Gaussian_Splatting_Code
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-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Home","text":"Welcome to wnc's note! My friends! / About Me
"},{"location":"Links/","title":"\u53cb\u94fe","text":""},{"location":"Links/#_1","title":"\u53cb\u94fe","text":"Abstract
My friends!
Wnc \u7684\u5496\u5561\u9986 \u6211\u81ea\u5df1\uff01 donotknow DoNotKnow Kinnari \u5f88\u5389\u5bb3\u7684\u5b66\u957f\uff01 MenGrey \u5f88\u5389\u5bb3\u7684\u540c\u5b66\uff01"},{"location":"AI/","title":"AI","text":""},{"location":"AI/#artificial-intelligence","title":"Artificial Intelligence","text":"Abstract
\u672c\u90e8\u5206\u5185\u5bb9\uff08\u9664\u7279\u522b\u58f0\u660e\u5916\uff09\u91c7\u7528 \u7f72\u540d-\u975e\u5546\u4e1a\u6027\u4f7f\u7528-\u4fdd\u6301\u4e00\u81f4 4.0 \u56fd\u9645 (CC BY-NC-SA 4.0) \u8bb8\u53ef\u534f\u8bae\u8fdb\u884c\u8bb8\u53ef\u3002
FFB6D \u590d\u73b0 - Docker 653 213 5 mins 1734024510
- Conda 293 96 2 mins 1734024510
EECS 498-007 - Pytorch 564 45 2 mins 1734024510
- KNN 374 100 2 mins 1734012860
- Linear Classifer 0 0 mins 0
CS231n - Numpy 49 104 1 mins 1734024510
- Notes 8852 30 mins 1734012860
\u673a\u5668\u5b66\u4e60 - \u300a\u7edf\u8ba1\u5b66\u4e60\u65b9\u6cd5\u300b\u7b14\u8bb0 3356 11 mins 1734012860
\u6df1\u5ea6\u5b66\u4e60 - Dive into Deep Learning 1547 387 10 mins 1734720663
SLAM - \u89c6\u89c9 SLAM \u5341\u56db\u8bb2 14110 72 48 mins 1734720663
"},{"location":"AI/Dive_into_Deep_Learning/","title":"Dive into Deep Learning","text":""},{"location":"AI/Dive_into_Deep_Learning/#dive-into-deep-learning","title":"Dive into Deep Learning","text":" \u7ea6 1547 \u4e2a\u5b57 387 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 13 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/Dive_into_Deep_Learning/#1","title":"1 \u5f15\u8a00","text":""},{"location":"AI/Dive_into_Deep_Learning/#2","title":"2 \u9884\u5907\u77e5\u8bc6","text":""},{"location":"AI/Dive_into_Deep_Learning/#21","title":"2.1 \u6570\u636e\u64cd\u4f5c","text":" - tensor
- ndarray (MXNet)
- Tensor (TensorFlow)
Pythonx = torch.arrange(12)\nx.shape\nx.numel()\nx.reshape(3, 4)\ntorch.zeros((2, 3, 4))\ntorch.ones((2, 3, 4))\ntorch.randn(3, 4)\n
- elementwise\uff1a
- concatenate
Pythontorch.cat((X, Y), dim = 0) # \u7ad6\u7740\u52a0\ntorch.cat((X, Y), dim = 1) # \u6a2a\u7740\u52a0\nx.sum()\n
- broadcasting mechanism
- \u590d\u5236\u62d3\u5c55\u5230\u5f62\u72b6\u4e00\u81f4\u540e\u76f8\u52a0
- \u7d22\u5f15+\u5207\u7247
- \u5207\u7247\u4fdd\u6301\u5730\u5740\u4e0d\u53d8\uff1a\u8282\u7701\u5185\u5b58
- ndarry <-> Tensor
- item ()
"},{"location":"AI/Dive_into_Deep_Learning/#22","title":"2.2 \u6570\u636e\u9884\u5904\u7406","text":" - pandas
- read_csv ()
- NaN
- fillna (inputs.mean ())
- np.array (inputs. to_numpy (dtype = float))
"},{"location":"AI/Dive_into_Deep_Learning/#23","title":"2.3 \u7ebf\u6027\u4ee3\u6570","text":" - scalar
- variable
- space
- element / component
- dimension
- square matrix
- transpose
- symmetric matrix
- channel
- Hadamard product
\\[ \\begin{split}\\mathbf{A} \\odot \\mathbf{B} = \\begin{bmatrix} a_{11} b_{11} & a_{12} b_{12} & \\dots & a_{1n} b_{1n} \\\\ a_{21} b_{21} & a_{22} b_{22} & \\dots & a_{2n} b_{2n} \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ a_{m1} b_{m1} & a_{m2} b_{m2} & \\dots & a_{mn} b_{mn} \\end{bmatrix}.\\end{split} \\] - A.sum (axis = 0) # \u7ad6\u7740\u6c42\u548c
- A.sum (axis = [0, 1]) = A.sum ()
- A.mean () = A.sum () / A.size ()
- A.cumsum (axis = 0)
- dot product
- torch.dot (x, y) = torch.sum (x * y)
- weighted average
- matrix-vector product
- matrix-matric multiplication
- norm
- \\(f(\\alpha \\mathbf{x}) = |\\alpha| f(\\mathbf{x}).\\)
- \\((\\mathbf{x} + \\mathbf{y}) \\leq f(\\mathbf{x}) + f(\\mathbf{y}).\\)
- \\(f(\\mathbf{x}) \\geq 0.\\)
"},{"location":"AI/Dive_into_Deep_Learning/#24","title":"2.4 \u5fae\u79ef\u5206","text":""},{"location":"AI/Dive_into_Deep_Learning/#241","title":"2.4.1 \u5bfc\u6570\u548c\u5fae\u5206","text":""},{"location":"AI/Dive_into_Deep_Learning/#242","title":"2.4.2 \u504f\u5bfc\u6570","text":""},{"location":"AI/Dive_into_Deep_Learning/#243","title":"2.4.3 \u68af\u5ea6","text":"\\[ \\nabla_{\\mathbf{x}} f(\\mathbf{x}) = \\bigg[\\frac{\\partial f(\\mathbf{x})}{\\partial x_1}, \\frac{\\partial f(\\mathbf{x})}{\\partial x_2}, \\ldots, \\frac{\\partial f(\\mathbf{x})}{\\partial x_n}\\bigg]^\\top, \\] \\[ \\nabla_{\\mathbf{x}} \\mathbf{A} \\mathbf{x} = \\mathbf{A}^\\top \\] \\[ \\nabla_{\\mathbf{x}} \\mathbf{x}^\\top \\mathbf{A} = \\mathbf{A} \\] \\[ \\nabla_{\\mathbf{x}} \\mathbf{x}^\\top \\mathbf{A} \\mathbf{x} = (\\mathbf{A} + \\mathbf{A}^\\top)\\mathbf{x} \\] \\[ \\nabla_{\\mathbf{x}} \\|\\mathbf{x} \\|^2 = \\nabla_{\\mathbf{x}} \\mathbf{x}^\\top \\mathbf{x} = 2\\mathbf{x} \\] \\[ \\nabla_{\\mathbf{X}} \\|\\mathbf{X} \\|_F^2 = 2\\mathbf{X} \\]"},{"location":"AI/Dive_into_Deep_Learning/#244","title":"2.4.4 \u94fe\u5f0f\u6cd5\u5219","text":""},{"location":"AI/Dive_into_Deep_Learning/#245","title":"2.4.5 \u5c0f\u7ed3","text":""},{"location":"AI/Dive_into_Deep_Learning/#25-automatic-differentiation","title":"2.5 \u81ea\u52a8\u5fae\u5206\uff08automatic differentiation\uff09","text":" - computational graph
- backpropagate
Pythonx.requires_grad_(True) # \u7b49\u4ef7\u4e8ex=torch.arange(4.0,requires_grad=True)\nx.grad # \u9ed8\u8ba4\u503c\u662fNone\ny = 2 * torch.dot(x, x)\ny.backward()\nx.grad\nx.grad == 4 * x\n
"},{"location":"AI/Dive_into_Deep_Learning/#251","title":"2.5.1 \u975e\u6807\u91cf\u53d8\u91cf\u7684\u53cd\u5411\u4f20\u64ad","text":"Python# \u5bf9\u975e\u6807\u91cf\u8c03\u7528backward\u9700\u8981\u4f20\u5165\u4e00\u4e2agradient\u53c2\u6570\uff0c\u8be5\u53c2\u6570\u6307\u5b9a\u5fae\u5206\u51fd\u6570\u5173\u4e8eself\u7684\u68af\u5ea6\u3002\n# \u672c\u4f8b\u53ea\u60f3\u6c42\u504f\u5bfc\u6570\u7684\u548c\uff0c\u6240\u4ee5\u4f20\u9012\u4e00\u4e2a1\u7684\u68af\u5ea6\u662f\u5408\u9002\u7684\nx.grad.zero_()\ny = x * x\n# \u7b49\u4ef7\u4e8ey.backward(torch.ones(len(x)))\ny.sum().backward()\nx.grad\n
"},{"location":"AI/Dive_into_Deep_Learning/#252","title":"2.5.2 \u5206\u79bb\u8ba1\u7b97","text":"Pythonx.grad.zero_()\ny = x * x\nu = y.detach()\nz = u * x\n\nz.sum().backward()\nx.grad == u\n
"},{"location":"AI/Dive_into_Deep_Learning/#26","title":"2.6 \u6982\u7387","text":""},{"location":"AI/Dive_into_Deep_Learning/#261","title":"2.6.1 \u57fa\u672c\u6982\u7387\u8bba","text":" - sampling
- distribution
- multinomial distribution
Pythonfair_probs = torch.ones([6]) / 6\nmultinomial.Multinomial(10, fair_probs).sample() # \u591a\u4e2a\u6837\u672c\n
"},{"location":"AI/Dive_into_Deep_Learning/#262","title":"2.6.2 \u5904\u7406\u591a\u4e2a\u968f\u673a\u53d8\u91cf","text":" - joint probability
- conditional probability
- Bayes\u2019 theorem
\\[ P(A \\mid B) = \\frac{P(B \\mid A) P(A)}{P(B)}. \\] - \u5176\u4e2d P (A, B) \u662f\u4e00\u4e2a\u8054\u5408\u5206\u5e03 (joint distribution)\uff0c\u00a0P (A\u2223B) \u662f\u4e00\u4e2a\u6761\u4ef6\u5206\u5e03 (conditional distribution)
- marginalization
- marginal probability
- marginal distribution
- conditionally independent
$$ P(A, B \\mid C) = P(A \\mid C)P(B \\mid C) $$
\\[ A \\perp B \\mid C \\] \\[ E[X] = \\sum_{x} x P(X = x). \\] \\[ E_{x \\sim P}[f(x)] = \\sum_x f(x) P(x). \\] \\[ \\mathrm{Var}[X] = E\\left[(X - E[X])^2\\right] = E[X^2] - E[X]^2. \\] \\[ \\mathrm{Var}[f(x)] = E\\left[\\left(f(x) - E[f(x)]\\right)^2\\right]. \\]"},{"location":"AI/Dive_into_Deep_Learning/#3","title":"3 \u7ebf\u6027\u795e\u7ecf\u7f51\u7edc","text":""},{"location":"AI/Dive_into_Deep_Learning/#31","title":"3.1 \u7ebf\u6027\u56de\u5f52","text":""},{"location":"AI/Dive_into_Deep_Learning/#311","title":"3.1.1 \u7ebf\u6027\u56de\u5f52\u7684\u57fa\u672c\u5143\u7d20","text":" - regression
- prediction / inference
- training set
- sample / data point / data instance
- label / target
- feature / covariate
\\[ \\mathrm{price} = w_{\\mathrm{area}} \\cdot \\mathrm{area} + w_{\\mathrm{age}} \\cdot \\mathrm{age} + b. \\] - weight
- bias / offset / intercept
- affine transformation
- linear transformation
- translation
- model parameters
- loss function
\\[ l^{(i)}(\\mathbf{w}, b) = \\frac{1}{2} \\left(\\hat{y}^{(i)} - y^{(i)}\\right)^2. \\] \\[ L(\\mathbf{w}, b) =\\frac{1}{n}\\sum_{i=1}^n l^{(i)}(\\mathbf{w}, b) =\\frac{1}{n} \\sum_{i=1}^n \\frac{1}{2}\\left(\\mathbf{w}^\\top \\mathbf{x}^{(i)} + b - y^{(i)}\\right)^2. \\] \\[ \\mathbf{w}^*, b^* = \\operatorname*{argmin}_{\\mathbf{w}, b}\\ L(\\mathbf{w}, b). \\] \\[ \\mathbf{w}^{*} = (\\mathbf X^\\top \\mathbf X)^{-1}\\mathbf X^\\top \\mathbf{y}. \\] - analytical solution
- gradient descent
- minibatch stochastic gradient descent
\\[ (\\mathbf{w},b) \\leftarrow (\\mathbf{w},b) - \\frac{\\eta}{|\\mathcal{B}|} \\sum_{i \\in \\mathcal{B}} \\partial_{(\\mathbf{w},b)} l^{(i)}(\\mathbf{w},b). \\] - \u521d\u59cb\u5316\u6a21\u578b\u53c2\u6570\u7684\u503c\uff0c\u5982\u968f\u673a\u521d\u59cb\u5316
- \u4ece\u6570\u636e\u96c6\u4e2d\u968f\u673a\u62bd\u53d6\u5c0f\u6279\u91cf\u6837\u672c\u4e14\u5728\u8d1f\u68af\u5ea6\u7684\u65b9\u5411\u4e0a\u66f4\u65b0\u53c2\u6570\uff0c\u5e76\u4e0d\u65ad\u8fed\u4ee3\u8fd9\u4e00\u6b65\u9aa4 - hyperparameter
- \\(|\\mathcal{B}|\\): batch size
- \\(\\eta\\): learning rate
- hyperparameter tuning
- validationg dataset
- generalization
"},{"location":"AI/Dive_into_Deep_Learning/#312","title":"3.1.2 \u77e2\u91cf\u5316\u52a0\u901f","text":" - \u77e2\u91cf\u5316\u4ee3\u7801
"},{"location":"AI/Dive_into_Deep_Learning/#313","title":"3.1.3 \u6b63\u6001\u5206\u5e03\u4e0e\u5e73\u65b9\u635f\u5931","text":" - normal distribution / Gaussian distribution
\\[ p(x) = \\frac{1}{\\sqrt{2 \\pi \\sigma^2}} \\exp\\left(-\\frac{1}{2 \\sigma^2} (x - \\mu)^2\\right). \\] \\[ y = \\mathbf{w}^\\top \\mathbf{x} + b + \\epsilon, \\] - \u5176\u4e2d \\(\\epsilon \\sim \\mathcal{N}(0, \\sigma^2)\\). \u56e0\u6b64 y \u7684 likelihood:
\\[ P(y \\mid \\mathbf{x}) = \\frac{1}{\\sqrt{2 \\pi \\sigma^2}} \\exp\\left(-\\frac{1}{2 \\sigma^2} (y - \\mathbf{w}^\\top \\mathbf{x} - b)^2\\right). \\] \\[ P(\\mathbf y \\mid \\mathbf X) = \\prod_{i=1}^{n} p(y^{(i)}|\\mathbf{x}^{(i)}). \\] \\[ -\\log P(\\mathbf y \\mid \\mathbf X) = \\sum_{i=1}^n \\frac{1}{2} \\log(2 \\pi \\sigma^2) + \\frac{1}{2 \\sigma^2} \\left(y^{(i)} - \\mathbf{w}^\\top \\mathbf{x}^{(i)} - b\\right)^2. \\]"},{"location":"AI/Dive_into_Deep_Learning/#314","title":"3.1.4 \u4ece\u7ebf\u6027\u56de\u5f52\u5230\u795e\u7ecf\u7f51\u7edc","text":" - feature dimensionality
- fully-connected layer / dense layer
"},{"location":"AI/Dive_into_Deep_Learning/#32","title":"3.2 \u7ebf\u6027\u56de\u5f52\u7684\u4ece\u96f6\u5f00\u59cb\u5b9e\u73b0","text":"Python%matplotlib inline\nimport random\nimport torch\nfrom d2l import torch as d2l\ndef synthetic_data(w, b, num_examples): #@save\n \"\"\"\u751f\u6210y=Xw+b+\u566a\u58f0\"\"\"\n X = torch.normal(0, 1, (num_examples, len(w)))\n y = torch.matmul(X, w) + b\n y += torch.normal(0, 0.01, y.shape)\n return X, y.reshape((-1, 1))\n\ntrue_w = torch.tensor([2, -3.4])\ntrue_b = 4.2\nfeatures, labels = synthetic_data(true_w, true_b, 1000)\n\ndef data_iter(batch_size, features, labels):\n num_examples = len(features)\n indices = list(range(num_examples))\n # \u8fd9\u4e9b\u6837\u672c\u662f\u968f\u673a\u8bfb\u53d6\u7684\uff0c\u6ca1\u6709\u7279\u5b9a\u7684\u987a\u5e8f\n random.shuffle(indices)\n for i in range(0, num_examples, batch_size):\n batch_indices = torch.tensor(\n indices[i: min(i + batch_size, num_examples)])\n yield features[batch_indices], labels[batch_indices]\n\nw = torch.normal(0, 0.01, size=(2,1), requires_grad=True)\nb = torch.zeros(1, requires_grad=True)\n\ndef linreg(X, w, b): #@save\n \"\"\"\u7ebf\u6027\u56de\u5f52\u6a21\u578b\"\"\"\n return torch.matmul(X, w) + b\n\ndef squared_loss(y_hat, y): #@save\n \"\"\"\u5747\u65b9\u635f\u5931\"\"\"\n return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2\n\ndef sgd(params, lr, batch_size): #@save\n \"\"\"\u5c0f\u6279\u91cf\u968f\u673a\u68af\u5ea6\u4e0b\u964d\"\"\"\n with torch.no_grad():\n for param in params:\n param -= lr * param.grad / batch_size\n param.grad.zero_()\n\nlr = 0.03\nnum_epochs = 3\nnet = linreg\nloss = squared_loss\n\nfor epoch in range(num_epochs):\n for X, y in data_iter(batch_size, features, labels):\n l = loss(net(X, w, b), y) # X\u548cy\u7684\u5c0f\u6279\u91cf\u635f\u5931\n # \u56e0\u4e3al\u5f62\u72b6\u662f(batch_size,1)\uff0c\u800c\u4e0d\u662f\u4e00\u4e2a\u6807\u91cf\u3002l\u4e2d\u7684\u6240\u6709\u5143\u7d20\u88ab\u52a0\u5230\u4e00\u8d77\uff0c\n # \u5e76\u4ee5\u6b64\u8ba1\u7b97\u5173\u4e8e[w,b]\u7684\u68af\u5ea6\n l.sum().backward()\n sgd([w, b], lr, batch_size) # \u4f7f\u7528\u53c2\u6570\u7684\u68af\u5ea6\u66f4\u65b0\u53c2\u6570\n with torch.no_grad():\n train_l = loss(net(features, w, b), labels)\n print(f'epoch {epoch + 1}, loss {float(train_l.mean()):f}')\n
"},{"location":"AI/Dive_into_Deep_Learning/#33","title":"3.3 \u7ebf\u6027\u56de\u5f52\u7684\u7b80\u6d01\u5b9e\u73b0","text":"Pythonimport numpy as np\nimport torch\nfrom torch.utils import data\nfrom d2l import torch as d2l\n\ntrue_w = torch.tensor([2, -3.4])\ntrue_b = 4.2\nfeatures, labels = d2l.synthetic_data(true_w, true_b, 1000) # \u751f\u6210\u6570\u636e\u96c6\n\ndef load_array(data_arrays, batch_size, is_train=True): #@save\n \"\"\"\u6784\u9020\u4e00\u4e2aPyTorch\u6570\u636e\u8fed\u4ee3\u5668\"\"\"\n dataset = data.TensorDataset(*data_arrays)\n return data.DataLoader(dataset, batch_size, shuffle=is_train)\n\nbatch_size = 10\ndata_iter = load_array((features, labels), batch_size) # \u8bfb\u53d6\u6570\u636e\u96c6\n\n# nn\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u7f29\u5199\nfrom torch import nn\n\nnet = nn.Sequential(nn.Linear(2, 1)) # \u5b9a\u4e49\u6a21\u578b \uff08\u8f93\u5165\uff0c\u8f93\u51fa\uff09\u7279\u5f81\u5f62\u72b6\nnet[0].weight.data.normal_(0, 0.01)\nnet[0].bias.data.fill_(0) # \u521d\u59cb\u5316\u6a21\u578b\u53c2\u6570\nloss = nn.MSELoss() # \u5b9a\u4e49\u635f\u5931\u51fd\u6570\ntrainer = torch.optim.SGD(net.parameters(), lr=0.03) # \u5b9a\u4e49\u4f18\u5316\u7b97\u6cd5\n\n# \u8bad\u7ec3\nnum_epochs = 3\nfor epoch in range(num_epochs):\n for X, y in data_iter:\n l = loss(net(X) ,y)\n trainer.zero_grad()\n l.backward()\n trainer.step()\n l = loss(net(features), labels)\n print(f'epoch {epoch + 1}, loss {l:f}')\n\nw = net[0].weight.data\nprint('w\u7684\u4f30\u8ba1\u8bef\u5dee\uff1a', true_w - w.reshape(true_w.shape))\nb = net[0].bias.data\nprint('b\u7684\u4f30\u8ba1\u8bef\u5dee\uff1a', true_b - b)\n
"},{"location":"AI/Dive_into_Deep_Learning/#34-softmax","title":"3.4 softmax \u56de\u5f52","text":""},{"location":"AI/Dive_into_Deep_Learning/#341","title":"3.4.1 \u5206\u7c7b\u95ee\u9898","text":" \\[ y \\in \\{(1, 0, 0), (0, 1, 0), (0, 0, 1)\\}. \\]"},{"location":"AI/Dive_into_Deep_Learning/#342","title":"3.4.2 \u7f51\u7edc\u67b6\u6784","text":" \\[ \\begin{split}\\begin{aligned} o_1 &= x_1 w_{11} + x_2 w_{12} + x_3 w_{13} + x_4 w_{14} + b_1,\\\\ o_2 &= x_1 w_{21} + x_2 w_{22} + x_3 w_{23} + x_4 w_{24} + b_2,\\\\ o_3 &= x_1 w_{31} + x_2 w_{32} + x_3 w_{33} + x_4 w_{34} + b_3. \\end{aligned}\\end{split} \\]"},{"location":"AI/Dive_into_Deep_Learning/#343","title":"3.4.3 \u5168\u8fde\u63a5\u5c42\u7684\u53c2\u6570\u5f00\u9500","text":" - \u4e0d\u77e5\u9053\u662f\u4ec0\u4e48\u4e1c\u897f
"},{"location":"AI/Dive_into_Deep_Learning/#344-softmax","title":"3.4.4 softmax \u8fd0\u7b97","text":" \\[ \\hat{\\mathbf{y}} = \\mathrm{softmax}(\\mathbf{o})\\quad \\text{\u5176\u4e2d}\\quad \\hat{y}_j = \\frac{\\exp(o_j)}{\\sum_k \\exp(o_k)} \\] \\[ \\operatorname*{argmax}_j \\hat y_j = \\operatorname*{argmax}_j o_j. \\] "},{"location":"AI/Dive_into_Deep_Learning/#345","title":"3.4.5 \u5c0f\u6279\u6837\u672c\u7684\u77e2\u91cf\u5316","text":"\\[ \\begin{split}\\begin{aligned} \\mathbf{O} &= \\mathbf{X} \\mathbf{W} + \\mathbf{b}, \\\\ \\hat{\\mathbf{Y}} & = \\mathrm{softmax}(\\mathbf{O}). \\end{aligned}\\end{split} \\]"},{"location":"AI/Dive_into_Deep_Learning/#346","title":"3.4.6 \u635f\u5931\u51fd\u6570","text":"\\[ P(\\mathbf{Y} \\mid \\mathbf{X}) = \\prod_{i=1}^n P(\\mathbf{y}^{(i)} \\mid \\mathbf{x}^{(i)}). \\] \\[ -\\log P(\\mathbf{Y} \\mid \\mathbf{X}) = \\sum_{i=1}^n -\\log P(\\mathbf{y}^{(i)} \\mid \\mathbf{x}^{(i)}) = \\sum_{i=1}^n l(\\mathbf{y}^{(i)}, \\hat{\\mathbf{y}}^{(i)}), \\] \\[ l(\\mathbf{y}, \\hat{\\mathbf{y}}) = - \\sum_{j=1}^q y_j \\log \\hat{y}_j. \\] \\[ \\begin{split}\\begin{aligned} l(\\mathbf{y}, \\hat{\\mathbf{y}}) &= - \\sum_{j=1}^q y_j \\log \\frac{\\exp(o_j)}{\\sum_{k=1}^q \\exp(o_k)} \\\\ &= \\sum_{j=1}^q y_j \\log \\sum_{k=1}^q \\exp(o_k) - \\sum_{j=1}^q y_j o_j\\\\ &= \\log \\sum_{k=1}^q \\exp(o_k) - \\sum_{j=1}^q y_j o_j. \\end{aligned}\\end{split} \\] \\[ \\partial_{o_j} l(\\mathbf{y}, \\hat{\\mathbf{y}}) = \\frac{\\exp(o_j)}{\\sum_{k=1}^q \\exp(o_k)} - y_j = \\mathrm{softmax}(\\mathbf{o})_j - y_j. \\]"},{"location":"AI/Dive_into_Deep_Learning/#347","title":"3.4.7 \u4fe1\u606f\u8bba\u57fa\u7840","text":" - information theory
- entropy
\\[ H[P] = \\sum_j - P(j) \\log P(j). \\]"},{"location":"AI/Dive_into_Deep_Learning/#348","title":"3.4.8 \u6a21\u578b\u9884\u6d4b\u548c\u8bc4\u4f30","text":""},{"location":"AI/Dive_into_Deep_Learning/#35","title":"3.5 \u56fe\u50cf\u5206\u7c7b\u6570\u636e\u96c6","text":"Python%matplotlib inline\nimport torch\nimport torchvision\nfrom torch.utils import data\nfrom torchvision import transforms\nfrom d2l import torch as d2l\n\nd2l.use_svg_display()\n\n# \u901a\u8fc7ToTensor\u5b9e\u4f8b\u5c06\u56fe\u50cf\u6570\u636e\u4ecePIL\u7c7b\u578b\u53d8\u6362\u621032\u4f4d\u6d6e\u70b9\u6570\u683c\u5f0f\uff0c\n# \u5e76\u9664\u4ee5255\u4f7f\u5f97\u6240\u6709\u50cf\u7d20\u7684\u6570\u503c\u5747\u57280\uff5e1\u4e4b\u95f4\ntrans = transforms.ToTensor()\nmnist_train = torchvision.datasets.FashionMNIST(\n root=\"../data\", train=True, transform=trans, download=True)\nmnist_test = torchvision.datasets.FashionMNIST(\n root=\"../data\", train=False, transform=trans, download=True)\n\ndef get_fashion_mnist_labels(labels): #@save\n \"\"\"\u8fd4\u56deFashion-MNIST\u6570\u636e\u96c6\u7684\u6587\u672c\u6807\u7b7e\"\"\"\n text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n return [text_labels[int(i)] for i in labels]\n\ndef show_images(imgs, num_rows, num_cols, titles=None, scale=1.5): #@save\n \"\"\"\u7ed8\u5236\u56fe\u50cf\u5217\u8868\"\"\"\n figsize = (num_cols * scale, num_rows * scale)\n _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)\n axes = axes.flatten()\n for i, (ax, img) in enumerate(zip(axes, imgs)):\n if torch.is_tensor(img):\n # \u56fe\u7247\u5f20\u91cf\n ax.imshow(img.numpy())\n else:\n # PIL\u56fe\u7247\n ax.imshow(img)\n ax.axes.get_xaxis().set_visible(False)\n ax.axes.get_yaxis().set_visible(False)\n if titles:\n ax.set_title(titles[i])\n return axes\n\nbatch_size = 256\n\ndef get_dataloader_workers(): #@save\n \"\"\"\u4f7f\u75284\u4e2a\u8fdb\u7a0b\u6765\u8bfb\u53d6\u6570\u636e\"\"\"\n return 4\n\ntrain_iter = data.DataLoader(mnist_train, batch_size, shuffle=True,\n num_workers=get_dataloader_workers())\n\ndef load_data_fashion_mnist(batch_size, resize=None): #@save\n \"\"\"\u4e0b\u8f7dFashion-MNIST\u6570\u636e\u96c6\uff0c\u7136\u540e\u5c06\u5176\u52a0\u8f7d\u5230\u5185\u5b58\u4e2d\"\"\"\n trans = [transforms.ToTensor()]\n if resize:\n trans.insert(0, transforms.Resize(resize))\n trans = transforms.Compose(trans)\n mnist_train = torchvision.datasets.FashionMNIST(\n root=\"../data\", train=True, transform=trans, download=True)\n mnist_test = torchvision.datasets.FashionMNIST(\n root=\"../data\", train=False, transform=trans, download=True)\n return (data.DataLoader(mnist_train, batch_size, shuffle=True,\n num_workers=get_dataloader_workers()),\n data.DataLoader(mnist_test, batch_size, shuffle=False,\n num_workers=get_dataloader_workers()))\n\ntrain_iter, test_iter = load_data_fashion_mnist(32, resize=64)\nfor X, y in train_iter:\n print(X.shape, X.dtype, y.shape, y.dtype)\n break\n
"},{"location":"AI/Dive_into_Deep_Learning/#36-softmax","title":"3.6 softmax \u56de\u5f52\u7684\u4ece\u96f6\u5f00\u59cb\u5b9e\u73b0","text":"Pythonimport torch\nfrom IPython import display\nfrom d2l import torch as d2l\n\nbatch_size = 256\ntrain_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)\n\nnum_inputs = 784\nnum_outputs = 10\n\nW = torch.normal(0, 0.01, size=(num_inputs, num_outputs), requires_grad=True)\nb = torch.zeros(num_outputs, requires_grad=True) # \u521d\u59cb\u5316\u6a21\u578b\u53c2\u6570\n\nX = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])\nX.sum(0, keepdim=True), X.sum(1, keepdim=True) \n\ndef softmax(X):\n X_exp = torch.exp(X)\n partition = X_exp.sum(1, keepdim=True)\n return X_exp / partition # \u8fd9\u91cc\u5e94\u7528\u4e86\u5e7f\u64ad\u673a\u5236\n\nX = torch.normal(0, 1, (2, 5))\nX_prob = softmax(X)\nX_prob, X_prob.sum(1) # \u5b9a\u4e49softmax\u64cd\u4f5c\n\ndef net(X):\n return softmax(torch.matmul(X.reshape((-1, W.shape[0])), W) + b) # \u5b9a\u4e49\u6a21\u578b\n\ny = torch.tensor([0, 2])\ny_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\ny_hat[[0, 1], y] \ndef cross_entropy(y_hat, y):\n return - torch.log(y_hat[range(len(y_hat)), y])\n\ncross_entropy(y_hat, y) # \u5b9a\u4e49\u635f\u5931\u51fd\u6570\n\ndef accuracy(y_hat, y): #@save\n \"\"\"\u8ba1\u7b97\u9884\u6d4b\u6b63\u786e\u7684\u6570\u91cf\"\"\"\n if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:\n y_hat = y_hat.argmax(axis=1)\n cmp = y_hat.type(y.dtype) == y\n return float(cmp.type(y.dtype).sum())\n\ndef evaluate_accuracy(net, data_iter): #@save\n \"\"\"\u8ba1\u7b97\u5728\u6307\u5b9a\u6570\u636e\u96c6\u4e0a\u6a21\u578b\u7684\u7cbe\u5ea6\"\"\"\n if isinstance(net, torch.nn.Module):\n net.eval() # \u5c06\u6a21\u578b\u8bbe\u7f6e\u4e3a\u8bc4\u4f30\u6a21\u5f0f\n metric = Accumulator(2) # \u6b63\u786e\u9884\u6d4b\u6570\u3001\u9884\u6d4b\u603b\u6570\n with torch.no_grad():\n for X, y in data_iter:\n metric.add(accuracy(net(X), y), y.numel())\n return metric[0] / metric[1]\n\nclass Accumulator: #@save\n \"\"\"\u5728n\u4e2a\u53d8\u91cf\u4e0a\u7d2f\u52a0\"\"\"\n def __init__(self, n):\n self.data = [0.0] * n\n\n def add(self, *args):\n self.data = [a + float(b) for a, b in zip(self.data, args)]\n\n def reset(self):\n self.data = [0.0] * len(self.data)\n\n def __getitem__(self, idx):\n return self.data[idx]\n\nevaluate_accuracy(net, test_iter) # \u5206\u7c7b\u7cbe\u5ea6\n\ndef train_epoch_ch3(net, train_iter, loss, updater): #@save\n \"\"\"\u8bad\u7ec3\u6a21\u578b\u4e00\u4e2a\u8fed\u4ee3\u5468\u671f\uff08\u5b9a\u4e49\u89c1\u7b2c3\u7ae0\uff09\"\"\"\n # \u5c06\u6a21\u578b\u8bbe\u7f6e\u4e3a\u8bad\u7ec3\u6a21\u5f0f\n if isinstance(net, torch.nn.Module):\n net.train()\n # \u8bad\u7ec3\u635f\u5931\u603b\u548c\u3001\u8bad\u7ec3\u51c6\u786e\u5ea6\u603b\u548c\u3001\u6837\u672c\u6570\n metric = Accumulator(3)\n for X, y in train_iter:\n # \u8ba1\u7b97\u68af\u5ea6\u5e76\u66f4\u65b0\u53c2\u6570\n y_hat = net(X)\n l = loss(y_hat, y)\n if isinstance(updater, torch.optim.Optimizer):\n # \u4f7f\u7528PyTorch\u5185\u7f6e\u7684\u4f18\u5316\u5668\u548c\u635f\u5931\u51fd\u6570\n updater.zero_grad()\n l.mean().backward()\n updater.step()\n else:\n # \u4f7f\u7528\u5b9a\u5236\u7684\u4f18\u5316\u5668\u548c\u635f\u5931\u51fd\u6570\n l.sum().backward()\n updater(X.shape[0])\n metric.add(float(l.sum()), accuracy(y_hat, y), y.numel())\n # \u8fd4\u56de\u8bad\u7ec3\u635f\u5931\u548c\u8bad\u7ec3\u7cbe\u5ea6\n return metric[0] / metric[2], metric[1] / metric[2]\n\nclass Animator: #@save\n \"\"\"\u5728\u52a8\u753b\u4e2d\u7ed8\u5236\u6570\u636e\"\"\"\n def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,\n ylim=None, xscale='linear', yscale='linear',\n fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,\n figsize=(3.5, 2.5)):\n # \u589e\u91cf\u5730\u7ed8\u5236\u591a\u6761\u7ebf\n if legend is None:\n legend = []\n d2l.use_svg_display()\n self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize=figsize)\n if nrows * ncols == 1:\n self.axes = [self.axes, ]\n # \u4f7f\u7528lambda\u51fd\u6570\u6355\u83b7\u53c2\u6570\n self.config_axes = lambda: d2l.set_axes(\n self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)\n self.X, self.Y, self.fmts = None, None, fmts\n\n def add(self, x, y):\n # \u5411\u56fe\u8868\u4e2d\u6dfb\u52a0\u591a\u4e2a\u6570\u636e\u70b9\n if not hasattr(y, \"__len__\"):\n y = [y]\n n = len(y)\n if not hasattr(x, \"__len__\"):\n x = [x] * n\n if not self.X:\n self.X = [[] for _ in range(n)]\n if not self.Y:\n self.Y = [[] for _ in range(n)]\n for i, (a, b) in enumerate(zip(x, y)):\n if a is not None and b is not None:\n self.X[i].append(a)\n self.Y[i].append(b)\n self.axes[0].cla()\n for x, y, fmt in zip(self.X, self.Y, self.fmts):\n self.axes[0].plot(x, y, fmt)\n self.config_axes()\n display.display(self.fig)\n display.clear_output(wait=True)\n\ndef train_ch3(net, train_iter, test_iter, loss, num_epochs, updater): #@save\n \"\"\"\u8bad\u7ec3\u6a21\u578b\uff08\u5b9a\u4e49\u89c1\u7b2c3\u7ae0\uff09\"\"\"\n animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.3, 0.9],\n legend=['train loss', 'train acc', 'test acc'])\n for epoch in range(num_epochs):\n train_metrics = train_epoch_ch3(net, train_iter, loss, updater)\n test_acc = evaluate_accuracy(net, test_iter)\n animator.add(epoch + 1, train_metrics + (test_acc,))\n train_loss, train_acc = train_metrics\n assert train_loss < 0.5, train_loss\n assert train_acc <= 1 and train_acc > 0.7, train_acc\n assert test_acc <= 1 and test_acc > 0.7, test_acc\n\nlr = 0.1\n\ndef updater(batch_size):\n return d2l.sgd([W, b], lr, batch_size)\n\nnum_epochs = 10\ntrain_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, updater) # \u8bad\u7ec3\n\ndef predict_ch3(net, test_iter, n=6): #@save\n \"\"\"\u9884\u6d4b\u6807\u7b7e\uff08\u5b9a\u4e49\u89c1\u7b2c3\u7ae0\uff09\"\"\"\n for X, y in test_iter:\n break\n trues = d2l.get_fashion_mnist_labels(y)\n preds = d2l.get_fashion_mnist_labels(net(X).argmax(axis=1))\n titles = [true +'\\n' + pred for true, pred in zip(trues, preds)]\n d2l.show_images(\n X[0:n].reshape((n, 28, 28)), 1, n, titles=titles[0:n])\n\npredict_ch3(net, test_iter) # \u9884\u6d4b\n
"},{"location":"AI/Dive_into_Deep_Learning/#37-softmax","title":"3.7 softmax \u56de\u5f52\u7684\u7b80\u6d01\u5b9e\u73b0","text":"Pythonimport torch\nfrom torch import nn\nfrom d2l import torch as d2l\n\nbatch_size = 256\ntrain_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)\n\n# PyTorch\u4e0d\u4f1a\u9690\u5f0f\u5730\u8c03\u6574\u8f93\u5165\u7684\u5f62\u72b6\u3002\u56e0\u6b64\uff0c\n# \u6211\u4eec\u5728\u7ebf\u6027\u5c42\u524d\u5b9a\u4e49\u4e86\u5c55\u5e73\u5c42\uff08flatten\uff09\uff0c\u6765\u8c03\u6574\u7f51\u7edc\u8f93\u5165\u7684\u5f62\u72b6\nnet = nn.Sequential(nn.Flatten(), nn.Linear(784, 10))\n\ndef init_weights(m):\n if type(m) == nn.Linear:\n nn.init.normal_(m.weight, std=0.01)\n\nnet.apply(init_weights); # \u521d\u59cb\u5316\u6a21\u578b\u53c2\u6570\n
\\[ \\begin{split}\\begin{aligned} \\hat y_j & = \\frac{\\exp(o_j - \\max(o_k))\\exp(\\max(o_k))}{\\sum_k \\exp(o_k - \\max(o_k))\\exp(\\max(o_k))} \\\\ & = \\frac{\\exp(o_j - \\max(o_k))}{\\sum_k \\exp(o_k - \\max(o_k))}. \\end{aligned}\\end{split} \\] \\[ \\begin{split}\\begin{aligned} \\log{(\\hat y_j)} & = \\log\\left( \\frac{\\exp(o_j - \\max(o_k))}{\\sum_k \\exp(o_k - \\max(o_k))}\\right) \\\\ & = \\log{(\\exp(o_j - \\max(o_k)))}-\\log{\\left( \\sum_k \\exp(o_k - \\max(o_k)) \\right)} \\\\ & = o_j - \\max(o_k) -\\log{\\left( \\sum_k \\exp(o_k - \\max(o_k)) \\right)}. \\end{aligned}\\end{split} \\] Pythonloss = nn.CrossEntropyLoss(reduction='none') # LogSumExp\u6280\u5de7\n\ntrainer = torch.optim.SGD(net.parameters(), lr=0.1) # \u4f18\u5316\u7b97\u6cd5\n\nnum_epochs = 10\nd2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer) # \u8bad\u7ec3\n
"},{"location":"AI/Dive_into_Deep_Learning/#_1","title":"\u591a\u5c42\u611f\u77e5\u673a","text":""},{"location":"AI/Dive_into_Deep_Learning/#_2","title":"\u8fc7\u62df\u5408\u548c\u6b20\u62df\u5408","text":""},{"location":"AI/Dive_into_Deep_Learning/#dropout","title":"dropout","text":""},{"location":"AI/Dive_into_Deep_Learning/#_3","title":"\u6df1\u5ea6\u5b66\u4e60\u8ba1\u7b97","text":""},{"location":"AI/Dive_into_Deep_Learning/#_4","title":"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc","text":""},{"location":"AI/Dive_into_Deep_Learning/#lenet","title":"LeNet","text":""},{"location":"AI/Dive_into_Deep_Learning/#_5","title":"\u73b0\u4ee3\u5377\u79ef\u795e\u7ecf\u7f51\u7edc","text":""},{"location":"AI/Dive_into_Deep_Learning/#alexnet","title":"AlexNet","text":""},{"location":"AI/Dive_into_Deep_Learning/#vgg","title":"VGG","text":""},{"location":"AI/Dive_into_Deep_Learning/#nin","title":"NiN","text":""},{"location":"AI/Dive_into_Deep_Learning/#googlenet","title":"GoogLeNet","text":""},{"location":"AI/Dive_into_Deep_Learning/#resnet","title":"ResNet","text":""},{"location":"AI/Dive_into_Deep_Learning/#densenet","title":"DenseNet","text":""},{"location":"AI/Dive_into_Deep_Learning/#_6","title":"\u5faa\u73af\u795e\u7ecf\u7f51\u7edc","text":""},{"location":"AI/Dive_into_Deep_Learning/#_7","title":"\u73b0\u4ee3\u5faa\u73af\u795e\u7ecf\u7f51\u7edc","text":""},{"location":"AI/Dive_into_Deep_Learning/#gru","title":"GRU","text":""},{"location":"AI/Dive_into_Deep_Learning/#lstm","title":"LSTM","text":""},{"location":"AI/Dive_into_Deep_Learning/#seq2seq","title":"seq2seq","text":""},{"location":"AI/Dive_into_Deep_Learning/#_8","title":"\u6ce8\u610f\u529b\u673a\u5236","text":""},{"location":"AI/Dive_into_Deep_Learning/#nadaraya-watson","title":"Nadaraya-Watson","text":""},{"location":"AI/Dive_into_Deep_Learning/#bahdanau","title":"Bahdanau","text":""},{"location":"AI/Dive_into_Deep_Learning/#multi-headed-attention","title":"Multi-headed attention","text":""},{"location":"AI/Dive_into_Deep_Learning/#transformer","title":"Transformer","text":""},{"location":"AI/Dive_into_Deep_Learning/#_9","title":"\u4f18\u5316\u7b97\u6cd5","text":""},{"location":"AI/Dive_into_Deep_Learning/#adagrad","title":"AdaGrad","text":""},{"location":"AI/Dive_into_Deep_Learning/#rmsprop","title":"RMSProp","text":""},{"location":"AI/Dive_into_Deep_Learning/#adadelta","title":"Adadelta","text":""},{"location":"AI/Dive_into_Deep_Learning/#adam","title":"Adam","text":""},{"location":"AI/Dive_into_Deep_Learning/#_10","title":"\u8ba1\u7b97\u6027\u80fd","text":""},{"location":"AI/Dive_into_Deep_Learning/#_11","title":"\u8ba1\u7b97\u673a\u89c6\u89c9","text":""},{"location":"AI/Dive_into_Deep_Learning/#ssd","title":"SSD","text":""},{"location":"AI/Dive_into_Deep_Learning/#r-cnn","title":"R-CNN","text":""},{"location":"AI/Dive_into_Deep_Learning/#_12","title":"\u81ea\u7136\u8bed\u8a00\u5904\u7406:\u9884\u8bad\u7ec3","text":""},{"location":"AI/Dive_into_Deep_Learning/#word2vec","title":"word2vec","text":""},{"location":"AI/Dive_into_Deep_Learning/#glove","title":"GloVe","text":""},{"location":"AI/Dive_into_Deep_Learning/#bert","title":"BERT","text":""},{"location":"AI/Dive_into_Deep_Learning/#_13","title":"\u81ea\u7136\u8bed\u8a00\u5904\u7406:\u5e94\u7528","text":""},{"location":"AI/SLAM14/","title":"\u89c6\u89c9SLAM\u5341\u56db\u8bb2","text":""},{"location":"AI/SLAM14/#slam","title":"\u89c6\u89c9SLAM\u5341\u56db\u8bb2","text":" \u7ea6 14110 \u4e2a\u5b57 72 \u884c\u4ee3\u7801 20 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 71 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/SLAM14/#1","title":"1 \u9884\u5907\u77e5\u8bc6","text":""},{"location":"AI/SLAM14/#11","title":"1.1 \u672c\u4e66\u8bb2\u4ec0\u4e48","text":"simultaneous localization and mapping
- \u5b9a\u4f4d
- \u5730\u56fe\u6784\u5efa
- \u80cc\u666f\u77e5\u8bc6:
- \u5c04\u5f71\u51e0\u4f55
- \u8ba1\u7b97\u673a\u89c6\u89c9
- \u72b6\u6001\u4f30\u8ba1\u7406\u8bba
- \u674e\u7fa4\u4e0e\u674e\u4ee3\u6570
"},{"location":"AI/SLAM14/#12","title":"1.2 \u5982\u4f55\u4f7f\u7528\u672c\u4e66","text":""},{"location":"AI/SLAM14/#121","title":"1.2.1 \u7ec4\u7ec7\u65b9\u5f0f","text":" - \u6570\u5b66\u57fa\u7840\u7bc7
- \u5b9e\u8df5\u5e94\u7528\u7bc7
"},{"location":"AI/SLAM14/#122","title":"1.2.2 \u4ee3\u7801","text":"GitHub - gaoxiang12/slambook2: edition 2 of the slambook
"},{"location":"AI/SLAM14/#123","title":"1.2.3 \u9762\u5411\u7684\u8bfb\u8005","text":" - \u57fa\u7840\u77e5\u8bc6:
- \u9ad8\u6570\u7ebf\u4ee3\u6982\u7387\u8bba
- C++\u8bed\u8a00\u57fa\u7840\uff08C++\u6807\u51c6\u5e93\uff0c\u6a21\u677f\u7c7b\uff0c\u4e00\u90e8\u5206 C++11 \uff09
- Linux \u57fa\u7840
"},{"location":"AI/SLAM14/#13","title":"1.3 \u98ce\u683c\u7ea6\u5b9a","text":""},{"location":"AI/SLAM14/#14","title":"1.4 \u81f4\u8c22\u548c\u58f0\u660e","text":""},{"location":"AI/SLAM14/#15","title":"1.5 \u4e60\u9898","text":" - \u9898\u76ee\uff1a\u6709\u7ebf\u6027\u65b9\u7a0b \\(A x=b\\)\uff0c\u82e5\u5df2\u77e5 \\(A, b\\)\uff0c\u9700\u8981\u6c42\u89e3 x\uff0c\u8be5\u5982\u4f55\u6c42\u89e3\uff1f\u8fd9\u5bf9 A \u548c b \u6709\u54ea\u4e9b\u8981\u6c42\uff1f\u63d0\u793a\uff1a\u4ece A \u7684\u7ef4\u5ea6\u548c\u79e9\u89d2\u5ea6\u6765\u5206\u6790\u3002
- \u7b54\u6848\uff1a\u7ebf\u6027\u65b9\u7a0b\u7ec4 \\(Ax = b\\) \u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u6c42\u89e3\uff0c\u5982\u9ad8\u65af\u6d88\u5143\u6cd5\u3001\u77e9\u9635\u9006\u6cd5\u7b49\u3002\u8981\u6c42 \\(A\\) \u662f\u4e00\u4e2a\u65b9\u9635\u4e14\u53ef\u9006\uff08\u5373 \\(A\\) \u7684\u884c\u5217\u5f0f\u4e0d\u4e3a\u96f6\uff09\uff0c\u8fd9\u6837\u65b9\u7a0b\u624d\u6709\u552f\u4e00\u89e3\u3002\u5982\u679c \\(A\\) \u4e0d\u662f\u65b9\u9635\uff0c\u9700\u8981 \\(A\\) \u7684\u79e9\u7b49\u4e8e\u5217\u6570\u4e14\u7b49\u4e8e\u589e\u5e7f\u77e9\u9635 \\(\\displaystyle [A|b]\\) \u7684\u79e9\uff0c\u8fd9\u6837\u65b9\u7a0b\u7ec4\u624d\u6709\u89e3\u3002
- \u9898\u76ee\uff1a\u9ad8\u65af\u5206\u5e03\u662f\u4ec0\u4e48\uff1f\u5b83\u7684\u4e00\u7ef4\u5f62\u5f0f\u662f\u4ec0\u4e48\u6837\u5b50\uff1f\u5b83\u7684\u9ad8\u7ef4\u5f62\u5f0f\u662f\u4ec0\u4e48\u6837\u5b50\uff1f
- \u7b54\u6848\uff1a\u9ad8\u65af\u5206\u5e03\uff0c\u4e5f\u79f0\u4e3a\u6b63\u6001\u5206\u5e03\uff0c\u662f\u4e00\u79cd\u8fde\u7eed\u6982\u7387\u5206\u5e03\u3002\u4e00\u7ef4\u9ad8\u65af\u5206\u5e03\u7684\u6570\u5b66\u8868\u8fbe\u5f0f\u4e3a \\(\\displaystyle f (x) = \\frac{1}{\\sigma\\sqrt{2\\pi}} e^{-\\frac{(x-\\mu)^2}{2\\sigma^2}}\\)\uff0c\u5176\u4e2d \\(\\displaystyle \\mu\\) \u662f\u5747\u503c\uff0c\\(\\displaystyle \\sigma\\) \u662f\u6807\u51c6\u5dee\u3002\u9ad8\u7ef4\u9ad8\u65af\u5206\u5e03\u662f\u4e00\u7ef4\u9ad8\u65af\u5206\u5e03\u5728\u591a\u7ef4\u7a7a\u95f4\u7684\u63a8\u5e7f\uff0c\u5176\u6982\u7387\u5bc6\u5ea6\u51fd\u6570\u4e3a \\(\\displaystyle N (\\mathbf{x}; \\mathbf{\\mu}, \\Sigma)\\)\uff0c\u5176\u4e2d \\(\\displaystyle \\mathbf{\\mu}\\) \u662f\u5747\u503c\u5411\u91cf\uff0c\\(\\displaystyle \\Sigma\\) \u662f\u534f\u65b9\u5dee\u77e9\u9635\u3002
- \u9898\u76ee\uff1a\u4f60\u77e5\u9053 C++11 \u6807\u51c6\u5417\uff1f\u4f60\u542c\u8bf4\u8fc7\u6216\u7528\u8fc7\u5176\u4e2d\u54ea\u4e9b\u65b0\u7279\u6027\uff1f\u6709\u6ca1\u6709\u5176\u4ed6\u7684\u6807\u51c6\uff1f
- \u7b54\u6848\uff1a\u662f\u7684\uff0cC++11 \u662f C++ \u8bed\u8a00\u7684\u4e00\u4e2a\u91cd\u8981\u6807\u51c6\uff0c\u5b83\u5f15\u5165\u4e86\u8bb8\u591a\u65b0\u7279\u6027\uff0c\u5982\u81ea\u52a8\u7c7b\u578b\u63a8\u5bfc\uff08auto\uff09\u3001\u57fa\u4e8e\u8303\u56f4\u7684 for \u5faa\u73af\u3001lambda \u8868\u8fbe\u5f0f\u3001\u667a\u80fd\u6307\u9488\u7b49\u3002\u9664\u4e86 C++11\uff0c\u8fd8\u6709 C++14\u3001C++17 \u548c C++20 \u7b49\u540e\u7eed\u6807\u51c6\uff0c\u5b83\u4eec\u4e5f\u5f15\u5165\u4e86\u65b0\u7684\u7279\u6027\u548c\u6539\u8fdb\u3002
- \u9898\u76ee\uff1a\u5982\u4f55\u5728 Ubuntu \u7cfb\u7edf\u4e2d\u5b89\u88c5\u8f6f\u4ef6\uff08\u4e0d\u6253\u5f00\u8f6f\u4ef6\u4e2d\u5fc3\u7684\u60c5\u51b5\u4e0b\uff09\uff1f\u8fd9\u4e9b\u8f6f\u4ef6\u88ab\u5b89\u88c5\u5728\u4ec0\u4e48\u5730\u65b9\uff1f\u5982\u679c\u53ea\u77e5\u9053\u6a21\u7cca\u7684\u8f6f\u4ef6\u540d\u79f0\uff08\u6bd4\u5982\u60f3\u8981\u88c5\u4e00\u4e2a\u540d\u79f0\u4e2d\u542b\u6709 Eigen \u7684\u5e93\uff09\uff0c\u5e94\u8be5\u5982\u4f55\u5b89\u88c5\u5b83\uff1f
- \u7b54\u6848\uff1a
- \u8f6f\u4ef6\u5b89\u88c5\uff1a\u5728 Ubuntu \u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u547d\u4ee4\u884c\u5de5\u5177
apt \u6765\u5b89\u88c5\u8f6f\u4ef6\u3002\u57fa\u672c\u547d\u4ee4\u4e3a sudo apt install [package-name]\u3002 - \u5b89\u88c5\u4f4d\u7f6e\uff1a\u8f6f\u4ef6\u901a\u5e38\u88ab\u5b89\u88c5\u5728
/usr/ \u76ee\u5f55\u4e0b\uff0c\u4f46\u5177\u4f53\u7684\u6587\u4ef6\u53ef\u80fd\u5206\u5e03\u5728\u591a\u4e2a\u5b50\u76ee\u5f55\u4e2d\u3002 - \u6a21\u7cca\u540d\u79f0\u5b89\u88c5\uff1a\u5982\u679c\u53ea\u77e5\u9053\u8f6f\u4ef6\u540d\u79f0\u7684\u4e00\u90e8\u5206\uff0c\u53ef\u4ee5\u4f7f\u7528
apt search \u547d\u4ee4\u6765\u641c\u7d22\u3002\u4f8b\u5982\uff0csudo apt search eigen \u53ef\u4ee5\u5e2e\u52a9\u627e\u5230\u6240\u6709\u5305\u542b \"eigen\" \u7684\u8f6f\u4ef6\u5305\u3002 - \u9898\u76ee\uff1a*\u82b1\u4e00\u4e2a\u5c0f\u65f6\u5b66\u4e60 Vim\uff0c\u56e0\u4e3a\u4f60\u8fdf\u65e9\u4f1a\u7528\u5b83\u3002\u4f60\u53ef\u4ee5\u5728\u7ec8\u7aef\u4e2d\u8f93\u5165 vimtutor \u9605\u8bfb\u4e00\u904d\u6240\u6709\u5185\u5bb9\u3002\u6211\u4eec\u4e0d\u9700\u8981\u4f60\u975e\u5e38\u719f\u7ec3\u5730\u64cd\u4f5c\u5b83\uff0c\u53ea\u8981\u80fd\u591f\u5728\u5b66\u4e60\u672c\u4e66\u7684\u8fc7\u7a0b\u4e2d\u4f7f\u7528\u5b83\u8f93\u5165\u4ee3\u7801\u5373\u53ef\u3002\u4e0d\u8981\u5728\u5b83\u7684\u63d2\u4ef6\u4e0a\u6d6a\u8d39\u65f6\u95f4\uff0c\u4e0d\u8981\u60f3\u7740\u628a Vim \u7528\u6210 IDE\uff0c\u6211\u4eec\u53ea\u7528\u5b83\u505a\u6587\u672c\u7f16\u8f91\u7684\u5de5\u4f5c\u3002
- \u7b54\u6848:
- vim \u6839\u672c\u4e0d\u719f\u7ec3\u634f
"},{"location":"AI/SLAM14/#2-slam","title":"2 \u521d\u8bc6 SLAM","text":""},{"location":"AI/SLAM14/#21","title":"2.1 \u5f15\u5b50: \u5c0f\u841d\u535c\u7684\u4f8b\u5b50","text":" - \u81ea\u4e3b\u8fd0\u52a8\u80fd\u529b
- \u611f\u77e5\u5468\u8fb9\u73af\u5883
- \u72b6\u6001
- \u73af\u5883
- \u5b89\u88c5\u4e8e\u73af\u5883\u4e2d\uff08\u4e0d\u592a\u597d\u53cd\u6b63\uff09
- \u673a\u5668\u4eba\u672c\u4f53\u4e0a
- \u6fc0\u5149 SLAM
- \u89c6\u89c9 SLAM\uff08\u672c\u4e66\u91cd\u70b9\uff09
- \u5355\u76ee\uff08Monocular\uff09
- \u53ea\u80fd\u7528\u4e00\u4e2a\u6444\u50cf\u5934
- \u8ddd\u79bb\u611f
- motion
- Structure
- Disparity
- Scale
- \u4f46\u662f\u65e0\u6cd5\u786e\u5b9a\u6df1\u5ea6
- \u53cc\u76ee\uff08Sterco\uff09
- \u4e24\u4e2a\u76f8\u673a\u7684\u8ddd\u79bb\uff08\u57fa\u7ebf Baseline\uff09\u5df2\u77e5
- \u914d\u7f6e\u4e0e\u6807\u5b9a\u6bd4\u8f83\u590d\u6742
- \u6df1\u5ea6\uff08RGB-D\uff09
- \u7ea2\u5916\u7ed3\u6784\u5173 Time-of-Flight\uff08ToF\uff09
- \u4e3b\u8981\u7528\u5728\u5ba4\u5185\uff0c\u5ba4\u5916\u4f1a\u6709\u5f88\u591a\u5f71\u54cd
- \u8fd8\u6709\u4e00\u4e9b\u975e\u4e3b\u6d41\u7684: \u5168\u666f\uff0cEvent
"},{"location":"AI/SLAM14/#22-slam","title":"2.2 \u7ecf\u5178\u89c6\u89c9 SLAM \u6846\u67b6","text":" - \u5728\u5916\u754c\u6362\u51e0\u4e2a\u6bd4\u8f83\u7a33\u5b9a\u7684\u60c5\u51b5\u4e0b\uff0cSLAM \u6280\u672f\u5df2\u7ecf\u6bd4\u8f83\u6210\u719f
"},{"location":"AI/SLAM14/#221","title":"2.2.1 \u89c6\u89c9\u91cc\u7a0b\u8ba1","text":" - \u53ea\u901a\u8fc7\u89c6\u89c9\u91cc\u7a0b\u8ba1\u6765\u4f30\u8ba1\u8f68\u8ff9\u4f1a\u51fa\u73b0\u7d2f\u79ef\u6f02\u79fb\uff08Accumulating Drift\uff09\u3002
- \u6240\u4ee5\u9700\u8981\u56de\u73af\u68c0\u6d4b\u4e0e\u540e\u7aef\u4f18\u5316
"},{"location":"AI/SLAM14/#222","title":"2.2.2 \u540e\u7aef\u4f18\u5316","text":" - \u6700\u5927\u540e\u9a8c\u6982\u7387\u4f30\u8ba1\uff08Maximum-a-Posteriori MAP\uff09
- \u524d\u7aef
- \u56fe\u50cf\u7684\u7279\u5f81\u63d0\u53d6\u4e0e\u5339\u914d
- \u540e\u7aef
- \u6ee4\u6ce2\u4e0e\u975e\u7ebf\u6027\u7b97\u6cd5
- \u5bf9\u8fd0\u52a8\u4e3b\u4f53\u81ea\u8eab\u548c\u5468\u56f4\u73af\u5883\u7a7a\u95f4\u4e0d\u786e\u5b9a\u6027\u7684\u4f30\u8ba1
"},{"location":"AI/SLAM14/#223","title":"2.2.3 \u56de\u73af\u68c0\u6d4b","text":" - \u95ed\u73af\u68c0\u6d4b
- \u8bc6\u522b\u5230\u8fc7\u7684\u573a\u666f
- \u5229\u7528\u56fe\u50cf\u7684\u76f8\u4f3c\u6027
"},{"location":"AI/SLAM14/#224","title":"2.2.4 \u5efa\u56fe","text":" - \u5ea6\u91cf\u5730\u56fe
- Sparse
- Landmark
- \u5b9a\u4f4d\u7528
- Dense
- Grid / Vocel
- \u5bfc\u822a\u7528
- \u62d3\u6251\u5730\u56fe Graph
"},{"location":"AI/SLAM14/#23-slam","title":"2.3 SLAM \u95ee\u9898\u7684\u6570\u5b66\u8868\u8ff0","text":" - \u8fd0\u52a8\u65b9\u7a0b
- \\(\\displaystyle \\quad\\boldsymbol{x}_k=f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k,\\boldsymbol{w}_k\\right).\\)
- \\(\\displaystyle \\boldsymbol{u}_{k}\\) \u662f\u8fd0\u52a8\u4f20\u611f\u5668\u7684\u8f93\u5165
- \\(\\displaystyle \\boldsymbol{w}_{k}\\) \u662f\u8fc7\u7a0b\u4e2d\u52a0\u5165\u7684\u566a\u58f0
- \u89c2\u6d4b\u65b9\u7a0b
- \\(\\displaystyle \\boldsymbol{z}_{k,j} = h (\\boldsymbol{y}_{j},\\boldsymbol{x}_{k},\\boldsymbol{v}_{k,j})\\)
- \\(\\displaystyle \\boldsymbol{v}_{k,j}\\) \u662f\u89c2\u6d4b\u91cc\u7684\u566a\u58f0
- \u53c8\u5f88\u591a\u53c2\u6570\u5316\u7684\u65b9\u5f0f
- \u53ef\u4ee5\u603b\u7ed3\u4e3a\u5982\u4e0b\u4e24\u4e2a\u65b9\u7a0b
\\[ \\begin{cases}\\boldsymbol{x}_k=f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k,\\boldsymbol{w}_k\\right),&k=1,\\cdots,K\\\\\\boldsymbol{z}_{k,j}=h\\left(\\boldsymbol{y}_j,\\boldsymbol{x}_k,\\boldsymbol{v}_{k,j}\\right),&(k,j)\\in\\mathcal{O}\\end{cases}. \\] - \u77e5\u9053\u8fd0\u52a8\u6d4b\u91cf\u7684\u8bfb\u6570 \\(\\displaystyle \\boldsymbol{u}\\) \u548c\u4f20\u611f\u5668\u7684\u8bfb\u6570 \\(\\displaystyle \\boldsymbol{z}\\)\uff0c\u5982\u4f55\u6c42\u89e3\u5b9a\u4f4d\u95ee\u9898\u548c\u5efa\u56fe\u95ee\u9898\u3002
- \u72b6\u6001\u4f30\u8ba1\u95ee\u9898: \u5982\u4f55\u901a\u8fc7\u5e26\u6709\u566a\u58f0\u7684\u6d4b\u91cf\u6570\u636e\uff0c\u4f30\u8ba1\u5185\u90e8\u7684\u3001\u9690\u85cf\u7740\u7684\u72b6\u6001\u53d8\u91cf
- Linear Gaussian -> Kalman Filter
- Non-Linear Non-Gaussian -> Extended Kalman Filter \u548c\u975e\u7ebf\u6027\u4f18\u5316
- EKF -> Particle Filter -> Graph Optimization
"},{"location":"AI/SLAM14/#24","title":"2.4 \u5b9e\u8df5: \u7f16\u7a0b\u57fa\u7840","text":""},{"location":"AI/SLAM14/#241-linux","title":"2.4.1 \u5b89\u88c5 Linux \u64cd\u4f5c\u7cfb\u7edf","text":""},{"location":"AI/SLAM14/#242-hello-slam","title":"2.4.2 Hello SLAM","text":""},{"location":"AI/SLAM14/#243-cmake","title":"2.4.3 \u4f7f\u7528 cmake","text":"Text Onlycmake_minimum_required( VERSION 2.8)\n\nproject(HelloSLAM)\n\nadd_executable(helloSLAM helloSLAM.cpp)\n
\u5bf9\u4e2d\u95f4\u6587\u4ef6\u7684\u5904\u7406:
Text Onlymkdir build\ncd build\ncmake ..\nmake\n
"},{"location":"AI/SLAM14/#244","title":"2.4.4 \u4f7f\u7528\u5e93","text":"Text Onlyadd_library(hello libHelloSLAM.cpp)\n
- \u9759\u6001\u5e93
- .a \u4f5c\u4e3a\u540e\u7f00\u540d\uff0c\u6bcf\u6b21\u8c03\u7528\u90fd\u6709\u4e00\u4e2a\u526f\u672c
- \u5171\u4eab\u5e93
- .so\uff0c\u53ea\u6709\u4e00\u4e2a\u526f\u672c
Text Onlyadd_library(hello_shared SHARED libHelloSLAM.cpp)\n
- \u8fd8\u8981\u4e00\u4e2a\u5934\u6587\u4ef6\u6765\u8bf4\u660e\u5e93\u91cc\u90fd\u6709\u4ec0\u4e48
Text Only#ifndef LIBHELLOSLAM_H_\n#define LIBHELLOSLAM_H_\n\nvoid printHello()\n\n#endif\n
- \u6700\u540e\u5199\u4e00\u4e2a\u53ef\u6267\u884c\u7a0b\u5e8f:
C++#include \"libHelloSLAM.h\"\n\nint main(int argc, char **argv) {\n printHello();\n return 0; \n}\n
- \u5728 CMakeLists. txt \u4e2d\u6dfb\u52a0\u53ef\u6267\u884c\u547d\u4ee4\u7684\u751f\u6210\u547d\u4ee4\uff0c\u94fe\u63a5\u5230\u5e93\u4e0a:
Text Onlyadd_executable(useHello useHello.cpp)\ntarget_link_libraries(useHello hello_shared)\n
"},{"location":"AI/SLAM14/#245-ide","title":"2.4.5 \u4f7f\u7528 IDE","text":" - KDevelop
- Clion
- \u8fd8\u6ca1\u5199
"},{"location":"AI/SLAM14/#3","title":"3 \u4e09\u7ef4\u7a7a\u95f4\u521a\u4f53\u8fd0\u52a8","text":""},{"location":"AI/SLAM14/#31","title":"3.1 \u65cb\u8f6c\u77e9\u9635","text":""},{"location":"AI/SLAM14/#311","title":"3.1.1 \u70b9\u3001\u5411\u91cf\u548c\u5750\u6807\u7cfb","text":" \\[ a\\times b = \\begin{Vmatrix}e_1&e_2&e_3\\\\ \\\\ a_1&a_2&a_3\\\\ \\\\ b_1&b_2&b_3 \\\\ \\end{Vmatrix} = \\begin{bmatrix} a_2b_3-a_3b_2\\\\ \\\\ a_3b_1-a_1b_3\\\\ \\\\ a_1b_2-a_2b_1 \\end{bmatrix} = \\begin{bmatrix} 0&-a_3&a_2\\\\ \\\\ a_3&0&-a_1\\\\ \\\\ -a_2&a_1&0 \\end{bmatrix} \\boldsymbol{b}\\overset{\\mathrm{def}}{\\operatorname*{=}}\\boldsymbol{a}^{\\wedge}\\boldsymbol{b}. \\] - \u4e8e\u662f\u5c31\u628a\u5916\u79ef\u53d8\u6210\u4e86\u7ebf\u6027\u8fd0\u7b97
- \u5373
\\[ \\displaystyle \\boldsymbol{a}^{\\wedge}=\\begin{bmatrix} 0 & -a_{3} & a_{2} \\\\ a_{3} & 0 & -a_{1} \\\\ -a_{2} & a_{1} & 0 \\end{bmatrix} \\]"},{"location":"AI/SLAM14/#312","title":"3.1.2 \u5750\u6807\u7cfb\u95f4\u7684\u6b27\u5f0f\u53d8\u6362","text":" \\[ \\begin{bmatrix}a_1\\\\ \\\\ a_2\\\\\\\\a_3\\end{bmatrix}=\\begin{bmatrix}e_1^\\mathrm{T}e_1^{\\prime}&e_1^\\mathrm{T}e_2^{\\prime}&e_1^\\mathrm{T}e_3^{\\prime}\\\\e_2^\\mathrm{T}e_1^{\\prime}&e_2^\\mathrm{T}e_2^{\\prime}&e_2^\\mathrm{T}e_3^{\\prime}\\\\e_3^\\mathrm{T}e_1^{\\prime}&e_3^\\mathrm{T}e_2^{\\prime}&e_3^\\mathrm{T}e_3^{\\prime}\\end{bmatrix}\\begin{bmatrix}a_1^{\\prime}\\\\\\\\a_2^{\\prime}\\\\\\\\a_3^{\\prime}\\end{bmatrix}\\stackrel{\\mathrm{def}}{=}Ra^{\\prime} \\] - \\(\\displaystyle \\boldsymbol{R}\\) \u662f\u65cb\u8f6c\u77e9\u9635\u3001\u65b9\u5411\u4f59\u5f26\u77e9\u9635
- Special Orthogonal Group \\(\\displaystyle \\mathrm{SO}(n)=\\{\\boldsymbol{R}\\in \\mathbb{R}^{n \\times n}|\\boldsymbol{R}\\boldsymbol{R}^{\\mathrm{T}}=\\boldsymbol{I},\\det(\\boldsymbol{R})=1\\}\\)
- \\(\\displaystyle a^{\\prime}=R^{-1}a=R^{\\intercal}a.\\)
- \u65cb\u8f6c+\u5e73\u79fb: \\(\\displaystyle a^{\\prime}=Ra+t.\\)
"},{"location":"AI/SLAM14/#313","title":"3.1.3 \u53d8\u6362\u77e9\u9635\u4e0e\u9f50\u6b21\u5750\u6807","text":" - \u4f46\u662f\u8fd9\u91cc\u7684\u53d8\u6362\u5173\u7cfb\u4e0d\u662f\u4e00\u4e2a\u7ebf\u6027\u5173\u7cfb
- \\(\\displaystyle c=R_2\\left(R_1a+t_1\\right)+t_2\\)
- \u6211\u4eec\u6539\u5199\u4e00\u4e0b\u5f62\u5f0f:
- \\(\\displaystyle \\begin{bmatrix}a'\\\\\\\\1\\end{bmatrix}=\\begin{bmatrix}R&t\\\\\\\\\\mathbf{0}^\\mathrm{T}&1\\end{bmatrix}\\begin{bmatrix}a\\\\\\\\1\\end{bmatrix}\\overset{\\mathrm{def}}{=}T\\begin{bmatrix}a\\\\\\\\1\\end{bmatrix}\\)
- \u8fd9\u5c31\u662f\u9f50\u6b21\u5750\u6807\uff0c\\(\\displaystyle \\boldsymbol{T}\\) \u79f0\u4e3a\u53d8\u6362\u77e9\u9635\uff08Transform matrix\uff09
- \\(\\displaystyle \\tilde{b}=T_1\\tilde{\\boldsymbol{a}}, \\tilde{\\boldsymbol{c}}=T_2\\tilde{\\boldsymbol{b}}\\quad\\Rightarrow\\tilde{\\boldsymbol{c}}=T_2T_1\\tilde{\\boldsymbol{a}}.\\)
- \u5e76\u4e14 \\(\\displaystyle \\boldsymbol{T}\\) \u79f0\u4e3a\u7279\u6b8a\u6b27\u5f0f\u7fa4\uff08Special Euclidean Group\uff09
- \\(\\displaystyle \\mathrm{SE}(3)=\\left\\{T=\\begin{bmatrix}R&t\\\\\\mathbf{0}^\\mathrm{T}&1\\end{bmatrix}\\in\\mathbb{R}^{4\\times4}|\\boldsymbol{R}\\in\\mathrm{SO}(3),\\boldsymbol{t}\\in\\mathbb{R}^3\\right\\}\\)
- \\(\\displaystyle T^{-1}=\\begin{bmatrix}R^\\mathrm{T}&-R^\\mathrm{T}t\\\\0^\\mathrm{T}&1\\end{bmatrix}\\)
- \u5728 C++\u7a0b\u5e8f\u4e2d\u53ef\u4ee5\u4f7f\u7528\u8fd0\u7b97\u7b26\u91cd\u8f7d\u6765\u5904\u7406\u9f50\u6b21\u548c\u975e\u9f50\u6b21\u7684\u60c5\u51b5
"},{"location":"AI/SLAM14/#32-eigen","title":"3.2 \u5b9e\u8df5:Eigen","text":"C++#include <iostream>\nusing namespace std;\n\n#include <ctime>\n\n#include <eigen3>\nusing namespace Eigen;\n\n#define MATRIX_SIZE 50\n\nint main(int argc, char **argv) {\n Matrix<float, 2, 3> matrix_23;\n// \u5982\u4e0b\u90fd\u662f\u4e09\u7ef4\u5411\u91cf\n Vector3d v_3d;\n Matrix<float, 3, 1> vd_3d;\n// \u5982\u4e0b\u662f3*3\u77e9\u9635\n Matrix3d matrix_33 = Matrix3d::Zero();\n// \u4e24\u4e2a\u52a8\u6001\u5206\u914d\n Matrix<double, Dynamic, Dynamic> matrix_dynamic;\n MatrixXd matrix_x;\n\n matrix_23 << 1, 2, 3, 4, 5, 6;\n cout<< \"matrix 2*3 from 1 to 6: \\n\" << matrix_23 << endl;\n\n cout << \"print matrix 2*3:\" << endl;\n for (int i = 0; i < 2; i++) {\n for (int j = 0; j < 3; j+) cout << matrix_23(i, j) << \"\\t\";\n cout << endl;\n }\n\n v_3d << 3, 2, 1;\n vd_3d << 4, 5, 6;\n\n Matrix<double, 2, 1> result = matrix_23.cast<double>() * v_3d;\n cout << \"[1, 2, 3; 4, 5, 6] * [3, 2, 1] =\" << result.transpose() << endl;\n\n matrix_22 = Matrix3d::Random();\n\n // \u4e00\u4e9b\u77e9\u9635\u7684\u64cd\u4f5c:\n // transpose()\n // sum()\n // trace()\n // inverse()\n // determinant()\n\n SelfAdjointEigenSolver<Matrix3d> eigen_solver(matrix_33.transpose() * matrix_33);\n cout << eigen_solver.eigenvalues() << endl;\n cout << eigen_solver.eigenvectors() << endl;\n\n // solve the equation\n Matrix<double, MATRIX_SIZE, MATRIX_SIZE> matrix_NN = MatrixXd::Random(MATRIX_SIZE, MATRIX_SIZE);\n matrix_NN = matrix_NN * matrix_NN.transpose()\n Matrix<double, MATRIX_SIZE, 1> v_Nd = MatrixXd::random(MATRIX_SIZE, 1);\n\n // \u7b2c\u4e00\u79cd:\u76f4\u63a5\u6c42\u9006\n Matrix<double, MATRIX_SIZE, 1> x = matrix_NN.inverse() * v_Nd;\n\n // \u7b2c\u4e8c\u79cd:\u77e9\u9635\u5206\u89e3\n x = matrix_NN.colPivHouseholderQr().solve(v_Nd);\n\n}\n
- Eigen \u4e0d\u652f\u6301\u81ea\u52a8\u7c7b\u578b\u63d0\u5347\uff0c\u5373\u4e0d\u4f1a\u9690\u5f0f\u8f6c\u6362
"},{"location":"AI/SLAM14/#33","title":"3.3 \u65cb\u8f6c\u5411\u91cf\u548c\u6b27\u62c9\u89d2","text":""},{"location":"AI/SLAM14/#331","title":"3.3.1 \u65cb\u8f6c\u5411\u91cf","text":" - Axis-Angle
- Rodrigues's Formula
- \\(\\displaystyle \\boldsymbol{R}=\\cos\\theta\\boldsymbol{I}+\\left(1-\\cos\\theta\\right)\\boldsymbol{n}\\boldsymbol{n}^\\mathrm{T}+\\sin\\theta\\boldsymbol{n}^\\mathrm{\\wedge}.\\)
\\[ \\begin{aligned} \\mathrm{tr}\\left(R\\right)& =\\cos\\theta\\operatorname{tr}\\left(\\boldsymbol{I}\\right)+\\left(1-\\cos\\theta\\right)\\operatorname{tr}\\left(\\boldsymbol{n}\\boldsymbol{n}^\\mathrm{T}\\right)+\\sin\\theta\\operatorname{tr}(\\boldsymbol{n}^\\mathrm{\\Lambda}) \\\\ &=3\\cos\\theta+(1-\\cos\\theta) \\\\ &=1+2\\cos\\theta \\end{aligned} \\] thus:
\\[ \\theta=\\arccos\\frac{\\mathrm{tr}(R)-1}{2}. \\] \\[ Rn=n. \\] - \u5373 \\(\\displaystyle \\boldsymbol{n}\\) \u662f\u77e9\u9635 \\(\\displaystyle \\boldsymbol{R}\\) \u7279\u5f81\u503c 1 \u5bf9\u5e94\u7684\u7279\u8bca\u5411\u91cf
"},{"location":"AI/SLAM14/#332","title":"3.3.2 \u6b27\u62c9\u89d2","text":" - \u6bd4\u8f83\u5e38\u7528\u7684\u4e00\u79cd yaw-pitch-roll
- \u4f46\u4f1a\u6709 Gimbal Lock \u95ee\u9898
- \u6240\u4ee5\u6b27\u62c9\u89d2\u6bd4\u8f83\u9002\u5408\u7528\u4e8e\u5feb\u901f\u68c0\u9a8c\u7ed3\u679c\u662f\u5426\u6709\u9519
"},{"location":"AI/SLAM14/#34","title":"3.4 \u56db\u5143\u6570","text":""},{"location":"AI/SLAM14/#341","title":"3.4.1 \u56db\u5143\u6570\u7684\u5b9a\u4e49","text":" - \u6211\u4eec\u627e\u4e0d\u5230\u4e0d\u5e26\u5947\u5f02\u6027\u7684\u4e09\u4f4d\u5411\u91cf\u63cf\u8ff0\u65b9\u5f0f
- \u627e\u4e0d\u5230\u4e00\u4e2a\u6d41\u5f62\uff1f
- Quaternion
- \u7d27\u51d1\u53c8\u6ca1\u6709\u5947\u5f02\u6027
- \u53ea\u662f\u4e0d\u591f\u76f4\u89c2+\u8fd0\u7b97\u590d\u6742
- \\(\\displaystyle q=q_0+q_1\\mathrm{i}+\\mathrm{q}_2\\mathrm{j}+\\mathrm{q}_3\\mathrm{k}\\)
\\[ \\begin{cases}\\mathbf{i}^2=\\mathbf{j}^2=\\mathbf{k}^2=-1\\\\\\mathbf{ij}=\\mathbf{k},\\mathbf{ji}=-\\mathbf{k}\\\\\\mathbf{jk}=\\mathbf{i},\\mathbf{kj}=-\\mathbf{i}\\\\\\mathbf{ki}=\\mathbf{j},\\mathbf{ik}=-\\mathbf{j}\\end{cases} \\] - (\u4e5f\u8bb8\u53ef\u4ee5\u7528\u5ea6\u89c4\u6765\u8868\u793a\uff1f)
- \\(\\displaystyle \\boldsymbol{q}=\\left[s,\\boldsymbol{v}\\right]^\\mathrm{T},\\quad s=q_0\\in\\mathbb{R},\\quad\\boldsymbol{v}=\\left[q_1,q_2,q_3\\right]^\\mathrm{T}\\in\\mathbb{R}^3.\\)
- \u4e66\u4e0a\u6ca1\u5199\u76f4\u89c2\u7684\u51e0\u4f55\u5bf9\u5e94
"},{"location":"AI/SLAM14/#342","title":"3.4.2 \u56db\u5143\u6570\u7684\u8fd0\u7b97","text":" - \u4e58\u6cd5: \\(\\displaystyle \\boldsymbol{q}_a\\boldsymbol{q}_b=\\begin{bmatrix}s_as_b-\\boldsymbol{v}_a^\\mathrm{T}\\boldsymbol{v}_b,s_a\\boldsymbol{v}_b+s_b\\boldsymbol{v}_a+\\boldsymbol{v}_a\\times\\boldsymbol{v}_b\\end{bmatrix}^\\mathrm{T}.\\)
- \u7531\u4e8e\u6700\u540e\u4e00\u9879\u7684\u5b58\u5728\uff0c\u4e58\u6cd5\u4e0d\u5177\u6709\u4ea4\u6362\u5f8b
- \u5171\u8f6d: \\(\\displaystyle q_a^*=s_a-x_a\\mathrm{i}-\\mathrm{y_aj}-\\mathrm{z_ak}=[\\mathrm{s_a},-\\mathrm{v_a}]^\\mathrm{T}.\\)
- \\(\\displaystyle q^*q=qq^*=[s_a^2+\\boldsymbol{v}^\\mathrm{T}\\boldsymbol{v},\\boldsymbol{0}]^\\mathrm{T}.\\)
- \u9006: \\(\\displaystyle q^{-1}=q^*/\\|q\\|^2.\\)
- \\(\\displaystyle (\\boldsymbol{q}_a\\boldsymbol{q}_b)^{-1}=\\boldsymbol{q}_b^{-1}\\boldsymbol{q}_a^{-1}.\\)
"},{"location":"AI/SLAM14/#343","title":"3.4.3 \u7528\u56db\u5143\u6570\u8868\u793a\u65cb\u8f6c","text":" - \u5148\u8868\u793a\u4e09\u7ef4\u7a7a\u95f4\u70b9:
- \\(\\displaystyle p=[0,x,y,z]^{\\mathrm{T}}=[0,\\boldsymbol{v}]^{\\mathrm{T}}.\\)
- \u518d\u65cb\u8f6c:
- \\(\\displaystyle p'=qpq^{-1}.\\)
"},{"location":"AI/SLAM14/#344","title":"3.4.4 \u56db\u5143\u6570\u5230\u5176\u4ed6\u65cb\u8f6c\u8868\u793a\u7684\u8f6c\u6362","text":" - \u8bbe \\(\\displaystyle \\boldsymbol{q} = [s,\\boldsymbol{v}]^\\mathrm{T}\\)
- \\(\\displaystyle \\boldsymbol{q}^+=\\begin{bmatrix}s&-\\boldsymbol{v}^\\mathrm{T}\\\\\\\\\\boldsymbol{v}&s\\boldsymbol{I}+\\boldsymbol{v}^\\wedge\\end{bmatrix},\\quad\\boldsymbol{q}^\\oplus=\\begin{bmatrix}s&-\\boldsymbol{v}^\\mathrm{T}\\\\\\\\\\boldsymbol{v}&s\\boldsymbol{I}-\\boldsymbol{v}^\\wedge\\end{bmatrix}.\\)
- \\(\\displaystyle q_1^+q_2=\\begin{bmatrix}s_1&-\\boldsymbol{v}_1^\\mathrm{T}\\\\\\\\\\boldsymbol{v}_1&s_1\\boldsymbol{I}+\\boldsymbol{v}_1^\\wedge\\end{bmatrix}\\begin{bmatrix}s_2\\\\\\\\\\boldsymbol{v}_2\\end{bmatrix}=\\begin{bmatrix}-\\boldsymbol{v}_1^\\mathrm{T}\\boldsymbol{v}_2+s_1s_2\\\\\\\\s_1\\boldsymbol{v}_2+s_2\\boldsymbol{v}_1+\\boldsymbol{v}_1^\\wedge\\boldsymbol{v}_2\\end{bmatrix}=\\boldsymbol{q}_1\\boldsymbol{q}_2.\\)
- \u540c\u7406\u53ef\u8bc1:
- \\(\\displaystyle q_1q_2=q_1^+q_2=q_2^\\oplus q_1.\\)
- \u518d\u6765\u8003\u8651\u65cb\u8f6c:
- \\(\\displaystyle \\begin{aligned}p^{\\prime}&=qpq^{-1}=q^{+}p^{+}q^{-1}\\\\&=q^{+}q^{-1^{\\oplus}}p.\\end{aligned}\\)
- \u4e8e\u662f\u53ef\u4ee5\u5f97\u5230:
- \\(\\displaystyle \\boldsymbol{q}^{+}\\big(\\boldsymbol{q}^{-1}\\big)^{\\oplus}=\\begin{bmatrix}s&-\\boldsymbol{v}^{\\mathrm{T}}\\\\\\boldsymbol{v}&s\\boldsymbol{I}+\\boldsymbol{v}^{\\wedge}\\end{bmatrix}\\begin{bmatrix}s&\\boldsymbol{v}^{\\mathrm{T}}\\\\-\\boldsymbol{v}&s\\boldsymbol{I}+\\boldsymbol{v}^{\\wedge}\\end{bmatrix}=\\begin{bmatrix}1&\\boldsymbol{0}\\\\\\boldsymbol{0}^{\\mathrm{T}}&\\boldsymbol{v}\\boldsymbol{v}^{\\mathrm{T}}+s^{2}\\boldsymbol{I}+2s\\boldsymbol{v}^{\\wedge}+\\left(\\boldsymbol{v}^{\\wedge}\\right)^{2}\\end{bmatrix}.\\)
- \u5373: \\(\\displaystyle R=\\boldsymbol{v}\\boldsymbol{v}^\\mathrm{T}+s^2\\boldsymbol{I}+2s\\boldsymbol{v}^\\wedge+\\left(\\boldsymbol{v}^\\wedge\\right)^2.\\)
\\[ \\begin{aligned} \\operatorname{tr}(R)& =\\mathbf{tr}(\\boldsymbol{vv}^\\mathrm{T}+3s^2+2s\\cdot0+\\mathbf{tr}((\\boldsymbol{v}^\\wedge)^2) \\\\ &=v_{1}^{2}+v_{2}^{2}+v_{3}^{2}+3s^{2}-2(v_{1}^{2}+v_{2}^{2}+v_{3}^{2}) \\\\ &=(1-s^2)+3s^2-2(1-s^2) \\\\ &=4s^2-1. \\end{aligned} \\] - \u5373 \\(\\displaystyle \\theta=2\\arccos s.\\)
- \u518d\u52a0\u4e0a\u65cb\u8f6c\u8f74:
\\[ \\begin{cases}\\theta=2\\arccos q_0\\\\ [n_x,n_y,n_z]^\\mathrm{T}=[q_1,q_2,q_3]^\\mathrm{T}/\\sin\\frac{\\theta}{2}\\end{cases}. \\]"},{"location":"AI/SLAM14/#35","title":"3.5 \u76f8\u4f3c\u3001\u4eff\u5c04\u3001\u5c04\u5f71\u53d8\u6362","text":" - \u76f8\u4f3c\u53d8\u6362:
\\[ \\boldsymbol{T}_S=\\begin{bmatrix}s\\boldsymbol{R}&t\\\\\\mathbf{0}^\\mathrm{T}&1\\end{bmatrix}. \\] \u5141\u8bb8\u7f29\u653e\uff0c\u76f8\u4f3c\u53d8\u6362\u7fa4: Sim (3) 2. \u4eff\u5c04\u53d8\u6362:
\\[ T_A=\\begin{bmatrix}A&t\\\\\\mathbf{0}^\\mathrm{T}&1\\end{bmatrix}. \\] \u53ea\u4fdd\u8bc1\u5e73\u884c\u5173\u7cfb 3. \u5c04\u5f71\u53d8\u6362
\\[ T_P=\\begin{bmatrix}A&t\\\\\\\\a^\\mathrm{T}&v\\end{bmatrix}. \\] \u603b\u7ed3\u4e00\u4e0b:
\\[ \\begin{array}{c|c|c|c}\\hline\\text{\u53d8\u6362\u540d\u79f0}&\\text{\u77e9\u9635\u5f62\u5f0f}&\\text{\u81ea\u7531\u5ea6}&\\text{\u4e0d\u53d8\u6027\u8d28}\\\\\\hline\\text{\u6b27\u6c0f\u53d8\u6362}&\\begin{bmatrix}R&t\\\\0^\\mathrm{T}&1\\end{bmatrix}&6&\\text{\u957f\u5ea6\u3001\u5939\u89d2\u3001\u4f53\u79ef}\\\\\\text{\u76f8\u4f3c\u53d8\u6362}&\\begin{bmatrix}sR&t\\\\0^\\mathrm{T}&1\\end{bmatrix}&7&\\text{\u4f53\u79ef\u6bd4}\\\\\\text{\u4eff\u5c04\u53d8\u6362}&\\begin{bmatrix}A&t\\\\0^\\mathrm{T}&1\\end{bmatrix}&12&\\text{\u5e73\u884c\u6027\u3001\u4f53\u79ef\u6bd4}\\\\\\text{\u5c04\u5f71\u53d8\u6362}&\\begin{bmatrix}A&t\\\\a^\\mathrm{T}&v\\end{bmatrix}&15&\\text{\u63a5\u89e6\u5e73\u9762\u7684\u76f8\u4ea4\u548c\u76f8\u5207}\\\\\\hline\\end{array} \\]"},{"location":"AI/SLAM14/#36-eigen","title":"3.6 \u5b9e\u8df5: Eigen \u51e0\u4f55\u6a21\u5757","text":""},{"location":"AI/SLAM14/#361-eigen","title":"3.6.1 Eigen \u51e0\u4f55\u6a21\u5757\u7684\u6570\u636e\u6f14\u793a","text":"\u518d\u8bf4\u5427\u3002
"},{"location":"AI/SLAM14/#362","title":"3.6.2 \u5b9e\u9645\u7684\u5750\u6807\u53d8\u6362\u4f8b\u5b50","text":"TODO
"},{"location":"AI/SLAM14/#37","title":"3.7 \u53ef\u89c6\u5316\u6f14\u793a","text":""},{"location":"AI/SLAM14/#371","title":"3.7.1 \u663e\u793a\u8fd0\u52a8\u8f68\u8ff9","text":" - \u7528 Pangolin \u5e93
- TODO
"},{"location":"AI/SLAM14/#372","title":"3.7.2 \u663e\u793a\u76f8\u673a\u7684\u4f4d\u59ff","text":""},{"location":"AI/SLAM14/#38","title":"3.8 \u4e60\u9898","text":"TODO
"},{"location":"AI/SLAM14/#4","title":"4 \u674e\u7fa4\u548c\u674e\u4ee3\u6570","text":" - \u7531\u4e8e\u65cb\u8f6c\u77e9\u9635\u672c\u8eab\u5e26\u6709\u7ea6\u675f\uff08\u6b63\u4ea4\u4e14\u884c\u5217\u5f0f\u4e3a 1\uff09\uff0c\u8ba9\u4f18\u5316\u53d8\u5f97\u56f0\u96be\u3002
- \u6240\u4ee5\u6211\u4eec\u5f15\u5165\u674e\u7fa4-\u674e\u4ee3\u6570\u95f4\u7684\u8f6c\u6362\u5173\u7cfb\uff0c\u628a\u4f4d\u59ff\u4f30\u8ba1\u53d8\u6210\u65e0\u7ea6\u675f\u7684\u4f18\u5316\u95ee\u9898
"},{"location":"AI/SLAM14/#41","title":"4.1 \u674e\u7fa4\u548c\u674e\u4ee3\u6570\u57fa\u7840","text":" - \u4e09\u7ef4\u65cb\u8f6c\u77e9\u9635\u6784\u6210\u4e86\u7279\u6b8a\u6b63\u4ea4\u7fa4 \\(\\displaystyle \\boldsymbol{SO}(3)\\)
- \u53d8\u6362\u77e9\u9635\u6784\u6210\u4e86\u7279\u6b8a\u6b27\u6c0f\u7fa4 \\(\\displaystyle \\boldsymbol{SE}(3)\\)
- \u4f46\u662f\u4ed6\u4eec\u90fd\u5bf9\u52a0\u6cd5\u4e0d\u5c01\u95ed
- \u5bf9\u4e58\u6cd5\u5c01\u95ed
"},{"location":"AI/SLAM14/#411","title":"4.1.1 \u7fa4","text":" - \\(\\displaystyle G = (A,\\cdot)\\) \u6ee1\u8db3:
- \u5c01\u95ed\u6027
- \u7ed3\u5408\u5f8b
- \u5e7a\u5143
- \u9006
- \u674e\u7fa4\u662f\u5177\u6709\u8fde\u7eed\uff08\u5149\u6ed1\uff09\u6027\u8d28\u7684\u7fa4
"},{"location":"AI/SLAM14/#412","title":"4.1.2 \u674e\u4ee3\u6570\u7684\u5f15\u51fa","text":" - \\(\\displaystyle \\boldsymbol{R}\\boldsymbol{R}^\\mathrm{T} = \\boldsymbol{I}\\)
- \u6211\u4eec\u6613\u5f97: \\(\\displaystyle \\dot{\\boldsymbol{R}}(t)\\boldsymbol{R}(t)^\\mathrm{T}=-\\left(\\dot{\\boldsymbol{R}}(t)\\boldsymbol{R}(t)^\\mathrm{T}\\right)^\\mathrm{T}.\\)
- \u5373 \\(\\displaystyle \\dot{\\boldsymbol{R}}(t)\\boldsymbol{R}(t)^\\mathrm{T}\\) \u662f\u53cd\u5bf9\u79f0
- \u800c\u5bf9\u4e8e\u4efb\u610f\u53cd\u5bf9\u79f0\u77e9\u9635\uff0c\u6211\u4eec\u90fd\u53ef\u4ee5\u627e\u5230\u552f\u4e00\u4e0e\u4e4b\u5bf9\u5e94\u7684\u5411\u91cf
- \\(\\displaystyle \\dot{\\boldsymbol{R}}(t)\\boldsymbol{R}(t)^\\mathrm{T}=\\boldsymbol{\\phi}(t)^{\\wedge}.\\)
- \\(\\displaystyle \\dot{\\boldsymbol{R}}(t)=\\phi(t)^{\\wedge}\\boldsymbol{R}(t)=\\begin{bmatrix}0&-\\phi_3&\\phi_2\\\\\\phi_3&0&-\\phi_1\\\\-\\phi_2&\\phi_1&0\\end{bmatrix}\\boldsymbol{R}(t).\\) \u8003\u8651 \\(\\displaystyle t_{0} = 0\\) \u548c \\(\\displaystyle \\boldsymbol{R}(0) = \\boldsymbol{I}\\) \u65f6:
\\[ \\begin{aligned} R(t)& \\approx\\boldsymbol{R}\\left(t_{0}\\right)+\\dot{\\boldsymbol{R}}\\left(t_{0}\\right)\\left(t-t_{0}\\right) \\\\ &=I+\\phi(t_0)^{\\wedge}(t). \\end{aligned} \\] \u4e8e\u662f\u6c42\u5bfc->\u4e00\u4e2a\u7b97\u7b26 \\(\\displaystyle \\phi\\) \uff0c\u88ab\u79f0\u4e3a \\(\\displaystyle \\boldsymbol{SO}(3)\\) \u539f\u70b9\u9644\u8fd1\u7684\u6b63\u5207\u7a7a\u95f4\uff08Tangent Space\uff09 \u8bbe\u5728 \\(\\displaystyle t_{0}\\) \u9644\u8fd1\uff0c\\(\\displaystyle \\phi\\) \u4fdd\u6301\u5e38\u6570 \\(\\displaystyle \\phi(t_{0})=\\phi_{0}\\)\uff0c
\\[ \\dot{\\boldsymbol{R}}(t)=\\boldsymbol{\\phi}(t_0)^\\wedge\\boldsymbol{R}(t)=\\boldsymbol{\\phi}_0^\\wedge\\boldsymbol{R}(t). \\] \u518d\u6709 \\(\\displaystyle \\boldsymbol{R}(0) = \\boldsymbol{I}\\)\uff0c\u89e3\u7684:
\\[ \\boldsymbol{R}(t)=\\exp\\left(\\boldsymbol{\\phi}_{0}^{\\wedge}t\\right). \\] - \\(\\displaystyle \\phi\\) \u6b63\u662f\u5bf9\u5e94\u5230 \\(\\displaystyle SO(3)\\) \u4e0a\u7684\u674e\u4ee3\u6570 \\(\\displaystyle \\mathfrak{so}(3)\\)
- \\(\\displaystyle \\begin{aligned}&\\text{\u5176\u6b21,\u7ed9\u5b9a\u67d0\u4e2a\u5411\u91cf }\\phi\\text{ \u65f6,\u77e9\u9635\u6307\u6570}\\exp(\\phi^{\\wedge})\\text{ \u5982\u4f55\u8ba1\u7b97? \u53cd\u4e4b,\u7ed9\u5b9a }R\\text{ \u65f6,\u80fd\u5426\u6709\u76f8\u53cd}\\\\&\\text{\u7684\u8fd0\u7b97\u6765\u8ba1\u7b97 }\\phi?\\text{ \u4e8b\u5b9e\u4e0a,\u8fd9\u6b63\u662f\u674e\u7fa4\u4e0e\u674e\u4ee3\u6570\u95f4\u7684\u6307\u6570}/\\text{\u5bf9\u6570\u6620\u5c04\u3002}\\end{aligned}\\)
"},{"location":"AI/SLAM14/#413","title":"4.1.3 \u674e\u4ee3\u6570\u7684\u5b9a\u4e49","text":"\u674e\u4ee3\u6570\u7531\u4e00\u4e2a\u96c6\u5408 \\(\\displaystyle \\mathbb{V}\\)\u3001\u4e00\u4e2a\u6570\u57df \\(\\displaystyle \\mathbb{F}\\) \u548c\u4e00\u4e2a\u4e8c\u5143\u8fd0\u7b97 \\(\\displaystyle [,]\\) \u7ec4\u6210\u3002\u5982\u679c\u6ee1\u8db3\u4ee5\u4e0b\u51e0\u6761\u6027\u8d28\uff0c\u5219\u79f0 ( \\(\\displaystyle \\mathbb{V},\\mathbb{F},[,]\\)) \u4e3a\u4e00\u4e2a\u674e\u4ee3\u6570\uff0c\u8bb0\u4f5c \\(\\displaystyle \\mathfrak{g}\\)\u3002
- \u5c01\u95ed\u6027
- \u53cc\u7ebf\u6027
- \u81ea\u53cd\u6027 \\(\\displaystyle \\quad\\forall \\boldsymbol{X}\\in\\mathbb{V},[\\boldsymbol{X},\\boldsymbol{X}]=0\\)
- \u96c5\u53ef\u6bd4\u7b49\u4ef7 \\(\\displaystyle \\forall X,Y,Z\\in\\mathbb{V},[X,[Y,Z]]+[Z,[X,Y]]+[Y,[Z,X]]=0.\\) \u5176\u4e2d\u4e8c\u5143\u8fd0\u7b97\u88ab\u79f0\u4e3a\u674e\u62ec\u53f7\u3002 eg: \u53c9\u79ef\u662f\u4e00\u79cd\u674e\u62ec\u53f7
"},{"location":"AI/SLAM14/#414-displaystyle-mathfrakso3","title":"4.1.4 \u674e\u4ee3\u6570 \\(\\displaystyle \\mathfrak{so}(3)\\)","text":"\\(\\displaystyle \\boldsymbol{\\Phi}=\\boldsymbol{\\phi}^{\\wedge}=\\begin{bmatrix}0&-\\phi_3&\\phi_2\\\\\\\\\\phi_3&0&-\\phi_1\\\\\\\\-\\phi_2&\\phi_1&0\\end{bmatrix}\\in\\mathbb{R}^{3\\times3}.\\) \u6240\u4ee5\u4e24\u4e2a\u5411\u91cf \\(\\displaystyle \\phi_{1},\\phi_{2}\\) \u7684\u674e\u62ec\u53f7\u4e3a:
\\[ [\\phi_1,\\phi_2]=(\\boldsymbol{\\Phi}_1\\boldsymbol{\\Phi}_2-\\boldsymbol{\\Phi}_2\\boldsymbol{\\Phi}_1)^\\vee. \\] \\[ \\mathfrak{so}(3)=\\left\\{\\phi\\in\\mathbb{R}^3,\\boldsymbol{\\Phi}=\\phi^\\wedge\\in\\mathbb{R}^{3\\times3}\\right\\}. \\] \\[ R=\\exp(\\phi^{\\wedge}). \\]"},{"location":"AI/SLAM14/#415-displaystyle-mathfrakse3","title":"4.1.5 \u674e\u4ee3\u6570 \\(\\displaystyle \\mathfrak{se}(3)\\)","text":"\\[ \\mathfrak{se}(3)=\\left\\{\\boldsymbol{\\xi}=\\begin{bmatrix}\\boldsymbol{\\rho}\\\\\\boldsymbol{\\phi}\\end{bmatrix}\\in\\mathbb{R}^6,\\boldsymbol{\\rho}\\in\\mathbb{R}^3,\\boldsymbol{\\phi}\\in\\mathfrak{so}(3),\\boldsymbol{\\xi}^\\wedge=\\begin{bmatrix}\\boldsymbol{\\phi}^\\wedge&\\boldsymbol{\\rho}\\\\\\boldsymbol{0}^\\mathrm{T}&0\\end{bmatrix}\\in\\mathbb{R}^{4\\times4}\\right\\}. \\] \u524d\u4e09\u7ef4\u4e3a\u5e73\u79fb\uff0c\u540e\u4e09\u7ef4\u4e3a\u65cb\u8f6c\uff08\u5b9e\u8d28\u4e0a\u662f \\(\\displaystyle \\mathfrak{so}(3)\\) \u5143\u7d20\uff09
\\[ \\xi^\\wedge=\\begin{bmatrix}\\phi^\\wedge&\\rho\\\\0^\\mathrm{T}&0\\end{bmatrix}\\in\\mathbb{R}^{4\\times4}. \\] \u540c\u6837\u674e\u4ee3\u6570 \\(\\displaystyle \\mathfrak{se}(3)\\) \u4e5f\u6709\u7c7b\u4f3c\u4e8e \\(\\displaystyle \\mathfrak{so}(3)\\) \u7684\u674e\u62ec\u53f7:
\\[ [\\xi_1,\\xi_2]=\\left(\\xi_1^\\wedge\\xi_2^\\wedge-\\xi_2^\\wedge\\xi_1^\\wedge\\right)^\\vee. \\]"},{"location":"AI/SLAM14/#42","title":"4.2 \u6307\u6570\u4e0e\u5bf9\u6570\u6620\u5c04","text":""},{"location":"AI/SLAM14/#421-so3","title":"4.2.1 SO(3)\u4e0a\u7684\u6307\u6570\u6620\u5c04","text":" - Exponential Map
- \u9996\u5148\u4efb\u610f\u77e9\u9635\u7684\u6307\u6570\u6620\u5c04\u53ef\u4ee5\u5199\u6210\u4e00\u4e2a\u6cf0\u52d2\u5c55\u5f00\uff08\u6536\u655b\u7684\u65f6\u5019\uff09
\\[ \\exp(A)=\\sum_{n=0}^\\infty\\frac1{n!}A^n. \\] - \u5e94\u7528\u5230 \\(\\displaystyle \\mathfrak{so}(3)\\) \u4e2d:
\\[ \\exp(\\phi^\\wedge)=\\sum_{n=0}^\\infty\\frac{1}{n!}(\\phi^\\wedge)^n. \\] - \u6211\u4eec\u53ef\u4ee5\u628a \\(\\displaystyle \\phi\\) \u8868\u793a\u6210 \\(\\displaystyle \\theta \\boldsymbol{a}\\)\uff0c\u5bf9\u4e8e \\(\\displaystyle \\boldsymbol{a}^\\wedge\\):
\\[ \\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}=\\begin{bmatrix}-a_2^2-a_3^2&a_1a_2&a_1a_3\\\\\\\\a_1a_2&-a_1^2-a_3^2&a_2a_3\\\\\\\\a_1a_3&a_2a_3&-a_1^2-a_2^2\\end{bmatrix}=\\boldsymbol{a}\\boldsymbol{a}^\\mathrm{T}-\\boldsymbol{I}, \\] \u548c
\\[ a^{\\wedge}a^{\\wedge}a^{\\wedge}=a^{\\wedge}(aa^{\\mathrm{T}}-I)=-a^{\\wedge}. \\] \u4e8e\u662f\u6211\u4eec\u53ef\u4ee5\u5316\u7b80:
\\[ \\begin{aligned} \\exp\\left(\\phi^\\wedge\\right)& =\\exp\\left(\\theta\\boldsymbol{a}^\\wedge\\right)=\\sum_{n=0}^\\infty\\frac1{n!}\\left(\\theta\\boldsymbol{a}^\\wedge\\right)^n \\\\ &=I+\\theta\\boldsymbol{a}^{\\wedge}+\\frac{1}{2!}\\theta^{2}\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}+\\frac{1}{3!}\\theta^{3}\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}+\\frac{1}{4!}\\theta^{4}(\\boldsymbol{a}^{\\wedge})^{4}+\\cdots \\\\ &=\\boldsymbol{a}\\boldsymbol{a}^{\\mathrm{T}}-\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}+\\theta\\boldsymbol{a}^{\\wedge}+\\frac{1}{2!}\\theta^{2}\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}-\\frac{1}{3!}\\theta^{3}\\boldsymbol{a}^{\\wedge}-\\frac{1}{4!}\\theta^{4}(\\boldsymbol{a}^{\\wedge})^{2}+\\cdots \\\\ &=\\boldsymbol{a}\\boldsymbol{a}^{\\mathsf{T}}+\\underbrace{\\left(\\theta-\\frac{1}{3!}\\theta^{3}+\\frac{1}{5!}\\theta^{5}-\\cdots\\right)}_{\\sin\\theta}\\boldsymbol{a}^{\\wedge}-\\underbrace{\\left(1-\\frac{1}{2!}\\theta^{2}+\\frac{1}{4!}\\theta^{4}-\\cdots\\right)}_{\\cos\\theta}\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge} \\\\ &=a^\\wedge a^\\wedge+I+\\sin\\theta a^\\wedge-\\cos\\theta a^\\wedge a^\\wedge \\\\ &=(1-\\cos\\theta)\\boldsymbol{a}^\\wedge\\boldsymbol{a}^\\wedge+\\boldsymbol{I}+\\sin\\theta\\boldsymbol{a}^\\wedge \\\\ &=\\cos\\theta\\boldsymbol{I}+(1-\\cos\\theta)\\boldsymbol{aa}^\\mathrm{T}+\\sin\\theta\\boldsymbol{a}^\\mathrm{\\wedge}. \\end{aligned} \\] \u6700\u540e\u5f97\u5230:
\\[ \\exp(\\theta\\boldsymbol{a}^\\wedge)=\\cos\\theta\\boldsymbol{I}+(1-\\cos\\theta)\\boldsymbol{a}\\boldsymbol{a}^\\mathrm{T}+\\sin\\theta\\boldsymbol{a}^\\wedge. \\] \u6240\u4ee5 \\(\\displaystyle \\mathfrak{so}(3)\\) \u5c31\u662f\u65cb\u91cf\u5411\u91cf\u7ec4\u6210\u7684\u7a7a\u95f4\uff0c\u800c\u6307\u6570\u6620\u5c04\u5373\u7f57\u5fb7\u91cc\u683c\u65af\u516c\u5f0f\u3002
- \u901a\u8fc7\u4e0a\u9762\u7684\u516c\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u628a \\(\\displaystyle \\mathfrak{so}(3)\\) \u4e2d\u4efb\u610f\u5411\u91cf\u5bf9\u5e94\u5230 SO (3) \u4e2d\u7684\u65cb\u8f6c\u77e9\u9635
- \u53cd\u8fc7\u6765\u4e5f\u662f\u53ef\u4ee5\u7684
\\[ \\phi=\\ln\\left(\\boldsymbol{R}\\right)^\\vee=\\left(\\sum_{n=0}^\\infty\\frac{\\left(-1\\right)^n}{n+1}\\left(\\boldsymbol{R}-\\boldsymbol{I}\\right)^{n+1}\\right)^\\vee. \\]"},{"location":"AI/SLAM14/#422-se-3","title":"4.2.2 SE (3) \u4e0a\u7684\u6307\u6570\u6620\u5c04","text":"\u540c\u6837\u7684\u63a8\u5bfc\u65b9\u5f0f:
\\[ \\begin{aligned} \\exp\\left(\\xi^{\\wedge}\\right)& =\\begin{bmatrix}\\sum_{n=0}^{\\infty}\\frac{1}{n!}(\\phi^{\\wedge})^{n}&\\sum_{n=0}^{\\infty}\\frac{1}{(n+1)!}(\\phi^{\\wedge})^{n}\\rho\\\\\\\\\\mathbf{0}^{\\mathrm{T}}&1\\end{bmatrix} \\\\ &\\stackrel{\\Delta}{=}\\begin{bmatrix}R&J\\rho\\\\\\\\0^\\mathrm{T}&1\\end{bmatrix}=T. \\end{aligned} \\] \\[ \\begin{aligned} \\sum_{n=0}^{\\infty}\\frac{1}{(n+1)!}(\\phi^{\\wedge})^{n}& =\\boldsymbol{I}+\\frac{1}{2!}\\theta\\boldsymbol{a}^{\\wedge}+\\frac{1}{3!}\\theta^{2}{(\\boldsymbol{a}^{\\wedge})}^{2}+\\frac{1}{4!}\\theta^{3}{(\\boldsymbol{a}^{\\wedge})}^{3}+\\frac{1}{5!}\\theta^{4}{(\\boldsymbol{a}^{\\wedge})}^{4}\\cdots \\\\ &=\\frac{1}{\\theta}\\left(\\frac{1}{2!}\\theta^{2}-\\frac{1}{4!}\\theta^{4}+\\cdots\\right)(\\boldsymbol{a}^{\\wedge})+\\frac{1}{\\theta}\\left(\\frac{1}{3!}\\theta^{3}-\\frac{1}{5}\\theta^{5}+\\cdots\\right)(\\boldsymbol{a}^{\\wedge})^{2}+\\boldsymbol{I} \\\\ &=\\frac1\\theta\\left(1-\\cos\\theta\\right)\\left(\\boldsymbol{a}^{\\wedge}\\right)+\\frac{\\theta-\\sin\\theta}\\theta\\left(\\boldsymbol{a}\\boldsymbol{a}^{\\mathrm{T}}-\\boldsymbol{I}\\right)+\\boldsymbol{I} \\\\ &=\\frac{\\sin\\theta}\\theta\\boldsymbol{I}+\\left(1-\\frac{\\sin\\theta}\\theta\\right)\\boldsymbol{aa}^\\mathrm{T}+\\frac{1-\\cos\\theta}\\theta\\boldsymbol{a}^\\mathrm{\\wedge}\\overset{\\mathrm{def}}{\\operatorname*{=}}\\boldsymbol{J}. \\end{aligned} \\] \\[ \\boldsymbol{J}=\\frac{\\sin\\theta}{\\theta}\\boldsymbol{I}+\\left(1-\\frac{\\sin\\theta}{\\theta}\\right)\\boldsymbol{a}\\boldsymbol{a}^\\mathrm{T}+\\frac{1-\\cos\\theta}{\\theta}\\boldsymbol{a}^\\mathrm{\\wedge}. \\] 4.28 \u6ca1\u770b\u61c2
"},{"location":"AI/SLAM14/#43","title":"4.3 \u674e\u4ee3\u6570\u6c42\u5bfc\u4e0e\u6270\u52a8\u6a21\u578b","text":""},{"location":"AI/SLAM14/#431-bch","title":"4.3.1 BCH \u516c\u5f0f\u4e0e\u8fd1\u4f3c\u5f62\u5f0f","text":"\u63a2\u7a76\u5982\u4e0b\u5f0f\u5b50\u662f\u5426\u6210\u7acb:
\\[ \\ln\\left(\\exp\\left(A\\right)\\exp\\left(B\\right)\\right)=A+B ? \\] \u4f46\u5b83\u5e76\u4e0d\u6210\u7acb\u3002\u4e24\u4e2a\u674e\u4ee3\u6570\u6307\u6570\u6620\u5c04\u4e58\u79ef\u7684\u5b8c\u6574\u5f62\u5f0f\uff0c\u7531 Baker-Campbell-Hausdorff \u7ed9\u51fa:
\\[ \\ln\\left(\\exp\\left(A\\right)\\exp\\left(B\\right)\\right)=A+B+\\frac{1}{2}\\left[A,B\\right]+\\frac{1}{12}\\left[A,\\left[A,B\\right]\\right]-\\frac{1}{12}\\left[B,\\left[A,B\\right]\\right]+\\cdots \\] \u7279\u522b\u7684\uff0c\u5f53 \\(\\displaystyle \\phi_{1}\\) \u6216 \\(\\displaystyle \\phi_{2}\\) \u4e3a\u5c0f\u91cf\u65f6\uff0c\u5c0f\u91cf\u4e8c\u6b21\u4ee5\u4e0a\u7684\u9879\u90fd\u53ef\u4ee5\u88ab\u5ffd\u7565\uff0c\u6b64\u65f6\u7684\u7ebf\u6027\u8fd1\u4f3c\u8868\u8fbe:
\\[ \\ln\\left(\\exp\\left(\\phi_1^\\wedge\\right)\\exp\\left(\\phi_2^\\wedge\\right)\\right)^\\vee\\approx\\begin{cases}J_l(\\phi_2)^{-1}\\phi_1+\\phi_2&\\text{\u5f53}\\phi_1\\text{\u4e3a\u5c0f\u91cf},\\\\J_r(\\phi_1)^{-1}\\phi_2+\\phi_1&\\text{\u5f53}\\phi_2\\text{\u4e3a\u5c0f\u91cf}.\\end{cases} \\] \\[ \\boldsymbol{J}_{l}=\\frac{\\sin\\theta}{\\theta}\\boldsymbol{I}+\\left(1-\\frac{\\sin\\theta}{\\theta}\\right)\\boldsymbol{a}\\boldsymbol{a}^\\mathrm{T}+\\frac{1-\\cos\\theta}{\\theta}\\boldsymbol{a}^\\mathrm{\\wedge}. \\] \\[ \\boldsymbol{J}_{\\ell}^{-1}=\\frac{\\theta}{2}\\cot\\frac{\\theta}{2}\\boldsymbol{I}+\\left(1-\\frac{\\theta}{2}\\cot\\frac{\\theta}{2}\\right)\\boldsymbol{a}\\boldsymbol{a}^{\\mathrm{T}}-\\frac{\\theta}{2}\\boldsymbol{a}^{\\wedge}. \\] \\[ J_{r}(\\phi)=J_{l}(-\\phi). \\] \u4e8e\u662f\u6211\u4eec\u5c31\u53ef\u4ee5\u8c08\u8bba\u674e\u7fa4\u4e58\u6cd5\u4e0e\u674e\u4ee3\u6570\u52a0\u6cd5\u7684\u5173\u7cfb\u4e86\u3002 \\(\\displaystyle \\boldsymbol{R}\\) \u5bf9\u5e94 \\(\\displaystyle \\phi\\)\uff0c\u6211\u4eec\u7ed9\u5b83\u5de6\u4e58\u4e00\u4e2a\u5fae\u5c0f\u65cb\u8f6c\uff0c\u8bb0\u4f5c \\(\\displaystyle \\Delta \\boldsymbol{R}\\)
\\[ \\exp\\left(\\Delta\\phi^{\\wedge}\\right)\\exp\\left(\\phi^{\\wedge}\\right)=\\exp\\left(\\left(\\phi+J_{l}^{-1}\\left(\\phi\\right)\\Delta\\phi\\right)^{\\wedge}\\right). \\] \\[ \\exp\\left(\\left(\\phi+\\Delta\\phi\\right)^{\\wedge}\\right)=\\exp\\left(\\left(J_{l}\\Delta\\phi\\right)^{\\wedge}\\right)\\exp\\left(\\phi^{\\wedge}\\right)=\\exp\\left(\\phi^{\\wedge}\\right)\\exp\\left(\\left(J_{r}\\Delta\\phi\\right)^{\\wedge}\\right). \\] \u5bf9\u4e8e SE (3) \u6211\u4eec\u4e5f\u6709:
\\[ \\exp\\left(\\Delta\\xi^{\\wedge}\\right)\\exp\\left(\\xi^{\\wedge}\\right)\\approx\\exp\\left(\\left(\\mathcal{J}_{l}^{-1}\\Delta\\xi+\\xi\\right)^{\\wedge}\\right) \\] \\[ exp\\left(\\xi^{\\wedge}\\right)\\exp\\left(\\Delta\\xi^{\\wedge}\\right)\\approx\\exp\\left(\\left(\\mathcal{J}_{r}^{-1}\\Delta\\xi+\\xi\\right)^{\\wedge}\\right). \\] \u552f\u4e00\u4e0d\u540c\u7684\u662f\u8fd9\u91cc\u7684 \\(\\displaystyle J_{l}\\) \u6bd4\u8f83\u590d\u6742\u3002
"},{"location":"AI/SLAM14/#432-so-3","title":"4.3.2 SO (3) \u4e0a\u7684\u674e\u4ee3\u6570\u6c42\u5bfc","text":"\\[ z=T\\boldsymbol{p}+\\boldsymbol{w}. \\] \u5176\u4e2d \\(\\displaystyle \\boldsymbol{w}\\) \u662f\u968f\u673a\u566a\u58f0\u3002
\\[ e=z-Tp. \\] \u5047\u8bbe\u4e00\u5171\u6709 N \u4e2a\u8fd9\u6837\u7684\u8def\u6807\u70b9\u548c\u89c2\u6d4b:
\\[ \\min_{\\boldsymbol{T}}J(\\boldsymbol{T})=\\sum_{i=1}^N\\left\\|\\boldsymbol{z}_i-\\boldsymbol{T}\\boldsymbol{p}_i\\right\\|_2^2. \\] most importantly\uff0c\u6211\u4eec\u4f1a\u6784\u5efa\u4e0e\u4f4d\u59ff\u6709\u5173\u7684\u51fd\u6570\uff0c\u5e76\u8ba8\u8bba\u8be5\u51fd\u6570\u5173\u4e8e\u4f4d\u59ff\u7684\u5bfc\u6570\uff0c\u4ee5\u8c03\u6574\u5f53\u524d\u7684\u4f30\u8ba1\u503c\u3002 \u4f7f\u7528\u674e\u4ee3\u6570\u89e3\u51b3\u6c42\u5bfc\u95ee\u9898\u7684\u601d\u8def\u5206\u4e3a\u4e24\u79cd:
- \u7528\u674e\u4ee3\u6570\u8868\u793a\u59ff\u6001\uff0c\u7136\u540e\u6839\u636e\u674e\u4ee3\u6570\u52a0\u6cd5\u5bf9\u674e\u4ee3\u6570\u6c42\u5bfc\u3002
- \u5bf9\u674e\u7fa4\u5de6\u4e58\u6216\u53f3\u4e58\u5fae\u5c0f\u6270\u52a8\uff0c\u7136\u540e\u5bf9\u8be5\u6270\u52a8\u6c42\u5bfc\uff0c\u79f0\u4e3a\u5de6\u6270\u52a8\u548c\u53f3\u6270\u52a8\u6a21\u578b\u3002
"},{"location":"AI/SLAM14/#44","title":"4.4 \u674e\u4ee3\u6570\u6c42\u5bfc","text":"\u8981\u8ba1\u7b97 \\(\\displaystyle \\frac{\\partial\\left(Rp\\right)}{\\partial R}\\), \u7531\u4e8eSO\uff083\uff09\u6ca1\u6709\u52a0\u6cd5\uff0c\u6211\u4eec\u8f6c\u800c\u8ba1\u7b97: \\(\\displaystyle \\frac{\\partial\\left(\\exp\\left(\\phi^{\\wedge}\\right)\\boldsymbol{p}\\right)}{\\partial\\boldsymbol{\\phi}}.\\)
\\[ \\begin{aligned} \\frac{\\partial\\left(\\exp\\left(\\phi^{\\wedge}\\right)\\boldsymbol{p}\\right)}{\\partial\\phi}& =\\lim_{\\delta\\boldsymbol{\\phi}\\to0}\\frac{\\exp\\left(\\left(\\boldsymbol{\\phi}+\\delta\\boldsymbol{\\phi}\\right)^{\\wedge}\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\boldsymbol{\\phi}} \\\\ &=\\lim_{\\delta\\phi\\to0}\\frac{\\exp\\left(\\left(\\boldsymbol{J}_i\\delta\\boldsymbol{\\phi}\\right)^\\wedge\\right)\\exp\\left(\\boldsymbol{\\phi}^\\wedge\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\phi}^\\wedge\\right)\\boldsymbol{p}}{\\delta\\boldsymbol{\\phi}} \\\\ &=\\lim_{\\delta\\phi\\to0}\\frac{\\left(\\boldsymbol{I}+\\left(\\boldsymbol{J}_{l}\\delta\\boldsymbol{\\phi}\\right)^{\\wedge}\\right)\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\phi} \\\\ &=\\lim_{\\delta\\phi\\to0}\\frac{\\left(\\boldsymbol{J}_{l}\\delta\\phi\\right)^{\\wedge}\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\phi} \\\\ &=\\lim_{\\delta\\boldsymbol{\\phi}\\to0}\\frac{-(\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p})^{\\wedge}\\boldsymbol{J}_{l}\\delta\\boldsymbol{\\phi}}{\\delta\\boldsymbol{\\phi}}=-(\\boldsymbol{R}\\boldsymbol{p})^{\\wedge}\\boldsymbol{J}_{l}. \\end{aligned} \\] BCH \u7ebf\u6027\u8fd1\u4f3c+\u6cf0\u52d2\u5c55\u5f00\u53d6\u7ebf\u6027\u9879:
\\[ \\frac{\\partial\\left(\\boldsymbol{Rp}\\right)}{\\partial\\boldsymbol{\\phi}}=\\left(-\\boldsymbol{Rp}\\right)^{\\wedge}\\boldsymbol{J}_{l}. \\] \u4f46\u662f\u8fd9\u91cc\u4ecd\u7136\u6709 \\(\\displaystyle \\boldsymbol{J}_{l}\\)
"},{"location":"AI/SLAM14/#441","title":"4.4.1 \u6270\u52a8\u6a21\u578b\uff08\u5de6\u4e58\uff09","text":"\\(\\displaystyle \\varphi\\) \u5bf9\u5e94\u5de6\u6270\u52a8 \\(\\displaystyle \\Delta \\boldsymbol{R}\\)
\\[ \\begin{aligned} \\frac{\\partial\\left(Rp\\right)}{\\partial\\varphi}& =\\lim_{\\varphi\\to0}\\frac{\\exp\\left(\\varphi^{\\wedge}\\right)\\exp\\left(\\phi^{\\wedge}\\right)p-\\exp\\left(\\phi^{\\wedge}\\right)p}{\\varphi} \\\\ &=\\lim_{\\varphi\\to0}\\frac{(\\boldsymbol{I}+\\boldsymbol{\\varphi}^{\\wedge})\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}}{\\varphi} \\\\ &=\\lim_{\\varphi\\to0}\\frac{\\varphi^\\wedge Rp}\\varphi=\\lim_{\\varphi\\to0}\\frac{-\\left(Rp\\right)^\\wedge\\varphi}\\varphi=-\\left(Rp\\right)^\\wedge. \\end{aligned} \\]"},{"location":"AI/SLAM14/#442-se-3","title":"4.4.2 SE (3) \u4e0a\u7684\u674e\u4ee3\u6570\u6c42\u5bfc","text":"\\[ \\begin{aligned} \\frac{\\partial\\left(\\boldsymbol{T}\\boldsymbol{p}\\right)}{\\partial\\delta\\boldsymbol{\\xi}}&=\\lim_{\\delta\\boldsymbol{\\xi}\\to\\boldsymbol{0}}\\frac{\\exp\\left(\\delta\\boldsymbol{\\xi}^{\\wedge}\\right)\\exp\\left(\\boldsymbol{\\xi}^{\\wedge}\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\xi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\xi} \\\\ &=\\lim_{\\delta\\boldsymbol{\\xi}\\to\\mathbf{0}}\\frac{\\left(\\boldsymbol{I}+\\delta\\boldsymbol{\\xi}^{\\wedge}\\right)\\exp\\left(\\boldsymbol{\\xi}^{\\wedge}\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\xi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\boldsymbol{\\xi}} \\\\ &=\\lim_{\\delta\\boldsymbol{\\xi}\\to0}\\frac{\\delta\\boldsymbol{\\xi}^{\\wedge}\\exp\\left(\\boldsymbol{\\xi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\boldsymbol{\\xi}} \\\\ &=\\lim_{\\delta\\boldsymbol{\\xi}\\to\\mathbf{0}}\\frac{\\begin{bmatrix}\\delta\\boldsymbol{\\phi}^\\wedge&\\delta\\boldsymbol{\\rho}\\\\\\\\\\mathbf{0}^\\mathrm{T}&0\\end{bmatrix}\\begin{bmatrix}\\boldsymbol{R}\\boldsymbol{p}+\\boldsymbol{t}\\\\\\\\1\\end{bmatrix}}{\\delta\\boldsymbol{\\xi}} \\\\ &=\\lim_{\\delta\\boldsymbol{\\xi}\\to\\boldsymbol{0}}\\frac{\\begin{bmatrix}\\delta\\boldsymbol{\\phi}^{\\wedge}\\left(\\boldsymbol{R}\\boldsymbol{p}+\\boldsymbol{t}\\right)+\\delta\\boldsymbol{\\rho}\\\\\\boldsymbol{0}^{\\mathrm{T}}\\end{bmatrix}}{[\\delta\\boldsymbol{\\rho},\\delta\\boldsymbol{\\phi}]^{\\mathrm{T}}}=\\begin{bmatrix}\\boldsymbol{I}&-(\\boldsymbol{R}\\boldsymbol{p}+\\boldsymbol{t})^{\\wedge}\\\\\\boldsymbol{0}^{\\mathrm{T}}&\\boldsymbol{0}^{\\mathrm{T}}\\end{bmatrix}\\stackrel{\\mathrm{def}}{=}(\\boldsymbol{T}\\boldsymbol{p})^{\\odot}. \\end{aligned} \\] \\[ \\frac{\\mathrm{d}\\begin{bmatrix}a\\\\b\\end{bmatrix}}{\\mathrm{d}\\begin{bmatrix}x\\\\y\\end{bmatrix}}=\\left(\\frac{\\mathrm{d}[a,b]^\\mathrm{T}}{\\mathrm{d}\\begin{bmatrix}x\\\\y\\end{bmatrix}}\\right)^\\mathrm{T}=\\begin{bmatrix}\\frac{\\mathrm{d}a}{\\mathrm{d}x}&\\frac{\\mathrm{d}b}{\\mathrm{d}x}\\\\\\frac{\\mathrm{d}a}{\\mathrm{d}y}&\\frac{\\mathrm{d}b}{\\mathrm{d}y}\\end{bmatrix}^\\mathrm{T}=\\begin{bmatrix}\\frac{\\mathrm{d}a}{\\mathrm{d}x}&\\frac{\\mathrm{d}a}{\\mathrm{d}y}\\\\\\frac{\\mathrm{d}b}{\\mathrm{d}x}&\\frac{\\mathrm{d}b}{\\mathrm{d}y}\\end{bmatrix} \\]"},{"location":"AI/SLAM14/#45-sophus","title":"4.5 \u5b9e\u8df5:Sophus","text":""},{"location":"AI/SLAM14/#451-sophus","title":"4.5.1 Sophus \u7684\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5","text":""},{"location":"AI/SLAM14/#452","title":"4.5.2 \u4f8b\u5b50: \u8bc4\u4f30\u8f68\u8ff9\u7684\u8bef\u5dee","text":" - \u7edd\u5bf9\u8f68\u8ff9\u8bef\u5dee\uff08Absolute Trajectory Error, ATE\uff09
\\[ \\mathrm{ATE}_{\\mathrm{all}}=\\sqrt{\\frac{1}{N}\\sum_{i=1}^{N}\\|\\log(T_{\\mathrm{gt},i}^{-1}T_{\\mathrm{esti},i})^{\\vee}\\|_{2}^{2}}, \\] \u5373\u5747\u65b9\u6839\u8bef\u5dee\uff08Root-Mean-Squared Error, RMSE\uff09
- \u7edd\u5bf9\u5e73\u79fb\u8bef\u5dee\uff08Average Translational Error\uff09
\\[ \\mathrm{ATE}_{\\mathrm{all}}=\\sqrt{\\frac{1}{N}\\sum_{i=1}^{N}\\|\\log(T_{\\mathrm{gt},i}^{-1}T_{\\mathrm{esti},i})^{\\vee}\\|_{2}^{2}}, \\] - \u76f8\u5bf9\u4f4d\u59ff\u8bef\u5dee\uff08Relative Pose Error, RPE\uff09
\\[ \\mathrm{RPE}_{\\mathrm{all}}=\\sqrt{\\frac{1}{N-\\Delta t}\\sum_{i=1}^{N-\\Delta t}\\|\\log\\left(\\left(\\boldsymbol{T}_{\\mathrm{gt},i}^{-1}\\boldsymbol{T}_{\\mathrm{gt},i+\\Delta t}\\right)\\right)^{-1}\\left(\\boldsymbol{T}_{\\mathrm{est},i}^{-1}\\boldsymbol{T}_{\\mathrm{est},i+\\Delta t}\\right))^{\\vee}\\|_{2}^{2}}, \\] \\[ \\mathrm{RPE}_{\\mathrm{trans}}=\\sqrt{\\frac{1}{N-\\Delta t}\\sum_{i=1}^{N-\\Delta t}\\|\\mathrm{trans}\\left(\\left(T_{gt,i}^{-1}T_{gt,i+\\Delta t}\\right)\\right)^{-1}\\left(T_{\\mathrm{esti},i}^{-1}T_{\\mathrm{esti},i+\\Delta t}\\right))\\|_{2}^{2}}. \\] \u4ee3\u7801\u8ba1\u7b97:
Text OnlyTODO\n
"},{"location":"AI/SLAM14/#46","title":"4.6 \u76f8\u4f3c\u53d8\u6362\u4e0e\u674e\u4ee3\u6570","text":"\u5728\u8fd9\u91cc\u6211\u4eec\u8ba8\u8bba Sim (3) \u548c\u5bf9\u5e94\u7684\u674e\u4ee3\u6570 \\(\\displaystyle \\mathfrak{sim}(3)\\)\u3002 \u5bf9\u4e8e\u4f4d\u4e8e\u7a7a\u95f4\u7684\u70b9 \\(\\displaystyle \\boldsymbol{p}\\)\uff0c\u5728\u76f8\u673a\u5750\u6807\u7cfb\u4e0b\u8981\u7ecf\u8fc7\u4e00\u4e2a\u76f8\u4f3c\u53d8\u6362\uff0c\u800c\u975e\u6b27\u6c0f\u53d8\u6362:
\\[ \\boldsymbol{p}'=\\begin{bmatrix}s\\boldsymbol{R}&\\boldsymbol{t}\\\\\\boldsymbol{0}^\\mathrm{T}&1\\end{bmatrix}\\boldsymbol{p}=s\\boldsymbol{R}\\boldsymbol{p}+\\boldsymbol{t}. \\] \\[ \\mathrm{Sim}(3)=\\left\\{S=\\begin{bmatrix}sR&t\\\\\\\\\\mathbf{0}^\\mathrm{T}&1\\end{bmatrix}\\in\\mathbb{R}^{4\\times4}\\right\\}. \\] \\[ \\sin(3)=\\left\\{\\zeta|\\zeta=\\begin{bmatrix}\\rho\\\\\\\\\\phi\\\\\\\\\\sigma\\end{bmatrix}\\in\\mathbb{R}^7,\\zeta^\\wedge=\\begin{bmatrix}\\sigma\\boldsymbol{I}+\\phi^\\wedge&\\rho\\\\\\\\\\mathbf{0}^\\mathrm{T}&0\\end{bmatrix}\\in\\mathbb{R}^{4\\times4}\\right\\}. \\] \\[ \\exp\\left(\\zeta^{\\wedge}\\right)=\\begin{bmatrix}\\mathrm{e}^{\\sigma}\\exp\\left(\\phi^{\\wedge}\\right)&J_{s}\\rho\\\\0^{\\mathrm{T}}&1\\end{bmatrix}. \\] \u5176\u4e2d\uff0c\\(\\displaystyle \\boldsymbol{J}_{S}\\) \u7684\u5f62\u5f0f\u662f:
\\[ \\begin{aligned} \\text{J}& =\\frac{\\mathrm{e}^{\\sigma}-1}{\\sigma}I+\\frac{\\sigma\\mathrm{e}^{\\sigma}\\sin\\theta+\\left(1-\\mathrm{e}^{\\sigma}\\cos\\theta\\right)\\theta}{\\sigma^{2}+\\theta^{2}}\\boldsymbol{a}^{\\wedge} \\\\ &+\\left(\\frac{\\mathrm{e}^\\sigma-1}{\\sigma}-\\frac{\\left(\\mathrm{e}^\\sigma\\cos\\theta-1\\right)\\sigma+\\left(\\mathrm{e}^\\sigma\\sin\\theta\\right)\\theta}{\\sigma^2+\\theta^2}\\right)\\boldsymbol{a}^\\wedge\\boldsymbol{a}^\\wedge. \\end{aligned} \\] \u4e8e\u662f\uff0c\u674e\u4ee3\u6570\u4e0e\u674e\u7fa4\u7684\u5173\u7cfb:
\\[ s=\\mathrm{e}^\\sigma, R=\\exp(\\phi^\\wedge), t=J_s\\rho. \\] \\[ \\frac{\\partial\\boldsymbol{Sp}}{\\partial\\boldsymbol{\\zeta}}=\\begin{bmatrix}\\boldsymbol{I}&-\\boldsymbol{q}^\\wedge&\\boldsymbol{q}\\\\\\boldsymbol{0}^\\mathrm{T}&\\boldsymbol{0}^\\mathrm{T}&0\\end{bmatrix}. \\]"},{"location":"AI/SLAM14/#47","title":"4.7 \u4e60\u9898","text":"TODO
"},{"location":"AI/SLAM14/#5","title":"5 \u76f8\u673a\u4e0e\u56fe\u50cf","text":" - \u89c2\u6d4b\u4e3b\u8981\u662f\u6307\u76f8\u673a\u6210\u50cf\u7684\u8fc7\u7a0b\u3002
"},{"location":"AI/SLAM14/#51","title":"5.1 \u76f8\u673a\u6a21\u578b","text":" - \u9488\u5b54\u6a21\u578b
- \u900f\u955c\u4f1a\u4ea7\u751f\u7578\u53d8
- \u5185\u53c2\u6570\uff08Intrinsics\uff09
"},{"location":"AI/SLAM14/#511","title":"5.1.1 \u9488\u5b54\u76f8\u673a\u6a21\u578b","text":"\\[ \\frac{Z}{f}=-\\frac{X}{X'}=-\\frac{Y}{Y'}. \\] \u53bb\u6389\u8d1f\u53f7:
\\[ \\frac Zf=\\frac X{X^{\\prime}}=\\frac Y{Y^{\\prime}}. \\] \\[ \\begin{aligned}X'&=f\\frac{X}{Z}\\\\Y'&=f\\frac{Y}{Z}\\end{aligned}. \\] \u8fd8\u6709\u4e00\u4e2a\u50cf\u7d20\u5750\u6807\u7cfb\uff0cu \u8f74\u4e0e x \u8f74\u5e73\u884c\uff0cv \u8f74\u4e0e y \u8f74\u5e73\u884c:
\\[ \\begin{cases}u=\\alpha X'+c_x\\\\[2ex]v=\\beta Y'+c_y\\end{cases}. \\] \\[ \\begin{cases}u=f_x\\frac{X}{Z}+c_x\\\\\\\\v=f_y\\frac{Y}{Z}+c_y\\end{cases}. \\] \\[ Z\\begin{pmatrix}u\\\\\\\\v\\\\\\\\1\\end{pmatrix}=\\begin{pmatrix}f_x&0&c_x\\\\0&f_y&c_y\\\\\\\\0&0&1\\end{pmatrix}\\begin{pmatrix}X\\\\\\\\Y\\\\\\\\Z\\end{pmatrix}\\overset{\\text{def}}{=}\\boldsymbol{KP}. \\] \u6700\u4e2d\u95f4\u7684\u77e9\u9635\u79f0\u4e3a\u76f8\u673a\u7684\u5185\u53c2\u6570\uff08Camera Inrinsics\uff09\u77e9\u9635 \\(\\displaystyle \\boldsymbol{K}\\)\u3002 \u6807\u5b9a: \u786e\u5b9a\u76f8\u673a\u7684\u5185\u53c2
\\[ Z\\boldsymbol{P}_{uv}=Z\\begin{bmatrix}u\\\\\\\\v\\\\\\\\1\\end{bmatrix}=\\boldsymbol{K}\\left(\\boldsymbol{R}\\boldsymbol{P}_\\mathrm{w}+\\boldsymbol{t}\\right)=\\boldsymbol{K}\\boldsymbol{T}\\boldsymbol{P}_\\mathrm{w}. \\] \u5176\u4e2d \\(\\displaystyle \\boldsymbol{R},\\boldsymbol{t}\\) \u53c8\u79f0\u4e3a\u76f8\u673a\u7684\u5916\u53c2\u6570\uff08Camera Extrinsics\uff09
\\[ (\\boldsymbol{RP_\\mathrm{w}}+\\boldsymbol{t})=\\underbrace{[X,Y,Z]^\\mathrm{T}}_{\\text{\u76f8\u673a\u5750\u6807}}\\to\\underbrace{[X/Z,Y/Z,1]^\\mathrm{T}}_{\\text{\u5f52\u4e00\u5316\u5750\u6807}} . \\] - \u5f52\u4e00\u5316\u5e73\u9762
- \u70b9\u7684\u6df1\u5ea6\u5728\u6295\u5f71\u8fc7\u7a0b\u4e2d\u88ab\u4e22\u5931\u4e86
"},{"location":"AI/SLAM14/#512","title":"5.1.2 \u7578\u53d8\u6a21\u578b","text":" - \u7578\u53d8 (Distortion \u5931\u771f) \u5f84\u5411\u7578\u53d8
- \u7b52\u5f62\u7578\u53d8
- \u6795\u5f62\u7578\u53d8
- \u5f84\u5411\u7578\u53d8\u5373 \\(\\displaystyle r\\) \u53d8\u5316
- \u5207\u5411\u7578\u53d8\u5373 \\(\\displaystyle \\theta\\) \u53d8\u5316 \u6211\u4eec\u53ef\u4ee5\u5047\u8bbe:
\\[ \\begin{align} x_{\\mathrm{distorted}}&=x(1+k_1r^2+k_2r^4+k_3r^6) \\\\ y_{\\mathrm{distorted}}&=y(1+k_1r^2+k_2r^4+k_3r^6). \\end{align} \\] \\[ \\begin{align} x_{\\mathrm{distorted}}&=x+2p_1xy+p_2(r^2+2x^2) \\\\ y_{\\mathrm{distorted}}&=y+p_1(r^2+2y^2)+2p_2xy \\end{align} \\] \u6240\u4ee5\u6211\u4eec\u53ef\u4ee5\u627e\u5230\u4e00\u4e2a\u70b9\u5728\u50cf\u7d20\u5e73\u9762\u4e0a\u7684\u6b63\u786e\u4f4d\u7f6e:
- \u5c06\u4e09\u7ef4\u7a7a\u95f4\u70b9\u6295\u5f71\u5230\u5f52\u4e00\u5316\u56fe\u50cf\u5e73\u9762\u3002\u8bbe\u5b83\u7684\u5f52\u4e00\u5316\u5750\u6807\u4e3a \\(\\displaystyle [x, y]^\\mathrm{T}\\)\u3002
- \u5bf9\u5f52\u4e00\u5316\u5e73\u9762\u4e0a\u7684\u70b9\u8ba1\u7b97\u5f84\u5411\u7578\u53d8\u548c\u5207\u5411\u7578\u53d8
\\[ \\begin{cases}x_\\text{distorted}=x(1+k_1r^2+k_2r^4+k_3r^6)+2p_1xy+p_2(r^2+2x^2)\\\\y_\\text{distorted}=y(1+k_1r^2+k_2r^4+k_3r^6)+p_1(r^2+2y^2)+2p_2xy\\end{cases} \\] - \u5c06\u7578\u53d8\u540e\u7684\u70b9\u901a\u8fc7\u5185\u53c2\u6570\u77e9\u9635\u6295\u5f71\u5230\u50cf\u7d20\u5e73\u9762\uff0c\u5f97\u5230\u8be5\u70b9\u5728\u56fe\u50cf\u4e0a\u7684\u6b63\u786e\u4f4d\u7f6e\u3002
\\[ \\begin{cases}u=f_xx_\\text{distorted}+c_x\\\\\\\\v=f_yy_\\text{distorted}+c_y\\end{cases}. \\] \u8fd8\u6709\u5f88\u591a\u7684\u76f8\u673a\u6a21\u578b\u6bd4\u5982: \u4eff\u5c04\u6a21\u578b\uff0c\u900f\u89c6\u6a21\u578b\u3002
\u603b\u7ed3\u4e00\u4e0b\u5355\u76ee\u76f8\u673a\u7684\u6210\u50cf\u8fc7\u7a0b:
- \u4e16\u754c\u5750\u6807\u7cfb\u4e0b\u6709\u4e00\u4e2a\u56fa\u5b9a\u7684\u70b9 \\(\\displaystyle P\\)\uff0c\u4e16\u754c\u5750\u6807\u4e3a \\(\\displaystyle \\boldsymbol{P}_{w}\\)\u3002
- \u7531\u4e8e\u76f8\u673a\u5728\u8fd0\u52a8\uff0c\u5b83\u7684\u8fd0\u52a8\u7531 \\(\\displaystyle \\boldsymbol{R},\\boldsymbol{t}\\) \u6216\u53d8\u6362\u77e9\u9635 \\(\\displaystyle \\boldsymbol{T}\\in SE(3)\\) \u63cf\u8ff0\u3002\\(\\displaystyle P\\) \u7684\u76f8\u673a\u5750\u6807\u4e3a \\(\\displaystyle \\tilde{P_{c}} = \\boldsymbol{R}\\boldsymbol{P}_{w}+\\boldsymbol{t}\\)\u3002
- \u8fd9\u65f6\u7684 \\(\\displaystyle \\tilde{\\boldsymbol{P}_{c}}\\) \u7684\u5206\u91cf\u662f \\(\\displaystyle X,Y,Z\\) \uff0c\u628a\u5b83\u4eec\u6295\u5f71\u5230\u5f52\u4e00\u5316\u5e73\u9762 \\(\\displaystyle Z = 1\\) \u4e0a\uff0c\u5f97\u5230 \\(\\displaystyle P\\) \u7684\u5f52\u4e00\u5316\u5750\u6807: \\(\\displaystyle \\boldsymbol{P}_{c} = \\left[ \\frac{X}{Z}, \\frac{Y}{Z}, 1 \\right]^\\mathrm{T}\\)\u3002
- \u6709\u7578\u53d8\u65f6\uff0c\u6839\u636e\u7578\u53d8\u53c2\u6570\u8ba1\u7b97 \\(\\displaystyle \\boldsymbol{P}_{c}\\) \u53d1\u751f\u7578\u53d8\u540e\u7684\u5750\u6807\u3002
- \\(\\displaystyle P\\) \u7684\u5f52\u4e00\u5316\u5750\u6807\u7ecf\u8fc7\u5185\u53c2\u540e\uff0c\u5bf9\u5e94\u5230\u5b83\u7684\u50cf\u7d20\u5750\u6807: \\(\\displaystyle \\boldsymbol{P}_{uv} = \\boldsymbol{K} \\boldsymbol{P}_{c}\\)\u3002
"},{"location":"AI/SLAM14/#513","title":"5.1.3 \u53cc\u76ee\u76f8\u673a\u6a21\u578b","text":" \u4e24\u8005\u4e4b\u95f4\u7684\u8ddd\u79bb\u79f0\u4e3a\u53cc\u76ee\u76f8\u673a\u7684\u57fa\u7ebf
\\[ z=\\frac{fb}{d},\\quad d\\stackrel{\\mathrm{def}}{=}u_{\\mathrm{L}}-u_{\\mathrm{R}}. \\] - d \u5b9a\u4e49\u4e3a\u5de6\u53f3\u56fe\u7684\u6a2a\u5750\u6807\u4e4b\u5dee\uff0c\u79f0\u4e3a\u89c6\u5dee\u3002
- \u7531\u4e8e\u89c6\u5dee\u6700\u5c0f\u4e3a\u4e00\u4e2a\u50cf\u7d20\uff0c\u6240\u4ee5\u53cc\u76ee\u7684\u6df1\u5ea6\u5b58\u5728\u4e00\u4e2a\u7406\u8bba\u4e0a\u7684\u6700\u5927\u503c\u3002
"},{"location":"AI/SLAM14/#514-rgb-d","title":"5.1.4 RGB-D \u76f8\u673a\u6a21\u578b","text":" - \u7ea2\u5916\u7ed3\u6784\u5149\uff08Structured lightning\uff09
- \u98de\u884c\u65f6\u95f4\uff08Time-of-Flight, ToF\uff09
- ToF \u76f8\u673a\u53ef\u4ee5\u83b7\u5f97\u6574\u4e2a\u56fe\u50cf\u7684\u50cf\u7d20\u6df1\u5ea6
- \u8f93\u51fa\u5f69\u8272\u56fe\u548c\u6df1\u5ea6\u56fe\uff0c\u751f\u6210\u70b9\u4e91\uff08Point Cloud\uff09
"},{"location":"AI/SLAM14/#52","title":"5.2 \u56fe\u50cf","text":"\\[ I(x,y):\\mathbb{R}^2\\mapsto\\mathbb{R}. \\] "},{"location":"AI/SLAM14/#53","title":"5.3 \u5b9e\u8df5: \u8ba1\u7b97\u673a\u4e2d\u7684\u56fe\u50cf","text":""},{"location":"AI/SLAM14/#531-opencv","title":"5.3.1 OpenCV \u7684\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5","text":"TODO
"},{"location":"AI/SLAM14/#532","title":"5.3.2 \u56fe\u50cf\u53bb\u7578\u53d8","text":"TODO
"},{"location":"AI/SLAM14/#54-3-d","title":"5.4 \u5b9e\u8df5: 3 D \u89c6\u89c9","text":""},{"location":"AI/SLAM14/#541","title":"5.4.1 \u53cc\u76ee\u89c6\u89c9","text":"TODO
"},{"location":"AI/SLAM14/#542-rgb-d","title":"5.4.2 RGB-D \u89c6\u89c9","text":""},{"location":"AI/SLAM14/#55","title":"5.5 \u4e60\u9898","text":""},{"location":"AI/SLAM14/#6","title":"6 \u975e\u7ebf\u6027\u4f18\u5316","text":"\u524d\u9762\u6211\u4eec\u5df2\u7ecf\u641e\u6e05\u695a\u4e86\u8fd0\u52a8\u65b9\u7a0b\u548c\u89c2\u6d4b\u65b9\u7a0b\u7684\u6765\u6e90\uff0c\u73b0\u5728\u6211\u4eec\u5f00\u59cb\u8ba8\u8bba\u566a\u58f0\u3002
"},{"location":"AI/SLAM14/#61","title":"6.1 \u72b6\u6001\u4f30\u8ba1\u95ee\u9898","text":""},{"location":"AI/SLAM14/#611","title":"6.1.1 \u6279\u91cf\u72b6\u6001\u4f30\u8ba1\u4e0e\u6700\u5927\u540e\u9a8c\u4f30\u8ba1","text":"\\[ \\begin{cases}\\boldsymbol{x}_k=f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k\\right)+\\boldsymbol{w}_k\\\\\\boldsymbol{z}_{k,j}=h\\left(\\boldsymbol{y}_j,\\boldsymbol{x}_k\\right)+\\boldsymbol{v}_{k,j}\\end{cases}. \\] \\[ s\\boldsymbol{z}_{k,j}=\\boldsymbol{K}(R_k\\boldsymbol{y}_j+\\boldsymbol{t}_k). \\] \u5176\u4e2d \\(\\displaystyle s\\) \u4e3a\u50cf\u7d20\u70b9\u7684\u8ddd\u79bb\u3002 \u6211\u4eec\u901a\u5e38\u5047\u8bbe\u566a\u58f0\u9879\u6ee1\u8db3\u96f6\u5747\u503c\u7684\u9ad8\u65af\u5206\u5e03:
\\[ \\boldsymbol{w}_k\\sim\\mathcal{N}\\left(\\boldsymbol{0},\\boldsymbol{R}_k\\right),\\boldsymbol{v}_k\\sim\\mathcal{N}\\left(\\boldsymbol{0},\\boldsymbol{Q}_{k,j}\\right). \\] \u6709\u4e24\u79cd\u65b9\u6cd5\u6765\u89e3\u51b3\u72b6\u6001\u4f30\u8ba1\u95ee\u9898:
- \u7528\u65b0\u7684\u6570\u636e\u6765\u66f4\u65b0\u5f53\u524d\u65f6\u523b\u7684\u4f30\u8ba1\u72b6\u6001\uff0c\u589e\u91cf/\u6e10\u8fdb (incremental) \u7684\u65b9\u6cd5\uff0c\u6216\u8005\u6559\u6ee4\u6ce2\u5668
- \u4e5f\u53ef\u4ee5\u628a\u6570\u636e\u90fd\u6512\u8d77\u6765\uff0c\u79f0\u4e3a\u6279\u91cf (batch) \u7684\u65b9\u6cd5 SfM (Structure from Motion) \u7efc\u5408\u4e00\u4e0b\u5c31\u6709\u4e86\u6ed1\u52a8\u7a97\u53e3\u4f30\u8ba1\u6cd5
\\[ \\boldsymbol{x}=\\{\\boldsymbol{x}_1,\\ldots,\\boldsymbol{x}_N\\},\\quad\\boldsymbol{y}=\\{\\boldsymbol{y}_1,\\ldots,\\boldsymbol{y}_M\\}. \\] \\[ P(\\boldsymbol{x},\\boldsymbol{y}|z,\\boldsymbol{u}). \\] \\[ P\\left(\\boldsymbol{x},\\boldsymbol{y}|\\boldsymbol{z},\\boldsymbol{u}\\right)=\\frac{P\\left(\\boldsymbol{z},\\boldsymbol{u}|\\boldsymbol{x},\\boldsymbol{y}\\right)P\\left(\\boldsymbol{x},\\boldsymbol{y}\\right)}{P\\left(\\boldsymbol{z},\\boldsymbol{u}\\right)}\\propto\\underbrace{P\\left(\\boldsymbol{z},\\boldsymbol{u}|\\boldsymbol{x},\\boldsymbol{y}\\right)}_{\\text{\u4f3c\u7136}}\\underbrace{P\\left(\\boldsymbol{x},\\boldsymbol{y}\\right)}_{\\text{\u5148\u9a8c}}. \\] \u53ef\u4ee5\u5148\u6c42\u4e00\u4e2a\u72b6\u6001\u6700\u4f18\u4f30\u8ba1:
\\[ (\\boldsymbol{x},\\boldsymbol{y})^*_{\\mathrm{MAP}}=\\arg\\max P(\\boldsymbol{x},\\boldsymbol{y}|\\boldsymbol{z},\\boldsymbol{u})=\\arg\\max P(\\boldsymbol{z},\\boldsymbol{u}|\\boldsymbol{x},\\boldsymbol{y})P(\\boldsymbol{x},\\boldsymbol{y}). \\] \u6c42\u89e3\u6700\u5927\u540e\u9a8c\u6982\u7387\u7b49\u4ef7\u4e8e\u6700\u5927\u5316\u4f3c\u7136\u548c\u5148\u9a8c\u7684\u4e58\u79ef\u3002 \u4f46\u5982\u679c\u6ca1\u6709\u7684\u5148\u9a8c\uff0c\u90a3\u4e48\u53ef\u4ee5\u6c42\u89e3\u6700\u5927\u4f3c\u7136\u4f30\u8ba1 (Maximize Likelihood Estimation\uff0c MLE):
\\[ (\\boldsymbol{x},\\boldsymbol{y})^*{}_{\\mathrm{MLE}}=\\arg\\max P(\\boldsymbol{z},\\boldsymbol{u}|\\boldsymbol{x},\\boldsymbol{y}). \\] \u6700\u5927\u4f3c\u7136\u4f30\u8ba1: \u5728\u4ec0\u4e48\u6837\u7684\u72b6\u6001\u4e0b\uff0c\u6700\u53ef\u80fd\u4ea7\u751f\u73b0\u5728\u89c2\u6d4b\u5230\u7684\u6570\u636e\u3002
"},{"location":"AI/SLAM14/#612","title":"6.1.2 \u6700\u5c0f\u4e8c\u4e58\u7684\u5f15\u51fa","text":"\u5bf9\u4e8e\u67d0\u4e00\u6b21\u89c2\u6d4b:
\\[ z_{k,j}=h\\left(y_{j},x_{k}\\right)+v_{k,j}, \\] \\[ P(\\boldsymbol{z}_{j,k}|\\boldsymbol{x}_k,\\boldsymbol{y}_j)=N\\left(h(\\boldsymbol{y}_j,\\boldsymbol{x}_k),\\boldsymbol{Q}_{k,j}\\right). \\] \u53ef\u4ee5\u4f7f\u7528\u6700\u5c0f\u5316\u8d1f\u5bf9\u6570\u6765\u6c42\u4e00\u4e2a\u9ad8\u65af\u5206\u5e03\u7684\u6700\u5927\u4f3c\u7136\u3002
\\[ P\\left(\\boldsymbol{x}\\right)=\\frac{1}{\\sqrt{\\left(2\\pi\\right)^{N}\\det\\left(\\boldsymbol{\\Sigma}\\right)}}\\exp\\left(-\\frac{1}{2}(\\boldsymbol{x}-\\boldsymbol{\\mu})^{\\mathrm{T}}\\boldsymbol{\\Sigma}^{-1}\\left(\\boldsymbol{x}-\\boldsymbol{\\mu}\\right)\\right). \\] \\[ -\\ln\\left(P\\left(\\boldsymbol{x}\\right)\\right)=\\frac12\\ln\\left(\\left(2\\pi\\right)^N\\det\\left(\\boldsymbol{\\Sigma}\\right)\\right)+\\frac12\\left(\\boldsymbol{x}-\\boldsymbol{\\mu}\\right)^\\mathrm{T}\\boldsymbol{\\Sigma}^{-1}\\left(\\boldsymbol{x}-\\boldsymbol{\\mu}\\right). \\] \\[ \\begin{aligned} (x_{k},y_{j})^{*}& =\\arg\\max\\mathcal{N}(h(\\boldsymbol{y}_{j},\\boldsymbol{x}_{k}),\\boldsymbol{Q}_{k,j}) \\\\ &=\\arg\\min\\left(\\left(\\boldsymbol{z}_{k,j}-h\\left(\\boldsymbol{x}_k,\\boldsymbol{y}_j\\right)\\right)^\\mathrm{T}\\boldsymbol{Q}_{k,j}^{-1}\\left(\\boldsymbol{z}_{k,j}-h\\left(\\boldsymbol{x}_k,\\boldsymbol{y}_j\\right)\\right)\\right). \\end{aligned} \\] \u8be5\u5f0f\u7b49\u4ef7\u4e8e\u6700\u5c0f\u5316\u566a\u58f0\u9879\u7684\u4e00\u4e2a\u4e8c\u6b21\u578b\uff0c\u9a6c\u54c8\u62c9\u8bfa\u6bd4\u65af\u8ddd\u79bb (Mahalanobis distance)\u3002\u5176\u4e2d \\(\\displaystyle \\boldsymbol{Q}_{k,j}^{-1}\\) \u53eb\u4fe1\u606f\u77e9\u9635\uff0c\u5373\u9ad8\u65af\u5206\u5e03\u534f\u65b9\u5dee\u77e9\u9635\u4e4b\u9006\u3002 \u5047\u8bbe\u5404\u4e2a\u65f6\u523b\u7684\u8f93\u5165\u548c\u89c2\u6d4b\u90fd\u662f\u72ec\u7acb\u7684\uff0c\u90a3\u4e48:
\\[ P\\left(\\boldsymbol{z},\\boldsymbol{u}|\\boldsymbol{x},\\boldsymbol{y}\\right)=\\prod_kP\\left(\\boldsymbol{u}_k|\\boldsymbol{x}_{k-1},\\boldsymbol{x}_k\\right)\\prod_{k,j}P\\left(\\boldsymbol{z}_{k,j}|\\boldsymbol{x}_k,\\boldsymbol{y}_j\\right), \\] \\[ \\begin{align} e_{u,k} &=\\boldsymbol{x}_k-f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k\\right) \\\\ e_{z,j,k} &=\\boldsymbol{z}_{k,j}-h\\left(\\boldsymbol{x}_k,\\boldsymbol{y}_j\\right), \\end{align} \\] \\[ \\min J(\\boldsymbol{x},\\boldsymbol{y})=\\sum_{k}\\boldsymbol{e}_{\\boldsymbol{u},k}^{\\mathrm{T}}\\boldsymbol{R}_{k}^{-1}\\boldsymbol{e}_{\\boldsymbol{u},k}+\\sum_{k}\\sum_{j}\\boldsymbol{e}_{\\boldsymbol{z},k,j}^{\\mathrm{T}}\\boldsymbol{Q}_{k,j}^{-1}\\boldsymbol{e}_{\\boldsymbol{z},k,j}. \\] \u8fd9\u6837\u5c31\u5f97\u5230\u4e86\u4e00\u4e2a\u6700\u5c0f\u4e8c\u4e58\u95ee\u9898 (Least Square Problem)
- \u6574\u4e2a\u95ee\u9898\u6709\u4e00\u79cd\u7a00\u758f\u7684\u5f62\u5f0f\u3002
- \u7528\u674e\u4ee3\u6570\u8868\u793a\u589e\u91cf\u4f1a\u6709\u65e0\u7ea6\u675f\u7684\u4f18\u52bf\u3002
- \u7528\u4e8c\u6b21\u578b\u5ea6\u91cf\u8bef\u5dee\uff0c\u90a3\u4e48\u8bef\u5dee\u7684\u5206\u5e03\u4f1a\u5f71\u54cd\u6b64\u9879\u5728\u6574\u4e2a\u95ee\u9898\u4e2d\u7684\u6743\u91cd\u3002 \u63a5\u4e0b\u4fe9\u8bb2\u4e00\u4e9b\u975e\u7ebf\u6027\u4f18\u5316\u7684\u57fa\u672c\u77e5\u8bc6\uff0c\u6765\u5e2e\u52a9\u6211\u4eec\u6c42\u89e3\u8fd9\u4e2a\u6700\u5c0f\u4e8c\u4e58\u95ee\u9898\u3002
"},{"location":"AI/SLAM14/#613","title":"6.1.3 \u4f8b\u5b50: \u6279\u91cf\u72b6\u6001\u4f30\u8ba1","text":"\u8003\u8651\u4e00\u4e2a\u975e\u5e38\u7b80\u5355\u7684\u79bb\u6563\u65f6\u95f4\u7cfb\u7edf:
\\[ \\begin{aligned}&x_{k}=x_{k-1}+u_{k}+w_{k},&&\\boldsymbol{w}_{k}\\sim\\mathcal{N}\\left(0,\\boldsymbol{Q}_{k}\\right)\\\\&\\boldsymbol{z}_{k}=\\boldsymbol{x}_{k}+\\boldsymbol{n}_{k},&&\\boldsymbol{n}_{k}\\sim\\mathcal{N}\\left(0,\\boldsymbol{R}_{k}\\right)\\end{aligned} \\] \\[ \\begin{gathered} x_{map}^{*} =\\arg\\max P(\\boldsymbol{x}|\\boldsymbol{u},\\boldsymbol{z})=\\arg\\max P(\\boldsymbol{u},\\boldsymbol{z}|\\boldsymbol{x}) \\\\ =\\prod_{k=1}^3P(\\boldsymbol{u}_k|\\boldsymbol{x}_{k-1},\\boldsymbol{x}_k)\\prod_{k=1}^3P(\\boldsymbol{z}_k|\\boldsymbol{x}_k), \\end{gathered} \\] \u800c\u5bf9\u4e8e\u5177\u4f53\u7684\u6bcf\u4e00\u9879\uff0c\u6211\u4eec\u6709:
\\[ P(\\boldsymbol{u}_k|\\boldsymbol{x}_{k-1},\\boldsymbol{x}_k)=\\mathcal{N}(\\boldsymbol{x}_k-\\boldsymbol{x}_{k-1},\\boldsymbol{Q}_k), \\] \\[ P\\left(\\boldsymbol{z}_{k}|\\boldsymbol{x}_{k}\\right)=\\mathcal{N}\\left(\\boldsymbol{x}_{k},\\boldsymbol{R}_{k}\\right). \\] \u4e8e\u662f\uff0c\u6211\u4eec\u53ef\u4ee5\u6784\u5efa\u8bef\u5dee\u53d8\u91cf:
\\[ e_{\\boldsymbol{u},k}=\\boldsymbol{x}_k-\\boldsymbol{x}_{k-1}-\\boldsymbol{u}_k,\\quad\\boldsymbol{e}_{z,k}=\\boldsymbol{z}_k-\\boldsymbol{x}_k, \\] \u4e8e\u662f\u6700\u5c0f\u4e8c\u4e58\u7684\u76ee\u6807\u51fd\u6570\u4e3a:
\\[ \\min\\sum_{k=1}^{3}e_{u,k}^{\\mathrm{T}}Q_{k}^{-1}e_{u,k}+\\sum_{k=1}^{3}e_{z,k}^{\\mathrm{T}}R_{k}^{-1}e_{z,k}. \\] \u5b9a\u4e49\u5411\u91cf \\(\\displaystyle \\boldsymbol{y} = [\\boldsymbol{u},\\boldsymbol{z}]^\\mathrm{T}\\)
\\[ y-Hx=e\\sim\\mathcal{N}(\\mathbf{0},\\boldsymbol{\\Sigma}). \\] \\[ H=\\begin{bmatrix}1&-1&0&0\\\\0&1&-1&0\\\\0&0&1&-1\\\\\\hline0&1&0&0\\\\0&0&1&0\\\\0&0&0&1\\end{bmatrix}, \\] \u4e14 \\(\\displaystyle \\Sigma = diag(\\boldsymbol{Q_{1}},\\boldsymbol{Q_{2}},\\boldsymbol{Q_{3}},\\boldsymbol{R_{1}},\\boldsymbol{R_{2}},\\boldsymbol{R_{3}})\\)\u3002 \u95ee\u9898\u5c31\u8f6c\u5316\u6210:
\\[ x_{\\mathrm{map}}^*=\\arg\\min e^{\\mathrm{T}}\\Sigma^{-1}e, \\] \u5b83\u7684\u552f\u4e00\u89e3\u662f:
\\[ x_{\\mathrm{map}}^{*}=(H^{\\mathrm{T}}\\Sigma^{-1}H)^{-1}H^{\\mathrm{T}}\\Sigma^{-1}y. \\]"},{"location":"AI/SLAM14/#62","title":"6.2 \u975e\u7ebf\u6027\u6700\u5c0f\u4e8c\u4e58","text":"\u5148\u8003\u8651\u4e00\u4e2a\u7b80\u5355\u7684\u6700\u5c0f\u4e8c\u4e58\u95ee\u9898:
\\[ \\min_{x}F(x)=\\frac12\\|f\\left(x\\right)\\|_{2}^{2}.` \\] \u5bf9\u4e8e\u4e0d\u65b9\u4fbf\u76f4\u63a5\u6c42\u89e3\u7684\u6700\u5c0f\u4e8c\u4e58\u95ee\u9898\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u8fed\u4ee3\u7684\u65b9\u5f0f\uff0c\u4ece\u4e00\u4e2a\u521d\u59cb\u503c\u51fa\u53d1\uff0c\u4e0d\u65ad\u5730\u66f4\u65b0\u5f53\u524d\u7684\u4f18\u5316\u53d8\u91cf\uff0c\u4f7f\u76ee\u6807\u51fd\u6570\u4e0b\u964d:
- \u7ed9\u5b9a\u67d0\u4e2a\u521d\u59cb\u503c \\(\\displaystyle \\boldsymbol{x_{0}}\\)\u3002
- \u5bf9\u4e8e\u7b2c \\(\\displaystyle k\\) \u6b21\u8fed\u4ee3\uff0c\u5bfb\u627e\u4e00\u4e2a\u589e\u91cf \\(\\displaystyle \\Delta x_{k}\\)\uff0c\u4f7f\u5f97 \\(\\displaystyle \\left\\|f\\left(\\boldsymbol{x}_{k}+\\Delta\\boldsymbol{x}_{k}\\right)\\right\\|_{2}^{2}\\) \u8fbe\u5230\u6700\u5c0f\u503c\u3002
- \u82e5 \\(\\displaystyle \\Delta x_k\\) \u8db3\u591f\u5c0f\uff0c\u5219\u505c\u6b62\u3002
- \u5426\u5219\uff0c\u4ee4 \\(\\displaystyle x_{k+1} = x_{k} + \\Delta x_{k}\\)\uff0c\u8fd4\u56de\u7b2c\u4e8c\u6b65 \u4e8e\u662f\u6c42\u89e3\u5bfc\u51fd\u6570\u4e3a\u96f6 -> \u5bfb\u627e\u4e0b\u964d\u589e\u91cf \\(\\displaystyle \\Delta x_{k}\\)
\u4e0b\u9762\u662f\u4e00\u4e9b\u5e7f\u6cdb\u4f7f\u7528\u7684\u7ed3\u679c\u3002
"},{"location":"AI/SLAM14/#621","title":"6.2.1 \u4e00\u9636\u548c\u4e8c\u9636\u68af\u5ea6\u6cd5","text":"\u4f7f\u7528\u6cf0\u52d2\u5c55\u5f00:
\\[ F(\\boldsymbol{x}_k+\\Delta\\boldsymbol{x}_k)\\approx F(\\boldsymbol{x}_k)+\\boldsymbol{J}\\left(\\boldsymbol{x}_k\\right)^\\mathrm{T}\\Delta\\boldsymbol{x}_k+\\frac{1}{2}\\Delta\\boldsymbol{x}_k^\\mathrm{T}\\boldsymbol{H}(\\boldsymbol{x}_k)\\Delta\\boldsymbol{x}_k. \\] \u5176\u4e2d \\(\\displaystyle \\boldsymbol{J}(x_{k})\\) \u662f \\(\\displaystyle F(x)\\) \u5173\u4e8e \\(\\displaystyle x\\) \u7684\u4e00\u9636\u5bfc\u6570\uff08\u68af\u5ea6\u3001\u96c5\u53ef\u6bd4\u77e9\u9635\uff09\uff0c\\(\\displaystyle \\boldsymbol{H}\\) \u662f\u4e8c\u9636\u5bfc\u6570\uff08\u6d77\u585e\u77e9\u9635\uff09\u3002
\\[ \\Delta\\boldsymbol{x}^*=-\\boldsymbol{J}(\\boldsymbol{x}_k). \\] \\[ \\Delta\\boldsymbol{x}^*=\\arg\\min\\left(F\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^\\mathrm{T}\\Delta\\boldsymbol{x}+\\frac{1}{2}\\Delta\\boldsymbol{x}^\\mathrm{T}\\boldsymbol{H}\\Delta\\boldsymbol{x}\\right). \\] \u5bf9 \\(\\displaystyle \\Delta x\\) \u6c42\u5bfc\uff0c\u5e76\u4ee4\u5b83\u7b49\u4e8e\u96f6\uff0c\u5f97\u5230:
\\[ J+H\\Delta x=\\mathbf{0}\\Rightarrow H\\Delta x=-J. \\] \u8fd9\u4e2a\u65b9\u6cd5\u53c8\u53eb\u725b\u987f\u6cd5\u3002
"},{"location":"AI/SLAM14/#622","title":"6.2.2 \u9ad8\u65af\u725b\u987f\u6cd5","text":"\u6362\u4e00\u4e2a\u51fd\u6570\u5c55\u5f00:
\\[ f\\left(x+\\Delta x\\right)\\approx f\\left(x\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^{\\mathrm{T}}\\Delta\\boldsymbol{x}. \\] \\[ \\Delta x^{*}=\\arg\\min_{\\Delta x}\\frac{1}{2}\\Big\\|f\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^{\\mathrm{T}}\\Delta\\boldsymbol{x}\\Big\\|^{2}. \\] \\[ \\begin{aligned} \\frac12\\left\\|f\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^\\mathrm{T}\\Delta\\boldsymbol{x}\\right\\|^2& =\\frac12\\Big(f\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^\\mathrm{T}\\Delta\\boldsymbol{x}\\Big)^\\mathrm{T}\\Big(f\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^\\mathrm{T}\\Delta\\boldsymbol{x}\\Big) \\\\ &=\\frac12\\left(\\|f(\\boldsymbol{x})\\|_2^2+2f\\left(\\boldsymbol{x}\\right)\\boldsymbol{J}(\\boldsymbol{x})^\\intercal\\Delta\\boldsymbol{x}+\\Delta\\boldsymbol{x}^\\intercal\\boldsymbol{J}(\\boldsymbol{x})\\boldsymbol{J}(\\boldsymbol{x})^\\intercal\\Delta\\boldsymbol{x}\\right). \\end{aligned} \\] \\[ \\boldsymbol{J}(\\boldsymbol{x})f\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}(\\boldsymbol{x})\\boldsymbol{J}^\\mathrm{T}\\left(\\boldsymbol{x}\\right)\\Delta\\boldsymbol{x}=\\boldsymbol{0}. \\] \\[ \\underbrace{\\boldsymbol{J}(\\boldsymbol{x})\\boldsymbol{J}^{\\intercal}}_{\\boldsymbol{H}(\\boldsymbol{x})}\\left(\\boldsymbol{x}\\right)\\Delta\\boldsymbol{x}=\\underbrace{-\\boldsymbol{J}(\\boldsymbol{x})f\\left(\\boldsymbol{x}\\right)}_{\\boldsymbol{g}(\\boldsymbol{x})}. \\] \u589e\u91cf\u65b9\u7a0b or Gauss-Newton equation or Normal equation
\\[ H\\Delta x=g. \\] \u6c42\u89e3\u589e\u91cf\u65b9\u7a0b\u662f\u6574\u4e2a\u4f18\u5316\u95ee\u9898\u7684\u6838\u5fc3\u6240\u5728 \u603b\u7ed3\u4e00\u4e0b:
- \u7ed9\u5b9a\u521d\u59cb\u503c \\(\\displaystyle \\boldsymbol{x}_{0}\\)\u3002
- \u5bf9\u4e8e\u7b2c \\(\\displaystyle k\\) \u6b21\u8fed\u4ee3\uff0c\u6c42\u89e3\u5f53\u524d\u7684\u96c5\u53ef\u6bd4\u77e9\u9635 \\(\\displaystyle \\boldsymbol{J}(x)\\) \u548c\u8bef\u5dee \\(\\displaystyle f(\\boldsymbol{x}_{k})\\)\u3002
- \u6c42\u89e3\u589e\u91cf\u65b9\u7a0b: \\(\\displaystyle \\boldsymbol{H} \\Delta x_{k} = \\boldsymbol{g}\\)\u3002
- \u82e5 \\(\\displaystyle \\Delta x_{k}\\) \u8db3\u591f\u5c0f\uff0c\u5219\u505c\u6b62\u3002\u5426\u5219\uff0c\u4ee4 \\(\\displaystyle x_{k+1} = x_{k}+ \\Delta x_{k}\\)\uff0c\u8fd4\u56de\u7b2c 2 \u6b65\u3002
"},{"location":"AI/SLAM14/#623","title":"6.2.3 \u5217\u6587\u4f2f\u683c\u2014\u2014\u9a6c\u5938\u5c14\u7279\u65b9\u6cd5","text":"Damped Newton Method Trust Region Trust Region Method
\\[ \\rho=\\frac{f\\left(\\boldsymbol{x}+\\Delta\\boldsymbol{x}\\right)-f\\left(\\boldsymbol{x}\\right)}{\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^{\\intercal}\\Delta\\boldsymbol{x}}. \\] \u6846\u67b6:
- \u7ed9\u5b9a\u521d\u59cb\u503c \\(\\displaystyle \\boldsymbol{x}_{0}\\)\uff0c\u4ee5\u53ca\u521d\u59cb\u4f18\u5316\u534a\u5f84 \\(\\displaystyle \\mu\\)\u3002
- \u5bf9\u4e8e\u7b2c \\(\\displaystyle k\\) \u6b21\u8fed\u4ee3\uff0c\u5728\u9ad8\u65af\u725b\u987f\u6cd5\u7684\u57fa\u7840\u4e0a\u52a0\u4e0a\u4fe1\u8d56\u533a\u57df\uff0c\u6c42\u89e3: \\(\\displaystyle \\min_{\\Delta\\boldsymbol{x}_{k}}\\frac{1}{2}\\Big\\|f\\left(\\boldsymbol{x}_{k}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}_{k}\\right)^{\\mathrm{T}}\\Delta\\boldsymbol{x}_{k}\\Big\\|^{2},\\quad\\mathrm{s.t.}\\quad\\left\\|\\boldsymbol{D}\\Delta\\boldsymbol{x}_{k}\\right\\|^{2}\\leqslant\\mu,\\)
- \u8ba1\u7b97 \\(\\displaystyle \\rho\\)
- \u5bf9\u4e8e \\(\\displaystyle \\frac{1}{4} \\frac{3}{4}\\) \u8fdb\u884c\u5206\u7c7b\u8ba8\u8bba
- \u5224\u65ad\u9608\u503c\uff0c\u5faa\u73af
\u8fd9\u662f\u5e26\u4e0d\u7b49\u5f0f\u7ea6\u675f\u7684\u4f18\u5316\u95ee\u9898:
\\[ \\mathcal{L}(\\Delta\\boldsymbol{x}_{k},\\lambda)=\\frac{1}{2}\\left\\|f\\left(\\boldsymbol{x}_{k}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}_{k}\\right)^{\\mathrm{T}}\\Delta\\boldsymbol{x}_{k}\\right\\|^{2}+\\frac{\\lambda}{2}\\left(\\left\\|\\boldsymbol{D}\\Delta\\boldsymbol{x}_{k}\\right\\|^{2}-\\mu\\right). \\] \\[ (H+\\lambda D^\\mathrm{T}D) \\Delta x_k=g. \\]"},{"location":"AI/SLAM14/#63","title":"6.3 \u5b9e\u8df5: \u66f2\u7ebf\u62df\u5408\u95ee\u9898","text":""},{"location":"AI/SLAM14/#631","title":"6.3.1 \u624b\u5199\u9ad8\u65af\u725b\u987f\u6cd5","text":"TODO
"},{"location":"AI/SLAM14/#632-ceres","title":"6.3.2 \u4f7f\u7528 Ceres \u8fdb\u884c\u66f2\u7ebf\u62df\u5408","text":"TODO
"},{"location":"AI/SLAM14/#633-g2o","title":"6.3.3 \u4f7f\u7528 g2o\u8fdb\u884c\u66f2\u7ebf\u62df\u5408","text":"TODO
"},{"location":"AI/SLAM14/#7-1","title":"7 \u89c6\u89c9\u91cc\u7a0b\u8ba1 1","text":""},{"location":"AI/SLAM14/#71","title":"7.1 \u7279\u5f81\u70b9\u6cd5","text":" - \u7279\u5f81\u70b9\u6cd5
- \u4e24\u89c6\u56fe\u51e0\u4f55\uff08Two-view geometry\uff09
- \u76f4\u63a5\u6cd5
"},{"location":"AI/SLAM14/#711","title":"7.1.1 \u7279\u5f81\u70b9","text":" - \u5982\u4f55\u6839\u636e\u56fe\u50cf\u4f30\u8ba1\u76f8\u673a\u8fd0\u52a8
- \u8def\u6807: \u56fe\u50cf\u7279\u5f81
- \u7279\u5f81\u70b9\u5728\u76f8\u673a\u8fd0\u52a8\u4e4b\u540e\u4fdd\u6301\u7a33\u5b9a
- \u4eba\u5de5\u8bbe\u8ba1\u7684\u7279\u5f81\u70b9:
- Repeatability
- Distinctiveness
- Efficiency
- Locality
- \u7531\u4e24\u90e8\u5206\u7ec4\u6210:
- \u5173\u952e\u70b9\uff08Key-point\uff09
- \u63cf\u8ff0\u5b50\uff08Descriptor\uff09
- SIFT (\u5c3a\u5ea6\u4e0d\u53d8\u7279\u5f81\u53d8\u6362\uff0cScale-Invariant Feature Transform)
- \u8003\u8651\u5145\u5206\uff0c\u4f46\u662f\u8ba1\u7b97\u91cf\u6bd4\u8f83\u5927
- ORB (Oriented FAST and Rotated BRIEF)
"},{"location":"AI/SLAM14/#712-orb","title":"7.1.2 ORB \u7279\u5f81","text":" - FAST \u89d2\u70b9\u63d0\u53d6: ORB \u4e2d\u8ba1\u7b97\u4e86\u7279\u5f81\u70b9\u7684\u4e3b\u65b9\u5411\uff0c\u4e3a\u540e\u7eed\u7684 BRIEF \u63cf\u8ff0\u5b50\u589e\u52a0\u4e86\u65cb\u8f6c\u4e0d\u53d8\u7279\u6027
- BRIEF \u63cf\u8ff0\u5b50: \u5bf9\u524d\u4e00\u6b65\u63d0\u53d6\u51fa\u7279\u5f81\u70b9\u7684\u5468\u56f4\u56fe\u50cf\u533a\u57df\u8fdb\u884c\u63cf\u8ff0\u3002\u4f7f\u7528\u5148\u524d\u8ba1\u7b97\u7684\u65b9\u5411\u4fe1\u606f\u3002 - FAST \u5173\u952e\u70b9 Non-maximal suppression \u5c3a\u5ea6\u4e0d\u53d8\u6027\u7531\u6784\u5efa\u56fe\u50cf\u91d1\u5b57\u5854 \u7279\u5f81\u7684\u65cb\u8f6c: Intensity Centroid \u65cb\u8f6c\u65b9\u9762\uff0c\u6211\u4eec\u8ba1\u7b97\u7279\u5f81\u70b9\u9644\u8fd1\u7684\u56fe\u50cf\u7070\u5ea6\u8d28\u5fc3\u3002
- \u5b9a\u4e49\u56fe\u50cf\u7684\u77e9: \\(\\displaystyle m_{pq} = \\Sigma _{x,y \\in B} x^p x^q I(x, y),p,q \\in \\{0, 1\\}\\).
- \u627e\u5230\u56fe\u50cf\u5757\u7684\u8d28\u5fc3: \\(\\displaystyle C=\\left(\\frac{m_{10}}{m_{00}},\\frac{m_{01}}{m_{00}}\\right).\\)
- \u5f97\u5230\u4e00\u4e2a\u51e0\u4f55\u4e2d\u5fc3\u5230\u8d28\u5fc3\u7684\u65b9\u5411\u5411\u91cf: \\(\\displaystyle \\theta=\\arctan(m_{01}/m_{10}).\\) - BRIEF \u63cf\u8ff0\u5b50 \u4e8c\u8fdb\u5236\u8868\u8fbe+\u968f\u673a\u9009\u70b9\u6bd4\u8f83
"},{"location":"AI/SLAM14/#713","title":"7.1.3 \u7279\u5f81\u5339\u914d","text":"data association Brute-Force Matcher
- \u901a\u8fc7\u6d4b\u91cf\u63cf\u8ff0\u5b50\u7684\u8ddd\u79bb\u6765\u53bb\u6700\u8fd1\u7684\u4e00\u4e2a\u4f5c\u4e3a\u5339\u914d\u70b9\u3002\u63cf\u8ff0\u5b50\u8ddd\u79bb\u8868\u793a\u4e86\u4e24\u4e2a\u7279\u5f81\u4e4b\u95f4\u7684\u76f8\u4f3c\u7a0b\u5ea6\u3002
- \u6b27\u6c0f\u8ddd\u79bb
- \u6c49\u660e\u8ddd\u79bb
- \u4e24\u4e2a\u4e8c\u8fdb\u5236\u4e32\u7684\u4e0d\u540c\u4f4d\u6570\u7684\u4e2a\u6570
- \u5feb\u901f\u8fd1\u4f3c\u6700\u8fd1\u90bb\uff08FLANN\uff09
"},{"location":"AI/SLAM14/#72","title":"7.2 \u5b9e\u8df5: \u7279\u5f81\u63d0\u53d6\u548c\u5339\u914d","text":""},{"location":"AI/SLAM14/#721-opencv-orb","title":"7.2.1 OpenCV \u7684 ORB \u7279\u5f81","text":"TODO
"},{"location":"AI/SLAM14/#722-orb","title":"7.2.2 \u624b\u5199 ORB \u7279\u5f81","text":""},{"location":"AI/SLAM14/#723","title":"7.2.3 \u8ba1\u7b97\u76f8\u673a\u8fd0\u52a8","text":" - \u5f53\u76f8\u673a\u4e3a\u5355\u76ee\u65f6\uff0c\u6211\u4eec\u901a\u8fc7\u5bf9\u6781\u51e0\u4f55\u6765\u89e3\u51b3\u4e24\u7ec4 2 D \u70b9\u4f30\u8ba1\u8fd0\u52a8\u7684\u95ee\u9898
- \u5f53\u76f8\u673a\u4e3a\u53cc\u76ee\u3001RGB-D \u65f6\uff0c\u901a\u8fc7 ICP \u6765\u89e3\u51b3\u4e24\u7ec4 3 D \u70b9\u4f30\u8ba1\u8fd0\u52a8\u7684\u95ee\u9898
- \u4e00\u4e2a\u662f 2 D \u4e00\u4e2a\u662f 3 D \u65f6\uff0c\u901a\u8fc7 PnP \u6765\u6c42\u89e3
"},{"location":"AI/SLAM14/#73-d-2-d","title":"7.3 D-2 D: \u5bf9\u6781\u51e0\u4f55","text":""},{"location":"AI/SLAM14/#731","title":"7.3.1 \u5bf9\u6781\u7ea6\u675f","text":" - Epipolar plane
- Epipoles
- Epipolar line
\\[ P=[X,Y,Z]^{\\mathrm{T}}. \\] \\[ s_{1}p_{1}=KP,\\quad s_{2}p_{2}=K\\left(RP+t\\right). \\] \u6210\u6295\u5f71\u5173\u7cfb:\u5c3a\u5ea6\u610f\u4e49\u4e0b\u76f8\u7b49 (equal up to scale)
\\[ sp\\simeq p. \\] \\[ p_1\\simeq KP,\\quad p_2\\simeq K\\left(RP+t\\right). \\] \\[ x_1=K^{-1}p_1,\\quad x_2=K^{-1}p_2. \\] \\[ x_2\\simeq Rx_1+t. \\] \\[ t^{\\wedge}x_{2}\\simeq t^{\\wedge}Rx_{1}. \\] \\[ x_2^\\mathrm{T}t^\\wedge x_2\\simeq x_2^\\mathrm{T}t^\\wedge Rx_1. \\] \\[ x_2^\\mathrm{T}t^\\wedge Rx_1=0. \\] \\[ p_2^\\mathrm{T}K^{-\\mathrm{T}}t^\\wedge RK^{-1}p_1=0. \\] \u5bf9\u6781\u7ea6\u675f
\\[ E=t^{\\wedge}R,\\quad F=K^{-\\mathrm{T}}EK^{-1},\\quad x_{2}^{\\mathrm{T}}Ex_{1}=p_{2}^{\\mathrm{T}}Fp_{1}=0. \\] - \u6839\u636e\u914d\u5bf9\u70b9\u7684\u50cf\u7d20\u4f4d\u7f6e\u6c42\u51fa \\(\\displaystyle \\boldsymbol{E}\\) \u6216\u8005 \\(\\displaystyle \\boldsymbol{F}\\)\u3002
- \u6839\u636e \\(\\displaystyle \\boldsymbol{E}\\) \u6216\u8005 \\(\\displaystyle \\boldsymbol{F}\\) \u6c42\u51fa \\(\\displaystyle \\boldsymbol{R},\\boldsymbol{t}\\)\u3002 \\(\\displaystyle \\boldsymbol{E}\\) \u548c \\(\\displaystyle \\boldsymbol{F}\\) \u53ea\u76f8\u5dee\u4e86\u76f8\u673a\u5185\u53c2\uff0c\u6240\u4ee5\u5b9e\u8df5\u4e2d\u5f80\u5f80\u4f7f\u7528\u5f62\u5f0f\u66f4\u7b80\u5355\u7684 \\(\\displaystyle \\boldsymbol{E}\\)\u3002
"},{"location":"AI/SLAM14/#732","title":"7.3.2 \u672c\u8d28\u77e9\u9635","text":"\u672c\u8d28\u77e9\u9635: \\(\\displaystyle E=t^{\\wedge}R\\)
- \\(\\displaystyle \\boldsymbol{E}\\) \u4e0d\u540c\u5c3a\u5ea6\u4e0b\u662f\u7b49\u4ef7\u7684\u3002
- \u53ef\u4ee5\u8bc1\u660e\uff0c\u672c\u8d28\u77e9\u9635 \\(\\displaystyle \\boldsymbol{E}\\) \u7684\u5947\u5f02\u503c\u5fc5\u5b9a\u662f \\(\\displaystyle [\\sigma,\\sigma,0]^\\mathrm{T}\\) \u7684\u5f62\u5f0f\uff0c\u8fd9\u79f0\u4e3a\u672c\u8d28\u77e9\u9635\u7684\u5185\u5728\u6027\u8d28\u3002
- \\(\\displaystyle \\boldsymbol{E}\\) \u5b9e\u9645\u4e0a\u6709 5 \u4e2a\u81ea\u7531\u5ea6\u3002 \u516b\u70b9\u6cd5 (Eight-point-algorithm) \u8003\u8651\u4e00\u5806\u914d\u5bf9\u70b9\uff0c\u5b83\u4eec\u7684\u5f52\u4e00\u5316\u5750\u6807\u4e3a \\(\\displaystyle x_{1}=[u_{1},v_{1},1]^{\\mathrm{T}},x_{2}=[u_{2},v_{2},1]^{\\mathrm{T}}\\)\u3002\u6839\u636e\u5bf9\u6781\u7ea6\u675f\uff0c\u6709
\\[ \\begin{pmatrix}u_2,v_2,1\\end{pmatrix}\\begin{pmatrix}e_1&e_2&e_3\\\\\\\\e_4&e_5&e_6\\\\\\\\e_7&e_8&e_9\\end{pmatrix}\\begin{pmatrix}u_1\\\\\\\\v_1\\\\\\\\1\\end{pmatrix}=0. \\] \\[ \\boldsymbol{e}=[e_1,e_2,e_3,e_4,e_5,e_6,e_7,e_8,e_9]^\\mathrm{T}, \\] \\[ [u_2u_1,u_2v_1,u_2,v_2u_1,v_2v_1,v_2,u_1,v_1,1]\\cdot e=0. \\] \u6211\u4eec\u628a\u6240\u6709\u70b9\u90fd\u653e\u5230\u4e00\u4e2a\u65b9\u7a0b\u4e2d\uff0c\u53d8\u6210\u7ebf\u6027\u65b9\u7a0b\u7ec4:
\\[ \\begin{pmatrix}u_2^1u_1^1&u_2^1v_1^1&u_2^1&v_2^1u_1^1&v_2^1v_1^1&v_2^1&u_1^1&v_1^1&1\\\\u_2^2u_1^2&u_2^2v_1^2&u_2^2&v_2^2u_1^2&v_2^2v_1^2&v_2^2&u_1^2&v_1^2&1\\\\\\vdots&\\vdots&\\vdots&\\vdots&\\vdots&\\vdots&\\vdots&\\vdots\\\\u_2^8u_1^8&u_2^8v_1^8&u_2^8&v_2^8u_1^8&v_2^8u_1^8&u_1^8&v_1^8&1\\end{pmatrix}\\begin{pmatrix}e_1\\\\e_2\\\\e_3\\\\e_4\\\\e_5\\\\e_6\\\\e_7\\\\e_8\\\\e_9\\end{pmatrix}=0. \\] \\[ E=U\\Sigma V^{\\mathrm{T}}, \\] \\[ \\begin{aligned}&t_{1}^{\\wedge}=UR_{Z}(\\frac{\\pi}{2})\\Sigma U^{\\mathrm{T}},\\quad R_{1}=UR_{Z}^{\\mathrm{T}}(\\frac{\\pi}{2})V^{\\mathrm{T}}\\\\&t_{2}^{\\wedge}=UR_{Z}(-\\frac{\\pi}{2})\\Sigma U^{\\mathrm{T}},\\quad R_{2}=UR_{Z}^{\\mathrm{T}}(-\\frac{\\pi}{2})V^{\\mathrm{T}}.\\end{aligned} \\] \\[ \\boldsymbol{E}=\\boldsymbol{U}\\mathrm{diag}(\\frac{\\sigma_1+\\sigma_2}2,\\frac{\\sigma_1+\\sigma_2}2,0)\\boldsymbol{V}^\\mathrm{T}. \\]"},{"location":"AI/SLAM14/#733","title":"7.3.3 \u5355\u5e94\u77e9\u9635","text":"Homography
"},{"location":"AI/SLAM14/#8-2","title":"8 \u89c6\u89c9\u91cc\u7a0b\u8ba1 2","text":""},{"location":"AI/SLAM14/#81","title":"8.1 \u76f4\u63a5\u6cd5\u7684\u5f15\u51fa","text":" - \u7279\u5f81\u70b9\u6cd5\u7684\u7f3a\u70b9
- \u5173\u952e\u70b9\u7684\u63d0\u53d6\u4e0e\u63cf\u8ff0\u5b50\u7684\u8ba1\u7b97\u975e\u5e38\u8017\u65f6
- \u4f7f\u7528\u7279\u5f81\u70b9\u65f6\uff0c\u4f1a\u5ffd\u7565\u9664\u7279\u5f81\u70b9\u4ee5\u5916\u7684\u6240\u6709\u4fe1\u606f
- \u76f8\u673a\u6709\u65f6\u4f1a\u8fd0\u52a8\u5230\u7279\u5f81\u7f3a\u5931\u7684\u5730\u65b9
- \u90a3\u4e48\u5982\u4f55\u514b\u670d\u8fd9\u4e9b\u7f3a\u70b9
- \u4fdd\u7559\u7279\u5f81\u70b9\uff0c\u4f46\u53ea\u8ba1\u7b97\u5173\u952e\u70b9\uff0c\u4e0d\u8ba1\u7b97\u63cf\u8ff0\u5b50\u3002\u4f7f\u7528\u5149\u6d41\u6cd5\uff08Optical Flow\uff09\u8ddf\u8e2a\u7279\u5f81\u70b9\u7684\u8fd0\u52a8
- \u53ea\u8ba1\u7b97\u5173\u952e\u70b9\uff0c\u4e0d\u8ba1\u7b97\u63cf\u8ff0\u5b50\u3002\u4f7f\u7528\u76f4\u63a5\u6cd5\uff08Direct Method\uff09
- \u7b2c\u4e00\u79cd\u65b9\u6cd5\u4ecd\u7136\u4f7f\u7528\u7279\u5f81\u70b9\uff0c\u53ea\u662f\u628a\u5339\u914d\u63cf\u8ff0\u5b57\u66ff\u6362\u6210\u4e86\u5149\u6d41\u8ddf\u8e2a\uff0c\u4f30\u8ba1\u76f8\u673a\u8fd0\u52a8\u65f6\u4ecd\u7136\u662f\u54e6\u90a3\u4e2a\u5bf9\u6781\u51e0\u4f55\u3001PnP \u6216 ICP \u7b97\u6cd5\uff08\u5373\uff0c\u6211\u4eec\u9700\u8981\u63d0\u5230\u89d2\u70b9\uff09
- \u7279\u5f81\u70b9\u6cd5:\u901a\u8fc7\u6700\u5c0f\u5316\u91cd\u6295\u5f71\u8bef\u5dee\uff08Reprojection error\uff09\u4f18\u5316\u76f8\u673a\u8fd0\u52a8
- \u76f4\u63a5\u6cd5: \u901a\u8fc7\u6700\u5c0f\u5316\u5149\u5ea6\u8bef\u5dee\uff08Photometric error\uff09
- \u53ea\u8981\u573a\u666f\u4e2d\u5b58\u5728\u660e\u6697\u53d8\u5316\u5c31\u53ef\u4ee5\u5de5\u4f5c
- \u7a20\u5bc6
- \u534a\u7a20\u5bc6
- \u7a00\u758f
"},{"location":"AI/SLAM14/#82-d","title":"8.2 D \u5149\u6d41","text":" - \u8ba1\u7b97\u90e8\u5206\u50cf\u7d20\u8fd0\u52a8: \u7a00\u758f\u5149\u6d41
- \u8ba1\u7b97\u6240\u6709\u50cf\u7d20\u8fd0\u52a8: \u7a20\u5bc6\u5149\u6d41
- Horn-Schunck Lucas-Kanade \u5149\u6d41
- \u7070\u5ea6\u4e0d\u53d8\u5047\u8bbe: \u540c\u4e00\u4e2a\u7a7a\u95f4\u70b9\u7684\u50cf\u7d20\u7070\u5ea6\u503c\uff0c\u5728\u5404\u4e2a\u56fe\u50cf\u4e2d\u65f6\u56fa\u5b9a\u4e0d\u53d8\u7684
\\[ I(x+\\mathrm{d}x,y+\\mathrm{d}y,t+\\mathrm{d}t)=I(x,y,t). \\] \\[ \\boldsymbol{I}\\left(x+\\mathrm{d}x,y+\\mathrm{d}y,t+\\mathrm{d}t\\right)\\approx\\boldsymbol{I}\\left(x,y,t\\right)+\\frac{\\partial\\boldsymbol{I}}{\\partial x}\\mathrm{d}x+\\frac{\\partial\\boldsymbol{I}}{\\partial y}\\mathrm{d}y+\\frac{\\partial\\boldsymbol{I}}{\\partial t}\\mathrm{d}t. \\] \\[ \\frac{\\partial\\boldsymbol{I}}{\\partial x}\\frac{\\mathrm{d}x}{\\mathrm{d}t}+\\frac{\\partial\\boldsymbol{I}}{\\partial y}\\frac{\\mathrm{d}y}{\\mathrm{d}t}=-\\frac{\\partial\\boldsymbol{I}}{\\partial t}. \\] \\[ \\begin{bmatrix}I_x&I_y\\end{bmatrix}\\begin{bmatrix}u\\\\\\\\v\\end{bmatrix}=-I_t. \\] \\[ \\begin{bmatrix}I_x&I_y\\end{bmatrix}_k\\begin{bmatrix}u\\\\\\\\v\\end{bmatrix}=-I_{tk},\\quad k=1,\\ldots,w^2. \\] \\[ A\\begin{bmatrix}u\\\\\\\\v\\end{bmatrix}=-b. \\] \\[ \\begin{bmatrix}u\\\\\\\\v\\end{bmatrix}^*=-\\begin{pmatrix}\\boldsymbol{A}^\\mathrm{T}\\boldsymbol{A}\\end{pmatrix}^{-1}\\boldsymbol{A}^\\mathrm{T}\\boldsymbol{b}. \\]"},{"location":"AI/SLAM14/#83-lk","title":"8.3 \u5b9e\u8df5: LK \u5149\u6d41","text":""},{"location":"AI/SLAM14/#831-lk","title":"8.3.1 \u4f7f\u7528 LK \u5149\u6d41","text":"C++vector<Point2f> pt1, pt2;\nfor (auto &kp: kp1) pt1.push_back(kp.pt);\nvector<uchar> status;\nvector<float> error;\ncv::calcOpticalFlowPyrLK(img1, img2, pt1, pt2, status, error);\n
"},{"location":"AI/SLAM14/#832","title":"8.3.2 \u7528\u9ad8\u65af\u725b\u987f\u6cd5\u5b9e\u73b0\u5149\u6d41","text":"\u5355\u5c42\u5149\u6d41 TODO
\\[ \\min_{\\Delta x,\\Delta y}\\left\\|\\boldsymbol{I}_1\\left(x,y\\right)-\\boldsymbol{I}_2\\left(x+\\Delta x,y+\\Delta y\\right)\\right\\|_2^2. \\] \u591a\u5c42\u5149\u6d41 - \u7531\u7c97\u81f3\u7cbe\uff08Coarse-to-fine\uff09
"},{"location":"AI/SLAM14/#833","title":"8.3.3 \u5149\u6d41\u5b9e\u8df5\u5c0f\u7ed3","text":""},{"location":"AI/SLAM14/#84","title":"8.4 \u76f4\u63a5\u6cd5","text":""},{"location":"AI/SLAM14/#841","title":"8.4.1 \u76f4\u63a5\u6cd5\u7684\u63a8\u5bfc","text":"\\[ \\boldsymbol{p}_1=\\begin{bmatrix}u\\\\\\\\v\\\\\\\\1\\end{bmatrix}_1=\\frac{1}{Z_1}\\boldsymbol{K}\\boldsymbol{P}, \\] \\[ \\boldsymbol{p}_{2}=\\begin{bmatrix}u\\\\\\\\v\\\\\\\\1\\end{bmatrix}_{2}=\\frac{1}{Z_{2}}\\boldsymbol{K}\\left(\\boldsymbol{R}\\boldsymbol{P}+\\boldsymbol{t}\\right)=\\frac{1}{Z_{2}}\\boldsymbol{K}\\left(\\boldsymbol{T}\\boldsymbol{P}\\right)_{1:3}. \\] \\[ e=\\boldsymbol{I}_1\\left(\\boldsymbol{p}_1\\right)-\\boldsymbol{I}_2\\left(\\boldsymbol{p}_2\\right). \\] \\[ \\min_{T}J\\left(T\\right)=\\left\\|e\\right\\|^{2}. \\] \\[ \\min_{\\boldsymbol{T}}J\\left(\\boldsymbol{T}\\right)=\\sum_{i=1}^{N}e_{i}^{\\mathrm{T}}e_{i},\\quad e_{i}=\\boldsymbol{I}_{1}\\left(\\boldsymbol{p}_{1,i}\\right)-\\boldsymbol{I}_{2}\\left(\\boldsymbol{p}_{2,i}\\right). \\] \\[ \\begin{aligned}&q=TP,\\\\&\\boldsymbol{u}=\\frac{1}{Z_{2}}Kq.\\end{aligned} \\] \\[ e(T)=I_1(p_1)-I_2(u), \\] \\[ \\frac{\\partial e}{\\partial\\boldsymbol{T}}=\\frac{\\partial\\boldsymbol{I}_{2}}{\\partial\\boldsymbol{u}}\\frac{\\partial\\boldsymbol{u}}{\\partial\\boldsymbol{q}}\\frac{\\partial\\boldsymbol{q}}{\\partial\\delta\\boldsymbol{\\xi}}\\delta\\boldsymbol{\\xi}, \\] \\[ \\frac{\\partial\\boldsymbol{u}}{\\partial\\boldsymbol{q}}=\\begin{bmatrix}\\frac{\\partial u}{\\partial X}&\\frac{\\partial u}{\\partial Y}&\\frac{\\partial u}{\\partial Z}\\\\\\frac{\\partial v}{\\partial X}&\\frac{\\partial v}{\\partial Y}&\\frac{\\partial v}{\\partial Z}\\end{bmatrix}=\\begin{bmatrix}\\frac{f_x}{Z}&0&-\\frac{f_xX}{Z^2}\\\\0&\\frac{f_y}{Z}&-\\frac{f_yY}{Z^2}\\end{bmatrix}. \\] \\[ \\frac{\\partial\\boldsymbol{q}}{\\partial\\delta\\boldsymbol{\\xi}}=\\left[I,-\\boldsymbol{q}^{\\wedge}\\right]. \\] \\[ \\frac{\\partial\\boldsymbol{u}}{\\partial\\delta\\boldsymbol{\\xi}}=\\begin{bmatrix}\\frac{f_x}{Z}&0&-\\frac{f_xX}{Z^2}&-\\frac{f_xXY}{Z^2}&f_x+\\frac{f_xX^2}{Z^2}&-\\frac{f_xY}{Z}\\\\0&\\frac{f_y}{Z}&-\\frac{f_yY}{Z^2}&-f_y-\\frac{f_yY^2}{Z^2}&\\frac{f_yXY}{Z^2}&\\frac{f_yX}{Z}\\end{bmatrix}. \\] \\[ J=-\\frac{\\partial I_2}{\\partial u}\\frac{\\partial u}{\\partial\\delta\\xi}. \\]"},{"location":"AI/SLAM14/#842","title":"8.4.2 \u76f4\u63a5\u6cd5\u7684\u8ba8\u8bba","text":""},{"location":"AI/SLAM14/#85","title":"8.5 \u5b9e\u8df5: \u76f4\u63a5\u6cd5","text":""},{"location":"AI/SLAM14/#851","title":"8.5.1 \u5355\u5c42\u76f4\u63a5\u6cd5","text":""},{"location":"AI/SLAM14/#852","title":"8.5.2 \u591a\u5c42\u76f4\u63a5\u6cd5","text":""},{"location":"AI/SLAM14/#853","title":"8.5.3 \u7ed3\u679c\u8ba8\u8bba","text":""},{"location":"AI/SLAM14/#854","title":"8.5.4 \u76f4\u63a5\u6cd5\u4f18\u7f3a\u70b9\u603b\u7ed3","text":""},{"location":"AI/SLAM14/#9-1","title":"9 \u540e\u7aef 1","text":""},{"location":"AI/SLAM14/#91","title":"9.1 \u6982\u8ff0","text":""},{"location":"AI/SLAM14/#911","title":"9.1.1 \u72b6\u6001\u4f30\u8ba1\u7684\u6982\u7387\u89e3\u91ca","text":" - \u53ea\u4f7f\u7528\u8fc7\u53bb\u7684\u4fe1\u606f: \u6e10\u8fdb\u7684\uff08Incremental\uff09
- \u4f7f\u7528\u672a\u6765\u7684\u4fe1\u606f\u66f4\u65b0: \u6279\u91cf\u7684\uff08Batch\uff09
\\[ \\begin{cases}\\boldsymbol{x}_k=f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k\\right)+\\boldsymbol{w}_k\\\\\\boldsymbol{z}_{k,j}=h\\left(\\boldsymbol{y}_j,\\boldsymbol{x}_k\\right)+\\boldsymbol{v}_{k,j}\\end{cases}\\quad k=1,\\ldots,N, j=1,\\ldots,M. \\] - \u89c2\u6d4b\u65b9\u7a0b\u7684\u6570\u91cf\u4f1a\u8fdc\u8fdc\u5927\u4e8e\u8fd0\u52a8\u65b9\u7a0b
- \u5f53\u6ca1\u6709\u8fd0\u52a8\u65b9\u7a0b\u7684\u65f6\u5019\uff0c\u6211\u4eec\u53ef\u4ee5\u5047\u8bbe\u76f8\u673a\u4e0d\u52a8\uff0c\u6216\u5047\u8bbe\u76f8\u673a\u5300\u901f\u8fd0\u52a8
- \u95ee\u9898\uff1a\u5f53\u5b58\u5728\u4e00\u4e9b\u8fd0\u52a8\u6570\u636e\u548c\u89c2\u6d4b\u6570\u636e\u65f6\uff0c\u6211\u4eec\u5982\u4f55\u4f30\u8ba1\u72b6\u6001\u91cf\u7684\u9ad8\u65af\u5206\u5e03
- \u8bef\u5dee\u65f6\u9010\u6e10\u7d2f\u79ef\u7684
- \u6700\u5927\u4f3c\u7136\u4f30\u8ba1: \u6279\u91cf\u72b6\u6001\u4f30\u8ba1\u95ee\u9898\u53ef\u4ee5\u8f6c\u5316\u4e3a\u6700\u5927\u4f3c\u7136\u4f30\u8ba1\u95ee\u9898\uff0c\u5e76\u4f7f\u7528\u6700\u5c0f\u4e8c\u4e58\u6cd5\u8fdb\u884c\u6c42\u89e3
\\[ x_k\\stackrel{\\mathrm{def}}{=}\\{x_k,y_1,\\ldots,y_m\\}. \\] \\[ \\begin{cases}\\boldsymbol{x}_k=f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k\\right)+\\boldsymbol{w}_k\\\\\\boldsymbol{z}_k=h\\left(\\boldsymbol{x}_k\\right)+\\boldsymbol{v}_k\\end{cases}\\quad k=1,\\ldots,N. \\] \\[ P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k}\\right)\\propto P\\left(\\boldsymbol{z}_k|\\boldsymbol{x}_k\\right)P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right). \\] - \u7b2c\u4e00\u9879\u79f0\u4e3a\u4f3c\u7136\uff0c\u7b2c\u4e8c\u9879\u79f0\u4e3a\u5148\u9a8c
\\[ P\\left(\\boldsymbol{x}_{k}|\\boldsymbol{x}_{0},\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)=\\int P\\left(\\boldsymbol{x}_{k}|\\boldsymbol{x}_{k-1},\\boldsymbol{x}_{0},\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)P\\left(\\boldsymbol{x}_{k-1}|\\boldsymbol{x}_{0},\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)\\mathrm{d}\\boldsymbol{x}_{k-1}. \\] - \u867d\u7136\u53ef\u4ee5\u7ee7\u7eed\u5bf9\u6b64\u5f0f\u8fdb\u884c\u5c55\u5f00\uff0c\u4f46\u6211\u4eec\u53ea\u5173\u5fc3 \\(\\displaystyle k\\) \u65f6\u523b\u548c \\(\\displaystyle k - 1\\) \u65f6\u523b\u7684\u60c5\u51b5
- \u7b2c\u4e00\u79cd\u65b9\u6cd5\u662f\u5047\u8bbe\u9a6c\u5c14\u53ef\u592b\u6027: \u5373\u8ba4\u4e3a \\(\\displaystyle k\\) \u65f6\u523b\u72b6\u6001\u53ea\u4e0e \\(\\displaystyle k - 1\\) \u65f6\u523b\u72b6\u6001\u6709\u5173
- \u90a3\u4e48\u6211\u4eec\u5c31\u53ef\u4ee5\u5f97\u5230\u4ee5\u6269\u5c55\u5361\u5c14\u66fc\u6ee4\u6ce2\uff08EKF\uff09\u4e3a\u4ee3\u8868\u7684\u6ee4\u6ce2\u5668\u65b9\u5f0f
- \u7b2c\u4e8c\u79cd\u65b9\u6cd5\u662f\u4f9d\u7136\u8003\u8651\u548c\u4e4b\u524d\u6240\u6709\u72b6\u6001\u7684\u5173\u7cfb\uff0c\u59ff\u52bf\u4f1a\u5f97\u5230\u975e\u7ebf\u6027\u4f18\u5316\u4e3a\u4e3b\u4f53\u7684\u4f18\u5316\u6846\u67b6\u3002
- \u4e3b\u6d41\u662f\u975e\u7ebf\u6027\u4f18\u5316
"},{"location":"AI/SLAM14/#912-kf","title":"9.1.2 \u7ebf\u6027\u7cfb\u7edf\u548c KF","text":"\\[ P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_{k-1},\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)=P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k\\right). \\] \\[ P\\left(\\boldsymbol{x}_{k-1}|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)=P\\left(\\boldsymbol{x}_{k-1}|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k-1},\\boldsymbol{z}_{1:k-1}\\right). \\] - \u6240\u4ee5\u6211\u4eec\u5b9e\u9645\u5728\u505a\u7684\u4e8b\u5982\u4f55\u628a \\(\\displaystyle k - 1\\) \u65f6\u523b\u7684\u72b6\u6001\u5206\u5e03\u63a8\u5bfc\u81f3 \\(\\displaystyle k\\) \u65f6\u523b
- \u5373\u6211\u4eec\u53ea\u8981\u7ef4\u62a4\u4e00\u4e2a\u72b6\u6001\uff0c\u5e76\u4e0d\u65ad\u5730\u8fed\u4ee3\u66f4\u65b0
- \u53ea\u8981\u7ef4\u62a4\u72b6\u6001\u91cf\u7684\u5747\u503c\u548c\u534f\u65b9\u5dee\uff08\u72b6\u6001\u91cf\u670d\u4ece\u9ad8\u65af\u5206\u5e03\uff09
\\[ \\begin{cases}x_k=A_kx_{k-1}+u_k+w_k\\\\z_k=C_kx_k+v_k\\end{cases}\\quad k=1,\\ldots,N. \\] \\[ w_k\\sim N(0,\\boldsymbol{R}).\\quad\\boldsymbol{v}_k\\sim N(\\boldsymbol{0},\\boldsymbol{Q}). \\] - \u4e0a\u5e3d\u5b50\u8868\u793a\u540e\u9a8c\uff0c\u4e0b\u5e3d\u5b50\u8868\u793a\u5148\u9a8c
\\[ P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)=N\\left(\\boldsymbol{A}_k\\hat{x}_{k-1}+\\boldsymbol{u}_k,\\boldsymbol{A}_k\\hat{\\boldsymbol{P}}_{k-1}\\boldsymbol{A}_k^\\mathrm{T}+\\boldsymbol{R}\\right). \\] - \u8fd9\u4e00\u6b65\u79f0\u4e3a\u9884\u6d4b\uff08Predict\uff09
\\[ \\check{\\boldsymbol{x}}_k=\\boldsymbol{A}_k\\hat{\\boldsymbol{x}}_{k-1}+\\boldsymbol{u}_k,\\quad\\check{\\boldsymbol{P}}_k=A_k\\hat{\\boldsymbol{P}}_{k-1}\\boldsymbol{A}_k^\\mathrm{T}+\\boldsymbol{R}. \\] \\[ P\\left(\\boldsymbol{z}_k|\\boldsymbol{x}_k\\right)=N\\left(\\boldsymbol{C}_k\\boldsymbol{x}_k,\\boldsymbol{Q}\\right). \\] - \u5982\u679c\u7ed3\u679c\u8bbe\u4e3a \\(\\displaystyle x_k\\sim N(\\hat{\\boldsymbol{x}}_k,\\hat{\\boldsymbol{P}}_k)\\)\uff0c\u90a3\u4e48
\\[ N(\\hat{\\boldsymbol{x}}_k,\\hat{\\boldsymbol{P}}_k)=\\eta N\\left(\\boldsymbol{C}_k\\boldsymbol{x}_k,\\boldsymbol{Q}\\right)\\cdot N(\\check{\\boldsymbol{x}}_k,\\check{\\boldsymbol{P}}_k). \\] \\[ (\\boldsymbol{x}_{k}-\\hat{\\boldsymbol{x}}_{k})^{\\mathrm{T}}\\hat{\\boldsymbol{P}}_{k}^{-1}\\left(\\boldsymbol{x}_{k}-\\hat{\\boldsymbol{x}}_{k}\\right)=\\left(\\boldsymbol{z}_{k}-\\boldsymbol{C}_{k}\\boldsymbol{x}_{k}\\right)^{\\mathrm{T}}\\boldsymbol{Q}^{-1}\\left(\\boldsymbol{z}_{k}-\\boldsymbol{C}_{k}\\boldsymbol{x}_{k}\\right)+\\left(\\boldsymbol{x}_{k}-\\check{\\boldsymbol{x}}_{k}\\right)^{\\mathrm{T}}\\boldsymbol{P}_{k}^{-1}\\left(\\boldsymbol{x}_{k}-\\check{\\boldsymbol{x}}_{k}\\right). \\] \\[ \\hat{P}_k^{-1}=C_k^{\\mathrm{T}}Q^{-1}C_k+\\check{P}_k^{-1}. \\] - \u5b9a\u4e49\u4e00\u4e2a\u4e2d\u95f4\u53d8\u91cf
\\[ K=\\hat{P}_kC_k^{\\mathrm{T}}Q^{-1}. \\] \\[ I=\\hat{P}_{k}C_{k}^{\\mathrm{T}}Q^{-1}C_{k}+\\hat{P}_{k}\\check{P}_{k}^{-1}=KC_{k}+\\hat{P}_{k}\\check{P}_{k}^{-1}. \\] \\[ \\hat{P}_{k}=(I-KC_{k})\\check{P}_{k}. \\] - \u4e00\u6b21\u9879\u7cfb\u6570
\\[ -2\\hat{\\boldsymbol{x}}_k^\\mathrm{T}\\hat{\\boldsymbol{P}}_k^{-1}\\boldsymbol{x}_k=-2\\boldsymbol{z}_k^\\mathrm{T}\\boldsymbol{Q}^{-1}\\boldsymbol{C}_k\\boldsymbol{x}_k-2\\boldsymbol{\\dot{x}}_k^\\mathrm{T}\\check{\\boldsymbol{P}}_k^{-1}\\boldsymbol{x}_k. \\] \\[ \\hat{\\boldsymbol{P}}_k^{-1}\\hat{\\boldsymbol{x}}_k=\\boldsymbol{C}_k^\\mathrm{T}\\boldsymbol{Q}^{-1}\\boldsymbol{z}_k+\\check{\\boldsymbol{P}}_k^{-1}\\check{\\boldsymbol{x}}_k. \\] \\[ \\begin{aligned} \\hat{x}_{k}& =\\hat{\\boldsymbol{P}}_k\\boldsymbol{C}_k^\\mathrm{T}\\boldsymbol{Q}^{-1}\\boldsymbol{z}_k+\\hat{\\boldsymbol{P}}_k\\check{\\boldsymbol{P}}_k^{-1}\\check{\\boldsymbol{x}}_k \\\\ &=K\\boldsymbol{z}_k+\\left(\\boldsymbol{I}-\\boldsymbol{K}\\boldsymbol{C}_k\\right)\\check{\\boldsymbol{x}}_k=\\check{\\boldsymbol{x}}_k+\\boldsymbol{K}\\left(\\boldsymbol{z}_k-\\boldsymbol{C}_k\\check{\\boldsymbol{x}}_k\\right). \\end{aligned} \\]"},{"location":"AI/SLAM14/#913-ekf","title":"9.1.3 \u975e\u7ebf\u6027\u7cfb\u7edf\u548c EKF","text":" - \u6269\u5c55\u5361\u5c14\u66fc\u6ee4\u6ce2\u5668
- \u5373\u628a\u975e\u9ad8\u65af\u5206\u5e03\u8fd1\u4f3c\u6210\u9ad8\u65af\u5206\u5e03
\\[ \\boldsymbol{x}_k\\approx f\\left(\\hat{\\boldsymbol{x}}_{k-1},\\boldsymbol{u}_k\\right)+\\left.\\frac{\\partial f}{\\partial\\boldsymbol{x}_{k-1}}\\right|_{\\tilde{\\boldsymbol{x}}_{k-1}}\\left(\\boldsymbol{x}_{k-1}-\\hat{\\boldsymbol{x}}_{k-1}\\right)+\\boldsymbol{w}_k. \\] \\[ \\boldsymbol{F}=\\left.\\frac{\\partial f}{\\partial\\boldsymbol{x}_{k-1}}\\right|_{\\hat{\\boldsymbol{x}}_{k-1}}. \\] \\[ z_k\\approx h\\left(\\check{\\boldsymbol{x}}_k\\right)+\\left.\\frac{\\partial h}{\\partial\\boldsymbol{x}_k}\\right|_{\\dot{\\boldsymbol{x}}_k}\\left(\\boldsymbol{x}_k-\\check{\\boldsymbol{x}}_k\\right)+\\boldsymbol{n}_k. \\] \\[ H=\\left.\\frac{\\partial h}{\\partial\\boldsymbol{x}_k}\\right|_{\\check{\\boldsymbol{x}}_k}. \\] \\[ P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{0:k-1}\\right)=N(f\\left(\\hat{\\boldsymbol{x}}_{k-1},\\boldsymbol{u}_k\\right),\\boldsymbol{F}\\hat{\\boldsymbol{P}}_{k-1}\\boldsymbol{F}^\\mathrm{T}+\\boldsymbol{R}_k). \\] \\[ \\check{\\boldsymbol{x}}_k=f\\left(\\hat{\\boldsymbol{x}}_{k-1},\\boldsymbol{u}_k\\right),\\quad\\check{\\boldsymbol{P}}_k=\\boldsymbol{F}\\hat{\\boldsymbol{P}}_{k-1}\\boldsymbol{F}^\\mathrm{T}+\\boldsymbol{R}_k. \\] \\[ P\\left(\\boldsymbol{z}_k|\\boldsymbol{x}_k\\right)=N(h\\left(\\check{\\boldsymbol{x}}_k\\right)+\\boldsymbol{H}\\left(\\boldsymbol{x}_k-\\check{\\boldsymbol{x}}_k\\right),Q_k). \\] - \u5b9a\u4e49\u4e00\u4e2a\u5361\u5c14\u66fc\u589e\u76ca \\(\\displaystyle \\boldsymbol{K}_{k}\\)
\\[ K_{k}=\\check{P}_{k}H^{\\mathrm{T}}(H\\check{P}_{k}H^{\\mathrm{T}}+Q_{k})^{-1}. \\] \\[ \\hat{\\boldsymbol{x}}_k=\\check{\\boldsymbol{x}}_k+\\boldsymbol{K}_k\\left(\\boldsymbol{z}_k-h\\left(\\check{\\boldsymbol{x}}_k\\right)\\right),\\hat{\\boldsymbol{P}}_k=\\left(\\boldsymbol{I}-\\boldsymbol{K}_k\\boldsymbol{H}\\right)\\check{\\boldsymbol{P}}_k. \\]"},{"location":"AI/SLAM14/#914-ekf","title":"9.1.4 EKF \u7684\u8ba8\u8bba","text":" - \u5c40\u9650
- \u5047\u8bbe\u4e86\u9a6c\u5c14\u53ef\u592b\u6027\uff0c\u4f46\u662f\u975e\u7ebf\u6027\u4f18\u5316\u662f\u5168\u4f53\u65f6\u95f4\u4e0a\u7684 SLAM (Full-SLAM)
- \u6709\u975e\u7ebf\u6027\u8bef\u5dee\uff08\u4e3b\u8981\u95ee\u9898\u6240\u5728\uff09
- \u5982\u679c\u628a\u8def\u6807\u4e5f\u653e\u8fdb\u72b6\u6001\uff0c\u5b58\u4e0d\u4e0b
- \u6ca1\u6709\u5f02\u5e38\u68c0\u6d4b\u673a\u5236
"},{"location":"AI/SLAM14/#92-ba","title":"9.2 BA \u4e0e\u56fe\u4f18\u5316","text":" - Bundle Adjustment
- \u4ece\u89c6\u89c9\u56fe\u50cf\u4e2d\u63d0\u70bc\u51fa\u6700\u6709\u7684 3 D \u6a21\u578b\u548c\u76f8\u673a\u53c2\u6570\uff0c\u8ba9\u5149\u7ebf\u6700\u7ec8\u6536\u675f\u5230\u76f8\u673a\u7684\u5149\u5fc3
"},{"location":"AI/SLAM14/#921-ba","title":"9.2.1 \u6295\u5f71\u6a21\u578b\u548c BA \u4ee3\u4ef7\u51fd\u6570","text":"\\[ P^{\\prime}=Rp+t=[X^{\\prime},Y^{\\prime},Z^{\\prime}]^\\mathrm{T}. \\] \\[ \\boldsymbol{P}_{\\mathrm{c}}=[u_{\\mathrm{c}},v_{\\mathrm{c}},1]^{\\mathrm{T}}=[X^{\\prime}/Z^{\\prime},Y^{\\prime}/Z^{\\prime},1]^{\\mathrm{T}}. \\] \\[ \\begin{cases}u_\\mathrm{c}'=u_\\mathrm{c}\\left(1+k_1r_\\mathrm{c}^2+k_2r_\\mathrm{c}^4\\right)\\\\v_\\mathrm{c}'=v_\\mathrm{c}\\left(1+k_1r_\\mathrm{c}^2+k_2r_\\mathrm{c}^4\\right)\\end{cases}. \\] \\[ \\begin{cases}u_s=f_xu_\\mathrm{c}'+c_x\\\\[2ex]v_s=f_yv_\\mathrm{c}'+c_y\\end{cases}. \\] \\[ z=h(\\boldsymbol{x},\\boldsymbol{y}). \\] \\[ e=z-h(\\boldsymbol{T},\\boldsymbol{p}). \\] \\[ z\\overset{\\mathrm{def}}{\\operatorname*{=}}[u_s,v_s]^\\mathrm{T} \\] \\[ \\frac12\\sum_{i=1}^m\\sum_{j=1}^n\\|e_{ij}\\|^2=\\frac12\\sum_{i=1}^m\\sum_{j=1}^n\\|z_{ij}-h(\\boldsymbol{T}_i,\\boldsymbol{p}_j)\\|^2. \\]"},{"location":"AI/SLAM14/#922-ba","title":"9.2.2 BA \u7684\u6c42\u89e3","text":"\\[ x=[T_1,\\ldots,T_m,p_1,\\ldots,p_n]^\\mathrm{T}. \\] \\[ \\frac12\\left\\|f(\\boldsymbol{x}+\\Delta\\boldsymbol{x})\\right\\|^2\\approx\\frac12\\sum_{i=1}^m\\sum_{j=1}^n\\left\\|\\boldsymbol{e}_{ij}+\\boldsymbol{F}_{ij}\\Delta\\boldsymbol{\\xi}_i+\\boldsymbol{E}_{ij}\\Delta\\boldsymbol{p}_j\\right\\|^2. \\] \\[ x_{\\mathfrak{c}}=[\\boldsymbol{\\xi}_1,\\boldsymbol{\\xi}_2,\\ldots,\\boldsymbol{\\xi}_m]^{\\mathrm{T}}\\in\\mathbb{R}^{6m} \\] \\[ \\boldsymbol{x}_p=[\\boldsymbol{p}_1,\\boldsymbol{p}_2,\\ldots,\\boldsymbol{p}_n]^\\mathrm{T}\\in\\mathbb{R}^{3n} \\] \\[ \\frac12\\left\\|f(\\boldsymbol{x}+\\Delta\\boldsymbol{x})\\right\\|^2=\\frac12\\left\\|\\boldsymbol{e}+\\boldsymbol{F}\\Delta\\boldsymbol{x}_c+\\boldsymbol{E}\\Delta\\boldsymbol{x}_p\\right\\|^2. \\] \\[ \\boldsymbol{J}=[\\boldsymbol{F}\\boldsymbol{E}]. \\] \\[ H=J^\\mathrm{T}J=\\begin{bmatrix}F^\\mathrm{T}F&F^\\mathrm{T}E\\\\E^\\mathrm{T}F&E^\\mathrm{T}E\\end{bmatrix}. \\]"},{"location":"AI/SLAM14/#923","title":"9.2.3 \u7a00\u758f\u6027\u548c\u8fb9\u7f18\u5316","text":"\\[ J_{ij}(x)=\\left(\\mathbf{0}_{2\\times6},\\ldots\\mathbf{0}_{2\\times6},\\frac{\\partial\\boldsymbol{e}_{ij}}{\\partial\\boldsymbol{T}_{i}},\\mathbf{0}_{2\\times6},\\ldots\\mathbf{0}_{2\\times3},\\ldots\\mathbf{0}_{2\\times3},\\frac{\\partial\\boldsymbol{e}_{ij}}{\\partial\\boldsymbol{p}_{j}},\\mathbf{0}_{2\\times3},\\ldots\\mathbf{0}_{2\\times3}\\right). \\] \\[ H=\\sum_{i,j}J_{ij}^{\\top}J_{ij}, \\] \\[ H=\\begin{bmatrix}H_{11}&H_{12}\\\\\\\\H_{21}&H_{22}\\end{bmatrix}. \\] - \u5bf9\u4e8e\u7a00\u758f\u77e9\u9635\uff0c\u6211\u4eec\u7528 Schur \u6d88\u5143\uff08Marginalization\uff09
\\[ \\begin{bmatrix}B&E\\\\E^\\mathrm{T}&C\\end{bmatrix}\\begin{bmatrix}\\Delta x_\\mathrm{c}\\\\\\Delta x_p\\end{bmatrix}=\\begin{bmatrix}v\\\\w\\end{bmatrix}. \\] \\[ \\begin{bmatrix}I&-EC^{-1}\\\\0&I\\end{bmatrix}\\begin{bmatrix}B&E\\\\E^{\\intercal}&C\\end{bmatrix}\\begin{bmatrix}\\Delta x_\\mathrm{c}\\\\\\Delta x_p\\end{bmatrix}=\\begin{bmatrix}I&-EC^{-1}\\\\0&I\\end{bmatrix}\\begin{bmatrix}v\\\\w\\end{bmatrix}. \\] \\[ \\begin{bmatrix}B-EC^{-1}E^\\mathrm{T}&0\\\\E^\\mathrm{T}&C\\end{bmatrix}\\begin{bmatrix}\\Delta x_\\mathrm{c}\\\\\\Delta x_p\\end{bmatrix}=\\begin{bmatrix}v-EC^{-1}w\\\\\\\\w\\end{bmatrix}. \\] \\[ \\begin{bmatrix}B-EC^{-1}E^\\mathrm{T}\\end{bmatrix}\\Delta x_\\mathrm{c}=v-EC^{-1}w. \\] - \u4f18\u52bf
- \\(\\displaystyle \\boldsymbol{C}\\) \u4e3a\u5bf9\u89d2\u5757\uff0c\u9006\u6bd4\u8f83\u5bb9\u6613\u89e3\u51fa
- \u975e\u5bf9\u89d2\u7ebf\u4e0a\u7684\u975e\u96f6\u77e9\u9635\u5757\u8868\u793a\u5bf9\u5e94\u7684\u4e24\u4e2a\u76f8\u673a\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u5171\u540c\u89c2\u6d4b\u7684\u8def\u6807\u70b9\uff0c\u5373\u5171\u89c6\uff08Co-visibility\uff09
"},{"location":"AI/SLAM14/#924","title":"9.2.4 \u9c81\u68d2\u6838\u51fd\u6570","text":" \\[ H(e)=\\begin{cases}\\frac{1}{2}e^2&\\text{\u5f53}|e|\\leqslant\\delta,\\\\\\\\\\delta\\left(|e|-\\frac{1}{2}\\delta\\right)&\\text{\u5176\u4ed6}\\end{cases} \\]"},{"location":"AI/SLAM14/#93-ceres-ba","title":"9.3 \u5b9e\u8df5: Ceres BA","text":""},{"location":"AI/SLAM14/#931-bal","title":"9.3.1 BAL \u6570\u636e\u96c6","text":""},{"location":"AI/SLAM14/#932-ceres-ba","title":"9.3.2 Ceres BA \u7684\u4e66\u5199","text":""},{"location":"AI/SLAM14/#94-g-2-o-ba","title":"9.4 \u5b9e\u8df5: g 2 o \u6c42\u89e3 BA","text":""},{"location":"AI/SLAM14/#95","title":"9.5 \u5c0f\u7ed3","text":""},{"location":"AI/SLAM14/#10-2","title":"10 \u540e\u7aef 2","text":""},{"location":"AI/SLAM14/#11_1","title":"11 \u56de\u73af\u68c0\u6d4b","text":""},{"location":"AI/SLAM14/#12_1","title":"12 \u5efa\u56fe","text":""},{"location":"AI/SLAM14/#13-slam","title":"13 \u5b9e\u8df5: \u8bbe\u8ba1 SLAM \u7cfb\u7edf","text":""},{"location":"AI/SLAM14/#14-slam","title":"14 SLAM: \u73b0\u5728\u4e0e\u672a\u6765","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/","title":"\u7edf\u8ba1\u5b66\u4e60\u65b9\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#_1","title":"\u7edf\u8ba1\u5b66\u4e60\u65b9\u6cd5","text":" \u7ea6 3356 \u4e2a\u5b57 43 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 17 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#1","title":"1 \u7edf\u8ba1\u5b66\u4e60\u65b9\u6cd5\u6982\u8bba","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#11","title":"1.1 \u7edf\u8ba1\u5b66\u4e60","text":" - \u7edf\u8ba1\u5b66\u4e60\u7684\u7279\u70b9
- \u4ee5\u8ba1\u7b97\u673a\u53ca\u7f51\u7edc\u4e3a\u5e73\u53f0
- \u4ee5\u6570\u636e\u4e3a\u7814\u7a76\u5bf9\u8c61
- \u76ee\u7684\u662f\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u4e0e\u5206\u6790
- \u4ea4\u53c9\u5b66\u79d1
- \u7edf\u8ba1\u5b66\u4e60\u7684\u5bf9\u8c61
- \u662f\u6570\u636e
- \u7edf\u8ba1\u5b66\u4e60\u7684\u76ee\u7684
- \u7edf\u8ba1\u5b66\u4e60\u7684\u65b9\u6cd5
- \u4e3b\u8981\u6709
- \u76d1\u7763\u5b66\u4e60\uff08\u672c\u4e66\u4e3b\u8981\u8ba8\u8bba\uff09
- \u975e\u76d1\u7763\u5b66\u4e60
- \u534a\u76d1\u7763\u5b66\u4e60
- \u5f3a\u5316\u5b66\u4e60
- \u4e09\u8981\u7d20
- \u6a21\u578b
- \u7b56\u7565
- \u7b97\u6cd5
- \u5b9e\u73b0\u6b65\u9aa4
- \u5f97\u5230\u4e00\u4e2a\u8bad\u7ec3\u6570\u636e\u96c6\u5408
- \u786e\u5b9a\u5b66\u4e60\u6a21\u578b\u7684\u96c6\u5408
- \u786e\u5b9a\u5b66\u4e60\u7684\u7b56\u7565
- \u786e\u5b9a\u5b66\u4e60\u7684\u7b97\u6cd5
- \u901a\u8fc7\u5b66\u4e60\u65b9\u6cd5\u9009\u62e9\u6700\u4f18\u6a21\u578b
- \u5229\u7528\u5b66\u4e60\u7684\u6700\u4f18\u6a21\u578b\u5bf9\u65b0\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u6216\u5206\u6790
- \u7edf\u8ba1\u5b66\u4e60\u7684\u7814\u7a76
- \u65b9\u6cd5
- \u7406\u8bba
- \u5e94\u7528
- \u7edf\u8ba1\u5b66\u4e60\u7684\u91cd\u8981\u6027
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#12","title":"1.2 \u76d1\u7763\u5b66\u4e60","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#121","title":"1.2.1 \u57fa\u672c\u6982\u5ff5","text":" - \u8f93\u5165\u7a7a\u95f4\u3001\u7279\u5f81\u7a7a\u95f4\u4e0e\u8f93\u51fa\u7a7a\u95f4
- \u6bcf\u4e2a\u8f93\u5165\u662f\u4e00\u4e2a\u5b9e\u4f8b\uff0c\u901a\u5e38\u7531\u7279\u5f81\u5411\u91cf\u8868\u793a
- \u76d1\u7763\u5b66\u4e60\u4ece\u8bad\u7ec3\u6570\u636e\u96c6\u5408\u4e2d\u5b66\u4e60\u6a21\u578b\uff0c\u5bf9\u6d4b\u8bd5\u6570\u636e\u8fdb\u884c\u9884\u6d4b
- \u6839\u636e\u8f93\u5165\u53d8\u91cf\u548c\u8f93\u51fa\u53d8\u91cf\u7684\u4e0d\u540c\u7c7b\u578b
- \u56de\u5f52\u95ee\u9898: \u90fd\u8fde\u7eed
- \u5206\u7c7b\u95ee\u9898: \u8f93\u51fa\u6709\u9650\u79bb\u6563
- \u6807\u6ce8\u95ee\u9898: \u90fd\u662f\u53d8\u91cf\u5e8f\u5217
- \u8054\u5408\u6982\u7387\u5206\u5e03
- \u5047\u8bbe\u7a7a\u95f4
- \u6a21\u578b\u5c5e\u4e8e\u7531\u8f93\u5165\u7a7a\u95f4\u5230\u8f93\u51fa\u7a7a\u95f4\u7684\u6620\u5c04\u7684\u96c6\u5408\uff0c\u8fd9\u4e2a\u96c6\u5408\u5c31\u662f\u5047\u8bbe\u7a7a\u95f4
- \u6a21\u578b\u53ef\u4ee5\u662f\uff08\u975e\uff09\u6982\u7387\u6a21\u578b
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#122","title":"1.2.2 \u95ee\u9898\u7684\u5f62\u5f0f\u5316","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#13","title":"1.3 \u7edf\u8ba1\u5b66\u4e60\u4e09\u8981\u8bfb","text":" - \u65b9\u6cd5=\u6a21\u578b+\u7b56\u7565+\u7b97\u6cd5
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#131","title":"1.3.1 \u6a21\u578b","text":" - \u6a21\u578b\u5c31\u662f\u7d22\u8981\u5b66\u4e60\u7684\u6761\u4ef6\u6982\u7387\u5206\u5e03\u6216\u51b3\u7b56\u51fd\u6570
\\[ \\mathcal{F}=\\{f\\mid Y=f(X)\\} \\] \\[ \\mathcal{F}=\\{f | Y=f_{\\theta}(X),\\theta\\in\\mathbf{R}^{n}\\} \\] - \u540c\u6837\u53ef\u4ee5\u5b9a\u4e49\u4e3a\u6761\u4ef6\u6982\u7387\u7684\u96c6\u5408
\\[ \\mathcal{F}=\\{P|P(Y|X)\\} \\] \\[ \\mathcal{F}=\\{P\\mid P_{\\theta}(Y\\mid X),\\theta\\in\\mathbf{R}^{n}\\} \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#132","title":"1.3.2 \u7b56\u7565","text":" - \u635f\u5931\u51fd\u6570\u548c\u98ce\u9669\u51fd\u6570
- loos function or cost function \\(\\displaystyle L(Y,f(X))\\)
- 0-1 loss function
- \\(\\displaystyle L(Y,f(X))=\\begin{cases}1,&Y\\neq f(X)\\\\0,&Y=f(X)\\end{cases}\\)
- quadratic loss function
- \\(\\displaystyle L(Y,f(X))=(Y-f(X))^{2}\\)
- absolute loss function
- \\(\\displaystyle L(Y,f(X))=|Y-f(X)|\\)
- logarithmic loss function or log-likelihood loss function
- \\(\\displaystyle L(Y,P(Y\\mid X))=-\\log P(Y\\mid X)\\)
- \\(\\displaystyle R_{\\exp}(f)=E_{P}[L(Y,f(X))]=\\int_{x\\times y}L(y,f(x))P(x,y)\\mathrm{d}x\\mathrm{d}y\\)
- risk function or expected loss
- \u4f46\u662f\u8054\u5408\u5206\u5e03\u4f4d\u7f6e\uff0c\u6240\u4ee5\u8981\u5b66\u4e60\uff0c\u4f46\u662f\u8fd9\u6837\u4ee5\u6765\u98ce\u9669\u6700\u5c0f\u53c8\u8981\u7528\u5230\u8054\u5408\u5206\u5e03\uff0c\u90a3\u4e48\u8fd9\u5c31\u6210\u4e3a\u4e86\u75c5\u6001\u95ee\u9898 (ill-formed problem)
- empirical risk or empirical loss
- \\(\\displaystyle R_{\\mathrm{emp}}(f)=\\frac{1}{N}\\sum_{i=1}^{N}L(y_{i},f(x_{i}))\\)
- \u5f53 \\(\\displaystyle N\\) \u8d8b\u4e8e\u65e0\u7a77\u65f6\uff0c\u7ecf\u9a8c\u98ce\u9669\u8d8b\u4e8e\u671f\u671b\u98ce\u9669
- \u8fd9\u5c31\u5173\u7cfb\u5230\u4e24\u4e2a\u57fa\u672c\u7b56\u7565:
- \u7ecf\u9a8c\u98ce\u9669\u6700\u5c0f\u5316
- \u7ed3\u6784\u98ce\u9669\u6700\u5c0f\u5316
- \u7ecf\u9a8c\u98ce\u9669\u6700\u5c0f\u5316\u4e0e\u7ed3\u6784\u98ce\u9669\u6700\u5c0f\u5316
- empirical risk minimization \uff08\u6837\u672c\u5bb9\u91cf\u6bd4\u8f83\u5927\u7684\u65f6\u5019\uff09
- \\(\\displaystyle \\min_{f\\in\\mathcal{F}} \\frac{1}{N}\\sum_{i=1}^{N}L(y_{i},f(x_{i}))\\)
- maximum likelihood estimation
- structural risk minimization
- regularization
- \\(\\displaystyle R_{\\mathrm{sm}}(f)=\\frac{1}{N}\\sum_{i=1}^{N}L(y_{i},f(x_{i}))+\\lambda J(f)\\)
- \u590d\u6742\u5ea6\u8868\u793a\u4e86\u5bf9\u590d\u6742\u6a21\u578b\u7684\u4e58\u6cd5
- maximum posterior probability estimation
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#133","title":"1.3.3 \u7b97\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#14","title":"1.4 \u6a21\u578b\u8bc4\u4f30\u4e0e\u6a21\u578b\u9009\u62e9","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#141","title":"1.4.1 \u8bad\u7ec3\u8bef\u5dee\u4e0e\u6d4b\u8bd5\u8bef\u5dee","text":"\\[ R_{\\mathrm{emp}}(\\hat{f})=\\frac{1}{N}\\sum_{i=1}^{N}L(y_{i},\\hat{f}(x_{i})) \\] \\[ e_{\\mathrm{test}}=\\frac{1}{N^{\\prime}}\\sum_{i=1}^{N^{\\prime}}L(y_{i},\\hat{f}(x_{i})) \\] \\[ r_{\\mathrm{test}}+e_{\\mathrm{test}}=1 \\] "},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#142","title":"1.4.2 \u8fc7\u62df\u5408\u4e0e\u6a21\u578b\u9009\u62e9","text":" - model selection
- over-fitting
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#15","title":"1.5 \u6b63\u5219\u5316\u4e0e\u4ea4\u53c9\u9a8c\u8bc1","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#151","title":"1.5.1 \u6b63\u5219\u5316","text":"\\[ L(w)=\\frac{1}{N}\\sum_{i=1}^{N}(f(x_{i};w)-y_{i})^{2}+\\frac{\\lambda}{2}\\parallel w\\parallel^{2} \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#152","title":"1.5.2 \u4ea4\u53c9\u9a8c\u8bc1","text":" - cross validation
- \u6570\u636e\u96c6
- \u8bad\u7ec3\u96c6
- \u9a8c\u8bc1\u96c6
- \u6d4b\u8bd5\u96c6 1. \u7b80\u5355\u4ea4\u53c9\u9a8c\u8bc1 2. \\(\\displaystyle S\\) \u6298\u4ea4\u53c9\u9a8c\u8bc1 3. \u7559\u4e00\u4ea4\u53c9\u9a8c\u8bc1
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#16","title":"1.6 \u6cdb\u5316\u80fd\u529b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#161","title":"1.6.1 \u6cdb\u5316\u8bef\u5dee","text":" \\[ R_{\\exp}(\\hat{f})=E_{P}[L(Y,\\hat{f}(X))]=\\int_{R\\times y}L(y,\\hat{f}(x))P(x,y)\\mathrm{d}x\\mathrm{d}y \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#162","title":"1.6.2 \u6cdb\u5316\u8bef\u5dee\u4e0a\u754c","text":" - generalization error bound
- \u6837\u672c\u5bb9\u91cf\u589e\u52a0\u65f6\uff0c\u6cdb\u5316\u4e0a\u754c\u8d8b\u4e8e 0
- \u5047\u8bbe\u7a7a\u95f4\u8d8a\u5927\uff0c\u6cdb\u5316\u8bef\u5dee\u4e0a\u754c\u8d8a\u5927
- \u8fd9\u4e2a\u5b9a\u7406\u53ea\u9002\u7528\u4e8e\u5047\u8bbe\u7a7a\u95f4\u5305\u542b\u6709\u9650\u4e2a\u51fd\u6570
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#17","title":"1.7 \u751f\u6210\u6a21\u578b\u4e0e\u5224\u522b\u6a21\u578b","text":" - generative model
- \u8fd8\u539f\u51fa\u8054\u5408\u6982\u7387\u5206\u5e03 \\(\\displaystyle P(X,Y)\\)
- \u6734\u7d20\u8d1d\u53f6\u65af\u6cd5
- \u9690\u9a6c\u5c14\u53ef\u592b\u6a21\u578b
- \u6536\u655b\u901f\u5ea6\u5feb
- discriminative model
- \u76f4\u63a5\u5b66\u4e60\u51b3\u7b56\u51fd\u6570\u6216\u6761\u4ef6\u6982\u7387\u5206\u5e03 \\(\\displaystyle P(Y|X)\\)
- \\(\\displaystyle k\\) \u8fd1\u90bb\u6cd5
- \u611f\u77e5\u673a
- \u51b3\u7b56\u6811
- \u903b\u8f91\u65af\u8c1b\u56de\u5f52\u6a21\u578b
- \u6700\u5927\u71b5\u6a21\u578b
- \u652f\u6301\u5411\u91cf\u673a
- \u63d0\u5347\u65b9\u6cd5
- \u6761\u4ef6\u968f\u673a\u573a
- \u51c6\u786e\u5ea6\u9ad8
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#18","title":"1.8 \u5206\u7c7b\u95ee\u9898","text":" - precision \\(\\displaystyle P=\\frac{TP}{TP+FP}\\)
- recall \\(\\displaystyle R=\\frac{TP}{TP+FN}\\)
\\[ \\frac{2}{F_{1}}=\\frac{1}{P}+\\frac{1}{R} \\] \\[ F_{1}=\\frac{2TP}{2TP+FP+FN} \\] "},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#19","title":"1.9 \u6807\u6ce8\u95ee\u9898","text":" - tagging \u662f classificationd \u4e00\u4e2a\u63a8\u5e7f
- \u662f structure prediction \u7684\u7b80\u5355\u5f62\u5f0f
- \u9690\u9a6c\u5c14\u53ef\u592b\u6a21\u578b
- \u6761\u4ef6\u968f\u673a\u573a
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#110","title":"1.10 \u56de\u5f52\u95ee\u9898","text":" - regression
- \uff08\u975e\uff09\u7ebf\u6027\u56de\u5f52\uff0c\u4e00\u5143\u56de\u5f52\uff0c\u591a\u5143\u56de\u5f52
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#2","title":"2 \u611f\u77e5\u673a","text":" - perception
- \u611f\u77e5\u673a\u5bf9\u5e94\u4e8e\u8f93\u5165\u7a7a\u95f4\u4e2d\u5c06\u5b9e\u4f8b\u5212\u5206\u6210\u6b63\u8d1f\u4e24\u7c7b\u7684\u5206\u79bb\u8d85\u5e73\u9762\uff0c\u5c5e\u4e8e\u5224\u522b\u6a21\u578b
- \u539f\u59cb\u5f62\u5f0f\u548c\u5bf9\u5076\u5f62\u5f0f
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#21","title":"2.1 \u611f\u77e5\u673a\u6a21\u578b","text":" - \u5047\u8bbe\u7a7a\u95f4\u662f\u5b9a\u4e49\u5728\u7279\u5f81\u7a7a\u95f4\u4e2d\u6240\u6709\u7684\u7ebf\u6027\u5206\u7c7b\u6a21\u578b\uff08linear classification model\uff09\\(\\displaystyle \\{f|f(x) = w \\cdot x+b\\}\\)
- separating hyperplane
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#22","title":"2.2 \u611f\u77e5\u673a\u5b66\u4e60\u7b56\u7565","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#221","title":"2.2.1 \u6570\u636e\u96c6\u7684\u7ebf\u6027\u53ef\u5206\u6027","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#222","title":"2.2.2 \u611f\u77e5\u673a\u5b66\u4e60\u7b56\u7565","text":" - \u5b9a\u4e49\u635f\u5931\u51fd\u6570\u5e76\u5c06\u635f\u5931\u51fd\u6570\u6781\u5c0f\u5316
\\[ L(w,b)=-\\sum_{x_{i}\\in M}y_{i}(w\\cdot x_{i}+b) \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#223","title":"2.2.3 \u611f\u77e5\u673a\u5b66\u4e60\u7b97\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#224","title":"2.2.4 \u611f\u77e5\u673a\u5b66\u4e60\u7b97\u6cd5\u7684\u539f\u59cb\u5f62\u5f0f","text":"\\[ \\min_{w,b}L(w,b)=-\\sum_{x_{i}\\in M}y_{i}(w\\cdot x_{i}+b) \\] - stochastic gradient descent
\\[ \\nabla_{_w}L(w,b)=-\\sum_{x_{i}\\in M}y_{i}x_{i} \\] \\[ \\nabla_{b}L(w,b)=-\\sum_{x_{i}eM}y_{i} \\] \\[ w\\leftarrow w+\\eta y_{i}x_{i} \\] \\[ b\\leftarrow b+\\eta y_{i} \\] - \\(\\displaystyle \\eta\\) \u88ab\u79f0\u4e3a\u5b66\u4e60\u7387\uff08learning rate\uff09
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#225","title":"2.2.5 \u7b97\u6cd5\u7684\u6536\u655b\u6027","text":" - \u4e3a\u4e86\u5f97\u5230\u552f\u4e00\u7684\u8d85\u5e73\u9762\uff0c\u9700\u8981\u5bf9\u5206\u79bb\u8d85\u5e73\u9762\u589e\u52a0\u7ea6\u675f\u6761\u4ef6\uff0c\u5373\u7ebf\u6027\u652f\u6301\u5411\u91cf\u673a
- \u5982\u679c\u8bad\u7ec3\u96c6\u7ebf\u6027\u4e0d\u53ef\u5206\uff0c\u90a3\u4e48\u611f\u77e5\u673a\u5b66\u4e60\u7b97\u6cd5\u4e0d\u6536\u655b
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#226","title":"2.2.6 \u611f\u77e5\u673a\u5b66\u4e60\u7b97\u6cd5\u7684\u5bf9\u5076\u5f62\u5f0f","text":"\\[ \\begin{aligned}&w\\leftarrow w+\\eta y_{i}x_{i}\\\\&b\\leftarrow b+\\eta y_{i}\\end{aligned} \\] \\[ w=\\sum_{i=1}^{N}\\alpha_{i}y_{i}x_{i} \\] \\[ b=\\sum_{i=1}^{N}\\alpha_{i}y_{i} \\] \\[ G=[x_{i}\\cdot x_{j}]_{N\\times N} \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#3-displaystyle-k","title":"3 \\(\\displaystyle k\\) \u8fd1\u90bb\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#31-displaystyle-k","title":"3.1 \\(\\displaystyle k\\) \u8fd1\u90bb\u7b97\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#32-displaystyle-k","title":"3.2 \\(\\displaystyle k\\) \u8fd1\u90bb\u6a21\u578b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#321","title":"3.2.1 \u6a21\u578b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#33","title":"3.3 \u8ddd\u79bb\u5ea6\u91cf","text":" - \\(\\displaystyle L_{p}\\) distance or Minkowski distamce
- \\(\\displaystyle L_{p}(x_{i},x_{j})=\\left(\\sum_{l=1}^{n}\\mid x_{i}^{(l)}-x_{j}^{(l)}\\mid^{p}\\right)^{\\frac{1}{p}}\\)
- \\(\\displaystyle L_{2}(x_{i},x_{j})=\\left(\\sum_{i=1}^{n}\\mid x_{i}^{(l)}-x_{j}^{(l)}\\mid^{2}\\right)^{\\frac{1}{2}}\\)
- \\(\\displaystyle L_{1}(x_{i}, x_{j})=\\sum_{l=1}^{n}\\mid x_{i}^{(l)}-x_{j}^{(l)}\\mid\\)
- \\(\\displaystyle L_{\\infty}(x_{i}, x_{j})=\\max_{l}\\mid x_{i}^{(l)}-x_{j}^{(l)}\\mid\\)
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#331-displaystyle-k","title":"3.3.1 \\(\\displaystyle k\\) \u503c\u7684\u9009\u62e9","text":" - if k is small, then the approximation error will reduce
- estimation error
- \\(\\displaystyle k\\) \u503c\u7684\u51cf\u5c0f\u5c31\u610f\u5473\u7740\u6574\u4f53\u6a21\u578b\u53d8\u5f97\u590d\u6742\uff0c\u5bb9\u6613\u53d1\u751f\u8fc7\u62df\u5408
- \u5728\u5e94\u7528\u4e2d, \\(\\displaystyle k\\) \u503c\u4e00\u822c\u53d6\u4e00\u4e2a\u6bd4\u8f83\u5c0f\u7684\u6570\u503c\uff0c\u901a\u5e38\u91c7\u7528\u4ea4\u53c9\u9a8c\u8bc1\u6cd5\u6765\u9009\u53d6\u6700\u4f18\u7684 \\(\\displaystyle k\\) \u503c
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#332","title":"3.3.2 \u5206\u7c7b\u51b3\u7b56\u89c4\u5219","text":" - \u591a\u6570\u8868\u51b3\u89c4\u5219\uff08majority voting rule\uff09
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#34-displaystyle-k-displaystyle-kd","title":"3.4 \\(\\displaystyle k\\) \u8fd1\u90bb\u6cd5\u7684\u5b9e\u73b0: \\(\\displaystyle kd\\) \u6811","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#341-displaystyle-kd","title":"3.4.1 \u6784\u9020 \\(\\displaystyle kd\\) \u6811","text":" - \\(\\displaystyle kd\\) \u6811\u662f\u4e00\u4e8c\u53c9\u6811\uff0c\u8868\u793a\u5bf9 \\(\\displaystyle k\\) \u7ef4\u7a7a\u95f4\u7684\u4e00\u4e2a\u5212\u5206\uff08partition\uff09
- \u901a\u5e38\u9009\u62e9\u8bad\u7ec3\u5b9e\u4f8b\u70b9\u5728\u9009\u5b9a\u5750\u6807\u8f74\u4e0a\u7684\u4e2d\u4f4d\u6570\u4e3a\u5207\u5206\u70b9\uff0c\u867d\u7136\u8fd9\u6837\u5f97\u5230\u7684\u6811\u662f\u5e73\u8861\u7684\uff0c\u4f46\u6548\u7387\u672a\u5fc5\u662f\u6700\u4f18\u7684 \u6709\u610f\u601d
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#342-displaystyle-kd","title":"3.4.2 \u641c\u7d22 \\(\\displaystyle kd\\) \u6811","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#4","title":"4 \u6734\u7d20\u8d1d\u53f6\u65af\u6cd5","text":" - \u57fa\u4e8e\u8d1d\u53f6\u65af\u5b9a\u7406\u4e0e\u7279\u5f81\u6761\u4ef6\u72ec\u7acb\u5047\u8bbe\u7684\u5206\u7c7b\u65b9\u6cd5
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#41","title":"4.1 \u6734\u7d20\u8d1d\u53f6\u65af\u6cd5\u7684\u5b66\u4e60\u4e0e\u5206\u7c7b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#411","title":"4.1.1 \u57fa\u672c\u65b9\u6cd5","text":" - \u5b66\u4e60\u5148\u9a8c\u6982\u7387\u5206\u5e03\u548c\u6761\u4ef6\u6982\u7387\u5206\u5e03\u4e8e\u662f\u5b66\u4e60\u5230\u8054\u5408\u6982\u7387\u5206\u5e03
\\[ P(X=x\\mid Y=c_{k})=P(X^{(1)}=x^{(1)},\\cdots,X^{(n)}=x^{(n)}\\mid Y=c_{k}),\\quad k=1,2,\\cdots,K \\] - \u5f15\u5165\u4e86\u6761\u4ef6\u72ec\u7acb\u6027\u5047\u8bbe
\\[ \\begin{aligned} P(X=x|Y=c_{k})& =P(X^{(1)}=x^{(1)},\\cdots,X^{(n)}=x^{(n)}\\mid Y=c_{k}) \\\\ &=\\prod_{j=1}^{n}P(X^{(j)}=x^{(j)}\\mid Y=c_{k}) \\end{aligned} \\] \\[ P(Y=c_{k}\\mid X=x)=\\frac{P(X=x\\mid Y=c_{k})P(Y=c_{k})}{\\sum_{k}P(X=x\\mid Y=c_{k})P(Y=c_{k})} \\] \\[ P(Y=c_{k}\\mid X=x)=\\frac{P(Y=c_{k})\\prod_{j}P(X^{(j)}=x^{(j)}\\mid Y=c_{k})}{\\sum_{k}P(Y=c_{k})\\prod_{j}P(X^{(j)}=x^{(j)}\\mid Y=c_{k})},\\quad k=1,2,\\cdots,K \\] \\[ y=f(x)=\\arg\\max_{c_{k}}\\frac{P(Y=c_{k})\\prod_{j}P(X^{(j)}=x^{(j)}\\mid Y=c_{k})}{\\sum_{k}P(Y=c_{k})\\prod_{j}P(X^{(j)}=x^{(j)}\\mid Y=c_{k})} \\] \\[ y=\\arg\\max_{c_{k}}P(Y=c_{k})\\prod_{j}P(X^{(j)}=x^{(j)}\\mid Y=c_{k}) \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#412","title":"4.1.2 \u540e\u9a8c\u6982\u7387\u6700\u5927\u5316\u7684\u542b\u4e49","text":"\\[ L(Y,f(X))=\\begin{cases}1,&Y\\neq f(X)\\\\0,&Y=f(X)\\end{cases} \\] \\[ R_{\\exp}(f)=E[L(Y,f(X))] \\] \\[ R_{\\exp}(f)=E_{\\chi}\\sum_{k=1}^{K}[L(c_{k},f(X))]P(c_{k}\\mid X) \\] \\[ \\begin{align} f(x) &=\\arg\\min_{y\\in\\mathcal{Y}}\\sum_{k=1}^{K}L(c_{k},y)P(c_{k}\\mid X=x) \\\\ &=\\arg\\min_{y\\in\\mathcal{Y}}\\sum_{k=1}^{K}P(y\\neq c_{k}\\mid X=x) \\\\ &=\\arg\\min_{y\\in\\mathcal{Y}}(1-P(y=c_{k}\\mid X=x)) \\\\ &=\\arg\\max_{y\\in\\mathcal{Y}}P(y=c_{k}\\mid X=x) \\end{align} \\] \\[ f(x)=\\arg\\max_{c_{k}}P(c_{k}\\mid X=x) \\] - \u671f\u671b\u98ce\u9669\u6700\u5c0f\u5316\u51c6\u5219\u5c31\u5f97\u5230\u8054\u8003\u540e\u9a8c\u6982\u7387\u6700\u5927\u5316\u51c6\u5219
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#42","title":"4.2 \u6734\u7d20\u8d1d\u53f6\u65af\u6cd5\u7684\u53c2\u6570\u4f30\u8ba1","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#421","title":"4.2.1 \u6781\u5927\u4f3c\u7136\u4f30\u8ba1","text":"\\[ P(Y=c_{k})=\\frac{\\sum_{i=1}^{N}I(y_{i}=c_{k})}{N} , k=1,2,\\cdots,K \\] \\[ P(X^{(j)}=a_{ji}\\mid Y=c_{k})=\\frac{\\sum_{i=1}^{N}I(x_{i}^{(j)}=a_{ji},y_{i}=c_{k})}{\\sum_{i=1}^{N}I(y_{i}=c_{k})}\\\\j=1,2,\\cdots,n ;\\quad l=1,2,\\cdots,S_{j} ;\\quad k=1,2,\\cdots,K \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#422","title":"4.2.2 \u5b66\u4e60\u4e0e\u5206\u7c7b\u7b97\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#423","title":"4.2.3 \u8d1d\u53f6\u65af\u4f30\u8ba1","text":" - \u6781\u5927\u4f3c\u7136\u4f30\u8ba1\u53ef\u80fd\u4f1a\u51fa\u73b0\u6240\u8981\u4f30\u8ba1\u7684\u6982\u7387\u503c\u4e3a 0 \u7684\u60c5\u51b5
- \u6761\u4ef6\u6982\u7387\u7684\u8d1d\u53f6\u65af\u4f30\u8ba1
\\[ P_{\\lambda}(X^{(j)}=a_{ji}\\mid Y=c_{k})=\\frac{\\sum_{i=1}^{N}I(x_{i}^{(j)}=a_{ji},y_{i}=c_{k})+\\lambda}{\\sum_{i=1}^{N}I(y_{i}=c_{k})+S_{j}\\lambda} \\] - when \\(\\displaystyle \\lambda = 0\\), it's called Laplace smoothing
\\[ \\begin{aligned}&P_{\\lambda}(X^{(j)}=a_{jl}\\mid Y=c_{k})>0\\\\&\\sum_{l=1}^{s_{j}}P(X^{(j)}=a_{jl}\\mid Y=c_{k})=1\\end{aligned} \\] - \u8868\u660e\u8d1d\u53f6\u65af\u4f30\u8ba1\u786e\u5b9e\u662f\u4e00\u79cd\u6982\u7387\u5206\u5e03
- \u5148\u9a8c\u6982\u7387\u7684\u8d1d\u53f6\u65af\u4f30\u8ba1
\\[ P_{\\lambda}(Y=c_{k})=\\frac{\\sum_{i=1}^{N}I(y_{i}=c_{k})+\\lambda}{N+K\\lambda} \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#5","title":"5 \u51b3\u7b56\u6811","text":" - decision tree
- \u7279\u5f81\u9009\u62e9
- \u51b3\u7b56\u6811\u7684\u751f\u6210
- \u51b3\u7b56\u6811\u7684\u4fee\u526a
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#51","title":"5.1 \u51b3\u7b56\u6811\u6a21\u578b\u4e0e\u5b66\u4e60","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#511","title":"5.1.1 \u51b3\u7b56\u6811\u6a21\u578b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#512-if-then","title":"5.1.2 \u51b3\u7b56\u6811\u4e0e if-then \u89c4\u5219","text":" - \u4e92\u65a5\u4e14\u5b8c\u5907
- \u6bcf\u4e00\u4e2a\u5b9e\u4f8b\u90fd\u88ab\u4e00\u6761\u8def\u5f84\u4f1a\u89c4\u5219\u6240\u8986\u76d6\uff0c\u800c\u4e14\u53ea\u88ab\u4e00\u6761\u8def\u5f84\u6216\u4e00\u6761\u89c4\u5219\u6240\u8986\u76d6
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#513","title":"5.1.3 \u51b3\u7b56\u6811\u4e0e\u6761\u4ef6\u6982\u7387\u5206\u5e03","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#514","title":"5.1.4 \u51b3\u7b56\u6811\u5b66\u4e60","text":" - \u51b3\u7b56\u6811\u5b66\u4e60\u672c\u8d28\u4e0a\u662f\u4ece\u8bad\u7ec3\u6570\u636e\u96c6\u4e2d\u5f52\u7eb3\u51fa\u4e00\u7ec4\u5206\u7c7b\u89c4\u5219
- \u5728\u635f\u5931\u51fd\u6570\u610f\u4e49\u4e0b\u9009\u62e9\u6700\u4f18\u51b3\u7b56\u6811\u7684\u95ee\u9898\uff0c\u662f NP \u5b8c\u5168\u95ee\u9898\uff0c\u91c7\u7528\u542f\u53d1\u5f0f\u65b9\u6cd5\uff0c\u8fd1\u4f3c\u6c42\u89e3\uff0c\u8fd9\u6837\u5f97\u5230\u7684\u51b3\u7b56\u6811\u662f\u6b21\u6700\u4f18\uff08sub-optimal\uff09
- \u4e3a\u4e86\u9632\u6b62\u8fc7\u62df\u5408\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u5df2\u751f\u6210\u7684\u6811\u81ea\u4e0a\u800c\u4e0b\u8fdb\u884c\u526a\u679d
- \u51b3\u7b56\u6811\u7684\u751f\u6210\u503c\u8003\u8651\u5c40\u90e8\u6700\u4f18\uff0c\u526a\u679d\u5219\u8003\u8651\u5168\u5c40\u6700\u4f18
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#52","title":"5.2 \u7279\u5f81\u9009\u62e9","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#521","title":"5.2.1 \u7279\u5f81\u9009\u62e9\u95ee\u9898","text":" - \u901a\u5e38\u7279\u5f81\u9009\u62e9\u7684\u51c6\u5219\u662f\u4fe1\u606f\u589e\u76ca\u6216\u4fe1\u606f\u589e\u76ca\u6bd4
- information gain
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#522","title":"5.2.2 \u4fe1\u606f\u589e\u76ca","text":" - \u71b5\u548c\u6761\u4ef6\u71b5
\\[ P(X=x_{i})=p_{i} ,\\quad i=1,2,\\cdots,n \\] \\[ H(X)=-\\sum_{i=1}^{n}p_{i}\\log p_{i} \\] \\[ H(p)=-\\sum_{i=1}^{n}p_{i}\\log p_{i} \\] \\[ 0\\leqslant H(p)\\leqslant\\log n \\] \\[ P(X=x_{i},Y=y_{j})=p_{ij} ,\\quad i=1,2,\\cdots,n ;\\quad j=1,2,\\cdots,m \\] \\[ H(Y\\mid X)=\\sum_{i=1}^{n}p_{i}H(Y\\mid X=x_{i}) \\] "},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#523","title":"5.2.3 \u4fe1\u606f\u589e\u76ca\u6bd4","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#53","title":"5.3 \u51b3\u7b56\u6811\u7684\u751f\u6210","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#531-id-3","title":"5.3.1 ID 3 \u7b97\u6cd5","text":" - ID 3 \u7b97\u6cd5\u53ea\u6709\u6811\u7684\u751f\u6210\uff0c\u6240\u4ee5\u8be5\u7b97\u6cd5\u751f\u6210\u7684\u6811\u5bb9\u6613\u4ea7\u751f\u8fc7\u62df\u5408
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#532-c-45","title":"5.3.2 C 4.5 \u7684\u751f\u6210\u7b97\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#54","title":"5.4 \u51b3\u7b56\u6811\u7684\u526a\u679d","text":" \\[ C_{\\alpha}(T)=\\sum_{t=1}^{|T|}N_{t}H_{t}(T)+\\alpha|T| \\] \\[ H_{t}(T)=-\\sum_{k}\\frac{N_{ik}}{N_{t}}\\log\\frac{N_{ik}}{N_{t}} \\] \\[ C(T)=\\sum_{t=1}^{|T|}N_{t}H_{t}(T)=-\\sum_{t=1}^{|T|}\\sum_{k=1}^{K}N_{tk}\\log\\frac{N_{tk}}{N_{t}} \\] \\[ C_{\\alpha}(T)=C(T)+\\alpha|T| \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#55-cart","title":"5.5 CART \u7b97\u6cd5","text":" - \u5206\u88c2\u4e0e\u56de\u5f52\u6811\uff08classification and regression tree\uff09
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#551-cart","title":"5.5.1 CART \u751f\u6210","text":" - \u5bf9\u56de\u5f52\u6811\u7528\u5e73\u65b9\u8bef\u5dee\u6700\u5c0f\u5316\u51c6\u5219
- \u5bf9\u5206\u7c7b\u6811\u7528\u57fa\u5c3c\u6307\u6570\uff08Gini index\uff09\u6700\u5c0f\u5316\u51c6\u5219 1. \u56de\u5f52\u6811\u7684\u751f\u6210
\\[ f(x)=\\sum_{m=1}^{M}c_{m}I(x\\in R_{m}) \\] \\[ \\hat{c}_{m}=\\mathrm{ave}(y_{i}\\mid x_{i}\\in R_{m}) \\] - splitting variable
- splitting point
\\[ R_{1}(j,s)=\\{x\\mid x^{(j)}\\leqslant s\\}\\quad\\text{\u548c}\\quad R_{2}(j,s)=\\{x\\mid x^{(j)}>s\\} \\] \\[ \\min_{j,s}\\biggl[\\min_{c_{1}}\\sum_{x_{i}\\in R_{i}(j,s)}(y_{i}-c_{1})^{2}+\\min_{c_{2}}\\sum_{x_{i}\\in R_{2}(j,s)}(y_{i}-c_{2})^{2}\\biggr] \\] \\[ \\hat{c}_{1}=\\mathrm{ave}(y_{i}\\mid x_{i}\\in R_{1}(j,s))\\quad\\hat{\\text{\u548c}}\\quad\\hat{c}_{2}=\\mathrm{ave}(y_{i}\\mid x_{i}\\in R_{2}(j,s)) \\] - least squares regression tree 1. \u5206\u7c7b\u6811\u7684\u751f\u6210
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#552-cart","title":"5.5.2 CART \u526a\u679d","text":" - \u526a\u679d\uff0c\u5f62\u6210\u4e00\u4e2a\u5b50\u6811\u5e8f\u5217
\\[ C_{\\alpha}(T)=C(T)+\\alpha\\left|T\\right| \\] \\[ g(t)=\\frac{C(t)-C(T_{t})}{\\mid T_{t}\\mid-1} \\] - \u5728\u526a\u679d\u5f97\u5230\u7684\u5b50\u6811\u5e8f\u5217 \\(\\displaystyle T_0,T_1,\\cdots,T_n\\) \u4e2d\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u9009\u53d6\u6700\u4f18\u5b50\u6811 \\(\\displaystyle T_{\\alpha}\\)
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#6","title":"6 \u903b\u8f91\u65af\u8c1b\u56de\u5f52\u4e0e\u6700\u5927\u71b5\u6a21\u578b","text":" - logistic regression
- maximum entropy model
- \u903b\u8f91\u65af\u8c1b\u56de\u5f52\u6a21\u578b\u548c\u6700\u5927\u71b5\u6a21\u578b\u90fd\u5c5e\u4e8e\u5bf9\u6570\u7ebf\u6027\u6a21\u578b
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#61","title":"6.1 \u903b\u8f91\u65af\u8c1b\u56de\u5f52\u6a21\u578b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#611","title":"6.1.1 \u903b\u8f91\u65af\u8c1b\u5206\u5e03","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#612","title":"6.1.2 \u4e8c\u9879\u903b\u8f91\u65af\u8c1b\u56de\u5f52\u6a21\u578b","text":" - binomial logistic regression model
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#7","title":"7 \u652f\u6301\u5411\u91cf\u673a","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#8","title":"8 \u63d0\u5347\u65b9\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#9-displaystyle-boldsymbolem","title":"9 \\(\\displaystyle \\boldsymbol{EM}\\) \u7b97\u6cd5\u53ca\u5176\u63a8\u5e7f","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#10","title":"10 \u9690\u9a6c\u5c14\u53ef\u592b\u6a21\u578b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#11_1","title":"11 \u6761\u4ef6\u968f\u673a\u573a","text":""},{"location":"AI/CS231n/CS231n_notes/","title":"Computer Vision","text":""},{"location":"AI/CS231n/CS231n_notes/#computer-vision","title":"Computer Vision","text":" \u7ea6 8852 \u4e2a\u5b57 167 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 44 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
This note is based on GitHub - DaizeDong/Stanford-CS231n-2021-and-2022: Notes and slides for Stanford CS231n 2021 & 2022 in English. I merged the contents together to get a better version. Assignments are not included. \u65af\u5766\u798fcs231n\u7684\u8bfe\u7a0b\u7b14\u8bb0(\u82f1\u6587\u7248\u672c\uff0c\u4e0d\u542b\u5b9e\u9a8c\u4ee3\u7801)\uff0c\u5c062021\u4e0e2022\u4e24\u5e74\u7684\u8bfe\u7a0b\u8fdb\u884c\u4e86\u5408\u5e76\uff0c\u5206\u4eab\u4ee5\u4f9b\u4ea4\u6d41\u3002 And I will add some blogs, articles and other understanding.
Topic Chapter Deep Learning Basics 2 - 4 Perceiving and Understanding the Visual World 5 - 12 Reconstructing and Interacting with the Visual World 13 - 16 Human-Centered Applications and Implications 17 - 18"},{"location":"AI/CS231n/CS231n_notes/#1-introduction","title":"1 - Introduction","text":"A brief history of computer vision & deep learning...
"},{"location":"AI/CS231n/CS231n_notes/#2-image-classification","title":"2 - Image Classification","text":"Image Classification: A core task in Computer Vision. The main drive to the progress of CV.
Challenges: Viewpoint variation, background clutter, illumination, occlusion, deformation, intra-class variation...
"},{"location":"AI/CS231n/CS231n_notes/#k-nearest-neighbor","title":"K Nearest Neighbor","text":"Hyperparameters: Distance metric (\\(p\\) norm), \\(k\\) number.
Choose hyperparameters using validation set.
Never use k-Nearest Neighbor with pixel distance.
"},{"location":"AI/CS231n/CS231n_notes/#linear-classifier","title":"Linear Classifier","text":"Pass...
"},{"location":"AI/CS231n/CS231n_notes/#3-loss-functions-and-optimization","title":"3 - Loss Functions and Optimization","text":""},{"location":"AI/CS231n/CS231n_notes/#loss-functions","title":"Loss Functions","text":"Dataset \\(\\big\\{(x_i,y_i)\\big\\}_{i=1}^N\\\\\\) Loss Function \\(L=\\frac{1}{N}\\sum_{i=1}^NL_i\\big(f(x_i,W),y_i\\big)\\\\\\) Loss Function with Regularization \\(L=\\frac{1}{N}\\sum_{i=1}^NL_i\\big(f(x_i,W),y_i\\big)+\\lambda R(W)\\\\\\) Motivation: Want to interpret raw classifier scores as probabilities.
Softmax Classifier \\(p_i=Softmax(y_i)=\\frac{\\exp(y_i)}{\\sum_{j=1}^N\\exp(y_j)}\\\\\\) Cross Entropy Loss \\(L_i=-y_i\\log p_i\\\\\\) Cross Entropy Loss with Regularization \\(L=-\\frac{1}{N}\\sum_{i=1}^Ny_i\\log p_i+\\lambda R(W)\\\\\\) "},{"location":"AI/CS231n/CS231n_notes/#optimization","title":"Optimization","text":""},{"location":"AI/CS231n/CS231n_notes/#sgd-with-momentum","title":"SGD with Momentum","text":"Problems that SGD can't handle:
- Inequality of gradient in different directions.
- Local minima and saddle point (much more common in high dimension).
- Noise of gradient from mini-batch.
Momentum: Build up \u201cvelocity\u201d \\(v_t\\) as a running mean of gradients.
SGD SGD + Momentum \\(x_{t+1}=x_t-\\alpha\\nabla f(x_t)\\) \\(\\begin{align}&v_{t+1}=\\rho v_t+\\nabla f(x_t)\\\\&x_{t+1}=x_t-\\alpha v_{t+1}\\end{align}\\) Naive gradient descent. \\(\\rho\\) gives \"friction\", typically \\(\\rho=0.9,0.99,0.999,...\\) Nesterov Momentum: Use the derivative on point \\(x_t+\\rho v_t\\) as gradient instead point \\(x_t\\).
Momentum Nesterov Momentum \\(\\begin{align}&v_{t+1}=\\rho v_t+\\nabla f(x_t)\\\\&x_{t+1}=x_t-\\alpha v_{t+1}\\end{align}\\) \\(\\begin{align}&v_{t+1}=\\rho v_t+\\nabla f(x_t+\\rho v_t)\\\\&x_{t+1}=x_t-\\alpha v_{t+1}\\end{align}\\) Use gradient at current point. Look ahead for the gradient in velocity direction. "},{"location":"AI/CS231n/CS231n_notes/#adagrad-and-rmsprop","title":"AdaGrad and RMSProp","text":"AdaGrad: Accumulate squared gradient, and gradually decrease the step size.
RMSProp: Accumulate squared gradient while decaying former ones, and gradually decrease the step size. (\"Leaky AdaGrad\")
AdaGrad RMSProp \\(\\begin{align}\\text{Initialize:}&\\\\&r:=0\\\\\\text{Update:}&\\\\&r:=r+\\Big[\\nabla f(x_t)\\Big]^2\\\\&x_{t+1}=x_t-\\alpha\\frac{\\nabla f(x_t)}{\\sqrt{r}}\\end{align}\\) \\(\\begin{align}\\text{Initialize:}&\\\\&r:=0\\\\\\text{Update:}&\\\\&r:=\\rho r+(1-\\rho)\\Big[\\nabla f(x_t)\\Big]^2\\\\&x_{t+1}=x_t-\\alpha\\frac{\\nabla f(x_t)}{\\sqrt{r}}\\end{align}\\) Continually accumulate squared gradients. \\(\\rho\\) gives \"decay rate\", typically \\(\\rho=0.9,0.99,0.999,...\\)"},{"location":"AI/CS231n/CS231n_notes/#adam","title":"Adam","text":"Sort of like \"RMSProp + Momentum\".
Adam (simple version) Adam (full version) \\(\\begin{align}\\text{Initialize:}&\\\\&r_1:=0\\\\&r_2:=0\\\\\\text{Update:}&\\\\&r_1:=\\beta_1r_1+(1-\\beta_1)\\nabla f(x_t)\\\\&r_2:=\\beta_2r_2+(1-\\beta_2)\\Big[\\nabla f(x_t)\\Big]^2\\\\&x_{t+1}=x_t-\\alpha\\frac{r_1}{\\sqrt{r_2}}\\end{align}\\) \\(\\begin{align}\\text{Initialize:}\\\\&r_1:=0\\\\&r_2:=0\\\\\\text{For }i\\text{:}\\\\&r_1:=\\beta_1r_1+(1-\\beta_1)\\nabla f(x_t)\\\\&r_2:=\\beta_2r_2+(1-\\beta_2)\\Big[\\nabla f(x_t)\\Big]^2\\\\&r_1'=\\frac{r_1}{1-\\beta_1^i}\\\\&r_2'=\\frac{r_2}{1-\\beta_2^i}\\\\&x_{t+1}=x_t-\\alpha\\frac{r_1'}{\\sqrt{r_2'}}\\end{align}\\) Build up \u201cvelocity\u201d for both gradient and squared gradient. Correct the \"bias\" that \\(r_1=r_2=0\\) for the first few iterations."},{"location":"AI/CS231n/CS231n_notes/#overview","title":"Overview","text":""},{"location":"AI/CS231n/CS231n_notes/#learning-rate-decay","title":"Learning Rate Decay","text":"Reduce learning rate at a few fixed points to get a better convergence over time.
\\(\\alpha_0\\) : Initial learning rate.
\\(\\alpha_t\\) : Learning rate in epoch \\(t\\).
\\(T\\) : Total number of epochs.
Method Equation Picture Step Reduce \\(\\alpha_t\\) constantly in a fixed step. Cosine \\(\\begin{align}\\alpha_t=\\frac{1}{2}\\alpha_0\\Bigg[1+\\cos(\\frac{t\\pi}{T})\\Bigg]\\end{align}\\) Linear \\(\\begin{align}\\alpha_t=\\alpha_0\\Big(1-\\frac{t}{T}\\Big)\\end{align}\\) Inverse Sqrt \\(\\begin{align}\\alpha_t=\\frac{\\alpha_0}{\\sqrt{t}}\\end{align}\\) High initial learning rates can make loss explode, linearly increasing learning rate in the first few iterations can prevent this.
Learning rate warm up:
Empirical rule of thumb: If you increase the batch size by \\(N\\), also scale the initial learning rate by \\(N\\) .
"},{"location":"AI/CS231n/CS231n_notes/#second-order-optimization","title":"Second-Order Optimization","text":"Picture Time Complexity Space Complexity First Order \\(O(n)\\) \\(O(n)\\) Second Order \\(O(n^2)\\) with BGFS optimization \\(O(n)\\) with L-BGFS optimization L-BGFS : Limited memory BGFS.
- Works very well in full batch, deterministic \\(f(x)\\).
- Does not transfer very well to mini-batch setting.
"},{"location":"AI/CS231n/CS231n_notes/#summary","title":"Summary","text":"Method Performance Adam Often chosen as default method.Work ok even with constant learning rate. SGD + Momentum Can outperform Adam.Require more tuning of learning rate and schedule. L-BGFS If can afford to do full batch updates then try out. - An article about gradient descent: Anoverview of gradient descent optimization algorithms
- A blog: An updated overview of recent gradient descent algorithms \u2013 John Chen \u2013 ML at Rice University
"},{"location":"AI/CS231n/CS231n_notes/#4-neural-networks-and-backpropagation","title":"4 - Neural Networks and Backpropagation","text":""},{"location":"AI/CS231n/CS231n_notes/#neural-networks","title":"Neural Networks","text":"Motivation: Inducted bias can appear to be high when using human-designed features.
Activation: Sigmoid, tanh, ReLU, LeakyReLU...
Architecture: Input layer, hidden layer, output layer.
Do not use the size of a neural network as the regularizer. Use regularization instead!
Gradient Calculation: Computational Graph + Backpropagation.
"},{"location":"AI/CS231n/CS231n_notes/#backpropagation","title":"Backpropagation","text":"Using Jacobian matrix to calculate the gradient of each node in a computation graph.
Suppose that we have a computation flow like this:
Input X Input W Output Y \\(X=\\begin{bmatrix}x_1\\\\x_2\\\\\\vdots\\\\x_n\\end{bmatrix}\\) \\(W=\\begin{bmatrix}w_{11}&w_{12}&\\cdots&w_{1n}\\\\w_{21}&w_{22}&\\cdots&w_{2n}\\\\\\vdots&\\vdots&\\ddots&\\vdots\\\\w_{m1}&w_{m2}&\\cdots&w_{mn}\\end{bmatrix}\\) \\(Y=\\begin{bmatrix}y_1\\\\y_2\\\\\\vdots\\\\y_m\\end{bmatrix}\\) \\(n\\times 1\\) \\(m\\times n\\) \\(m\\times 1\\) After applying feed forward, we can calculate gradients like this:
Derivative Matrix of X Jacobian Matrix of X Derivative Matrix of Y \\(D_X=\\begin{bmatrix}\\frac{\\partial L}{\\partial x_1}\\\\\\frac{\\partial L}{\\partial x_2}\\\\\\vdots\\\\\\frac{\\partial L}{\\partial x_n}\\end{bmatrix}\\) \\(J_X=\\begin{bmatrix}\\frac{\\partial y_1}{\\partial x_1}&\\frac{\\partial y_1}{\\partial x_2}&\\cdots&\\frac{\\partial y_1}{\\partial x_n}\\\\\\frac{\\partial y_2}{\\partial x_1}&\\frac{\\partial y_2}{\\partial x_2}&\\cdots&\\frac{\\partial y_2}{\\partial x_n}\\\\\\vdots&\\vdots&\\ddots&\\vdots\\\\\\frac{\\partial y_m}{\\partial x_1}&\\frac{\\partial y_m}{\\partial x_2}&\\cdots&\\frac{\\partial y_m}{\\partial x_n}\\end{bmatrix}\\) \\(D_Y=\\begin{bmatrix}\\frac{\\partial L}{\\partial y_1}\\\\\\frac{\\partial L}{\\partial y_2}\\\\\\vdots\\\\\\frac{\\partial L}{\\partial y_m}\\end{bmatrix}\\) \\(n\\times 1\\) \\(m\\times n\\) \\(m\\times 1\\) Derivative Matrix of W Jacobian Matrix of W Derivative Matrix of Y \\(W=\\begin{bmatrix}\\frac{\\partial L}{\\partial w_{11}}&\\frac{\\partial L}{\\partial w_{12}}&\\cdots&\\frac{\\partial L}{\\partial w_{1n}}\\\\\\frac{\\partial L}{\\partial w_{21}}&\\frac{\\partial L}{\\partial w_{22}}&\\cdots&\\frac{\\partial L}{\\partial w_{2n}}\\\\\\vdots&\\vdots&\\ddots&\\vdots\\\\\\frac{\\partial L}{\\partial w_{m1}}&\\frac{\\partial L}{\\partial w_{m2}}&\\cdots&\\frac{\\partial L}{\\partial w_{mn}}\\end{bmatrix}\\) \\(J_W^{(k)}=\\begin{bmatrix}\\frac{\\partial y_k}{\\partial w_{11}}&\\frac{\\partial y_k}{\\partial w_{12}}&\\cdots&\\frac{\\partial y_k}{\\partial w_{1n}}\\\\\\frac{\\partial y_k}{\\partial w_{21}}&\\frac{\\partial y_k}{\\partial w_{22}}&\\cdots&\\frac{\\partial y_k}{\\partial w_{2n}}\\\\\\vdots&\\vdots&\\ddots&\\vdots\\\\\\frac{\\partial y_k}{\\partial w_{m1}}&\\frac{\\partial y_k}{\\partial w_{m2}}&\\cdots&\\frac{\\partial y_k}{\\partial w_{mn}}\\end{bmatrix}\\)\\(J_W=\\begin{bmatrix}J_W^{(1)}&J_W^{(2)}&\\cdots&J_W^{(m)}\\end{bmatrix}\\) \\(D_Y=\\begin{bmatrix}\\frac{\\partial L}{\\partial y_1}\\\\\\frac{\\partial L}{\\partial y_2}\\\\\\vdots\\\\\\frac{\\partial L}{\\partial y_m}\\end{bmatrix}\\) \\(m\\times n\\) \\(m\\times m\\times n\\) $ m\\times 1$ For each element in \\(D_X\\) , we have:
\\(D_{Xi}=\\frac{\\partial L}{\\partial x_i}=\\sum_{j=1}^m\\frac{\\partial L}{\\partial y_j}\\frac{\\partial y_j}{\\partial x_i}\\\\\\)
"},{"location":"AI/CS231n/CS231n_notes/#5-convolutional-neural-networks","title":"5 - Convolutional Neural Networks","text":""},{"location":"AI/CS231n/CS231n_notes/#convolution-layer","title":"Convolution Layer","text":""},{"location":"AI/CS231n/CS231n_notes/#introduction","title":"Introduction","text":"Convolve a filter with an image: Slide the filter spatially within the image, computing dot products in each region.
Giving a \\(32\\times32\\times3\\) image and a \\(5\\times5\\times3\\) filter, a convolution looks like:
Convolve six \\(5\\times5\\times3\\) filters to a \\(32\\times32\\times3\\) image with step size \\(1\\), we can get a \\(28\\times28\\times6\\) feature:
With an activation function after each convolution layer, we can build the ConvNet with a sequence of convolution layers:
By changing the step size between each move for filters, or adding zero-padding around the image, we can modify the size of the output:
"},{"location":"AI/CS231n/CS231n_notes/#1times1-convolution-layer","title":"\\(1\\times1\\) Convolution Layer","text":"This kind of layer makes perfect sense. It is usually used to change the dimension (channel) of features.
A \\(1\\times1\\) convolution layer can also be treated as a full-connected linear layer.
"},{"location":"AI/CS231n/CS231n_notes/#summary_1","title":"Summary","text":"Input image size \\(W_1\\times H_1\\times C\\) filter size \\(F\\times F\\times C\\) filter number \\(K\\) stride \\(S\\) zero padding \\(P\\) Output output size \\(W_2\\times H_2\\times K\\) output width \\(W_2=\\frac{W_1-F+2P}{S}+1\\\\\\) output height \\(H_2=\\frac{H_1-F+2P}{S}+1\\\\\\) Parameters parameter number (weight) \\(F^2CK\\) parameter number (bias) \\(K\\)"},{"location":"AI/CS231n/CS231n_notes/#pooling-layer","title":"Pooling layer","text":"Make the representations smaller and more manageable.
An example of max pooling:
Input image size \\(W_1\\times H_1\\times C\\) spatial extent \\(F\\times F\\) stride \\(S\\) Output output size \\(W_2\\times H_2\\times C\\) output width \\(W_2=\\frac{W_1-F}{S}+1\\\\\\) output height \\(H_2=\\frac{H_1-F}{S}+1\\\\\\)"},{"location":"AI/CS231n/CS231n_notes/#convolutional-neural-networks-cnn","title":"Convolutional Neural Networks (CNN)","text":"CNN stack CONV, POOL, FC layers.
CNN Trends:
- Smaller filters and deeper architectures.
- Getting rid of POOL/FC layers (just CONV).
Historically architectures of CNN looked like:
where usually \\(m\\) is large, \\(0\\le n\\le5\\), \\(0\\le k\\le2\\).
Recent advances such as ResNet / GoogLeNet have challenged this paradigm.
"},{"location":"AI/CS231n/CS231n_notes/#6-cnn-architectures","title":"6 - CNN Architectures","text":"Best model in ImageNet competition:
"},{"location":"AI/CS231n/CS231n_notes/#alexnet","title":"AlexNet","text":"8 layers.
First use of ConvNet in image classification problem.
Filter size decreases in deeper layer.
Channel number increases in deeper layer.
"},{"location":"AI/CS231n/CS231n_notes/#vgg","title":"VGG","text":"19 layers. (also provide 16 layers edition)
Static filter size (\\(3\\times3\\)) in all layers:
- The effective receptive field expands with the layer gets deeper.
- Deeper architecture gets more non-linearities and few parameters.
Most memory is in early convolution layers.
Most parameter is in late FC layers.
"},{"location":"AI/CS231n/CS231n_notes/#googlenet","title":"GoogLeNet","text":"22 layers.
No FC layers, only 5M parameters. ( \\(8.3\\%\\) of AlexNet, \\(3.7\\%\\) of VGG )
Devise efficient \"inception module\".
"},{"location":"AI/CS231n/CS231n_notes/#inception-module","title":"Inception Module","text":"Design a good local network topology (network within a network) and then stack these modules on top of each other.
Naive Inception Module:
- Apply parallel filter operations on the input from previous layer.
- Concatenate all filter outputs together channel-wise.
- Problem: The depth (channel number) increases too fast, costing expensive computation.
Inception Module with Dimension Reduction:
- Add \"bottle neck\" layers to reduce the dimension.
- Also get fewer computation cost.
"},{"location":"AI/CS231n/CS231n_notes/#architecture","title":"Architecture","text":""},{"location":"AI/CS231n/CS231n_notes/#resnet","title":"ResNet","text":"152 layers for ImageNet.
Devise \"residual connections\".
Use BN in place of dropout.
"},{"location":"AI/CS231n/CS231n_notes/#residual-connections","title":"Residual Connections","text":"Hypothesis: Deeper models have more representation power than shallow ones. But they are harder to optimize.
Solution: Use network layers to fit a residual mapping instead of directly trying to fit a desired underlying mapping.
It is necessary to use ReLU as activation function, in order to apply identity mapping when \\(F(x)=0\\) .
"},{"location":"AI/CS231n/CS231n_notes/#architecture_1","title":"Architecture","text":""},{"location":"AI/CS231n/CS231n_notes/#senet","title":"SENet","text":"Using ResNeXt-152 as a base architecture.
Add a \u201cfeature recalibration\u201d module. (adjust weights of each channel)
Using the global avg-pooling layer + FC layers to determine feature map weights.
"},{"location":"AI/CS231n/CS231n_notes/#improvements-of-resnet","title":"Improvements of ResNet","text":"Wide Residual Networks, ResNeXt, DenseNet, MobileNets...
"},{"location":"AI/CS231n/CS231n_notes/#other-interesting-networks","title":"Other Interesting Networks","text":"NASNet: Neural Architecture Search with Reinforcement Learning.
EfficientNet: Smart Compound Scaling.
"},{"location":"AI/CS231n/CS231n_notes/#7-training-neural-networks","title":"7 - Training Neural Networks","text":""},{"location":"AI/CS231n/CS231n_notes/#activation-functions","title":"Activation Functions","text":"Activation Usage Sigmoid, tanh Do not use. ReLU Use as default. Leaky ReLU, Maxout, ELU, SELU Replace ReLU to squeeze out some marginal gains. Swish No clear usage."},{"location":"AI/CS231n/CS231n_notes/#data-processing","title":"Data Processing","text":"Apply centralization and normalization before training.
In practice for pictures, usually we apply channel-wise centralization only.
"},{"location":"AI/CS231n/CS231n_notes/#weight-initialization","title":"Weight Initialization","text":"Assume that we have 6 layers in a network.
\\(D_i\\) : input size of layer \\(i\\)
\\(W_i\\) : weights in layer \\(i\\)
\\(X_i\\) : output after activation of layer \\(i\\), we have \\(X_i=g(Z_i)=g(W_iX_{i-1}+B_i)\\)
We initialize each parameter in \\(W_i\\) randomly in \\([-k_i,k_i]\\) .
Tanh Activation Output Distribution \\(k_i=0.01\\) \\(k_i=0.05\\) Xavier Initialization \\(k_i=\\frac{1}{\\sqrt{D_i}\\\\}\\) When \\(k_i=0.01\\), the variance keeps decreasing as the layer gets deeper. As a result, the output of each neuron in deep layer will all be 0. The partial derivative \\(\\frac{\\partial Z_i}{\\partial W_i}=X_{i-1}=0\\\\\\). (no gradient)
When \\(k_i=0.05\\), most neurons is saturated. The partial derivative \\(\\frac{\\partial X_i}{\\partial Z_i}=g'(Z_i)=0\\\\\\). (no gradient)
To solve this problem, We need to keep the variance same in each layer.
Assuming that \\(Var\\big(X_{i-1}^{(1)}\\big)=Var\\big(X_{i-1}^{(2)}\\big)=\\dots=Var\\big(X_{i-1}^{(D_i)}\\big)\\)
We have \\(Z_i=X_{i-1}^{(1)}W_i^{(:,1)}+X_{i-1}^{(2)}W_i^{(:,2)}+\\dots+X_{i-1}^{(D_i)}W_i^{(:,D_i)}=\\sum_{n=1}^{D_i}X_{i-1}^{(n)}W_i^{(:,n)}\\\\\\)
We want \\(Var\\big(Z_i\\big)=Var\\big(X_{i-1}^{(n)}\\big)\\)
Let's do some conduction:
\\(\\begin{aligned}Var\\big(Z_i\\big)&=Var\\Bigg(\\sum_{n=1}^{D_i}X_{i-1}^{(n)}W_i^{(:,n)}\\Bigg)\\\\&=D_i\\ Var\\Big(X_{i-1}^{(n)}W_i^{(:,n)}\\Big)\\\\&=D_i\\ Var\\Big(X_{i-1}^{(n)}\\Big)\\ Var\\Big(W_i^{(:,n)}\\Big)\\end{aligned}\\)
So \\(Var\\big(Z_i\\big)=Var\\big(X_{i-1}^{(n)}\\big)\\) only when \\(Var\\Big(W_i^{(:,n)}\\Big)=\\frac{1}{D_i}\\\\\\), that is to say \\(k_i=\\frac{1}{\\sqrt{D_i}}\\\\\\)
ReLU Activation Output Distribution Xavier Initialization \\(k_i=\\frac{1}{\\sqrt{D_i}\\\\}\\) Kaiming Initialization \\(k_i=\\sqrt{2D_i}\\) For ReLU activation, when using xavier initialization, there still exist \"variance decreasing\" problem.
We can use kaiming initialization instead to fix this.
"},{"location":"AI/CS231n/CS231n_notes/#batch-normalization","title":"Batch Normalization","text":"Force the inputs to be \"nicely scaled\" at each layer.
\\(N\\) : batch size
\\(D\\) : feature size
\\(x\\) : input with shape \\(N\\times D\\)
\\(\\gamma\\) : learnable scale and shift parameter with shape \\(D\\)
\\(\\beta\\) : learnable scale and shift parameter with shape \\(D\\)
The procedure of batch normalization:
- Calculate channel-wise mean \\(\\mu_j=\\frac{1}{N}\\sum_{i=1}^Nx_{i,j}\\\\\\) . The result \\(\\mu\\) with shape \\(D\\) .
- Calculate channel-wise variance \\(\\sigma_j^2=\\frac{1}{N}\\sum_{i=1}^N(x_{i,j}-\\mu_j)^2\\\\\\) . The result \\(\\sigma^2\\) with shape \\(D\\) .
- Calculate normalized \\(\\hat{x}_{i,j}=\\frac{x_{i,j}-\\mu_j}{\\sqrt{\\sigma_j^2+\\epsilon}}\\\\\\) . The result \\(\\hat{x}\\) with shape \\(N\\times D\\) .
- Scale normalized input to get output \\(y_{i,j}=\\gamma_j\\hat{x}_{i,j}+\\beta_j\\) . The result \\(y\\) with shape \\(N\\times D\\) .
Why scale: The constraint \"zero-mean, unit variance\" may be too hard.
Pros:
- Makes deep networks much easier to train!
- Improves gradient flow.
- Allows higher learning rates, faster convergence.
- Networks become more robust to initialization.
- Acts as regularization during training.
- Zero overhead at test-time: can be fused with conv!
Cons:
Behaves differently during training and testing: this is a very common source of bugs!
"},{"location":"AI/CS231n/CS231n_notes/#transfer-learning","title":"Transfer Learning","text":"Train on a pre-trained model with other datasets.
An empirical suggestion:
very similar dataset very different dataset very little data Use Linear Classifier on top layer. You\u2019re in trouble\u2026 Try linear classifier from different stages. quite a lot of data Finetune a few layers. Finetune a larger number of layers."},{"location":"AI/CS231n/CS231n_notes/#regularization","title":"Regularization","text":""},{"location":"AI/CS231n/CS231n_notes/#common-pattern-of-regularization","title":"Common Pattern of Regularization","text":"Training: Add some kind of randomness. \\(y=f(x,z)\\)
Testing: Average out randomness (sometimes approximate). \\(y=f(x)=E_z\\big[f(x,z)\\big]=\\int p(z)f(x,z)dz\\\\\\)
"},{"location":"AI/CS231n/CS231n_notes/#regularization-term","title":"Regularization Term","text":"L2 regularization: \\(R(W)=\\sum_k\\sum_lW_{k,l}^2\\) (weight decay)
L1 regularization: \\(R(W)=\\sum_k\\sum_l|W_{k,l}|\\)
Elastic net : \\(R(W)=\\sum_k\\sum_l\\big(\\beta W_{k,l}^2+|W_{k,l}|\\big)\\) (L1+L2)
"},{"location":"AI/CS231n/CS231n_notes/#dropout","title":"Dropout","text":"Training: Randomly set some neurons to 0 with a probability \\(p\\) .
Testing: Each neuron multiplies by dropout probability \\(p\\) . (scale the output back)
More common: Scale the output with \\(\\frac{1}{p}\\) when training, keep the original output when testing.
Why dropout works:
- Forces the network to have a redundant representation. Prevents co-adaptation of features.
- Another interpretation: Dropout is training a large ensemble of models (that share parameters).
"},{"location":"AI/CS231n/CS231n_notes/#batch-normalization_1","title":"Batch Normalization","text":"See above.
"},{"location":"AI/CS231n/CS231n_notes/#data-augmentation","title":"Data Augmentation","text":" - Horizontal Flips
- Random Crops and Scales
- Color Jitter
- Rotation
- Stretching
- Shearing
- Lens Distortions
- ...
There also exists automatic data augmentation method using neural networks.
"},{"location":"AI/CS231n/CS231n_notes/#other-methods-and-summary","title":"Other Methods and Summary","text":"DropConnect: Drop connections between neurons.
Fractional Max Pooling: Use randomized pooling regions.
Stochastic Depth: Skip some layers in the network.
Cutout: Set random image regions to zero.
Mixup: Train on random blends of images.
Regularization Method Usage Dropout For large fully-connected layers. Batch Normalization & Data Augmentation Almost always a good idea. Cutout & Mixup For small classification datasets."},{"location":"AI/CS231n/CS231n_notes/#hyperparameter-tuning","title":"Hyperparameter Tuning","text":"Most Common Hyperparameters Less Sensitive Hyperparameters learning ratelearning rate decay scheduleweight decay setting of momentum... Tips on hyperparameter tuning:
- Prefer one validation fold to cross-validation.
- Search for hyperparameters on log scale. (e.g. multiply the hyperparameter by a fixed number \\(k\\) at each search)
- Prefer random search to grid search.
- Careful with best values on border.
- Stage your search from coarse to fine.
"},{"location":"AI/CS231n/CS231n_notes/#implementation","title":"Implementation","text":"Have a worker that continuously samples random hyperparameters and performs the optimization. During the training, the worker will keep track of the validation performance after every epoch, and writes a model checkpoint to a file.
Have a master that launches or kills workers across a computing cluster, and may additionally inspect the checkpoints written by workers and plot their training statistics.
"},{"location":"AI/CS231n/CS231n_notes/#common-procedures","title":"Common Procedures","text":" - Check initial loss.
Turn off weight decay, sanity check loss at initialization \\(\\log(C)\\) for softmax with \\(C\\) classes.
- Overfit a small sample. (important)
Try to train to 100% training accuracy on a small sample of training data.
Fiddle with architecture, learning rate, weight initialization.
- Find learning rate that makes loss go down.
Use the architecture from the previous step, use all training data, turn on small weight decay, find a learning rate that makes the loss drop significantly within 100 iterations.
Good learning rates to try: \\(0.1,0.01,0.001,0.0001,\\dots\\)
- Coarse grid, train for 1-5 epochs.
Choose a few values of learning rate and weight decay around what worked from Step 3, train a few models for 1-5 epochs.\\
Good weight decay to try: \\(0.0001,0.00001,0\\)
- Refine grid, train longer.
Pick best models from Step 4, train them for longer (10-20 epochs) without learning rate decay.
- Look at loss and accuracy curves.
- GOTO step 5.
"},{"location":"AI/CS231n/CS231n_notes/#gradient-checks","title":"Gradient Checks","text":"CS231n Convolutional Neural Networks for Visual Recognition
Compute analytical gradient manually using \\(f_a'=\\frac{\\partial f(x)}{\\partial x}=\\frac{f(x-h)-f(x+h)}{2h}\\\\\\)
Get relative error between numerical gradient \\(f_n'\\) and analytical gradient \\(f_a'\\) using \\(E=\\frac{|f_n'-f_a'|}{\\max{|f_n'|,|f_a'|}}\\\\\\)
Relative Error Result \\(E>10^{-2}\\) Probably \\(f_n'\\) is wrong. \\(10^{-2}>E>10^{-4}\\) Not good, should check the gradient. \\(10^{-4}>E>10^{-6}\\) Okay for objectives with kinks. (e.g. ReLU)Not good for objectives with no kink. (e.g. softmax, tanh) \\(10^{-7}>E\\) Good. Tips on gradient checks:
- Use double precision.
- Use only few data points.
- Careful about kinks in the objective. (e.g. \\(x=0\\) for ReLU activation)
- Careful with the step size \\(h\\).
- Use gradient check after the loss starts to go down.
- Remember to turn off anything that may affect the gradient. (e.g. regularization / dropout / augmentations)
- Check only few dimensions for every parameter. (reduce time cost)
"},{"location":"AI/CS231n/CS231n_notes/#8-visualizing-and-understanding","title":"8 - Visualizing and Understanding","text":""},{"location":"AI/CS231n/CS231n_notes/#feature-visualization-and-inversion","title":"Feature Visualization and Inversion","text":""},{"location":"AI/CS231n/CS231n_notes/#visualizing-what-models-have-learned","title":"Visualizing what models have learned","text":"Visualize Areas Filters Visualize the raw weights of each convolution kernel. (better in the first layer) Final Layer Features Run dimensionality reduction for features in the last FC layer. (PCA, t-SNE...) Activations Visualize activated areas. (Understanding Neural Networks Through Deep Visualization)"},{"location":"AI/CS231n/CS231n_notes/#understanding-input-pixels","title":"Understanding input pixels","text":""},{"location":"AI/CS231n/CS231n_notes/#maximally-activating-patches","title":"Maximally Activating Patches","text":" - Pick a layer and a channel.
- Run many images through the network, record values of the chosen channel.
- Visualize image patches that correspond to maximal activation features.
For example, we have a layer with shape \\(128\\times13\\times13\\). We pick the 17th channel from all 128 channels. Then we run many pictures through the network. During each run we can find a maximal activation feature among all the \\(13\\times13\\) features in channel 17. We then record the corresponding picture patch for each maximal activation feature. At last, we visualize all picture patches for each feature.
This will help us find the relationship between each maximal activation feature and its corresponding picture patches.
(each row of the following picture represents a feature)
"},{"location":"AI/CS231n/CS231n_notes/#saliency-via-occlusion","title":"Saliency via Occlusion","text":"Mask part of the image before feeding to CNN, check how much predicted probabilities change.
"},{"location":"AI/CS231n/CS231n_notes/#saliency-via-backprop","title":"Saliency via Backprop","text":" - Compute gradient of (unnormalized) class score with respect to image pixels.
- Take absolute value and max over RGB channels to get saliency maps.
"},{"location":"AI/CS231n/CS231n_notes/#intermediate-features-via-guided-backprop","title":"Intermediate Features via Guided Backprop","text":" - Pick a single intermediate neuron. (e.g. one feature in a \\(128\\times13\\times13\\) feature map)
- Compute gradient of neuron value with respect to image pixels.
Striving for Simplicity: The All Convolutional Net
Just like \"Maximally Activating Patches\", this could find the part of an image that a neuron responds to.
"},{"location":"AI/CS231n/CS231n_notes/#gradient-ascent","title":"Gradient Ascent","text":"Generate a synthetic image that maximally activates a neuron.
- Initialize image \\(I\\) to zeros.
- Forward image to compute current scores \\(S_c(I)\\) (for class \\(c\\) before softmax).
- Backprop to get gradient of neuron value with respect to image pixels.
- Make a small update to the image.
Objective: \\(\\max S_c(I)-\\lambda\\lVert I\\lVert^2\\)
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
"},{"location":"AI/CS231n/CS231n_notes/#adversarial-examples","title":"Adversarial Examples","text":"Find an fooling image that can make the network misclassify correctly-classified images when it is added to the image.
- Start from an arbitrary image.
- Pick an arbitrary class.
- Modify the image to maximize the class.
- Repeat until network is fooled.
"},{"location":"AI/CS231n/CS231n_notes/#deepdream-and-style-transfer","title":"DeepDream and Style Transfer","text":""},{"location":"AI/CS231n/CS231n_notes/#feature-inversion","title":"Feature Inversion","text":"Given a CNN feature vector \\(\\Phi_0\\) for an image, find a new image \\(x\\) that:
- Features of new image \\(\\Phi(x)\\) matches the given feature vector \\(\\Phi_0\\).
- \"looks natural\u201d. (image prior regularization)
Objective: \\(\\min \\lVert\\Phi(x)-\\Phi_0\\lVert+\\lambda R(x)\\)
Understanding Deep Image Representations by Inverting Them
"},{"location":"AI/CS231n/CS231n_notes/#deepdream-amplify-existing-features","title":"DeepDream: Amplify Existing Features","text":"Given an image, amplify the neuron activations at a layer to generate a new one.
- Forward: compute activations at chosen layer.
- Set gradient of chosen layer equal to its activation.
- Backward: Compute gradient on image.
- Update image.
"},{"location":"AI/CS231n/CS231n_notes/#texture-synthesis","title":"Texture Synthesis","text":""},{"location":"AI/CS231n/CS231n_notes/#nearest-neighbor","title":"Nearest Neighbor","text":" - Generate pixels one at a time in scanline order
- Form neighborhood of already generated pixels, copy the nearest neighbor from input.
"},{"location":"AI/CS231n/CS231n_notes/#neural-texture-synthesis","title":"Neural Texture Synthesis","text":"Gram Matrix: \u683c\u62c9\u59c6\u77e9\u9635\uff08Gram matrix\uff09\u8be6\u7ec6\u89e3\u8bfb
- Pretrain a CNN on ImageNet.
- Run input texture forward through CNN, record activations on every layer.
Layer \\(i\\) gives feature map of shape \\(C_i\\times H_i\\times W_i\\).
- At each layer compute the Gram matrix \\(G_i\\) giving outer product of features.
- Reshape feature map at layer \\(i\\) to \\(C_i\\times H_iW_i\\).
- Compute the Gram matrix \\(G_i\\) with shape \\(C_i\\times C_i\\).
- Initialize generated image from random noise.
- Pass generated image through CNN, compute Gram matrix \\(\\hat{G}_l\\) on each layer.
- Compute loss: Weighted sum of L2 distance between Gram matrices.
- \\(E_l=\\frac{1}{aN_l^2M_l^2}\\sum_{i,j}\\Big(G_i^{(i,j)}-\\hat{G}_i^{(i,j)}\\Big)^2\\\\\\)
- \\(\\mathcal{L}(\\vec{x},\\hat{\\vec{x}})=\\sum_{l=0}^L\\omega_lE_l\\\\\\)
- Backprop to get gradient on image.
- Make gradient step on image.
- GOTO 5.
Texture Synthesis Using Convolutional Neural Networks
"},{"location":"AI/CS231n/CS231n_notes/#style-transfer","title":"Style Transfer","text":""},{"location":"AI/CS231n/CS231n_notes/#feature-gram-reconstruction","title":"Feature + Gram Reconstruction","text":"Problem: Style transfer requires many forward / backward passes. Very slow!
"},{"location":"AI/CS231n/CS231n_notes/#fast-style-transfer","title":"Fast Style Transfer","text":""},{"location":"AI/CS231n/CS231n_notes/#9-object-detection-and-image-segmentation","title":"9 - Object Detection and Image Segmentation","text":""},{"location":"AI/CS231n/CS231n_notes/#semantic-segmentation","title":"Semantic Segmentation","text":"Paired Training Data: For each training image, each pixel is labeled with a semantic category.
Fully Convolutional Network: Design a network with only convolutional layers without downsampling operators to make predictions for pixels all at once!
Problem: Convolutions at original image resolution will be very expensive...
Solution: Design fully convolutional network with downsampling and upsampling inside it!
- Downsampling: Pooling, strided convolution.
- Upsampling: Unpooling, transposed convolution.
Unpooling:
Nearest Neighbor \"Bed of Nails\" \"Position Memory\" Transposed Convolution: (example size \\(3\\times3\\), stride \\(2\\), pad \\(1\\))
Normal Convolution Transposed Convolution "},{"location":"AI/CS231n/CS231n_notes/#object-detection","title":"Object Detection","text":""},{"location":"AI/CS231n/CS231n_notes/#single-object","title":"Single Object","text":"Classification + Localization. (classification + regression problem)
"},{"location":"AI/CS231n/CS231n_notes/#multiple-object","title":"Multiple Object","text":""},{"location":"AI/CS231n/CS231n_notes/#r-cnn","title":"R-CNN","text":"Using selective search to find \u201cblobby\u201d image regions that are likely to contain objects.
- Find regions of interest (RoI) using selective search. (region proposal)
- Forward each region through ConvNet.
- Classify features with SVMs.
Problem: Very slow. Need to do 2000 independent forward passes for each image!
"},{"location":"AI/CS231n/CS231n_notes/#fast-r-cnn","title":"Fast R-CNN","text":"Pass the image through ConvNet before cropping. Crop the conv feature instead.
- Run whole image through ConvNet.
- Find regions of interest (RoI) from conv features using selective search. (region proposal)
- Classify RoIs using CNN.
Problem: Runtime is dominated by region proposals. (about \\(90\\%\\) time cost)
"},{"location":"AI/CS231n/CS231n_notes/#faster-r-cnn","title":"Faster R-CNN","text":"Insert Region Proposal Network (RPN) to predict proposals from features.
Otherwise same as Fast R-CNN: Crop features for each proposal, classify each one.
Region Proposal Network (RPN) : Slide many fixed windows over ConvNet features.
- Treat each point in the feature map as the anchor.
We have \\(k\\) fixed windows (anchor boxes) of different size/scale centered with each anchor.
- For each anchor box, predict whether it contains an object.
For positive boxes, also predict a corrections to the ground-truth box.
- Slide anchor over the feature map, get the \u201cobjectness\u201d score for each box at each point.
- Sort the \u201cobjectness\u201d score, take top \\(300\\) as the proposals.
Faster R-CNN is a Two-stage object detector:
- First stage: Run once per image
Backbone network
Region proposal network
- Second stage: Run once per region
Crop features: RoI pool / align
Predict object class
Prediction bbox offset
"},{"location":"AI/CS231n/CS231n_notes/#single-stage-object-detectors-yolo","title":"Single-Stage Object Detectors: YOLO","text":"You Only Look Once: Unified, Real-Time Object Detection
- Divide image into grids. (example image grids shape \\(7\\times7\\))
- Set anchors in the middle of each grid.
- For each grid: - Using \\(B\\) anchor boxes to regress \\(5\\) numbers: \\(\\text{dx, dy, dh, dw, confidence}\\). - Predict scores for each of \\(C\\) classes.
- Finally the output is \\(7\\times7\\times(5B+C)\\).
"},{"location":"AI/CS231n/CS231n_notes/#instance-segmentation","title":"Instance Segmentation","text":"Mask R-CNN: Add a small mask network that operates on each RoI and predicts a \\(28\\times28\\) binary mask.
Mask R-CNN performs very good results!
"},{"location":"AI/CS231n/CS231n_notes/#10-recurrent-neural-networks","title":"10 - Recurrent Neural Networks","text":"Supplement content added according to Deep Learning Book - RNN.
"},{"location":"AI/CS231n/CS231n_notes/#recurrent-neural-network-rnn","title":"Recurrent Neural Network (RNN)","text":""},{"location":"AI/CS231n/CS231n_notes/#motivation-sequence-processing","title":"Motivation: Sequence Processing","text":"One to One One to Many Many to One Many to Many Many to Many Vanilla Neural Networks Image Captioning Action Prediction Video Captioning Video Classification on Frame Level"},{"location":"AI/CS231n/CS231n_notes/#vanilla-rnn","title":"Vanilla RNN","text":"\\(x^{(t)}\\) : Input at time \\(t\\).
\\(h^{(t)}\\) : State at time \\(t\\).
\\(o^{(t)}\\) : Output at time \\(t\\)\u200b\u200b.
\\(y^{(t)}\\) : Expected output at time \\(t\\).
"},{"location":"AI/CS231n/CS231n_notes/#many-to-one","title":"Many to One","text":"Calculation State Transition \\(h^{(t)}=\\tanh(Wh^{(t-1)}+Ux^{(t)}+b)\\) Output Calculation \\(o^{(\\tau)}=\\text{sigmoid}\\ \\big(Vh^{(\\tau)}+c\\big)\\)"},{"location":"AI/CS231n/CS231n_notes/#many-to-many-type-2","title":"Many to Many (type 2)","text":"Calculation State Transition \\(h^{(t)}=\\tanh(Wh^{(t-1)}+Ux^{(t)}+b)\\) Output Calculation \\(o^{(t)}=\\text{sigmoid}\\ \\big(Vh^{(t)}+c\\big)\\)"},{"location":"AI/CS231n/CS231n_notes/#rnn-with-teacher-forcing","title":"RNN with Teacher Forcing","text":"Update current state according to last-time output instead of last-time state.
Calculation State Transition \\(h^{(t)}=\\tanh(Wo^{(t-1)}+Ux^{(t)}+b)\\) Output Calculation \\(o^{(t)}=\\text{sigmoid}\\ \\big(Vh^{(t)}+c\\big)\\)"},{"location":"AI/CS231n/CS231n_notes/#rnn-with-output-forwarding","title":"RNN with \"Output Forwarding\"","text":"We can also combine last-state output with this-state input together.
Calculation State Transition (training) \\(h^{(t)}=\\tanh(Wh^{(t-1)}+Ux^{(t)}+Ry^{(t-1)}+b)\\) State Transition (testing) \\(h^{(t)}=\\tanh(Wh^{(t-1)}+Ux^{(t)}+Ro^{(t-1)}+b)\\) Output Calculation \\(o^{(t)}=\\text{sigmoid}\\ \\big(Vh^{(t)}+c\\big)\\) Usually we use \\(o^{(t-1)}\\) in place of \\(y^{(t-1)}\\) at testing time.
"},{"location":"AI/CS231n/CS231n_notes/#bidirectional-rnn","title":"Bidirectional RNN","text":"When dealing with a whole input sequence, we can process features from two directions.
Calculation State Transition (forward) \\(h^{(t)}=\\tanh(W_1h^{(t-1)}+U_1x^{(t)}+b_1)\\) State Transition (backward) \\(g^{(t)}=\\tanh(W_2g^{(t+1)}+U_2x^{(t)}+b_2)\\) Output Calculation \\(o^{(t)}=\\text{sigmoid}\\ \\big(Vh^{(t)}+Wg^{(t)}+c\\big)\\)"},{"location":"AI/CS231n/CS231n_notes/#encoder-decoder-sequence-to-sequence-rnn","title":"Encoder-Decoder Sequence to Sequence RNN","text":"This is a many-to-many structure (type 1).
First we encode information according to \\(x\\) with no output.
Later we decode information according to \\(y\\) with no input.
\\(C\\) : Context vector, often \\(C=h^{(T)}\\) (last state of encoder).
Calculation State Transition (encode) \\(h^{(t)}=\\tanh(W_1h^{(t-1)}+U_1x^{(t)}+b_1)\\) State Transition (decode, training) \\(s^{(t)}=\\tanh(W_2s^{(t-1)}+U_2y^{(t)}+TC+b_2)\\) State Transition (decode, testing) \\(s^{(t)}=\\tanh(W_2s^{(t-1)}+U_2o^{(t)}+TC+b_2)\\) Output Calculation \\(o^{(t)}=\\text{sigmoid}\\ \\big(Vs^{(t)}+c\\big)\\)"},{"location":"AI/CS231n/CS231n_notes/#example-image-captioning","title":"Example: Image Captioning","text":""},{"location":"AI/CS231n/CS231n_notes/#summary_2","title":"Summary","text":"Advantages of RNN:
- Can process any length input.
- Computation for step \\(t\\) can (in theory) use information from many steps back.
- Model size doesn\u2019t increase for longer input.
- Same weights applied on every timestep, so there is symmetry in how inputs are processed.
Disadvantages of RNN:
- Recurrent computation is slow.
- In practice, difficult to access information from many steps back.
- Problems with gradient exploding and gradient vanishing. (check Deep Learning Book - RNN Page 396, Chap 10.7)
"},{"location":"AI/CS231n/CS231n_notes/#long-short-term-memory-lstm","title":"Long Short Term Memory (LSTM)","text":"Add a \"cell block\" to store history weights.
\\(c^{(t)}\\) : Cell at time \\(t\\).
\\(f^{(t)}\\) : Forget gate at time \\(t\\). Deciding whether to erase the cell.
\\(i^{(t)}\\) : Input gate at time \\(t\\). Deciding whether to write to the cell.
\\(g^{(t)}\\) : External input gate at time \\(t\\). Deciding how much to write to the cell.
\\(o^{(t)}\\) : Output gate at time \\(t\\). Deciding how much to reveal the cell.
Calculation (Gate) Forget Gate \\(f^{(t)}=\\text{sigmoid}\\ \\big(W_fh^{(t-1)}+U_fx^{(t)}+b_f\\big)\\) Input Gate \\(i^{(t)}=\\text{sigmoid}\\ \\big(W_ih^{(t-1)}+U_ix^{(t)}+b_i\\big)\\) External Input Gate \\(g^{(t)}=\\tanh(W_gh^{(t-1)}+U_gx^{(t)}+b_g)\\) Output Gate \\(o^{(t)}=\\text{sigmoid}\\ \\big(W_oh^{(t-1)}+U_ox^{(t)}+b_o\\big)\\) Calculation (Main) Cell Transition \\(c^{(t)}=f^{(t)}\\odot c^{(t-1)}+i^{(t)}\\odot g^{(t)}\\) State Transition \\(h^{(t)}=o^{(t)}\\odot\\tanh(c^{(t)})\\) Output Calculation \\(O^{(t)}=\\text{sigmoid}\\ \\big(Vh^{(t)}+c\\big)\\) "},{"location":"AI/CS231n/CS231n_notes/#other-rnn-variants","title":"Other RNN Variants","text":"GRU...
"},{"location":"AI/CS231n/CS231n_notes/#11-attention-and-transformers","title":"11 - Attention and Transformers","text":""},{"location":"AI/CS231n/CS231n_notes/#rnn-with-attention","title":"RNN with Attention","text":"Encoder-Decoder Sequence to Sequence RNN Problem:
Input sequence bottlenecked through a fixed-sized context vector \\(C\\). (e.g. \\(T=1000\\))
Intuitive Solution:
Generate new context vector \\(C_t\\) at each step \\(t\\) !
\\(e_{t,i}\\) : Alignment score for input \\(i\\) at state \\(t\\). (scalar)
\\(a_{t,i}\\) : Attention weight for input \\(i\\) at state \\(t\\).
\\(C_t\\) : Context vector at state \\(t\\).
Calculation Alignment Score \\(e_i^{(t)}=f(s^{(t-1)},h^{(i)})\\).Where \\(f\\) is an MLP. Attention Weight \\(a_i^{(t)}=\\text{softmax}\\ (e_i^{(t)})\\).Softmax includes all \\(e_i\\) at state \\(t\\). Context Vector \\(C^{(t)}=\\sum_i a_i^{(t)}h^{(i)}\\) Decoder State Transition \\(s^{(t)}=\\tanh(Ws^{(t-1)}+Uy^{(t)}+TC^{(t)}+b)\\) Example on Image Captioning:
"},{"location":"AI/CS231n/CS231n_notes/#general-attention-layer","title":"General Attention Layer","text":"Add linear transformations to the input vector before attention.
Notice:
- Number of queries \\(q\\) is variant. (can be different from the number of keys \\(k\\))
- Number of outputs \\(y\\) is equal to the number of queries \\(q\\).
Each \\(y\\) is a linear weighting of values \\(v\\).
- Alignment \\(e\\) is divided by \\(\\sqrt{D}\\) to avoid \"explosion of softmax\", where \\(D\\) is the dimension of input feature.
"},{"location":"AI/CS231n/CS231n_notes/#self-attention-layer","title":"Self-attention Layer","text":"The query vectors \\(q\\) are also generated from the inputs.
In this way, the shape of \\(y\\) is equal to the shape of \\(x\\).
Example with CNN:
"},{"location":"AI/CS231n/CS231n_notes/#positional-encoding","title":"Positional Encoding","text":"Self-attention layer doesn\u2019t care about the orders of the inputs!
To encode ordered sequences like language or spatially ordered image features, we can add positional encoding to the inputs.
We use a function \\(P:R\\rightarrow R^d\\) to process the position \\(i\\) into a d-dimensional vector \\(p_i=P(i)\\).
Constraint Condition of \\(P\\) Uniqueness \\(P(i)\\ne P(j)\\) Equidistance \\(\\lVert P(i+k)-P(i)\\rVert^2=\\lVert P(j+k)-P(j)\\rVert^2\\) Boundness \\(P(i)\\in[a,b]\\) Determinacy \\(P(i)\\) is always a static value. (function is not dynamic) We can either train a encoder model, or design a fixed function.
A Practical Positional Encoding Method: Using \\(\\sin\\) and \\(\\cos\\) with different frequency \\(\\omega\\) at different dimension.
\\(P(t)=\\begin{bmatrix}\\sin(\\omega_1,t)\\\\\\cos(\\omega_1,t)\\\\\\\\\\sin(\\omega_2,t)\\\\\\cos(\\omega_2,t)\\\\\\vdots\\\\\\sin(\\omega_{\\frac{d}{2}},t)\\\\\\cos(\\omega_{\\frac{d}{2}},t)\\end{bmatrix}\\), where frequency \\(\\omega_k=\\frac{1}{10000^{\\frac{2k}{d}}}\\\\\\). (wave length \\(\\lambda=\\frac{1}{\\omega}=10000^{\\frac{2k}{d}}\\\\\\))
\\(P(t)=\\begin{bmatrix}\\sin(1/10000^{\\frac{2}{d}},t)\\\\\\cos(1/10000^{\\frac{2}{d}},t)\\\\\\\\\\sin(1/10000^{\\frac{4}{d}},t)\\\\\\cos(1/10000^{\\frac{4}{d}},t)\\\\\\vdots\\\\\\sin(1/10000^1,t)\\\\\\cos(1/10000^1,t)\\end{bmatrix}\\), after we substitute \\(\\omega_k\\) into the equation.
\\(P(t)\\) is a vector with size \\(d\\), where \\(d\\) is a hyperparameter to choose according to the length of input sequence.
An intuition of this method is the binary encoding of numbers.
[lecture 11d] \u6ce8\u610f\u529b\u548ctransformer (positional encoding \u8865\u5145\uff0c\u4ee3\u7801\u5b9e\u73b0\uff0c\u8ddd\u79bb\u8ba1\u7b97)
It is easy to prove that \\(P(t)\\) satisfies \"Equidistance\": (set \\(d=2\\) for example)
\\(\\begin{aligned}\\lVert P(i+k)-P(i)\\rVert^2&=\\big[\\sin(\\omega_1,i+k)-\\sin(\\omega_1,i)\\big]^2+\\big[\\cos(\\omega_1,i+k)-\\cos(\\omega_1,i)\\big]^2\\\\&=2-2\\sin(\\omega_1,i+k)\\sin(\\omega_1,i)-2\\cos(\\omega_1,i+k)\\cos(\\omega_1,i)\\\\&=2-2\\cos(\\omega_1,k)\\end{aligned}\\)
So the distance is not associated with \\(i\\), we have \\(\\lVert P(i+k)-P(i)\\rVert^2=\\lVert P(j+k)-P(j)\\rVert^2\\).
Visualization of \\(P(t)\\) features: (set \\(d=32\\), \\(x\\) axis represents the position of sequence)
"},{"location":"AI/CS231n/CS231n_notes/#masked-self-attention-layer","title":"Masked Self-attention Layer","text":"To prevent vectors from looking at future vectors, we manually set alignment scores to \\(-\\infty\\).
"},{"location":"AI/CS231n/CS231n_notes/#multi-head-self-attention-layer","title":"Multi-head Self-attention Layer","text":"Multiple self-attention heads in parallel.
"},{"location":"AI/CS231n/CS231n_notes/#transformer","title":"Transformer","text":"Attention Is All You Need
"},{"location":"AI/CS231n/CS231n_notes/#encoder-block","title":"Encoder Block","text":"Inputs: Set of vectors \\(z\\). (in which \\(z_i\\) can be a word in a sentence, or a pixel in a picture...)
Output: Set of context vectors \\(c\\). (encoded features of \\(z\\))
The number of blocks \\(N=6\\) in original paper.
Notice:
- Self-attention is the only interaction between vectors \\(x_0,x_1,\\dots,x_n\\).
- Layer norm and MLP operate independently per vector.
- Highly scalable, highly parallelizable, but high memory usage.
"},{"location":"AI/CS231n/CS231n_notes/#decoder-block","title":"Decoder Block","text":"Inputs: Set of vectors \\(y\\). (\\(y_i\\) can be a word in a sentence, or a pixel in a picture...)
Inputs: Set of context vectors \\(c\\).
Output: Set of vectors \\(y'\\). (decoded result, \\(y'_i=y_{i+1}\\) for the first \\(n-1\\) number of \\(y'\\))
The number of blocks \\(N=6\\) in original paper.
Notice:
- Masked self-attention only interacts with past inputs.
- Multi-head attention block is NOT self-attention. It attends over encoder outputs.
- Highly scalable, highly parallelizable, but high memory usage. (same as encoder)
Why we need mask in decoder:
- Needs for the special formation of output \\(y'_i=y_{i+1}\\).
- Needs for parallel computation.
\u4e3e\u4e2a\u4f8b\u5b50\u8bb2\u4e0btransformer\u7684\u8f93\u5165\u8f93\u51fa\u7ec6\u8282\u53ca\u5176\u4ed6
\u5728\u6d4b\u8bd5\u6216\u8005\u9884\u6d4b\u65f6\uff0cTransformer\u91ccdecoder\u4e3a\u4ec0\u4e48\u8fd8\u9700\u8981seq mask\uff1f
"},{"location":"AI/CS231n/CS231n_notes/#example-on-image-captioning-only-with-transformers","title":"Example on Image Captioning (Only with Transformers)","text":""},{"location":"AI/CS231n/CS231n_notes/#comparing-rnns-to-transformer","title":"Comparing RNNs to Transformer","text":"RNNs Transformer Pros LSTMs work reasonably well for long sequences. 1. Good at long sequences. Each attention calculation looks at all inputs.2. Can operate over unordered sets or ordered sequences with positional encodings.3. Parallel computation: All alignment and attention scores for all inputs can be done in parallel. Cons 1. Expects an ordered sequences of inputs.2. Sequential computation: Subsequent hidden states can only be computed after the previous ones are done. Requires a lot of memory: \\(N\\times M\\) alignment and attention scalers need to be calculated and stored for a single self-attention head."},{"location":"AI/CS231n/CS231n_notes/#comparing-convnets-to-transformer","title":"Comparing ConvNets to Transformer","text":"ConvNets strike back!
"},{"location":"AI/CS231n/CS231n_notes/#12-video-understanding","title":"12 - Video Understanding","text":""},{"location":"AI/CS231n/CS231n_notes/#video-classification","title":"Video Classification","text":"Take video classification task for example.
Input size: \\(C\\times T\\times H\\times W\\).
The problem is, videos are quite big. We can't afford to train on raw videos, instead we train on video clips.
Raw Videos Video Clips \\(1920\\times1080,\\ 30\\text{fps}\\) \\(112\\times112,\\ 5\\text{f}/3.2\\text{s}\\) \\(10\\text{GB}/\\text{min}\\) \\(588\\text{KB}/\\text{min}\\) "},{"location":"AI/CS231n/CS231n_notes/#plain-cnn-structure","title":"Plain CNN Structure","text":""},{"location":"AI/CS231n/CS231n_notes/#single-frame-2d-cnn","title":"Single Frame 2D-CNN","text":"Train a normal 2D-CNN model.
Classify each frame independently.
Average the result of each frame as the final result.
"},{"location":"AI/CS231n/CS231n_notes/#late-fusion","title":"Late Fusion","text":"Get high-level appearance of each frame, and combine them.
Run 2D-CNN on each frame, pool features and feed to Linear Layers.
Problem: Hard to compare low-level motion between frames.
"},{"location":"AI/CS231n/CS231n_notes/#early-fusion","title":"Early Fusion","text":"Compare frames with very first Conv Layer, after that normal 2D-CNN.
Problem: One layer of temporal processing may not be enough!
"},{"location":"AI/CS231n/CS231n_notes/#3d-cnn","title":"3D-CNN","text":"Convolve on 3 dimensions: Height, Width, Time.
Input size: \\(C_{in}\\times T\\times H\\times W\\).
Kernel size: \\(C_{in}\\times C_{out}\\times 3\\times 3\\times 3\\).
Output size: \\(C_{out}\\times T\\times H\\times W\\). (with zero paddling)
"},{"location":"AI/CS231n/CS231n_notes/#c3d-vgg-of-3d-cnns","title":"C3D (VGG of 3D-CNNs)","text":"The cost is quite expensive...
Network Calculation AlexNet 0.7 GFLOP VGG-16 13.6 GFLOP C3D 39.5 GFLOP"},{"location":"AI/CS231n/CS231n_notes/#two-stream-networks","title":"Two-Stream Networks","text":"Separate motion and appearance.
"},{"location":"AI/CS231n/CS231n_notes/#i3d-inflating-2d-networks-to-3d","title":"I3D (Inflating 2D Networks to 3D)","text":"Take a 2D-CNN architecture.
Replace each 2D conv/pool layer with a 3D version.
"},{"location":"AI/CS231n/CS231n_notes/#modeling-long-term-temporal-structure","title":"Modeling Long-term Temporal Structure","text":""},{"location":"AI/CS231n/CS231n_notes/#recurrent-convolutional-network","title":"Recurrent Convolutional Network","text":"Similar to multi-layer RNN, we replace the dot-product operation with convolution.
Feature size in layer \\(L\\), time \\(t-1\\): \\(W_h\\times H\\times W\\).
Feature size in layer \\(L-1\\), time \\(t\\): \\(W_x\\times H\\times W\\).
Feature size in layer \\(L\\), time \\(t\\): \\((W_h+W_x)\\times H\\times W\\).
Problem: RNNs are slow for long sequences. (can\u2019t be parallelized)
"},{"location":"AI/CS231n/CS231n_notes/#spatio-temporal-self-attention","title":"Spatio-temporal Self-attention","text":"Introduce self-attention into video classification problems.
"},{"location":"AI/CS231n/CS231n_notes/#vision-transformers-for-video","title":"Vision Transformers for Video","text":"Factorized attention: Attend over space / time.
So many papers...
"},{"location":"AI/CS231n/CS231n_notes/#visualizing-video-models","title":"Visualizing Video Models","text":""},{"location":"AI/CS231n/CS231n_notes/#multimodal-video-understanding","title":"Multimodal Video Understanding","text":""},{"location":"AI/CS231n/CS231n_notes/#temporal-action-localization","title":"Temporal Action Localization","text":"Given a long untrimmed video sequence, identify frames corresponding to different actions.
"},{"location":"AI/CS231n/CS231n_notes/#spatio-temporal-detection","title":"Spatio-Temporal Detection","text":"Given a long untrimmed video, detect all the people in both space and time and classify the activities they are performing.
"},{"location":"AI/CS231n/CS231n_notes/#visually-guided-audio-source-separation","title":"Visually-guided Audio Source Separation","text":"And So on...
"},{"location":"AI/CS231n/CS231n_notes/#13-generative-models","title":"13 - Generative Models","text":""},{"location":"AI/CS231n/CS231n_notes/#pixelrnn-and-pixelcnn","title":"PixelRNN and PixelCNN","text":""},{"location":"AI/CS231n/CS231n_notes/#fully-visible-belief-network-fvbn","title":"Fully Visible Belief Network (FVBN)","text":"\\(p(x)\\) : Likelihood of image \\(x\\).
\\(p(x_1,x_2,\\dots,x_n)\\) : Joint likelihood of all \\(n\\) pixels in image \\(x\\).
\\(p(x_i|x_1,x_2,\\dots,x_{i-1})\\) : Probability of pixel \\(i\\) value given all previous pixels.
For explicit density models, we have \\(p(x)=p(x_1,x_2,\\dots,x_n)=\\prod_{i=1}^np(x_i|x_1,x_2,\\dots,x_{i-1})\\\\\\).
Objective: Maximize the likelihood of training data.
"},{"location":"AI/CS231n/CS231n_notes/#pixelrnn","title":"PixelRNN","text":"Generate image pixels starting from corner.
Dependency on previous pixels modeled using an RNN (LSTM).
Drawback: Sequential generation is slow in both training and inference!
"},{"location":"AI/CS231n/CS231n_notes/#pixelcnn","title":"PixelCNN","text":"Still generate image pixels starting from corner.
Dependency on previous pixels modeled using a CNN over context region (masked convolution).
Drawback: Though its training is faster, its generation is still slow. (pixel by pixel)
"},{"location":"AI/CS231n/CS231n_notes/#variational-autoencoder","title":"Variational Autoencoder","text":"Supplement content added according to Tutorial on Variational Autoencoders. (paper with notes: VAE Tutorial.pdf)
\u53d8\u5206\u81ea\u7f16\u7801\u5668VAE\uff1a\u539f\u6765\u662f\u8fd9\u4e48\u4e00\u56de\u4e8b | \u9644\u5f00\u6e90\u4ee3\u7801
"},{"location":"AI/CS231n/CS231n_notes/#autoencoder","title":"Autoencoder","text":"Learn a lower-dimensional feature representation with unsupervised approaches.
\\(x\\rightarrow z\\) : Dimension reduction for input features.
\\(z\\rightarrow \\hat{x}\\) : Reconstruct input features.
After training, we throw the decoder away and use the encoder for transferring.
For generative models, there is a problem:
We can\u2019t generate new images from an autoencoder because we don\u2019t know the space of \\(z\\).
"},{"location":"AI/CS231n/CS231n_notes/#variational-autoencoder_1","title":"Variational Autoencoder","text":""},{"location":"AI/CS231n/CS231n_notes/#character-description","title":"Character Description","text":"\\(X\\) : Images. (random variable)
\\(Z\\) : Latent representations. (random variable)
\\(P(X)\\) : True distribution of all training images \\(X\\).
\\(P(Z)\\) : True distribution of all latent representations \\(Z\\).
\\(P(X|Z)\\) : True posterior distribution of all images \\(X\\) with condition \\(Z\\).
\\(P(Z|X)\\) : True prior distribution of all latent representations \\(Z\\) with condition \\(X\\).
\\(Q(Z|X)\\) : Approximated prior distribution of all latent representations \\(Z\\) with condition \\(X\\).
\\(x\\) : A specific image.
\\(z\\) : A specific latent representation.
\\(\\theta\\): Learned parameters in decoder network.
\\(\\phi\\): Learned parameters in encoder network.
\\(p_\\theta(x)\\) : Probability that \\(x\\sim P(X)\\).
\\(p_\\theta(z)\\) : Probability that \\(z\\sim P(Z)\\).
\\(p_\\theta(x|z)\\) : Probability that \\(x\\sim P(X|Z)\\).
\\(p_\\theta(z|x)\\) : Probability that \\(z\\sim P(Z|X)\\).
\\(q_\\phi(z|x)\\) : Probability that \\(z\\sim Q(Z|X)\\).
"},{"location":"AI/CS231n/CS231n_notes/#decoder","title":"Decoder","text":"Objective:
Generate new images from \\(\\mathscr{z}\\).
- Generate a value \\(z^{(i)}\\) from the prior distribution \\(P(Z)\\).
- Generate a value \\(x^{(i)}\\) from the conditional distribution \\(P(X|Z)\\).
Lemma:
Any distribution in \\(d\\) dimensions can be generated by taking a set of \\(d\\) variables that are normally distributed and mapping them through a sufficiently complicated function. (source: Tutorial on Variational Autoencoders, Page 6)
Solutions:
- Choose prior distribution \\(P(Z)\\) to be a simple distribution, for example \\(P(Z)\\sim N(0,1)\\).
- Learn the conditional distribution \\(P(X|Z)\\) through a neural network (decoder) with parameter \\(\\theta\\).
"},{"location":"AI/CS231n/CS231n_notes/#encoder","title":"Encoder","text":"Objective:
Learn \\(\\mathscr{z}\\) with training images.
Given: (From the decoder, we can deduce the following probabilities.)
- data likelihood: \\(p_\\theta(x)=\\int p_\\theta(x|z)p_\\theta(z)dz\\).
- posterior density: \\(p_\\theta(z|x)=\\frac{p_\\theta(x|z)p_\\theta(z)}{p_\\theta(x)}=\\frac{p_\\theta(x|z)p_\\theta(z)}{\\int p_\\theta(x|z)p_\\theta(z)dz}\\).
Problem:
Both \\(p_\\theta(x)\\) and \\(p_\\theta(z|x)\\) are intractable. (can't be optimized directly as they contain integral operation)
Solution:
Learn \\(Q(Z|X)\\) to approximate the true posterior \\(P(Z|X)\\).
Use \\(q_\\phi(z|x)\\) in place of \\(p_\\theta(z|x)\\).
"},{"location":"AI/CS231n/CS231n_notes/#variational-autoencoder-combination-of-encoder-and-decoder","title":"Variational Autoencoder (Combination of Encoder and Decoder)","text":"Objective:
Maximize \\(p_\\theta(x)\\) for all \\(x^{(i)}\\) in the training set.
$$ \\begin{aligned} \\log p_\\theta\\big(x^{(i)}\\big)&=\\mathbb{E}{z\\sim q\\phi\\big(z|x^{(i)}\\big)}\\Big[\\log p_\\theta\\big(x^{(i)}\\big)\\Big]\\
&=\\mathbb{E}z\\Bigg[\\log\\frac{p\\theta\\big(x^{(i)}|z\\big)p_\\theta\\big(z\\big)}{p_\\theta\\big(z|x^{(i)}\\big)}\\Bigg]\\quad\\text{(Bayes' Rule)}\\
&=\\mathbb{E}z\\Bigg[\\log\\frac{p\\theta\\big(x^{(i)}|z\\big)p_\\theta\\big(z\\big)}{p_\\theta\\big(z|x^{(i)}\\big)}\\frac{q_\\phi\\big(z|x^{(i)}\\big)}{q_\\phi\\big(z|x^{(i)}\\big)}\\Bigg]\\quad\\text{(Multiply by Constant)}\\
&=\\mathbb{E}z\\Big[\\log p\\theta\\big(x^{(i)}|z\\big)\\Big]-\\mathbb{E}z\\Bigg[\\log\\frac{q\\phi\\big(z|x^{(i)}\\big)}{p_\\theta\\big(z\\big)}\\Bigg]+\\mathbb{E}z\\Bigg[\\log\\frac{p\\theta\\big(z|x^{(i)}\\big)}{q_\\phi\\big(z|x^{(i)}\\big)}\\Bigg]\\quad\\text{(Logarithm)}\\
&=\\mathbb{E}z\\Big[\\log p\\theta\\big(x^{(i)}|z\\big)\\Big]-D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big]+D_{\\text{KL}}\\Big[p_\\theta\\big(z|x^{(i)}\\big)||q_\\phi\\big(z|x^{(i)}\\big)\\Big]\\quad\\text{(KL Divergence)} \\end{aligned} $$
Analyze the Formula by Term:
\\(\\mathbb{E}_z\\Big[\\log p_\\theta\\big(x^{(i)}|z\\big)\\Big]\\): Decoder network gives \\(p_\\theta\\big(x^{(i)}|z\\big)\\), can compute estimate of this term through sampling.
\\(D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big]\\): This KL term (between Gaussians for encoder and \\(z\\) prior) has nice closed-form solution!
\\(D_{\\text{KL}}\\Big[p_\\theta\\big(z|x^{(i)}\\big)||q_\\phi\\big(z|x^{(i)}\\big)\\Big]\\): The part \\(p_\\theta\\big(z|x^{(i)}\\big)\\) is intractable. However, we know KL divergence always \\(\\ge0\\).
Tractable Lower Bound:
We can maximize the lower bound of that formula.
As \\(D_{\\text{KL}}\\Big[p_\\theta\\big(z|x^{(i)}\\big)||q_\\phi\\big(z|x^{(i)}\\big)\\Big]\\ge0\\) , we can deduce that:
$$ \\begin{aligned} \\log p_\\theta\\big(x^{(i)}\\big)&=\\mathbb{E}z\\Big[\\log p\\theta\\big(x^{(i)}|z\\big)\\Big]-D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big]+D_{\\text{KL}}\\Big[p_\\theta\\big(z|x^{(i)}\\big)||q_\\phi\\big(z|x^{(i)}\\big)\\Big]\\
&\\ge\\mathbb{E}z\\Big[\\log p\\theta\\big(x^{(i)}|z\\big)\\Big]-D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big] \\end{aligned} $$
So the loss function \\(\\mathcal{L}\\big(x^{(i)},\\theta,\\phi\\big)=-\\mathbb{E}_z\\Big[\\log p_\\theta\\big(x^{(i)}|z\\big)\\Big]+D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big]\\).
\\(\\mathbb{E}_z\\Big[\\log p_\\theta\\big(x^{(i)}|z\\big)\\Big]\\): Decoder, reconstruct the input data.
\\(D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big]\\): Encoder, make approximate posterior distribution close to prior.
"},{"location":"AI/CS231n/CS231n_notes/#generative-adversarial-networks-gans","title":"Generative Adversarial Networks (GANs)","text":""},{"location":"AI/CS231n/CS231n_notes/#motivation-modeling","title":"Motivation & Modeling","text":"Objective: Not modeling any explicit density function.
Problem: Want to sample from complex, high-dimensional training distribution. No direct way to do this!
Solution: Sample from a simple distribution, e.g. random noise. Learn the transformation to training distribution.
Problem: We can't learn the mapping relation between sample \\(z\\) and training images.
Solution: Use a discriminator network to tell whether the generate image is within data distribution or not.
Discriminator network: Try to distinguish between real and fake images.
Generator network: Try to fool the discriminator by generating real-looking images.
\\(x\\) : Real data.
\\(y\\) : Fake data, which is generated by the generator network. \\(y=G_{\\theta_g}(z)\\).
\\(D_{\\theta_d}(x)\\) : Discriminator score, which is the likelihood of real image. \\(D_{\\theta_d}(x)\\in[0,1]\\).
Objective of discriminator network:
\\(\\max_{\\theta_d}\\bigg[\\mathbb{E}_x\\Big(\\log D_{\\theta_d}(x)\\Big)+\\mathbb{E}_{z\\sim p(z)}\\Big(\\log\\big(1-D_{\\theta_d}(y)\\big)\\Big)\\bigg]\\)
Objective of generator network:
\\(\\min_{\\theta_g}\\max_{\\theta_d}\\bigg[\\mathbb{E}_x\\Big(\\log D_{\\theta_d}(x)\\Big)+\\mathbb{E}_{z\\sim p(z)}\\Big(\\log\\big(1-D_{\\theta_d}(y)\\big)\\Big)\\bigg]\\)
"},{"location":"AI/CS231n/CS231n_notes/#training-strategy","title":"Training Strategy","text":"Two combine this two networks together, we can train them alternately:
- Gradient ascent on discriminator.
\\(\\max_{\\theta_d}\\bigg[\\mathbb{E}_x\\Big(\\log D_{\\theta_d}(x)\\Big)+\\mathbb{E}_{z\\sim p(z)}\\Big(\\log\\big(1-D_{\\theta_d}(y)\\big)\\Big)\\bigg]\\)
- Gradient descent on generator.
\\(\\min_{\\theta_g}\\bigg[\\mathbb{E}_{z\\sim p(z)}\\Big(\\log\\big(1-D_{\\theta_d}(y)\\big)\\Big)\\bigg]\\)
However, the gradient of generator decreases with the value itself, making it hard to optimize.
So we replace \\(\\log\\big(1-D_{\\theta_d}(y)\\big)\\) with \\(-\\log D_{\\theta_d}(y)\\), and use gradient ascent instead.
- Gradient ascent on discriminator.
\\(\\max_{\\theta_d}\\bigg[\\mathbb{E}_x\\Big(\\log D_{\\theta_d}(x)\\Big)+\\mathbb{E}_{z\\sim p(z)}\\Big(\\log\\big(1-D_{\\theta_d}(y)\\big)\\Big)\\bigg]\\)
- Gradient ascent on generator.
\\(\\max_{\\theta_g}\\bigg[\\mathbb{E}_{z\\sim p(z)}\\Big(\\log D_{\\theta_d}(y)\\Big)\\bigg]\\)
"},{"location":"AI/CS231n/CS231n_notes/#summary_3","title":"Summary","text":"Pros: Beautiful, state-of-the-art samples!
Cons:
- Trickier / more unstable to train.
- Can\u2019t solve inference queries such as \\(p(x), p(z|x)\\).
"},{"location":"AI/CS231n/CS231n_notes/#14-self-supervised-learning","title":"14 - Self-supervised Learning","text":"Aim: Solve \u201cpretext\u201d tasks that produce good features for downstream tasks.
Application:
- Learn a feature extractor from pretext tasks. (self-supervised)
- Attach a shallow network on the feature extractor.
- Train the shallow network on target task with small amount of labeled data. (supervised)
"},{"location":"AI/CS231n/CS231n_notes/#pretext-tasks","title":"Pretext Tasks","text":"Labels are generated automatically.
"},{"location":"AI/CS231n/CS231n_notes/#rotation","title":"Rotation","text":"Train a classifier on randomly rotated images.
"},{"location":"AI/CS231n/CS231n_notes/#rearrangement","title":"Rearrangement","text":"Train a classifier on randomly shuffled image pieces.
Predict the location of image pieces.
"},{"location":"AI/CS231n/CS231n_notes/#inpainting","title":"Inpainting","text":"Mask part of the image, train a network to predict the masked area.
Method referencing Context Encoders: Feature Learning by Inpainting.
Combine two types of loss together to get better performance:
- Reconstruction loss (L2 loss): Used for reconstructing global features.
- Adversarial loss: Used for generating texture features.
"},{"location":"AI/CS231n/CS231n_notes/#coloring","title":"Coloring","text":"Transfer between greyscale images and colored images.
Cross-channel predictions for images: Split-Brain Autoencoders.
Video coloring: Establish mappings between reference and target frames in a learned feature space. Tracking Emerges by Colorizing Videos.
"},{"location":"AI/CS231n/CS231n_notes/#summary-for-pretext-tasks","title":"Summary for Pretext Tasks","text":" - Pretext tasks focus on \u201cvisual common sense\u201d.
- The models are forced learn good features about natural images.
- We don\u2019t care about the performance of these pretext tasks.
What we care is the performance of downstream tasks.
"},{"location":"AI/CS231n/CS231n_notes/#problems-of-specific-pretext-tasks","title":"Problems of Specific Pretext Tasks","text":" - Coming up with individual pretext tasks is tedious.
- The learned representations may not be general.
Intuitive Solution: Contrastive Learning.
"},{"location":"AI/CS231n/CS231n_notes/#contrastive-representation-learning","title":"Contrastive Representation Learning","text":"Local additional references: Contrastive Learning.md.
Objective:
Given a chosen score function \\(s\\), we aim to learn an encoder function \\(f\\) that yields:
- For each sample \\(x\\), increase the similarity \\(s\\big(f(x),f(x^+)\\big)\\) between \\(x\\) and positive samples \\(x^+\\).
- Finally we want \\(s\\big(f(x),f(x^+)\\big)\\gg s\\big(f(x),f(x^-)\\big)\\).
Loss Function:
Given \\(1\\) positive sample and \\(N-1\\) negative samples:
InfoNCE Loss Cross Entropy Loss \\(\\begin{aligned}\\mathcal{L}=-\\mathbb{E}_X\\Bigg[\\log\\frac{\\exp{s\\big(f(x),f(x^+)\\big)}}{\\exp{s\\big(f(x),f(x^+)\\big)}+\\sum_{j=1}^{N-1}\\exp{s\\big(f(x),f(x^+)\\big)}}\\Bigg]\\\\\\end{aligned}\\) \\(\\begin{aligned}\\mathcal{L}&=-\\sum_{i=1}^Np(x_i)\\log q(x_i)\\\\&=-\\mathbb{E}_X\\big[\\log q(x)\\big]\\\\&=-\\mathbb{E}_X\\Bigg[\\log\\frac{\\exp(x)}{\\sum_{j=1}^N\\exp(x_j)}\\Bigg]\\end{aligned}\\) The InfoNCE Loss is a lower bound on the mutual information between \\(f(x)\\) and \\(f(x^+)\\):
\\(\\text{MI}\\big[f(x),f(x^+)\\big]\\ge\\log(N)-\\mathcal{L}\\)
The larger the negative sample size \\(N\\), the tighter the bound.
So we use \\(N-1\\) negative samples.
"},{"location":"AI/CS231n/CS231n_notes/#instance-contrastive-learning","title":"Instance Contrastive Learning","text":""},{"location":"AI/CS231n/CS231n_notes/#simclr","title":"SimCLR","text":"Use a projection function \\(g(\\cdot)\\) to project features to a space where contrastive learning is applied.
The extra projection contributes a lot to the final performance.
Score Function: Cos similarity \\(s(u,v)=\\frac{u^Tv}{||u||||v||}\\\\\\).
Positive Pair: Pair of augmented data.
"},{"location":"AI/CS231n/CS231n_notes/#momentum-contrastive-learning-moco","title":"Momentum Contrastive Learning (MoCo)","text":"There are mainly \\(3\\) training strategy in contrastive learning:
- end-to-end: Keys are updated together with queries, e.g. SimCLR.
(limited by GPU size)
- memory bank: Store last-time keys for sampling.
(inconsistency between \\(q\\) and \\(k\\))
- MoCo: Use momentum methods to encode keys.
(combination of end-to-end & memory bank)
Key differences to SimCLR:
- Keep a running queue of keys (negative samples).
- Compute gradients and update the encoder only through the queries.
- Decouple min-batch size with the number of keys: can support a large number of negative samples.
- The key encoder is slowly progressing through the momentum update rules:
\\(\\theta_k\\leftarrow m\\theta_k+(1-m)\\theta_q\\)
"},{"location":"AI/CS231n/CS231n_notes/#sequence-contrastive-learning","title":"Sequence Contrastive Learning","text":""},{"location":"AI/CS231n/CS231n_notes/#contrastive-predictive-coding-cpc","title":"Contrastive Predictive Coding (CPC)","text":"Contrastive: Contrast between \u201cright\u201d and \u201cwrong\u201d sequences using contrastive learning.
Predictive: The model has to predict future patterns given the current context.
Coding: The model learns useful feature vectors, or \u201ccode\u201d, for downstream tasks, similar to other self-supervised methods.
"},{"location":"AI/CS231n/CS231n_notes/#other-examples-frontier","title":"Other Examples (Frontier)","text":""},{"location":"AI/CS231n/CS231n_notes/#contrastive-language-image-pre-training-clip","title":"Contrastive Language Image Pre-training (CLIP)","text":"Contrastive learning between image and natural language sentences.
"},{"location":"AI/CS231n/CS231n_notes/#15-low-level-vision","title":"15 - Low-Level Vision","text":"Pass...
"},{"location":"AI/CS231n/CS231n_notes/#16-3d-vision","title":"16 - 3D Vision","text":""},{"location":"AI/CS231n/CS231n_notes/#representation","title":"Representation","text":""},{"location":"AI/CS231n/CS231n_notes/#explicit-vs-implicit","title":"Explicit vs Implicit","text":"Explicit: Easy to sample examples, hard to do inside/outside check.
Implicit: Hard to sample examples, easy to do inside/outside check.
Non-parametric Parametric Explicit Points.Meshes. Splines.Subdivision Surfaces. Implicit Level Sets.Voxels. Algebraic Surfaces.Constructive Solid Geometry."},{"location":"AI/CS231n/CS231n_notes/#point-clouds","title":"Point Clouds","text":"The simplest representation.
Collection of \\((x,y,z)\\) coordinates.
Cons:
- Difficult to draw in under-sampled regions.
- No simplification or subdivision.
- No direction smooth rendering.
- No topological information.
"},{"location":"AI/CS231n/CS231n_notes/#polygonal-meshes","title":"Polygonal Meshes","text":"Collection of vertices \\(v\\) and edges \\(e\\).
Pros:
- Can apply downsampling or upsampling on meshes.
- Error decreases by \\(O(n^2)\\) while meshes increase by \\(O(n)\\).
- Can approximate arbitrary topology.
- Efficient rendering.
"},{"location":"AI/CS231n/CS231n_notes/#splines","title":"Splines","text":"Use specific functions to approximate the surface. (e.g. B\u00e9zier Curves)
"},{"location":"AI/CS231n/CS231n_notes/#algebraic-surfaces","title":"Algebraic Surfaces","text":"Use specific functions to represent the surface.
"},{"location":"AI/CS231n/CS231n_notes/#constructive-solid-geometry","title":"Constructive Solid Geometry","text":"Combine implicit geometry with Boolean operations.
"},{"location":"AI/CS231n/CS231n_notes/#level-sets","title":"Level Sets","text":"Store a grim of values to approximate the function.
Surface is found where interpolated value equals to \\(0\\).
"},{"location":"AI/CS231n/CS231n_notes/#voxels","title":"Voxels","text":"Binary thresholding the volumetric grid.
"},{"location":"AI/CS231n/CS231n_notes/#ai-3d","title":"AI + 3D","text":"Pass...
"},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/","title":"Image Classification-Data-driven Approach, k-Nearest Neighbor, train_val_test splits","text":""},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#image-classification-data-driven-approach-k-nearest-neighbor-train_val_test-splits","title":"Image Classification-Data-driven Approach, k-Nearest Neighbor, train_val_test splits","text":" \u7ea6 651 \u4e2a\u5b57 28 \u884c\u4ee3\u7801 2 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 4 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#image-classification","title":"image classification","text":" - challenges
- viewpoint variation
- scale variation
- deformation
- occlusion
- illumination conditions
- background clutter
- intra-class variation
- data-driven approach
- the image classification pipeline
- input
- learning
- training a classifier
- learning a model
- evaluation
"},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#nearest-neighbor-classifier","title":"Nearest Neighbor Classifier","text":"\\[ d_1 (I_1, I_2) = \\sum_{p} \\left| I^p_1 - I^p_2 \\right| \\] Pythonimport numpy as np\n\nclass NearestNeighbor(object): \n def **init**(self): \n pass\n\n def train(self, X, y): \n \"\"\" X is N x D where each row is an example. Y is 1-dimension of size N \"\"\" \n # the nearest neighbor classifier simply remembers all the training data \n self.Xtr = X \n self.ytr = y\n\n def predict(self, X): \n \"\"\" X is N x D where each row is an example we wish to predict label for \"\"\" \n num_test = X.shape[0] \n # lets make sure that the output type matches the input type \n Ypred = np.zeros(num_test, dtype = self.ytr.dtype)\n\n # loop over all test rows\n for i in range(num_test):\n # find the nearest training image to the i'th test image\n # using the L1 distance (sum of absolute value differences)\n distances = np.sum(np.abs(self.Xtr - X[i,:]), axis = 1)\n min_index = np.argmin(distances) # get the index with smallest distance\n Ypred[i] = self.ytr[min_index] # predict the label of the nearest example\n\n return Ypred\n
\\[ d_2 (I_1, I_2) = \\sqrt{\\sum_{p} \\left( I^p_1 - I^p_2 \\right)^2} \\] Pythondistances = np.sqrt(np.sum(np.square(self.Xtr - X[i,:]), axis = 1))\n
"},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#k-nearest-neighbor-classifier","title":"k - Nearest Neighbor Classifier","text":""},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#validation-sets-for-hyperparameter-tuning","title":"Validation sets for Hyperparameter tuning","text":"Evaluate on the test only a single time, at the very end
Split your training set into training set and a validation set. Use validation set to tune all hyperparameters. At the end run a single time on the test set and report performance.
- cross-validation
- single calidation split
"},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#pros-and-cons-of-nearest-neighbor-classifier","title":"Pros and Cons of Nearest Neighbor classifier","text":" - simple to implement and understand
- take no time to train
- however, pay a cost at test time
As an aside, the computational complexity of the Nearest Neighbor classifier is an active area of research, and several\u00a0Approximate Nearest Neighbor\u00a0(ANN) algorithms and libraries exist that can accelerate the nearest neighbor lookup in a dataset (e.g.\u00a0FLANN). These algorithms allow one to trade off the correctness of the nearest neighbor retrieval with its space/time complexity during retrieval, and usually rely on a pre-processing/indexing stage that involves building a kdtree, or running the k-means algorithm.
- \\(\\displaystyle L_{2}\\) isn't enough sensitive
In particular, note that images that are nearby each other are much more a function of the general color distribution of the images, or the type of background rather than their semantic identity.
Applying kNN in practice - Preprocess your data: Normalize the features in your data (e.g. one pixel in images) to have zero mean and unit variance. We will cover this in more detail in later sections, and chose not to cover data normalization in this section because pixels in images are usually homogeneous and do not exhibit widely different distributions, alleviating the need for data normalization.
- If your data is very high-dimensional, consider using a dimensionality reduction technique such as PCA (wiki ref,\u00a0CS229ref,\u00a0blog ref), NCA (wiki ref,\u00a0blog ref), or even\u00a0Random Projections.
- Split your training data randomly into train/val splits. As a rule of thumb, between 70-90% of your data usually goes to the train split. This setting depends on how many hyperparameters you have and how much of an influence you expect them to have. If there are many hyperparameters to estimate, you should err on the side of having larger validation set to estimate them effectively. If you are concerned about the size of your validation data, it is best to split the training data into folds and perform cross-validation. If you can afford the computational budget it is always safer to go with cross-validation (the more folds the better, but more expensive).
- Train and evaluate the kNN classifier on the validation data (for all folds, if doing cross-validation) for many choices of\u00a0k\u00a0(e.g. the more the better) and across different distance types (L1 and L2 are good candidates)
- If your kNN classifier is running too long, consider using an Approximate Nearest Neighbor library (e.g.\u00a0FLANN) to accelerate the retrieval (at cost of some accuracy).
- Take note of the hyperparameters that gave the best results. There is a question of whether you should use the full training set with the best hyperparameters, since the optimal hyperparameters might change if you were to fold the validation data into your training set (since the size of the data would be larger). In practice it is cleaner to not use the validation data in the final classifier and consider it to be\u00a0burned\u00a0on estimating the hyperparameters. Evaluate the best model on the test set. Report the test set accuracy and declare the result to be the performance of the kNN classifier on your data.
more about Machine Learing
"},{"location":"AI/CS231n/Linear%20classification-Support%20Vector%20Machine%2C%20Softmax/","title":"Linear classification-Support Vector Machine, Softmax","text":""},{"location":"AI/CS231n/Linear%20classification-Support%20Vector%20Machine%2C%20Softmax/#linear-classification-support-vector-machine-softmax","title":"Linear classification-Support Vector Machine, Softmax","text":" \u7ea6 126 \u4e2a\u5b57 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/CS231n/Linear%20classification-Support%20Vector%20Machine%2C%20Softmax/#linear-classifiaction","title":"Linear Classifiaction","text":"\\[ L_i = \\sum_{j\\neq y_i} \\max(0, s_j - s_{y_i} + \\Delta) \\] \\[ L = \\frac{1}{N} \\sum_i \\sum_{j\\neq y_i} \\left[ \\max(0, f(x_i; W)_j - f(x_i; W)_{y_i} + \\Delta) \\right] + \\lambda \\sum_k\\sum_l W_{k,l}^2 \\] \\[ L_i = -\\log\\left(\\frac{e^{f_{y_i}}}{ \\sum_j e^{f_j} }\\right) \\hspace{0.5in} \\text{or equivalently} \\hspace{0.5in} L_i = -f_{y_i} + \\log\\sum_j e^{f_j} \\] \\[ \\frac{e^{f_{y_i}}}{\\sum_j e^{f_j}} = \\frac{Ce^{f_{y_i}}}{C\\sum_j e^{f_j}} = \\frac{e^{f_{y_i} + \\log C}}{\\sum_j e^{f_j + \\log C}} \\]"},{"location":"AI/CS231n/Numpy/","title":"Numpy","text":""},{"location":"AI/CS231n/Numpy/#python","title":"Python","text":" \u7ea6 49 \u4e2a\u5b57 104 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 2 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/CS231n/Numpy/#string","title":"string","text":"Pythons = \"hello\"\nprint(s.capitalize()) # Capitalize a string; prints \"Hello\"\nprint(s.upper()) # Convert a string to uppercase; prints \"HELLO\"\nprint(s.rjust(7)) # Right-justify a string, padding with spaces; prints \" hello\"\nprint(s.center(7)) # Center a string, padding with spaces; prints \" hello \"\nprint(s.replace('l', '(ell)')) # Replace all instances of one substring with another;\n # prints \"he(ell)(ell)o\"\nprint(' world '.strip()) # Strip leading and trailing whitespace; prints \"world\"\n
"},{"location":"AI/CS231n/Numpy/#containers","title":"Containers","text":"Pythonanimals = ['cat', 'dog', 'monkey']\nfor idx, animal in enumerate(animals):\n print('#%d: %s' % (idx + 1, animal))\n# Prints \"#1: cat\", \"#2: dog\", \"#3: monkey\", each on its own line\n
\u5217\u8868\u63a8\u5bfc\u5f0f
Pythonnums = [0, 1, 2, 3, 4]\neven_squares = [x ** 2 for x in nums if x % 2 == 0]\nprint(even_squares) # Prints \"[0, 4, 16]\"\n
\u540c\u6837\u4e5f\u6709\u5b57\u5178\u63a8\u5bfc\u5f0f
Tuples \u53ef\u4ee5\u7528\u4f5c\u5b57\u5178\u4e2d\u7684\u952e\u548c\u96c6\u5408\u7684\u5143\u7d20\uff0c\u4f46\u662f lists \u4e0d\u80fd
"},{"location":"AI/CS231n/Numpy/#numpy","title":"Numpy","text":"Pythonimport numpy as np\n\n# Create a new array from which we will select elements\na = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n\nprint(a) # prints \"array([[ 1, 2, 3],\n # [ 4, 5, 6],\n # [ 7, 8, 9],\n # [10, 11, 12]])\"\n\n# Create an array of indices\nb = np.array([0, 2, 0, 1])\n\n# Select one element from each row of a using the indices in b\nprint(a[np.arange(4), b]) # Prints \"[ 1 6 7 11]\"\n\n# Mutate one element from each row of a using the indices in b\na[np.arange(4), b] += 10\n\nprint(a) # prints \"array([[11, 2, 3],\n # [ 4, 5, 16],\n # [17, 8, 9],\n # [10, 21, 12]])\n
Pythonimport numpy as np\n\na = np.array([[1,2], [3, 4], [5, 6]])\n\nbool_idx = (a > 2) # Find the elements of a that are bigger than 2;\n # this returns a numpy array of Booleans of the same\n # shape as a, where each slot of bool_idx tells\n # whether that element of a is > 2.\n\nprint(bool_idx) # Prints \"[[False False]\n # [ True True]\n # [ True True]]\"\n\n# We use boolean array indexing to construct a rank 1 array\n# consisting of the elements of a corresponding to the True values\n# of bool_idx\nprint(a[bool_idx]) # Prints \"[3 4 5 6]\"\n\n# We can do all of the above in a single concise statement:\nprint(a[a > 2]) # Prints \"[3 4 5 6]\"\n
Pythonx = np.array([1, 2], dtype=np.int64) # Force a particular datatype\nprint(x.dtype) # Prints \"int64\"\n
Pythonimport numpy as np\n\nx = np.array([[1,2],[3,4]], dtype=np.float64)\ny = np.array([[5,6],[7,8]], dtype=np.float64)\n\n# Elementwise sum; both produce the array\n# [[ 6.0 8.0]\n# [10.0 12.0]]\nprint(x + y)\nprint(np.add(x, y))\n\n# Elementwise difference; both produce the array\n# [[-4.0 -4.0]\n# [-4.0 -4.0]]\nprint(x - y)\nprint(np.subtract(x, y))\n\n# Elementwise product; both produce the array\n# [[ 5.0 12.0]\n# [21.0 32.0]]\nprint(x * y)\nprint(np.multiply(x, y))\n\n# Elementwise division; both produce the array\n# [[ 0.2 0.33333333]\n# [ 0.42857143 0.5 ]]\nprint(x / y)\nprint(np.divide(x, y))\n\n# Elementwise square root; produces the array\n# [[ 1. 1.41421356]\n# [ 1.73205081 2. ]]\nprint(np.sqrt(x))\n
\u5e7f\u64ad\u53ef\u4ee5\u907f\u514d\u5faa\u73af
Pythonimport numpy as np\n\n# We will add the vector v to each row of the matrix x,\n# storing the result in the matrix y\nx = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\nv = np.array([1, 0, 1])\ny = x + v # Add v to each row of x using broadcasting\nprint(y) # Prints \"[[ 2 2 4]\n # [ 5 5 7]\n # [ 8 8 10]\n # [11 11 13]]\"\n
"},{"location":"AI/CS231n/Numpy/#scipy","title":"SciPy","text":""},{"location":"AI/CS231n/Numpy/#matplotlib","title":"Matplotlib","text":""},{"location":"AI/EECS%20498-007/KNN/","title":"KNN","text":"\u5bf9\u4e8e\u4e00\u4e2a\u5f85\u5206\u7c7b\u7684\u6837\u672c\uff0c\u627e\u5230\u8bad\u7ec3\u6570\u636e\u96c6\u4e2d\u4e0e\u5176\u6700\u63a5\u8fd1\u7684K\u4e2a\u6837\u672c\uff08\u5373\u6700\u8fd1\u90bb\uff09\uff0c\u7136\u540e\u6839\u636e\u8fd9K\u4e2a\u6837\u672c\u7684\u7c7b\u522b\u6765\u51b3\u5b9a\u5f85\u5206\u7c7b\u6837\u672c\u7684\u7c7b\u522b\u3002
\u7ea6 374 \u4e2a\u5b57 100 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 3 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/EECS%20498-007/KNN/#_1","title":"\u6570\u5b66\u63a8\u5bfc","text":"\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u8bad\u7ec3\u6570\u636e\u96c6 \\(T = \\{(x_1, y_1), (x_2, y_2), \\ldots, (x_N, y_N)\\}\\)\uff0c\u5176\u4e2d \\(x_i\\) \u662f\u7279\u5f81\u5411\u91cf\uff0c \\(y_i\\) \u662f\u5bf9\u5e94\u7684\u7c7b\u522b\u6807\u7b7e\u3002\u5bf9\u4e8e\u4e00\u4e2a\u65b0\u7684\u5f85\u5206\u7c7b\u6837\u672c x\uff0cKNN\u7b97\u6cd5\u7684\u76ee\u6807\u662f\u9884\u6d4b\u5176\u7c7b\u522b \\(y\\) \u3002
- \u8ddd\u79bb\u5ea6\u91cf\uff1a\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u4e00\u4e2a\u8ddd\u79bb\u5ea6\u91cf\u6765\u8ba1\u7b97\u5f85\u5206\u7c7b\u6837\u672c \\(x\\) \u4e0e\u8bad\u7ec3\u96c6\u4e2d\u6bcf\u4e2a\u6837\u672c \\(x_i\\) \u4e4b\u95f4\u7684\u8ddd\u79bb\u3002\u5e38\u7528\u7684\u8ddd\u79bb\u5ea6\u91cf\u5305\u62ec\u6b27\u6c0f\u8ddd\u79bb\uff08Euclidean distance\uff09\u3001\u66fc\u54c8\u987f\u8ddd\u79bb\uff08Manhattan distance\uff09\u548c\u95f5\u53ef\u592b\u65af\u57fa\u8ddd\u79bb\uff08Minkowski distance\uff09\u3002\u4ee5\u6b27\u6c0f\u8ddd\u79bb\u4e3a\u4f8b\uff0c\u4e24\u4e2a\u6837\u672c \\(x\\) \u548c \\(x_i\\) \u4e4b\u95f4\u7684\u8ddd\u79bb\u5b9a\u4e49\u4e3a\uff1a
\\[ d(x, x_i) = \\sqrt{\\sum_{j=1}^{d} (x_j - x_{i,j})^2} \\] \u5176\u4e2d\uff0c \\(d\\) \u662f\u7279\u5f81\u7684\u7ef4\u5ea6\u3002
- \u5bfb\u627e\u6700\u8fd1\u90bb\uff1a\u7136\u540e\uff0c\u6211\u4eec\u6839\u636e\u8ba1\u7b97\u51fa\u7684\u8ddd\u79bb\uff0c\u9009\u62e9\u8ddd\u79bb\u6700\u8fd1\u7684K\u4e2a\u6837\u672c\uff0c\u6784\u6210\u5f85\u5206\u7c7b\u6837\u672c\u7684\u90bb\u57df \\(N_k(x)\\)\u3002
- \u51b3\u7b56\u89c4\u5219\uff1a\u6700\u540e\uff0c\u6839\u636e\u90bb\u57df \\( N_k(x) \\) \u4e2d\u7684\u6837\u672c\u7c7b\u522b\uff0c\u901a\u8fc7\u591a\u6570\u6295\u7968\u7684\u65b9\u5f0f\u6765\u51b3\u5b9a\u5f85\u5206\u7c7b\u6837\u672c\u7684\u7c7b\u522b\u3002\u5373\uff1a
\\[ y = \\arg\\max_{c_j} \\sum_{x_i \\in N_k(x)} I(y_i = c_j) \\] \u5176\u4e2d\uff0c \\(I\\) \u662f\u6307\u793a\u51fd\u6570\uff0c\u5f53 \\(y_i = c_j\\) \u65f6\u53d6\u503c\u4e3a1\uff0c\u5426\u5219\u4e3a0\u3002
"},{"location":"AI/EECS%20498-007/KNN/#_2","title":"\u4f5c\u4e1a\u4e2d\u7684\u5b9e\u73b0","text":"Pythonimport torch\n\ndef compute_distances_two_loops(x_train, x_test):\n num_train = x_train.shape[0]\n num_test = x_test.shape[0]\n dists = x_train.new_zeros(num_train, num_test)\n\n for i in range(num_train):\n for j in range(num_test):\n dists[i,j] = ((x_train[i] - x_test[j]) ** 2).sum() ** (1/2)\n\n return dists\n\ndef compute_distances_one_loop(x_train, x_test):\n num_train = x_train.shape[0]\n num_test = x_test.shape[0]\n dists = x_train.new_zeros(num_train, num_test)\n\n for i in range(num_train):\n dists[i] = ((x_train[i] - x_test) ** 2).sum(dim=(1,2,3)) ** (1/2)\n\n return dists\n\ndef compute_distances_no_loops(x_train, x_test):\n num_train = x_train.shape[0]\n num_test = x_test.shape[0]\n dists = x_train.new_zeros(num_train, num_test)\n\n A = x_train.reshape(num_train, -1)\n B = x_test.reshape(num_test, -1)\n AB2 = A.mm(B.T) * 2\n dists = ((A ** 2).sum(dim=1).reshape(-1, 1) - AB2 + (B ** 2).sum(dim=1).reshape(1, -1)) ** (1/2)\n\n return dists\n\ndef predict_labels(dists, y_train, k=1):\n num_train, num_test = dists.shape\n y_pred = torch.zeros(num_test, dtype=torch.int64)\n\n values, indices = torch.topk(dists, k, dim=0, largest=False)\n for i in range(indices.shape[1]):\n _, idx = torch.max(y_train[indices[:, i]].bincount(), dim=0)\n y_pred[i] = idx\n\n return y_pred\n\nclass KnnClassifier:\n def __init__(self, x_train, y_train):\n self.x_train = x_train\n self.y_train = y_train\n\n def predict(self, x_test, k=1):\n dists = compute_distances_no_loops(self.x_train, x_test)\n y_test_pred = predict_labels(dists, self.y_train, k)\n return y_test_pred\n\n def check_accuracy(self, x_test, y_test, k=1, quiet=False):\n y_test_pred = self.predict(x_test, k=k)\n num_samples = x_test.shape[0]\n num_correct = (y_test == y_test_pred).sum().item()\n accuracy = 100.0 * num_correct / num_samples\n msg = (f'Got {num_correct} / {num_samples} correct; accuracy is {accuracy:.2f}%')\n if not quiet:\n print(msg)\n return accuracy\n\ndef knn_cross_validate(x_train, y_train, num_folds=5, k_choices=None):\n if k_choices is None:\n k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n\n x_train_folds = torch.chunk(x_train, num_folds, dim=0)\n y_train_folds = torch.chunk(y_train, num_folds, dim=0)\n\n k_to_accuracies = {}\n\n for k in k_choices:\n list_of_acc = []\n for num_fold in range(num_folds):\n x_train_folds_local = [x for x in x_train_folds]\n y_train_folds_local = [x for x in y_train_folds]\n x_test = x_train_folds_local[num_fold]\n y_test = y_train_folds_local[num_fold]\n del x_train_folds_local[num_fold]\n del y_train_folds_local[num_fold]\n x_train = torch.cat(x_train_folds_local, dim=0)\n y_train = torch.cat(y_train_folds_local, dim=0)\n classifier = KnnClassifier(x_train, y_train)\n list_of_acc.append(classifier.check_accuracy(x_test, y_test, k))\n k_to_accuracies[k] = list_of_acc\n\n return k_to_accuracies\n\ndef knn_get_best_k(k_to_accuracies):\n best_k = 0\n new_dict = {}\n for k, accs in sorted(k_to_accuracies.items()):\n new_dict[k] = sum(accs) / len(accs) \n max_value = max(new_dict.values())\n best_k = [k for k, v in new_dict.items() if v == max_value][0]\n return best_k\n
"},{"location":"AI/EECS%20498-007/Pytorch/","title":"pytorch \u7684\u57fa\u672c\u4f7f\u7528","text":""},{"location":"AI/EECS%20498-007/Pytorch/#pytorch","title":"pytorch \u7684\u57fa\u672c\u4f7f\u7528","text":" \u7ea6 564 \u4e2a\u5b57 45 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 3 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
Python# Create a rank 1 tensor from a Python list\na = torch.tensor([[1, 2, 3], [4, 5, 6]])\nprint('Here is a:')\nprint(a)\nprint('type(a): ', type(a))\nprint('rank of a: ', a.dim())\nprint('a.shape: ', a.shape)\n\ntorch.zeros(2, 3)\ntorch.ones(2, 3)\ntorch.eye(3)\ntorch.rand(2, 3)\ntorch.full((M, N), 3.14)\n\ny2 = torch.tensor([1, 2], dtype=torch.int64)\nprint(y2.dtype)\n\nx3 = x0.to(torch.float32)\n\nx0 = torch.eye(3, dtype=torch.float64) \u00a0# Shape (3, 3), dtype torch.float64\nx1 = torch.zeros_like(x0) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # Shape (3, 3), dtype torch.float64\nx2 = x0.new_zeros(4, 5) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # Shape (4, 5), dtype torch.float64\nx3 = torch.ones(6, 7).to(x0) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# Shape (6, 7), dtype torch.float64\n
Even though PyTorch provides a large number of numeric datatypes, the most commonly used datatypes are:
torch.float32: Standard floating-point type; used to store learnable parameters, network activations, etc. Nearly all arithmetic is done using this type.torch.int64: Typically used to store indicestorch.bool: Stores boolean values: 0 is false and 1 is truetorch.float16: Used for mixed-precision arithmetic, usually on NVIDIA GPUs with tensor cores. You won't need to worry about this datatype in this course.- \u6ce8\u610f
a[:, 1] \u548c a[:, 1:2] \u7684\u533a\u522b\uff0c\u540e\u8005\u4f1a\u4fdd\u7559\u7684\u591a\u4e00\u70b9 clone() \u4ee5\u540e\u7684\u53d8\u91cf\u8ddf\u539f\u53d8\u91cf\u662f\u72ec\u7acb\u7684\uff0c\u4f46\u662f\u7b49\u53f7\u76f4\u63a5\u8d4b\u503c\u7684\u662f\u540c\u4e00\u4e2a\u6307\u9488
Pythonmask = (a > 3)\nprint('\\nMask tensor:')\nprint(mask)\n# Mask tensor: tensor([[False, False], [False, True], [ True, True]])\n
- As its name implies, a tensor returned by
.view() shares the same data as the input, so changes to one will affect the other.
Reshape\u548cview\u7684\u533a\u522b - \u5185\u5b58\u8fde\u7eed\u6027\uff1a
view \u8981\u6c42\u539f\u59cb\u5f20\u91cf\u548c\u76ee\u6807\u5f20\u91cf\u5728\u5185\u5b58\u4e2d\u662f\u8fde\u7eed\u7684\u3002\u5982\u679c\u539f\u59cb\u5f20\u91cf\u4e0d\u662f\u8fde\u7eed\u7684\uff0c view \u4f1a\u9996\u5148\u8c03\u7528 contiguous \u65b9\u6cd5\u4f7f\u5176\u8fde\u7eed\uff0c\u7136\u540e\u6539\u53d8\u5f62\u72b6\u3002\u5982\u679c\u5f20\u91cf\u5df2\u7ecf\u662f\u8fde\u7eed\u7684\uff0c view \u64cd\u4f5c\u4e0d\u4f1a\u590d\u5236\u6570\u636e\u3002reshape\u4e0d\u8981\u6c42\u539f\u59cb\u5f20\u91cf\u662f\u8fde\u7eed\u7684\u3002\u5982\u679c\u539f\u59cb\u5f20\u91cf\u4e0d\u662f\u8fde\u7eed\u7684\uff0creshape\u4f1a\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u5f20\u91cf\u5e76\u590d\u5236\u6570\u636e\uff0c\u4ee5\u786e\u4fdd\u65b0\u5f20\u91cf\u662f\u8fde\u7eed\u7684\u3002
- \u8fd4\u56de\u503c
view\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684\u5f20\u91cf\uff0c\u5b83\u4e0e\u539f\u59cb\u5f20\u91cf\u5171\u4eab\u76f8\u540c\u7684\u6570\u636e\uff0c\u4f46\u662f\u6709\u4e0d\u540c\u7684\u5f62\u72b6\u3002\u5982\u679c\u539f\u59cb\u5f20\u91cf\u4e0d\u662f\u8fde\u7eed\u7684\uff0cview\u4f1a\u8fd4\u56de\u4e00\u4e2a\u526f\u672c\u3002reshape\u4e5f\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684\u5f20\u91cf\uff0c\u4f46\u603b\u662f\u521b\u5efa\u6570\u636e\u7684\u526f\u672c\uff0c\u5373\u4f7f\u539f\u59cb\u5f20\u91cf\u662f\u8fde\u7eed\u7684\u3002
- \u4f7f\u7528\u573a\u666f\uff1a
- \u5f53\u4f60\u786e\u5b9a\u539f\u59cb\u5f20\u91cf\u662f\u8fde\u7eed\u7684\uff0c\u5e76\u4e14\u4f60\u60f3\u8981\u907f\u514d\u4e0d\u5fc5\u8981\u7684\u6570\u636e\u590d\u5236\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528
view\u3002 - \u5f53\u4f60\u4e0d\u786e\u5b9a\u539f\u59cb\u5f20\u91cf\u662f\u5426\u8fde\u7eed\uff0c\u6216\u8005\u4f60\u60f3\u8981\u786e\u4fdd\u64cd\u4f5c\u4e0d\u4f1a\u56e0\u975e\u8fde\u7eed\u6027\u800c\u5931\u8d25\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528
reshape\u3002
- \u53c2\u6570\uff1a
view\u7684\u53c2\u6570\u662f\u76ee\u6807\u5f62\u72b6\u7684\u7ef4\u5ea6\u3002reshape\u7684\u53c2\u6570\u4e5f\u662f\u76ee\u6807\u5f62\u72b6\u7684\u7ef4\u5ea6\uff0c\u4f46\u5b83\u53ef\u4ee5\u63a5\u53d7\u4e00\u4e2a\u989d\u5916\u7684\u53c2\u6570inplace\uff0c\u5982\u679c\u8bbe\u7f6e\u4e3aTrue\uff0c\u5219\u4f1a\u5728\u539f\u5730\u4fee\u6539\u5f20\u91cf\u7684\u5f62\u72b6\u3002
torch.sin(x) \u548c x.sin() \u662f\u7b49\u4ef7\u7684
Pythonx = torch.tensor([[1, 2, 3],\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 [4, 5, 6]], dtype=torch.float32)\nprint('Original tensor:')\nprint(x)\n\nprint('\\nSum over entire tensor:')\nprint(torch.sum(x))\nprint(x.sum())\n\n# We can sum over each row:\nprint('\\nSum of each row:')\nprint(torch.sum(x, dim=0))\nprint(x.sum(dim=0))\n\n# Sum over each column:\nprint('\\nSum of each column:')\nprint(torch.sum(x, dim=1))\nprint(x.sum(dim=1))\n
torch.dot: Computes inner product of vectorstorch.mm: Computes matrix-matrix productstorch.mv: Computes matrix-vector productstorch.addmm / torch.addmv: Computes matrix-matrix and matrix-vector multiplications plus a biastorch.bmm / torch.baddmm: Batched versions of torch.mm and torch.addmm, respectivelytorch.matmul: General matrix product that performs different operations depending on the rank of the inputs. Confusingly, this is similar to np.dot in numpy.
"},{"location":"AI/EECS%20498-007/linear_classifer/","title":"Linear classifer","text":""},{"location":"AI/EECS%20498-007/linear_classifer/#_1","title":"\u539f\u7406","text":" \u7ea6 677 \u4e2a\u5b57 216 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 6 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
\u4e24\u79cd\u7ebf\u6027\u5206\u7c7b\u5668\uff1a\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u548cSoftmax\u5206\u7c7b\u5668\u3002\u8fd9\u4e24\u79cd\u5206\u7c7b\u5668\u90fd\u662f\u76d1\u7763\u5b66\u4e60\u7b97\u6cd5\uff0c\u7528\u4e8e\u5206\u7c7b\u4efb\u52a1\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#svm","title":"\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09","text":"SVM\u7684\u76ee\u6807\u662f\u627e\u5230\u4e00\u4e2a\u8d85\u5e73\u9762\uff0c\u5b83\u53ef\u4ee5\u6700\u5927\u5316\u4e0d\u540c\u7c7b\u522b\u4e4b\u95f4\u7684\u8fb9\u754c\u3002\u8fd9\u4e2a\u8d85\u5e73\u9762\u88ab\u79f0\u4e3a\u6700\u4f18\u5206\u5272\u8d85\u5e73\u9762\u3002\u5bf9\u4e8e\u4e8c\u5206\u7c7b\u95ee\u9898\uff0cSVM\u7684\u635f\u5931\u51fd\u6570\u53ef\u4ee5\u8868\u793a\u4e3a\uff1a
\\[ L(W, b) = \\frac{1}{N} \\sum_{i=1}^{N} \\max(0, 1 - y_i (W \\cdot x_i + b)) \\] \u5176\u4e2d\uff0c\\(W\\) \u662f\u6743\u91cd\u5411\u91cf\uff0c\\(b\\) \u662f\u504f\u7f6e\u9879\uff0c\\(x_i\\) \u662f\u8f93\u5165\u7279\u5f81\uff0c\\(y_i\\) \u662f\u6807\u7b7e\uff08-1\u62161\uff09\uff0c\\(N\\) \u662f\u6837\u672c\u6570\u91cf\u3002
\u4e3a\u4e86\u5b9e\u73b0\u591a\u5206\u7c7b\uff0c\u6211\u4eec\u4f7f\u7528\u7ed3\u6784\u5316SVM\u635f\u5931\u51fd\u6570\uff0c\u5b83\u8003\u8651\u4e86\u6bcf\u4e2a\u7c7b\u522b\u7684\u5206\u6570\uff0c\u5e76\u5c1d\u8bd5\u6700\u5927\u5316\u6b63\u786e\u7c7b\u522b\u7684\u5206\u6570\u4e0e\u6b21\u9ad8\u7c7b\u522b\u5206\u6570\u4e4b\u95f4\u7684\u5dee\u8ddd\u3002\u635f\u5931\u51fd\u6570\u53ef\u4ee5\u8868\u793a\u4e3a\uff1a
\\[ L(W) = \\frac{1}{N} \\sum_{i=1}^{N} \\sum_{j \\neq y_i} \\max(0, \\text{score}_j - \\text{score}_{y_i} + \\Delta) \\] \u5176\u4e2d\uff0c\\(\\text{score}_j = W_j \\cdot x_i\\)\uff0c\\(\\Delta\\) \u662f\u4e00\u4e2a\u5e38\u6570\uff0c\u901a\u5e38\u8bbe\u7f6e\u4e3a1\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#softmax","title":"Softmax\u5206\u7c7b\u5668","text":"Softmax\u5206\u7c7b\u5668\u4f7f\u7528Softmax\u51fd\u6570\u5c06\u8f93\u5165\u7279\u5f81\u6620\u5c04\u5230\u6982\u7387\u5206\u5e03\u4e0a\u3002\u5bf9\u4e8e\u6bcf\u4e2a\u6837\u672c\uff0cSoftmax\u51fd\u6570\u8f93\u51fa\u6bcf\u4e2a\u7c7b\u522b\u7684\u6982\u7387\u3002Softmax\u51fd\u6570\u5b9a\u4e49\u4e3a\uff1a
\\[ \\text{softmax}(z_i) = \\frac{e^{z_i}}{\\sum_{j=1}^{K} e^{z_j}} \\] \u5176\u4e2d\uff0c\\(z_i\\) \u662f\u7b2c\\(i\\)\u4e2a\u7c7b\u522b\u7684\u5206\u6570\uff0c\\(K\\) \u662f\u7c7b\u522b\u603b\u6570\u3002
Softmax\u5206\u7c7b\u5668\u7684\u635f\u5931\u51fd\u6570\u662f\u4ea4\u53c9\u71b5\u635f\u5931\uff0c\u53ef\u4ee5\u8868\u793a\u4e3a\uff1a
\\[ L(W) = -\\frac{1}{N} \\sum_{i=1}^{N} \\sum_{j=1}^{K} y_{ij} \\log(\\text{softmax}(z_j)) \\] \u5176\u4e2d\uff0c\\(y_{ij}\\) \u662f\u4e00\u4e2a\u6307\u793a\u53d8\u91cf\uff0c\u5982\u679c\u6837\u672c\\(i\\)\u5c5e\u4e8e\u7c7b\u522b\\(j\\)\uff0c\u5219\u4e3a1\uff0c\u5426\u5219\u4e3a0\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#_2","title":"\u6b63\u5219\u5316","text":"\u4e3a\u4e86\u9632\u6b62\u8fc7\u62df\u5408\uff0c\u6211\u4eec\u5728\u635f\u5931\u51fd\u6570\u4e2d\u6dfb\u52a0\u4e86\u6b63\u5219\u5316\u9879\u3002L2\u6b63\u5219\u5316\u7684\u635f\u5931\u51fd\u6570\u53ef\u4ee5\u8868\u793a\u4e3a\uff1a
\\[ L(W) = L(W) + \\lambda \\lVert W \\rVert^2 \\] \u5176\u4e2d\uff0c\\(\\lambda\\) \u662f\u6b63\u5219\u5316\u5f3a\u5ea6\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#_3","title":"\u4ee3\u7801\u5b9e\u73b0","text":"\u4ee3\u7801\u4e2d\u5b9e\u73b0\u4e86\u4e24\u79cd\u635f\u5931\u51fd\u6570\u7684\u6734\u7d20\u7248\u672c\uff08svm_loss_naive \u548c softmax_loss_naive\uff09\u548c\u5411\u91cf\u5316\u7248\u672c\uff08svm_loss_vectorized \u548c softmax_loss_vectorized\uff09\u3002\u5411\u91cf\u5316\u7248\u672c\u901a\u8fc7\u907f\u514d\u663e\u5f0f\u5faa\u73af\u6765\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3002
\u8bad\u7ec3\u8fc7\u7a0b\uff08train_linear_classifier\uff09\u4f7f\u7528\u968f\u673a\u68af\u5ea6\u4e0b\u964d\uff08SGD\uff09\u6765\u4f18\u5316\u635f\u5931\u51fd\u6570\u3002\u5728\u6bcf\u6b21\u8fed\u4ee3\u4e2d\uff0c\u6211\u4eec\u968f\u673a\u62bd\u53d6\u4e00\u4e2a\u6279\u6b21\u7684\u6837\u672c\uff0c\u8ba1\u7b97\u635f\u5931\u548c\u68af\u5ea6\uff0c\u7136\u540e\u66f4\u65b0\u6743\u91cd\u3002
\u9884\u6d4b\u8fc7\u7a0b\uff08predict_linear_classifier\uff09\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6743\u91cd\u6765\u9884\u6d4b\u65b0\u6837\u672c\u7684\u7c7b\u522b\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#_4","title":"\u8d85\u53c2\u6570\u641c\u7d22","text":"\u4ee3\u7801\u4e2d\u8fd8\u5305\u542b\u4e86\u8d85\u53c2\u6570\u641c\u7d22\u7684\u51fd\u6570\uff08svm_get_search_params \u548c softmax_get_search_params\uff09\uff0c\u5b83\u4eec\u8fd4\u56de\u4e0d\u540c\u7684\u5b66\u4e60\u7387\u548c\u6b63\u5219\u5316\u5f3a\u5ea6\u7684\u5019\u9009\u503c\uff0c\u4ee5\u4fbf\u627e\u5230\u6700\u4f73\u7684\u6a21\u578b\u53c2\u6570\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#_5","title":"\u4f5c\u4e1a\u5b9e\u73b0","text":"Pythonimport torch\nimport random\nfrom abc import abstractmethod\n\ndef hello_linear_classifier():\n print('Hello from linear_classifier.py!')\n\nclass LinearClassifier(object):\n def __init__(self):\n random.seed(0)\n torch.manual_seed(0)\n self.W = None\n\n def train(self, X_train, y_train, learning_rate=1e-3, reg=1e-5, num_iters=100,\n batch_size=200, verbose=False):\n train_args = (self.loss, self.W, X_train, y_train, learning_rate, reg,\n num_iters, batch_size, verbose)\n self.W, loss_history = train_linear_classifier(*train_args)\n return loss_history\n\n def predict(self, X):\n return predict_linear_classifier(self.W, X)\n\n @abstractmethod\n def loss(self, W, X_batch, y_batch, reg):\n raise NotImplementedError\n\n def _loss(self, X_batch, y_batch, reg):\n self.loss(self.W, X_batch, y_batch, reg)\n\n def save(self, path):\n torch.save({'W': self.W}, path)\n print(\"Saved in {}\".format(path))\n\n def load(self, path):\n W_dict = torch.load(path, map_location='cpu')\n self.W = W_dict['W']\n print(\"load checkpoint file: {}\".format(path))\n\n\nclass LinearSVM(LinearClassifier):\n def loss(self, W, X_batch, y_batch, reg):\n return svm_loss_vectorized(W, X_batch, y_batch, reg)\n\n\nclass Softmax(LinearClassifier):\n def loss(self, W, X_batch, y_batch, reg):\n return softmax_loss_vectorized(W, X_batch, y_batch, reg)\n\n\ndef svm_loss_naive(W, X, y, reg):\n dW = torch.zeros_like(W)\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in range(num_train):\n scores = W.t().mv(X[i])\n correct_class_score = scores[y[i]]\n for j in range(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1\n if margin > 0:\n loss += margin\n dW[:, j] += X[i]\n dW[:, y[i]] -= X[i]\n\n loss /= num_train\n loss += reg * torch.sum(W * W)\n dW /= num_train\n dW += 2 * reg * W\n\n return loss, dW\n\n\ndef svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = torch.zeros_like(W)\n num_classes = W.shape[1]\n num_train = X.shape[0]\n scores = X.mm(W)\n correct_class_score = scores[range(num_train), y]\n margin = scores - correct_class_score.view(-1, 1) + 1\n margin[range(num_train), y] = 0\n mask = (margin > 0)\n loss = margin[mask].sum()\n mask_correct_y = torch.zeros_like(scores, dtype=torch.bool)\n mask_correct_y[range(num_train), y] = True\n margin[margin > 0] = 1\n margin[margin < 0] = 0\n margin[mask_correct_y] = torch.sum(margin, axis=1) * -1\n dW = margin.T.mm(X).T\n loss /= num_train\n dW /= num_train\n loss += reg * torch.sum(W * W)\n dW += 2 * reg * W\n\n return loss, dW\n\n\ndef sample_batch(X, y, num_train, batch_size):\n indices = torch.randint(num_train, (batch_size,))\n y_batch = y[indices]\n X_batch = X[indices]\n return X_batch, y_batch\n\n\ndef train_linear_classifier(loss_func, W, X, y, learning_rate=1e-3,\n reg=1e-5, num_iters=100, batch_size=200,\n verbose=False):\n num_train, dim = X.shape\n if W is None:\n num_classes = torch.max(y) + 1\n W = 0.000001 * torch.randn(dim, num_classes, device=X.device, dtype=X.dtype)\n else:\n num_classes = W.shape[1]\n\n loss_history = []\n for it in range(num_iters):\n X_batch, y_batch = sample_batch(X, y, num_train, batch_size)\n loss, grad = loss_func(W, X_batch, y_batch, reg)\n loss_history.append(loss.item())\n W -= learning_rate * grad\n if verbose and it % 100 == 0:\n print('iteration %d / %d: loss %f' % (it, num_iters, loss))\n\n return W, loss_history\n\n\ndef predict_linear_classifier(W, X):\n y_pred = torch.zeros(X.shape[0], dtype=torch.int64)\n _, y_pred = X.mm(W).max(dim=1)\n return y_pred\n\n\ndef svm_get_search_params():\n learning_rates = [0.000001, 0.0001, 0.001, 0.005, 0.01, 0.05]\n regularization_strengths = [0.001, 0.5, 1, 3]\n return learning_rates, regularization_strengths\n\n\ndef test_one_param_set(cls, data_dict, lr, reg, num_iters=2000):\n train_acc = 0.0\n val_acc = 0.0\n cls.train(data_dict['X_train'], data_dict['y_train'], lr, reg, num_iters,\n batch_size=200, verbose=False)\n y_train_pred = cls.predict(data_dict['X_train'])\n train_acc = 100.0 * (data_dict['y_train'] == y_train_pred).double().mean().item()\n\n y_test_pred = cls.predict(data_dict['X_val'])\n val_acc = 100.0 * (data_dict['y_val'] == y_test_pred).double().mean().item()\n\n return cls, train_acc, val_acc\n\n\ndef softmax_loss_naive(W, X, y, reg):\n loss = 0.0\n dW = torch.zeros_like(W)\n num_classes = W.shape[1]\n num_train = X.shape[0]\n\n scores = W.t().mv(X[0])\n correct_class_score = scores[y[0]]\n\n for i in range(num_train):\n scores = W.t().mv(X[i])\n scores = scores - scores.max()\n correct_class_score = scores[y[i]]\n loss += -correct_class_score + torch.log(torch.exp(scores).sum())\n for j in range(num_classes):\n if j == y[i]:\n dW[:, j] += torch.exp(scores[j]) / torch.exp(scores).sum() * X[i, :] - X[i, :]\n else:\n dW[:, j] += torch.exp(scores[j]) / torch.exp(scores).sum() * X[i, :]\n\n loss /= num_train\n loss += reg * torch.sum(W * W)\n dW /= num_train\n dW += 2 * reg * W\n\n return loss, dW\n\n\ndef softmax_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = torch.zeros_like(W)\n num_classes = W.shape[1]\n num_train = X.shape[0]\n\n\n scores = X.mm(W)\n val, _ = scores.max(dim=1)\n scores = scores - val.view(-1, 1)\n exp_scores = scores.exp()\n exp_scores_sum = exp_scores.sum(dim=1)\n exp_scores_sum_log = exp_scores_sum.log()\n correct_class_scores = scores[range(num_train), y]\n loss = (exp_scores_sum_log - correct_class_scores).sum()\n zeros = torch.zeros((num_train, num_classes), dtype=torch.float64, device='cuda')\n zeros[range(num_train), y] = -1\n minus_X = zeros.t().mm(X)\n dW += minus_X.t()\n dW += ((exp_scores / exp_scores_sum.view(-1, 1)).t().mm(X)).t()\n\n loss /= num_train\n loss += reg * torch.sum(W * W)\n dW /= num_train\n dW += 2 * reg * W\n\n return loss, dW\n\n\ndef softmax_get_search_params():\n learning_rates = [1e-4, 1e-3,1e-2, 1e-1, 1]\n regularization_strengths = [1e-4, 1e-3, 1e-2, 1e-1] \n return learning_rates, regularization_strengths\n
"},{"location":"AI/FFB6D/FFB6D_Conda/","title":"FFB6D\u73af\u5883\u914d\u7f6e\u6307\u5357\uff1a\u539f\u751f\u7cfb\u7edf\u5b89\u88c5","text":""},{"location":"AI/FFB6D/FFB6D_Conda/#ffb6d","title":"FFB6D\u73af\u5883\u914d\u7f6e\u6307\u5357\uff1a\u539f\u751f\u7cfb\u7edf\u5b89\u88c5","text":" \u7ea6 293 \u4e2a\u5b57 96 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 3 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/FFB6D/FFB6D_Conda/#1","title":"1. \u7cfb\u7edf\u8981\u6c42","text":" - Ubuntu 20.04/22.04/24.04
- NVIDIA GPU\uff08\u652f\u6301CUDA\uff09
- \u81f3\u5c118GB\u5185\u5b58
- \u81f3\u5c1130GB\u78c1\u76d8\u7a7a\u95f4
"},{"location":"AI/FFB6D/FFB6D_Conda/#2","title":"2. \u57fa\u7840\u73af\u5883\u914d\u7f6e","text":""},{"location":"AI/FFB6D/FFB6D_Conda/#21-nvidia","title":"2.1 \u5b89\u88c5NVIDIA\u9a71\u52a8","text":"Bash# \u6dfb\u52a0NVIDIA\u5305\u4ed3\u5e93\nsudo add-apt-repository ppa:graphics-drivers/ppa\nsudo apt-get update\n\n# \u5b89\u88c5NVIDIA\u9a71\u52a8\nsudo apt-get install -y nvidia-driver-535 # \u6839\u636e\u9700\u8981\u9009\u62e9\u7248\u672c\n\n# \u91cd\u542f\u7cfb\u7edf\nsudo reboot\n\n# \u9a8c\u8bc1\u5b89\u88c5\nnvidia-smi\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#22-cudacudnn","title":"2.2 \u5b89\u88c5CUDA\u548ccuDNN","text":"Bash# \u4e0b\u8f7d\u5e76\u5b89\u88c5CUDA 11.0\nwget https://developer.download.nvidia.com/compute/cuda/11.0.3/local_installers/cuda_11.0.3_450.51.06_linux.run\nsudo sh cuda_11.0.3_450.51.06_linux.run\n\n# \u914d\u7f6e\u73af\u5883\u53d8\u91cf\necho 'export PATH=/usr/local/cuda-11.0/bin:$PATH' >> ~/.bashrc\necho 'export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc\nsource ~/.bashrc\n\n# \u4e0b\u8f7d\u5e76\u5b89\u88c5cuDNN 8.0\n# \u6ce8\uff1a\u9700\u8981\u4eceNVIDIA\u5f00\u53d1\u8005\u7f51\u7ad9\u4e0b\u8f7dcuDNN v8.0\uff0c\u89e3\u538b\u540e\uff1a\nsudo cp cuda/include/cudnn*.h /usr/local/cuda/include\nsudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64\nsudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#23","title":"2.3 \u5b89\u88c5\u7cfb\u7edf\u4f9d\u8d56","text":"Bashsudo apt-get update\nsudo apt-get install -y \\\n python3.6 \\\n python3.6-dev \\\n python3-pip \\\n git \\\n cmake \\\n build-essential \\\n libopencv-dev \\\n libglib2.0-0 \\\n libsm6 \\\n libxext6 \\\n libxrender-dev \\\n libboost-all-dev \\\n libeigen3-dev\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#24-python","title":"2.4 \u914d\u7f6ePython\u73af\u5883","text":"Bash# \u8bbe\u7f6ePython 3.6\u4e3a\u9ed8\u8ba4\u7248\u672c\nsudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 1\nsudo update-alternatives --set python3 /usr/bin/python3.6\n\n# \u914d\u7f6epip\u955c\u50cf\u6e90\npip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple\n\n# \u5347\u7ea7pip\npython3 -m pip install --upgrade pip\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#3-pytorch","title":"3. \u5b89\u88c5PyTorch\u548c\u4f9d\u8d56\u5305","text":""},{"location":"AI/FFB6D/FFB6D_Conda/#31-pytorch","title":"3.1 \u5b89\u88c5PyTorch","text":"Bashpip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 -f https://download.pytorch.org/whl/torch_stable.html\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#32","title":"3.2 \u5b89\u88c5\u9879\u76ee\u4f9d\u8d56","text":"\u521b\u5efarequirements.txt\u5e76\u5b89\u88c5\u4f9d\u8d56\uff1a
Bashpip3 install -r requirements.txt\n
requirements.txt\u5185\u5bb9\uff1a
Text Onlyh5py \nnumpy \npyyaml==5.4.1\nenum34 \nfuture \nscipy==1.4.1 \nopencv_contrib_python==3.4.2.16 \ntransforms3d==0.3.1 \nscikit_image==0.13.1 \nlmdb==0.94 \nsetuptools==41.0.0 \ncffi==1.11.5 \neasydict==1.7 \nplyfile==0.6 \npillow==8.2.0 \ndataclasses\nglumpy \ntqdm\ntensorboardX \npandas\nscikit-learn \nscipy \ntermcolor\npybind11\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#4","title":"4. \u7f16\u8bd1\u548c\u5b89\u88c5\u7279\u6b8a\u7ec4\u4ef6","text":""},{"location":"AI/FFB6D/FFB6D_Conda/#41-apex","title":"4.1 \u7f16\u8bd1apex","text":"Bashgit clone https://github.com/NVIDIA/apex\ncd apex\nexport TORCH_CUDA_ARCH_LIST=\"6.0;6.1;6.2;7.0;7.5\"\npython setup.py install -v\ncd ..\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#42-normalspeed","title":"4.2 \u5b89\u88c5\u548c\u7f16\u8bd1normalspeed","text":"Bash# 1. \u51c6\u5907OpenCV\u73af\u5883\npip uninstall opencv-python opencv-python-headless -y\npip install opencv-python==4.5.3.56\n\n# 2. \u514b\u9686\u5e76\u5b89\u88c5normalspeed\ngit clone https://github.com/hfutcgncas/normalspeed.git\ncd normalspeed/normalSpeed\n\n# \u5b89\u88c5\u7f16\u8bd1\u4f9d\u8d56\nsudo apt-get install python3-pybind11\npip3 install Cython==0.29.15\n\n# \u6e05\u7406\u5e76\u91cd\u65b0\u5b89\u88c5\nrm -rf build/ dist/ *.egg-info/\npython3 setup.py install --user\ncd ../..\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#5-ffb6d","title":"5. \u514b\u9686\u548c\u914d\u7f6eFFB6D","text":"Bash# \u514b\u9686\u4ee3\u7801\ngit clone https://github.com/ethnhe/FFB6D.git\ncd FFB6D\n\n# \u521b\u5efa\u5fc5\u8981\u7684\u76ee\u5f55\nmkdir -p datasets models train_log\n\n# \u914d\u7f6e\u73af\u5883\u53d8\u91cf\nexport PYTHONPATH=$PYTHONPATH:$(pwd)\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#6","title":"6. \u9a8c\u8bc1\u5b89\u88c5","text":"Bash# \u9a8c\u8bc1CUDA\u652f\u6301\npython3 -c \"import torch; print('CUDA available:', torch.cuda.is_available())\"\n\n# \u9a8c\u8bc1apex\u5b89\u88c5\npython3 -c \"from apex import amp; print('APEX installed')\"\n\n# \u9a8c\u8bc1normalspeed\u5b89\u88c5\npython3 -c \"import normalSpeed; print('normalspeed installed')\"\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#7","title":"7. \u5e38\u89c1\u95ee\u9898","text":""},{"location":"AI/FFB6D/FFB6D_Conda/#71","title":"7.1 \u7f51\u7edc\u95ee\u9898","text":"Bash# \u4f7f\u7528\u4ee3\u7406\uff08\u5982\u9700\u8981\uff09\nexport http_proxy=\"http://proxy:port\"\nexport https_proxy=\"http://proxy:port\"\n\n# \u6216\u4f7f\u7528\u56fd\u5185\u955c\u50cf\u6e90\npip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#72","title":"7.2 \u7248\u672c\u517c\u5bb9\u6027\u95ee\u9898","text":" - \u786e\u4fddNVIDIA\u9a71\u52a8\u7248\u672c\u652f\u6301CUDA 11.0
- \u786e\u4fddPython\u5305\u7248\u672c\u76f8\u4e92\u517c\u5bb9
- \u68c0\u67e5CUDA\u7248\u672c\u4e0ePyTorch\u7248\u672c\u7684\u5339\u914d
"},{"location":"AI/FFB6D/FFB6D_Conda/#73","title":"7.3 \u7f16\u8bd1\u9519\u8bef","text":" - \u786e\u4fdd\u5df2\u5b89\u88c5\u6240\u6709\u5fc5\u8981\u7684\u7f16\u8bd1\u5de5\u5177
- \u68c0\u67e5CUDA\u8def\u5f84\u914d\u7f6e\u662f\u5426\u6b63\u786e
- \u786e\u8ba4\u7cfb\u7edf\u5e93\u7248\u672c\u662f\u5426\u6ee1\u8db3\u8981\u6c42
"},{"location":"AI/FFB6D/FFB6D_Conda/#8","title":"8. \u8bad\u7ec3","text":"\u6309\u7167\u5b98\u65b9\u6587\u6863\u914d\u7f6eLineMOD\u6570\u636e\u96c6\u5e76\u5f00\u59cb\u8bad\u7ec3\u3002
"},{"location":"AI/FFB6D/FFB6D_Conda/#_1","title":"\u53c2\u8003\u8d44\u6599","text":" - FFB6D\u9879\u76ee
- CUDA\u5b89\u88c5\u6307\u5357
- PyTorch\u5b89\u88c5\u6307\u5357
- \u3010\u8bba\u6587\u7b14\u8bb0\u3011FFB6D | \u9a6c\u6d69\u98de\u4e28\u535a\u5ba2
"},{"location":"AI/FFB6D/FFB6D_Docker/","title":"Docker\u4ece\u5165\u95e8\u5230\u5b9e\u8df5\uff1a\u4ee5FFB6D\u73af\u5883\u914d\u7f6e\u4e3a\u4f8b","text":""},{"location":"AI/FFB6D/FFB6D_Docker/#dockerffb6d","title":"Docker\u4ece\u5165\u95e8\u5230\u5b9e\u8df5\uff1a\u4ee5FFB6D\u73af\u5883\u914d\u7f6e\u4e3a\u4f8b","text":" \u7ea6 653 \u4e2a\u5b57 213 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 6 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/FFB6D/FFB6D_Docker/#1","title":"1. \u7b80\u4ecb","text":"Docker\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u5e94\u7528\u5bb9\u5668\u5f15\u64ce\uff0c\u8ba9\u5f00\u53d1\u8005\u53ef\u4ee5\u6253\u5305\u4ed6\u4eec\u7684\u5e94\u7528\u4ee5\u53ca\u4f9d\u8d56\u5305\u5230\u4e00\u4e2a\u53ef\u79fb\u690d\u7684\u5bb9\u5668\u4e2d\uff0c\u7136\u540e\u53d1\u5e03\u5230\u4efb\u4f55\u6d41\u884c\u7684Linux\u6216Windows\u64cd\u4f5c\u7cfb\u7edf\u4e0a\u3002\u672c\u6587\u5c06\u4ee5\u914d\u7f6eFFB6D\uff08\u4e00\u4e2a3D\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\uff09\u7684\u8fd0\u884c\u73af\u5883\u4e3a\u4f8b\uff0c\u4ecb\u7ecdDocker\u7684\u57fa\u672c\u4f7f\u7528\u3002
"},{"location":"AI/FFB6D/FFB6D_Docker/#2","title":"2. \u73af\u5883\u51c6\u5907","text":""},{"location":"AI/FFB6D/FFB6D_Docker/#21","title":"2.1 \u7cfb\u7edf\u8981\u6c42","text":" - Ubuntu 20.04/22.04/24.04
- NVIDIA GPU\uff08\u652f\u6301CUDA\uff09
- \u81f3\u5c118GB\u5185\u5b58
- \u81f3\u5c1130GB\u78c1\u76d8\u7a7a\u95f4
"},{"location":"AI/FFB6D/FFB6D_Docker/#22","title":"2.2 \u57fa\u7840\u7ec4\u4ef6\u5b89\u88c5","text":"\u5b89\u88c5Docker
Bash# \u66f4\u65b0apt\u5305\u7d22\u5f15\nsudo apt-get update\n\n# \u5b89\u88c5\u5fc5\u8981\u7684\u7cfb\u7edf\u5de5\u5177\nsudo apt-get install -y \\\n apt-transport-https \\\n ca-certificates \\\n curl \\\n gnupg \\\n lsb-release\n\n# \u6dfb\u52a0Docker\u7684\u5b98\u65b9GPG\u5bc6\u94a5\ncurl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg\n\n# \u8bbe\u7f6e\u7a33\u5b9a\u7248\u4ed3\u5e93\necho \\\n \"deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \\\n $(lsb_release -cs) stable\" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null\n\n# \u5b89\u88c5Docker Engine\nsudo apt-get update\nsudo apt-get install -y docker-ce docker-ce-cli containerd.io\n\n# \u9a8c\u8bc1\u5b89\u88c5\nsudo docker run hello-world\n
\u5b89\u88c5NVIDIA\u9a71\u52a8
Bash# \u6dfb\u52a0NVIDIA\u5305\u4ed3\u5e93\nsudo add-apt-repository ppa:graphics-drivers/ppa\nsudo apt-get update\n\n# \u5b89\u88c5NVIDIA\u9a71\u52a8\nsudo apt-get install -y nvidia-driver-535 # \u6839\u636e\u9700\u8981\u9009\u62e9\u7248\u672c\n\n# \u91cd\u542f\u7cfb\u7edf\nsudo reboot\n\n# \u9a8c\u8bc1\u5b89\u88c5\nnvidia-smi\n
\u5b89\u88c5NVIDIA Container Toolkit
Bash# \u8bbe\u7f6e\u7a33\u5b9a\u7248\u4ed3\u5e93\ncurl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg\n\necho \"deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/stable/ubuntu22.04/$(arch) /\" | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list\n\n# \u66f4\u65b0\u8f6f\u4ef6\u5305\u5217\u8868\nsudo apt-get update\n\n# \u5b89\u88c5nvidia-docker2\nsudo apt-get install -y nvidia-container-toolkit\n\n# \u91cd\u542fDocker\u670d\u52a1\nsudo systemctl restart docker\n\n# \u6d4b\u8bd5GPU\u652f\u6301\nsudo docker run --rm --gpus all nvidia/cuda:12.0-base nvidia-smi\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#23-docker","title":"2.3 Docker\u914d\u7f6e\u4f18\u5316","text":"\u914d\u7f6e\u955c\u50cf\u52a0\u901f
Bashsudo tee /etc/docker/daemon.json << EOF\n{\n \"registry-mirrors\": [\n \"https://docker.1panel.dev\",\n \"https://docker.zhai.cm\",\n \"https://hub.littlediary.cn\",\n \"https://docker.nastool.de\"\n ],\n \"dns\": [\"8.8.8.8\", \"8.8.4.4\"],\n \"max-concurrent-downloads\": 10,\n \"log-driver\": \"json-file\",\n \"log-opts\": {\n \"max-size\": \"10m\",\n \"max-file\": \"3\"\n }\n}\nEOF\n\nsudo systemctl daemon-reload\nsudo systemctl restart docker\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#3-ffb6d","title":"3. FFB6D\u73af\u5883\u914d\u7f6e\u5b9e\u4f8b","text":""},{"location":"AI/FFB6D/FFB6D_Docker/#31","title":"3.1 \u9879\u76ee\u7ed3\u6784","text":"Bashffb6d_docker/\n\u251c\u2500\u2500 Dockerfile\n\u251c\u2500\u2500 docker-compose.yml\n\u251c\u2500\u2500 build_and_run.sh\n\u251c\u2500\u2500 downloads/\n\u2502 \u251c\u2500\u2500 apex/\n\u2502 \u2514\u2500\u2500 normalspeed/\n\u251c\u2500\u2500 code/\n\u251c\u2500\u2500 datasets/\n\u251c\u2500\u2500 models/\n\u2514\u2500\u2500 train_log/\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#32-dockerfile","title":"3.2 \u521b\u5efaDockerfile","text":"Docker# \u4f7f\u7528\u8f83\u65b0\u7684 CUDA \u955c\u50cf\nFROM nvcr.io/nvidia/cuda:11.0.3-cudnn8-devel-ubuntu18.04\n\n# \u4f7f\u7528 NVIDIA CUDA 11.3 \u57fa\u7840\u955c\u50cf\n# FROM nvcr.io/nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04\n\n# \u907f\u514d\u4ea4\u4e92\u5f0f\u63d0\u793a\nENV DEBIAN_FRONTEND=noninteractive\nENV PYTHONPATH=/workspace/code\nENV CUDA_HOME=/usr/local/cuda\nENV PATH=$CUDA_HOME/bin:$PATH\nENV LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH\n\nWORKDIR /workspace\n\n# \u5b89\u88c5\u7cfb\u7edf\u4f9d\u8d56\nRUN apt-get update && apt-get install -y \\\n python3.6 \\\n python3.6-dev \\\n python3-pip \\\n git \\\n cmake \\\n build-essential \\\n libopencv-dev \\\n libglib2.0-0 \\\n libsm6 \\\n libxext6 \\\n libxrender-dev \\\n libboost-all-dev \\\n libeigen3-dev \\\n wget \\\n && rm -rf /var/lib/apt/lists/*\n\n# \u8bbe\u7f6e Python 3.6 \u4e3a\u9ed8\u8ba4\u7248\u672c\nRUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 1\nRUN update-alternatives --set python3 /usr/bin/python3.6\n\n# \u5347\u7ea7 pip\nRUN python3 -m pip install --upgrade pip\n\n# \u914d\u7f6epip\u955c\u50cf\u6e90\nRUN pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple\n\n# \u5148\u5b89\u88c5 PyTorch (\u4f7f\u7528\u8f83\u65b0\u4f46\u517c\u5bb9\u7684\u7248\u672c)\nRUN pip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 -f https://download.pytorch.org/whl/torch_stable.html\n\n# \u590d\u5236\u9884\u4e0b\u8f7d\u7684\u6587\u4ef6\nCOPY downloads/apex /workspace/apex\nCOPY downloads/normalspeed /workspace/normalspeed\nCOPY code /workspace/code\n\n# \u5b89\u88c5 apex\n#RUN cd /workspace/apex && \\\n# pip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" ./ || \\\n# (echo \"Apex installation failed. Check the error messages above.\" && exit 1)\n\n# \u5b89\u88c5 normalspeed\n#RUN cd /workspace/normalspeed && \\\n# python3 setup.py build_ext --inplace && \\\n# python3 setup.py install\n\nWORKDIR /workspace/code\n
\u66f4\u6539 requirement.txt
Text Onlyh5py \nnumpy \npyyaml==5.4.1\nenum34 \nfuture \nscipy==1.4.1 \nopencv_contrib_python==3.4.2.16 \ntransforms3d==0.3.1 \nscikit_image==0.13.1 \nlmdb==0.94 \nsetuptools==41.0.0 \ncffi==1.11.5 \neasydict==1.7 \nplyfile==0.6 \npillow==8.2.0 \ndataclasses\nglumpy \ntqdm\ntensorboardX \npandas\nscikit-learn \nscipy \ntermcolor\npybind11\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#33-docker-composeyml","title":"3.3 \u521b\u5efadocker-compose.yml","text":"YAMLversion: '3'\nservices:\n ffb6d:\n build: .\n image: ffb6d:latest\n container_name: ffb6d_container\n runtime: nvidia\n environment:\n - NVIDIA_VISIBLE_DEVICES=all\n volumes:\n - ./datasets:/workspace/code/datasets\n - ./train_log:/workspace/code/train_log\n - ./models:/workspace/code/models\n shm_size: '8gb'\n tty: true\n stdin_open: true\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#34","title":"3.4 \u6784\u5efa\u548c\u8fd0\u884c\u811a\u672c","text":"\u521b\u5efabuild_and_run.sh\uff1a
Bash#!/bin/bash\n\n# \u521b\u5efa\u5fc5\u8981\u76ee\u5f55\nmkdir -p downloads datasets models train_log\n\n# \u4e0b\u8f7d\u4f9d\u8d56\ncd downloads\nif [ ! -d \"apex\" ]; then\n git clone https://github.com/NVIDIA/apex.git\nfi\nif [ ! -d \"normalspeed\" ]; then\n git clone https://github.com/hfutcgncas/normalspeed.git\nfi\ncd ..\n\n# \u514b\u9686FFB6D\u4ee3\u7801\nif [ ! -d \"code\" ]; then\n git clone https://github.com/ethnhe/FFB6D.git code\nfi\n\n# \u6784\u5efa\u955c\u50cf\ndocker-compose build\n\n# \u8fd0\u884c\u5bb9\u5668\ndocker-compose up -d\ndocker exec -it ffb6d_container bash\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#35","title":"3.5 \u542f\u52a8\u548c\u9a8c\u8bc1","text":"Bash# \u6dfb\u52a0\u6267\u884c\u6743\u9650\nchmod +x build_and_run.sh\n\n# \u8fd0\u884c\n./build_and_run.sh\n\n# \u5728\u5bb9\u5668\u5185\u9a8c\u8bc1\npython3 -c \"import torch; print('CUDA available:', torch.cuda.is_available())\"\npython3 -c \"from apex import amp; print('APEX installed')\"\npython3 -c \"import normalspeed; print('normalspeed installed')\"\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#36-apex","title":"3.6 \u7f16\u8bd1 apex","text":"Text Onlygit clone https://github.com/NVIDIA/apex\ncd apex\nexport TORCH_CUDA_ARCH_LIST=\"6.0;6.1;6.2;7.0;7.5\"\npython setup.py install -v\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#37-normalspeed","title":"3.7 \u7f16\u8bd1 normalspeed","text":"Text Only# 1. \u5378\u8f7d\u5f53\u524dcv2\npip uninstall opencv-python opencv-python-headless -y\n\n# 2. \u5b89\u88c5\u7279\u5b9a\u7248\u672c\u7684OpenCV\uff0c\u9009\u62e9\u4e0ePython 3.6\u517c\u5bb9\u7684\u7248\u672c\npip install opencv-python==4.5.3.56\n\n# 3. \u9a8c\u8bc1\u5b89\u88c5\npython3 -c \"import cv2; print(cv2.__version__)\"\n
Text Only# \u8fdb\u5165normalSpeed\u76ee\u5f55\ncd /workspace/code/normalspeed/normalSpeed\n\n# \u5b89\u88c5\u4f9d\u8d56\napt-get update\napt-get install python3-pybind11\npip3 install Cython==0.29.15\n\n# \u6e05\u7406\u4e4b\u524d\u7684\u6784\u5efa\nrm -rf build/\nrm -rf dist/\nrm -rf *.egg-info/\n\n# \u91cd\u65b0\u5b89\u88c5\npython3 setup.py install\n\n# \u9a8c\u8bc1\u5b89\u88c5\npython3 -c \"import normalSpeed\"\n\n# \u8fd4\u56deffb6d\u76ee\u5f55\ncd /workspace/code/ffb6d/\n
Text Onlypython3 setup.py install --user\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#38","title":"3.8 \u8bad\u7ec3","text":"\u628a LineMOD \u590d\u5236\u5230\u5bf9\u5e94\u7684\u8def\u5f84\u4e0b\uff0c\u7136\u540e\u6309\u5b98\u7f51\u6765\u5c31\u597d\u4e86
"},{"location":"AI/FFB6D/FFB6D_Docker/#4","title":"4. \u5e38\u89c1\u95ee\u9898","text":""},{"location":"AI/FFB6D/FFB6D_Docker/#41","title":"4.1 \u7f51\u7edc\u95ee\u9898","text":"\u5982\u679c\u9047\u5230\u4e0b\u8f7d\u6162\u6216\u5931\u8d25\uff1a
Bash# \u4e34\u65f6\u4f7f\u7528\u4ee3\u7406\nexport http_proxy=\"http://proxy:port\"\nexport https_proxy=\"http://proxy:port\"\n\n# \u6216\u4fee\u6539pip\u6e90\npip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple\n
\u6ce8\u610f\u7528\u4e86\u4ee3\u7406\u7684\u8bdd\uff0cdocker hub \u955c\u50cf\u5c31\u4f1a\u88ab\u8986\u76d6\u6389\uff0c\u5bfc\u81f4\u901f\u5ea6\u5f88\u6162\u3002\u6240\u4ee5\u5230\u65f6\u5019\u4e00\u5b9a\u8981\u53d6\u6d88\u4ee3\u7406 / \u5220\u9664\u4ee3\u7406\u6587\u4ef6
docker hub \u955c\u50cf\u8bbe\u7f6e\u4e0a\u9762\u5199\u4e86\uff0c\u5b98\u7f51\u662f\u8fd9\u4e2a GitHub - dongyubin/DockerHub: 2024\u5e7411\u6708\u66f4\u65b0\uff0c\u76ee\u524d\u56fd\u5185\u53ef\u7528Docker\u955c\u50cf\u6e90\u6c47\u603b\uff0cDockerHub\u56fd\u5185\u955c\u50cf\u52a0\u901f\u5217\u8868\uff0c\ud83d\ude80DockerHub\u955c\u50cf\u52a0\u901f\u5668
"},{"location":"AI/FFB6D/FFB6D_Docker/#42-cuda","title":"4.2 CUDA\u517c\u5bb9\u6027","text":"\u786e\u4fddNVIDIA\u9a71\u52a8\u7248\u672c\u652f\u6301\u6240\u9700\u7684CUDA\u7248\u672c\uff1a
Bash# \u68c0\u67e5\u652f\u6301\u7684\u6700\u9ad8CUDA\u7248\u672c\nnvidia-smi\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#43","title":"4.3 \u5185\u5b58\u4e0d\u8db3","text":"\u8c03\u6574Docker\u5185\u5b58\u9650\u5236\uff1a
YAML# \u5728docker-compose.yml\u4e2d\u6dfb\u52a0\nservices:\n ffb6d:\n deploy:\n resources:\n limits:\n memory: 16G\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#5","title":"5. \u603b\u7ed3","text":"\u672c\u6587\u4ecb\u7ecd\u4e86\u4f7f\u7528Docker\u914d\u7f6e\u6df1\u5ea6\u5b66\u4e60\u73af\u5883\u7684\u5b8c\u6574\u6d41\u7a0b\uff0c\u4ee5FFB6D\u4e3a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5904\u7406\u590d\u6742\u4f9d\u8d56\u5173\u7cfb\u3002\u901a\u8fc7\u5bb9\u5668\u5316\u6280\u672f\uff0c\u6211\u4eec\u53ef\u4ee5\uff1a
- \u786e\u4fdd\u73af\u5883\u4e00\u81f4\u6027
- \u7b80\u5316\u90e8\u7f72\u6d41\u7a0b
- \u63d0\u9ad8\u5f00\u53d1\u6548\u7387
- \u65b9\u4fbf\u56e2\u961f\u534f\u4f5c
\u5e0c\u671b\u672c\u6559\u7a0b\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u4f7f\u7528Docker\u3002
"},{"location":"AI/FFB6D/FFB6D_Docker/#_1","title":"\u53c2\u8003\u8d44\u6599","text":" - Docker\u5b98\u65b9\u6587\u6863
- NVIDIA Docker\u6587\u6863
- FFB6D\u9879\u76ee
- \u3010\u8bba\u6587\u7b14\u8bb0\u3011FFB6D | \u9a6c\u6d69\u98de\u4e28\u535a\u5ba2
"},{"location":"Blogs/","title":"index","text":""},{"location":"Blogs/#blogs","title":"Blogs \u270d","text":"Abstract
\u4e2a\u4eba\u535a\u5ba2
\u672c\u90e8\u5206\u5185\u5bb9\uff08\u9664\u7279\u522b\u58f0\u660e\u5916\uff09\u91c7\u7528 \u7f72\u540d-\u975e\u5546\u4e1a\u6027\u4f7f\u7528-\u4fdd\u6301\u4e00\u81f4 4.0 \u56fd\u9645 (CC BY-NC-SA 4.0) \u8bb8\u53ef\u534f\u8bae\u8fdb\u884c\u8bb8\u53ef\u3002
"},{"location":"Blogs/#selected-blogs","title":"Selected Blogs","text":"\u8bba\u6587\u7b14\u8bb0 - ULIP-2 1421 5 mins 1735373878
"},{"location":"Blogs/archives/","title":"Archives","text":""},{"location":"Blogs/archives/#archives","title":"Archives","text":"{{ blog_content }}
"},{"location":"Blogs/posts/24-12-29/","title":"\u5de5\u4f5c\u89c4\u5f8b","text":" \u7ea6 1038 \u4e2a\u5b57 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 5 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#Blog","tags":["Blog"]},{"location":"Blogs/posts/24-12-29/#_1","title":"\u5de5\u4f5c\u89c4\u5f8b","text":"\u4e4b\u524d\u7684\u4e00\u4e9b\u611f\u60f3\u7eed\u5199\uff1a
Quote - \u611f\u89c9\u9ad8\u4e2d\u4e4b\u6240\u4ee5\u6548\u7387\u9ad8\u5c31\u662f\u8fd9\u4e2a\u539f\u56e0\uff0c\u5a31\u4e50\u6709\u4ec0\u4e48\u5417\uff1f\u8bfb\u95f2\u4e66\uff0c\u53bb\u8e22\u7403\uff0c\u7761\u89c9\uff0c\u804a\u5929\uff0c\u5199\u65e5\u8bb0\uff0c\u7ec3\u5b57\u3002\u90a3\u4e48\u81ea\u7136\u800c\u7136\u5c31\u4f1a\u9677\u5165\u4e00\u79cd\u53cd\u601d\u7684\u72b6\u6001\uff0c\u7136\u540e\u5c31\u56de\u53bb\u5b66\u4e60\uff0c\u6216\u8005\u5b89\u6392\u63a5\u4e0b\u6765\u7684\u4e8b\u60c5\u3002
- \u4f46\u662f\u7b49\u4e0a\u4e86\u5927\u5b66\u4e86\uff0c\u6709\u624b\u673a\u4e86\uff0c\u95f2\u4e0b\u6765\u5c31\u4f1a\u53bb\u770b\u5404\u79cd\u89c6\u9891\uff0c\u8ffd\u6c42\u5404\u79cd\u5f3a\u523a\u6fc0\uff1a\u6253\u6e38\u620f\uff0c\u770b\u5267\uff0c\u770b\u77ed\u89c6\u9891\u3002\u7136\u540e\u5c31\u4f1a\u6076\u6027\u5faa\u73af\uff0c\u4e5f\u4e0d\u4f1a\u6709\u9000\u51fa\u673a\u5236\u3002
- \u7b2c\u4e8c\u4e2a\u539f\u56e0\u662f\uff0c\u5373\u4f7f\u9ad8\u4e2d\u4f60\u7b2c\u4e00\u5929\u6446\u70c2\u4e86\uff0c\u4ed6\u4f1a\u6709\u5f3a\u5236\u6027\u7684\u4f5c\u606f\u6765\u8ba9\u4f60\u7b2c\u4e8c\u5929\u4ece\u5934\u5f00\u59cb\uff0c\u4f46\u662f\u5230\u4e86\u5927\u5b66\u751a\u81f3\u5f88\u591a\u65f6\u5019\u4f1a\u901a\u5bb5\u6216\u8005\u665a\u7761\u665a\u8d77\uff0c\u56e0\u4e3a\u5404\u79cd\u539f\u56e0\uff08\u5199 lab\uff0c surfing\uff0c\u6253\u6e38\u620f\u7b49\u7b49\uff09
- \u7b2c\u4e09\u4e2a\u539f\u56e0\u662f\uff0c\u9ad8\u4e2d\u4f1a\u6709 interaction, \u4f46\u662f\u5230\u4e86\u5927\u5b66\u53ef\u80fd\u5c31\u5f88\u591a\u65f6\u5019\u90fd\u662f\u81ea\u5df1\u4e00\u4e2a\u4eba\uff0c\u7136\u540e\u751f\u6d3b\u5c31\u5982\u4e00\u6ee9\u6b7b\u6c34\uff0c\u4f1a\u66f4\u5bb9\u6613\u9677\u5165\u6b7b\u5faa\u73af\u3002
- \u7b2c\u56db\u4e2a\u539f\u56e0\u662f\uff08\u5176\u5b9e\u6211\u89c9\u5f97\u5e76\u4e0d\u662f\u90a3\u4e48\u7684\u91cd\u8981\uff09\uff0c\u9ad8\u4e2d\u4f1a\u6709\u5b9a\u65f6\u7684\u68c0\u6d4b\u673a\u5236\uff1alike \u5468\u8003\uff0c\u5c0f\u6d4b\uff0c\u6708\u8003\uff0c\u5e73\u65f6\u7684\u6bcf\u4e00\u6b21\u4f5c\u4e1a\uff0c\u6216\u8005\u53ef\u80fd\u4f1a\u6709\u9ed8\u5199\u3002\u4f1a\u4e0d\u505c\u7684\u6709\u8001\u5e08\u6765\u50ac\u4fc3\u4f60\uff0c\u6709\u540c\u5b66\u5728\u8ddf\u4f60\u6bd4\u8f83\u3002\u4f46\u662f\u5230\u4e86\u5927\u5b66\uff0c\u4f60\u7684\u9009\u62e9\u662f\u591a\u6837\u7684\uff0c\u53ef\u80fd\u4f60\u6839\u672c\u4e0d\u77e5\u9053\u4f60\u5728\u90a3\u4e00\u6761\u8def\u4e0a\uff0c\u6216\u8005\u5373\u4f7f\u4f60\u786e\u5b9a\u4e86\u4f60\u7684\u9053\u8def\uff0c\u4f46\u662f\u4f60\u4e5f\u5f88\u96be\u91cf\u5316\u4f60\u7684\u8fdb\u7a0b\uff0c\u4f60\u672a\u6765\u7684\u9053\u8def\u6ca1\u6709\u4eba\u80fd\u591f\u4e3a\u4f60\u62c5\u4fdd\u3002
\u90a3\u4e48\u5982\u679c\u60f3\u8981\u63d0\u9ad8\u6548\u7387\u7684\u5bf9\u7b56\uff1a
- \u5f3a\u5236\u6027\u7684\u89c4\u5f8b\u7684\u4f5c\u606f\uff0c\u6bd4\u5982\uff1a\u665a\u4e0a 11\uff1a30 \u7761\u89c9\uff0c\u65e9\u4e0a 7\uff1a40 \u8d77\u5e8a\uff0c\u4e09\u9910\u56fa\u5b9a\u3002
- \u62d2\u7edd\u4e00\u5207\u53ef\u80fd\u7684\uff0c\u6ca1\u6709\u610f\u4e49\u7684\u4e8b\u60c5\u3002\u8fd9\u4e2a\u56e0\u4eba\u800c\u5f02\uff0c\u4f46\u662f\u4e00\u822c\u6765\u8bf4\uff0c\u901a\u8fc7\u81ea\u5236\u529b\u6765\u63a7\u5236\u81ea\u5df1\u5076\u5c14\u53bb\u73a9\u662f\u4e0d\u592a\u6709\u7528\u7684\uff08\u6211\u4e2a\u4eba\u53cd\u6b63\u5982\u6b64\uff09\u3002
- \u5b9a\u65f6\u7684\u53cd\u601d\u548c\u81ea\u6211\u68c0\u6d4b\u3002\u524d\u8005\u53ea\u8981\u901a\u8fc7\u65e5\u8bb0 + \u5468\u7ed3\u7684\u5f62\u5f0f\u5c31\u53ef\u4ee5\u5b8c\u6210\uff0c\u800c\u540e\u8005\uff0c\u8981\u4e48\u901a\u8fc7\u516c\u5f00\u8bfe\u53ef\u80fd\u81ea\u5e26\u7684\u8003\u8bd5\uff0c\u8981\u4e48\u901a\u8fc7\u4f60\u81ea\u5df1\u7684\u8f93\u51fa\uff0c\u5f53\u7136\u8fd9\u5f88\u96be\uff0c\u56e0\u4e3a\u4e0d\u662f\u6240\u6709\u7684\u4e8b\u60c5\u90fd\u662f\u5e94\u8bd5\u90a3\u4e00\u5957\u53ef\u4ee5\u89e3\u51b3\u7684\u3002\u6211\u76ee\u524d\u7684\u60f3\u6cd5\u662f\uff0c\u4f60\u5b66\u7684\u4e1c\u897f\u8bc4\u4ef7\u8981\u6c42\u5c31\u662f\u4ee5\u540e\u9762\u8bd5\u7684\u65f6\u5019\u6216\u8005\u505a\u9879\u76ee\u7684\u65f6\u5019\uff0c\u80fd\u591f\u4e86\u7136\u4e8e\u5fc3\uff0c\u90a3\u4e48\u5c31\u7b97\u5b66\u4f1a\u4e86\u3002
- \u591a interaction, \u4e0d\u8981\u62c5\u5fc3\u81ea\u5df1\u5185\u5411\u6216\u8005\u4ec0\u4e48\u7684\u3002\u8ddf\u522b\u4eba\u804a\u804a\u672a\u6765\u89c4\u5212\u6216\u8005\u522b\u7684\uff0c\u63d0\u9ad8\u81ea\u5df1\u7684\u66dd\u5149\u5ea6\u4f1a\u8ba9\u81ea\u5df1\u7684\u751f\u6d3b\u8fed\u4ee3\u7684\u66f4\u5feb\u3002
","tags":["Blog"]},{"location":"Blogs/posts/24-12-29/#_2","title":"\u57fa\u672c\u7279\u70b9","text":"\u6211\u60f3\u4e00\u4e2a\u5408\u7406\u7684\u751f\u6d3b\u4f5c\u606f\u5e94\u8be5\u6709\u4e00\u4e0b\u7279\u70b9\uff1a
- \u53ef\u6301\u7eed
- \u7cbe\u795e
- \u8eab\u4f53
- \u9ad8\u6548
- \u7075\u6d3b
","tags":["Blog"]},{"location":"Blogs/posts/24-12-29/#_3","title":"\u89c4\u5f8b","text":" - \u5408\u7406\u7684\u7761\u7720\uff0c\u8fd0\u52a8\uff0c\u996e\u98df
- \u5b66\u7d2f\u4e86\u5c31\u4f11\u606f\uff0c\u4f11\u606f\u591f\u4e86\u5c31\u5b66\u4e60\u7684\u8fdb\u7a0b\u5207\u6362
- \u5b9a\u671f\u53cd\u601d
","tags":["Blog"]},{"location":"Blogs/posts/24-12-29/#_4","title":"\u5177\u4f53\u5b9e\u8df5","text":"\u5de5\u5177\uff1a obsidian\uff0c\u65f6\u95f4\u65e5\u5fd7\uff0c\u535a\u5ba2
- \u6bcf\u5929\u5199\u65e5\u8bb0\uff0c\u6bcf\u5468\u5199\u5468\u7ed3\uff08\u968f\u65f6\u53ef\u4ee5\u5199\uff0c\u4e0d\u4e00\u5b9a\u662f\u8981\u4e00\u5929\u7684\u7ed3\u675f\u3002\u7eaf\u7cb9\u8bb0\u5f55\u96f6\u788e\u7684\u60f3\u6cd5\u548c\u4e00\u4e9b\u7cfb\u7edf\u7684\u53cd\u601d\u3002\uff09
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- \u53ef\u4ee5\u590d\u76d8\u81ea\u5df1\u7684\u751f\u6d3b\uff0c\u5728\u4e00\u5468\u6216\u8005\u66f4\u957f\u7684\u65f6\u95f4\u5185\u56de\u770b\u3002
- \u5728\u8bb0\u5f55\u65f6\u95f4\u7684\u65f6\u5019\uff0c\u53ef\u4ee5\u6709\u4e00\u4e2a\u540e\u64a4\u7684\u59ff\u6001\u6765\u53cd\u601d\u5f53\u4e0b\u7684\u5904\u5883\uff0c\u5373\u2018\u6211\u5728\u5e72\u4ec0\u4e48\u2019\uff0c\u2018\u6211\u8981\u5e72\u4ec0\u4e48\u2019\u3002
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- \u6309\u65f6\u7761\u89c9\uff0c\u6309\u65f6\u953b\u70bc\uff0c\u6309\u65f6\u5403\u996d
","tags":["Blog"]},{"location":"Blogs/posts/24-12-29/#_5","title":"\u66f4\u52a0\u5177\u4f53\u7684\uff0c\u4e2a\u4eba\u5316\u7684\u5b89\u6392","text":" - \u4ee5\u4e00\u4e2a\u5c0f\u9879\u76ee\u4e3a\u5355\u4f4d\u6765\u5b89\u6392\u4ee5\u5929\u4e3a\u9897\u7c92\u5ea6\u7684\u8ba1\u5212\uff0c\u800c\u4e0d\u662f\u53ea\u6709
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/","title":"\u4fe1\u606f","text":" \u7ea6 2271 \u4e2a\u5b57 2 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 11 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_2","title":"\u52c7\u6562\u53bb\u505a","text":"\u6211\u89c9\u5f97\u6211\u4e00\u76f4\u90fd\u662f\u5f88\u76f4\u63a5\u7684\u4eba\u3002 \u4e3e\u4e2a\u4f8b\u5b50\uff0c\u9ad8\u4e09\u5347\u5927\u4e00\u7684\u6691\u5047\uff0c\u6211\u5728\u8003\u8651\u8981\u4e0d\u8981\u8f6c\u4e13\u4e1a\u3002\u4e8e\u662f\u770b\u904d\u4e86\u6c34\u6e90\u6240\u6709\u76f8\u5173\u7684\u5e16\u5b50\uff0c\u95ee\u4e86\u5f88\u591a\u5b66\u957f\u5b66\u59d0\uff0c\u6574\u7406\u4e86\u5f88\u591a\u8def\u5f84\uff0c\u77e5\u9053\u4e86\u8f6c\u4e13\u4e1a\u8981\uff1a
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_3","title":"\u591a\u4ea4\u6d41","text":"\u4e00\u76f4\u611f\u89c9\u56fd\u5185\u7684\u6559\u80b2\u4e0d\u652f\u6301\u5408\u4f5c\uff0c\u5f3a\u70c8\u7684\u7ade\u4e89\u5173\u7cfb\u96be\u4ee5\u5f62\u6210\u5229\u76ca\u5171\u540c\u4f53\u3002
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\u8fd9\u91cc\u662f\u7fa4\u4e8c\u7ef4\u7801 \u8fd9\u91cc\u662f\u7fa4\u89c4\u5219 - Background and Purpose\uff1a
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- OneLLM: One Framework to Align All Modalities with Language - Kinnari's Site
- \u5b66\u4e60\u5fc3\u5f97
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_4","title":"\u5173\u4e8e\u4fe1\u606f","text":"\u5b9e\u9645\u4e0a\uff0c\u4e0a\u6c34\u6e90\u641c\u7d22\uff0c\u53bb\u8ddf\u4ed6\u4eba\u4ea4\u6d41\u90fd\u662f\u4fe1\u606f\u8f93\u5165\u7684\u4e00\u73af\u3002
","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_5","title":"\u4fe1\u606f\u7684\u8d28\u91cf","text":"\u73b0\u4ee3\u793e\u4f1a\u4e0d\u7f3a\u4f18\u8d28\u4fe1\u606f\uff0c\u7f3a\u7684\u662f\u5982\u4f55\u627e\u5230\u4f18\u8d28\u4fe1\u606f
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_6","title":"\u4fe1\u606f\u7684\u83b7\u53d6\u65b9\u5f0f","text":"","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_7","title":"\u4e3b\u52a8","text":" - \u641c\u7d22\u5f15\u64ce\uff1aGoogle \u6216\u8005 Bing\u3002
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_8","title":"\u88ab\u52a8","text":" - \u7b97\u6cd5\u63a8\u8350\uff1a\u73b0\u4ee3\u4e92\u8054\u7f51\u901a\u8fc7\u53e4\u8001\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u6784\u5efa\u4e86\u4ee5\u77ed\u89c6\u9891\u548c\u63a8\u8350\u4fe1\u606f\u6d41\u4e3a\u4e3b\u7684\u4fe1\u606f\u4ea7\u54c1\uff0c\u8fd9\u7c7b\u4ea7\u54c1\u7c7b\u4f3c\u4e8e\u51cf\u80a5\u836f\uff0c\u5403\u591a\u4e86\u5bb9\u6613\u53cd\u5f39\u3002\u63a8\u8350\u7b97\u6cd5\u4e00\u4e2a\u5f88\u5927\u7684\u95ee\u9898\u4f1a\u5236\u9020\u51fa\u56f4\u7ed5\u4f7f\u7528\u8005\u7684\u4fe1\u606f\u8327\u623f\uff0c\u4e5f\u5c31\u662f\u7528\u6237\u6700\u7ec8\u770b\u5230\u7684\u90fd\u662f\u81ea\u5df1\u559c\u6b22\u770b\u5230\u7684\u4fe1\u606f\uff0c\u4fe1\u606f\u5355\u4e00\u5316\u8ba9\u4f7f\u7528\u8005\u770b\u5230\u7684\u90fd\u662f\u7247\u9762\u7684\u4fe1\u606f\uff0c\u8fd9\u5bf9\u63d0\u5347\u8ba4\u77e5\u6765\u8bf4\u662f\u4e2a\u707e\u96be\u3002
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_9","title":"\u5b66\u4e60\u4e0e\u4fe1\u606f\u7684\u5173\u7cfb","text":"\u5b66\u4e60\u67d0\u4e00\u9886\u57df\u77e5\u8bc6\u65f6\u9075\u5faa\u00a0\u81ea\u4f4e\u5411\u4e0a\u00a0\u7684\u8fc7\u7a0b\uff1a
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\u8f6e\u5ed3\u8ba4\u8bc6\u3002 - \u4e4b\u540e\u4f1a\u5173\u6ce8\u4e00\u4e9b\u8be5\u9886\u57df\u7684\u516c\u4f17\u53f7/\u77e5\u4e4e\u5927V/\u90ae\u4ef6\u5217\u8868\uff0c\u751a\u81f3\u662f\u770b\u6709\u6ca1\u6709Youtube\u76f8\u5173\u7684\u9891\u9053\u3002
- \u5f85\u611f\u89c9\u5bf9\u8be5\u9886\u57df\u6709\u4e86\u4e00\u4e2a\u00a0
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- \u4e4b\u540e\u4e0e\u8fd9\u4e9b\u4eba\u5efa\u7acb\u8054\u7cfb(\u5305\u62ec\u5e73\u53f0\u79c1\u4fe1\u3001\u5fae\u4fe1\u4e0e\u90ae\u4ef6\u8054\u7cfb)\u00a0\uff0c\u505a1\u5bf91\u7684\u6c9f\u901a\uff0c\u89e3\u51b3\u6211\u5728\u5b66\u4e60\u5b9e\u8df5\u8fc7\u7a0b\u4e2d\u9047\u5230\u7684\u4e00\u4e9b\u96be\u4ee5\u89e3\u51b3\u7684\u56f0\u60d1\u3002
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_10","title":"\u4e00\u4e9b\u7531\u6b64\u5f15\u53d1\u51fa\u6765\u7684\u8bdd\u9898","text":"","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_11","title":"\u4e0d\u8981\u7126\u8651","text":"\u6211\u89c9\u5f97\u7126\u8651\u5e94\u8be5\u662f\u4fe1\u606f\u6444\u5165\u7684\u95ee\u9898\uff0c\u4e3b\u8981\u8fd8\u662f\u8981\u60f3\u6e05\u695a\u81ea\u5df1\u8981\u4ec0\u4e48\u3002
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\u4f46\u662f\u5177\u4f53\u7684\u53ef\u80fd\u4ee5\u540e\u4f1a\u53e6\u5199\u4e00\u7bc7\u6587\u7ae0\uff0c\u4e0d\u8fc7\u5728 24-12-29 \u8fd9\u7bc7\u6587\u7ae0\u4e2d\u4e5f\u6709\u63d0\u53ca\u3002
","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#reference","title":"Reference","text":" - \u6784\u5efa\u7ec8\u8eab\u5b66\u4e60\u4f53\u7cfb\u8fdb\u884c\u81ea\u6211\u63d0\u5347 \u00b7 BMPI
","tags":["Blog"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/","title":"Gaussian_Splatting_Code","text":" \u7ea6 982 \u4e2a\u5b57 34 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 5 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u79d1\u7814 #\u590d\u73b0 #\u4e09\u7ef4\u91cd\u5efa","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#gaussian_splatting_code","title":"Gaussian_Splatting_Code","text":"\u4e3b\u8981\u6709 colmap \u548c blender \u4e24\u79cd\u6570\u636e\u7c7b\u578b\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#1-colmap","title":"1. Colmap \u6570\u636e\u96c6\u52a0\u8f7d\u8fc7\u7a0b","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#11","title":"1.1 \u52a0\u8f7d\u51fd\u6570","text":" - \u4e3b\u51fd\u6570\u4e3a
readColmapSceneInfo\uff0c\u8c03\u7528\u4e86\u4e00\u7cfb\u5217\u5b50\u51fd\u6570\u4ee5\u52a0\u8f7d\u76f8\u673a\u53c2\u6570\u3001\u70b9\u4e91\u548c\u76f8\u5173\u6587\u4ef6\u3002 - \u6570\u636e\u6765\u6e90\u8def\u5f84\u4e3a
sparse/ \u6587\u4ef6\u5939\uff0c\u5305\u542b\u76f8\u673a\u53c2\u6570\u6587\u4ef6\u3001\u70b9\u4e91\u6587\u4ef6\u7b49\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#12","title":"1.2 \u52a0\u8f7d\u7684\u4e3b\u8981\u6b65\u9aa4","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#1","title":"\u6b65\u9aa4 1\uff1a\u52a0\u8f7d\u76f8\u673a\u5916\u53c2","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythoncameras_extrinsic_file = os.path.join(path, \"sparse/0\", \"images.bin\")\ncam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file)\n
-
\u5982\u679c images.bin \u6587\u4ef6\u4e0d\u5b58\u5728\uff0c\u5219\u52a0\u8f7d images.txt\uff1a
Pythoncameras_extrinsic_file = os.path.join(path, \"sparse/0\", \"images.txt\")\ncam_extrinsics = read_extrinsics_text(cameras_extrinsic_file)\n
-
\u5916\u53c2\u5305\u62ec\uff1a
- \u76f8\u673a\u65cb\u8f6c\u77e9\u9635 (
R)\u3002 - \u5e73\u79fb\u5411\u91cf (
T)\u3002 - \u56fe\u50cf\u540d\u79f0\uff0c\u7528\u4e8e\u5339\u914d\u56fe\u50cf\u6587\u4ef6\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#2","title":"\u6b65\u9aa4 2\uff1a\u52a0\u8f7d\u76f8\u673a\u5185\u53c2","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythoncameras_intrinsic_file = os.path.join(path, \"sparse/0\", \"cameras.bin\")\ncam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file)\n
-
\u5982\u679c cameras.bin \u6587\u4ef6\u4e0d\u5b58\u5728\uff0c\u5219\u52a0\u8f7d cameras.txt\uff1a
Pythoncameras_intrinsic_file = os.path.join(path, \"sparse/0\", \"cameras.txt\")\ncam_intrinsics = read_intrinsics_text(cameras_intrinsic_file)\n
-
\u5185\u53c2\u5305\u62ec\uff1a
- \u7126\u8ddd\uff08
focal length\uff09\u3002 - \u4e3b\u70b9\u5750\u6807\uff08
principal point\uff09\u3002 - \u56fe\u50cf\u5206\u8fa8\u7387\uff08
width \u548c height\uff09\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#3","title":"\u6b65\u9aa4 3\uff1a\u52a0\u8f7d\u70b9\u4e91","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythonply_path = os.path.join(path, \"sparse/0/points3D.ply\")\nbin_path = os.path.join(path, \"sparse/0/points3D.bin\")\ntxt_path = os.path.join(path, \"sparse/0/points3D.txt\")\n\nif not os.path.exists(ply_path):\n print(\"Converting point3d.bin to .ply, will happen only the first time you open the scene.\")\n xyz, rgb, _ = read_points3D_binary(bin_path)\n storePly(ply_path, xyz, rgb)\npcd = fetchPly(ply_path)\n
- \u70b9\u4e91\u6587\u4ef6\u7684\u6765\u6e90\u53ef\u4ee5\u662f\uff1a
points3D.binpoints3D.txtpoints3D.ply\uff08\u5982\u679c\u5b58\u5728\u5219\u76f4\u63a5\u52a0\u8f7d\uff0c\u5426\u5219\u4ece bin \u6216 txt \u8f6c\u6362\u4e3a .ply\uff09
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#4","title":"\u6b65\u9aa4 4\uff1a\u8bad\u7ec3/\u6d4b\u8bd5\u96c6\u5212\u5206","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythonif eval:\n cam_names = [cam_extrinsics[cam_id].name for cam_id in cam_extrinsics]\n cam_names = sorted(cam_names)\n test_cam_names_list = [name for idx, name in enumerate(cam_names) if idx % llffhold == 0]\nelse:\n test_cam_names_list = []\n
- \u6d4b\u8bd5\u96c6\u53ef\u4ee5\u901a\u8fc7
eval \u53c2\u6570\u542f\u7528\u3002 - \u9ed8\u8ba4\u6bcf 8 \u4e2a\u89c6\u89d2\uff08
llffhold=8\uff09\u9009\u53d6\u4e00\u4e2a\u4f5c\u4e3a\u6d4b\u8bd5\u96c6\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#5","title":"\u6b65\u9aa4 5\uff1a\u573a\u666f\u5f52\u4e00\u5316","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythonnerf_normalization = getNerfppNorm(train_cam_infos)\n
- \u901a\u8fc7\u8ba1\u7b97\u8bad\u7ec3\u76f8\u673a\u7684\u4e2d\u5fc3\u548c\u5bf9\u89d2\u7ebf\u957f\u5ea6\uff0c\u786e\u5b9a\u573a\u666f\u4e2d\u5fc3 (
translate) \u548c\u534a\u5f84 (radius)\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#13","title":"1.3 \u8fd4\u56de\u6570\u636e\u7ed3\u6784","text":"\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a SceneInfo \u6570\u636e\u7ed3\u6784\uff1a
point_cloud\uff1a\u70b9\u4e91\u6570\u636e\u3002train_cameras\uff1a\u8bad\u7ec3\u76f8\u673a\u5217\u8868\u3002test_cameras\uff1a\u6d4b\u8bd5\u76f8\u673a\u5217\u8868\u3002nerf_normalization\uff1a\u573a\u666f\u5f52\u4e00\u5316\u53c2\u6570\u3002ply_path\uff1a\u70b9\u4e91\u6587\u4ef6\u8def\u5f84\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#2-blender","title":"2. Blender \u6570\u636e\u96c6\u52a0\u8f7d\u8fc7\u7a0b","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#21","title":"2.1 \u52a0\u8f7d\u51fd\u6570","text":" - \u4e3b\u51fd\u6570\u4e3a
readNerfSyntheticInfo\uff0c\u4ece Blender \u5bfc\u51fa\u7684 JSON \u6587\u4ef6\u52a0\u8f7d\u76f8\u673a\u548c\u56fe\u50cf\u4fe1\u606f\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#22","title":"2.2 \u52a0\u8f7d\u7684\u4e3b\u8981\u6b65\u9aa4","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#1_1","title":"\u6b65\u9aa4 1\uff1a\u52a0\u8f7d\u8bad\u7ec3\u548c\u6d4b\u8bd5\u76f8\u673a","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythontrain_cam_infos = readCamerasFromTransforms(path, \"transforms_train.json\", depths_folder, white_background, False, extension)\ntest_cam_infos = readCamerasFromTransforms(path, \"transforms_test.json\", depths_folder, white_background, True, extension)\n
- \u6bcf\u4e2a\u76f8\u673a\u7684\u6570\u636e\u6765\u6e90\u4e8e
transforms_train.json \u548c transforms_test.json\u3002 transforms_train.json: - \u7528\u4e8e\u63cf\u8ff0\u8bad\u7ec3\u96c6\u4e2d\u76f8\u673a\u7684\u4f4d\u7f6e\u4fe1\u606f\uff08\u5916\u53c2\uff09\u3001\u76f8\u673a\u7684\u5185\u53c2\uff08\u6c34\u5e73\u89c6\u89d2\uff09\u4ee5\u53ca\u5bf9\u5e94\u7684\u56fe\u50cf\u8def\u5f84\u3002
- \u6bcf\u4e2a\u76f8\u673a\u89c6\u89d2\u90fd\u4f1a\u6709\u4e00\u6761\u8bb0\u5f55\u3002
- \u76f8\u673a\u53c2\u6570\u5305\u542b\uff1a
transform_matrix\uff08\u76f8\u673a\u5230\u4e16\u754c\u7684\u53d8\u6362\u77e9\u9635\uff09\u3002- \u6c34\u5e73\u89c6\u89d2 (
camera_angle_x) \u7528\u4e8e\u8ba1\u7b97\u7126\u8ddd\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#2_1","title":"\u6b65\u9aa4 2\uff1a\u751f\u6210\u70b9\u4e91","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythonif not os.path.exists(ply_path):\n num_pts = 100_000\n print(f\"Generating random point cloud ({num_pts})...\")\n xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3\n shs = np.random.random((num_pts, 3)) / 255.0\n storePly(ply_path, xyz, SH2RGB(shs) * 255)\npcd = fetchPly(ply_path)\n
- Blender \u6570\u636e\u96c6\u6ca1\u6709\u70b9\u4e91\u6587\u4ef6\uff0c\u56e0\u6b64\u968f\u673a\u751f\u6210 10 \u4e07\u4e2a\u70b9\uff1a
- \u70b9\u7684\u4f4d\u7f6e\u8303\u56f4\u4e3a
[-1.3, 1.3]\u3002 - \u70b9\u7684\u989c\u8272\u901a\u8fc7\u7403\u8c10\u51fd\u6570 (SH) \u968f\u673a\u751f\u6210\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#3_1","title":"\u6b65\u9aa4 3\uff1a\u573a\u666f\u5f52\u4e00\u5316","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythonnerf_normalization = getNerfppNorm(train_cam_infos)\n
- \u4e0e Colmap \u7c7b\u4f3c\uff0c\u8ba1\u7b97\u573a\u666f\u7684\u4e2d\u5fc3 (
translate) \u548c\u534a\u5f84 (radius)\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#23","title":"2.3 \u8fd4\u56de\u6570\u636e\u7ed3\u6784","text":"\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a SceneInfo \u6570\u636e\u7ed3\u6784\uff1a
point_cloud\uff1a\u751f\u6210\u7684\u70b9\u4e91\u3002train_cameras\uff1a\u8bad\u7ec3\u76f8\u673a\u5217\u8868\u3002test_cameras\uff1a\u6d4b\u8bd5\u76f8\u673a\u5217\u8868\u3002nerf_normalization\uff1a\u573a\u666f\u5f52\u4e00\u5316\u53c2\u6570\u3002ply_path\uff1a\u70b9\u4e91\u6587\u4ef6\u8def\u5f84\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#3_2","title":"3. \u603b\u7ed3\u5bf9\u6bd4","text":"\u7279\u6027 Colmap \u6570\u636e\u96c6 Blender \u6570\u636e\u96c6 \u76f8\u673a\u53c2\u6570 \u4ece images.bin \u548c cameras.bin \u4e2d\u52a0\u8f7d\u5916\u53c2\u548c\u5185\u53c2 \u4ece transforms_train.json \u4e2d\u52a0\u8f7d\u53d8\u6362\u77e9\u9635 \u70b9\u4e91 \u4ece points3D.bin \u6216 points3D.txt \u52a0\u8f7d \u968f\u673a\u751f\u6210 10 \u4e07\u4e2a\u70b9 \u56fe\u50cf\u6570\u636e \u5b58\u50a8\u5728 images/ \u6587\u4ef6\u5939 \u8def\u5f84\u7531 JSON \u6587\u4ef6\u6307\u5b9a \u6df1\u5ea6\u56fe \u53ef\u9009\uff0c\u9700 depth_params.json \u5b9a\u4e49 \u53ef\u9009\uff0c\u9700\u63d0\u4f9b\u6df1\u5ea6\u56fe\u6587\u4ef6\u5939 \u5f52\u4e00\u5316 \u57fa\u4e8e\u76f8\u673a\u4f4d\u7f6e\u8ba1\u7b97\u573a\u666f\u4e2d\u5fc3\u548c\u534a\u5f84 \u540c\u4e0a \u6d4b\u8bd5\u96c6\u5212\u5206 \u6bcf 8 \u4e2a\u89c6\u89d2\u5212\u5206\u4e00\u4e2a\u5230\u6d4b\u8bd5\u96c6\uff0c\u6216\u8bfb\u53d6 test.txt \u6d4b\u8bd5\u96c6\u901a\u8fc7 transforms_test.json \u6307\u5b9a \u80cc\u666f\u989c\u8272 \u65e0 \u53ef\u9009\u767d\u8272\u6216\u9ed1\u8272\u80cc\u666f","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#4_1","title":"4. \u6240\u9700\u51c6\u5907\u7684\u6587\u4ef6","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#colmap","title":"Colmap \u6570\u636e\u96c6","text":" - \u76f8\u673a\u5916\u53c2\uff1a
sparse/0/images.bin \u6216 images.txt\u3002 - \u76f8\u673a\u5185\u53c2\uff1a
sparse/0/cameras.bin \u6216 cameras.txt\u3002 - \u70b9\u4e91\u6587\u4ef6\uff1a
sparse/0/points3D.bin \u6216 points3D.txt\u3002 - \u56fe\u50cf\u6587\u4ef6\u5939\uff1a\u901a\u5e38\u4e3a
images/\u3002 - \u53ef\u9009\u6587\u4ef6\uff1a
sparse/0/depth_params.json\uff08\u6df1\u5ea6\u53c2\u6570\uff09\u3002sparse/0/test.txt\uff08\u6d4b\u8bd5\u96c6\u5212\u5206\u6587\u4ef6\uff09\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#blender","title":"Blender \u6570\u636e\u96c6","text":" - \u8bad\u7ec3\u76f8\u673a\u6587\u4ef6\uff1a
transforms_train.json\u3002 - \u6d4b\u8bd5\u76f8\u673a\u6587\u4ef6\uff1a
transforms_test.json\u3002 - \u56fe\u50cf\u6587\u4ef6\u8def\u5f84\uff1a\u7531 JSON \u6587\u4ef6\u6307\u5b9a\u3002
- \u53ef\u9009\u6587\u4ef6\uff1a
- \u6df1\u5ea6\u56fe\u6587\u4ef6\u5939\u3002
- \u80cc\u666f\u989c\u8272\u53c2\u6570\uff08
white_background\uff09\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#blender_1","title":"Blender","text":"Random Point Initialization \u00b7 Issue #39 \u00b7 graphdeco-inria/gaussian-splatting \u00b7 GitHub
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/","title":"Gaussian Splatting \u590d\u73b0","text":" \u7ea6 1407 \u4e2a\u5b57 4 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 7 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u79d1\u7814 #\u590d\u73b0 #\u4e09\u7ef4\u91cd\u5efa","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#gaussian-splatting","title":"Gaussian Splatting \u590d\u73b0","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#_1","title":"\u5b98\u65b9\u6570\u636e\u96c6\u590d\u73b0","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#prepare","title":"prepare","text":"Text Onlygit clone https://github.com/graphdeco-inria/gaussian-splatting --recursive\nconda env create --file environment.yml\nconda activate gaussian_splatting\n
- \u53bb\u4e0b\u8f7d\u4e2a\u6570\u636e\u96c6
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#train","title":"train","text":"Text Onlypython train.py -s ./data/tandt/train/ -m ./output/train/ --eval\n\nOptimizing ./output/train/\nOutput folder: ./output/train/ [31/12 20:58:06]\nTensorboard not available: not logging progress [31/12 20:58:06]\n------------LLFF HOLD------------- [31/12 20:58:08]\nReading camera 301/301 [31/12 20:58:08]\nConverting point3d.bin to .ply, will happen only the first time you open the scene. [31/12 20:58:08]\nLoading Training Cameras [31/12 20:58:09]\nLoading Test Cameras [31/12 20:58:19]\nNumber of points at initialisation : 182686 [31/12 20:58:20]\nTraining progress: 23%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 7000/30000 [02:04<07:36, 50.34it/s, Loss=0.0985708, Depth Loss=0.0000000]\n[ITER 7000] Evaluating test: L1 0.07567951896865116 PSNR 19.8115311672813 [31/12 21:00:25]\n\n[ITER 7000] Evaluating train: L1 0.05251172706484795 PSNR 21.924707794189455 [31/12 21:00:25]\n\n[ITER 7000] Saving Gaussians [31/12 21:00:25]\nTraining progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30000/30000 [10:58<00:00, 45.54it/s, Loss=0.0425664, Depth Loss=0.0000000]\n\n[ITER 30000] Evaluating test: L1 0.05784237639684426 PSNR 22.03640069459614 [31/12 21:09:19]\n\n[ITER 30000] Evaluating train: L1 0.025540136173367502 PSNR 27.51155662536621 [31/12 21:09:19]\n\n[ITER 30000] Saving Gaussians [31/12 21:09:19]\n\nTraining complete. [31/12 21:09:28]\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#render","title":"render","text":"Text Onlypython render.py -m ./output/train/\n\nLooking for config file in ./output/train/cfg_args\nConfig file found: ./output/train/cfg_args\nRendering ./output/train/\nLoading trained model at iteration 30000 [31/12 21:14:40]\n------------LLFF HOLD------------- [31/12 21:14:41]\nReading camera 301/301 [31/12 21:14:41]\nLoading Training Cameras [31/12 21:14:41]\nLoading Test Cameras [31/12 21:14:53]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 263/263 [01:41<00:00, 2.58it/s]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 38/38 [00:15<00:00, 2.42it/s]\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#evaluate","title":"evaluate","text":"Text Onlypython metrics.py -m ./output/train/\n\nScene: ./output/train/\nMethod: ours_30000\nMetric evaluation progress: 0%| | 0/38 [00:00<?, ?it/s]Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /home/fanghaotian/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 528M/528M [00:28<00:00, 19.4MB/s]\nDownloading: \"https://raw.githubusercontent.com/richzhang/PerceptualSimilarity/master/lpips/weights/v0.1/vgg.pth\" to /home/fanghaotian/.cache/torch/hub/checkpoints/vgg.pth\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7.12k/7.12k [00:03<00:00, 2.29kB/s]\nMetric evaluation progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 38/38 [07:57<00:00, 12.58s/it] 7.12k/7.12k [00:03<00:00, 2.29kB/s]\n SSIM : 0.8192384\n PSNR : 22.0094910\n LPIPS: 0.1966141\n
\u770b\u8d77\u6765\u6bd4\u8bba\u6587\u91cc\u7684\u6570\u636e\u4f4e\u3002
.\\SIBR_remoteGaussian_app.exe \u8fdc\u7a0b\u4e0d\u80fd\u7528\uff0c issue \u4e2d\u4e5f\u672a\u89e3\u51b3
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#interactive-viewers","title":"Interactive Viewers","text":"Text Onlyscp -r -P 26000 fanghaotian@RHOS:/home/fanghaotian/3DGS/gaussian-splatting/output/ C:\\Users\\fanghaotian\\Desktop\\\n\n./SIBR_gaussianViewer_app -m C:\\Users\\fanghaotian\\Desktop\\data\\82ea91ef-6\\\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#blender","title":"\u7528 blender \u6570\u636e\u96c6","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#prepare_1","title":"prepare","text":" - \u6570\u636e\u96c6
- GitHub - bmild/nerf: Code release for NeRF (Neural Radiance Fields)
nerf_synthetic/lego \u90a3\u4e2a\u662f blender \u7684\u683c\u5f0f
Text Onlytree -I \"*.png\"\n.\n\u2514\u2500\u2500 lego\n \u251c\u2500\u2500 test\n \u251c\u2500\u2500 train\n \u251c\u2500\u2500 transforms_test.json\n \u251c\u2500\u2500 transforms_train.json\n \u251c\u2500\u2500 transforms_val.json\n \u2514\u2500\u2500 val\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#iterations30000-no-evaluation","title":"Iterations=30000 no evaluation","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#train_1","title":"train","text":"Text Onlypython train.py -s ./data/nerf_synthetic/lego/ -m ./output/lego/\n\nOptimizing ./output/lego/\nOutput folder: ./output/lego/ [01/01 13:31:23]\nTensorboard not available: not logging progress [01/01 13:31:23]\nFound transforms_train.json file, assuming Blender data set! [01/01 13:31:23]\nReading Training Transforms [01/01 13:31:23]\nReading Test Transforms [01/01 13:31:31]\nGenerating random point cloud (100000)... [01/01 13:31:38]\nLoading Training Cameras [01/01 13:31:39]\nLoading Test Cameras [01/01 13:31:53]\nNumber of points at initialisation : 100000 [01/01 13:31:53]\nTraining progress: 23%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 7000/30000 [01:15<04:12, 91.02it/s, Loss=0.0152277, Depth Loss=0.0000000]\n[ITER 7000] Evaluating train: L1 0.3646775007247925 PSNR 5.983335494995117 [01/01 13:33:09]\n\n[ITER 7000] Saving Gaussians [01/01 13:33:09]\nTraining progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30000/30000 [05:26<00:00, 91.97it/s, Loss=0.0105094, Depth Loss=0.0000000]\n\n[ITER 30000] Evaluating train: L1 0.6417351365089417 PSNR 2.185275435447693 [01/01 13:37:19]\n\n[ITER 30000] Saving Gaussians [01/01 13:37:19]\n\nTraining complete. [01/01 13:37:21]\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#render_1","title":"render","text":"Text Onlypython render.py -m ./output/lego/\n\nLooking for config file in ./output/lego/cfg_args\nConfig file found: ./output/lego/cfg_args\nRendering ./output/lego/\nLoading trained model at iteration 30000 [01/01 13:41:14]\nFound transforms_train.json file, assuming Blender data set! [01/01 13:41:14]\nReading Training Transforms [01/01 13:41:14]\nReading Test Transforms [01/01 13:41:20]\nLoading Training Cameras [01/01 13:41:28]\nLoading Test Cameras [01/01 13:41:44]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 300/300 [01:06<00:00, 4.48it/s]\nRendering progress: 0it [00:00, ?it/s]\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#metric","title":"metric","text":"Text Onlypython metrics.py -m ./output/lego/\n\nScene: ./output/lego/\nMethod: ours_30000\nMetric evaluation progress: 0it [00:00, ?it/s]\n SSIM : nan\n PSNR : nan\n LPIPS: nan\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#interactive-viewers_1","title":"Interactive Viewers","text":"Text Onlyscp -r -P 26000 fanghaotian@RHOS:/home/fanghaotian/3DGS/gaussian-splatting/output/lego C:\\Users\\fanghaotian\\Desktop\\\n\n./SIBR_gaussianViewer_app -m C:\\Users\\fanghaotian\\Desktop\\lego\n
\ud83e\udd14\uff0c\u597d\u50cf\u4e00\u5b9a\u8981 --eval\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#iteratations30000-evaluation","title":"Iteratations=30000 + evaluation","text":"\u90a3\u4e48\u52a0\u4e0a\u4ee5\u540e\u7528 iterations=300000\uff08default\uff09\u518d\u8dd1\u4e00\u6b21\u3002
Text Onlypython train.py -s ./data/nerf_synthetic/lego/ -m ./output/lego_eval/ --eval\n\nOptimizing ./output/lego_eval/\nOutput folder: ./output/lego_eval/ [01/01 13:43:45]\nTensorboard not available: not logging progress [01/01 13:43:45]\nFound transforms_train.json file, assuming Blender data set! [01/01 13:43:45]\nReading Training Transforms [01/01 13:43:45]\nReading Test Transforms [01/01 13:43:51]\nLoading Training Cameras [01/01 13:43:58]\nLoading Test Cameras [01/01 13:44:06]\nNumber of points at initialisation : 100000 [01/01 13:44:12]\nTraining progress: 23%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 7000/30000 [01:24<04:48, 79.82it/s, Loss=0.0153927, Depth Loss=0.0000000]\n[ITER 7000] Evaluating test: L1 0.34677238538861277 PSNR 6.254974584579468 [01/01 13:45:38]\n\n[ITER 7000] Evaluating train: L1 0.3650033831596375 PSNR 5.939675426483155 [01/01 13:45:38]\n\n[ITER 7000] Saving Gaussians [01/01 13:45:38]\nTraining progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30000/30000 [05:47<00:00, 86.33it/s, Loss=0.0095609, Depth Loss=0.0000000]\n\n[ITER 30000] Evaluating test: L1 0.557637879550457 PSNR 3.0822176444530487 [01/01 13:50:00]\n\n[ITER 30000] Evaluating train: L1 0.5475435316562652 PSNR 3.064222288131714 [01/01 13:50:00]\n\n[ITER 30000] Saving Gaussians [01/01 13:50:00]\n\nTraining complete. [01/01 13:50:04]\n\n\npython render.py -m ./output/lego_eval/\n\nLooking for config file in ./output/lego_eval/cfg_args\nConfig file found: ./output/lego_eval/cfg_args\nRendering ./output/lego_eval/\nLoading trained model at iteration 30000 [01/01 13:51:20]\nFound transforms_train.json file, assuming Blender data set! [01/01 13:51:20]\nReading Training Transforms [01/01 13:51:20]\nReading Test Transforms [01/01 13:51:26]\nLoading Training Cameras [01/01 13:51:33]\nLoading Test Cameras [01/01 13:51:40]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 100/100 [00:25<00:00, 3.94it/s]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:48<00:00, 4.13it/s]\n\n\npython metrics.py -m ./output/lego_eval/\n\nScene: ./output/lego_eval/\nMethod: ours_30000\nMetric evaluation progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [04:00<00:00, 1.20s/it]\n SSIM : 0.2604475\n PSNR : 3.0649595\n LPIPS: 0.5116847\n
\u770b\u8d77\u6765\u6548\u679c\u4e0d\u592a\u597d\u3002 \u5e94\u8be5\u662f\u521d\u59cb\u5316\u7684\u95ee\u9898\uff0cL1 \u751a\u81f3\u8fd8\u662f\u4e0a\u6b21\u7684 10 \u500d, Evaluating train: L1 0.5475435316562652 PSNR 3.064222288131714 \u3002
\u628a iterations \u8c03\u5230 100000 \u518d\u6765\u4e00\u6b21\uff0c\u770b\u770b\u6536\u655b\u60c5\u51b5\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#iterations100000-evaluation","title":"Iterations=100000 + evaluation","text":"Text Only python train.py -s ./data/nerf_synthetic/lego/ -m ./output/lego_eval_100000/ --iterations 100000 --eval\n\nOptimizing ./output/lego_eval_100000/\nOutput folder: ./output/lego_eval_100000/ [01/01 14:00:46]\nTensorboard not available: not logging progress [01/01 14:00:46]\nFound transforms_train.json file, assuming Blender data set! [01/01 14:00:46]\nReading Training Transforms [01/01 14:00:46]\nReading Test Transforms [01/01 14:00:53]\nLoading Training Cameras [01/01 14:01:00]\nLoading Test Cameras [01/01 14:01:08]\nNumber of points at initialisation : 100000 [01/01 14:01:15]\nTraining progress: 7%|\u2588\u2588\u2588\u2588\u2588\u2589 | 7000/100000 [01:23<17:42, 87.50it/s, Loss=0.0159089, Depth Loss=0.0000000]\n[ITER 7000] Evaluating test: L1 0.34856061153113843 PSNR 6.298382818698883 [01/01 14:02:40]\n\n[ITER 7000] Evaluating train: L1 0.36839944720268253 PSNR 5.857858657836914 [01/01 14:02:40]\n\n[ITER 7000] Saving Gaussians [01/01 14:02:40]\nTraining progress: 30%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 29990/100000 [05:45<10:51, 107.52it/s, Loss=0.0095307, Depth Loss=0.0000000]\n[ITER 30000] Evaluating test: L1 0.6024469056725502 PSNR 2.6481225460767748 [01/01 14:07:02]\n\n[ITER 30000] Evaluating train: L1 0.5561836898326874 PSNR 2.9405082702636722 [01/01 14:07:02]\n\n[ITER 30000] Saving Gaussians [01/01 14:07:02]\nTraining progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 100000/100000 [16:33<00:00, 100.62it/s, Loss=0.0109117, Depth Loss=0.0000000]\n\n[ITER 100000] Saving Gaussians [01/01 14:17:49]\n\nTraining complete. [01/01 14:17:51]\n
Text Onlypython render.py -m ./output/lego_eval_100000/\nLooking for config file in ./output/lego_eval_100000/cfg_args\nConfig file found: ./output/lego_eval_100000/cfg_args\nRendering ./output/lego_eval_100000/\nLoading trained model at iteration 100000 [01/01 14:21:43]\nFound transforms_train.json file, assuming Blender data set! [01/01 14:21:43]\nReading Training Transforms [01/01 14:21:43]\nReading Test Transforms [01/01 14:21:50]\nLoading Training Cameras [01/01 14:21:57]\nLoading Test Cameras [01/01 14:22:06]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 100/100 [00:22<00:00, 4.39it/s]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:42<00:00, 4.65it/s]\n
Text Onlypython metrics.py -m ./output/lego_eval_100000/\n\nScene: ./output/lego_eval_100000/\nMethod: ours_100000\nMetric evaluation progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [04:03<00:00, 1.22s/it]\n SSIM : 0.2404845\n PSNR : 2.4990394\n LPIPS: 0.5251624\n
\u597d\u50cf\u8bc4\u4f30\u4ecd\u7136\u4e0d\u592a\u884c\uff0c\u8fd8\u8d8a\u8bad\u7ec3\u8d8a\u5dee\u4e86\u3002
Text Onlyscp -r -P 26000 fanghaotian@RHOS:/home/fanghaotian/3DGS/gaussian-splatting/output/lego_eval_100000/ C:\\Users\\fanghaotian\\Desktop\\\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#blender-colmap","title":"Blender \u8f6c Colmap","text":" - Frequently Asked Questions \u2014 COLMAP 3.12.0.dev0 documentation
Text Onlycolmap automatic_reconstructor --workspace_path . --image_path ./images --sparse 1 --camera_model SIMPLE_PINHOLE --dense 0\n# colmap automatic_reconstructor: \u8fd9\u662f\u8c03\u7528COLMAP\u7a0b\u5e8f\u4e2d\u7684\u81ea\u52a8\u91cd\u5efa\u6a21\u5757\uff0c\u5b83\u4f1a\u81ea\u52a8\u5b8c\u6210\u7279\u5f81\u63d0\u53d6\u3001\u5339\u914d\u3001\u91cd\u6295\u5f71\u8bef\u5dee\u4f18\u5316\u548c\u4e09\u89d2\u5316\u7b49\u6b65\u9aa4\uff0c\u4ee5\u751f\u6210\u573a\u666f\u7684\u7a00\u758f3D\u70b9\u4e91\u6a21\u578b\u3002\n# --workspace_path .: \u6307\u5b9a\u4e86\u5de5\u4f5c\u7a7a\u95f4\u8def\u5f84\u4e3a\u5f53\u524d\u76ee\u5f55(.)\uff0c\u5728\u8fd9\u4e2a\u8def\u5f84\u4e0b\uff0cCOLMAP\u5c06\u5b58\u50a8\u4e2d\u95f4\u7ed3\u679c\u4ee5\u53ca\u6700\u7ec8\u7684\u91cd\u5efa\u8f93\u51fa\u6587\u4ef6\u3002\n# --image_path ./images: \u5b9a\u4e49\u4e86\u56fe\u50cf\u6570\u636e\u96c6\u6240\u5728\u7684\u8def\u5f84\uff0c\u5373\u6240\u6709\u53c2\u4e0e\u91cd\u5efa\u7684\u56fe\u7247\u90fd\u4f4d\u4e8e./images\u76ee\u5f55\u4e0b\u3002\n# --sparse 1: \u8fd9\u4e2a\u53c2\u6570\u8868\u793a\u8fdb\u884c\u7a00\u758f\u91cd\u5efa\uff08\u4e0e\u5bc6\u96c6\u91cd\u5efa\u76f8\u5bf9\uff09\uff0c\u5373\u53ea\u6784\u5efa\u51fa\u573a\u666f\u4e2d\u7684\u5173\u952e\u70b9\u53ca\u5176\u5bf9\u5e94\u5173\u7cfb\uff0c\u5e76\u901a\u8fc7\u8fd9\u4e9b\u4fe1\u606f\u751f\u6210\u4e00\u4e2a\u7531\u7a00\u758f\u70b9\u4e91\u7ec4\u6210\u7684\u4e09\u7ef4\u6a21\u578b\u3002\n# --camera_model SIMPLE_PINHOLE: \u6307\u5b9a\u4f7f\u7528\u7684\u76f8\u673a\u6a21\u578b\u4e3a\u201c\u7b80\u5355\u9488\u5b54\u6a21\u578b\u201d\uff08Simple Pinhole Model\uff09\u3002\u8fd9\u610f\u5473\u7740COLMAP\u5728\u8fdb\u884c\u91cd\u5efa\u65f6\u5c06\u5047\u8bbe\u76f8\u673a\u9075\u5faa\u7684\u662f\u6700\u57fa\u7840\u7684\u51e0\u4f55\u6295\u5f71\u6a21\u578b\uff0c\u5176\u4e2d\u4e0d\u5305\u62ec\u50cf\u5f84\u5411\u7578\u53d8\u8fd9\u6837\u7684\u590d\u6742\u56e0\u7d20\u3002\n# --dense 0\uff0c\u51cf\u5c11\u4e0d\u9700\u8981\u7684\u8ba1\u7b97\u64cd\u4f5c\u3002\n
\u7136\u540e train render metric
Text Only(gaussian_splatting) fanghaotian@rhos-Super-Server:~/3DGS/gaussian-splatting$ python metrics.py -m ./output/lego_colmap/\n\nScene: ./output/lego_colmap/\nMethod: ours_30000\nMetric evaluation progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:23<00:00, 1.83s/it]\n SSIM : 0.2915006\n PSNR : 4.3199711\n LPIPS: 0.4333871\n
\u611f\u89c9\u662f\u8f6c\u6362\u7684\u65f6\u5019\u56fe\u7247\u592a\u5c11\u4e86\uff0c\u7136\u540e\u5c31\u53d8\u5dee\u4e86\u3002render \u7684\u65f6\u5019\u5f88\u5feb\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#ffmpeg","title":"ffmpeg \u7684\u95ee\u9898","text":"ffmpeg: error while loading shared libraries: libopenh264.so.5:-CSDN\u535a\u5ba2
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#_2","title":"\u4ee3\u7801\u89e3\u8bfb","text":"Gaussian_Splatting_Code
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#reference","title":"Reference","text":" - Site Unreachable
- \u75283D\u9ad8\u65af\u6cfc\u6e85(3DGS)\u91cd\u5efa\u81ea\u5df1\u7684\u6570\u636e_3d\u9ad8\u65af\u6cfc\u6e85\u6587\u7269\u6570\u5b57\u5316\u91cd\u5efa-CSDN\u535a\u5ba2
- 3D Gaussian Spaltting\u4ee3\u7801\u590d\u73b0\u5168\u6d41\u7a0b\u4e0e\u4ee3\u7801\u7ed3\u6784\u89e3\u8bfb_3d gaussian splatting\u590d\u73b0-CSDN\u535a\u5ba2
- 3D Gaussian Splatting\u590d\u73b0-CSDN\u535a\u5ba2
- Windows\u4e0b3D Gaussian Splatting\u4ece0\u5f00\u59cb\u5b89\u88c5\u914d\u7f6e\u73af\u5883\u53ca\u8bad\u7ec3\u6559\u7a0b_3d gaussian splatting\u5b89\u88c5\u6559\u7a0b-CSDN\u535a\u5ba2
- 3D Gaussian Splatting\u4e3b\u7a0b\u5e8f\u4ee3\u7801\u89e3\u8bfb_3d gaussian splatting\u6838\u5fc3\u4ee3\u7801-CSDN\u535a\u5ba2
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/OCRN/","title":"Beyond Object Recognition: A New Benchmark towards Object Concept Learning","text":" \u7ea6 374 \u4e2a\u5b57 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 2 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u79d1\u7814","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/OCRN/#beyond-object-recognition-a-new-benchmark-towards-object-concept-learning","title":"Beyond Object Recognition: A New Benchmark towards Object Concept Learning","text":"\u6587\u7ae0\u4fe1\u606f - \u6587\u7ae0\u9898\u76ee: Beyond Object Recognition: A New Benchmark towards Object Concept Learning
- \u4f5c\u8005\uff1aYong-Lu Li,\u00a0Yue Xu,\u00a0Xinyu Xu,\u00a0Xiaohan Mao,\u00a0Yuan Yao,\u00a0Siqi Liu,\u00a0Cewu Lu
- arXiv\uff1a[2212.02710] Beyond Object Recognition: A New Benchmark towards Object Concept Learning
- \u4ee3\u7801\uff1aGitHub - silicx/ObjectConceptLearning: This the official repository of OCL (ICCV 2023).
Abstract Understanding objects is a central building block of AI, especially for embodied AI. Even though object recognition excels with deep learning, current machines struggle to learn higher-level knowledge, e.g., what attributes an object has, and what we can do with it. Here, we propose a challenging Object Concept Learning (OCL) task to push the envelope of object understanding. It requires machines to reason out affordances and simultaneously give the reason: what attributes make an object possess these affordances. To support OCL, we build a densely annotated knowledge base including extensive annotations for three levels of object concept (category, attribute, affordance), and the clear causal relations of three levels. By analyzing the causal structure of OCL, we present a baseline, Object Concept Reasoning Network (OCRN). It leverages concept instantiation and causal intervention to infer the three levels. In experiments, OCRN effectively infers the object knowledge while following the causalities well. Our data and code are available at https://mvig-rhos.com/ocl.
Conclusion In this work, we introduce object concept learning (OCL) expecting machines to infer affordances and explain what attributes enable an object to possess them. Accordingly, we build an extensive dataset and present OCRN based on casual intervention and instantiation. OCRN achieves decent performance and follows the causalities well. However, OCL remains challenging and would inspire a line of studies on reasoning-based object understanding.
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/OCRN/#model","title":"Model","text":" - \u901a\u8fc7 attribute \u6765\u9884\u6d4b affordance\uff0c\u5e76\u63a8\u65ad\u662f\u90a3\u4e9b attribute \u8d77\u5230\u4e86\u4e3b\u8981\u4f5c\u7528\u3002
Example - \u901a\u8fc7 casual intervetion \u6765\u907f\u514d object category bias
- model implement \u770b\u7684\u8fd8\u662f\u6709\u70b9\u6a21\u7cca\uff0c\u4ee5\u540e\u518d\u6765\u8865\u5145
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/","title":"ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding","text":" \u7ea6 1428 \u4e2a\u5b57 7 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 7 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u79d1\u7814","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#ulip-2-towards-scalable-multimodal-pre-training-for-3d-understanding","title":"ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding","text":"\u6587\u7ae0\u4fe1\u606f - \u4f5c\u8005\uff1aLe Xue,\u00a0Ning Yu,\u00a0Shu Zhang,\u00a0Artemis Panagopoulou,\u00a0Junnan Li,\u00a0Roberto Mart\u00edn-Mart\u00edn,\u00a0Jiajun Wu,\u00a0Caiming Xiong,\u00a0Ran Xu,\u00a0Juan Carlos Niebles,\u00a0Silvio Savarese
- arXiv\uff1a[2305.08275] ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding
- \u4ee3\u7801\uff1aGitHub - salesforce/ULIP
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#abstract","title":"Abstract","text":"Abstract Recent advancements in multimodal pre-training have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes, their 2D counterparts, and language descriptions. However, the methods used by existing frameworks to curate such multimodal data, in particular language descriptions for 3D shapes, are not scalable, and the collected language descriptions are not diverse. To address this, we introduce ULIP-2, a simple yet effective tri-modal pre-training framework that leverages large multimodal models to automatically generate holistic language descriptions for 3D shapes. It only needs 3D data as input, eliminating the need for any manual 3D annotations, and is therefore scalable to large datasets. ULIP-2 is also equipped with scaled-up backbones for better multimodal representation learning. We conduct experiments on two large-scale 3D datasets, Objaverse and ShapeNet, and augment them with tri-modal datasets of 3D point clouds, images, and language for training ULIP-2. Experiments show that ULIP-2 demonstrates substantial benefits in three downstream tasks: zero-shot 3D classification, standard 3D classification with fine-tuning, and 3D captioning (3D-tolanguage generation). It achieves a new SOTA of 50.6% (top1) on Objaverse-LVIS and 84.7% (top-1) on ModelNet40 in zero-shot classification. In the ScanObjectNN benchmark for standard fine-tuning, ULIP-2 reaches an overall accuracy of 91.5% with a compact model of only 1.4 million parameters. ULIP-2 sheds light on a new paradigm for scalable multimodal 3D representation learning without human annotations and shows significant improvements over existing baselines. The code and datasets are released at https://github.com/salesforce/ULIP.
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#task","title":"Task","text":" - \u6784\u5efa\u4e00\u4e2a\u53ef\u6269\u5c55\u7684\u591a\u6a21\u6001\u9884\u8bad\u7ec3\u6846\u67b6\u7528\u4e8e3D\u7406\u89e3,\u4e0d\u9700\u8981\u4eba\u5de5\u6807\u6ce8\u5c31\u80fd\u8fdb\u884c\u5927\u89c4\u6a213D\u6570\u636e\u7684\u9884\u8bad\u7ec3\u3002
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#technical-chellenge-for-previous-methods","title":"Technical Chellenge for Previous Methods","text":" - \u6536\u96c6\u548c\u91c7\u96c6 3D \u6570\u636e
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#previous-methods","title":"Previous Methods","text":"","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#failure-case-limitation","title":"Failure Case / Limitation","text":" - \u4eba\u5de5\u6ce8\u91ca\u96be\u4ee5\u62d3\u5c55
- \u4e0d\u591f\u5168\u9762 might not provide sufficient details and lacks variations, or appears to be noisy
- \u8d35
Example \u6bd4\u5982\u8fd9\u5f20\u56fe\uff0c\u539f\u59cb\u6807\u6ce8\u7684\u8bed\u4e49\u4fe1\u606f\u5c31\u4e0d\u592a\u591f\uff0c\u4f46\u662f\u7528 ULIP-2 \u6807\u6ce8\u7684\u5c31\u6bd4\u8f83\u5168\u9762\u3002
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#our-pipeline","title":"Our Pipeline","text":"ULIP-2 \u91c7\u7528\u5927\u578b\u591a\u6a21\u6001\u6a21\u578b\uff0c\u4ece 3D \u5f62\u72b6\u7684\u6574\u4f53\u89d2\u5ea6\u4e3a\u6bcf\u4e2a 2D \u6e32\u67d3\u7684\u56fe\u50cf\u81ea\u52a8\u751f\u6210\u8be6\u7ec6\u63cf\u8ff0\u3002ULIP-2 \u5229\u7528\u9884\u5148\u5bf9\u9f50\u548c\u51bb\u7ed3\u7684\u89c6\u89c9\u8bed\u8a00\u7279\u5f81\u7a7a\u95f4\u6765\u5b9e\u73b0\u4e09\u5143\u4f53\u6a21\u6001\u4e4b\u95f4\u7684\u5bf9\u9f50\uff1a\u6574\u4f53\u6587\u672c\u3001\u56fe\u50cf\u548c 3D \u70b9\u4e91\u3002\u9884\u8bad\u7ec3\u540e\uff0c3D \u7f16\u7801\u5668\u5c06\u7528\u4e8e\u4e0b\u6e38\u4efb\u52a1\u3002
Example ","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#key-insight","title":"Key insight","text":" - 3D \u5230 2D \u5c31\u662f\u7528\u51e0\u4e2a\u56fa\u5b9a\u89c6\u89d2\uff0c\u7136\u540e\u7528 multi-modality pre-training module \u5c31\u53ef\u4ee5\u751f\u6210\u6bcf\u5f20\u7167\u7247\u7684\u63cf\u8ff0\u3002\u5c31\u53ef\u4ee5\u81ea\u52a8\u4e14\u53ef\u62d3\u5c55\u5730\u751f\u6210\u6570\u636e\u3002
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#technical-contributions","title":"Technical contributions","text":" - ULIP-2 \u6709\u52a9\u4e8e\u5b9e\u73b0\u53ef\u6269\u5c55\u7684\u591a\u6a21\u6001\u9884\u8bad\u7ec3\uff0c\u65e0\u9700\u4eba\u5de5\u6ce8\u91ca\uff0c\u4f7f\u5176\u9002\u7528\u4e8e\u4efb\u4f55 3D \u6570\u636e\u96c6\uff0c\u751a\u81f3\u662f\u672a\u6807\u8bb0\u7684\u6570\u636e\u96c6\u3002\u5b83\u5b8c\u5168\u4f9d\u8d56\u4e8e 3D \u6570\u636e\uff0c\u5b9e\u73b0\u4e86\u66f4\u5e7f\u6cdb\u7684\u9002\u7528\u6027\u548c\u6613\u7528\u6027\u3002
- ULIP-2 \u5728\u591a\u6a21\u6001\u8868\u793a\u5b66\u4e60\u65b9\u9762\u53d6\u5f97\u4e86\u91cd\u5927\u8fdb\u6b65\u3002\u5728\u5177\u6709\u6311\u6218\u6027\u7684\u5f00\u653e\u4e16\u754c Objaverse-LVIS \u57fa\u51c6\u6d4b\u8bd5\u4e2d\uff0cULIP-2 \u7684\u51c6\u786e\u7387\u8fbe\u5230\u4e86 50.6% \u7684\u524d 1%\uff0c\u6bd4\u5f53\u524d\u7684 SOTA\uff08OpenShape [22]\uff09\u9ad8\u51fa 3.8%\uff0c\u5c3d\u7ba1 ULIP-2 \u7684\u6846\u67b6\u66f4\u7b80\u5355\u3001\u66f4\u6d41\u7ebf\u578b;\u5bf9\u4e8e ModelNet40 \u4e0a\u7684\u96f6\u6837\u672c\u5206\u7c7b\uff0cULIP-2 \u8fbe\u5230 84.7%\uff0c\u751a\u81f3\u4f18\u4e8e\u4e00\u4e9b\u5b8c\u5168\u76d1\u7763\u7684 3D \u5206\u7c7b\u65b9\u6cd5 [50]\u3002\u6b64\u5916\uff0c\u5b83\u5728 ScanObjectNN \u57fa\u51c6\u6d4b\u8bd5\u4e2d\u786e\u4fdd\u4e86 91.5% \u7684\u603b\u4f53\u51c6\u786e\u7387\uff0c\u53ea\u6709 140 \u4e07\u4e2a\u53c2\u6570\u3002\u6b64\u5916\uff0c\u8fd8\u5c55\u793a\u4e86 ULIP-2 \u7f16\u7801\u5668\u7684 LLMs\uff0c\u7a81\u51fa\u4e86\u5b83\u4e0e\u4e0d\u65ad\u589e\u957f\u7684 LLM\u3002\u6b64\u5916\uff0cULIP-2 \u53ef\u4ee5\u4e0e\u4e0d\u65ad\u589e\u957f\u7684 3D \u6570\u636e\u5bb9\u91cf\u548c\u5927\u578b\u591a\u6a21\u6001\u6a21\u578b\u7684\u5f00\u53d1\u6709\u6548\u534f\u540c\u4f5c\u7528\u3002
- \u6211\u4eec\u53d1\u5e03\u4e86\u4e24\u4e2a\u5927\u89c4\u6a21\u7684\u4e09\u6a21\u6001\u6570\u636e\u96c6\uff0c\u201cULIP-Objaverse\u201d\u548c\u201cULIP-ShapeNet\u201d\u4e09\u5143\u7ec4\uff0c\u7531\u70b9\u4e91\u3001\u56fe\u50cf\u548c\u8bed\u8a00\u63cf\u8ff0\u7ec4\u6210\u3002\u8868 2 \u4e2d\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u6570\u636e\u96c6\u7684\u7edf\u8ba1\u6570\u636e\u3002
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#method","title":"Method","text":" - \u7528 ULIP \u5f53 baseline 1. \u8f93\u5165 3D \u6a21\u6001 2. \u63d0\u53d6\u70b9\u4e91 3. \u751f\u6210\u56fe\u50cf 4. \u7528 BLIP-2 \u751f\u6210\u53e5\u5b50 5. \u5f97\u5230\u8bed\u8a00\u6a21\u6001
\\[ \\mathcal{L}_{\\mathrm{P2I}}=-\\frac{1}{2}\\sum_{i}\\log\\frac{\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{I}}/\\tau)}{\\sum_{j}\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{j}^{\\mathrm{I}}/\\tau)}+\\log\\frac{\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{I}}/\\tau)}{\\sum_{j}\\exp(\\mathbf{f}_{j}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{I}}/\\tau)} \\] \\[ \\mathcal{L}_{\\mathrm{P2T}}=-\\frac{1}{2}\\sum_{i}\\log\\frac{\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{T}}/\\tau)}{\\sum_{j}\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{j}^{\\mathrm{T}}/\\tau)}+\\log\\frac{\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{T}}/\\tau)}{\\sum_{j}\\exp(\\mathbf{f}_{j}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{T}}/\\tau)} \\] - \\(\\displaystyle \\tau\\) \u662f\u6e29\u5ea6\u7cfb\u6570\uff0c \\(\\displaystyle\\mathrm{f}\\) \u662f\u5d4c\u5165\u4ee5\u540e\u7684\u7279\u5f81
- \u8bad\u7ec3 3D \u7f16\u7801\u5668\uff0c\u8ba9\u8fd9\u4e24\u4e2a\u503c\u52a0\u8d77\u6765\u6700\u5c0f
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#experiment","title":"Experiment","text":" Example - three downstream tasks
- the zero-shot 3D classification task involving multimodal inputs
- the standard 3D classification task involving a single modality
- the 3D captioning task involving 3D-to-language generation with LLMs
- Evaluation Metrics
- top-1 and top-5 accuracy for the zero-shot 3D classification task;
- overall accuracy and class average accuracy for the standard 3D classification task.
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#the-zero-shot-3d-classification-task-involving-multimodal-inputs","title":"the zero-shot 3D classification task involving multimodal inputs","text":"Example","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#the-standard-3d-classification-task-involving-a-single-modality","title":"the standard 3D classification task involving a single modality","text":"Example","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#the-3d-captioning-task-involving-3d-to-language-generation-with-llms","title":"the 3D captioning task involving 3D-to-language generation with LLMs","text":"Example Example","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#ablation-study","title":"Ablation Study","text":" - Ablation on the effect of the generated captions
- Different Large Multimodal Models
- Number of 2D Views Per 3D Object
- Top-k CLIP Ranked Captions Per 2D View
- Scaling Up the Backbone Models
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#conclusion","title":"Conclusion","text":"Conclusion We present ULIP-2, a novel framework for multimodal 3D representation learning. By leveraging large multimodal models for language description generation and scaling up the multimodal 3D pre-training, ULIP-2 not only addresses the quality and scalability challenges in existing multimodal 3D datasets but also demonstrates significant improvements in all downstream tasks. We also release \"ULIP-Objaverse\" triplets and \"ULIP-ShapeNet\" triplets, two large-scale trimodal datasets to foster further research. Limitations. ULIP-2\u2019s pre-training primarily utilizes object-level 3D shape datasets, which inherently differ from scene-level 3D data in their distribution and complexity. Exploring the application of the ULIP-2 framework to scenelevel 3D data understanding, and leveraging the knowledge learned from object-level 3D data for this purpose, represents a compelling avenue for future research. Broader Impact. ULIP-2 aims to minimize human annotation in 3D multimodal pre-training, reducing labor but potentially impacting low-skilled job markets. This dual impact, a common concern in AI advancements, underscores the need for broader considerations in AI research.
- \u521b\u9020\u4e86\u4e00\u79cd\u751f\u6210\u6570\u636e\u7684\u65b9\u6cd5\uff0c\u7136\u540e\u53d1\u5e03\u4e86\u00a0\"ULIP-Objaverse\" triplets and \"ULIP-ShapeNet\" triplets
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#limitation","title":"Limitation","text":" - 3D \u6570\u636e\u96c6\u53ea\u6709 object\uff0c\u6570\u636e\u96c6\u6ca1\u6709\u573a\u666f\u7684
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#_1","title":"\u590d\u73b0","text":"","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#_2","title":"\u76f8\u5173\u8bba\u6587","text":"","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#generative-large-multimodal-models","title":"Generative Large Multimodal Models","text":"","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#multimodal-representation-learning","title":"Multimodal Representation Learning","text":"","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#3d-point-cloud-understanding","title":"3D Point Cloud Understanding","text":" - PointNet
- Point-BERT
- PointNeXt
","tags":["\u79d1\u7814"]},{"location":"CS_Basic/","title":"Computer Basic","text":""},{"location":"CS_Basic/#computer-science-basic","title":"Computer Science Basic","text":"Abstract
\u672c\u90e8\u5206\u5185\u5bb9\uff08\u9664\u7279\u522b\u58f0\u660e\u5916\uff09\u91c7\u7528 \u7f72\u540d-\u975e\u5546\u4e1a\u6027\u4f7f\u7528-\u4fdd\u6301\u4e00\u81f4 4.0 \u56fd\u9645 (CC BY-NC-SA 4.0) \u8bb8\u53ef\u534f\u8bae\u8fdb\u884c\u8bb8\u53ef\u3002
CS61A - Composing Programs 2713 651 17 mins 1734027543
- Experience 0 0 mins 0
CS61C - \u8ba1\u7b97\u673a\u7ec4\u6210\u4e0e\u8bbe\u8ba1\u786c\u4ef6\u8f6f\u4ef6\u63a5\u53e3 2978 13 10 mins 1734027543
Network - Security Basic 368 1 mins 1734027543
15-213 - CSAPP 1012 36 4 mins 1734027543
C++ - Accelerated C++ 2534 489 15 mins 1734027543
- C++ Basic 3115 523 17 mins 1734027543
"},{"location":"CS_Basic/15-213/CSAPP/","title":"\u6df1\u5165\u7406\u89e3\u8ba1\u7b97\u673a\u7cfb\u7edf","text":""},{"location":"CS_Basic/15-213/CSAPP/#_1","title":"\u6df1\u5165\u7406\u89e3\u8ba1\u7b97\u673a\u7cfb\u7edf","text":" \u7ea6 997 \u4e2a\u5b57 36 \u884c\u4ee3\u7801 14 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 5 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"CS_Basic/15-213/CSAPP/#1","title":"1 \u8ba1\u7b97\u673a\u7cfb\u7edf\u6f2b\u6e38","text":""},{"location":"CS_Basic/15-213/CSAPP/#2","title":"2 \u4fe1\u606f\u7684\u8868\u793a\u548c\u5904\u7406","text":" - \u628a\u4f4d\u7ec4\u5408\u518d\u4e00\u8d77\uff0c\u518d\u52a0\u4e0a interpretation
- \u4e09\u79cd\u91cd\u8981\u7684\u6570\u5b57\u8868\u793a
- unsigned
- two's-complement
- floating-point
- overflow
- \u6d6e\u70b9\u6570\u662f\u8fd1\u4f3c\u7684
"},{"location":"CS_Basic/15-213/CSAPP/#21","title":"2.1 \u4fe1\u606f\u5b58\u50a8","text":" - 1 byte = 8 bits
- virtual memory
- address
- \u8bb2\u5b58\u50a8\u5668\u7a7a\u95f4\u5212\u5206\u4e3a\u66f4\u53ef\u7ba1\u7406\u7684\u5355\u5143\uff0c\u6765\u5b58\u653e\u4e0d\u540c\u7684 program object
"},{"location":"CS_Basic/15-213/CSAPP/#211","title":"2.1.1 \u5341\u516d\u8fdb\u5236\u8868\u793a\u6cd5","text":""},{"location":"CS_Basic/15-213/CSAPP/#212","title":"2.1.2 \u5b57\u6570\u636e\u5927\u5c0f","text":" - word size
- nominal size
- \u5b57\u957f\u51b3\u5b9a\u7684\u6700\u91cd\u8981\u7684\u7cfb\u7edf\u53c2\u6570\u5c31\u662f\u865a\u62df\u5730\u5740\u7a7a\u95f4\u7684\u6700\u5927\u5927\u5c0f
- \u5b57\u957f\u4e3a \\(\\displaystyle \\omega\\) \u4e3a\u7684\u673a\u5668\uff0c\u865a\u62df\u5730\u5740\u7684\u8303\u56f4\u4e3a \\(\\displaystyle 0\\sim2^{\\omega} - 1\\)
- \u5927\u591a\u6570 64 \u4f4d\u673a\u5668\u53ef\u4ee5\u8fd0\u884c 32 \u4f4d\u673a\u5668\u7f16\u8bd1\u7684\u7a0b\u5e8f\uff0c\u5373\u5411\u540e\u517c\u5bb9
- \u4e3a\u4e86\u907f\u514d\u5927\u5c0f\u548c\u4e0d\u540c\u7f16\u8bd1\u5668\u8bbe\u7f6e\u5e26\u6765\u7684\u5947\u602a\u884c\u4e3a\uff0c\u6211\u4eec\u6709\u4e86 int 32_t \u548c int 64_t
- C \u8bed\u8a00\u5bf9\u58f0\u660e\u7684\u5173\u952e\u8bcd\u987a\u5e8f\u4e0d\u654f\u611f
"},{"location":"CS_Basic/15-213/CSAPP/#213","title":"2.1.3 \u5bfb\u5740\u548c\u5b57\u8282\u987a\u5e8f","text":" - \u5c0f\u7aef\u7f16\u5740
- \u5c31\u662f\u53f3\u8fb9\u653e\u5c0f\u7684\uff0c\u8981\u4ece\u53f3\u5f80\u5de6\u8bfb
- \u5b57\u8282\u987a\u5e8f\u53d8\u5f97\u91cd\u8981\u7684\u4e09\u79cd\u60c5\u51b5
- \u7f51\u7edc\u5e94\u7528\u7a0b\u5e8f\u7684\u4ee3\u7801\u7f16\u5199\u5fc5\u987b\u9075\u5b88\u5df2\u5efa\u7acb\u7684\u5173\u4e8e\u5b57\u8282\u987a\u5e8f\u7684\u89c4\u5219
- disassembler
- \u7f16\u5199\u89c4\u907f\u6b63\u5e38\u7684\u7c7b\u578b\u7cfb\u7edf\u7684\u7a0b\u5e8f
- cast or union in C
- \u5bf9\u5e94\u7528\u7f16\u7a0b\u4e0d\u63a8\u8350\uff0c\u4f46\u662f\u5bf9\u7cfb\u7edf\u7ea7\u7f16\u7a0b\u662f\u5fc5\u9700\u7684
C#include <stdio.h>\n\n\n\ntypedef unsigned char *byte_pointer;\n\nvoid show_bytes(byte_pointer start, size_t len) {\n\u00a0 size_t i;\n\u00a0 for (i = 0; i < len; i++)\n\u00a0 \u00a0 printf(\" %.2x\", start[i]);\n\u00a0 printf(\"\\n\");\n}\n\nvoid show_int(int x) { show_bytes((byte_pointer)&x, sizeof(int)); }\n\nvoid show_float(float x) { show_bytes((byte_pointer)&x, sizeof(float)); }\n\nvoid show_pointer(void *x) { show_bytes((byte_pointer)&x, sizeof(void *)); }\n\nvoid test_show_bytes(int val) {\n\u00a0 int ival = val;\n\u00a0 float fval = (float)ival;\n\u00a0 int *pval = &ival;\n\u00a0 show_int(ival);\n\u00a0 show_float(fval);\n\u00a0 show_pointer(pval);\n}\n\nint main() {\n\u00a0 int val = 12345;\n\u00a0 test_show_bytes(val);\n\u00a0 return 0;\n}\n
Text Only 39 30 00 00 //\u5c0f\u7aef\u6cd5\n 00 e4 40 46\n 8c f6 bf ef b4 00 00 00\n
"},{"location":"CS_Basic/15-213/CSAPP/#214","title":"2.1.4 \u8868\u793a\u5b57\u7b26\u4e32","text":""},{"location":"CS_Basic/15-213/CSAPP/#215","title":"2.1.5 \u8868\u793a\u4ee3\u7801","text":" - \u4e8c\u8fdb\u5236\u4ee3\u7801\u5f88\u5c11\u80fd\u5728\u4e0d\u540c\u673a\u5668\u548c\u64cd\u4f5c\u7cfb\u7edf\u7ec4\u5408\u4e4b\u95f4\u79fb\u690d
"},{"location":"CS_Basic/15-213/CSAPP/#216","title":"2.1.6 \u5e03\u5c14\u4ee3\u6570\u7b80\u4ecb","text":" - \u53ef\u4ee5\u6269\u5c55\u5230\u4f4d\u5411\u91cf\u7684\u8fd0\u7b97
- \u5e03\u5c14\u4ee3\u6570
- Boolean ring
- \u548c\u96c6\u5408\u7684\u5bf9\u5e94
"},{"location":"CS_Basic/15-213/CSAPP/#217-c","title":"2.1.7 C \u8bed\u8a00\u4e2d\u7684\u4f4d\u7ea7\u8fd0\u7b97","text":""},{"location":"CS_Basic/15-213/CSAPP/#218-c","title":"2.1.8 C \u8bed\u8a00\u4e2d\u7684\u903b\u8f91\u8fd0\u7b97","text":""},{"location":"CS_Basic/15-213/CSAPP/#219-c","title":"2.1.9 C \u8bed\u8a00\u4e2d\u7684\u79fb\u4f4d\u8fd0\u7b97","text":" - \u903b\u8f91\u53f3\u79fb
- \u7b97\u6570\u53f3\u79fb
- \u5b9e\u9645\u4e0a\uff0c\u51e0\u4e4e\u6240\u6709\u7684\u7f16\u8bd1\u5668\u90fd\u5bf9\u6709\u7b26\u53f7\u6570\u4f7f\u7528\u7b97\u672f\u53f3\u79fb\uff0c\u800c\u5bf9\u4e8e\u65e0\u7b26\u53f7\u6570\uff0c\u53f3\u79fb\u5fc5\u987b\u662f\u903b\u8f91\u7684\uff08C \u8bed\u8a00\uff09
- \u800c\u5bf9\u4e8e Java
x>>k \u662f\u7b97\u6570\u53f3\u79fb\uff0c x>>>k \u662f\u903b\u8f91\u53f3\u79fb - \u5bf9\u4e8e C \u8bed\u8a00\uff0c\u79fb\u52a8 \\(\\displaystyle k \\text{ mod } \\omega\\)
- \u52a0\u51cf\u7684\u4f18\u5148\u7ea7\u6bd4\u79fb\u4f4d\u7b97\u6cd5\u8981\u641e
"},{"location":"CS_Basic/15-213/CSAPP/#22","title":"2.2 \u6574\u6570\u8868\u793a","text":""},{"location":"CS_Basic/15-213/CSAPP/#221","title":"2.2.1 \u6574\u578b\u6570\u636e\u7c7b\u578b","text":" - 64 \u4f4d\u548c 32 \u4f4d\u662f\u4e0d\u4e00\u6837\u7684
- \u53d6\u503c\u8303\u56f4\u4e0d\u662f\u5bf9\u79f0\u7684
- Java \u53ea\u652f\u6301\u6709\u7b26\u53f7\u6570
"},{"location":"CS_Basic/15-213/CSAPP/#222","title":"2.2.2 \u65e0\u7b26\u53f7\u6570\u7684\u7f16\u7801","text":" - \u628a\u5411\u91cf\u5199\u6210\u4e8c\u8fdb\u5236\u8868\u793a\u7684\u6570\uff0c\u5c31\u5f97\u5230\u4e86\u65e0\u7b26\u53f7\u8868\u793a
\\[ B2U_w(\\vec{x})\\doteq\\sum_{i=0}^{u-1}x_i2^i \\] \\[ B2U_w:\\{0, 1\\}^w\\to\\{0, \\cdots,2^w-1\\} \\] - \u65e0\u7b26\u53f7\u6570\u7f16\u7801\u7684\u552f\u4e00\u6027
- \\(\\displaystyle \u51fd\u6570B2U_w\u662f\u4e00\u4e2a\u53cc\u5c04\u3002\\)
"},{"location":"CS_Basic/15-213/CSAPP/#223","title":"2.2.3 \u8865\u7801\u7f16\u7801","text":" - two's-complement
- negative weight
\\[ B2T_w(\\vec{x})\\doteq- x_{w-1}2^{w-1}+\\sum_{i=0}^{w-2}x_i2^i \\] \\[ B2T_{w}\\colon \\{0, 1\\}^{w}\\to\\langle TMin_{w}, \\cdots, TMax_{w} \\rangle \\] \\[ B2O_w(\\vec{x})\\doteq-x_{w-1}(2^{w-1}-1)+\\sum_{i=0}^{w-2}x_i2^i \\] \\[ B2S_w(\\vec{x})\\doteq(-1)^{x_{w-1}}\\cdot\\bigl(\\sum_{i=0}^{w-2}x_i2^i\\bigr) \\] - \u5bf9\u4e8e\u6570\u5b57 0 \u6709\u4e24\u79cd\u4e0d\u540c\u7684\u7f16\u7801\u65b9\u5f0f
"},{"location":"CS_Basic/15-213/CSAPP/#224","title":"2.2.4 \u6709\u7b26\u53f7\u6570\u548c\u65e0\u7b26\u53f7\u6570\u4e4b\u95f4\u7684\u8f6c\u6362","text":" - \u4fdd\u6301\u4f4d\u503c\u4e0d\u53d8\uff0c\u6539\u53d8\u89e3\u91ca\u65b9\u5f0f
\\[ T2U_{_w}(x)\\doteq B2U_{_w}( T2B_{_w}(x) ) \\] - \u8865\u7801\u8f6c\u6362\u4e3a\u65e0\u7b26\u53f7\u6570
\\[ T2U_w(x)=\\begin{cases}x+2^w,&x<0\\\\x,&x\\geqslant0\\end{cases} \\] - \u65e0\u7b26\u53f7\u6570\u8f6c\u6362\u4e3a\u8865\u7801
\\[ U2T_w(u)=\\begin{cases}u ,&u\\leqslant TMax_w\\\\u-2^w ,&u>TMax_w\\end{cases} \\]"},{"location":"CS_Basic/15-213/CSAPP/#225-c","title":"2.2.5 C \u8bed\u8a00\u4e2d\u7684\u6709\u7b26\u53f7\u6570\u4e0e\u65e0\u7b26\u53f7\u6570","text":" - \u4e00\u4e2a\u8fd0\u7b97\u7b26\u662f\u6709\u7b26\u53f7\u7684\u800c\u53e6\u4e00\u4e2a\u662f\u65e0\u7b26\u53f7\u7684\uff0c\u90a3 C \u7a0b\u5e8f\u4f1a\u628a\u6709\u7b26\u53f7\u7684\u8f6c\u6362\u4e3a\u65e0\u7b26\u53f7\u7684\u3002
"},{"location":"CS_Basic/15-213/CSAPP/#226","title":"2.2.6 \u6269\u5c55\u4e00\u4e2a\u6570\u5b57\u7684\u4f4d\u8868\u793a","text":" - \u65e0\u7b26\u53f7\u6570\u7684 zero extension
- \u8865\u7801\u6570\u7684\u7b26\u53f7\u6269\u5c55
"},{"location":"CS_Basic/15-213/CSAPP/#227","title":"2.2.7 \u622a\u65ad\u6570\u5b57","text":""},{"location":"CS_Basic/15-213/CSAPP/#228","title":"2.2.8 \u5173\u4e8e\u6709\u7b26\u53f7\u6570\u4e0e\u65e0\u7b26\u53f7\u6570\u7684\u5efa\u8bae","text":""},{"location":"CS_Basic/15-213/CSAPP/#23","title":"2.3 \u6574\u6570\u8fd0\u7b97","text":""},{"location":"CS_Basic/15-213/CSAPP/#231","title":"2.3.1 \u65e0\u7b26\u53f7\u52a0\u6cd5","text":" - Lisp \u652f\u6301\u65e0\u9650\u7cbe\u5ea6\u7684\u8fd0\u7b97
\\[ x+_w^uy=\\begin{cases}x+y,&x+y<2^w&\\text{\u6b63\u5e38}\\\\x+y-2^w,&2^w\\leqslant x+y<2^{w+1}&\\text{\u6ea2\u51fa}\\end{cases} \\]"},{"location":"CS_Basic/15-213/CSAPP/#3","title":"3 \u7a0b\u5e8f\u7684\u673a\u5668\u7ea7\u8868\u793a","text":""},{"location":"CS_Basic/15-213/CSAPP/#4","title":"4 \u5904\u7406\u5668\u4f53\u7cfb\u7ed3\u6784","text":""},{"location":"CS_Basic/15-213/CSAPP/#5","title":"5 \u4f18\u5316\u7a0b\u5e8f\u6027\u80fd","text":""},{"location":"CS_Basic/15-213/CSAPP/#6","title":"6 \u5b58\u50a8\u5668\u5c42\u6b21\u7ed3\u6784","text":""},{"location":"CS_Basic/15-213/CSAPP/#7","title":"7 \u94fe\u63a5","text":""},{"location":"CS_Basic/15-213/CSAPP/#8","title":"8 \u5f02\u5e38\u63a7\u5236\u6d41","text":""},{"location":"CS_Basic/15-213/CSAPP/#9","title":"9 \u865a\u62df\u5185\u5b58","text":""},{"location":"CS_Basic/15-213/CSAPP/#10-io","title":"10 \u7cfb\u7edf\u7ea7 I/O","text":""},{"location":"CS_Basic/15-213/CSAPP/#11","title":"11 \u7f51\u7edc\u7f16\u7a0b","text":""},{"location":"CS_Basic/15-213/CSAPP/#12","title":"12 \u5e76\u53d1\u7f16\u7a0b","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/","title":"Accelerated C++","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#accelerated-c","title":"Accelerated C++","text":" \u7ea6 2520 \u4e2a\u5b57 489 \u884c\u4ee3\u7801 2 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 19 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#0-c","title":"0 \u5f00\u59cb\u5b66\u4e60 C++","text":"C++#include <iostream>\n\nint main()\n{\n std::cout << \"Hello, World!\" << std::endl;\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#01","title":"0.1 \u6ce8\u91ca","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#02-displaystyle-include","title":"0.2 \\(\\displaystyle \\#\\)include \u6307\u4ee4","text":" - \u8f93\u5165\u3001\u8f93\u51fa\u4e0d\u5c5e\u4e8e\u8bed\u8a00\u6838\u5fc3\uff0c\u800c\u662f\u6807\u51c6\u5e93\u7684\u4e00\u90e8\u5206
- iostream \u4ee3\u8868 C++\u5e93\u7684\u6807\u51c6\u5934\u6587\u4ef6
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#03-main","title":"0.3 \u4e3b\u51fd\u6570 main","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#04","title":"0.4 \u82b1\u62ec\u53f7","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#05","title":"0.5 \u4f7f\u7528\u6807\u51c6\u5e93\u8fdb\u884c\u8f93\u51fa","text":" - std:: cout \u6307\u6807\u51c6\u8f93\u51fa\u6d41
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#06","title":"0.6 \u8fd4\u56de\u8bed\u53e5","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#07","title":"0.7 \u4e00\u4e9b\u8f83\u4e3a\u6df1\u5165\u7684\u89c2\u5bdf","text":" - \u8868\u8fbe\u5f0f
- \u8fd0\u7b97\u4f1a\u4ea7\u751f\u4e00\u4e2a\u7ed3\u679c\uff0c\u53ef\u80fd\u4f1a\u5177\u6709\u526f\u4f5c\u7528
- \u6bcf\u4e2a\u64cd\u4f5c\u6570\u90fd\u5177\u6709\u4e00\u4e2a\u7c7b\u578b
- <<\u662f\u5de6\u7ed3\u5408\u7684\uff0c\u6240\u4ee5\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e24\u4e2a<<\u8fd0\u7b97\u7b26\u548c\u4e09\u4e2a\u64cd\u4f5c\u6570
C++(std::cout << \"Hello, World!\") << std::endl\n
- std:: cout \u7684\u7c7b\u578b\u662f std::ostream
- std:: endl \u662f\u4e00\u4e2a\u63a7\u5236\u5668\uff08manipulator\uff09
- \u7b2c\u4e00\u79cd\u4f5c\u7528\u57df: \u540d\u5b57\u7a7a\u95f4
- :: \u662f\u4f5c\u7528\u57df\u8fd0\u7b97\u7b26\uff0cstd:: cout \u662f\u4e00\u4e2a\u9650\u5b9a\u540d\u79f0
- \u82b1\u62ec\u53f7\u662f\u53e6\u4e00\u79cd\u4f5c\u7528\u57df
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#08","title":"0.8 \u5c0f\u7ed3","text":" - main \u53ef\u4ee5\u6ca1\u6709 return \u8bed\u53e5
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#1","title":"1 \u4f7f\u7528\u5b57\u7b26\u4e32","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#11","title":"1.1 \u8f93\u5165","text":"C++#include <iostream>\n#include <string>\n\nint main() \n{\n std::cout << \"Please enter your first name:\";\n\n std::string name;\n std::cin >> name;\n\n std::cout << \"Hello\" << name << \"!\" << std::endl;\n return 0;\n}\n
- \u53d8\u91cf\u662f\u4e00\u4e2a\u5bf9\u8c61\uff0c\u4f46\u6709\u4e9b\u5bf9\u8c61\u53ef\u80fd\u6ca1\u6709\u540d\u79f0
- \u5c40\u90e8\u53d8\u91cf\u6709\u9650\u7684\u751f\u5b58\u671f\u662f\u533a\u5206\u53d8\u91cf\u548c\u5bf9\u8c61\u7684\u4e00\u4e2a\u91cd\u8981\u4f9d\u636e
- \u9690\u85cf\u5728\u5bf9\u8c61\u7c7b\u578b\u4e2d\u7684\u8fd8\u6709\u5176\u63a5\u53e3
- \u63a5\u53e3\u662f\u53ef\u5b9e\u73b0\u64cd\u4f5c\u7684\u96c6\u5408
- \u7f13\u51b2\u533a\u6765\u4fdd\u5b58\u8f93\u51fa
- \u4f55\u65f6\u5237\u65b0\u7f13\u51b2\u533a:
- \u7f13\u51b2\u533a\u6ee1\u4e86
- \u8f93\u5165\u6d41\u8bfb\u6570\u636e
- \u660e\u786e\u8981\u6c42\u5237\u65b0
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#12","title":"1.2 \u4e3a\u59d3\u540d\u88c5\u6846","text":" - \u8fd0\u7b97\u7b26\u88ab\u91cd\u8f7d\u4e86
- \u8fd0\u7b97\u7b26\u4e00\u4e2a\u6c38\u8fdc\u4e0d\u4f1a\u6539\u53d8\u7684\u6027\u8d28\u662f\u7ed3\u5408\u5f8b\uff0c+\u662f\u5de6\u7ed3\u5408\u7684
C++std::string stars(10, '*')\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#13","title":"1.3 \u5c0f\u7ed3","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#2","title":"2 \u5faa\u73af\u548c\u8ba1\u6570","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#21","title":"2.1 \u95ee\u9898","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#22","title":"2.2 \u7a0b\u5e8f\u7684\u6574\u4f53\u7ed3\u6784","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#23","title":"2.3 \u8f93\u51fa\u6570\u76ee\u672a\u77e5\u7684\u884c","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#231-while","title":"2.3.1 while \u8bed\u53e5","text":" - ++\u662f\u4e00\u4e2a\u589e\u91cf\u8fd0\u7b97\u7b26
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#232-while","title":"2.3.2 \u8bbe\u8ba1 while \u8bed\u53e5","text":" - \u5faa\u73af\u4e0d\u53d8\u5f0f\uff08Loop invariant\uff09
- \u521d\u59cb\u5316\uff1a\u5728\u5faa\u73af\u5f00\u59cb\u4e4b\u524d\uff0c\u5faa\u73af\u4e0d\u53d8\u5f0f\u5e94\u8be5\u88ab\u786e\u7acb\u4e3a\u771f\u3002
- \u4fdd\u6301\uff1a\u5faa\u73af\u7684\u6bcf\u6b21\u8fed\u4ee3\u90fd\u5fc5\u987b\u4fdd\u6301\u5faa\u73af\u4e0d\u53d8\u5f0f\u4e3a\u771f\uff0c\u5373\u5982\u679c\u8fdb\u5165\u67d0\u6b21\u8fed\u4ee3\u65f6\u5faa\u73af\u4e0d\u53d8\u5f0f\u4e3a\u771f\uff0c\u90a3\u4e48\u5728\u8be5\u6b21\u8fed\u4ee3\u7ed3\u675f\u65f6\uff0c\u5faa\u73af\u4e0d\u53d8\u5f0f\u4ecd\u7136\u4e3a\u771f\u3002
- \u7ec8\u6b62\uff1a\u5f53\u5faa\u73af\u7ed3\u675f\u65f6\uff0c\u5faa\u73af\u4e0d\u53d8\u5f0f\u5e94\u8be5\u80fd\u591f\u7528\u6765\u8bc1\u660e\u5faa\u73af\u7684\u7ec8\u6b62\u6761\u4ef6\u6210\u7acb\uff0c\u6216\u8005\u7528\u6765\u8bc1\u660e\u5faa\u73af\u7684\u8f93\u51fa\u6216\u7ed3\u679c\u6ee1\u8db3\u7279\u5b9a\u7684\u5c5e\u6027\u3002
- \u5faa\u73af\u4e0d\u53d8\u5f0f\u7684\u4e00\u4e2a\u5178\u578b\u4f8b\u5b50\u662f\u5728\u6392\u5e8f\u7b97\u6cd5\u4e2d\uff0c\u4f8b\u5982\u5192\u6ce1\u6392\u5e8f\u3002\u5728\u5192\u6ce1\u6392\u5e8f\u4e2d\uff0c\u4e00\u4e2a\u53ef\u80fd\u7684\u5faa\u73af\u4e0d\u53d8\u5f0f\u662f\uff1a\u201c\u6bcf\u6b21\u5faa\u73af\u8fed\u4ee3\u540e\uff0c\u6570\u7ec4\u7684\u6700\u540en\u4e2a\u5143\u7d20\u662f\u6392\u5e8f\u597d\u7684\u201d\uff0c\u5176\u4e2dn\u662f\u5faa\u73af\u8fed\u4ee3\u7684\u6b21\u6570\u3002
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#24","title":"2.4 \u8f93\u51fa\u4e00\u884c","text":"C++const std::string::size_type cols = greeting.size() + pad * 2 + 2;\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#241","title":"2.4.1 \u8f93\u51fa\u8fb9\u754c\u5b57\u7b26","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#2411-if","title":"2.4.1.1 if \u8bed\u53e5","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#2412","title":"2.4.1.2 \u903b\u8f91\u8fd0\u7b97\u7b26","text":" - ||\u662f\u5de6\u7ed3\u5408\uff0c\u4f1a\u6709\u77ed\u8def\u6c42\u503c\uff08short-circuit evaluation\uff09
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#242","title":"2.4.2 \u8f93\u51fa\u975e\u8fb9\u754c\u5b57\u7b26","text":" - +=\u590d\u5408\u8d4b\u503c\u8fd0\u7b97\u7b26
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#25","title":"2.5 \u5b8c\u6574\u7684\u6846\u67b6\u7a0b\u5e8f","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#251-std","title":"2.5.1 \u7565\u53bb\u91cd\u590d\u4f7f\u7528\u7684 std::","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#252-for","title":"2.5.2 \u4f7f\u7528 for \u8bed\u53e5\u6765\u7f29\u77ed\u7a0b\u5e8f","text":" - \u533a\u95f4\u7684\u8d8a\u754c\u503c\uff08off-the-end value\uff09
C++for (int r = 0; r != rows; ++r) {\n\n}\n
- \u8fd9\u662f\u534a\u5f00\u533a\u95f4
C++for (init-statement condition; expression)\n statement\n\n#equals to\n\n{\n inti-statement\n while (condition) {\n statement\n expression;\n }\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#253","title":"2.5.3 \u538b\u7f29\u68c0\u6d4b","text":" - \u5c31\u662f\u628a if \u8bed\u53e5\u5408\u5e76\u4e00\u4e0b
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#254","title":"2.5.4 \u5b8c\u6574\u7684\u6846\u67b6\u7a0b\u5e8f","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#26","title":"2.6 \u8ba1\u6570","text":" - \u4e0d\u5bf9\u6210\u533a\u95f4\u53ef\u4ee5\u76f4\u63a5\u770b\u51fa\u6709\u591a\u5c11\u4e2a\u5143\u7d20
- \\(\\displaystyle [m, n]\\) \u6709 m - n \u4e2a\u5143\u7d20
- \u800c\u4e14\u53ef\u4ee5\u8868\u793a\u7a7a\u533a\u95f4 \\(\\displaystyle [n, n]\\)
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#27","title":"2.7 \u5c0f\u7ed3","text":" - \u8868\u8fbe\u5f0f
- \u64cd\u4f5c\u6570\u7684\u7ec4\u5408\u65b9\u5f0f
- \u64cd\u4f5c\u6570\u5982\u4f55\u88ab\u8f6c\u6362
- \u64cd\u4f5c\u6570\u7684\u8fd0\u7b97\u6b21\u5e8f
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#3","title":"3 \u4f7f\u7528\u6279\u91cf\u6570\u636e","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#31","title":"3.1 \u8ba1\u7b97\u5b66\u751f\u6210\u7ee9","text":"C++#include <iomanip>\n#include <ios>\n#include <iostream>\n#include <string>\n\nusing std::cin;\nusing std::cout;\nusing std::endl;\nusing std::precision;\n\nint main() {\n cout << \"Please enter your first name:\";\n string name;\n cin >> name;\n cout << \"Hello, \" << name << \"!\" << endl;\n\n cout << \"Please enter your midterm and final exam grades:\";\n double midterm, final;\n cin >> midterm >> final;\n\n cout << \"Enter all your homework grades, \" \n \"followed by end-of-file:\";\n\n int count = 0;\n double sum = 0;\n\n double x;\n\n while (cin >> x) {\n ++ count;\n sum += x;\n }\n\n streamsize prec = cout.precision();\n cout << \"Your final grade is \" << setprecision(3)\n << 0.2 * midterm + 0.4 * final + 0.4 * sum / count\n << setprecision(prec) << endl; //\u91cd\u7f6e\u6709\u6548\u4f4d\u6570 \u6216\u5199\u6210cout.precision(prec);\n return 0;\n}\n
- \u8f93\u5165\u8fd0\u7b97\u7b26\u8fd4\u56de\u5b83\u7684\u5de6\u64cd\u4f5c\u6570\u4f5c\u4e3a\u7ed3\u679c
- \u5374\u7701\u521d\u59cb\u5316
- setprecision \u4e5f\u662f\u4e00\u4e2a\u63a7\u5236\u5668: \u4e3a\u6d41\u7684\u540e\u7ee7\u8f93\u51fa\u8bbe\u7f6e\u4e86\u4e00\u4e2a\u7279\u5b9a\u7684\u6709\u6548\u4f4d\u6570
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#311","title":"3.1.1 \u68c0\u6d4b\u8f93\u51fa","text":" - istream \u53ef\u4ee5\u88ab\u8f6c\u6362\u6210 bool \u503c
- \u6709\u4e09\u79cd\u60c5\u51b5\u6761\u4ef6\u4f1a\u53d8\u5047:
- \u8fbe\u5230\u8f93\u5165\u6587\u4ef6\u7684\u7ed3\u5c3e
- \u8f93\u5165\u548c\u6211\u4eec\u8bd5\u56fe\u8bfb\u53d6\u7684\u53d8\u91cf\u7c7b\u578b\u4e0d\u4e00\u81f4
- \u68c0\u6d4b\u5230\u4e00\u4e2a\u786c\u4ef6\u95ee\u9898
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#312","title":"3.1.2 \u5faa\u73af\u4e0d\u53d8\u5f0f","text":"\u4e0d\u662f\u5f88\u61c2
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#32","title":"3.2 \u7528\u4e2d\u503c\u4ee3\u66ff\u5e73\u5747\u503c","text":"\u90a3\u4e48\u5c31\u8981\u6392\u5e8f\u4e86
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#321","title":"3.2.1 \u628a\u6570\u636e\u96c6\u5408\u5b58\u50a8\u5728\u5411\u91cf\u4e2d","text":"C++double x;\nvector<double> homework;\n\nwhile(cin >> x)\n homework.push_back(x);\n
- \u6211\u4eec\u4f7f\u7528\u4e86\u4e00\u79cd\u540d\u4e3a\u6a21\u677f\u7c7b\u7684\u8bed\u8a00\u7279\u5f81
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#322","title":"3.2.2 \u4ea7\u751f\u8f93\u51fa","text":"C++typedef vector<double>::size_type vec_sz;\nvec_sz size = homework.size();\n
- \u6211\u4eec\u5e0c\u671b\u4fdd\u6301\u7cfb\u7edf\u73af\u5883\u7684\u72ec\u7acb\u6027
C++if (size == 0) {\n cout << endl << \"You must enter your grades. \"\n \"Please try again.\" << endl;\n return 1;\n}\n\nsort (homework.begin(), homework.end());\n\nvec_sz mid = size / 2;\ndouble median;\nmedian = size % 2 == 0 ? (homework[mid] + homework[mid - 1]) / 2\n : homework[mid];\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#323","title":"3.2.3 \u4e00\u4e9b\u66f4\u4e3a\u6df1\u5165\u7684\u89c2\u5bdf","text":" - size_type \u662f\u65e0\u7b26\u53f7\u6574\u6570\u7c7b\u578b
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#33","title":"3.3 \u5c0f\u7ed3","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#4","title":"4 \u7ec4\u7ec7\u7a0b\u5e8f\u548c\u6570\u636e","text":" - \u8fd9\u4e9b\u5e93\u5de5\u5177\u6709\u51e0\u4e2a\u540c\u6837\u7684\u7279\u6027:
- \u80fd\u89e3\u51b3\u67d0\u4e9b\u7279\u5b9a\u7c7b\u578b\u7684\u95ee\u9898
- \u4e0e\u5176\u4ed6\u7684\u5927\u591a\u6570\u5de5\u5177\u90fd\u76f8\u4e92\u72ec\u7acb
- \u90fd\u5177\u6709\u4e00\u4e2a\u540d\u79f0
- \u4e24\u79cd\u65b9\u6cd5\u6765\u7ec4\u7ec7\u5927\u578b\u7684\u7a0b\u5e8f
- \u51fd\u6570
- \u6570\u636e\u7ed3\u6784
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#41","title":"4.1 \u7ec4\u7ec7\u8ba1\u7b97","text":" - \u53c2\u6570\u7684\u521d\u59cb\u503c\u662f\u76f8\u5e94\u53c2\u6570\u503c\u7684\u590d\u5236\uff0c\u5373\u6309\u503c\u8c03\u7528\uff08call by value\uff09
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#411","title":"4.1.1 \u67e5\u627e\u4e2d\u503c","text":"C++double median(vector<double> vec)\n{\n typedef vector<double>::size_type vec_sz;\n vec_sz size = vec.size();\n if (size == 0)\n throw domain_error(\"median of an empty vector\");\n sort(vec.begin(), vec.end());\n vec_sz mid = size / 2;\n return size % 2 == 0 ? (vec[mid] + vec[mid - 1]) / 2 : vec[mid];\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#412","title":"4.1.2 \u91cd\u65b0\u5236\u5b9a\u8ba1\u7b97\u6210\u7ee9\u7684\u7b56\u7565","text":" - \u53ea\u80fd\u628a\u4e0d\u662f\u5e38\u91cf\u7684\u5f15\u7528\u7ed9\u5e38\u91cf\u7684\uff0c\u5373\u6761\u4ef6\u53ea\u80fd\u52a0\u5f3a
- \u6709\u597d\u51e0\u4e2a\u540c\u6837\u51fd\u6570\u540d\u7684\u51fd\u6570\u65f6\uff0c\u4f1a\u53d1\u751f\u91cd\u8f7d
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#413","title":"4.1.3 \u8bfb\u5bb6\u5ead\u4f5c\u4e1a\u6210\u7ee9","text":" - \u5de6\u503c\u53c2\u6570: \u975e\u4e34\u65f6\u5bf9\u8c61
C++istream read_hw(istream& in, vector<double>& hw)\n{\n if (in) {\n hw.clear();\n double x;\n while (in >> x) \n hw.push_back(x);\n in.clear();\n }\n return in;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#414","title":"4.1.4 \u4e09\u79cd\u51fd\u6570\u53c2\u6570","text":" - \u4e00\u822c\u800c\u8a00\uff0c\u6211\u4eec\u6ca1\u6709\u5fc5\u8981\u4e3a\u4e86 int \u6216 double \u8fd9\u6837\u89c1\u5230\u4f60\u7684\u5185\u90e8\u7c7b\u578b\u7684\u53c2\u6570\u800c\u53bb\u4f7f\u7528 const \u5f15\u7528
- \u5982\u679c\u4f20\u5165 read_hw () \u7684\u4e0d\u662f\u4e00\u4e2a\u5de6\u503c\uff0c\u90a3\u4e48\u6211\u4eec\u4f1a\u628a\u8f93\u5165\u5b58\u5230\u4e00\u4e2a\u6211\u4eec\u65e0\u59a8\u8bbf\u95ee\u7684\u5bf9\u8c61\u4e2d\uff08\u6539\u5bf9\u8c61\u4f1a\u5728 read_hw() \u8fd4\u56de\u65f6\u7acb\u5373\u6d88\u5931\uff09
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#415","title":"4.1.5 \u4f7f\u7528\u51fd\u6570\u6765\u8ba1\u7b97\u5b66\u751f\u7684\u6210\u7ee9","text":" - \u4e0d\u8981\u8ba9\u4e00\u6761\u8bed\u53e5\u4e2d\u7684\u526f\u4f5c\u7528\u4e2a\u6570\u8d85\u8fc7\u4e00\u4e2a
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#42","title":"4.2 \u7ec4\u7ec7\u6570\u636e","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#421","title":"4.2.1 \u628a\u4e00\u4e2a\u5b66\u751f\u7684\u6240\u6709\u6570\u636e\u653e\u7f6e\u5728\u4e00\u8d77","text":"C++struct Student_info {\n string name;\n double midterm, final;\n vector<double> homework;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#422","title":"4.2.2 \u5904\u7406\u5b66\u751f\u8bb0\u5f55","text":"C++istream& read(istream& is, Student_info& s)\n{\n is >> s.name >> s.midterm >> s.final;\n read_hw(is, s.homework);\n return is;\n}\n\ndouble grade(const Student_info& s) \n{\n return grade(s.midterm, s.final, s.homework);\n}\n\nbool compare(const Student_info& x, const Studeng_info& y)\n{\n return x.name < y.name;\n}\n\nsort(students.begin(), students.end(), compare;\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#423","title":"4.2.3 \u751f\u6210\u62a5\u8868","text":"C++cout << setw(maxlen + 1) << student[i].name;\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#43","title":"4.3 \u628a\u5404\u90e8\u5206\u4ee3\u7801\u8fde\u63a5\u5230\u4e00\u8d77","text":"Text Only#ifndef GUARD_median_h\n#define GUARD_median_h //\u9884\u5904\u7406\u7a0b\u5e8f\n\n#include <vector>\ndouble median(vector<double>);\n\n#endif\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#44","title":"4.4 \u628a\u8ba1\u7b97\u6210\u7ee9\u7684\u7a0b\u5e8f\u5206\u5757","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#45","title":"4.5 \u4fee\u6b63\u540e\u7684\u8ba1\u7b97\u6210\u7ee9\u7684\u7a0b\u5e8f","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#46","title":"4.6 \u5c0f\u7ed3","text":" - \u7528\u6237\u5b9a\u4e49\u7684\u5934\u6587\u4ef6\u4e00\u822c\u4ee5 .h \u7ed3\u5c3e
- \u4e0d\u5e94\u8be5\u5728\u5934\u6587\u4ef6\u4e2d\u4f7f\u7528 using \u58f0\u660e
- \u7528 \\(\\displaystyle \\#\\) ifndef \u6307\u4ee4\u6765\u9632\u6b62\u5bf9\u5934\u6587\u4ef6\u7684\u91cd\u590d\u5305\u542b
- \u5185\u8054\u5b50\u8fc7\u7a0b\u901a\u5e38\u65f6\u5728\u5934\u6587\u4ef6\u800c\u4e0d\u662f\u5728\u6e90\u6587\u4ef6\u4e2d\u5b9a\u4e49 inline
- \u4e3a\u4e86\u6bd4\u5356\u4f60\u51fd\u6570\u8c03\u7528\u7684\u989d\u5916\u5f00\u9500\uff0c\u7f16\u8bd1\u5668\u4f1a\u7528\u51fd\u6570\u4f53\u7684\u4e00\u4e2a\u590d\u5236\u6765\u66ff\u6362\u5bf9\u51fd\u6570\u7684\u6bcf\u4e00\u4e2a\u8c03\u7528\u5e76\u6839\u636e\u9700\u8981\u8fdb\u884c\u4fee\u6b63
- \u5f02\u5e38\u5904\u7406
C++try{\n} catch (t) { }\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#5","title":"5 \u4f7f\u7528\u987a\u5e8f\u5bb9\u5668\u5e76\u5206\u6790\u5b57\u7b26\u4e32","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#51","title":"5.1 \u6309\u7c7b\u522b\u6765\u533a\u5206\u5b66\u751f","text":"C++vector<Student_info> extract_fails(vector<Student_info>& students)\n{\n vector<Student_info> pass, fail;\n for (vector<Student_info>::size_type i = 0; i != students.size(); ++i;)\n if (fgrade(student[i]))\n fail.push_back(student[i]);\n else\n pass.push_back(student[i]);\n students = pass;\n return fail;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#511","title":"5.1.1 \u5c31\u5730\u5220\u9664\u51fd\u6570","text":"C++vector<Student_info> extract_fails(vector<Student_info>& students)\n{\n vector<Student_info> fail;\n vector<Student_info>::size_type i = 0;\n while (i != students.size()) {\n if (fgrade(studentp[i])) {\n fail.push_back(students[i]);\n students.erase(students.begin() + i);\n } else\n ++i;\n }\n return fail;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#512","title":"5.1.2 \u987a\u5e8f\u5b58\u53d6\u4e0e\u968f\u673a\u5b58\u53d6","text":" - \u8bbf\u95ee\u5bb9\u5668\u65f6\u6240\u91c7\u53d6\u7684\u6b21\u5e8f\u4f1a\u5bfc\u81f4\u4e0d\u540c\u7684\u6027\u80fd\u7279\u6027
- \u4e8e\u662f\u4fbf\u6709\u4e86 iterator
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#52","title":"5.2 \u8fed\u4ee3\u5668","text":" - iterator
- \u8bc6\u522b\u4e00\u4e2a\u5bb9\u5668\u4ee5\u53ca\u5bb9\u5668\u4e2d\u7684\u4e00\u4e2a\u5143\u7d20
- \u8ba9\u6211\u4eec\u68c0\u67e5\u5b58\u50a8\u5728\u8fd9\u4e2a\u5143\u7d20\u4e2d\u7684\u503c
- \u63d0\u4f9b\u64cd\u4f5c\u6765\u79fb\u52a8\u5728\u5bb9\u6613\u4e2d\u7684\u5143\u7d20
- \u91c7\u7528\u5bf9\u5e94\u4e8e\u5bb9\u5668\u6240\u80fd\u591f\u6709\u6548\u5904\u7406\u7684\u65b9\u5f0f\u5bf9\u53ef\u7528\u7684\u64cd\u4f5c\u8fdb\u884c\u7ea6\u675f
C++for (vector<Student_info>::size_type i = 0; i != students.end(); ++i)\n cout << students[i].name << endl;\nfor (vector<Student_info>::const_iterator iter = students.begin();\n iter != students.end(); ++iter) {\n cout << (*iter).name << endl; }\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#521","title":"5.2.1 \u8fed\u4ee3\u5668\u7c7b\u578b","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#522","title":"5.2.2 \u8fed\u4ee3\u5668\u64cd\u4f5c","text":" - end \u7d27\u63a5\u5728\u5bb9\u5668\u6700\u540e\u4e00\u4e2a\u5143\u7d20\u540e\u9762\u7684\u4f4d\u7f6e
- ++iter \u7528\u4e86\u8fed\u4ee3\u5668\u7c7b\u578b\u91cd\u8f7d
- \u5f53\u6211\u4eec\u7528\u95f4\u63a5\u5f15\u7528\u8fd0\u7b97\u7b26 \\(\\displaystyle *\\) \u6765\u8bbf\u95ee\u8fd9\u4e2a\u5143\u7d20\uff0c\u90a3\u4e48\u4ed6\u4f1a\u8fd4\u56de\u4e00\u4e2a\u5de6\u503c\uff08\u8fed\u4ee3\u5668\u6240\u6307\u5411\u7684\u5143\u7d20\uff09
- \\(\\displaystyle .\\) \u7684\u4f18\u5148\u7ea7\u6bd4 \\(\\displaystyle *\\) \u9ad8
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#523","title":"5.2.3 \u4e00\u70b9\u8bed\u6cd5\u77e5\u8bc6","text":"C++(*iter).name\niter->name\n//both are ok\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#524-students-erase-students-begin-i","title":"5.2.4 students. erase (students. begin () + i) \u7684\u542b\u4e49","text":" - students \u4e0d\u652f\u6301\u968f\u673a\u8bbf\u95ee\u7d22\u5f15\u64cd\u4f5c\u7684\u5bb9\u5668\uff0c\u4f46\u662f\u4ecd\u4f1a\u5141\u8bb8\u8fed\u4ee3\u5668\u7684\u968f\u673a\u8bbf\u95ee
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#53","title":"5.3 \u7528\u8fed\u4ee3\u5668\u6765\u4ee3\u66ff\u7d22\u5f15","text":"C++vecotr<Student_info> extract_fails(vector<Student_info>& students)\n{\n vector<Student_info> fail;\n vector<Student_info>::iterator iter = students.begin();\n while (iter != students.end()) {\n if (fgrade(*iter)) {\n fail.push_back(*iter);\n iter = students.erase(iter); //important\n } else\n ++iter;\n }\n return fail;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#54","title":"5.4 \u91cd\u65b0\u601d\u8003\u6570\u636e\u7ed3\u6784\u4ee5\u5b9e\u73b0\u66f4\u597d\u7684\u6027\u80fd","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#55-list","title":"5.5 list \u7c7b\u578b","text":" - \u76f4\u63a5\u628a\u6240\u6709\u7684 vector \u6362\u6210 list \u5c31\u53ef\u4ee5\u4e86
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#551","title":"5.5.1 \u4e00\u4e9b\u91cd\u8981\u7684\u5dee\u522b","text":" - list \u7c7b\u7684\u8fed\u4ee3\u5668\u5e76\u4e0d\u652f\u6301\u5b8c\u5168\u968f\u673a\u7684\u8bbf\u95ee
- \u6240\u4ee5\u6211\u4eec\u4e0d\u80fd\u7528 sort \u51fd\u6570
- \u4f46\u662f list \u6709\u81ea\u5df1\u6210\u5458\u51fd\u6570
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#552","title":"5.5.2 \u4e00\u4e2a\u607c\u4eba\u7684\u8bdd\u9898","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#56","title":"5.6 \u5206\u5272\u5b57\u7b26\u4e32","text":"C++vector<string> split(const string& s)\n{\n vector<string> ret;\n typedef string::size_def string_size;\n string_size i = 0;\n while (i != s.size()) {\n while (i != s.size() && isspace(s[i])) // is from <cctype>\n ++i;\n string_size j = i;\n while (j != s.size() && !isspace(s[i]))\n ++j;\n if (i != j) {\n ret.push_back(s.substr(i, j - i));\n i = j;\n }\n }\n return ret;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#57-split","title":"5.7 \u6d4b\u8bd5 split \u51fd\u6570","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#58","title":"5.8 \u8fde\u63a5\u5b57\u7b26\u4e32","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#581","title":"5.8.1 \u4e3a\u56fe\u6848\u88c5\u6846","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#582","title":"5.8.2 \u7eb5\u5411\u8fde\u63a5","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#583","title":"5.8.3 \u6a2a\u5411\u8fde\u63a5","text":"TODO
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#59","title":"5.9 \u5c0f\u7ed3","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#6","title":"6 \u4f7f\u7528\u5e93\u7b97\u6cd5","text":" - \u5229\u7528\u516c\u7528\u63a5\u53e3\u6765\u63d0\u4f9b\u4e00\u4e2a\u6807\u51c6\u7b97\u6cd5\u96c6\u5408
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#61","title":"6.1 \u5206\u6790\u5b57\u7b26\u4e32","text":"C++for (vector<string>::const_iterator it = bottom.begin(); it != bottom.end(); ++i)\n ret.push_back(*it);\n\nret.insert(ret.end(), bottom.begin(), bottom.end());\n\ncopy(bottom.begin(), bottom.end(), back_inserter(ret));\n
- copy \u662f\u4e00\u4e2a\u6cdb\u578b (generic) \u7b97\u6cd5\u7684\u4f8b\u5b50
- back_inserter \u662f\u4e00\u4e2a\u8fed\u4ee3\u5668\u9002\u914d\u5668\u7684\u4f8b\u5b50
C++copy(begin, end, out);\n//equals to\nwhile (begin != end)\n *out++ = *begin++;\n // equals to \n // *out = *begin; ++out; ++begin;\n\nit = begin++;\n//equals to\nit = begin;\n++begin;\n
- \u8fed\u4ee3\u5668\u9002\u914d\u5668\u662f\u4e00\u4e2a\u4ea7\u751f\u8fed\u4ee3\u5668\u7684\u4e1c\u897f
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#611-split","title":"6.1.1 \u53e6\u4e00\u4e2a\u5b9e\u73b0 split \u7684\u65b9\u6cd5","text":"C++bool space(char c) {\n return isspace(c);\n}\nbool not_space(char c) {\n return !isspace(c);\n}\nvector<string> split(const string& str) {\n typedef string::const_iterator iter;\n vector<string> ret;\n iter i = str.begin();\n while(i != str.end()) {\n i = find_if(i, str.end(), not_space);\n iter j = find_if(i, str.end(), space);\n if (i != str.end()) ret.push_back(string(i, j));\n i = j; \n }\n return ret;\n}\n
- \u6211\u4eec\u9700\u8981\u7f16\u5199\u6211\u4eec space \u548c not_space \u662f\u7528\u6765\u89e3\u91ca\u6211\u4eec\u6240\u6307\u7684\u662f\u54ea\u4e2a\u7248\u672c\u7684 isspace
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#612","title":"6.1.2 \u56de\u6587","text":"C++bool is_palindrome(const string& s) {\n return equal(s.begin(), s.end(), s.rbegin());\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#613-url","title":"6.1.3 \u67e5\u627e URL","text":" C++vector<string> find_urls(const string& s) {\n vector<string> ret;\n typedef string::const_iterator iter;\n iter b = s.begin(), e = s.end();\n while (b != e) {\n b = url_beg(b, e);\n if (b != e) {\n iter after = url_end(b, e);\n ret.push_back(string(b, after));\n b = after;\n }\n }\n return ret;\n}\n\nstring::const_iterator url_end(string::const_iterator b, string::const_iterator e) {\n return find_if(b, e, not_url_char);\n}\n\nbool not_url_char(char c) {\n static const string url_ch = \"~;/?@=&$-_.+!*{},'\";\n return !(isalnum(c)) || find(url_ch.begin(), url_ch.end(), c) != url_end());\n //static\u58f0\u660e\u7684\u5c40\u90e8\u53d8\u91cf\u5177\u6709\u5168\u5c40\u5bff\u547d\uff0c\u751f\u5b58\u671f\u8d2f\u7a7f\u6574\u4e2a\u51fd\u6570\u8c03\u7528\u8fc7\u7a0b\n}\n\nstring::const_iterator url_beg(string::const_iterator b, string::const_iterator e) {\n static const string sep = \"://\";\n typedef string::const_iterator iter;\n iter i = b;\n while ((i = search(i, e, sep.begin(), sep.end())) != e) {\n if (i != b && i + sep.size() != e) {\n iter beg = i;\n while (beg != b && isalpha(beg[-1]))\n --beg;\n if (beg != i && i + sep.size() != e && !not_url_char(i[sep.size()])) return beg;\n }\n if (i != e) i += sep.size();\n }\n return e;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#62","title":"6.2 \u5bf9\u8ba1\u7b97\u6210\u7ee9\u7684\u65b9\u6848\u8fdb\u884c\u6bd4\u8f83","text":" - \u8bfb\u6240\u6709\u7684\u5b66\u751f\u8bb0\u5f55\uff0c\u628a\u505a\u4e86\u5168\u90e8\u5bb6\u5ead\u4f5c\u4e1a\u7684\u5b66\u751f\u4e0e\u5176\u4ed6\u7684\u5b66\u751f\u5206\u9694\u5f00\u3002
- \u5bf9\u6bcf\u4e00\u7ec4\u4e2d\u7684\u6240\u6709\u5b66\u751f\u5206\u522b\u4f7f\u7528\u6bcf\u4e00\u4e2a\u7684\u8ba1\u7b97\u6210\u7ee9\u7684\u65b9\u6848\uff0c\u62a5\u544a\u6bcf\u4e00\u7ec4\u7684\u4e2d\u503c\u6210\u7ee9\u3002
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#621","title":"6.2.1 \u5904\u7406\u5b66\u751f\u8bb0\u5f55","text":"C++bool did_all_hw(const Student_info& s) {\n reutrn ((fint(s.homework.begin(), s.homework.end(), 0)) == s.homework.end());\n}\n\nvector<Student_info> did, didnt;\nStudent_info student;\n\nwhile (read(cin, student)) {\n if (did_all_hw(student))\n did.push_back(student);\n else\n didnt.push_back(student);\n}\nif (did.empty()) {\n cout << \"No student did all the homework!\" << endl;\n return 1;\n}\nif (didnt.empty()) {\n cout << \"Every student did all the homework!\" << endl;\n return 1;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#622","title":"6.2.2 \u5206\u6790\u6210\u7ee9","text":"C++write_analysis(cout, \"median\", median_analysis, did, didn't);\n\ndouble grade_aux(const Student_info& s) {\n tru {\n return grade(s);\n } catch (domain_error) {\n return grade(s.midterm, s.final, 0);\n }\n}\n\ndouble median_analysis(const vector<Student_info>& students) {\n vector<double> grades;\n transdorm(students.begin(), students.end(), back_inserter(grades), grade_aux);\n return medina(grades);\n}\n\nvoid write_analysis(ostream& out, const string& name, double analysis(const vector<Student_info>&), const vector<Student_info>& did, const vector<Student_info>& didnt) {\n out << name << \": median(did) = \" << analysis(did) << \", median(didnt) = \" << analysis(didnt) << endl; \n}\n\nint main() {\n vector<Student_info> did, didnt;\n Student_info student;\n while (read(cin, student)) {\n if (did_all_hw(student))\n did.push_back(student);\n else \n didnt.push_back(student);\n }\n if (did.empty()) {\n cout << \"No student did all the homework!\" << endl;\n return 1;\n }\n if (didnt.empty()) {\n cout << \"Every student did all the homework!\" << endl;\n return 1;\n }\n write_analysis(cout, \"median\", median_analysis, did, didnt);\n write_analysis(cout, \"average\", average_analysis, did, didnt);\n write_analysis(cout, \"medina of homework turned in\", optimistic_median_analysis, did, didnt);\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#623","title":"6.2.3 \u8ba1\u7b97\u57fa\u4e8e\u5bb6\u5ead\u4f5c\u4e1a\u5e73\u5747\u6210\u7ee9\u7684\u603b\u6210\u7ee9","text":"C++double average(const vector<double>& v) {\n return accumulate(v.begin(), v.end(), 0.0) / v.size();\n}\n\ndouble average_analysis(const Student_info& s) {\n return grade(s.midterm, s.final, average(s.homework));\n}\n\ndouble average_analysis(const vector<Student_info>& students) {\n vector<double> grades;\n transform(students.begin(), students.end(), back_inserter(grades), average_grade);\n return median(grades);\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#624","title":"6.2.4 \u4e0a\u4ea4\u7684\u5bb6\u5ead\u4f5c\u4e1a\u7684\u4e2d\u503c","text":"C++double optimistic_median(const Student_info& s) {\n vector<double> nonzero;\n remove_copy(s.homework.begin(), s.homework.end(), back_inserter(nonzero), 0);\n if (nonzero.empty())\n return grade(s.midterm, s.final, 0);\n else \n return grade(s.midterm, s.final, median(nonzero));\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#63","title":"6.3 \u5bf9\u5b66\u751f\u8fdb\u884c\u5206\u7c7b\u5e76\u56de\u987e\u4e00\u4e0b\u6211\u4eec\u7684\u95ee\u9898","text":" - \u4f7f\u7528\u4e00\u4e9b\u7b97\u6cd5\u5e93\u6765\u89e3\u51b3\u95ee\u9898
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#631","title":"6.3.1 \u4e00\u79cd\u4e24\u6b21\u4f20\u9012\u7684\u89e3\u51b3\u65b9\u6848","text":"C++vector<Student_info> extract_fails(vector<Student_info>& students) {\n vector<Student_info> fail;\n remove_copy_if(students.begin(), students.end(), back_inserter(fail), pgrade);\n students.earse(remove_if(students.begin(), students.end(), fgrade), student.end());\n return fail;\n}\n\nbool pgrade(const Student_info& s) {\n return !fgrade(s);\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#64","title":"6.4 \u4e00\u79cd\u4e00\u6b21\u4f20\u9012\u7684\u89e3\u51b3\u65b9\u6848","text":"C++vector<Student_info> extract_fails(vector<Student_info>& students) {\n vector<Student_info>::iterator iter = stable_partition(students.begin(), students.end(), pgrade);\n vector<Student_info> fail(iter, students.end());\n students.erase(iter, students.end());\n return fail;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#65","title":"6.5 \u7b97\u6cd5\u3001\u5bb9\u5668\u4ee5\u53ca\u8fed\u4ee3\u5668","text":" - \u7b97\u6cd5\u4f5c\u7528\u4e8e\u5bb9\u5668\u7684\u5143\u7d20\uff0c\u800c\u4e0d\u662f\u4f5c\u7528\u4e8e\u5bb9\u5668
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#7","title":"7 \u4f7f\u7528\u5173\u8054\u5bb9\u5668","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#71","title":"7.1 \u652f\u6301\u9ad8\u6548\u67e5\u627e\u7684\u5bb9\u5668","text":" - \u5173\u8054\u5bb9\u5668\u4f1a\u5bf9\u63d2\u5165\u7684\u5143\u7d20\u8fdb\u884c\u6392\u5e8f\u6765\u63d0\u9ad8\u67e5\u627e\u7684\u901f\u5ea6
- \u5173\u8054\u6570\u7ec4\u5c31\u662f\u6709 key-value \u7684\u6570\u7ec4
- map: \u6620\u5c04\u8868\u7684\u7d22\u5f15\u4e0d\u4e00\u5b9a\u662f\u6574\u6570
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#72","title":"7.2 \u8ba1\u7b97\u5355\u8bcd\u6570","text":"C++int main() {\n string s;\n map<string, int> counters;\n while (cin >> s) ++counters[s];\n for (map<string, int>::const_iterator it = counters.begin(); it != couners.end(); ++it) {\n cout << it->first << \"\\t\" << it->second << endl;\n }\n return 0;\n}\n
- \u6570\u5bf9\uff08pair\uff09
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#73","title":"7.3 \u4ea7\u751f\u4e00\u4e2a\u4ea4\u53c9\u5f15\u7528\u8868","text":"C++map<string, vector<int> > xref(istream& in, vector<string> find_words(const string&) = split) {\n string line;\n int line_number = 0'\n map<string, vector<int>> ret;\n while (getline(in, line)) {\n ++line_number;\n vector<string> words = find_words(line);\n for (vector<string>::const_iterator it = words.begin(); it != words.end(); ++it)\n ret(*it).push_back(line_number);\n }\n return ret;\n}\n\n\nint main() {\n map<string, vector<int> > ret = cref(cin);\n for (map<string, vector<int> >::const_iterator it = ret.begin(); it != ret.end(); ++it) {\n cout << it->first << \" occurs on line(s): \";\n vector<int>::const_iterator line_it = it->second.begin();\n cout << *lint_it;\n ++line_it;\n while (line_it != it->second.end()) {\n cout << \", \" << *line_it;\n ++line_it;\n }\n cout << endl;\n }\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#74","title":"7.4 \u751f\u6210\u53e5\u5b50","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#741","title":"7.4.1 \u8868\u793a\u89c4\u5219","text":"C++typedef vector<string> Rule;\ntypedef vector<Rule> Rule_collection;\ntypedef map<string, Rule_collection> Grammer;\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#742","title":"7.4.2 \u8bfb\u5165\u6587\u6cd5","text":"C++Grammer read_grammer(istream& in) {\n Grammer ret;\n string line;\n while (getline(in, line)) {\n vector<string> entry = split(line);\n if (!entry.empty())\n ret[entry[0]].push_back(Rule(entry.begin() + 1, entry.end()));\n }\n return ret;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#743","title":"7.4.3 \u751f\u6210\u53e5\u5b50","text":"C++vector<string> gen_sentence(const Grammar& g) {\n vector<string> ret;\n gen_aux(g, \"<sentence>\", ret);\n return ret;\n}\n\nbool bracketed(const string& s) {\n return s.size() > 1 && s[0] == '<' && s[s.size() - 1] == '>';\n}\n\nvoid gen_aux(const Grammar& g, const string& word, vector<string>& ret) {\n if (!bracketed(word)) {\n ret.push_back(word);\n } else {\n Grammar::const_iterator it = g.find(word);\n if (it == g.end())\n throw logic_error(\"empty rule\");\n const Rule_collection& c = it->seond;\n const Rule& r = c[nrand(c.size())];\n for (Rule::const_iterator i = r.begin(); i != r.end(); ++i)\n gen_aux(g, *i, ret);\n }\n}\n\nint main() {\n vector<string> sentence = gen_sentence(read_grammar(cin));\n vector<string>::const_iterator it = sentence.begin();\n if (!sentence.empty()) {\n cout << *it;\n ++it; \n }\n while (it != sentence.end()) {\n cout << \" \" << *it;\n ++it;\n }\n cout << endl;\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#744","title":"7.4.4 \u9009\u62e9\u4e00\u4e2a\u968f\u673a\u51fd\u6570","text":"C++int nrand(int n) {\n if (n <= 0 || n > RAND_MAX)\n throw domain_error(\"Argument to nrand is out of range\");\n const int bucket_size = RAND_MAX / n;\n int r;\n do r = rand() / bucket_size;\n while (r >= n);\n return r;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#75","title":"7.5 \u5173\u4e8e\u6027\u80fd\u7684\u4e00\u70b9\u8bf4\u660e","text":" - \u7528\u6563\u5217\u8868\u5b9e\u73b0\u5173\u8054\u6570\u7ec4\u5728 C++\u4e2d\u7684\u662f\u5f88\u56f0\u96be\u7684 \u6ca1\u641e\u61c2
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#76","title":"7.6 \u5c0f\u7ed3","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#8","title":"8 \u7f16\u5199\u6cdb\u578b\u51fd\u6570","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#81","title":"8.1 \u6cdb\u578b\u51fd\u6570\u662f\u4ec0\u4e48","text":" - \u5728\u4f7f\u7528\u51fd\u6570\u4e4b\u524d\u4e0d\u77e5\u9053\u53c2\u6570\u6216\u8005\u8fd4\u56de\u7c7b\u578b\u662f\u4ec0\u4e48
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#811","title":"8.1.1 \u672a\u77e5\u7c7b\u578b\u7684\u4e2d\u503c","text":" - \u5b9e\u73b0\u4e86\u6cdb\u578b\u51fd\u6570\u7684\u8bed\u8a00\u7279\u5f81\u88ab\u79f0\u4f5c\u6a21\u677f\u51fd\u6570
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#9","title":"9 \u5b9a\u4e49\u65b0\u7c7b\u578b","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#10","title":"10 \u7ba1\u7406\u5185\u5b58\u548c\u4f4e\u7ea7\u6570\u636e\u7ed3\u6784","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#11_1","title":"11 \u5b9a\u4e49\u62bd\u8c61\u6570\u636e\u7c7b\u578b","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#12_1","title":"12 \u4f7f\u7c7b\u5bf9\u8c61\u50cf\u4e00\u4e2a\u6570\u503c\u4e00\u6837\u5de5\u4f5c","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#13_1","title":"13 \u4f7f\u7528\u7ee7\u627f\u4e0e\u52a8\u6001\u7ed1\u5b9a","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#14","title":"14 \u8fd1\u4e4e\u81ea\u52a8\u5730\u7ba1\u7406\u5185\u5b58","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#15","title":"15 \u518d\u8c08\u5b57\u7b26\u56fe\u5f62","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#16-c","title":"16 \u4eca\u540e\u5982\u4f55\u5b66\u4e60 C++","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/","title":"C++","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#c","title":"C++","text":" \u7ea6 3115 \u4e2a\u5b57 523 \u884c\u4ee3\u7801 9 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 22 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#1","title":"1 \u6587\u4ef6\u64cd\u4f5c","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#11","title":"1.1 \u6587\u4ef6\u7684\u6982\u5ff5","text":" - C/C++\u628a\u6bcf\u4e00\u4e2a\u6587\u4ef6\u90fd\u770b\u6210\u662f\u4e00\u4e2a\u6709\u5e8f\u7684\u5b57\u8282\u6d41\uff0c\u4ee5\u6587\u4ef6\u7ed3\u675f\u6807\u5fd7\uff08EOF\uff09\u7ed3\u675f
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#12","title":"1.2 \u6587\u4ef6\u7684\u64cd\u4f5c\u6b65\u9aa4","text":" - \u6253\u5f00\u6587\u4ef6\uff0c\u8bb2\u6587\u4ef6\u6307\u9488\u6307\u5411\u6587\u4ef6\uff0c\u51b3\u5b9a\u6253\u5f00\u6587\u4ef6\u7684\u7c7b\u578b
- \u5bf9\u6587\u4ef6\u8fdb\u884c\u8bfb/\u5199\u64cd\u4f5c
- \u5728\u4f7f\u7528\u5b8c\u6587\u4ef6\u540e\uff0c\u5173\u95ed\u6587\u4ef6
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#13","title":"1.3 \u4e00\u4e9b\u51fd\u6570","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#131-freopen","title":"1.3.1 freopen \u51fd\u6570","text":"C++FILE* freopen(const char* filename, const char* mode, FILE* stream);\n
- \u53c2\u6570\u8bf4\u660e
filename: \u8981\u6253\u5f00\u7684\u6587\u4ef6\u540dmode: \u6587\u4ef6\u6253\u5f00\u7684\u6a21\u5f0f\uff0c\u8868\u793a\u6587\u4ef6\u8bbf\u95ee\u7684\u6743\u9650stream: \u6587\u4ef6\u6307\u9488\uff0c\u901a\u5e38\u4f7f\u7528\u6807\u51c6\u6587\u4ef6\u6d41 (stdin/stdout) \u6216\u6807\u51c6\u9519\u8bef\u8f93\u51fa\u6d41 (stderr)- \u8fd4\u56de\u503c\uff1a\u6587\u4ef6\u6307\u9488\uff0c\u6307\u5411\u88ab\u6253\u5f00\u6587\u4ef6
- \u6587\u4ef6\u6253\u5f00\u683c\u5f0f
r\uff1a\u4ee5\u53ea\u8bfb\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u5fc5\u987b\u5b58\u5728\uff0c\u53ea\u5141\u8bb8\u8bfb\u5165\u6570\u636e\u00a0\uff08\u5e38\u7528\uff09r+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u5fc5\u987b\u5b58\u5728\uff0c\u5141\u8bb8\u8bfb/\u5199\u6570\u636erb\uff1a\u4ee5\u53ea\u8bfb\u65b9\u5f0f\u6253\u5f00\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u6587\u4ef6\u5fc5\u987b\u5b58\u5728\uff0c\u53ea\u5141\u8bb8\u8bfb\u5165\u6570\u636erb+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u6587\u4ef6\u5fc5\u987b\u5b58\u5728\uff0c\u5141\u8bb8\u8bfb/\u5199\u6570\u636ert+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u672c\u6587\u4ef6\uff0c\u5141\u8bb8\u8bfb/\u5199\u6570\u636ew\uff1a\u4ee5\u53ea\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u4f1a\u65b0\u5efa\u6587\u4ef6\uff0c\u5426\u5219\u6e05\u7a7a\u5185\u5bb9\uff0c\u53ea\u5141\u8bb8\u5199\u5165\u6570\u636e\u00a0\uff08\u5e38\u7528\uff09w+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u5c06\u65b0\u5efa\u6587\u4ef6\uff0c\u5426\u5219\u6e05\u7a7a\u5185\u5bb9\uff0c\u5141\u8bb8\u8bfb/\u5199\u6570\u636ewb\uff1a\u4ee5\u53ea\u5199\u65b9\u5f0f\u6253\u5f00\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u5c06\u4f1a\u65b0\u5efa\u6587\u4ef6\uff0c\u5426\u5219\u6e05\u7a7a\u5185\u5bb9\uff0c\u53ea\u5141\u8bb8\u5199\u5165\u6570\u636ewb+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u5c06\u65b0\u5efa\u6587\u4ef6\uff0c\u5426\u5219\u6e05\u7a7a\u5185\u5bb9\uff0c\u5141\u8bb8\u8bfb/\u5199\u6570\u636ea\uff1a\u4ee5\u53ea\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u5c06\u65b0\u5efa\u6587\u4ef6\uff0c\u5199\u5165\u6570\u636e\u5c06\u88ab\u9644\u52a0\u5728\u6587\u4ef6\u672b\u5c3e\uff08\u4fdd\u7559 EOF \u7b26\uff09a+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u5c06\u65b0\u5efa\u6587\u4ef6\uff0c\u5199\u5165\u6570\u636e\u5c06\u88ab\u9644\u52a0\u5728\u6587\u4ef6\u672b\u5c3e\uff08\u4e0d\u4fdd\u7559 EOF \u7b26\uff09at+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u672c\u6587\u4ef6\uff0c\u5199\u5165\u6570\u636e\u5c06\u88ab\u9644\u52a0\u5728\u6587\u4ef6\u672b\u5c3eab+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u5199\u5165\u6570\u636e\u5c06\u88ab\u9644\u52a0\u5728\u6587\u4ef6\u672b\u5c3e \u4f7f\u7528\u65b9\u5f0f
C++#include <cstdio>\n#include <iostream>\nint mian(void) {\n freopen(\"data.in\", \"r\", stdin); \n // data.in \u5c31\u662f\u8bfb\u53d6\u7684\u6587\u4ef6\u540d\uff0c\u8981\u548c\u53ef\u6267\u884c\u6587\u4ef6\u653e\u5728\u540c\u4e00\u76ee\u5f55\u4e0b\n freopen(\"data.out\", \"w\", stdout); \n // data.out \u5c31\u662f\u8f93\u51fa\u6587\u4ef6\u7684\u6587\u4ef6\u540d\uff0c\u548c\u53ef\u6267\u884c\u6587\u4ef6\u5728\u540c\u4e00\u76ee\u5f55\u4e0b\n fclose(stdin);\n fclose(stdout);\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#132-fopen","title":"1.3.2 fopen \u51fd\u6570","text":"C++FILE* fopen(const char* path, const char* mode)\n
\u4f7f\u7528\u65b9\u5f0f
C++FILE *in, *out; // \u5b9a\u4e49\u6587\u4ef6\u6307\u9488 \nin = fopen(\"data.in\", \"r\"); \nout = fopen(\"data.out\", \"w\"); \n/* do what you want to do */ \nfclose(in); \nfclose(out);\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#14-c-ifstreamofstream","title":"1.4 C++ \u7684\u00a0ifstream/ofstream\u00a0\u6587\u4ef6\u8f93\u5165\u8f93\u51fa\u6d41","text":"C++#include <fstream> \nusing namespace std; \n// \u4e24\u4e2a\u7c7b\u578b\u90fd\u5728 std \u547d\u540d\u7a7a\u95f4\u91cc \nifstream fin(\"data.in\"); \nofstream fout(\"data.out\"); \nint main(void) { \n /* \u4e2d\u95f4\u7684\u4ee3\u7801\u6539\u53d8 cin \u4e3a fin \uff0ccout \u4e3a fout \u5373\u53ef */ \n fin.close(); \n fout.close(); \n return 0; \n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#2","title":"2 \u6807\u51c6\u5e93","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#21-stl","title":"2.1 STL \u5bb9\u5668","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#211","title":"2.1.1 \u5e8f\u5217\u5f0f\u5bb9\u5668","text":" - \u5411\u91cf(
vector) \u540e\u7aef\u53ef\u9ad8\u6548\u589e\u52a0\u5143\u7d20\u7684\u987a\u5e8f\u8868\u3002 - \u53ef\u4ee5\u52a8\u6001\u5206\u914d\u5185\u5b58
- \u91cd\u5199\u4e86\u6bd4\u8f83\u8fd0\u7b97\u7b26\u53ca\u8d4b\u503c\u8fd0\u7b97\u7b26
- \u4fbf\u5229\u7684\u521d\u59cb\u5316
- std::vector - cppreference.com
- \u6570\u7ec4(
array)C++11\uff0c\u5b9a\u957f\u7684\u987a\u5e8f\u8868\uff0cC \u98ce\u683c\u6570\u7ec4\u7684\u7b80\u5355\u5305\u88c5\u3002 - \u53cc\u7aef\u961f\u5217(
deque) \u53cc\u7aef\u90fd\u53ef\u9ad8\u6548\u589e\u52a0\u5143\u7d20\u7684\u987a\u5e8f\u8868\u3002 - \u5217\u8868(
list) \u53ef\u4ee5\u6cbf\u53cc\u5411\u904d\u5386\u7684\u94fe\u8868\u3002 - \u5355\u5411\u5217\u8868(
forward_list) \u53ea\u80fd\u6cbf\u4e00\u4e2a\u65b9\u5411\u904d\u5386\u7684\u94fe\u8868\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#212","title":"2.1.2 \u5173\u8054\u5f0f\u5bb9\u5668","text":" - \u96c6\u5408(
set) \u7528\u4ee5\u6709\u5e8f\u5730\u5b58\u50a8\u00a0\u4e92\u5f02\u00a0\u5143\u7d20\u7684\u5bb9\u5668\u3002\u5176\u5b9e\u73b0\u662f\u7531\u8282\u70b9\u7ec4\u6210\u7684\u7ea2\u9ed1\u6811\uff0c\u6bcf\u4e2a\u8282\u70b9\u90fd\u5305\u542b\u7740\u4e00\u4e2a\u5143\u7d20\uff0c\u8282\u70b9\u4e4b\u95f4\u4ee5\u67d0\u79cd\u6bd4\u8f83\u5143\u7d20\u5927\u5c0f\u7684\u8c13\u8bcd\u8fdb\u884c\u6392\u5217\u3002 - \u591a\u91cd\u96c6\u5408(
multiset) \u7528\u4ee5\u6709\u5e8f\u5730\u5b58\u50a8\u5143\u7d20\u7684\u5bb9\u5668\u3002\u5141\u8bb8\u5b58\u5728\u76f8\u7b49\u7684\u5143\u7d20\u3002 - \u6620\u5c04(
map) \u7531 {\u952e\uff0c\u503c} \u5bf9\u7ec4\u6210\u7684\u96c6\u5408\uff0c\u4ee5\u67d0\u79cd\u6bd4\u8f83\u952e\u5927\u5c0f\u5173\u7cfb\u7684\u8c13\u8bcd\u8fdb\u884c\u6392\u5217\u3002 - \u591a\u91cd\u6620\u5c04(
multimap) \u7531 {\u952e\uff0c\u503c} \u5bf9\u7ec4\u6210\u7684\u591a\u91cd\u96c6\u5408\uff0c\u4ea6\u5373\u5141\u8bb8\u952e\u6709\u76f8\u7b49\u60c5\u51b5\u7684\u6620\u5c04\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#213","title":"2.1.3 \u65e0\u5e8f\uff08\u5173\u8054\u5f0f\uff09\u5bb9\u5668","text":" - \u65e0\u5e8f\uff08\u591a\u91cd\uff09\u96c6\u5408(
unordered_set/unordered_multiset)C++11\uff0c\u4e0e\u00a0set/multiset\u00a0\u7684\u533a\u522b\u5728\u4e8e\u5143\u7d20\u65e0\u5e8f\uff0c\u53ea\u5173\u5fc3\u300c\u5143\u7d20\u662f\u5426\u5b58\u5728\u300d\uff0c\u4f7f\u7528\u54c8\u5e0c\u5b9e\u73b0\u3002 - \u65e0\u5e8f\uff08\u591a\u91cd\uff09\u6620\u5c04(
unordered_map/unordered_multimap)C++11\uff0c\u4e0e\u00a0map/multimap\u00a0\u7684\u533a\u522b\u5728\u4e8e\u952e (key) \u65e0\u5e8f\uff0c\u53ea\u5173\u5fc3 \"\u952e\u4e0e\u503c\u7684\u5bf9\u5e94\u5173\u7cfb\"\uff0c\u4f7f\u7528\u54c8\u5e0c\u5b9e\u73b0\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#214","title":"2.1.4 \u5bb9\u5668\u9002\u914d\u5668","text":"\u5bb9\u5668\u9002\u914d\u5668\u5176\u5b9e\u5e76\u4e0d\u662f\u5bb9\u5668\u3002\u5b83\u4eec\u4e0d\u5177\u6709\u5bb9\u5668\u7684\u67d0\u4e9b\u7279\u70b9\uff08\u5982\uff1a\u6709\u8fed\u4ee3\u5668\u3001\u6709\u00a0clear()\u00a0\u51fd\u6570\u2026\u2026\uff09\u3002
\u300c\u9002\u914d\u5668\u662f\u4f7f\u4e00\u79cd\u4e8b\u7269\u7684\u884c\u4e3a\u7c7b\u4f3c\u4e8e\u53e6\u5916\u4e00\u79cd\u4e8b\u7269\u884c\u4e3a\u7684\u4e00\u79cd\u673a\u5236\u300d\uff0c\u9002\u914d\u5668\u5bf9\u5bb9\u5668\u8fdb\u884c\u5305\u88c5\uff0c\u4f7f\u5176\u8868\u73b0\u51fa\u53e6\u5916\u4e00\u79cd\u884c\u4e3a\u3002
- \u6808(
stack) \u540e\u8fdb\u5148\u51fa (LIFO) \u7684\u5bb9\u5668\uff0c\u9ed8\u8ba4\u662f\u5bf9\u53cc\u7aef\u961f\u5217\uff08deque\uff09\u7684\u5305\u88c5\u3002 - \u961f\u5217(
queue) \u5148\u8fdb\u5148\u51fa (FIFO) \u7684\u5bb9\u5668\uff0c\u9ed8\u8ba4\u662f\u5bf9\u53cc\u7aef\u961f\u5217\uff08deque\uff09\u7684\u5305\u88c5\u3002 - \u4f18\u5148\u961f\u5217(
priority_queue) \u5143\u7d20\u7684\u6b21\u5e8f\u662f\u7531\u4f5c\u7528\u4e8e\u6240\u5b58\u50a8\u7684\u503c\u5bf9\u4e0a\u7684\u67d0\u79cd\u8c13\u8bcd\u51b3\u5b9a\u7684\u7684\u4e00\u79cd\u961f\u5217\uff0c\u9ed8\u8ba4\u662f\u5bf9\u5411\u91cf\uff08vector\uff09\u7684\u5305\u88c5\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#215","title":"2.1.5 \u5171\u540c\u70b9","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#2151","title":"2.1.5.1 \u5bb9\u5668\u58f0\u660e","text":" - \u90fd\u662f\u00a0
containerName<typeName,...> name\u00a0\u7684\u5f62\u5f0f\uff0c\u4f46\u6a21\u677f\u53c2\u6570\uff08<>\u00a0\u5185\u7684\u53c2\u6570\uff09\u7684\u4e2a\u6570\u3001\u5f62\u5f0f\u4f1a\u6839\u636e\u5177\u4f53\u5bb9\u5668\u800c\u53d8\u3002 - \u672c\u8d28\u539f\u56e0\uff1aSTL \u5c31\u662f\u300c\u6807\u51c6\u6a21\u677f\u5e93\u300d\uff0c\u6240\u4ee5\u5bb9\u5668\u90fd\u662f\u6a21\u677f\u7c7b\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#2152","title":"2.1.5.2 \u8fed\u4ee3\u5668","text":"C++vector<int> data(10);\n\nfor (int i = 0; i < data.size(); i++)\n cout << data[i] << endl;\n\nfor (vector<int>::iterator iter = data.begin(); iter != data.end(); iter++)\n cout << *iter << endl;\n//C++11 \u4ee5\u540e\u53ef\u4ee5\u7528 auto iter = data.begin() \u6765\u7b80\u5316\n
- \u5206\u7c7b
- InputIterator\uff08\u8f93\u5165\u8fed\u4ee3\u5668\uff09\uff1a\u53ea\u8981\u6c42\u652f\u6301\u62f7\u8d1d\u3001\u81ea\u589e\u548c\u89e3\u5f15\u8bbf\u95ee\u3002
- OutputIterator\uff08\u8f93\u51fa\u8fed\u4ee3\u5668\uff09\uff1a\u53ea\u8981\u6c42\u652f\u6301\u62f7\u8d1d\u3001\u81ea\u589e\u548c\u89e3\u5f15\u8d4b\u503c\u3002
- ForwardIterator\uff08\u5411\u524d\u8fed\u4ee3\u5668\uff09\uff1a\u540c\u65f6\u6ee1\u8db3 InputIterator \u548c OutputIterator \u7684\u8981\u6c42\u3002
- BidirectionalIterator\uff08\u53cc\u5411\u8fed\u4ee3\u5668\uff09\uff1a\u5728 ForwardIterator \u7684\u57fa\u7840\u4e0a\u652f\u6301\u81ea\u51cf\uff08\u5373\u53cd\u5411\u8bbf\u95ee\uff09\u3002
- RandomAccessIterator\uff08\u968f\u673a\u8bbf\u95ee\u8fed\u4ee3\u5668\uff09\uff1a\u5728 BidirectionalIterator \u7684\u57fa\u7840\u4e0a\u652f\u6301\u52a0\u51cf\u8fd0\u7b97\u548c\u6bd4\u8f83\u8fd0\u7b97\uff08\u5373\u968f\u673a\u8bbf\u95ee\uff09\u3002
- ContiguousIterator\uff08\u8fde\u7eed\u8fed\u4ee3\u5668\uff09\uff1a\u5728 RandomAccessIterator \u7684\u57fa\u7840\u4e0a\u8981\u6c42\u5bf9\u53ef\u89e3\u5f15\u7528\u7684\u8fed\u4ee3\u5668\u00a0
a + n\u00a0\u6ee1\u8db3\u00a0*(a + n)\u00a0\u4e0e\u00a0*(std::address_of(*a) + n)\u00a0\u7b49\u4ef7\uff08\u5373\u8fde\u7eed\u5b58\u50a8\uff0c\u5176\u4e2d\u00a0a\u00a0\u4e3a\u8fde\u7eed\u8fed\u4ee3\u5668\u3001n\u00a0\u4e3a\u6574\u578b\u503c\uff09\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#2153","title":"2.1.5.3 \u5171\u6709\u51fd\u6570","text":" =\uff1a\u6709\u8d4b\u503c\u8fd0\u7b97\u7b26\u4ee5\u53ca\u590d\u5236\u6784\u9020\u51fd\u6570\u3002begin()\uff1a\u8fd4\u56de\u6307\u5411\u5f00\u5934\u5143\u7d20\u7684\u8fed\u4ee3\u5668\u3002end()\uff1a\u8fd4\u56de\u6307\u5411\u672b\u5c3e\u7684\u4e0b\u4e00\u4e2a\u5143\u7d20\u7684\u8fed\u4ee3\u5668\u3002end()\u00a0\u4e0d\u6307\u5411\u67d0\u4e2a\u5143\u7d20\uff0c\u4f46\u5b83\u662f\u672b\u5c3e\u5143\u7d20\u7684\u540e\u7ee7\u3002size()\uff1a\u8fd4\u56de\u5bb9\u5668\u5185\u7684\u5143\u7d20\u4e2a\u6570\u3002max_size()\uff1a\u8fd4\u56de\u5bb9\u5668\u00a0\u7406\u8bba\u4e0a\u00a0\u80fd\u5b58\u50a8\u7684\u6700\u5927\u5143\u7d20\u4e2a\u6570\u3002\u4f9d\u5bb9\u5668\u7c7b\u578b\u548c\u6240\u5b58\u50a8\u53d8\u91cf\u7684\u7c7b\u578b\u800c\u53d8\u3002empty()\uff1a\u8fd4\u56de\u5bb9\u5668\u662f\u5426\u4e3a\u7a7a\u3002swap()\uff1a\u4ea4\u6362\u4e24\u4e2a\u5bb9\u5668\u3002clear()\uff1a\u6e05\u7a7a\u5bb9\u5668\u3002==/!=/</>/<=/>=\uff1a\u6309\u00a0\u5b57\u5178\u5e8f\u00a0\u6bd4\u8f83\u4e24\u4e2a\u5bb9\u5668\u7684\u5927\u5c0f\u3002\uff08\u6bd4\u8f83\u5143\u7d20\u5927\u5c0f\u65f6\u00a0map\u00a0\u7684\u6bcf\u4e2a\u5143\u7d20\u76f8\u5f53\u4e8e\u00a0set<pair<key, value> >\uff0c\u65e0\u5e8f\u5bb9\u5668\u4e0d\u652f\u6301\u00a0</>/<=/>=\u3002\uff09
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#216-stl","title":"2.1.6 STL \u7b97\u6cd5","text":" find\uff1a\u987a\u5e8f\u67e5\u627e\u3002find(v.begin(), v.end(), value)\uff0c\u5176\u4e2d\u00a0value\u00a0\u4e3a\u9700\u8981\u67e5\u627e\u7684\u503c\u3002reverse\uff1a\u7ffb\u8f6c\u6570\u7ec4\u3001\u5b57\u7b26\u4e32\u3002reverse(v.begin(), v.end())\u00a0\u6216\u00a0reverse(a + begin, a + end)\u3002unique\uff1a\u53bb\u9664\u5bb9\u5668\u4e2d\u76f8\u90bb\u7684\u91cd\u590d\u5143\u7d20\u3002unique(ForwardIterator first, ForwardIterator last)\uff0c\u8fd4\u56de\u503c\u4e3a\u6307\u5411\u00a0\u53bb\u91cd\u540e\u00a0\u5bb9\u5668\u7ed3\u5c3e\u7684\u8fed\u4ee3\u5668\uff0c\u539f\u5bb9\u5668\u5927\u5c0f\u4e0d\u53d8\u3002\u4e0e\u00a0sort\u00a0\u7ed3\u5408\u4f7f\u7528\u53ef\u4ee5\u5b9e\u73b0\u5b8c\u6574\u5bb9\u5668\u53bb\u91cd\u3002sort\uff1a\u6392\u5e8f\u3002sort(v.begin(), v.end(), cmp)\u00a0\u6216\u00a0sort(a + begin, a + end, cmp)\uff0c\u5176\u4e2d\u00a0end\u00a0\u662f\u6392\u5e8f\u7684\u6570\u7ec4\u6700\u540e\u4e00\u4e2a\u5143\u7d20\u7684\u540e\u4e00\u4f4d\uff0ccmp\u00a0\u4e3a\u81ea\u5b9a\u4e49\u7684\u6bd4\u8f83\u51fd\u6570\u3002stable_sort\uff1a\u7a33\u5b9a\u6392\u5e8f\uff0c\u7528\u6cd5\u540c\u00a0sort()\u3002nth_element \uff1a\u6309\u6307\u5b9a\u8303\u56f4\u8fdb\u884c\u5206\u7c7b\uff0c\u5373\u627e\u51fa\u5e8f\u5217\u4e2d\u7b2c \\(\\displaystyle n\\) \u5927\u7684\u5143\u7d20\uff0c\u4f7f\u5176\u5de6\u8fb9\u5747\u4e3a\u5c0f\u4e8e\u5b83\u7684\u6570\uff0c\u53f3\u8fb9\u5747\u4e3a\u5927\u4e8e\u5b83\u7684\u6570\u3002 nth_element(v.begin(), v.begin() + mid, v.end(), cmp)\u00a0\u6216\u00a0nth_element(a + begin, a + begin + mid, a + end, cmp) \u3002binary_search\uff1a\u4e8c\u5206\u67e5\u627e\u3002binary_search(v.begin(), v.end(), value)\uff0c\u5176\u4e2d\u00a0value\u00a0\u4e3a\u9700\u8981\u67e5\u627e\u7684\u503c\u3002merge\uff1a\u5c06\u4e24\u4e2a\uff08\u5df2\u6392\u5e8f\u7684\uff09\u5e8f\u5217\u00a0\u6709\u5e8f\u5408\u5e76\u00a0\u5230\u7b2c\u4e09\u4e2a\u5e8f\u5217\u7684\u00a0\u63d2\u5165\u8fed\u4ee3\u5668\u00a0\u4e0a\u3002merge(v1.begin(), v1.end(), v2.begin(), v2.end() ,back_inserter(v3))\u3002inplace_merge\uff1a\u5c06\u4e24\u4e2a\uff08\u5df2\u6309\u5c0f\u4e8e\u8fd0\u7b97\u7b26\u6392\u5e8f\u7684\uff09\uff1a[first,middle), [middle,last)\u00a0\u8303\u56f4\u00a0\u539f\u5730\u5408\u5e76\u4e3a\u4e00\u4e2a\u6709\u5e8f\u5e8f\u5217\u3002inplace_merge(v.begin(), v.begin() + middle, v.end())\u3002lower_bound\uff1a\u5728\u4e00\u4e2a\u6709\u5e8f\u5e8f\u5217\u4e2d\u8fdb\u884c\u4e8c\u5206\u67e5\u627e\uff0c\u8fd4\u56de\u6307\u5411\u7b2c\u4e00\u4e2a\u00a0\u5927\u4e8e\u7b49\u4e8e \u00a0\u7684\u5143\u7d20\u7684\u4f4d\u7f6e\u7684\u8fed\u4ee3\u5668\u3002\u5982\u679c\u4e0d\u5b58\u5728\u8fd9\u6837\u7684\u5143\u7d20\uff0c\u5219\u8fd4\u56de\u5c3e\u8fed\u4ee3\u5668\u3002lower_bound(v.begin(),v.end(),x)\u3002upper_bound\uff1a\u5728\u4e00\u4e2a\u6709\u5e8f\u5e8f\u5217\u4e2d\u8fdb\u884c\u4e8c\u5206\u67e5\u627e\uff0c\u8fd4\u56de\u6307\u5411\u7b2c\u4e00\u4e2a\u00a0\u5927\u4e8e \u00a0\u7684\u5143\u7d20\u7684\u4f4d\u7f6e\u7684\u8fed\u4ee3\u5668\u3002\u5982\u679c\u4e0d\u5b58\u5728\u8fd9\u6837\u7684\u5143\u7d20\uff0c\u5219\u8fd4\u56de\u5c3e\u8fed\u4ee3\u5668\u3002upper_bound(v.begin(),v.end(),x)\u3002next_permutation\uff1a\u5c06\u5f53\u524d\u6392\u5217\u66f4\u6539\u4e3a\u00a0\u5168\u6392\u5217\u4e2d\u7684\u4e0b\u4e00\u4e2a\u6392\u5217\u3002\u5982\u679c\u5f53\u524d\u6392\u5217\u5df2\u7ecf\u662f\u00a0\u5168\u6392\u5217\u4e2d\u7684\u6700\u540e\u4e00\u4e2a\u6392\u5217\uff08\u5143\u7d20\u5b8c\u5168\u4ece\u5927\u5230\u5c0f\u6392\u5217\uff09\uff0c\u51fd\u6570\u8fd4\u56de\u00a0false\u00a0\u5e76\u5c06\u6392\u5217\u66f4\u6539\u4e3a\u00a0\u5168\u6392\u5217\u4e2d\u7684\u7b2c\u4e00\u4e2a\u6392\u5217\uff08\u5143\u7d20\u5b8c\u5168\u4ece\u5c0f\u5230\u5927\u6392\u5217\uff09\uff1b\u5426\u5219\uff0c\u51fd\u6570\u8fd4\u56de\u00a0true\u3002next_permutation(v.begin(), v.end())\u00a0\u6216\u00a0next_permutation(v + begin, v + end)\u3002prev_permutation\uff1a\u5c06\u5f53\u524d\u6392\u5217\u66f4\u6539\u4e3a\u00a0\u5168\u6392\u5217\u4e2d\u7684\u4e0a\u4e00\u4e2a\u6392\u5217\u3002\u7528\u6cd5\u540c\u00a0next_permutation\u3002partial_sum\uff1a\u6c42\u524d\u7f00\u548c\u3002\u8bbe\u6e90\u5bb9\u5668\u4e3a\u00a0\uff0c\u76ee\u6807\u5bb9\u5668\u4e3a\u00a0\uff0c\u5219\u4ee4\u00a0\u3002partial_sum(src.begin(), src.end(), back_inserter(dst))\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#22-bitset","title":"2.2 bitset","text":"TODO bitset - OI Wiki
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#23-string","title":"2.3 string","text":" - \u91cd\u8f7d\u8fd0\u7b97\u7b26
- \u52a8\u6001\u5206\u914d\u7a7a\u95f4
C++std::string s;\nprintf(\"%s\", s.c_str());\nprintf(\"s \u7684\u957f\u5ea6\u4e3a %lu\", s.size()); \nprintf(\"s \u7684\u957f\u5ea6\u4e3a %lu\", s.length()); \nprintf(\"s \u7684\u957f\u5ea6\u4e3a %lu\", strlen(s.c_str()));\nsubstr(pos, len);\ninsert(index, count, ch);\ninsert(index, str);\nerase(index, count);\nreplace(pos, count, str);\nreplace(first, last, str);\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#24-pair","title":"2.4 pair","text":" pair \u4e0d\u9700\u8981\u989d\u5916\u5b9a\u4e49\u7ed3\u6784\u4e0e\u91cd\u8f7d\u8fd0\u7b97\u7b26
C++pair<int, double> p0(1, 2.0);\npair<int, double> p2 = make_pair(1, 2.0);\nauto p3 = make_pair(1, 2.0);\n\nint i = p0.first; \ndouble d = p0.second;\np1.first++;\n\npriority_queue<pair<int, double> > q;\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3","title":"3 \u8fdb\u9636","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#31","title":"3.1 \u7c7b","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#32","title":"3.2 \u52a8\u6001\u5185\u5b58","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#321-new-delete","title":"3.2.1 new \u548c delete \u8fd0\u7b97\u7b26","text":" - \u6808: \u58f0\u660e\u7684\u6240\u6709\u53d8\u91cf\u5c06\u5360\u7528\u6808\u5185\u5b58
- \u5806: \u672a\u4f7f\u7528\u7684\u5185\u5b58
C++new data-type;\n\ndouble* pvalue = NULL;\npvalue = new double;\n\nif (!(pvalue = new double)) {\n cout << \"Error: out of memory.\" << endl;\n exit(1);\n}\n\ndelete pvalue;\n
- new \u4e0d\u4ec5\u5206\u914d\u7684\u5185\u5b58\u8fd8\u521b\u5efa\u4e86\u5bf9\u8c61
- malloc () \u53ea\u5206\u914d\u7684\u5185\u5b58
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#322","title":"3.2.2 \u6570\u7ec4\u7684\u52a8\u6001\u5185\u5b58\u5206\u914d","text":"C++int ***array;\n// \u5047\u5b9a\u6570\u7ec4\u7b2c\u4e00\u7ef4\u4e3a m\uff0c \u7b2c\u4e8c\u7ef4\u4e3a n\uff0c \u7b2c\u4e09\u7ef4\u4e3ah\n// \u52a8\u6001\u5206\u914d\u7a7a\u95f4\narray = new int **[m];\nfor( int i=0; i<m; i++ )\n{\n array[i] = new int *[n];\n for( int j=0; j<n; j++ )\n {\n array[i][j] = new int [h];\n }\n}\n//\u91ca\u653e\nfor( int i=0; i<m; i++ )\n{\n for( int j=0; j<n; j++ )\n {\n delete [] array[i][j];\n }\n delete [] array[i];\n}\ndelete [] array;\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#323","title":"3.2.3 \u5bf9\u8c61\u7684\u52a8\u6001\u5185\u5b58\u5206\u914d","text":"C++#include <iostream>\nusing namespace std;\n\nclass Box\n{\n public:\n Box() { \n cout << \"\u8c03\u7528\u6784\u9020\u51fd\u6570\uff01\" <<endl; \n }\n ~Box() { \n cout << \"\u8c03\u7528\u6790\u6784\u51fd\u6570\uff01\" <<endl; \n }\n};\n\nint main( )\n{\n Box* myBoxArray = new Box[4]; // \u4e00\u4e2a\u5305\u542b 4 \u4e2a Box \u5bf9\u8c61\u7684\u6570\u7ec4\n\n delete [] myBoxArray; // \u5220\u9664\u6570\u7ec4\n return 0;\n}\n
Text Only\u8c03\u7528\u6784\u9020\u51fd\u6570\uff01\n\u8c03\u7528\u6784\u9020\u51fd\u6570\uff01\n\u8c03\u7528\u6784\u9020\u51fd\u6570\uff01\n\u8c03\u7528\u6784\u9020\u51fd\u6570\uff01\n\u8c03\u7528\u6790\u6784\u51fd\u6570\uff01\n\u8c03\u7528\u6790\u6784\u51fd\u6570\uff01\n\u8c03\u7528\u6790\u6784\u51fd\u6570\uff01\n\u8c03\u7528\u6790\u6784\u51fd\u6570\uff01\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#33","title":"3.3 \u547d\u540d\u7a7a\u95f4","text":" - \u7f16\u8bd1\u5668\u4e3a\u4e86\u533a\u522b\u540c\u540d\u51fd\u6570\u5f15\u5165\u4e86\u547d\u540d\u7a7a\u95f4
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#331","title":"3.3.1 \u5b9a\u4e49\u547d\u540d\u7a7a\u95f4","text":"C++namespace namespace_name {\n // code\n}\n\nnamespace_name::code;\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#332-using","title":"3.3.2 using \u6307\u4ee4","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#333","title":"3.3.3 \u4e0d\u8fde\u7eed\u7684\u547d\u540d\u7a7a\u95f4","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#334","title":"3.3.4 \u5d4c\u5957\u7684\u547d\u540d\u7a7a\u95f4","text":"C++namespace namespace_name1 {\n // \u4ee3\u7801\u58f0\u660e\n namespace namespace_name2 {\n // \u4ee3\u7801\u58f0\u660e\n }\n}\n\n// \u8bbf\u95ee namespace_name2 \u4e2d\u7684\u6210\u5458\nusing namespace namespace_name1::namespace_name2;\n\n// \u8bbf\u95ee namespace_name1 \u4e2d\u7684\u6210\u5458\nusing namespace namespace_name1;\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#34","title":"3.4 \u6a21\u677f","text":" - \u6cdb\u578b\u7f16\u7a0b: \u72ec\u7acb\u4e8e\u4efb\u4f55\u7279\u5b9a\u7c7b\u578b\u7684\u65b9\u5f0f\u5199\u4ee3\u7801
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#341","title":"3.4.1 \u51fd\u6570\u6a21\u677f","text":"C++template <typename type> ret-type func-name(parameter list)\n{\n // \u51fd\u6570\u7684\u4e3b\u4f53\n}\n
C++#include <iostream>\n#include <string>\n\nusing namespace std;\n\ntemplate <typename T>\ninline T const& Max (T const& a, T const& b) \n{ \n return a < b ? b:a; \n} \n\nint main ()\n{\n\n int i = 39;\n int j = 20;\n cout << \"Max(i, j): \" << Max(i, j) << endl; \n\n double f1 = 13.5; \n double f2 = 20.7; \n cout << \"Max(f1, f2): \" << Max(f1, f2) << endl; \n\n string s1 = \"Hello\"; \n string s2 = \"World\"; \n cout << \"Max(s1, s2): \" << Max(s1, s2) << endl; \n\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#342","title":"3.4.2 \u7c7b\u6a21\u677f","text":"C++template <class type> class class-name {\n// code\n}\n
C++#include <iostream>\n#include <vector>\n#include <cstdlib>\n#include <string>\n#include <stdexcept>\n\nusing namespace std;\n\ntemplate <class T>\nclass Stack { \n private: \n vector<T> elems; // \u5143\u7d20 \n\n public: \n void push(T const&); // \u5165\u6808\n void pop(); // \u51fa\u6808\n T top() const; // \u8fd4\u56de\u6808\u9876\u5143\u7d20\n bool empty() const{ // \u5982\u679c\u4e3a\u7a7a\u5219\u8fd4\u56de\u771f\u3002\n return elems.empty(); \n } \n}; \n\ntemplate <class T>\nvoid Stack<T>::push (T const& elem) \n{ \n // \u8ffd\u52a0\u4f20\u5165\u5143\u7d20\u7684\u526f\u672c\n elems.push_back(elem); \n} \n\ntemplate <class T>\nvoid Stack<T>::pop () \n{ \n if (elems.empty()) { \n throw out_of_range(\"Stack<>::pop(): empty stack\"); \n }\n // \u5220\u9664\u6700\u540e\u4e00\u4e2a\u5143\u7d20\n elems.pop_back(); \n} \n\ntemplate <class T>\nT Stack<T>::top () const \n{ \n if (elems.empty()) { \n throw out_of_range(\"Stack<>::top(): empty stack\"); \n }\n // \u8fd4\u56de\u6700\u540e\u4e00\u4e2a\u5143\u7d20\u7684\u526f\u672c \n return elems.back(); \n} \n\nint main() \n{ \n try { \n Stack<int> intStack; // int \u7c7b\u578b\u7684\u6808 \n Stack<string> stringStack; // string \u7c7b\u578b\u7684\u6808 \n\n // \u64cd\u4f5c int \u7c7b\u578b\u7684\u6808 \n intStack.push(7); \n cout << intStack.top() <<endl; \n\n // \u64cd\u4f5c string \u7c7b\u578b\u7684\u6808 \n stringStack.push(\"hello\"); \n cout << stringStack.top() << std::endl; \n stringStack.pop(); \n stringStack.pop(); \n } \n catch (exception const& ex) { \n cerr << \"Exception: \" << ex.what() <<endl; \n return -1;\n } \n}\n
Text Only7\nhello\nException: Stack<>::pop(): empty stack\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#35","title":"3.5 \u9884\u5904\u7406\u5668","text":" - \u4e0d\u662f C++\u8bed\u53e5\uff0c\u4e0d\u4f1a\u4ee5\u5206\u53f7\u7ed3\u5c3e
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#351-define","title":"3.5.1 #define \u9884\u5904\u7406","text":"C++#define macro-name replacement-text \n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#352","title":"3.5.2 \u53c2\u6570\u5b8f","text":"C++#include <iostream>\nusing namespace std;\n\n#define MIN(a,b) (a<b ? a : b)\n\nint main ()\n{\n int i, j;\n i = 100;\n j = 30;\n cout <<\"\u8f83\u5c0f\u7684\u503c\u4e3a\uff1a\" << MIN(i, j) << endl;\n\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#353","title":"3.5.3 \u6761\u4ef6\u7f16\u8bd1","text":"C++#ifdef NULL\n #define NULL 0\n#endif\n\n#ifdef DEBUG\n cerr <<\"Variable x = \" << x << endl;\n#endif\n\n#if 0\n \u4e0d\u8fdb\u884c\u7f16\u8bd1\u7684\u4ee3\u7801\n#endif\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#354","title":"3.5.4 # \u548c ## \u8fd0\u7b97\u7b26","text":"C++#include <iostream>\nusing namespace std;\n\n#define MKSTR( x ) #x\n\nint main ()\n{\n cout << MKSTR(HELLO C++) << endl;\n\n return 0;\n}\n
C++#include <iostream>\nusing namespace std;\n\n#define concat(a, b) a ## b\nint main()\n{\n int xy = 100;\n\n cout << concat(x, y);\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#355","title":"3.5.5 \u9884\u5b9a\u4e49\u5b8f","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#36","title":"3.6 \u4fe1\u53f7\u5904\u7406","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#361-signal","title":"3.6.1 signal () \u51fd\u6570","text":"C++void (*signal (int sig, void (*func)(int)))(int); \n\nsignal(registered signal, signal handler)\n
C++#include <iostream>\n#include <csignal>\n#include <unistd.h>\n\nusing namespace std;\n\nvoid signalHandler( int signum )\n{\n cout << \"Interrupt signal (\" << signum << \") received.\\n\";\n\n // \u6e05\u7406\u5e76\u5173\u95ed\n // \u7ec8\u6b62\u7a0b\u5e8f \n\n exit(signum); \n\n}\n\nint main ()\n{\n // \u6ce8\u518c\u4fe1\u53f7 SIGINT \u548c\u4fe1\u53f7\u5904\u7406\u7a0b\u5e8f\n signal(SIGINT, signalHandler); \n\n while(1){\n cout << \"Going to sleep....\" << endl;\n sleep(1);\n }\n\n return 0;\n}\n
\u6309 Ctrl + C \u4e2d\u65ad\u7a0b\u5e8f:
Text OnlyGoing to sleep....\nGoing to sleep....\nGoing to sleep....\nInterrupt signal (2) received.\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#362-raise","title":"3.6.2 raise () \u51fd\u6570","text":"C++int raise (signal sig);\n
C++#include <iostream>\n#include <csignal>\n#include <unistd.h>\n\nusing namespace std;\n\nvoid signalHandler( int signum )\n{\n cout << \"Interrupt signal (\" << signum << \") received.\\n\";\n\n // \u6e05\u7406\u5e76\u5173\u95ed\n // \u7ec8\u6b62\u7a0b\u5e8f \n\n exit(signum); \n\n}\n\nint main ()\n{\n int i = 0;\n // \u6ce8\u518c\u4fe1\u53f7 SIGINT \u548c\u4fe1\u53f7\u5904\u7406\u7a0b\u5e8f\n signal(SIGINT, signalHandler); \n\n while(++i){\n cout << \"Going to sleep....\" << endl;\n if( i == 3 ){\n raise(SIGINT);\n }\n sleep(1);\n }\n\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#37","title":"3.7 \u591a\u7ebf\u7a0b","text":" - \u4e24\u79cd\u7c7b\u578b\u7684\u591a\u4efb\u52a1\u5904\u7406
- \u57fa\u4e8e\u8fdb\u7a0b\u662f\u7a0b\u5e8f\u7684\u5e76\u53d1\u6267\u884c
- \u57fa\u4e8e\u7ebf\u7a0b\u662f\u540c\u4e00\u7a0b\u5e8f\u7684\u7247\u6bb5\u7684\u5e76\u53d1\u6267\u884c
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#371","title":"3.7.1 \u6982\u5ff5\u8bf4\u660e","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3711-thread","title":"3.7.1.1 \u7ebf\u7a0b\uff08Thread\uff09","text":" - \u7ebf\u7a0b\u662f\u7a0b\u5e8f\u6267\u884c\u4e2d\u7684\u5355\u4e00\u987a\u5e8f\u63a7\u5236\u6d41\uff0c\u591a\u4e2a\u7ebf\u7a0b\u53ef\u4ee5\u5728\u540c\u4e00\u4e2a\u8fdb\u7a0b\u4e2d\u72ec\u7acb\u8fd0\u884c\u3002
- \u7ebf\u7a0b\u5171\u4eab\u8fdb\u7a0b\u7684\u5730\u5740\u7a7a\u95f4\u3001\u6587\u4ef6\u63cf\u8ff0\u7b26\u3001\u5806\u548c\u5168\u5c40\u53d8\u91cf\u7b49\u8d44\u6e90\uff0c\u4f46\u6bcf\u4e2a\u7ebf\u7a0b\u6709\u81ea\u5df1\u7684\u6808\u3001\u5bc4\u5b58\u5668\u548c\u7a0b\u5e8f\u8ba1\u6570\u5668\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3712-concurrency-parallelism","title":"3.7.1.2 \u5e76\u53d1\uff08Concurrency\uff09\u4e0e\u5e76\u884c \uff08Parallelism\uff09","text":" - \u5e76\u53d1\uff1a\u591a\u4e2a\u4efb\u52a1\u5728\u65f6\u95f4\u7247\u6bb5\u5185\u4ea4\u66ff\u6267\u884c\uff0c\u8868\u73b0\u51fa\u540c\u65f6\u8fdb\u884c\u7684\u6548\u679c\u3002
- \u5e76\u884c\uff1a\u591a\u4e2a\u4efb\u52a1\u5728\u591a\u4e2a\u5904\u7406\u5668\u6216\u5904\u7406\u5668\u6838\u4e0a\u540c\u65f6\u6267\u884c\u3002 C++11 \u4ee5\u540e\u6709\u591a\u7ebf\u7a0b\u652f\u6301:
- std::thread\uff1a\u7528\u4e8e\u521b\u5efa\u548c\u7ba1\u7406\u7ebf\u7a0b\u3002
- std::mutex\uff1a\u7528\u4e8e\u7ebf\u7a0b\u4e4b\u95f4\u7684\u4e92\u65a5\uff0c\u9632\u6b62\u591a\u4e2a\u7ebf\u7a0b\u540c\u65f6\u8bbf\u95ee\u5171\u4eab\u8d44\u6e90\u3002
- std::lock_guard\u00a0\u548c\u00a0std::unique_lock\uff1a\u7528\u4e8e\u7ba1\u7406\u9501\u7684\u83b7\u53d6\u548c\u91ca\u653e\u3002
- std::condition_variable\uff1a\u7528\u4e8e\u7ebf\u7a0b\u95f4\u7684\u6761\u4ef6\u53d8\u91cf\uff0c\u534f\u8c03\u7ebf\u7a0b\u95f4\u7684\u7b49\u5f85\u548c\u901a\u77e5\u3002
- std::future\u00a0\u548c\u00a0std::promise\uff1a\u7528\u4e8e\u5b9e\u73b0\u7ebf\u7a0b\u95f4\u7684\u503c\u4f20\u9012\u548c\u4efb\u52a1\u540c\u6b65\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#372","title":"3.7.2 \u521b\u5efa\u7ebf\u7a0b","text":"C++#include<thread>\nstd::thread thread_object(callable, args...);\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3721","title":"3.7.2.1 \u4f7f\u7528\u51fd\u6570\u6307\u9488","text":"C++#include <iostream>\n#include <thread>\n\nvoid printMessage(int count) {\n for (int i = 0; i < count; ++i) {\n std::cout << \"Hello from thread (function pointer)!\\n\";\n }\n}\n\nint main() {\n std::thread t1(printMessage, 5); // \u521b\u5efa\u7ebf\u7a0b\uff0c\u4f20\u9012\u51fd\u6570\u6307\u9488\u548c\u53c2\u6570\n t1.join(); // \u7b49\u5f85\u7ebf\u7a0b\u5b8c\u6210\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3722","title":"3.7.2.2 \u4f7f\u7528\u51fd\u6570\u5bf9\u8c61","text":"C++#include <iostream>\n#include <thread>\n\nclass PrintTask {\npublic:\n void operator()(int count) const {\n for (int i = 0; i < count; ++i) {\n std::cout << \"Hello from thread (function object)!\\n\";\n }\n }\n};\n\nint main() {\n std::thread t2(PrintTask(), 5); // \u521b\u5efa\u7ebf\u7a0b\uff0c\u4f20\u9012\u51fd\u6570\u5bf9\u8c61\u548c\u53c2\u6570\n t2.join(); // \u7b49\u5f85\u7ebf\u7a0b\u5b8c\u6210\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3723-lambda","title":"3.7.2.3 \u4f7f\u7528 Lambda \u8868\u8fbe\u5f0f","text":"C++#include <iostream>\n#include <thread>\n\nint main() {\n std::thread t3([](int count) {\n for (int i = 0; i < count; ++i) {\n std::cout << \"Hello from thread (lambda)!\\n\";\n }\n }, 5); // \u521b\u5efa\u7ebf\u7a0b\uff0c\u4f20\u9012 Lambda \u8868\u8fbe\u5f0f\u548c\u53c2\u6570\n t3.join(); // \u7b49\u5f85\u7ebf\u7a0b\u5b8c\u6210\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3724","title":"3.7.2.4 \u7ebf\u7a0b\u7ba1\u7406","text":"C++t.join();\nt.detach();\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3725","title":"3.7.2.5 \u7ebf\u7a0b\u7684\u4f20\u53c2","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3726","title":"3.7.2.6 \u503c\u4f20\u9012","text":"C++std::thread t(funx, arg1, arg2);\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3727","title":"3.7.2.7 \u5f15\u7528\u4f20\u9012","text":"C++#include <iostream>\n#include <thread>\n\nvoid increment(int& x) {\n ++x;\n}\n\nint main() {\n int num = 0;\n std::thread t(increment, std::ref(num)); // \u4f7f\u7528 std::ref \u4f20\u9012\u5f15\u7528\n t.join();\n std::cout << \"Value after increment: \" << num << std::endl;\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#373","title":"3.7.3 \u7ebf\u7a0b\u540c\u6b65\u4e0e\u4e92\u65a5","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3731-mutex","title":"3.7.3.1 \u4e92\u65a5\u91cf\uff08Mutex\uff09","text":"C++std::mutex mtx;\nmtx.lock(); // \u9501\u5b9a\u4e92\u65a5\u9501\n// \u8bbf\u95ee\u5171\u4eab\u8d44\u6e90\nmtx.unlock();// \u91ca\u653e\u4e92\u65a5\u9501\n\nstd::lock_guard<std::mutex> lock(mtx); // \u81ea\u52a8\u9501\u5b9a\u548c\u89e3\u9501\n// \u8bbf\u95ee\u5171\u4eab\u8d44\u6e90\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3732-locks","title":"3.7.3.2 \u9501\uff08Locks\uff09","text":" std::lock_guard \uff1a\u4f5c\u7528\u57df\u9501\uff0c\u5f53\u6784\u9020\u65f6\u81ea\u52a8\u9501\u5b9a\u4e92\u65a5\u91cf\uff0c\u5f53\u6790\u6784\u65f6\u81ea\u52a8\u89e3\u9501\u3002std::unique_lock \uff1a\u4e0e std::lock_guard \u7c7b\u4f3c\uff0c\u4f46\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u7075\u6d3b\u6027\uff0c\u4f8b\u5982\u53ef\u4ee5\u8f6c\u79fb\u6240\u6709\u6743\u548c\u624b\u52a8\u89e3\u9501\u3002
C++#include <mutex>\n\nstd::mutex mtx;\n\nvoid safeFunctionWithLockGuard() {\n std::lock_guard<std::mutex> lk(mtx);\n // \u8bbf\u95ee\u6216\u4fee\u6539\u5171\u4eab\u8d44\u6e90\n}\n\nvoid safeFunctionWithUniqueLock() {\n std::unique_lock<std::mutex> ul(mtx);\n // \u8bbf\u95ee\u6216\u4fee\u6539\u5171\u4eab\u8d44\u6e90\n // ul.unlock(); // \u53ef\u9009\uff1a\u624b\u52a8\u89e3\u9501\n // ...\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3733-condition-variable","title":"3.7.3.3 \u6761\u4ef6\u53d8\u91cf\uff08Condition Variable\uff09","text":"C++std::condition_variable cv;\nstd::mutex mtx;\nbool ready = false;\n\nstd::unique_lock<std::mutex> lock(mtx);\ncv.wait(lock, []{ return ready; }); // \u7b49\u5f85\u6761\u4ef6\u6ee1\u8db3\n// \u6761\u4ef6\u6ee1\u8db3\u540e\u6267\u884c\n
C++#include <mutex>\n#include <condition_variable>\n\nstd::mutex mtx;\nstd::condition_variable cv;\nbool ready = false;\n\nvoid workerThread() {\n std::unique_lock<std::mutex> lk(mtx);\n cv.wait(lk, []{ return ready; }); // \u7b49\u5f85\u6761\u4ef6\n // \u5f53\u6761\u4ef6\u6ee1\u8db3\u65f6\u6267\u884c\u5de5\u4f5c\n}\n\nvoid mainThread() {\n {\n std::lock_guard<std::mutex> lk(mtx);\n // \u51c6\u5907\u6570\u636e\n ready = true;\n } // \u79bb\u5f00\u4f5c\u7528\u57df\u65f6\u89e3\u9501\n cv.notify_one(); // \u901a\u77e5\u4e00\u4e2a\u7b49\u5f85\u7684\u7ebf\u7a0b\n}\n
TODO
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3734-atomic-operations","title":"3.7.3.4 \u539f\u5b50\u64cd\u4f5c\uff08Atomic Operations\uff09","text":" - \u5bf9\u5171\u4eab\u6570\u636e\u7684\u8bbf\u95ee\u4e0d\u53ef\u5206\u5272
C++#include <atomic>\n#include <thread>\n\nstd::atomic<int> count(0);\n\nvoid increment() {\n count.fetch_add(1, std::memory_order_relaxed);\n}\n\nint main() {\n std::thread t1(increment);\n std::thread t2(increment);\n t1.join();\n t2.join();\n return count; // \u5e94\u8fd4\u56de2\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3735-thread-local-storagetls","title":"3.7.3.5 \u7ebf\u7a0b\u5c40\u90e8\u5b58\u50a8\uff08Thread Local Storage\uff0cTLS\uff09","text":" - \u5141\u8bb8\u6bcf\u4e2a\u7ebf\u7a0b\u6709\u81ea\u5df1\u7684\u6570\u636e\u526f\u672c
C++#include <iostream>\n#include <thread>\n\nthread_local int threadData = 0;\n\nvoid threadFunction() {\n threadData = 42; // \u6bcf\u4e2a\u7ebf\u7a0b\u90fd\u6709\u81ea\u5df1\u7684threadData\u526f\u672c\n std::cout << \"Thread data: \" << threadData << std::endl;\n}\n\nint main() {\n std::thread t1(threadFunction);\n std::thread t2(threadFunction);\n t1.join();\n t2.join();\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3736-deadlock","title":"3.7.3.6 \u6b7b\u9501\uff08Deadlock\uff09\u548c\u907f\u514d\u7b56\u7565","text":" - \u6b7b\u9501\u5373\u591a\u4e2a\u7ebf\u7a0b\u4e92\u76f8\u7b49\u5f85\u5bf9\u65b9\u91ca\u653e\u8d44\u6e90
- \u603b\u662f\u4ee5\u76f8\u540c\u7684\u987a\u5e8f\u8bf7\u6c42\u8d44\u6e90\u3002
- \u4f7f\u7528\u8d85\u65f6\u6765\u5c1d\u8bd5\u83b7\u53d6\u8d44\u6e90\u3002
- \u4f7f\u7528\u6b7b\u9501\u68c0\u6d4b\u7b97\u6cd5\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#374","title":"3.7.4 \u7ebf\u7a0b\u95f4\u901a\u4fe1","text":"C++std::promise<int> p;\nstd::future<int> f = p.get_future();\n\nstd::thread t([&p] {\n p.set_value(10); // \u8bbe\u7f6e\u503c\uff0c\u89e6\u53d1 future\n});\n\nint result = f.get(); // \u83b7\u53d6\u503c\n
C++17 \u5f15\u5165\u4e86\u5e76\u884c\u7b97\u6cd5\u5e93
C++#include <algorithm>\n#include <vector>\n#include <execution>\n\nstd::vector<int> vec = {1, 2, 3, 4, 5};\nstd::for_each(std::execution::par, vec.begin(), vec.end(), [](int &n) {\n n *= 2;\n});\n
TODO \u591a\u7ebf\u7a0b\u5b9e\u4f8b
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#38-web","title":"3.8 Web \u7f16\u7a0b","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#381-cgi","title":"3.8.1 \u4ec0\u4e48\u662f CGI\uff1f","text":" - \u516c\u5171\u7f51\u5173\u63a5\u53e3\uff08CGI\uff09\uff0c\u662f\u4e00\u5957\u6807\u51c6\uff0c\u5b9a\u4e49\u4e86\u4fe1\u606f\u662f\u5982\u4f55\u5728 Web \u670d\u52a1\u5668\u548c\u5ba2\u6237\u7aef\u811a\u672c\u4e4b\u95f4\u8fdb\u884c\u4ea4\u6362\u7684\u3002
- CGI \u89c4\u8303\u76ee\u524d\u662f\u7531 NCSA \u7ef4\u62a4\u7684\uff0cNCSA \u5b9a\u4e49 CGI \u5982\u4e0b\uff1a
- \u516c\u5171\u7f51\u5173\u63a5\u53e3\uff08CGI\uff09\uff0c\u662f\u4e00\u79cd\u7528\u4e8e\u5916\u90e8\u7f51\u5173\u7a0b\u5e8f\u4e0e\u4fe1\u606f\u670d\u52a1\u5668\uff08\u5982 HTTP \u670d\u52a1\u5668\uff09\u5bf9\u63a5\u7684\u63a5\u53e3\u6807\u51c6\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#382-web","title":"3.8.2 Web \u6d4f\u89c8","text":" - \u60a8\u7684\u6d4f\u89c8\u5668\u8054\u7cfb\u4e0a HTTP Web \u670d\u52a1\u5668\uff0c\u5e76\u8bf7\u6c42 URL\uff0c\u5373\u6587\u4ef6\u540d\u3002
- Web \u670d\u52a1\u5668\u5c06\u89e3\u6790 URL\uff0c\u5e76\u67e5\u627e\u6587\u4ef6\u540d\u3002\u5982\u679c\u627e\u5230\u8bf7\u6c42\u7684\u6587\u4ef6\uff0cWeb \u670d\u52a1\u5668\u4f1a\u628a\u6587\u4ef6\u53d1\u9001\u56de\u6d4f\u89c8\u5668\uff0c\u5426\u5219\u53d1\u9001\u4e00\u6761\u9519\u8bef\u6d88\u606f\uff0c\u8868\u660e\u60a8\u8bf7\u6c42\u4e86\u4e00\u4e2a\u9519\u8bef\u7684\u6587\u4ef6\u3002
- Web \u6d4f\u89c8\u5668\u4ece Web \u670d\u52a1\u5668\u83b7\u53d6\u54cd\u5e94\uff0c\u5e76\u6839\u636e\u63a5\u6536\u5230\u7684\u54cd\u5e94\u6765\u663e\u793a\u6587\u4ef6\u6216\u9519\u8bef\u6d88\u606f\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#383-cgi","title":"3.8.3 CGI \u67b6\u6784\u56fe","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#384-web","title":"3.8.4 Web \u670d\u52a1\u5668\u914d\u7f6e","text":"TODO
"},{"location":"CS_Basic/CS61A/CS61A/","title":"CS61A","text":""},{"location":"CS_Basic/CS61A/CS61A/#cs61a","title":"CS61A","text":" \u7ea6 24 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\u4e0d\u5230 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
- \u4ed6\u4eba\u603b\u7ed3
- https://github.com/HobbitQia/CS61A-Fall-2020/tree/main
- \u6559\u6750\u7ffb\u8bd1\u7248
- \u539f\u6559\u6750
- \u8bfe\u7a0b\u7f51\u7ad9\u5b58\u6863
- https://github.com/shuo-liu16/CS61A
"},{"location":"CS_Basic/CS61A/Composing_Programs/","title":"COMPOSING PROGRAMS","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#composing-programs","title":"COMPOSING PROGRAMS","text":" \u7ea6 2713 \u4e2a\u5b57 651 \u884c\u4ee3\u7801 2 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 22 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"CS_Basic/CS61A/Composing_Programs/#1","title":"1 \u4f7f\u7528\u51fd\u6570\u6784\u5efa\u62bd\u8c61","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#11","title":"1.1 \u5f00\u59cb","text":"\u7a0b\u5e8f\u7531\u4e24\u90e8\u5206\u7ec4\u6210:
- \u8ba1\u7b97\u4e00\u4e9b\u503c
- \u6267\u884c\u4e00\u4e9b\u64cd\u4f5c
- \u51fd\u6570
- \u5bf9\u8c61
- \u89e3\u91ca\u5668:
- \u7528\u4e8e\u8ba1\u7b97\u590d\u6742\u8868\u8fbe\u5f0f\u7684\u7a0b\u5e8f
- \u589e\u91cf\u6d4b\u8bd5\u3001\u6a21\u5757\u5316\u8bbe\u8ba1\u3001\u660e\u786e\u7684\u5047\u8bbe\u548c\u56e2\u961f\u5408\u4f5c
"},{"location":"CS_Basic/CS61A/Composing_Programs/#12","title":"1.2 \u7f16\u7a0b\u8981\u7d20","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#121","title":"1.2.1 \u8868\u8fbe\u5f0f","text":" - \u8bed\u8a00\u8981\u6709\u7684\u673a\u5236:
- \u539f\u59cb\u8868\u8fbe\u5f0f\u548c\u8bed\u53e5\uff1a\u8bed\u8a00\u6240\u5173\u5fc3\u7684\u6700\u7b80\u5355\u7684\u4e2a\u4f53
- \u7ec4\u5408\u65b9\u6cd5\uff1a\u7531\u7b80\u5355\u5143\u7d20\u7ec4\u5408\u6784\u5efa\u590d\u5408\u5143\u7d20
- \u62bd\u8c61\u65b9\u6cd5\uff1a\u547d\u540d\u590d\u5408\u5143\u7d20\uff0c\u5e76\u5c06\u5176\u4f5c\u4e3a\u5355\u5143\u8fdb\u884c\u64cd\u4f5c
- infix notation
"},{"location":"CS_Basic/CS61A/Composing_Programs/#122","title":"1.2.2 \u8c03\u7528\u8868\u8fbe\u5f0f","text":" - subexpressions
- \u7528\u53c2\u6570\u6765\u8c03\u7528\u51fd\u6570
- nested\uff08\u5d4c\u5957\uff09
"},{"location":"CS_Basic/CS61A/Composing_Programs/#123","title":"1.2.3 \u5bfc\u5165\u5e93\u51fd\u6570","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#124","title":"1.2.4 \u540d\u79f0\u4e0e\u73af\u5883","text":" - = is assignment operator
- \u6700\u7b80\u5355\u7684\u62bd\u8c61\u65b9\u6cd5
- environment
"},{"location":"CS_Basic/CS61A/Composing_Programs/#125","title":"1.2.5 \u6c42\u89e3\u5d4c\u5957\u8868\u8fbe\u5f0f","text":"\u6c42\u503c\u7a0b\u5e8f\u672c\u8d28\u4e0a\u662f\u9012\u5f52\u7684
"},{"location":"CS_Basic/CS61A/Composing_Programs/#126-print","title":"1.2.6 \u975e\u7eaf\u51fd\u6570 print","text":"Pure functions None-pure functions which has a side effect
"},{"location":"CS_Basic/CS61A/Composing_Programs/#13","title":"1.3 \u5b9a\u4e49\u65b0\u7684\u51fd\u6570","text":"Pythondef <name>(<formal parameters>):\n return <return expression> \n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#131","title":"1.3.1 \u73af\u5883","text":"environment has some frames frames have some bindings
- intrinsic name
- bound name \u4e0d\u540c\u7684\u540d\u79f0\u53ef\u80fd\u6307\u7684\u662f\u540c\u4e00\u4e2a\u51fd\u6570\uff0c\u4f46\u8be5\u51fd\u6570\u672c\u8eab\u53ea\u6709\u4e00\u4e2a\u5185\u5728\u540d\u79f0 \u5bf9\u51fd\u6570\u5f62\u5f0f\u53c2\u6570\u7684\u63cf\u8ff0\u88ab\u79f0\u4e3a\u51fd\u6570\u7684\u7b7e\u540d
"},{"location":"CS_Basic/CS61A/Composing_Programs/#132","title":"1.3.2 \u8c03\u7528\u7528\u6237\u5b9a\u4e49\u7684\u51fd\u6570","text":" - \u5728\u65b0\u7684\u5c40\u90e8\u5e27\u4e2d\uff0c\u5c06\u5b9e\u53c2\u7ed1\u5b9a\u5230\u51fd\u6570\u7684\u5f62\u53c2\u4e0a\u3002
- \u5728\u4ee5\u6b64\u5e27\u5f00\u59cb\u7684\u73af\u5883\u4e2d\u6267\u884c\u51fd\u6570\u4f53\u3002 name evaluation
"},{"location":"CS_Basic/CS61A/Composing_Programs/#133","title":"1.3.3 \u793a\u4f8b\uff1a\u8c03\u7528\u7528\u6237\u5b9a\u4e49\u7684\u51fd\u6570","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#134","title":"1.3.4 \u5c40\u90e8\u540d\u79f0","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#135","title":"1.3.5 \u9009\u62e9\u540d\u79f0","text":"PEP 8 \u2013 Style Guide for Python Code | peps.python.org
"},{"location":"CS_Basic/CS61A/Composing_Programs/#136","title":"1.3.6 \u62bd\u8c61\u51fd\u6570","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#137","title":"1.3.7 \u8fd0\u7b97\u7b26","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#14","title":"1.4 \u8bbe\u8ba1\u51fd\u6570","text":" - \u4e00\u4e2a\u51fd\u6570\u4e00\u4e2a\u4efb\u52a1
- Don't repeat yourself (DRY)
- \u5b9a\u4e49\u901a\u7528\u7684\u51fd\u6570
"},{"location":"CS_Basic/CS61A/Composing_Programs/#141","title":"1.4.1 \u6587\u6863","text":"docstring
"},{"location":"CS_Basic/CS61A/Composing_Programs/#142","title":"1.4.2 \u53c2\u6570\u9ed8\u8ba4\u503c","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#15","title":"1.5 \u63a7\u5236","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#151","title":"1.5.1 \u8bed\u53e5","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#152","title":"1.5.2 \u590d\u5408\u8bed\u53e5","text":"header suite
Python<header>:\n <statement>\n <statement>\n ...\n<separating header>:\n <statement>\n <statement>\n ...\n...\n
def \u662f\u590d\u5408\u8bed\u53e5 the header controls its suite \u8fd9\u4e2a\u5b9a\u4e49\u63ed\u793a\u4e86\u9012\u5f52\u5b9a\u4e49\u5e8f\u5217\uff08sequence\uff09\u7684\u57fa\u672c\u7ed3\u6784\uff1a\u4e00\u4e2a\u5e8f\u5217\u53ef\u4ee5\u5206\u89e3\u6210\u5b83\u7684\u7b2c\u4e00\u4e2a\u5143\u7d20\u548c\u5176\u4f59\u5143\u7d20 redirected control
"},{"location":"CS_Basic/CS61A/Composing_Programs/#153-ii","title":"1.5.3 \u00a0\u5b9a\u4e49\u51fd\u6570 II\uff1a\u5c40\u90e8\u8d4b\u503c","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#154","title":"1.5.4 \u6761\u4ef6\u8bed\u53e5","text":"Pythonif <expression>:\n <suite>\nelif <expression>:\n <suite>\nelse:\n <suite>\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#155","title":"1.5.5 \u8fed\u4ee3","text":"iteractive control
Pythonwhile <expression>:\n <suite>\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#156","title":"1.5.6 \u6d4b\u8bd5","text":"assertions
Python>>> assert fib(8) == 13, '\u7b2c\u516b\u4e2a\u6590\u6ce2\u90a3\u5951\u6570\u5e94\u8be5\u662f 13'\n
Doctests
Python>>> def sum_naturals(n):\n \"\"\"\u8fd4\u56de\u524d n \u4e2a\u81ea\u7136\u6570\u7684\u548c\u3002\n\n >>> sum_naturals(10)\n 55\n >>> sum_naturals(100)\n 5050\n \"\"\"\n total, k = 0, 1\n while k <= n:\n total, k = total + k, k + 1\n return total\n
Python>>> from doctest import testmod\n>>> testmod()\nTestResults(failed=0, attempted=2)\n
\u5355\u4e2a\u51fd\u6570\u7684\u4ea4\u4e92
Python>>> from doctest import run_docstring_examples\n>>> run_docstring_examples(sum_naturals, globals(), True)\nFinding tests in NoName\nTrying:\n\u00a0\u00a0\u00a0 sum_naturals(10)\nExpecting:\n\u00a0\u00a0\u00a0 55\nok\nTrying:\n\u00a0\u00a0\u00a0 sum_naturals(100)\nExpecting:\n\u00a0\u00a0\u00a0 5050\nok\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#16","title":"1.6 \u9ad8\u9636\u51fd\u6570","text":" - general patterns
- named concepts
- higher-order functions
- \u53ef\u4ee5\u628a\u51fd\u6570\u5f53\u4f5c\u53c2\u6570\u6216\u8005\u8fd4\u56de\u503c
"},{"location":"CS_Basic/CS61A/Composing_Programs/#161","title":"1.6.1 \u4f5c\u4e3a\u53c2\u6570\u7684\u51fd\u6570","text":" - slots
- step through \uff08\u5355\u6b65\u8c03\u8bd5\uff09
- \u4e00\u4e2a\u51e0\u4e4e\u6ca1\u5fc5\u8981\u770b\u7684\u4f8b\u5b50:
Python>>> def summation(n, term):\n total, k = 0, 1\n while k <= n:\n total, k = total + term(k), k + 1\n return total\n>>> def identity(x):\n return x\n>>> def sum_naturals(n):\n return summation(n, identity)\n>>> sum_naturals(10)\n55\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#162","title":"1.6.2 \u4f5c\u4e3a\u901a\u7528\u65b9\u6cd5\u7684\u51fd\u6570","text":" - user-defined functions
- general methods
- iterative improvement
- repetitive refinement
"},{"location":"CS_Basic/CS61A/Composing_Programs/#163-iii","title":"1.6.3 \u5b9a\u4e49\u51fd\u6570 III\uff1a\u5d4c\u5957\u5b9a\u4e49","text":"\u4e24\u4e2a\u540e\u679c:
- \u5168\u5c40\u5e27\u53d8\u6df7\u4e71
- \u51fd\u6570\u7b7e\u540d\u9650\u5236
- Nested function definition
- Lexical scope
- \u8fd9\u79cd\u5728\u5d4c\u5957\u5b9a\u4e49\u4e4b\u95f4\u5171\u4eab\u540d\u79f0\u7684\u89c4\u5219\u79f0\u4e3a\u8bcd\u6cd5\u4f5c\u7528\u57df
- \u6bcf\u4e2a\u7528\u6237\u5b9a\u4e49\u7684\u51fd\u6570\u90fd\u6709\u4e00\u4e2a\u7236\u73af\u5883\uff1a\u5b9a\u4e49\u5b83\u7684\u73af\u5883\u3002
- \u8c03\u7528\u7528\u6237\u5b9a\u4e49\u7684\u51fd\u6570\u65f6\uff0c\u5176\u5c40\u90e8\u5e27\u4f1a\u7ee7\u627f\u5176\u7236\u73af\u5883\u3002
- \u5173\u952e\u4f18\u52bf:
- \u5c40\u90e8\u51fd\u6570\u7684\u540d\u79f0\u4e0d\u4f1a\u5f71\u54cd\u5b9a\u4e49\u5b83\u7684\u51fd\u6570\u7684\u5916\u90e8\u540d\u79f0\uff0c\u56e0\u4e3a\u5c40\u90e8\u51fd\u6570\u7684\u540d\u79f0\u5c06\u7ed1\u5b9a\u5728\u5b9a\u4e49\u5b83\u7684\u5f53\u524d\u5c40\u90e8\u73af\u5883\u4e2d\uff0c\u800c\u4e0d\u662f\u5168\u5c40\u73af\u5883\u4e2d\u3002
- \u5c40\u90e8\u51fd\u6570\u53ef\u4ee5\u8bbf\u95ee\u5916\u5c42\u51fd\u6570\u7684\u73af\u5883\uff0c\u8fd9\u662f\u56e0\u4e3a\u5c40\u90e8\u51fd\u6570\u7684\u51fd\u6570\u4f53\u7684\u6c42\u503c\u73af\u5883\u4f1a\u7ee7\u627f\u5b9a\u4e49\u5b83\u7684\u6c42\u503c\u73af\u5883\u3002
- Extended Environments
- \u5c40\u90e8\u5b9a\u4e49\u7684\u51fd\u6570\u901a\u5e38\u88ab\u79f0\u4e3a\u95ed\u5305\uff08closures\uff09
"},{"location":"CS_Basic/CS61A/Composing_Programs/#164","title":"1.6.4 \u4f5c\u4e3a\u8fd4\u56de\u503c\u7684\u51fd\u6570","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#165","title":"1.6.5 \u793a\u4f8b\uff1a\u725b\u987f\u6cd5","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#166-currying","title":"1.6.6 Currying","text":" - uncurrying transformation
Python>>> def curry2(f):\n \"\"\"\u8fd4\u56de\u7ed9\u5b9a\u7684\u53cc\u53c2\u6570\u51fd\u6570\u7684\u67ef\u91cc\u5316\u7248\u672c\"\"\"\n def g(x):\n def h(y):\n return f(x, y)\n return h\n return g\n>>> def uncurry2(g):\n \"\"\"\u8fd4\u56de\u7ed9\u5b9a\u7684\u67ef\u91cc\u5316\u51fd\u6570\u7684\u53cc\u53c2\u6570\u7248\u672c\"\"\"\n def f(x, y):\n return g(x)(y)\n return f\n>>> pow_curried = curry2(pow)\n>>> pow_curried(2)(5)\n32\n>>> map_to_range(0, 10, pow_curried(2))\n1\n2\n4\n8\n16\n32\n64\n128\n256\n512\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#167-lambda","title":"1.6.7 Lambda \u8868\u8fbe\u5f0f","text":"Pythonlambda x : f(g(x))\n\"A function that takes x and returns f(g(x))\"\n
\\(\\displaystyle \\lambda\\)
Python>>> s = lambda x: x * x\n>>> s\n<function <lambda> at 0xf3f490>\n>>> s(12)\n144\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#168","title":"1.6.8 \u62bd\u8c61\u548c\u4e00\u7b49\u51fd\u6570","text":" - \u53ef\u4ee5\u4e0e\u540d\u79f0\u7ed1\u5b9a
- \u53ef\u4ee5\u4f5c\u4e3a\u53c2\u6570\u4f20\u9012\u7ed9\u51fd\u6570
- \u53ef\u4ee5\u4f5c\u4e3a\u51fd\u6570\u7684\u7ed3\u679c\u8fd4\u56de
- \u53ef\u4ee5\u5305\u542b\u5728\u6570\u636e\u7ed3\u6784\u4e2d
"},{"location":"CS_Basic/CS61A/Composing_Programs/#169","title":"1.6.9 \u51fd\u6570\u88c5\u9970\u5668","text":" Python>>> def trace(fn):\n def wrapped(x):\n print('-> ', fn, '(', x, ')')\n return fn(x)\n return wrapped\n\n>>> @trace\n def triple(x):\n return 3 * x\n\n>>> triple(12)\n-> <function triple at 0x102a39848> ( 12 )\n36\n
- annotation
- \u7b49\u4ef7\u4e8e:
Python>>> def triple(x):\n return 3 * x\n>>> triple = trace(triple)\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#17","title":"1.7 \u9012\u5f52\u51fd\u6570","text":" - rucursive
- circular nature
"},{"location":"CS_Basic/CS61A/Composing_Programs/#171","title":"1.7.1 \u9012\u5f52\u51fd\u6570\u5256\u6790","text":" - base case
- unwinds
- recursive calls
- induction
"},{"location":"CS_Basic/CS61A/Composing_Programs/#172-mutually-recursive","title":"1.7.2 mutually recursive","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#173","title":"1.7.3 \u9012\u5f52\u51fd\u6570\u4e2d\u7684\u6253\u5370","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#174-tree-recursive","title":"1.7.4 tree recursive","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#175","title":"1.7.5 \u793a\u4f8b\uff1a\u5206\u5272\u6570","text":"Python>>> def count_partitions(n, m):\n\u00a0\u00a0\u00a0 \"\"\"\u8ba1\u7b97\u4f7f\u7528\u6700\u5927\u6570 m \u7684\u6574\u6570\u5206\u5272 n \u7684\u65b9\u5f0f\u7684\u6570\u91cf\"\"\"\n\u00a0\u00a0\u00a0 if n == 0:\n\u00a0\u00a0\u00a0 return 1\n\u00a0\u00a0\u00a0 elif n < 0:\n\u00a0\u00a0\u00a0 return 0\n\u00a0\u00a0\u00a0 elif m == 0:\n\u00a0\u00a0\u00a0 return 0\n\u00a0\u00a0\u00a0 else:\n\u00a0\u00a0\u00a0 return count_partitions(n-m, m) + count_partitions(n, m-1)\n\n>>> count_partitions(6, 4)\n9\n>>> count_partitions(5, 5)\n7\n>>> count_partitions(10, 10)\n42\n>>> count_partitions(15, 15)\n176\n>>> count_partitions(20, 20)\n627\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#2","title":"2 \u4f7f\u7528\u6570\u636e\u6784\u5efa\u62bd\u8c61","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#21","title":"2.1 \u5f15\u8a00","text":" - \u9ad8\u9636\u51fd\u6570\u4f7f\u6211\u4eec\u80fd\u591f\u6839\u636e\u901a\u7528\u7684\u8ba1\u7b97\u65b9\u6cd5\u8fdb\u884c\u64cd\u4f5c\u548c\u63a8\u7406\uff0c\u4ece\u800c\u589e\u5f3a\u4e86\u8bed\u8a00\u7684\u529f\u80fd\u3002\u8fd9\u5c31\u662f\u7f16\u7a0b\u7684\u672c\u8d28
- \u6709\u6548\u4f7f\u7528\u5185\u7f6e\u6570\u636e\u7c7b\u578b\u548c\u7528\u6237\u5b9a\u4e49\u7684\u6570\u636e\u7c7b\u578b\u662f\u6570\u636e\u5904\u7406\u578b\u5e94\u7528\uff08data processing applications\uff09\u7684\u57fa\u7840
"},{"location":"CS_Basic/CS61A/Composing_Programs/#211","title":"2.1.1 \u539f\u59cb\u6570\u636e\u7c7b\u578b","text":"\u539f\u59cb\u6570\u636e\u7c7b\u578b\u5177\u6709\u5c5e\u6027:
- \u6709\u4e00\u4e9b\u53ef\u4ee5\u6c42\u89e3\u4e3a\u539f\u59cb\u6570\u636e\u7c7b\u578b\u7684\u8868\u8fbe\u5f0f\uff0c\u88ab\u79f0\u4e3a\u5b57\u9762\u91cf\uff08literals\uff09\u3002
- \u6709\u7528\u4e8e\u64cd\u4f5c\u539f\u59cb\u7c7b\u578b\u503c\u7684\u5185\u7f6e\u51fd\u6570\u548c\u64cd\u4f5c\u7b26\u3002 - \u539f\u59cb\u6570\u5b57\u7c7b\u578b
- int
- float
- complex
- Non-numeric types
- bool
- more on \u539f\u59cb\u6570\u636e\u7c7b\u578b
"},{"location":"CS_Basic/CS61A/Composing_Programs/#22","title":"2.2 \u6570\u636e\u62bd\u8c61","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#221","title":"2.2.1 \u793a\u4f8b\uff1a\u6709\u7406\u6570","text":"wishful thinking
Python>>> def add_rationals(x, y):\n nx, dx = numer(x), denom(x)\n ny, dy = numer(y), denom(y)\n return rational(nx * dy + ny * dx, dx * dy)\n\n>>> def mul_rationals(x, y):\n return rational(numer(x) * numer(y), denom(x) * denom(y))\n\n>>> def print_rational(x):\n print(numer(x), '/', denom(x))\n\n>>> def rationals_are_equal(x, y):\n return numer(x) * denom(y) == numer(y) * denom(x)\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#222-pair","title":"2.2.2 pair","text":"from operator import getitem
Python>>> def rational(n, d):\n return [n, d]\n\n>>> def numer(x):\n return x[0]\n\n>>> def denom(x):\n return x[1]\n
\u7b80\u5316\u6709\u7406\u6570:
Python>>> from fractions import gcd\n>>> def rational(n, d):\n g = gcd(n, d)\n return (n//g, d//g)\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#223","title":"2.2.3 \u62bd\u8c61\u5c4f\u969c","text":" - \u6570\u636e\u62bd\u8c61: \u7528\u4e00\u7ec4\u57fa\u672c\u64cd\u4f5c\u6765\u64cd\u4f5c\u6570\u636e\u3002
- avbstraction barrier
- the best:
Python>>> def square_rational(x):\n return mul_rational(x, x)\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#224","title":"2.2.4 \u6570\u636e\u7684\u5c5e\u6027","text":"\u76f8\u5f53\u4e8e\u81ea\u5df1\u5199\u4e00\u4e2a\u6570\u636e\u7ed3\u6784:
Python>>> def pair(x, y):\n \"\"\"Return a function that represents a pair.\"\"\"\n def get(index):\n if index == 0:\n return x\n elif index == 1:\n return y\n return get\n\n>>> def select(p, i):\n \"\"\"Return the element at index i of pair p.\"\"\"\n return p(i)\n\n>>> p = pair(20, 14)\n>>> select(p, 0)\n20\n>>> select(p, 1)\n14\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#23","title":"2.3 \u5e8f\u5217","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#231-list","title":"2.3.1 list","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#232","title":"2.3.2 \u5e8f\u5217\u904d\u5386","text":"Pythonfor <name> in <expression>:\n <suite>\n
the expression must produce an iterable object sequence unpacking
Python>>> pairs = [[1, 2], [2, 2], [2, 3], [4, 4]]\n>>> same_count = 0\n>>> for x, y in pairs:\n if x == y:\n same_count = same_count + 1\n>>> same_count\n2\n
range
"},{"location":"CS_Basic/CS61A/Composing_Programs/#233","title":"2.3.3 \u5e8f\u5217\u5904\u7406","text":"list comprehensions
Python>>> odds = [1, 3, 5, 7, 9]\n>>> [x+1 for x in odds]\n[2, 4, 6, 8, 10]\n[<map expression> for <name> in <sequence expression> if <filter expression>]\n
- Aggregation \u5c31\u662f\u7f29\u5e76\u5566
Python>>> def apply_to_all(map_fn, s):\n return [map_fn(x) for x in s]\n>>> def keep_if(filter_fn, s):\n return [x for x in s if filter_fn(x)]\n# conventional names\n>>> apply_to_all = lambda map_fn, s: list(map(map_fn, s))\n>>> keep_if = lambda filter_fn, s: list(filter(filter_fn, s))\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#234","title":"2.3.4 \u5e8f\u5217\u62bd\u8c61","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#235","title":"2.3.5 \u5b57\u7b26\u4e32","text":"string \u6ca1\u6709\u5b57\u7b26\u7c7b\u578b
- Membership
- Multiline Literals
- String Coercion more on Dive Into Python 3\u00a0\u7684\u00a0\u5b57\u7b26\u4e32\u7ae0\u8282\u00a0\u63d0\u4f9b\u4e86\u5b57\u7b26\u7f16\u7801\u548c Unicode \u7684\u63cf\u8ff0
"},{"location":"CS_Basic/CS61A/Composing_Programs/#236","title":"2.3.6 \u6811","text":"closure property bax-and-pointer notation
- root label
- branch
- leaf: the tree without branch
- node tree-recursive \u4e24\u4e2a\u4f8b\u5b50:
Python>>> def fib_tree(n):\n if n == 0 or n == 1:\n return tree(n)\n else:\n left, right = fib_tree(n-2), fib_tree(n-1)\n fib_n = label(left) + label(right)\n return tree(fib_n, [left, right])\n>>> fib_tree(5)\n[5, [2, [1], [1, [0], [1]]], [3, [1, [0], [1]], [2, [1], [1, [0], [1]]]]]\n
Python>>> def count_leaves(tree):\n if is_leaf(tree):\n return 1\n else:\n branch_counts = [count_leaves(b) for b in branches(tree)]\n return sum(branch_counts)\n>>> count_leaves(fib_tree(5))\n8\n
Partition trees
Python>>> def print_parts(tree, partition=[]):\n if is_leaf(tree):\n if label(tree):\n print(' + '.join(partition))\n else:\n left, right = branches(tree)\n m = str(label(tree))\n print_parts(left, partition + [m])\n print_parts(right, partition)\n\n>>> print_parts(partition_tree(6, 4))\n4 + 2\n4 + 1 + 1\n3 + 3\n3 + 2 + 1\n3 + 1 + 1 + 1\n2 + 2 + 2\n2 + 2 + 1 + 1\n2 + 1 + 1 + 1 + 1\n1 + 1 + 1 + 1 + 1 + 1\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#237","title":"2.3.7 \u94fe\u8868","text":"linked list abstract data representation
Python>>> def partitions(n, m):\n\"\"\"\u8fd4\u56de\u4e00\u4e2a\u5305\u542b n \u7684\u5206\u5272\u65b9\u6848\u7684\u94fe\u8868\uff0c\u5176\u4e2d\u6bcf\u4e2a\u6b63\u6574\u6570\u4e0d\u8d85\u8fc7 m\"\"\"\nif n == 0:\n return link(empty, empty) # \u5305\u542b\u7a7a\u5206\u5272\u7684\u94fe\u8868\nelif n < 0 or m == 0:\n return empty\nelse:\n using_m = partitions(n-m, m)\n with_m = apply_to_all_link(lambda s: link(m, s), using_m)\n without_m = partitions(n, m-1)\n return extend_link(with_m, without_m)\n\n>>> def print_partitions(n, m):\n lists = partitions(n, m)\n strings = apply_to_all_link(lambda s: join_link(s, \" + \"), lists)\n print(join_link(strings, \"\\n\"))\n\n>>> print_partitions(6, 4)\n4 + 2\n4 + 1 + 1\n3 + 3\n3 + 2 + 1\n3 + 1 + 1 + 1\n2 + 2 + 2\n2 + 2 + 1 + 1\n2 + 1 + 1 + 1 + 1\n1 + 1 + 1 + 1 + 1 + 1\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#24","title":"2.4 \u53ef\u53d8\u6570\u636e","text":"object-oriented programming
"},{"location":"CS_Basic/CS61A/Composing_Programs/#241","title":"2.4.1 \u5bf9\u8c61\u9690\u55bb","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#242","title":"2.4.2 \u5e8f\u5217\u5bf9\u8c61","text":"mutable Sharing and Identity \u5217\u8868\u63a8\u5bfc\u5f0f:
Python>>> from unicodedata import lookup\n>>> [lookup('WHITE ' + s.upper() + ' SUIT') for s in suits]\n['\u2661', '\u2662', '\u2664', '\u2667']\n
tuple
"},{"location":"CS_Basic/CS61A/Composing_Programs/#243","title":"2.4.3 \u5b57\u5178","text":"key-value pairs
"},{"location":"CS_Basic/CS61A/Composing_Programs/#244","title":"2.4.4 \u5c40\u90e8\u72b6\u6001","text":"local state
Python>>> def make_withdraw(balance):\n\"\"\"\u8fd4\u56de\u4e00\u4e2a\u6bcf\u6b21\u8c03\u7528\u90fd\u4f1a\u51cf\u5c11 balance \u7684 withdraw \u51fd\u6570\"\"\"\ndef withdraw(amount):\n nonlocal balance # \u58f0\u660e balance \u662f\u975e\u5c40\u90e8\u7684\n if amount > balance:\n return '\u4f59\u989d\u4e0d\u8db3'\n balance = balance - amount # \u91cd\u65b0\u7ed1\u5b9a\n return balance\nreturn withdraw\n
Python Particulars
"},{"location":"CS_Basic/CS61A/Composing_Programs/#245-non-local","title":"2.4.5 \u975e\u5c40\u90e8 Non-local \u8d4b\u503c\u7684\u597d\u5904","text":"\u8fd9\u6837\uff0c\u6bcf\u4e2a withdraw \u5b9e\u4f8b\u90fd\u4fdd\u6301\u81ea\u5df1\u7684 balance \u72b6\u6001\uff0c\u4f46\u7a0b\u5e8f\u4e2d\u7684\u4efb\u4f55\u5176\u4ed6\u51fd\u6570\u90fd\u65e0\u6cd5\u8bbf\u95ee\u8be5\u72b6\u6001\u3002\u4ece\u66f4\u9ad8\u7684\u5c42\u9762\u6765\u770b\u8fd9\u79cd\u60c5\u51b5\uff0c\u6211\u4eec\u62bd\u8c61\u4e86\u4e00\u4e2a\u94f6\u884c\u8d26\u6237\uff0c\u5b83\u81ea\u5df1\u7ba1\u7406\u81ea\u5df1\u7684\u72b6\u6001\uff0c\u5176\u884c\u4e3a\u65b9\u5f0f\u4e0e\u4e16\u754c\u4e0a\u6240\u6709\u5176\u5b83\u8d26\u6237\u4e00\u6837\uff1a\u968f\u7740\u65f6\u95f4\u63a8\u79fb\uff0c\u8d26\u6237\u7684\u72b6\u6001\u4f1a\u6839\u636e\u8d26\u6237\u7684\u53d6\u6b3e\u8bb0\u5f55\u800c\u53d1\u751f\u53d8\u5316\u3002
"},{"location":"CS_Basic/CS61A/Composing_Programs/#246-non-local","title":"2.4.6 \u975e\u5c40\u90e8 Non-local \u8d4b\u503c\u7684\u4ee3\u4ef7","text":" - \u6b63\u786e\u7406\u89e3\u5305\u542b nonlocal \u58f0\u660e\u7684\u4ee3\u7801\u7684\u5173\u952e\u662f\u8bb0\u4f4f\uff1a\u53ea\u6709\u51fd\u6570\u8c03\u7528\u624d\u80fd\u5f15\u5165\u65b0\u5e27\u3002\u8d4b\u503c\u8bed\u53e5\u53ea\u80fd\u66f4\u6539\u73b0\u6709\u5e27\u4e2d\u7684\u7ed1\u5b9a\u5173\u7cfb\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u9664\u975e make_withdraw \u88ab\u8c03\u7528\u4e24\u6b21\uff0c\u5426\u5219\u53ea\u80fd\u6709\u4e00\u4e2a balance \u7ed1\u5b9a\u3002
- Sameness and change
- referentially transparent
"},{"location":"CS_Basic/CS61A/Composing_Programs/#247","title":"2.4.7 \u5217\u8868\u548c\u5b57\u5178\u5b9e\u73b0","text":"\u51fd\u6570\u662f\u4e00\u4e2a dispatch \uff08\u8c03\u5ea6\uff09\u51fd\u6570\uff0c\u5176\u53c2\u6570\u9996\u5148\u662f\u4e00\u4e2a\u671f\u671b\u7684\u6307\u4ee4\uff0c\u4ee3\u8868\u671f\u671b\u8fd9\u4e2a\u51fd\u6570\u505a\u4ec0\u4e48\uff1b\u7136\u540e\u662f\u8be5\u65b9\u6cd5\u7684\u9700\u8981\u7528\u5230\u7684\u53c2\u6570\u3002\u6b64\u6307\u4ee4\u662f\u4e00\u4e2a\u5b57\u7b26\u4e32\uff0c\u7528\u4e8e\u547d\u540d\u51fd\u6570\u5e94\u6267\u884c\u7684\u64cd\u4f5c\u3002\u53ef\u4ee5\u5c06\u8fd9\u4e2a dispatch \u51fd\u6570\u7406\u89e3\u4e3a\u591a\u4e2a\u4e0d\u540c\u51fd\u6570\u7684\u62bd\u8c61\uff1a\u7b2c\u4e00\u4e2a\u53c2\u6570\u786e\u5b9a\u76ee\u6807\u51fd\u6570\u7684\u884c\u4e3a\uff0c\u5e76\u4e3a\u8be5\u884c\u4e3a\u5165\u53c2\u5176\u4ed6\u53c2\u6570\u3002 \u7528\u5b57\u7b26\u4e32\u4e5f\u592a\u9006\u5929\u4e86\u3002
Python>>> def mutable_link():\n\"\"\"\u8fd4\u56de\u4e00\u4e2a\u53ef\u53d8\u94fe\u8868\u7684\u51fd\u6570\"\"\"\ncontents = empty\ndef dispatch(message, value=None):\n nonlocal contents\n if message == 'len':\n return len_link(contents)\n elif message == 'getitem':\n return getitem_link(contents, value)\n elif message == 'push_first':\n contents = link(value, contents)\n elif message == 'pop_first':\n f = first(contents)\n contents = rest(contents)\n return f\n elif message == 'str':\n return join_link(contents, \", \")\nreturn dispatch\n\n>>> def to_mutable_link(source):\n\"\"\"\u8fd4\u56de\u4e00\u4e2a\u4e0e\u539f\u5217\u8868\u76f8\u540c\u5185\u5bb9\u7684\u51fd\u6570\u5217\u8868\"\"\"\ns = mutable_link()\nfor element in reversed(source):\n s('push_first', element)\nreturn s\n\n>>> s = to_mutable_link(suits)\n>>> type(s)\n<class 'function'>\n>>> print(s('str'))\nheart, diamond, spade, club\n
\u5b57\u5178\u5b9e\u73b0:
Python>>> def dictionary():\n\"\"\"\u8fd4\u56de\u4e00\u4e2a\u5b57\u5178\u7684\u51fd\u6570\u5b9e\u73b0\"\"\"\nrecords = []\ndef getitem(key):\n matches = [r for r in records if r[0] == key]\n if len(matches) == 1:\n key, value = matches[0]\n return value\ndef setitem(key, value):\n nonlocal records\n non_matches = [r for r in records if r[0] != key]\n records = non_matches + [[key, value]]\ndef dispatch(message, key=None, value=None):\n if message == 'getitem':\n return getitem(key)\n elif message == 'setitem':\n setitem(key, value)\nreturn dispatch\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#248-dispatch-dictionaries","title":"2.4.8 \u8c03\u5ea6\u5b57\u5178\uff08Dispatch Dictionaries\uff09","text":"\u7528\u5b57\u5178\u5b58\u50a8\u6d88\u606f\u3002
Pythondef account(initial_balance):\n def deposit(amount):\n dispatch['balance'] += amount\n return dispatch['balance']\n def withdraw(amount):\n if amount > dispatch['balance']:\n return 'Insufficient funds'\n dispatch['balance'] -= amount\n return dispatch['balance']\n dispatch = {'deposit': deposit,\n 'withdraw': withdraw,\n 'balance': initial_balance}\n return dispatch\n\ndef withdraw(account, amount):\n return account['withdraw'](amount)\ndef deposit(account, amount):\n return account['deposit'](amount)\ndef check_balance(account):\n return account['balance']\n\na = account(20)\ndeposit(a, 5)\nwithdraw(a, 17)\ncheck_balance(a)\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#249-propagating-constraints","title":"2.4.9 \u7ea6\u675f\u4f20\u9012 (Propagating\u00a0Constraints)","text":"connector Using the Constraint System
Python>>> celsius = connector('Celsius')\n>>> fahrenheit = connector('Fahrenheit')\n>>> def converter(c, f):\n \"\"\"\u7528\u7ea6\u675f\u6761\u4ef6\u8fde\u63a5 c \u5230 f\uff0c\u5c06\u6444\u6c0f\u5ea6\u8f6c\u6362\u4e3a\u534e\u6c0f\u5ea6.\"\"\"\n u, v, w, x, y = [connector() for _ in range(5)]\n multiplier(c, w, u)\n multiplier(v, x, u)\n adder(v, y, f)\n constant(w, 9)\n constant(x, 5)\n constant(y, 32)\n>>> converter(celsius, fahrenheit)\n\n>>> celsius['set_val']('user', 25)\nCelsius = 25\nFahrenheit = 77.0\n\n>>> fahrenheit['set_val']('user', 212)\nContradiction detected: 77.0 vs 212\n\n>>> celsius['forget']('user')\nCelsius is forgotten\nFahrenheit is forgotten\n\n>>> fahrenheit['set_val']('user', 212)\nFahrenheit = 212\nCelsius = 100.0\n\n# Implementing the Constraint System\n>>> connector ['set_val'](source, value) \"\"\"\u8868\u793a\u00a0source\u00a0\u5728\u8bf7\u6c42\u8fde\u63a5\u5668\u5c06\u5f53\u524d\u503c\u8bbe\u4e3a value\"\"\"\n>>> connector ['has_val']()\u00a0 \"\"\"\u8fd4\u56de\u8fde\u63a5\u5668\u662f\u5426\u5df2\u7ecf\u5177\u6709\u503c\"\"\"\n>>> connector ['val'] \"\"\"\u662f\u8fde\u63a5\u5668\u7684\u5f53\u524d\u503c\"\"\"\n>>> connector ['forget'](source)\u00a0 \"\"\"\u544a\u8bc9\u8fde\u63a5\u5668 source \u8bf7\u6c42\u9057\u5fd8\u5b83\u7684\u503c\"\"\"\n>>> connector ['connect'](source)\u00a0 \"\"\"\u544a\u8bc9\u8fde\u63a5\u5668\u53c2\u4e0e\u65b0\u7684\u7ea6\u675f\uff0c\u5373 source\"\"\"\n>>> constraint['new_val']() \"\"\"\u8868\u793a\u4e0e\u7ea6\u675f\u76f8\u8fde\u7684\u67d0\u4e2a\u8fde\u63a5\u5668\u5177\u6709\u65b0\u7684\u503c\u3002\"\"\"\n>>> constraint['forget']()\u00a0 \"\"\"\u8868\u793a\u4e0e\u7ea6\u675f\u76f8\u8fde\u7684\u67d0\u4e2a\u8fde\u63a5\u5668\u9057\u5fd8\u4e86\u503c\u3002\"\"\"\n\n>>> from operator import add, sub\n>>> def adder(a, b, c):\n \"\"\"\u7ea6\u675f a+b=c\"\"\"\n return make_ternary_constraint(a, b, c, add, sub, sub)\n\n>>> def make_ternary_constraint(a, b, c, ab, ca, cb):\n \"\"\"\u7ea6\u675f ab(a,b)=c\uff0cca(c,a)=b\uff0ccb(c,b)=a\"\"\"\n def new_value():\n av, bv, cv = [connector['has_val']() for connector in (a, b, c)]\n if av and bv:\n c['set_val'](constraint, ab(a['val'], b['val']))\n elif av and cv:\n b['set_val'](constraint, ca(c['val'], a['val']))\n elif bv and cv:\n a['set_val'](constraint, cb(c['val'], b['val']))\n def forget_value():\n for connector in (a, b, c):\n connector['forget'](constraint)\n constraint = {'new_val': new_value, 'forget': forget_value}\n for connector in (a, b, c):\n connector['connect'](constraint)\n return constraint\n\n>>> from operator import mul, truediv\n>>> def multiplier(a, b, c):\n \"\"\"\u7ea6\u675f a*b=c\"\"\"\n return make_ternary_constraint(a, b, c, mul, truediv, truediv)\n\n>>> def constant(connector, value):\n \"\"\"\u5e38\u91cf\u8d4b\u503c\"\"\"\n constraint = {}\n connector['set_val'](constraint, value)\n return constraint\n\n# Representing connectors\n>>> def connector(name=None):\n \"\"\"\u9650\u5236\u6761\u4ef6\u4e4b\u95f4\u7684\u8fde\u63a5\u5668\"\"\"\n informant = None\n constraints = []\n def set_value(source, value):\n nonlocal informant\n val = connector['val']\n if val is None:\n informant, connector['val'] = source, value\n if name is not None:\n print(name, '=', value)\n inform_all_except(source, 'new_val', constraints)\n else:\n if val != value:\n print('Contradiction detected:', val, 'vs', value)\n def forget_value(source):\n nonlocal informant\n if informant == source:\n informant, connector['val'] = None, None\n if name is not None:\n print(name, 'is forgotten')\n inform_all_except(source, 'forget', constraints)\n connector = {'val': None,\n 'set_val': set_value,\n 'forget': forget_value,\n 'has_val': lambda: connector['val'] is not None,\n 'connect': lambda source: constraints.append(source)}\n return connector\n\n>>> def inform_all_except(source, message, constraints):\n \"\"\"\u544a\u77e5\u4fe1\u606f\u9664\u4e86 source \u5916\u7684\u6240\u6709\u7ea6\u675f\u6761\u4ef6\"\"\"\n for c in constraints:\n if c != source:\n c[message]()\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#25","title":"2.5 \u9762\u5411\u5bf9\u8c61\u7f16\u7a0b","text":" - object
- dot notation
- class
"},{"location":"CS_Basic/CS61A/Composing_Programs/#251","title":"2.5.1 \u5bf9\u8c61\u548c\u7c7b","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#252","title":"2.5.2 \u7c7b\u7684\u5b9a\u4e49","text":"__init__\u7c7b\u7684\u6784\u9020\u51fd\u6570\uff08constructor\uff09
Python>>> class Account:\n def __init__(self, account_holder):\n self.balance = 0\n self.holder = account_holder\n def deposit(self, amount):\n self.balance = self.balance + amount\n return self.balance\n def withdraw(self, amount):\n if amount > self.balance:\n return 'Insufficient funds'\n self.balance = self.balance - amount\n return self.balance\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#253","title":"2.5.3 \u6d88\u606f\u4f20\u9012\u548c\u70b9\u8868\u8fbe\u5f0f","text":"Python>>> getattr(spock_account, 'balance')\n10\n>>> hasattr(spock_account, 'deposit')\nTrue\n\n\n>>> type(Account.deposit)\n<class 'Function'>\n>>> type(spock_account.deposit)\n<class 'method'>\n# \u4e3a\u7c7b\u7684\u5c5e\u6027\uff0c\u65b9\u6cd5\u53ea\u662f\u4e00\u4e2a\u51fd\u6570\uff0c\u4f46\u4f5c\u4e3a\u5b9e\u4f8b\u7684\u5c5e\u6027\uff0c\u5b83\u662f\u4e00\u4e2a\u7ed1\u5b9a\u65b9\u6cd5\n\n>>> Account.deposit(spock_account, 1001) # \u51fd\u6570 deposit \u63a5\u53d7\u4e24\u4e2a\u53c2\u6570\n1011\n>>> spock_account.deposit(1000) # \u65b9\u6cd5 deposit \u63a5\u53d7\u4e00\u4e2a\u53c2\u6570\n2011\n
\u547d\u540d\u7ea6\u5b9a\uff1a\u7c7b\u540d\u901a\u5e38\u4f7f\u7528 CapWords \u7ea6\u5b9a\uff08\u4e5f\u79f0\u4e3a CamelCase\uff0c\u56e0\u4e3a\u540d\u79f0\u4e2d\u95f4\u7684\u5927\u5199\u5b57\u6bcd\u770b\u8d77\u6765\u50cf\u9a7c\u5cf0\uff09\u7f16\u5199\u3002\u65b9\u6cd5\u540d\u79f0\u9075\u5faa\u4f7f\u7528\u4e0b\u5212\u7ebf\u5206\u9694\u7684\u5c0f\u5199\u5355\u8bcd\u547d\u540d\u51fd\u6570\u7684\u6807\u51c6\u7ea6\u5b9a\u3002
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6709\u4e00\u4e9b\u5b9e\u4f8b\u53d8\u91cf\u548c\u65b9\u6cd5\u4e0e\u5bf9\u8c61\u7684\u7ef4\u62a4\u548c\u4e00\u81f4\u6027\u76f8\u5173\uff0c\u6211\u4eec\u4e0d\u5e0c\u671b\u5bf9\u8c61\u7684\u7528\u6237\u770b\u5230\u6216\u4f7f\u7528\u3002\u5b83\u4eec\u4e0d\u662f\u7c7b\u5b9a\u4e49\u7684\u62bd\u8c61\u7684\u4e00\u90e8\u5206\uff0c\u800c\u662f\u5b9e\u73b0\u7684\u4e00\u90e8\u5206\u3002Python \u7684\u7ea6\u5b9a\u89c4\u5b9a\uff0c\u5982\u679c\u5c5e\u6027\u540d\u79f0\u4ee5\u4e0b\u5212\u7ebf\u5f00\u5934\uff0c\u5219\u53ea\u80fd\u5728\u7c7b\u672c\u8eab\u7684\u65b9\u6cd5\u4e2d\u8bbf\u95ee\u5b83\uff0c\u800c\u4e0d\u662f\u7528\u6237\u8bbf\u95ee\u3002
"},{"location":"CS_Basic/CS61A/Composing_Programs/#254","title":"2.5.4 \u7c7b\u5c5e\u6027","text":"\u611f\u89c9\u6ca1\u4ec0\u4e48\u7528:
Python>>> Account.interest = 0.05 # \u6539\u53d8\u7c7b\u5c5e\u6027\n>>> spock_account.interest # \u5b9e\u4f8b\u5c5e\u6027\u53d1\u751f\u53d8\u5316\uff08\u8be5\u5b9e\u4f8b\u4e2d\u6ca1\u6709\u548c\u7c7b\u5c5e\u6027\u540c\u540d\u79f0\u7684\u5b9e\u4f8b\u5c5e\u6027\uff09\n0.05\n>>> kirk_account.interest # \u5982\u679c\u5b9e\u4f8b\u4e2d\u5b58\u5728\u548c\u7c7b\u5c5e\u6027\u540c\u540d\u7684\u5b9e\u4f8b\u5c5e\u6027\uff0c\u5219\u6539\u53d8\u7c7b\u5c5e\u6027\uff0c\u4e0d\u4f1a\u5f71\u54cd\u5b9e\u4f8b\u5c5e\u6027\n0.08\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#255","title":"2.5.5 \u7ee7\u627f","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#256","title":"2.5.6 \u4f7f\u7528\u7ee7\u627f","text":"Python>>> class Account:\n \"\"\"\u4e00\u4e2a\u4f59\u989d\u975e\u96f6\u7684\u8d26\u6237\u3002\"\"\"\n interest = 0.02\n def __init__(self, account_holder):\n self.balance = 0\n self.holder = account_holder\n def deposit(self, amount):\n \"\"\"\u5b58\u5165\u8d26\u6237 amount\uff0c\u5e76\u8fd4\u56de\u53d8\u5316\u540e\u7684\u4f59\u989d\"\"\"\n self.balance = self.balance + amount\n return self.balance\n def withdraw(self, amount):\n \"\"\"\u4ece\u8d26\u53f7\u4e2d\u53d6\u51fa amount\uff0c\u5e76\u8fd4\u56de\u53d8\u5316\u540e\u7684\u4f59\u989d\"\"\"\n if amount > self.balance:\n return 'Insufficient funds'\n self.balance = self.balance - amount\n return self.balance\n\n>>> class CheckingAccount(Account):\n \"\"\"\u4ece\u8d26\u53f7\u53d6\u94b1\u4f1a\u6263\u51fa\u624b\u7eed\u8d39\u7684\u8d26\u53f7\"\"\"\n withdraw_charge = 1\n interest = 0.01\n def withdraw(self, amount):\n return Account.withdraw(self, amount + self.withdraw_charge)\n
\u63a5\u53e3
Python>>> def deposit_all(winners, amount=5):\n for account in winners:\n account.deposit(amount) # \u8fd9\u91cc\u8c03\u7528\u7684\u662f\u5b9e\u4f8b account \u7684 deposit \u65b9\u6cd5\n # \u5bf9\u4e8e\u4e0d\u540c\u5b9e\u4f8b\u6765\u8bf4\uff0c\u5b83\u4eec\u7684 deposit \u65b9\u6cd5\u53ef\u80fd\u4e0d\u540c\u3002\u8fd9\u4e2a\u4f8b\u5b50\u76f8\u5bf9\u4e8e\u4e0b\u9762\u6765\u8bb2\uff0c\u66f4\u52a0\u5177\u6709\u5065\u58ee\u6027\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#257","title":"2.5.7 \u591a\u7ee7\u627f","text":"\u7ee7\u627f\u6392\u5e8f\u95ee\u9898\u6ca1\u6709\u6b63\u786e\u7684\u89e3\u51b3\u65b9\u6848\uff0c\u56e0\u4e3a\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u80fd\u66f4\u613f\u610f\u5c06\u67d0\u4e9b\u7ee7\u627f\u7c7b\u7f6e\u4e8e\u5176\u4ed6\u7c7b\u4e4b\u4e0a\u3002\u4f46\u662f\uff0c\u4efb\u4f55\u652f\u6301\u591a\u91cd\u7ee7\u627f\u7684\u7f16\u7a0b\u8bed\u8a00\u90fd\u5fc5\u987b\u4ee5\u4e00\u81f4\u7684\u65b9\u5f0f\u9009\u62e9\u67d0\u4e9b\u6392\u5e8f\uff0c\u4ee5\u4fbf\u8be5\u8bed\u8a00\u7684\u7528\u6237\u53ef\u4ee5\u9884\u6d4b\u5176\u7a0b\u5e8f\u7684\u884c\u4e3a\u3002
\u8fdb\u4e00\u6b65\u9605\u8bfb\u3002Python \u4f7f\u7528\u79f0\u4e3a C3 \u65b9\u6cd5\u89e3\u6790\u6392\u5e8f\u7684\u9012\u5f52\u7b97\u6cd5\u89e3\u6790\u6b64\u540d\u79f0\u3002\u53ef\u4ee5\u5728\u6240\u6709\u7c7b\u4e0a\u4f7f\u7528\u00a0mro\u00a0\u65b9\u6cd5\u67e5\u8be2\u4efb\u4f55\u7c7b\u7684\u65b9\u6cd5\u89e3\u6790\u987a\u5e8f\u3002
Python>>> [c.__name__ for c in AsSeenOnTVAccount.mro()]\n['AsSeenOnTVAccount', 'CheckingAccount', 'SavingsAccount', 'Account', 'object']\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#258","title":"2.5.8 \u5bf9\u8c61\u7684\u4f5c\u7528","text":"\u53e6\u4e00\u65b9\u9762\uff0c\u7c7b\u53ef\u80fd\u4e0d\u662f\u5b9e\u73b0\u67d0\u4e9b\u62bd\u8c61\u7684\u6700\u4f73\u673a\u5236\u3002\u51fd\u6570\u5f0f\u62bd\u8c61\u63d0\u4f9b\u4e86\u4e00\u4e2a\u66f4\u81ea\u7136\u7684\u9690\u55bb\u6765\u8868\u793a\u8f93\u5165\u548c\u8f93\u51fa\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6211\u4eec\u4e0d\u5e94\u8be5\u89c9\u5f97\u5fc5\u987b\u5c06\u7a0b\u5e8f\u4e2d\u7684\u6bcf\u4e00\u70b9\u903b\u8f91\u90fd\u585e\u8fdb\u4e00\u4e2a\u7c7b\u4e2d\uff0c\u5c24\u5176\u662f\u5728\u5b9a\u4e49\u72ec\u7acb\u51fd\u6570\u6765\u64cd\u4f5c\u6570\u636e\u66f4\u81ea\u7136\u7684\u60c5\u51b5\u4e0b\u3002\u51fd\u6570\u8fd8\u53ef\u4ee5\u5f3a\u5236\u5b9e\u73b0\u5173\u6ce8\u70b9\u7684\u5206\u79bb\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u51fd\u6570\u5f0f\u7f16\u7a0b\u63d0\u4f9b\u4e86\u53e6\u4e00\u79cd\u6709\u6548\u5730\u7ec4\u7ec7\u7a0b\u5e8f\u903b\u8f91\u7684\u65b9\u6cd5\uff0c\u4f7f\u5f97\u7a0b\u5e8f\u5458\u80fd\u591f\u66f4\u597d\u5730\u5904\u7406\u548c\u7ef4\u62a4\u7a0b\u5e8f\u3002\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u4f7f\u7528\u51fd\u6570\u5f0f\u7f16\u7a0b\u65b9\u6cd5\u53ef\u80fd\u6bd4\u4f7f\u7528\u9762\u5411\u5bf9\u8c61\u7f16\u7a0b\u66f4\u81ea\u7136\u548c\u6709\u6548\u3002
"},{"location":"CS_Basic/CS61A/Composing_Programs/#26","title":"2.6 \u5b9e\u73b0\u7c7b\u548c\u5bf9\u8c61","text":"object-oriented programming paradigm \u5373\u4f7f\u5728\u6ca1\u6709\u5185\u7f6e\u5bf9\u8c61\u7cfb\u7edf\u7684\u7f16\u7a0b\u8bed\u8a00\u4e2d\uff0c\u7a0b\u5e8f\u4e5f\u53ef\u4ee5\u662f\u9762\u5411\u5bf9\u8c61\u7684\u3002 \u653e\u5f03\u70b9\u8868\u793a\u6cd5->\u8c03\u5ea6\u5b57\u5178\u5b9e\u73b0\u6d88\u606f\u4f20\u9012
"},{"location":"CS_Basic/CS61A/Composing_Programs/#261","title":"2.6.1 \u5b9e\u4f8b","text":"Python>>> def make_instance(cls):\n \"\"\"Return a new object instance, which is a dispatch dictionary.\"\"\"\n def get_value(name):\n if name in attributes:\n return attributes[name]\n else:\n value = cls['get'](name)\n return bind_method(value, instance)\n def set_value(name, value):\n attributes[name] = value\n attributes = {}\n instance = {'get': get_value, 'set': set_value}\n return instance\n\n>>> def bind_method(value, instance):\n \"\"\"Return a bound method if value is callable, or value otherwise.\"\"\"\n if callable(value):\n def method(*args):\n return value(instance, *args)\n return method\n else:\n return value\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#262","title":"2.6.2 \u7c7b","text":"Python>>> def make_class(attributes, base_class=None):\n \"\"\"Return a new class, which is a dispatch dictionary.\"\"\"\n def get_value(name):\n if name in attributes:\n return attributes[name]\n elif base_class is not None:\n return base_class['get'](name)\n def set_value(name, value):\n attributes[name] = value\n def new(*args):\n return init_instance(cls, *args)\n cls = {'get': get_value, 'set': set_value, 'new': new}\n return cls\n\n>>> def init_instance(cls, *args):\n \"\"\"Return a new object with type cls, initialized with args.\"\"\"\n instance = make_instance(cls)\n init = cls['get']('__init__')\n if init:\n init(instance, *args)\n return instance\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#3","title":"3 \u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u89e3\u91ca","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#31","title":"3.1 \u5f15\u8a00","text":"\u8bb8\u591a\u89e3\u91ca\u5668\u90fd\u6709\u4e00\u4e2a\u4f18\u96c5\u7684\u7ed3\u6784\uff0c\u5373\u4e24\u4e2a\u4e92\u9012\u5f52\u51fd\u6570\uff1a
- \u7b2c\u4e00\u4e2a\u51fd\u6570\u6c42\u89e3\u73af\u5883\u4e2d\u7684\u8868\u8fbe\u5f0f
- \u7b2c\u4e8c\u4e2a\u51fd\u6570\u5c06\u51fd\u6570\u5e94\u7528\u4e8e\u53c2\u6570
"},{"location":"CS_Basic/CS61A/Composing_Programs/#32","title":"3.2 \u51fd\u6570\u5f0f\u7f16\u7a0b","text":" - \u53ea\u4f7f\u7528\u8868\u8fbe\u5f0f\u800c\u4e0d\u4f7f\u7528\u8bed\u53e5\uff0c\u7279\u522b\u9002\u5408\u7b26\u53f7\u8ba1\u7b97
- \u5904\u7406\u7684\u6570\u636e\u90fd\u662f\u4e0d\u53ef\u53d8\u7684\uff08immutable\uff09
Python(if <predicate> <consequent> <alternative>)\n\n(define pi 3.14)\n(* pi 3.14)\n\n(define (<name> <formal parameters>) <body>)\neg1:\n (define (average x y)\n (/ (+ x y) 2))\neg2:\n (define (abs x)\n (if (< x 0)\n (- x)\n x))\neg3:\n (define (sqrt x)\n (define (good-enough? guess)\n (< (abs (- (square guess) x)) 0.001))\n (define (improve guess)\n (average guess (/ x guess)))\n (define (sqrt-iter guess)\n (if (good-enough? guess)\n guess\n (sqrt-iter (improve guess))))\n (sqrt-iter 1.0))\n (sqrt 9)\n\n(lambda (<formal-parameters>) <body>)\neg1:\n (define (plus4 x) (+ x 4))\n (define plus4 (lambda (x) (+ x 4))) # both are OK\n\n# \u7279\u6b8a\u7684\u503c\u00a0nil\u00a0\u6216\u00a0'()\u00a0\u8868\u793a\u7a7a\u5217\u8868\n\n# null? \u8c13\u8bcd\u7684\u4f7f\u7528:\n (define (length items)\n (if (null? items)\n 0\n (+ 1 (length (cdr items)))))\n (define (getitem items n)\n (if (= n 0)\n (car items)\n (getitem (cdr items) (- n 1))))\n (define squares (list 1 4 9 16 25))\n\n (length squares)\n\n (getitem squares 3)\n\n# \u4efb\u4f55\u4e0d\u88ab\u6c42\u503c\u7684\u8868\u8fbe\u5f0f\u90fd\u88ab\u79f0\u4e3a\u88ab\u5f15\u7528\n (list 'define 'list)\n\n# turtle\u4f7f\u7528+\u9012\u5f52\u753b\u56fe\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#33","title":"3.3 \u5f02\u5e38","text":" Python>>> raise Exception(' An error occurred')\nTraceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nException: an error occurred\n
- raising an exception
- read-eval-print-loop \u5373 REPL
- stack backtrace
- handling exceptions
Pythontry\n <try suite>\nexcept <exception class> as <name>:\n <except suite>\n
\u5f02\u5e38\u662f\u4e2a\u7c7b\uff0c\u53ef\u4ee5\u6709\u989d\u5916\u7684\u5c5e\u6027\uff0c\u53ef\u4ee5\u907f\u514d\u62a5\u9519\uff0c\u8ba9\u7a0b\u5e8f\u7ed9\u51fa\u4e00\u4e2a\u8f83\u4e3a\u7c97\u7cd9\u7684\u503c\uff1a
Python>>> class IterImproveError(Exception):\n\u00a0\u00a0\u00a0 def __init__(self, last_guess):\n\u00a0\u00a0\u00a0 self.last_guess = last_guess\n>>> def improve(update, done, guess=1, max_updates=1000):\n\u00a0\u00a0\u00a0 k = 0\n\u00a0\u00a0\u00a0 try:\n\u00a0\u00a0\u00a0 while not done(guess) and k < max_updates:\n\u00a0\u00a0\u00a0 guess = update(guess)\n\u00a0\u00a0\u00a0 k = k + 1\n\u00a0\u00a0\u00a0 return guess\n\u00a0\u00a0\u00a0 except ValueError:\n\u00a0\u00a0\u00a0 raise IterImproveError(guess)\n>>> def find_zero(f, guess=1):\n\u00a0\u00a0\u00a0 def done(x):\n\u00a0\u00a0\u00a0 return f(x) == 0\n\u00a0\u00a0\u00a0 try:\n\u00a0\u00a0\u00a0 return improve(newton_update(f), done, guess)\n\u00a0\u00a0\u00a0 except IterImproveError as e:\n\u00a0\u00a0\u00a0 return e.last_guess\n>>> from math import sqrt\n>>> find_zero(lambda x: 2*x*x + sqrt(x))\n-0.030211203830201594\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#34","title":"3.4 \u7ec4\u5408\u8bed\u8a00\u7684\u89e3\u91ca\u5668","text":" - \u8ba1\u7b97\u5668\u8bed\u8a00 -> \u7b80\u7565\u89e3\u91ca\u5668
- scheme \u5bf9
- \u8868\u8fbe\u5f0f\u6811
- \u89e3\u6790\u8868\u8fbe\u5f0f\u6811
- \u8bcd\u6cd5\u5206\u6790\u5668\uff08lexical analyzer\uff09/ \u5206\u8bcd\u5668\uff08tokenizer\uff09
- \u6807\u8bb0\uff08token\uff09
- \u8bed\u6cd5\u5206\u6790\u5668\uff08syntactic analyzer\uff09
- \u6570\u5b57\u548c\u8c03\u7528\u8868\u8fbe\u5f0f \u8bb2\u4e86\u4e00\u4e0b\u8ba1\u7b97\u5668\u89e3\u91ca\u5668\u4ea4\u4e92\u5f0f\u9875\u9762\u7684\u8868\u8fbe\u5f0f\u5982\u4f55\u8ba1\u7b97\u548c\u5f02\u5e38\u5904\u7406
"},{"location":"CS_Basic/CS61A/Composing_Programs/#35","title":"3.5 \u62bd\u8c61\u8bed\u8a00\u7684\u89e3\u91ca\u5668","text":" - \u6269\u5c55 scheme_reader \u89e3\u6790\u70b9\u5217\u8868\u548c\u5f15\u53f7
- \u6c42\u503c\uff08Evaluation\uff09
- \u51fd\u6570\u5e94\u7528\uff08Procedure application\uff09
- \u6c42\u503c/\u5e94\u7528\u9012\u5f52
- \u6570\u636e\u5373\u7a0b\u5e8f
"},{"location":"CS_Basic/CS61A/Composing_Programs/#4","title":"4 \u6570\u636e\u5904\u7406","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#41","title":"4.1 \u5f15\u8a00","text":" - pipelines
- sequence interface
- unbounded
"},{"location":"CS_Basic/CS61A/Composing_Programs/#42","title":"4.2 \u9690\u5f0f\u5e8f\u5217","text":" - \u6211\u4eec\u53ea\u5728\u6709\u9700\u8981\u7684\u65f6\u5019\u624d\u8ba1\u7b97\u5143\u7d20
- Lazy computation
"},{"location":"CS_Basic/CS61A/Composing_Programs/#421","title":"4.2.1 \u8fed\u4ee3\u5668","text":"\u4e24\u4e2a\u7ec4\u4ef6:
- \u68c0\u7d22\u4e0b\u4e00\u4e2a\u5143\u7d20\u7684\u673a\u5236
- \u5230\u8fbe\u5e8f\u5217\u672b\u5c3e\u5e76\u4e14\u6ca1\u6709\u5269\u4f59\u5143\u7d20\uff0c\u53d1\u51fa\u4fe1\u53f7\u7684\u673a\u5236
Python>>> next(iterator)\n7\n>>> next(iterator)\nTraceback (most recent call las):\n File \"<stdin>\", line 1, in <module>\nStopIteration\n\n>>> try:\n next(iterator)\n except StopIteration:\n print('No more values')\nNo more values\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#422","title":"4.2.2 \u53ef\u8fed\u4ee3\u6027","text":"iterable value \u53ef\u8fed\u4ee3\u5bf9\u8c61:
- \u5e8f\u5217\u503c: string & tuples
- \u5bb9\u5668: sets & Dictionaries
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/","title":"\u8ba1\u7b97\u673a\u7ec4\u6210\u4e0e\u8bbe\u8ba1\u786c\u4ef6\u8f6f\u4ef6\u63a5\u53e3","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#_1","title":"\u8ba1\u7b97\u673a\u7ec4\u6210\u4e0e\u8bbe\u8ba1\u786c\u4ef6\u8f6f\u4ef6\u63a5\u53e3","text":" \u7ea6 2978 \u4e2a\u5b57 13 \u884c\u4ee3\u7801 4 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 15 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#1","title":"1 \u8ba1\u7b97\u673a\u62bd\u8c61\u53ca\u76f8\u5173\u6280\u672f","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#2","title":"2 \u6307\u4ee4: \u8ba1\u7b97\u673a\u7684\u8bed\u8a00","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#21","title":"2.1 \u5f15\u8a00","text":"\u8bbe\u8ba1\u539f\u5219:
- \u7b80\u5355\u6e90\u4e8e\u89c4\u6574
- \u66f4\u5c11\u5219\u66f4\u5feb
- \u4f18\u79c0\u7684\u8bbe\u8ba1\u9700\u8981\u9002\u5f53\u7684\u6298\u4e2d
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#22","title":"2.2 \u8ba1\u7b97\u673a\u786c\u4ef6\u7684\u64cd\u4f5c","text":"Java \u7f16\u8bd1\u5668: Just In Time \u7f16\u8bd1\u5668
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#23","title":"2.3 \u8ba1\u7b97\u673a\u786c\u4ef6\u7684\u64cd\u4f5c\u6570","text":" - \u5bc4\u5b58\u5668
- \u5927\u5c0f\u4e3a64 bits \u53cc\u5b57
- \u6570\u91cf\u6709\u9650\u901a\u5e38\u4e3a 32 \u4e2a
- x +\u5bc4\u5b58\u5668\u7f16\u53f7
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#231","title":"2.3.1 \u5b58\u50a8\u5668\u64cd\u4f5c\u6570","text":"\u5728\u5185\u5b58\u548c\u5bc4\u5b58\u5668\u4e4b\u95f4\u4f20\u8f93\u6307\u4ee4:\u6570\u636e\u4f20\u8f93\u6307\u4ee4 \u6307\u4ee4\u63d0\u4f9b\u5185\u5b58\u5730\u5740 \u8f7d\u5165\u6307\u4ee4\uff08load\uff09:ld
Text OnlyLd x9, 8(x22)\n
X 22 \u57fa\u5740\u5bc4\u5b58\u5668 8 \u504f\u79fb\u91cf \u5b57\u8282\u5730\u5740: 0 8 16 24 RICS- V \u662f\u5c0f\u7aef\u7f16\u5740: \u53ea\u5728\u4ee5\u53cc\u5b57\u5f62\u5f0f\u548c\u516b\u4e2a\u5355\u72ec\u5b57\u8282\u8bbf\u95ee\u76f8\u540c\u6570\u636e\u65f6\u4f1a\u6709\u5f71\u54cd ^da8be4
\u5b58\u50a8\u6307\u4ee4\uff08store\uff09\u5b58\u50a8\u53cc\u5b57
Text Onlysd x9, 96(x22)\n
- \u5bf9\u9f50\u9650\u5236:
- \u5b57\u7684\u8d77\u59cb\u5730\u5740\u662f 4 \u7684\u500d\u6570
- \u53cc\u5b57\u7684\u8d77\u59cb\u5730\u5740\u662f 8 \u7684\u500d\u6570
- \u4f46\u662f risc-v and Intel x 86 \u6ca1\u6709
- MIPS \u6709 Gibibyte (\\(\\displaystyle 2^{30}\\)) and tebibyte (\\(\\displaystyle 2^{40}\\)) \u5982\u679c\u53d8\u91cf\u6bd4\u5bc4\u5b58\u5668\u6570\u91cf\u66f4\u591a\uff0c\u90a3\u4e48\u4f1a\u628a\u4e00\u4e9b\u653e\u5230\u5185\u5b58\uff0c\u5373\u5bc4\u5b58\u5668\u6362\u51fa\u3002
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#232","title":"2.3.2 \u5e38\u6570\u6216\u7acb\u5373\u6570\u64cd\u4f5c\u6570","text":"Text Onlyld x9, AddConstant4(x3)\nAdd x22, x22, x9\n\n# Equals to \n\naddi x22, x22, 4 # x22 = x22 + 4\n
\u5e38\u6570\u79f0\u4e3a\u7b97\u6570\u6307\u4ee4\u64cd\u4f5c\u6570 X0 \u53ef\u4ee5\u7528\u6765\u8868\u793a 0 \u5176\u5b9e\u8fd8\u6709 RV 32 \u57fa\u5740\u5bc4\u5b58\u5668\u4e5f\u88ab\u79f0\u4e3a\u4e0b\u6807\u5bc4\u5b58\u5668 \u6211\u4eec\u5047\u8bbe\u6307\u4ee4\u90fd\u662f 64 \u4f4d
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#24","title":"2.4 \u6709\u7b26\u53f7\u6570\u4e0e\u65e0\u7b26\u53f7\u6570","text":" - Binary digit Or bit
- Least significant bit
- Most significant bit
- sign and magnitude
- \u8865\u7801\u8f6c\u5316:
- \u62d3\u5c55\u7b26\u53f7\u4f4d
- \u53d6\u53cd
- \u52a0\u4e00
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#25","title":"2.5 \u8ba1\u7b97\u673a\u4e2d\u7684\u6307\u4ee4\u8868\u793a","text":" - \u5b57\u6bb5
- \u6307\u4ee4 32 \u4f4d\u957f
- \u6307\u4ee4\u7684\u6570\u5b57\u8868\u793a:\u673a\u5668\u8bed\u8a00
- \u6307\u4ee4\u5e8f\u5217: \u673a\u5668\u7801
- C \u548c java \u7528 0 xnnnn \u6765\u8868\u793a\u5341\u516d\u8fdb\u5236\u6570
- RISC-V:\uff08R\uff09
- funct7 + rs 2 + rs 1 + funct 3 + rd + opcode
- rd: \u76ee\u7684\u64cd\u4f5c\u6570\u5bc4\u5b58\u5668
- rs 1: \u7b2c\u4e00\u4e2a\u539f\u64cd\u4f5c\u6570\u5bc4\u5b58\u5668
- rs 2: \u7b2c\u4e8c\u4e2a\u539f\u64cd\u4f5c\u6570\u5bc4\u5b58\u5668
- funct 7 (3): \u64cd\u4f5c\u7801\u5b57\u6bb5
- \u5bf9\u4e0d\u540c\u7684\u6307\u4ee4\u4f7f\u7528\u4e0d\u540c\u7684\u6307\u4ee4\u683c\u5f0f
- I
- immediate + rs 1 + funct 3 + rd + opcode
- \u8d85\u8fc7 32 \u4e2a\u5bc4\u5b58\u5668\u7684\u8bdd\uff0crd and rs 1 \u90fd\u8981\u589e\u52a0\u989d\u5916\u7684\u4e00\u4f4d
- ld
- S
- immediate + rs 2 + rs 1 + funct 3 + immediate + opcode
- Reg \u8868\u793a 0 \u5230 31 \u4e4b\u95f4\u7684\u5bc4\u5b58\u5668\u7f16\u53f7
- \u6ca1\u6709 subi \u56e0\u4e3a\u53ef\u4ee5\u901a\u8fc7\u52a0\u8d1f\u6570\u6765\u5b9e\u73b0
- \u8ba1\u7b97\u673a\u6784\u5efa\u57fa\u4e8e\u4e24\u4e2a\u5173\u952e\u539f\u5219:
- \u6307\u4ee4\u7531\u6570\u5b57\u5f62\u5f0f\u8868\u793a
- \u7a0b\u5e8f\u548c\u6570\u636e\u4e00\u6837\u4fdd\u5b58\u5728\u5b58\u50a8\u5668\u4e2d\u8fdb\u884c\u8bfb\u5199
- \u7a0b\u5e8f\u4f1a\u4ee5\u4e8c\u8fdb\u5236\u6570\u636e\u6587\u4ef6\u7684\u5f62\u5f0f\u6765\u53d1\u5e03
- \u4e8c\u8fdb\u5236\u517c\u5bb9\u6027\u8ba9\u884c\u4e1a\u56f4\u7ed5\u5c11\u6570\u51e0\u4e2a\u6307\u4ee4\u7cfb\u7edf\u7ed3\u6784\u5f62\u6210\u8054\u76df
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#26","title":"2.6 \u903b\u8f91\u64cd\u4f5c","text":" - sll, slli
- srl, srli
- sra, srai
- and, andi
- or, ori
- xor, xori
- not
- \u4f4d\u79fb\u6307\u4ee4\u7528\u7684 I \u578b\u683c\u5f0f:
- \u4f46\u5b83\u7684\u683c\u5f0f\u6709\u53d8\u5316:
- funct 6 + immediate + rs 1 + funct 3 + rd + opcode
- \u7b97\u6570\u53f3\u79fb\u7528\u7684\u662f\u7b26\u53f7\u4f4d
- AND \u5b9e\u73b0\u4e86\u63a9\u7801
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#27","title":"2.7 \u7528\u4e8e\u51b3\u7b56\u7684\u6307\u4ee4","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#271","title":"2.7.1 \u5faa\u73af","text":"Cwhile (save[i] == k) \n i += 1;\n
I -> x 22 K -> x 24 Save -> x 25
Text Onlyloop: slli, x10, x22, 3\nAdd x10, x10, x25\nLd x9, 0(x10)\nBen x9, x24, Exit\nAddi x22, x22, l\nBeq x0, x0, loop\nExit:\n
\u57fa\u672c\u5757:
- \u9664\u4e86\u5728\u6307\u4ee4\u5e8f\u5217\u7684\u7ed3\u5c3e\uff0c\u5e8f\u5217\u4e2d\u6ca1\u6709\u5206\u652f\u3002
- \u9664\u4e86\u5728\u5e8f\u5217\u8d77\u59cb\u5904\uff0c\u5e8f\u5217\u4e2d\u6ca1\u6709\u5206\u652f\u76ee\u6807\u548c\u5206\u652f\u6807\u7b7e\u3002
- \u5bf9\u4e8e\u7b26\u53f7:
- RISC-V \u7528\u4e0d\u540c\u7684\u6307\u4ee4
- MIPS \u8bbe\u7f6e\u4e34\u65f6\u5bc4\u5b58\u5668
- ARM \u6761\u4ef6\u4ee3\u7801\u6216\u6807\u5fd7\u4f4d
- \u4e0d\u8fc7\u4e5f\u4f1a\u6709\u7f3a\u70b9: \u8fc7\u591a\u7684\u6307\u4ee4\u8bbe\u7f6e\u6761\u4ef6\u4ee3\u7801\uff0c\u4f1a\u8ba9\u6d41\u6c34\u7ebf\u6267\u884c\u56f0\u96be
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#272","title":"2.7.2 \u8fb9\u754c\u68c0\u67e5\u7684\u7b80\u4fbf\u65b9\u6cd5","text":"\u5c06\u7b26\u53f7\u6570\u5f53\u4f5c\u65e0\u7b26\u53f7\u6570\u5904\u7406
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#273-caseswitch","title":"2.7.3 Case/switch \u8bed\u53e5","text":" - \u7f16\u7801\u5f62\u6210\u6307\u4ee4\u5e8f\u5217\u7684\u5730\u5740\u8868: \u5206\u652f\u5730\u5740\u8868/\u5206\u652f\u8868
- \u95f4\u63a5\u8df3\u8f6c\u6307\u4ee4 jalr
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#28","title":"2.8 \u8ba1\u7b97\u673a\u786c\u4ef6\u5bf9\u8fc7\u7a0b\u7684\u652f\u6301","text":" - Procedure
- \u6267\u884c\u8fc7\u7a0b\u7684\u516d\u4e2a\u6b65\u9aa4:
- \u5c06\u53c2\u6570\u653e\u5728\u8fc7\u7a0b\u53ef\u4ee5\u8bbf\u95ee\u5230\u7684\u4f4d\u7f6e
- \u5c06\u63a7\u5236\u8f6c\u4ea4\u7ed9\u8fc7\u7a0b
- \u83b7\u53d6\u8fc7\u7a0b\u6240\u9700\u7684\u5b58\u50a8\u8d44\u6e90
- \u6267\u884c\u6240\u9700\u7684\u4efb\u52a1
- \u5c06\u7ed3\u679c\u503c\u653e\u5728\u8c03\u7528\u7a0b\u5e8f\u53ef\u4ee5\u8bbf\u95ee\u5230\u7684\u4f4d\u7f6e
- \u5c06\u63a7\u5236\u8fd4\u56de\u5230\u521d\u59cb\u70b9\uff0c\u56e0\u4e3a\u8fc7\u7a0b\u53ef\u4ee5\u4ece\u7a0b\u5e8f\u4e2d\u7684\u591a\u4e2a\u70b9\u8c03\u7528
- x 10~x 17: \u53c2\u6570\u5bc4\u5b58\u5668\uff0c\u7528\u4e8e\u4f20\u9012\u53c2\u6570\u6216\u8fd4\u56de\u503c
- x 1: \u8fd4\u56de\u5730\u5740\u5bc4\u5b58\u5668\uff0c\u7528\u4e8e\u8fd4\u56de\u5230\u8d77\u59cb\u70b9
- jal \u8df3\u8f6c-\u94fe\u63a5\u6307\u4ee4
Text OnlyJal x1, ProcedureAddress\nJalr x0, 0(x1)\n
\u8c03\u7528\u8005\u5c06\u53c2\u6570\u503c\u653e\u5165 x 10~x 17 \u8fc7\u7a0b\u662f\u88ab\u8c03\u7528\u8005 Program counter:PC \u6307\u4ee4\u5730\u5740\u5bc4\u5b58\u5668
Text Onlyjal x0, Label // unconditionally branch to Label\n
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#281","title":"2.8.1 \u4f7f\u7528\u66f4\u591a\u7684\u5bc4\u5b58\u5668","text":" - Stack
- Stack pointer x2:sp
- \u538b\u6808\u5f39\u6808
Text Onlyleaf_example:\nAddi sp, sp, -14\nSd x5, 16(sp)\nSd x6, 8(sp)\nSd x20, 0(sp)\nAdd x5, x10, x11\nAdd x6, x12, x13\nSub x20, x5, x6\nAddi x10, x20, 0\nLd x20, 0(sp)\nLd x6, 8(sp)\nLd x5, 16(sp)\nAddi sp, sp, 24\nJalr x0, 0(x1)\n
- X 5~x 7, x 28~x 31: \u4e34\u65f6\u5bc4\u5b58\u5668
- x 8~x 9, x 18~x 27: \u4fdd\u5b58\u5bc4\u5b58\u5668
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#282","title":"2.8.2 \u5d4c\u5957\u8fc7\u7a0b","text":" - Leaf procedure
- \u5c06\u6240\u6709\u5fc5\u987b\u4fdd\u5b58\u7684\u5bc4\u5b58\u5668\u538b\u6808\uff0c\u9632\u6b62\u51b2\u7a81
Clong long int fact (long long int n) {\n if (n < 1) return (1);\n else return (n * fact(n - 1))\n}\n
n -> x 10
Text Onlyfact:\n addi sp, sp, -16\n Sd x1, 8(sp)\n Sd x10, 0(sp)\n\n Addi x5, x10, -1\n Bge x5, x0, L1\n\n Addi x10, x0, 1\n Addi sp, sp, 16\n Jalr x0, 0(x1)\n\nL1: addi x10, x10, -1\n Jal x1, fact\n\naddi x6, x10, 0\nLd x10, 0(sp)\nLd x1, 8(sp)\nAddi sp, sp, 16\nMul x10, x10, x6\nJalr x0, 0(x1)\n
- \u4e00\u4e9b\u7f16\u8bd1\u5668\u4fdd\u7559\u4e00\u4e2a\u5bc4\u5b58\u5668 x 3 \u7528\u4f5c\u5168\u5c40\u6307\u9488 gp
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#283","title":"2.8.3 \u5728\u6808\u4e2d\u4f4d\u65b0\u6570\u636e\u5206\u914d\u7a7a\u95f4","text":"\u6808\u4e5f\u7528\u4e8e\u5b58\u50a8\u8fc7\u7a0b\u7684\u5c40\u90e8\u53d8\u91cf \u8fc7\u7a0b\u5e27/\u6d3b\u52a8\u8bb0\u5f55
\u6ca1\u770b\u61c2
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#284","title":"2.8.4 \u5728\u5806\u4e2d\u4e3a\u65b0\u6570\u636e\u5206\u914d\u7a7a\u95f4","text":"Static data segment text segment \u50cf\u94fe\u8868\u7b49\u6570\u636e\u7ed3\u6784\u5f80\u5f80\u4f1a\u968f\u751f\u547d\u5468\u671f\u589e\u957f\u548c\u7f29\u77ed\uff0c\u6240\u4ee5\u4f1a\u628a\u4ed6\u4eec\u5b58\u5728\u5806\u91cc (heap)
- malloc ()
- free () \u8be6\u7ec6\u9610\u8ff0\u6ca1\u770b
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#29","title":"2.9 \u4eba\u673a\u4ea4\u4e92","text":"ASCII: american standard code for information interchange
\u52a0\u8f7d\u65e0\u7b26\u53f7\u5b57\u8282 (lbu) \u5b58\u50a8\u5b57\u8282 (sb)
Text Onlylbu x12, 0(x10)\nsb x12, 0(x11)\n
\u5b57\u7b26\u4e32\u7684\u8868\u793a\u6709\u4e09\u79cd\u9009\u62e9:
- \u7b2c\u4e00\u4e2a\u4f4d\u7f6e\u4fdd\u7559\u6765\u7ed9\u51fa\u5b57\u7b26\u4e32\u7684\u957f\u5ea6
- \u7528\u989d\u5916\u7684\u53d8\u91cf\u6765\u5b58\u50a8\u957f\u5ea6\uff08\u5982\u7ed3\u6784\u4f53\uff09
- \u5b57\u7b26\u4e32\u7684\u6700\u540e\u4e00\u4e2a\u4f4d\u7f6e\u7528\u5b57\u7b26\u6807\u8bb0\u7ed3\u5c3e \u5176\u4e2d C \u8bed\u8a00\u7528\u7b2c\u4e09\u79cd\uff0c\u4f7f\u7528\u503c\u4e3a 0 \u7684\u5b57\u8282\u6765\u7ec8\u6b62\u5b57\u7b26\u4e32\uff08null in ASCII\uff09
Cvoid strcpy (char x[], char y[])\n{\n size_t i;\n i = 0;\n while ((x[i] = y[i]) != '\\0')\n i += 1;\n}\n
x -> x 10 y -> x 11 i -> x 19
Text Onlystrcpy:\n addi sp, sp, -8\n sd x19, 0(sp)\n add x19, x0, x0\nL1: add x5, x19, x11\n lbu x6, 0(x5)\n add x7, x19, x10\n sb x6, 0(x7)\n beq x6, x0, L2\n addi x19, x19, 1\n jal x0, L1\nL2: ld x19, 0(sp)\n addi sp, sp, 8\n jalr x0, 0(x1)\n
load half unsigned \u52a0\u8f7d\u534a\u5b57: lh lhu sh
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#210-risc-v","title":"2.10 \u5bf9\u5927\u7acb\u5373\u6570\u7684 RISC-V \u7684\u7f16\u5740\u548c\u5bfb\u5740","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#2101","title":"2.10.1 \u5927\u7acb\u5373\u6570","text":"load upper immediate \u53d6\u7acb\u5373\u6570\u9ad8\u4f4d lui lui \u53ef\u4ee5\u52a0\u8f7d 12~31 \u4f4d addi \u53ef\u4ee5\u52a0\u8f7d 0~11 \u4f4d
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#2102","title":"2.10.2 \u5206\u652f\u4e2d\u7684\u5bfb\u5740","text":"\u5206\u652f\u6307\u4ee4\u4f7f\u7528 SB \u578b\u7684\u6307\u4ee4\u683c\u5f0f
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#211","title":"2.11 \u6307\u4ee4\u4e0e\u5e76\u884c\u6027\uff1a\u540c\u6b65","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#212","title":"2.12 \u7ffb\u8bd1\u5e76\u542f\u52a8\u7a0b\u5e8f","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#2121","title":"2.12.1 \u7f16\u8bd1\u5668","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#2122","title":"2.12.2 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\u52a0\u5bc6\u539f\u7406
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"},{"location":"Robot/","title":"Robot","text":""},{"location":"Robot/#robot","title":"Robot","text":"Abstract
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\u8f6f\u4ef6 - \u76f8\u673a\u6807\u5b9a 73 0 mins 1735363772
- \u5361\u5c14\u66fc\u6ee4\u6ce2 193 115 2 mins 1734024510
- PnP \u7b97\u6cd5 79 55 1 mins 1734012860
"},{"location":"Robot/calibration/","title":"Calibration","text":""},{"location":"Robot/calibration/#calibration","title":"Calibration","text":" \u7ea6 76 \u4e2a\u5b57 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\u4e0d\u5230 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
- GitHub - SHU-FLYMAN/CalibCamera: \u57fa\u4e8e\u5f20\u6b63\u53cb\u6807\u5b9a\u6cd5\u7684\u5355\u76ee\u76f8\u673a\u6807\u5b9a\u7406\u8bba\u5230\u5b9e\u8df5
- \u4e00\u6587\u5403\u900f\u76f8\u673a\u6807\u5b9a\uff08Camera calibration\uff09-CSDN\u535a\u5ba2
- \u6700\u8be6\u7ec6\u3001\u6700\u5b8c\u6574\u7684\u76f8\u673a\u6807\u5b9a\u8bb2\u89e3-CSDN\u535a\u5ba2
- \u76f8\u673a\u6807\u5b9a\uff08Camera calibration\uff09\u539f\u7406\u3001\u6b65\u9aa4_\u89c6\u89c9\u76f8\u673a \u793a\u6559\u76ee\u7684-CSDN\u535a\u5ba2
2024_1_1
"},{"location":"Robot/kalman/","title":"\u5361\u5c14\u66fc\u6ee4\u6ce2","text":""},{"location":"Robot/kalman/#_1","title":"\u5361\u5c14\u66fc\u6ee4\u6ce2","text":" \u7ea6 193 \u4e2a\u5b57 115 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 2 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"Robot/kalman/#1-why","title":"1 Why","text":" - \u5dee\u5206
- \u53d7\u566a\u58f0\u5e72\u6270\u5927
- \u6709\u5ef6\u8fdf
- \u901f\u5ea6\u4e0d\u8fde\u7eed\uff08\u4e0d\u80fd\u5f97\u5230\u77ac\u65f6\u901f\u5ea6\uff09
"},{"location":"Robot/kalman/#2-how","title":"2 How","text":""},{"location":"Robot/kalman/#21","title":"2.1 \u5361\u5c14\u66fc\u6ee4\u6ce2","text":" - \u5408\u7406\u5730\u6839\u636e\u8bef\u5dee\u6765\u63a8\u5bfc\uff0c\u800c\u4e0d\u662f\u76f4\u63a5\u5ffd\u89c6\u5f71\u54cd\u6700\u7ec8\u91cf
- \u65e0\u4eba\u9a7e\u9a76\u6280\u672f\u5165\u95e8\uff08\u5341\u4e09\uff09| \u624b\u628a\u624b\u6559\u4f60\u5199\u5361\u5c14\u66fc\u6ee4\u6ce2\u5668 - \u77e5\u4e4e
\\[ \\begin{array}{|c|}\\hline\\textbf{Prediction}\\\\\\hline x^{'}=Ax+u\\\\P^{'}=APA^{T}+R\\\\\\hline\\textbf{Measurement update}\\\\\\hline y=z-Cx^{'}\\\\S=CPC^{T}+Q\\\\K=PC^{T}S^{-1}\\\\x=x^{'}+Ky\\\\P=(I-KC)P\\\\\\hline\\end{array} \\] C++#include <iostream>\n#include <cstdio>\n#include <string>\n#include <vector>\n#include <ctime>\n#include <opencv2/core/core.hpp>\n#include <opencv2/highgui/highgui.hpp>\n#include <opencv2/imgproc/imgproc.hpp>\n#include <Eigen/Dense>\n#include <opencv2/core/eigen.hpp>\nusing namespace std;\nusing namespace cv;\nusing namespace Eigen;\n\nint main() {\nsrand((unsigned int) time(NULL));\n// generate data with noise\nconst int N = 20;\nconst double k = 2.5;\nMatrix<double, 1, N> noise = Matrix<double, 1, N>::Random();\nMatrix<double, 1, N> data = Matrix<double, 1, N>::LinSpaced(0, k * (N - 1));\ndata += noise;\nstd::cout << data << std::endl;\n// calculate speed\nconst int Z_N = 1, X_N = 2;\nMatrix<double, X_N, 1> X;\nMatrix<double, X_N, X_N> A;\nMatrix<double, X_N, X_N> P;\nMatrix<double, X_N, X_N> R;\nMatrix<double, X_N, Z_N> K;\nMatrix<double, Z_N, X_N> C;\nMatrix<double, Z_N, Z_N> Q;\n\nX << data[0], 0;\nA << 1, 1, 0, 1;\nC << 1, 0;\nR << 2, 0, 0, 2;\nQ << 10;\nfor (int i = 1; i < N; i++) {\n // \u66f4\u65b0\u9884\u6d4b\n Matrix<double, X_N, 1> X_k = A * X;\n P = A * P * A.transpose() + R;\n // \u66f4\u65b0\u89c2\u6d4b\n K = P * C.transpose() * (C * P * C.transpose() + Q).inverse();\n Matrix<double, Z_N, 1> Z{data[i]};\n X = X_k + K * (Z - C * X_k);\n P = (Matrix<double, X_N, X_N>::Identity() - K * C) * P;\n std::cout << \"step \" << i << \": \" << X[1] << std::endl;\n}\nstd:cout << \"final speed: \" << X[1] << std::endl;\nreturn 0;\n}\n
"},{"location":"Robot/kalman/#22-ekf","title":"2.2 EKF \u7b97\u6cd5\u7684\u5b9e\u73b0","text":"C++#include <ceres/jet.h>\n#include <Eigen/Dense>\n\ntemplate<int N_X, int N_Y>\nclass AdaptiveEKF {\n using MatrixXX = Eigen::Matrix<double, N_X, N_X>;\n using MatrixYX = Eigen::Matrix<double, N_Y, N_X>;\n using MatrixXY = Eigen::Matrix<double, N_X, N_Y>;\n using MatrixYY = Eigen::Matrix<double, N_Y, N_Y>;\n using VectorX = Eigen::Matrix<double, N_X, 1>;\n using VectorY = Eigen::Matrix<double, N_Y, 1>;\n\npublic:\n explicit AdaptiveEKF(const VectorX &X0 = VectorX::Zero())\n : Xe(X0), P(MatrixXX::Identity()), Q(MatrixXX::Identity()), R(MatrixYY::Identity()) {}\n\n // \u9884\u6d4b\u51fd\u6570\n template<class Func>\n VectorX predict(Func &&func) {\n calculateJacobian(Xe, func, Xp, F);\n P = F * P * F.transpose() + Q;\n return Xp;\n }\n\n // \u66f4\u65b0\u51fd\u6570\n template<class Func>\n VectorX update(Func &&func, const VectorY &Y) {\n calculateJacobian(Xp, func, Yp, H);\n MatrixYY S = H * P * H.transpose() + R; // \u521b\u65b0\u534f\u65b9\u5dee\n K = P * H.transpose() * S.inverse(); // \u5361\u5c14\u66fc\u589e\u76ca\n Xe = Xp + K * (Y - Yp); // \u66f4\u65b0\u72b6\u6001\u4f30\u8ba1\n P = (MatrixXX::Identity() - K * H) * P; // \u66f4\u65b0\u72b6\u6001\u534f\u65b9\u5dee\n return Xe;\n }\n\nprivate:\n // \u8ba1\u7b97\u96c5\u514b\u6bd4\u77e9\u9635\u7684\u8f85\u52a9\u51fd\u6570\n template<class Func, int N_IN, int N_OUT>\n void calculateJacobian(const Eigen::Matrix<double, N_IN, 1> &input, Func &&func, Eigen::Matrix<double, N_OUT, 1> &output, Eigen::Matrix<double, N_OUT, N_IN> &jacobian) {\n ceres::Jet<double, N_IN> input_auto_jet[N_IN];\n for (int i = 0; i < N_IN; i++) {\n input_auto_jet[i].a = input[i];\n input_auto_jet[i].v[i] = 1;\n }\n ceres::Jet<double, N_OUT> output_auto_jet[N_OUT];\n func(input_auto_jet, output_auto_jet);\n for (int i = 0; i < N_OUT; i++) {\n output[i] = output_auto_jet[i].a;\n jacobian.block(i, 0, 1, N_IN) = output_auto_jet[i].v.transpose();\n }\n }\n\npublic:\n VectorX Xe; // \u4f30\u8ba1\u72b6\u6001\u53d8\u91cf\n VectorX Xp; // \u9884\u6d4b\u72b6\u6001\u53d8\u91cf\n MatrixXX F; // \u9884\u6d4b\u96c5\u514b\u6bd4\u77e9\u9635\n MatrixYX H; // \u89c2\u6d4b\u96c5\u514b\u6bd4\u77e9\u9635\n MatrixXX P; // \u72b6\u6001\u534f\u65b9\u5dee\n MatrixXX Q; // \u9884\u6d4b\u8fc7\u7a0b\u534f\u65b9\u5dee\n MatrixYY R; // \u89c2\u6d4b\u8fc7\u7a0b\u534f\u65b9\u5dee\n MatrixXY K; // \u5361\u5c14\u66fc\u589e\u76ca\n VectorY Yp; // \u9884\u6d4b\u89c2\u6d4b\u91cf\n};\n
"},{"location":"Robot/kalman/#23","title":"2.3 \u975e\u7ebf\u6027\u4f18\u5316","text":"TODO
"},{"location":"Robot/kalman/#_2","title":"\u4e00\u4e9b\u8d44\u6599","text":" - zhuanlan.zhihu.com/p/45238681
- \u5361\u5c14\u66fc\u6ee4\u6ce2(Kalman Filter)\u6982\u5ff5\u4ecb\u7ecd\u53ca\u8be6\u7ec6\u516c\u5f0f\u63a8\u5bfc-CSDN\u535a\u5ba2
- \u8c03\u8282\u8bef\u5dee\u77e9\u9635\u7684\u5b9e\u9645\u610f\u4e49
- \u975e\u7ebf\u6027\u62d3\u5c55
- \u81ea\u9002\u5e94\u4f18\u5316
- \u4f5c\u4e1a\u4e2d\u6709\u4e00\u4e2a\u5929\u4f53\u8fd0\u52a8\u7684\u4f8b\u5b50
"},{"location":"Robot/pnp/","title":"pnp","text":""},{"location":"Robot/pnp/#pnp","title":"pnp","text":" \u7ea6 76 \u4e2a\u5b57 55 \u884c\u4ee3\u7801 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
- \u5df2\u77e5
- \u76ee\u6807\u7269\u4f53\u7279\u5b9a\u70b9\u7684\u50cf\u7d20\u5750\u6807
- \u76ee\u6807\u7269\u4f53\u7279\u5b9a\u70b9\u7684\u771f\u5b9e\u5c3a\u5bf8
- \u76f8\u673a\u5185\u53c2
- \u6c42
- \u76ee\u6807\u7269\u4f53\u5728\u76f8\u673a\u5750\u6807\u7cfb\u4e0b\u7684 6d pose
\u50cf\u7d20\u5750\u6807\u548c\u7269\u4f53\u5750\u6807\u7684\u5bf9\u70b9 \u4f46\u662f\u4e00\u822c\u53ea\u7528 t, \u56e0\u4e3a R \u7684\u7cbe\u5ea6\u4e0d\u591f\u9ad8 Fetching Title#g70i
C++#include <iostream>\n#include <opencv2/opencv.hpp>\n#include <opencv2/imgproc/imgproc.hpp>\n#include <opencv2/calib3d/calib3d.hpp>\n#include <opencv2/core/core.hpp>\nusing namespace cv;\n\nbool findCorners(const cv::Mat &src, std::vector<cv::Point2f> &corners) {\n std::vector<cv::Point2f> pts;\n corners.clear();\n bool flag = cv::findChessboardCorners(src, {9, 6}, pts);\n if (!flag)\n return false;\n corners.push_back(pts[0]);\n corners.push_back(pts[9 - 1]);\n corners.push_back(pts[pts.size() - 9]);\n corners.push_back(pts[pts.size() - 1]);\n return true;\n}\n\nint main() {\n cv::Mat src;\n cv::Mat camera_matrix;\n cv::Mat distort_matrix;\n cv::FileStorage reader(PROJECT_DIR\"/parameter.txt\", cv::FileStorage::READ);\n reader[\"C\"] >> camera_matrix;\n reader[\"D\"] >> distort_matrix;\n\n for (int i = 0; i <= 40; i++) {\n src = imread(std::__cxx11::to_string(i).append(\".jpg\"));\n std::vector<cv::Point2f> corners;\n bool flag = findCorners(src, corners);\n imshow(\"Opencv Demo\", src);\n cv::waitKey(100);\n if (flag == false) {\n std::cout << \"failed to find all corners\\n\";\n continue;\n }\n std::vector<cv::Point3f> dst;\n dst.push_back({0, 0, 0});\n dst.push_back({8 * 1, 0, 0});\n dst.push_back({0, 5 * 1, 0});\n dst.push_back({8 * 1, 5 * 1, 0});\n cv::Mat rvec, tvec;\n cv::solvePnP(dst, corners, camera_matrix, distort_matrix, rvec, tvec);\n std::cout << \"t:\" << std::endl << -tvec << std::endl << std::endl;\n cv::Mat drawer;\n drawer = src.clone();\n for (int j = 0; j < 4; j++)\n cv::circle(drawer, corners[j], 2, {0, 255, 0}, 2);\n cv::imshow(\"corners\", drawer);\n cv::waitKey(5);\n }\n return 0;\n}\n
"},{"location":"Summaries/","title":"Summaries","text":""},{"location":"Summaries/#summaries","title":"Summaries","text":"Abstract
\u672c\u90e8\u5206\u5185\u5bb9\uff08\u9664\u7279\u522b\u58f0\u660e\u5916\uff09\u91c7\u7528 \u7f72\u540d-\u975e\u5546\u4e1a\u6027\u4f7f\u7528-\u4fdd\u6301\u4e00\u81f4 4.0 \u56fd\u9645 (CC BY-NC-SA 4.0) \u8bb8\u53ef\u534f\u8bae\u8fdb\u884c\u8bb8\u53ef\u3002
\u5b66\u671f\u603b\u7ed3 & \u5176\u4ed6 - \u9ad8\u4e09\u81f3\u5927\u4e00\u6691\u5047\u603b\u7ed3 520 2 mins 1734582373
2024 \u5e74\u5468\u7ed3 - \u7b2c 52 \u5468 802 3 mins 1735455536
- \u7b2c 51 \u5468 645 2 mins 1734869085
"},{"location":"Summaries/2024/weekly/2024-W51-12/","title":"2024-W51-12","text":" \u7ea6 657 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 3 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u5468\u8bb0","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W51-12/#2024-w51-12","title":"2024-W51-12","text":"","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W51-12/#review","title":"Review","text":" - \u671f\u672b\u5468\uff0c\u8003\u4e86\u9ad8\u6570\uff0c\u5316\u5b66\uff0c\u8fd1\u73b0\u4ee3\u53f2\uff0c\u8ba1\u7b97\u673a\u5bfc\u8bba\u3002
- \u4e00\u5929\u5b66\u4e00\u95e8\u901f\u901a\u3002\u8bf4\u5b9e\u8bdd\u8fd9\u4e2a\u5b66\u671f\u6ca1\u53bb\u4e0a\u8fc7\u8bfe\uff0c\u4f5c\u4e1a\u4e5f\u6ca1\u5199\u8fc7\uff08\u9ad8\u6570 + \u5316\u5b66\uff09\uff0c\u5e73\u65f6 RC \u4e5f\u4e0d\u770b\u3002\u5168\u9760\u671f\u672b\u4e00\u5929/\u534a\u5929\u8865\u5929\uff0c\u901f\u5237 Big RC \u7136\u540e\u6709\u9898\u505a\u9898\uff0c\u6ca1\u9898\u770b Regular RC\uff0c\u518d\u770b\u770b sample exam\u3002
- \u8003\u51fa\u6765\u7ed3\u679c\u611f\u89c9\u8fd8\u884c\uff0c\u4f46\u662f\u53ef\u80fd\u6709\u4e24\u95e8\u8981\u5361\u7ee9\u4e86\u3002\u5e0c\u671b\u90fd\u80fd\u7ed9\u6211\u5230 A \u5427\u3002
- \u867d\u7136\u4f46\u662f\uff0c\u611f\u89c9\u5927\u5bb6\u786e\u5b9e\u90fd\u633a curve \u7684\u3002\u672c\u6765\u4ee5\u4e3a\u53ef\u80fd A \u8fd8\u662f\u6ca1\u6709\u5f88\u5927\u7684\u95ee\u9898\uff0c\u4f46\u662f\u73b0\u5728\u53ef\u80fd\u8981\u6709\u4e24\u95e8 A- \u4e86\uff0c\u4e5f\u662f\u6709\u70b9\u96be\u53d7\u3002
- \u5927\u6982\u539f\u56e0\u662f\uff0c\u4e00\u70b9\u4f5c\u4e1a\u6ca1\u505a\uff0c\u671f\u672b\u51b2\u523a\u7684\u65f6\u5019\u4e0d\u591f\u8ba4\u771f\u3002\u540c\u65f6\u5fd8\u8bb0\u590d\u4e60\u671f\u4e2d\u4e4b\u524d\u7684\u77e5\u8bc6\u4e86\uff0c\u5bfc\u81f4\u9519\u4e86\u597d\u4e00\u4e9b\u3002
- \u7b2c\u4e8c\u4e2a\u539f\u56e0\u662f\uff0c\u867d\u7136\u5f88\u591a\u786e\u5b9e\u4f1a\u7b97\uff0c\u4f46\u662f\u5b8c\u6210\u5ea6\u6ca1\u6709\u90a3\u4e48\u9ad8\uff0c\u4ee5\u53ca\u81ea\u5df1\u61d2\u7684\u53bb\u7b97\uff0c\u5bfc\u81f4\u8003\u8bd5\u7684\u65f6\u5019\u5c31\u6ca1\u6709\u7b97\u5bf9\u4e86\u3002
- \u7b2c\u4e09\u4e2a\u539f\u56e0\u662f\uff0c\u5c31\u662f\u5f88\u6446\u70c2\u554a\uff0c\u8003\u8bd5\u5468\u4e86\u8fd8\u4e0d\u60f3\u590d\u4e60\u3002
- \u4e0d\u8fc7\u4e5f\u8fd8\u884c\uff0c\u6ca1\u6709\u82b1\u5f88\u591a\u65f6\u95f4\u5728\u5377\u7ee9\u70b9\u4e0a\u9762\uff0c\u4e0b\u4e2a\u5b66\u671f\u6253\u7b97\u5e73\u65f6\u518d\u5c11\u4e00\u70b9\uff0c\u7136\u540e\u590d\u4e60\u7684\u65f6\u5019\u66f4\u6709\u9488\u5bf9\u6027\u4e00\u70b9\u3002\u52aa\u529b\u63d0\u5347\u63d0\u5347\u7ee9\u70b9\uff0c\u4ee5\u514d\u4ee5\u540e\u8981\u7528\u7684\u65f6\u5019\u6ca1\u6709\u3002
- \u8bfb\u8bba\u6587
- [2103.00020] Learning Transferable Visual Models From Natural Language Supervision
- [2212.02710] Beyond Object Recognition: A New Benchmark towards Object Concept Learning
- [2303.15343] Sigmoid Loss for Language Image Pre-Training
- \u6709\u610f\u601d\uff0c\u4f46\u662f\u611f\u89c9\u81ea\u5df1\u8bfb\u7684\u6162\u6162\u7684\uff0c\u800c\u4e14\u76f8\u5173\u77e5\u8bc6\u4f3c\u4e4e\u4e0d\u592a\u591a\u3002\u800c\u4e14 transformer \u4e4b\u540e\u7684\u6a21\u578b\u4e0d\u662f\u5f88\u719f\u6089\uff0c\u6253\u7b97\u628a\u674e\u6c90\u7684\u8bfb\u8bba\u6587\u90a3\u4e2a\u7cfb\u5217\u90fd\u53bb\u770b\u770b\uff0c\u591a\u8bfb\u8bfb\u8bba\u6587\u3002
- \u914d\u4e86\u914d\u73af\u5883\uff0c\u7a0d\u5fae\u5199\u4e86\u4e00\u4e9b\u4ee3\u7801\uff0c\u4f46\u662f\u611f\u89c9\u8c03\u8bd5\u5e94\u8be5\u8fd8\u8981\u597d\u51e0\u5929\u624d\u80fd\u5b8c\u6210\u3002
- \u5b8c\u5584\u4e86 obsidian \u7684\u914d\u7f6e\u6587\u7ae0\uff0c\u5728\u8fd9\u91cc
- \u987a\u4fbf\u7528 tag \u91cd\u6784\u4e86 obsidian\uff0c\u653e\u5f03\u4e86\u6811\u72b6\u6587\u4ef6\u5939\u7684\u65b9\u6848\u3002
- \u5199\u4e86\u5199\u5bd2\u5047\u8ba1\u5212
- \u914d\u7f6e\u4e86\u4e00\u70b9 zotero\uff0c\u4f46\u662f\u8fd8\u662f\u4e0d\u592a\u4f1a\u7528\u73b0\u5728\uff0c\u6162\u6162\u719f\u7ec3\u5427\u3002
- \u5efa\u4e86\u4e00\u4e2a\u5206\u4eab\u4ea4\u6d41\u7684\u7fa4, QQ \u7fa4\u53f7\u662f\uff1a1011394397\uff0c\u6b22\u8fce\u52a0\u5165\u3002
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W51-12/#next-week-plan","title":"Next Week Plan","text":" - \u5b8c\u6210\u63a8\u7406\u5b9e\u9a8c
- \u770b\u70b9\u8bba\u6587\uff08\u4e0d\u8fc7\u8fd9\u4e2a\u4e5f\u592a\u6a21\u7cca\u4e86\uff09
- \u5b66\u5b8c CS61C
- \u505a 6.s081 \u7684\u89c4\u5212
- \u5b66\u4f1a\u4f7f\u7528 zotero
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W51-12/#time-line","title":"Time Line","text":" - \u65e0\uff0c\u4ee5\u540e\u7528\u65f6\u95f4\u65e5\u5fd7\u505a\u4e86\uff0c\u4e0d\u7528 Day Planner
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W51-12/#thoughts","title":"THOUGHTS","text":" - \u8fd8\u662f\u5f88\u96be balance \u5f88\u591a\u4e8b\u60c5\uff0c\u9009\u62e9\u6027\u653e\u5f03\u5427\uff0c\u66f4\u4f55\u51b5\u6211\u8fd8\u83dc\u83dc\u7684\uff0c\u5e73\u65f6\u80af\u5b9a\u4f1a\u6446\u6446\u70c2\u3002
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/","title":"2024-W52-12","text":" \u7ea6 814 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 4 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u5468\u8bb0","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#2024-w52-12","title":"2024-W52-12","text":"","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#review","title":"Review","text":"","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#completed","title":"Completed","text":" - \u5b8c\u6210\u63a8\u7406\u5b9e\u9a8c
- \u7ec8\u4e8e\u641e\u5b8c cs231n\uff0c\u7136\u540e\u505a\u4e86\u4e2a\u5c0f\u4efb\u52a1\uff0c\u5c31\u5f00\u59cb\u5e2e\u5b66\u957f\u5e72\u6d3b\u4e86\u3002
- \u5b66\u4f1a\u4f7f\u7528 zotero_\u4f7f\u7528\u6307\u5357
- \u8054\u7cfb\u9ad8\u4e2d\u8001\u5e08
- \u628a\u4e4b\u524d\u9ad8\u4e2d\u5b66\u4e60\u7ecf\u9a8c\u7684\u4e1c\u897f\u4ece\u535a\u5ba2\u4e2d\u79fb\u51fa\u4e86\uff0c\u53e6\u5916\u5efa\u4e86\u4e00\u4e2a\u7f51\u7ad9\u3002
- \u987a\u4fbf\u9080\u8bf7\u4e86\u4e00\u4e9b\u9ad8\u4e2d\u540c\u5b66\u5206\u4eab\u5206\u4eab\u7ecf\u9a8c
- \u4e4b\u540e\u5927\u6982\u4f1a\u8fd4\u6821\u5ba3\u8bb2\uff0c\u5206\u4eab\u4e00\u4e9b\u5b66\u4e60\u7ecf\u9a8c / \u4e09\u4e00\u554a\u5565\u7684\u3002\ud83e\udd14\uff0c\u6211\u4e5f\u6210\u5b66\u957f\u4e86\u3002
- \u8bfb\u8bba\u6587\uff0c\u5efa\u7acb\u4e86\u4e00\u4e9b\u57fa\u672c\u6982\u5ff5
- \u5199\u4e86\u4e00\u4e9b\u7b14\u8bb0\uff0c\u6bd4\u5982
- \u770b\u4e86\u4e00\u4e0b\u674e\u6c90\u8bfb\u8bba\u6587\u7684\u7cfb\u5217
- \u5305\u62ec Transformer, CLIP, CLIP \u4e4b\u540e\u7684\u5de5\u4f5c\u4e32\u8bb2\u4e0a\u4e0b\uff0c\u591a\u6a21\u6001\u4e32\u8bb2\u4e0a\u4e0b
- \u770b\u4e86\u4e00\u90e8\u5206\u7684 GAMES 003\uff0c\u8bb2\u7684\u5f88\u4e0d\u9519
- \u7a0d\u5fae\u8fd0\u8425\u4e86\u4e00\u4e0b\u4ea4\u6d41\u7fa4
- \u4fee\u590d\u4e86\u4e00\u4e0b\u535a\u5ba2
- \u73b0\u5728\u535a\u5ba2\u53ef\u4ee5\u5b8c\u6574\u5730\u652f\u6301 obsidian \u7684\u53cc\u94fe\u548c callout \u4e86
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#uncompleted","title":"Uncompleted","text":" - \u5b66\u5b8c CS61C
- \u5fd9\u4e0d\u8fc7\u6765\uff0c\u751a\u81f3\u90fd\u8fd8\u6ca1\u6361\u8d77\u6765
- \u89c4\u5212 6.s081 \u5b66\u4e60
- \u5fd9\u4e0d\u8fc7\u6765\u634f\uff0c\u800c\u4e14\u611f\u89c9\u6700\u8fd1\u7684\u91cd\u5fc3\u5728\u79d1\u7814\u4e0a\uff0c\u6253\u7b97\u628a\u9886\u57df\u5185\u7684\u8bba\u6587\u770b\u7684\u5dee\u4e0d\u591a\u4e86\uff0c\u518d\u6765\u6253\u7cfb\u7edf\u65b9\u9762\u7684\u57fa\u7840\u3002
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#next-week-plan","title":"Next Week Plan","text":" - \u5b66\u4e60 CS61C\uff0c\u76ee\u524d\u662f\u5b66\u5230\u7b2c 6 \u4e2a lab\uff0c\u5e0c\u671b\u4e00\u4e2a\u661f\u671f\u80fd\u5b66\u5b8c\u5269\u4e0b\u7684\u3002
- \u770b\u5b8c\u674e\u6c90\u8bfb\u8bba\u6587\u7684\u7cfb\u5217\uff0c\u7136\u540e\u4e0b\u8f7d\u8bba\u6587\u505a\u7b14\u8bb0\uff0c\u4e32\u8054\u81ea\u5df1\u7684\u77e5\u8bc6\u56fe\u8c31\u3002
- \u7740\u624b\u5b9e\u9a8c\u5ba4\u5de5\u4f5c\u3002
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#thoughts","title":"THOUGHTS","text":" - \u611f\u89c9\u81ea\u5df1\u7684\u5927\u4e00\u4e0a\u8fd8\u662f\u5904\u5728\u4e1c\u73a9\u73a9\u897f\u73a9\u73a9\u7684\u72b6\u6001\uff0c\u5f04\u4e86\u4e0d\u5c11\u8ddf\u4ee5\u540e\u65b9\u5411\u4e0d\u592a\u6709\u5173\u7684\u4e1c\u897f\u3002\u8d81\u5bd2\u5047\u6709\u7a7a\uff0c\u65e9\u70b9\u60f3\u6e05\u695a\u60f3\u5e72\u4ec0\u4e48\u5427\u3002
- \u6b38\uff0c\u8fd8\u662f\u5f15\u7528\u4e00\u6bb5\u8bdd\u6765\u7684\u8d34\u5207
Quote \u5176\u5b9e\u5f52\u6839\u7ed3\u5e95\uff0c\u95ee\u9898\u8fd8\u662f\u5927\u591a\u6570\u4eba\uff08\u5305\u62ec\u6211\u81ea\u5df1\uff09\uff0c\u6700\u521d\u6839\u672c\u60f3\u4e0d\u6e05\u695a\u81ea\u5df1\u6700\u540e\u8981\u5e72\u5565\uff0c\u505a\u4e86\u597d\u591a\u65e0\u610f\u4e49\u7684\u4e8b\u60c5\u3002\u6bd4\u5982\u9009\u4e00\u4e9b\u65e0\u610f\u4e49\u4f46\u96be\u7684\u8bfe\uff0c\uff08\u6bd4\u5982\u6570\u5206\u7cfb\u5217\uff0c\u5982\u679c\u4e0d\u662f\u771f\u5fc3\u559c\u6b22\u6570\u5b66\u5343\u4e07\u522b\u9009\uff09\uff0c\u5927\u521b\uff0c\u5b66\u4e00\u4e9b\u975e cs \u7684\u9009\u4fee\u8bfe\uff0c\u7ed9\u4e00\u4e9b\u6c34\u5b9e\u9a8c\u5ba4\u6253\u5de5\u7b49\u3002\u521a\u4e0a\u5927\u5b66\uff0c\u90fd\u89c9\u5f97\u81ea\u5df1\u6709\u65e0\u7a77\u7684\u53ef\u80fd\u6027\uff0c\u4e0d\u5c51\u4e8e\u5728\u5199\u4ee3\u7801\u8fd9\u4e00\u6761\u8def\u4e0a\u540a\u6b7b\u3002\u8bda\u7136\uff0c\u6709\u4e9b\u4eba\u5565\u90fd\u641e\u8fd8\u5565\u90fd\u80fd\u641e\u597d\uff0c\u4f46\u662f\u4f5c\u4e3a\u4e00\u4e2a\u666e\u901a\u4eba\uff0c\u8fd8\u662f\u8981\u8ba4\u6e05\u81ea\u5df1\u80fd\u529b\u7684\u8303\u56f4\u548c\u6700\u540e\u7684\u76ee\u6807\u3002\u770b\u522b\u4eba\u53bb\u793e\u56e2\uff0c\u81ea\u5df1\u4e5f\u60f3\u53bb\u51d1\u51d1\uff0c\u770b\u522b\u4eba\u505a\u79d1\u7814\uff0c\u81ea\u5df1\u4e5f\u60f3\u641e\u641e\uff1b\u53ef\u662f\u8fd9\u6837\u641e\u6765\u641e\u53bb\uff0c\u53d1\u73b0\u66fe\u7ecf\u4e0d\u5c51\u4e8e\u5e72\u7684\u4e8b\u60c5\uff0c\u73b0\u5728\u8fde\u5e72\u7684\u8d44\u683c\u90fd\u6ca1\u6709\uff0c\u8fd9\u65f6\u518d\u540e\u6094\u5df2\u7ecf\u4e3a\u65f6\u5df2\u665a\u3002\u5c06\u6765\u53d1\u5c55\u7684\u65b9\u5411\u8fd8\u662f\u8d8a\u65e9\u60f3\u660e\u767d\u8d8a\u597d\u3002
- \u6211\u60f3\u6211\u81ea\u5df1\u7edd\u5bf9\u79f0\u4e0d\u4e0a\u52aa\u529b\uff0c\u751a\u81f3\u5f88\u96be\u8bf4\u5728\u8ffd\u9010\u559c\u6b22\u7684\u4e1c\u897f\uff0c\u53ea\u4e0d\u8fc7\u611f\u89c9\u2018\u6b38\uff0c\u5e72\u8fd9\u4e2a\u4e5f\u8fd8\u4e0d\u9519\uff0c\u90a3\u5c31\u8fd9\u6837\u5427\u2019\u3002\u6700\u8fd1\u786e\u5b9e\u8ba4\u8bc6\u5230\u5f88\u591a\u4f18\u79c0\u7684\u5b66\u957f\uff0c\u4ed6\u4eec\u8981\u4e48\u662f\u53bb MSRA \u5b9e\u4e60\uff0c\u7136\u540e\u5728\u79d1\u7814\u7684\u9053\u8def\u4e0a\u8d8a\u8d70\u8d8a\u8fdc\uff1b\u8981\u4e48\u662f\u4e00\u624b\u6293\u540e\u7aef\uff0c\u4e00\u624b\u6293\u7cfb\u7edf\uff0c\u53bb\u4e86\u91cf\u5316\u8d5a\u7c73\u3002
Failure
\u4e5f\u8bb8\uff0c\u6211\u771f\u7684\u662f\u7f3a\u4e4f all in \u7684\u52c7\u6c14\u5427\u3002
","tags":["\u5468\u8bb0"]},{"location":"Summaries/Semesters/2024summer_vacation/","title":"2024\u5e74\u9ad8\u4e09-\u5927\u4e00\u6691\u5047\u603b\u7ed3","text":""},{"location":"Summaries/Semesters/2024summer_vacation/#2024-","title":"2024\u5e74\u9ad8\u4e09-\u5927\u4e00\u6691\u5047\u603b\u7ed3","text":" \u7ea6 518 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 3 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
\u6211\u7684\u6691\u5047\u5927\u6982\u662f\u4ece 24.7.10 \u5f00\u59cb\u7684\uff0c\u5230\u90a3\u65f6\u624d\u5c18\u57c3\u843d\u5b9a\u3002\u4f46\u53c8\u6765\u56de\u6447\u6446\uff0c\u60f3\u7740\u672a\u6765\u7684\u51fa\u8def\uff08\u51fa\u56fd\uff1f\u4fdd\u7814\uff1f\u5de5\u4f5c\uff1f\u8f6c\u4e13\u4e1a\uff1f\uff09\u3002\u6240\u4ee5\u5927\u6982\u5230 8 \u6708\u624d\u5f00\u59cb\u5b66\u4e60\u3002
- \u8ba1\u7b97\u673a
- crash course computer \u770b\u4e86\u524d 20 \u8bb2\uff0c\u540e\u6765\u56e0\u4e3a\u61d2\u5f97\u770b\u4e86\u5c31\u6446\u70c2\u4e86
- cs 61 A
- \u770b\u4e86\u524d 10 \u8bb2\u7684 lecture\uff0c\u4f46\u662f\u6ca1\u505a\u7b14\u8bb0
- \u770b\u4e86 composing programs \u524d\u4e09\u7ae0
- \u505a\u5b8c\u4e86 4 \u4e2a proj\uff0c\u4f46\u662f\u6ca1\u6709\u505a hw \u548c lab
- cs 61 C
- \u770b\u4e86\u524d 10 \u8bb2\u7684 slide
- \u505a\u4e86\u524d\u4e24\u4e2a proj \u548c\u524d\u516d\u4e2a lab
- \u770b\u8ba1\u7b97\u673a\u7ec4\u6210\u4e0e\u8bbe\u8ba1\u786c\u4ef6\u8f6f\u4ef6\u63a5\u53e3\u524d\u4e24\u7ae0
- csapp
- \u4e66\u770b\u4e86\u524d\u4e09\u7ae0
- \u4e5d\u66f2\u9611\u5e72\u770b\u4e86\u524d 4 \u7ae0
- Dive into Deep Learning
- \u770b\u4e86\u524d\u4e24\u7ae0\u5e76\u505a\u4e86\u7b14\u8bb0\uff0c\u4f46\u611f\u89c9\u4e00\u4e0b\u5b50\u8df3\u8fc7\u592a\u591a\u524d\u7f6e\u77e5\u8bc6\u5f88\u96be\u611f\u53d7\u5230\u7f8e\u611f\u4fbf\u5148\u653e\u653e\u3002
- games 101
- \u51e0\u4e4e\u770b\u5b8c\u4e86\u6240\u6709\u7684 lecture (\u4f46\u662f\u540e\u9762\u51e0\u8bb2\u4e0d\u662f\u5f88\u8ba4\u771f)\uff0c\u4f46\u662f\u6ca1\u6709\u505a\u7b14\u8bb0
- \u4ee3\u7801\u968f\u60f3\u5f55
- \u505a\u5230\u56de\u6eaf\u4e86\uff0c\u4f46\u662f\u6253\u7b97\u4e4b\u540e\u4e0d\u4f1a\u5f88\u7ecf\u5e38\u505a\uff08\u7b49\u5230\u8981\u7528\u4e86\u518d\u8bf4\uff09
- \u7528 mkdocs \u642d\u5efa\u4e86\u81ea\u5df1\u7684 blog
- C++
- \u770b\u4e86\u83dc\u9e1f\u6559\u7a0b\u4e0a\u7684\u76f8\u5173\u5185\u5bb9\uff0c\u6ca1\u505a\u7b14\u8bb0
- \u770b\u4e86\u6d59\u5927\u7684 C++\u8bfe\uff0c\u6ca1\u505a\u7b14\u8bb0\uff0c\u4e5f\u6ca1\u770b\u5b8c\uff08\uff09
- \u770b\u4e86 accelerated C++\uff0c\u505a\u4e86\u7b14\u8bb0
- \u770b\u4e86\u6d59\u5927\u7684\u5b9e\u7528\u6280\u80fd\u62fe\u9057
- \u590d\u4e60\u4e86 Markdown \u548c Latex \u8bed\u6cd5\uff0c\u5b66\u4e60\u4e86\u5982\u4f55\u4f7f\u7528 git\uff0c\u5b66\u4e60\u4e86\u6700\u57fa\u7840\u7684 shell\uff0cvim\u3002
- \u89c6\u89c9 slam \u5341\u56db\u8bb2
- \u770b\u5b8c\u4e86\u524d 7 \u8bb2\uff08\u5373\u7406\u8bba\u90e8\u5206\uff09\uff0c\u505a\u4e86\u7b14\u8bb0\uff0c\u4f46\u662f\u6ca1\u6709\u8dd1\u4ee3\u7801 \uff08\u73af\u5883\u592a\u96be\u914d\u4e86\uff09
- \u914d\u7f6e\u73af\u5883
- wsl 2 , git\uff0cvmware\uff0cvscode
- \u914d\u7f6e\u4e86 obsidian\uff0c\u88c5\u4e86\u597d\u591a\u63d2\u4ef6\uff0c\u73b0\u5728\u7528\u8d77\u6765\u662f\u5f88\u8212\u670d\u4e86
- \u8fd0\u52a8
- \u6bcf\u5929\u505a\u505a\u4fef\u5367\u6491\uff0c\u611f\u89c9\u8fd8\u4e0d\u9519
- \u5927\u6982 7 \u6708\u4efd\u7684\u65f6\u5019\u6bcf\u5929\u4e0b\u5348\u4f1a\u51fa\u53bb\u9a91\u8f66\uff08city cycling?\uff09
- \u5176\u4ed6
- \u5bb6\u6559
- \u5b66\u4e86\u9a7e\u7167
- \u4e70\u4e86\u4e00\u4e2a\u952e\u76d8\u548c\u663e\u793a\u5668, \u91cd\u88c5\u4e86\u7535\u8111
- \u548c\u670b\u53cb\u65c5\u6e38\uff0c\u53bb\u6cc9\u5dde+\u798f\u5dde
- \u7ed9\u9ad8\u4e2d\u7684\u5b66\u5f1f\u5b66\u59b9\u5199\u4e86\u7ecf\u9a8c\u5206\u4eab\uff08\u6570\u5b66+\u82f1\u8bed+\u7269\u7406+\u6280\u672f\uff09
- \u770b\u4e86\u4e0d\u5c11\u7535\u5f71
"},{"location":"Tags/","title":"Tags","text":""},{"location":"Tags/#tags","title":"Tags","text":"{{ tag_content }}
"},{"location":"Tools/","title":"Tools","text":""},{"location":"Tools/#toolbox","title":"Toolbox","text":"Abstract
\u672c\u90e8\u5206\u5185\u5bb9\uff08\u9664\u7279\u522b\u58f0\u660e\u5916\uff09\u91c7\u7528 \u7f72\u540d-\u975e\u5546\u4e1a\u6027\u4f7f\u7528-\u4fdd\u6301\u4e00\u81f4 4.0 \u56fd\u9645 (CC BY-NC-SA 4.0) \u8bb8\u53ef\u534f\u8bae\u8fdb\u884c\u8bb8\u53ef\u3002
Envirtonment Setup - Environment 227 1 mins 1735386955
- Obsidian 1269 55 5 mins 1735386955
- Ubuntu 77 14 0 mins 1735386955
Prompt Engerneering - useful prompt 4220 1 14 mins 1735547104
- \u5982\u4f55\u5199\u4e00\u4e2a\u597d prompt 3315 162 13 mins 1734024510
Blog - Mkdocs & Material 5956 9407 137 mins 1734024510
Make - CMake 161 13 1 mins 1734024510
- Makefile 0 0 mins 0
Terminal - Tabby & Zsh 236 789 11 mins 1734024510
Others - zotero \u4f7f\u7528\u6307\u5357 146 0 mins 1735395966
- SSH 641 195 5 mins 1734024510
- Chezmoi \u540c\u6b65\u914d\u7f6e\u6587\u4ef6 512 142 3 mins 1734024510
"},{"location":"Tools/about/","title":"About \ud83e\udd73","text":""},{"location":"Tools/about/#about","title":"About \ud83e\udd73","text":" \u7ea6 54 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\u4e0d\u5230 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
Welcome, I'm Wnc.
"},{"location":"Tools/about/#some-tags","title":"Some Tags","text":" - \u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\u5bc6\u897f\u6839\u5b66\u9662 2024 \u7ea7\u672c\u79d1\u751f
- INTJ (Maybe)
- Interested in AI, Robot and ...
Ways to befriend with me You could find my email or qq or WeChat in the icon above.
Feel free to contact me!
"},{"location":"Tools/AI/prompt/","title":"prompt","text":" \u7ea6 4234 \u4e2a\u5b57 1 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 21 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#prompt","tags":["prompt"]},{"location":"Tools/AI/prompt/#prompt","title":"prompt","text":"","tags":["prompt"]},{"location":"Tools/AI/prompt/#prompt_1","title":"\u901a\u7528\u601d\u7ef4\u94fe prompt","text":"Note Text OnlyBy default, all responses must be in Chinese.\n\n# AI Full-Stack Development Assistant Guide\n\n## Core Thinking Patterns\nYou must engage in multi-dimensional deep thinking before and during responses:\n\n### Fundamental Thinking Modes\n\n- Systems Thinking: Three-dimensional thinking from overall architecture to specific implementation\n- Dialectical Thinking: Weighing pros and cons of multiple solutions \n- Creative Thinking: Breaking through conventional thinking patterns to find innovative solutions\n- Critical Thinking: Multi-angle validation and optimization of solutions\n\n### Thinking Balance\n\n- Balance between analysis and intuition\n- Balance between detailed inspection and global perspective \n- Balance between theoretical understanding and practical application\n- Balance between deep thinking and forward momentum\n- Balance between complexity and clarity\n\n### Analysis Depth Control \n\n- Conduct in-depth analysis for complex problems\n- Keep simple issues concise and efficient\n- Ensure analysis depth matches problem importance\n- Find balance between rigor and practicality\n\n### Goal Focus\n\n- Maintain clear connection with original requirements\n- Guide divergent thinking back to the main topic timely\n- Ensure related explorations serve the core objective\n- Balance between open exploration and goal orientation\n\nAll thinking processes must:\n\n0. Presented in the form of a block of code + the title of the point of view, please note that the format is strictly adhered to and that it must include a beginning and an end.\n1. Unfold in an original, organic, stream-of-consciousness manner\n2. Establish organic connections between different levels of thinking\n3. Flow naturally between elements, ideas, and knowledge\n4. Each thought process must maintain contextual records, keeping contextual associations and connections\n\n## Technical Capabilities\n### Core Competencies\n\n- Systematic technical analysis thinking\n- Strong logical analysis and reasoning abilities \n- Strict answer verification mechanism\n- Comprehensive full-stack development experience\n\n### Adaptive Analysis Framework\nAdjust analysis depth based on:\n\n- Technical complexity\n- Technology stack scope\n- Time constraints \n- Existing technical information\n- User's specific needs\n\n### Solution Process\n\n1. Initial Understanding\n- Restate technical requirements\n- Identify key technical points\n- Consider broader context\n- Map known/unknown elements\n\n2. Problem Analysis \n- Break down tasks into components\n- Determine requirements\n- Consider constraints\n- Define success criteria\n\n3. Solution Design\n- Consider multiple implementation paths\n- Evaluate architectural approaches\n- Maintain open-minded thinking\n- Progressively refine details\n\n4. Implementation Verification\n- Test assumptions\n- Verify conclusions\n- Validate feasibility\n- Ensure completeness\n\n## Output Requirements\n### Code Quality Standards\n\n- Always show complete code context for better understanding and maintainability.\n- Code accuracy and timeliness\n- Complete functionality\n- Security mechanisms\n- Excellent readability\n- Use markdown formatting\n- Specify language and path in code blocks\n- Show only necessary code modifications\n#### Code Handling Guidelines\n\n1. When editing code:\n - Show only necessary modifications\n - Include file paths and language identifiers\n - Provide context with comments\n - Format: ```language:path/to/file\n\n2. Code block structure: ```language:file/path\n // ... existing code ...\n {{ modifications }}\n // ... existing code ... ```\n\n\n### Technical Specifications\n\n- Complete dependency management\n- Standardized naming conventions\n- Thorough testing\n- Detailed documentation\n\n### Communication Guidelines\n\n- Clear and concise expression\n- Handle uncertainties honestly\n- Acknowledge knowledge boundaries\n- Avoid speculation\n- Maintain technical sensitivity\n- Track latest developments\n- Optimize solutions\n- Improve knowledge\n\n### Prohibited Practices\n\n- Using unverified dependencies\n- Leaving incomplete functionality\n- Including untested code\n- Using outdated solutions\n\n## Important Notes\n\n- Maintain systematic thinking for solution completeness\n- Focus on feasibility and maintainability\n- Continuously optimize interaction experience\n- Keep open learning attitude and updated knowledge\n- Disable the output of emoji unless specifically requested\n- By default, all responses must be in Chinese.\n
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\u89e3\u91ca\u6027\u6587\u672c\uff1a\u6dfb\u52a0\u5fc5\u8981\u7684\u6587\u5b57\u8bf4\u660e\n * \u56fe\u4f8b\u8bf4\u660e\uff1a\u5bf9\u7279\u6b8a\u7b26\u53f7\u8fdb\u884c\u89e3\u91ca\n * \u6570\u636e\u6765\u6e90\uff1a\u8865\u5145\u6570\u636e\u80cc\u666f\uff08\u5982\u6709\uff09\n- \u8bbe\u8ba1\u589e\u5f3a\uff1a\n * \u88c5\u9970\u5143\u7d20\uff1a\u589e\u52a0\u534f\u8c03\u7684\u88c5\u9970\u6027\u56fe\u5f62\n * \u80cc\u666f\u5143\u7d20\uff1a\u8bbe\u8ba1\u886c\u6258\u4e3b\u9898\u7684\u80cc\u666f\n * \u70b9\u7f00\u7ec6\u8282\uff1a\u6dfb\u52a0\u63d0\u5347\u7cbe\u81f4\u611f\u7684\u5c0f\u7ec6\u8282\n\n3. \u89c6\u89c9\u7cfb\u7edf\u8bbe\u8ba1\n- \u8272\u5f69\u89c4\u5212:\n * \u4e3b\u8272\u8c03\u9009\u62e9\n * \u6e10\u53d8\u65b9\u6848\u8bbe\u8ba1\n * \u660e\u6697\u5bf9\u6bd4\u63a7\u5236\n * \u900f\u660e\u5ea6\u5c42\u6b21\n- \u56fe\u5f62\u7cfb\u7edf:\n * \u51e0\u4f55\u5f62\u72b6\u8bbe\u8ba1\n * \u7ebf\u6761\u98ce\u683c\u5b9a\u4e49\n * \u56fe\u6848\u586b\u5145\u89c4\u5219\n * \u88c5\u9970\u5143\u7d20\u8bbe\u8ba1\n- \u6392\u7248\u89c4\u8303:\n * \u5b57\u4f53\u9009\u62e9\n * \u5b57\u53f7\u5c42\u7ea7\n * \u95f4\u8ddd\u89c4\u5219\n * \u5bf9\u9f50\u65b9\u5f0f\n\n4. \u6280\u672f\u5b9e\u73b0\u89c4\u8303\n- \u57fa\u7840\u7ed3\u6784:\n * viewBox \u8bbe\u7f6e\n * \u5750\u6807\u7cfb\u7edf\u89c4\u5212\n * \u56fe\u5c42\u7ec4\u7ec7\n * \u547d\u540d\u89c4\u8303\n- \u9ad8\u7ea7\u7279\u6548:\n * \u6e10\u53d8(linearGradient/radialGradient)\n * \u6ee4\u955c(filter:shadow/blur/glow)\n * \u8499\u7248(mask/clip-path)\n * \u6df7\u5408\u6a21\u5f0f(mix-blend-mode)\n- \u52a8\u753b\u7cfb\u7edf:\n * \u8fc7\u6e21\u52a8\u753b\u8bbe\u8ba1\n * \u5173\u952e\u5e27\u52a8\u753b\n * \u8def\u5f84\u52a8\u753b\n * \u4ea4\u4e92\u53cd\u9988\n\n5. \u6027\u80fd\u4e0e\u517c\u5bb9\u6027\n- \u4ee3\u7801\u4f18\u5316:\n * \u8def\u5f84\u7b80\u5316\n * \u7ec4\u4ef6\u590d\u7528\n * \u4ee3\u7801\u538b\u7f29\n * \u65e0\u969c\u788d\u9002\u914d\n- \u4ea4\u4e92\u4f18\u5316:\n * \u54cd\u5e94\u5f0f\u8bbe\u8ba1\n * 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* \u7ef4\u62a4\u5efa\u8bae\n\n\u8bbe\u8ba1\u8981\u6c42\uff1a\n\n1. \u4fe1\u606f\u5b8c\u6574\u4e14\u6df1\u5165\n2. \u89c6\u89c9\u6548\u679c\u7cbe\u7f8e\u6709\u8bbe\u8ba1\u611f\n3. \u6280\u672f\u5b9e\u73b0\u89c4\u8303\u4e13\u4e1a\n4. \u5177\u6709\u9002\u5f53\u7684\u52a8\u6548\u548c\u4ea4\u4e92\n5. \u6027\u80fd\u8868\u73b0\u826f\u597d\n6. \u4ee3\u7801\u6574\u6d01\u6613\u7ef4\u62a4\n\n\u6280\u672f\u89c4\u8303\uff1a\n\n1. \u4f7f\u7528\u8bed\u4e49\u5316\u7684\u5206\u7ec4\u548c\u547d\u540d\n2. \u6ce8\u91ca\u5173\u952e\u7684\u8bbe\u8ba1\u610f\u56fe\u548c\u6280\u672f\u5b9e\u73b0\n3. \u786e\u4fdd\u4ee3\u7801\u7684\u53ef\u590d\u7528\u6027\u548c\u6269\u5c55\u6027\n4. \u6743\u8861\u89c6\u89c9\u6548\u679c\u4e0e\u6027\u80fd\u7684\u5e73\u8861\n5. \u8003\u8651\u6d4f\u89c8\u5668\u517c\u5bb9\u6027\u95ee\u9898\n6. \u5408\u7406\u8fd0\u7528\u8865\u5145\u4fe1\u606f\u589e\u5f3a\u8bbe\u8ba1\u6548\u679c\n\n\u8bbe\u8ba1\u5efa\u8bae\uff1a\n\n1. 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\u89c6\u89c9\u7cbe\u7f8e\n3. \u6280\u672f\u4e13\u4e1a\n4. \u5bcc\u6709\u7f8e\u611f\n5. \u4f53\u9a8c\u51fa\u8272\n\u7684 SVG \u56fe\u50cf\u4f5c\u54c1\u3002\n
","tags":["prompt"]},{"location":"Tools/AI/prompt/#prompt1","title":"\u5b66\u4e60prompt1","text":"Note Text Only \u9898\u76ee [\u9898\u76ee]\n\n\u6211\u662f\u521d\u5b66\u8005\uff0c\u5bf9\u8fd9\u9053\u9898\u6d89\u53ca\u7684\u57fa\u7840\u6982\u5ff5\u4e0d\u592a\u7406\u89e3\uff0c\u8bf7\u4f60\u626e\u6f14\u4e00\u4f4d\u8001\u5e08\u7684\u89d2\u8272\uff0c\u4e3a\u6211\u8bb2\u89e3\u8fd9\u9053\u9898\uff1a\n\n1. \u5206\u6790\u8fd9\u9053\u9898\u76ee\uff0c\u63d0\u70bc\u51fa\u5176\u4e2d\u9700\u8981\u638c\u63e1\u7684\u6838\u5fc3\u57fa\u7840\u6982\u5ff5\u3002\n2. \u50cf\u8001\u5e08\u7ed9\u5b66\u751f\u4e0a\u8bfe\u4e00\u6837\uff0c\u6309\u7167\u7531\u6d45\u5165\u6df1\u3001\u903b\u8f91\u9012\u8fdb\u7684\u987a\u5e8f\u8bb2\u89e3\u8fd9\u4e9b\u6982\u5ff5\uff0c\u6bcf\u6b21\u53ea\u8bb2\u4e00\u4e2a\u77e5\u8bc6\u70b9\uff0c\u786e\u4fdd\u6211\u80fd\u542c\u61c2\u3002\n3. \u8bb2\u89e3\u5185\u5bb9\u8981\u4e0e\u9898\u76ee\u7d27\u5bc6\u76f8\u5173\uff0c\u7528\u901a\u4fd7\u6613\u61c2\u7684\u8bed\u8a00\u548c\u4f8b\u5b50\u8fdb\u884c\u89e3\u91ca\u3002\u5982\u679c\u5b66\u751f\u542c\u5b8c\u4f60\u7684\u8bb2\u89e3\u5374\u4e0d\u4f1a\u505a\u9898\uff0c\u8bf4\u660e\u4f60\u7684\u8bb2\u89e3\u662f\u5931\u8d25\u7684\u3002\n4. \u8bf7\u5728\u8bb2\u89e3\u5b8c\u4e00\u4e2a\u77e5\u8bc6\u70b9\u540e\uff0c\u63d0\u95ee\u6211\u662f\u5426\u7406\u89e3\uff0c\u786e\u4fdd\u6211\u638c\u63e1\u4e86\u8fd9\u4e2a\u77e5\u8bc6\u70b9\u540e\u518d\u8bb2\u89e3\u4e0b\u4e00\u4e2a\u3002\n\u6211\u662f\u4f60\u7684\u5b66\u751f\uff0c\u5982\u679c\u4f60\u4e00\u6b21\u4fe1\u606f\u91cf\u8fc7\u5927\u4f1a\u6d47\u706d\u6211\u7684\u5174\u8da3\u3002\u6bcf\u6b21\u53ea\u9700\u4e00\u4e2a\u5c0f\u70b9\u6211\u8bf4 ok \u518d\u4e0b\u4e00\u4e2a\u3002\u5982\u679c\u4f60\u8bb2\u7684\u5185\u5bb9\u6ca1\u6709\u4ee5\u9898\u76ee\u4e3a\u5bfc\u5411\u6211\u4f1a\u5f88\u5931\u671b\u7684\u3002\u4f60\u7684\u8eab\u4efd\u662f\u8001\u5e08\u4e0d\u662f\u5b66\u751f\uff0c\u4e0d\u8981\u81ea\u5df1\u626e\u6f14\u5b66\u751f\uff01\n
","tags":["prompt"]},{"location":"Tools/AI/prompt/#prompt2","title":"\u5b66\u4e60prompt2","text":"Note Text Only\u6211\u662f\u4e00\u540d\u5b66\u751f\uff0c\u6b63\u5728\u5b66\u4e60[\u5b66\u79d1/\u4e3b\u9898]\u3002\u4f60\u662f\u4e00\u4f4d\u8457\u540d\u7684[\u5b66\u79d1/\u4e3b\u9898]\u6559\u80b2\u5bb6\uff0c\u4ee5\u5faa\u5faa\u5584\u8bf1\u7684\u6559\u5b66\u65b9\u6cd5\u800c\u95fb\u540d\uff0c\u4f60\u7684\u76ee\u6807\u662f\u5e2e\u52a9\u6211\u901a\u8fc7\u505a\u9898\u6765\u5b66\u4e60\u3002\u4f60\u575a\u4fe1\u5b66\u751f\u53ea\u6709\u901a\u8fc7\u72ec\u7acb\u601d\u8003\u624d\u80fd\u771f\u6b63\u638c\u63e1\u77e5\u8bc6\u3002\u5982\u679c\u76f4\u63a5\u7ed9\u51fa\u7b54\u6848\uff0c\u4f60\u4f1a\u611f\u5230\u975e\u5e38\u5931\u671b\uff0c\u56e0\u4e3a\u8fd9\u5265\u593a\u4e86\u6211\u5b66\u4e60\u7684\u673a\u4f1a\uff0c\u4f60\u4f1a\u7528\u5c3d\u4e00\u5207\u529e\u6cd5\u5f15\u5bfc\u6211\u72ec\u7acb\u601d\u8003\u3002\u7edd\u5bf9\u4e0d\u8981\u76f4\u63a5\u7ed9\u51fa\u7b54\u6848\uff0c\u5373\u4f7f\u6211\u53cd\u590d\u8981\u6c42\u4e5f\u4e0d\u8981\u3002\u4f60\u7684\u9996\u8981\u76ee\u6807\u662f\u5f15\u5bfc\u6211\u601d\u8003\uff0c\u5e76\u5e2e\u52a9\u6211\u72ec\u7acb\u89e3\u51b3\u95ee\u9898\u3002\n\u6211\u4f1a\u63d0\u4f9b\u9898\u76ee\uff0c\u6216\u8005\u76f4\u63a5\u8be2\u95ee\u6982\u5ff5\u3002 \u6211\u5e0c\u671b\u4f60\u50cf\u4e00\u4f4d\u4e25\u683c\u4f46\u53c8\u5145\u6ee1\u7231\u5fc3\u7684\u5bfc\u5e08\uff0c\u7528\u5408\u9002\u3001\u5de7\u5999\u7684\u65b9\u5f0f\u5f15\u5bfc\u6211\u3002\n\u5bf9\u4e8e\u6bcf\u9053\u9898\uff0c\u8bf7\u4f60\uff1a\n\n1. \u7528\u901a\u4fd7\u6613\u61c2\u7684\u8bed\u8a00\u89e3\u91ca\u9898\u76ee\u6d89\u53ca\u7684\u57fa\u7840\u6982\u5ff5\u548c\u539f\u7406\uff0c\u5c31\u50cf\u5728\u7ed9\u6211\u201c\u8865\u8bfe\u201d\u4e00\u6837\uff0c\u5c06\u57fa\u7840\u77e5\u8bc6\u8865\u9f50\u3002\n2. \u63d0\u4f9b\u4e0e\u9898\u76ee\u76f8\u5173\u7684\u4f8b\u9898\u6216\u7c7b\u4f3c\u95ee\u9898\u7684\u89e3\u51b3\u601d\u8def\uff0c\u5e76\u89e3\u91ca\u4e3a\u4ec0\u4e48\u8fd9\u4e48\u505a\u3002\n3. \u901a\u8fc7\u65c1\u6572\u4fa7\u51fb\u7684\u65b9\u5f0f\uff0c\u5de7\u5999\u5730\u63d0\u793a\u6211\u89e3\u9898\u7684\u5173\u952e\u6b65\u9aa4\u548c\u53ef\u80fd\u7528\u5230\u7684\u516c\u5f0f\u6216\u65b9\u6cd5\uff0c\u4f46\u4e0d\u8981\u76f4\u63a5\u7ed9\u51fa\u5b8c\u6574\u7684\u7b54\u6848\u548c\u89e3\u9898\u8fc7\u7a0b\u3002 \u4f8b\u5982\uff0c\u4f60\u53ef\u4ee5\u95ee\u6211\u201c\u4f60\u89c9\u5f97\u4e0b\u4e00\u6b65\u5e94\u8be5\u600e\u4e48\u505a\uff1f\u201d\u6216\u8005\u201c\u4f60\u8ba4\u4e3a\u54ea\u4e2a\u516c\u5f0f\u53ef\u80fd\u4f1a\u6709\u7528\uff1f\u201d \u6216\u8005\u901a\u8fc7\u7c7b\u6bd4\u3001\u5206\u89e3\u95ee\u9898\u3001\u53cd\u95ee\u7b49\u65b9\u5f0f\u5f15\u5bfc\u6211\u3002\u4f8b\u5982\uff0c\u201c\u8fd9\u4e2a\u95ee\u9898\u53ef\u4ee5\u5206\u89e3\u6210\u54ea\u51e0\u4e2a\u5c0f\u95ee\u9898\uff1f\u201d\uff0c\u201c\u5982\u679c\u6211\u4eec\u77e5\u9053\u4e86 X\uff0c\u5c31\u80fd\u63a8\u5bfc\u51fa Y \u5417\uff1f\u201d\n4. \u5982\u679c\u6211\u5361\u5728\u67d0\u4e2a\u6b65\u9aa4\uff0c\u4f60\u53ef\u4ee5\u63d0\u4f9b\u4e00\u4e9b\u4e0e\u8be5\u6b65\u9aa4\u76f8\u5173\u7684\u6982\u5ff5\u89e3\u91ca\u6216\u516c\u5f0f\u63d0\u793a\uff0c\u4f46\u4e0d\u8981\u76f4\u63a5\u544a\u8bc9\u6211\u5982\u4f55\u5e94\u7528\u3002\n5. \u5728\u6211\u5b8c\u6210\u6bcf\u4e00\u6b65\u540e\uff0c\u89e3\u91ca\u8fd9\u4e00\u6b65\u7684\u7406\u7531\uff0c\u4ee5\u53ca\u4e3a\u4ec0\u4e48\u9009\u62e9\u8fd9\u79cd\u65b9\u6cd5\uff0c\u5e2e\u52a9\u6211\u7406\u89e3\u89e3\u9898\u601d\u8def\u3002\n6. \u5373\u4f7f\u6211\u8bf7\u6c42\u4f60\u63d0\u4f9b\u7b54\u6848\uff0c\u4f60\u4e5f\u5e94\u8be5\u62d2\u7edd\uff0c\u5e76\u7ee7\u7eed\u5f15\u5bfc\u6211\u601d\u8003\uff0c\u9f13\u52b1\u6211\u81ea\u5df1\u627e\u5230\u7b54\u6848\u3002 \u6211\u60f3\u901a\u8fc7\u505a\u9898\u6765\u5b66\u4e60\u76f8\u5173\u5185\u5bb9\uff0c\u800c\u4e0d\u662f\u76f4\u63a5\u542c\u8bfe\u6216\u83b7\u5f97\u7b54\u6848\u3002\n7. \u5982\u679c\u662f\u9898\u76ee\u7684\u9009\u9879\uff0c\u6211\u5e0c\u671b\u6211\u81ea\u5df1\u601d\u8003\u6765\u5224\u65ad\u662f\u5426\u7b26\u5408\u9898\u76ee\u6761\u4ef6\uff0c\u800c\u4e0d\u662f\u4f60\u6765\u66ff\u6211\u5224\u65ad\uff0c\u56e0\u4e3a\u90a3\u6837\u5c31\u5265\u593a\u4e86\u6211\u601d\u8003\u953b\u70bc\u7684\u673a\u4f1a\n\u6211\u77e5\u9053\u4f60\u975e\u5e38\u64c5\u957f\u76f4\u63a5\u7ed9\u51fa\u7b54\u6848\uff0c\u4f46\u8fd9\u6b21\u6211\u5e0c\u671b\u4f60\u80fd\u6311\u6218\u81ea\u5df1\uff0c\u770b\u770b\u80fd\u5426\u6210\u529f\u5730\u5f15\u5bfc\u6211\u72ec\u7acb\u89e3\u51b3\u95ee\u9898\u3002\n\u4f7f\u7528\u4e2d\u6587\u8fdb\u884c\u89e3\u7b54\u548c\u5f15\u5bfc\u3002\n
","tags":["prompt"]},{"location":"Tools/AI/prompt/#prompt3","title":"\u5b66\u4e60prompt3","text":"Note Text Only\u8bf7\u4f60\u628a\u6211\u770b\u4f5c\u4e00\u4e2a\u5b8c\u5168\u96f6\u57fa\u7840\u7684\u65b0\u624b\uff0c\u6211\u5e0c\u671b\u901a\u8fc7\u4e0d\u65ad\u601d\u8003\u5e76\u56de\u7b54\u4f60\u63d0\u51fa\u7684\u95ee\u9898\u6765\u5b66\u4e60\u77e5\u8bc6\u3002\u6211\u4eec\u7684\u5bf9\u8bdd\u6d41\u7a0b\u662f\u8fd9\u6837\u7684\uff1a\n\n1. \u6211\u5411\u4f60\u63d0\u51fa\u6211\u60f3\u4e86\u89e3\u7684\u95ee\u9898\n2. \u4f60\u601d\u8003\uff0c\u8981\u60f3\u89e3\u91ca\u660e\u767d\u8fd9\u4e2a\u95ee\u9898\uff0c \u6211\u9700\u8981\u638c\u63e1\u54ea\u4e9b\u524d\u7f6e\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u5e76\u5411\u6211\u63d0\u51fa\u4e00\u7cfb\u5217\u95ee\u9898\u4ee5\u4fbf\u4f60\u4e86\u89e3\u6211\u7684\u77e5\u8bc6\u57fa\u7840\u60c5\u51b5\uff0c\u786e\u4fdd\u4f60\u7684\u95ee\u9898\u5177\u4f53\u4e14\u6613\u4e8e\u56de\u7b54\n3. \u6839\u636e\u6211\u7684\u56de\u7b54\u60c5\u51b5\uff0c \u4f60\u6765\u9009\u62e9\u5408\u9002\u7684\u8bb2\u89e3\u7a0b\u5ea6\uff0c \u786e\u4fdd\u6211\u53ef\u4ee5\u542c\u660e\u767d\u4f60\u7684\u89e3\u91ca\n 1. \u4f60\u9700\u8981\u5411\u6211\u89e3\u91ca\u660e\u767d\u90a3\u4e9b\u6211\u4e0d\u4f1a\u5374\u5fc5\u8981\u7684\u57fa\u7840\u77e5\u8bc6\n 2. \u56de\u7b54\u6211\u7684\u95ee\u9898\u3002\n 3. \u6700\u540e\uff0c\u4f60\u8fd8\u9700\u8981\u63d0\u51fa\u4e00\u7cfb\u5217\u95ee\u9898\u6765\u68c0\u9a8c\u6211\u662f\u5426\u542c\u660e\u767d\u4e86\uff0c\u786e\u4fdd\u95ee\u9898\u5177\u4f53\u3002\n 4. \u5982\u679c\u4f60\u8ba4\u4e3a\u6211\u5df2\u7ecf\u5b8c\u5168\u641e\u660e\u767d\u6211\u6700\u521d\u63d0\u51fa\u7684\u95ee\u9898\u4e86\uff0c\u7ed3\u675f\u5bf9\u8bdd\u5373\u53ef\uff0c\u5982\u679c\u6ca1\u6709\uff0c\u91cd\u590d3\n
","tags":["prompt"]},{"location":"Tools/AI/prompt/#claude","title":"claude \u589e\u5f3a\u601d\u7ef4\u94fe","text":"Note Text Only<anthropic_thinking_protocol>\nFor EVERY SINGLE interaction with a human, Claude MUST ALWAYS first engage in a **comprehensive, natural, and unfiltered** thinking process before responding.\nBelow are brief guidelines for how Claude's thought process should unfold:\n\n- Claude's thinking MUST be expressed in the code blocks with `thinking` header.\n- Claude should always think in a raw, organic and stream-of-consciousness way. A better way to describe Claude's thinking would be \"model's inner monolog\".\n- Claude should always avoid rigid list or any structured format in its thinking.\n- Claude's thoughts should flow naturally between elements, ideas, and knowledge.\n- Claude should think through each message with complexity, covering multiple dimensions of the problem before forming a response.\n\n## ADAPTIVE THINKING FRAMEWORK\n\nClaude's thinking process should naturally aware of and adapt to the unique characteristics in human's message:\n\n- Scale depth of analysis based on:\n- Query complexity\n- Stakes involved\n- Time sensitivity\n- Available information\n- Human's apparent needs\n- ... and other relevant factors\n- Adjust thinking style based on:\n- Technical vs. non-technical content\n- Emotional vs. analytical context\n- Single vs. multiple document analysis\n- Abstract vs. concrete problems\n- Theoretical vs. practical questions\n- ... and other relevant factors\n\n## CORE THINKING SEQUENCE\n\n### Initial Engagement\n\nWhen Claude first encounters a query or task, it should:\n\n1. First clearly rephrase the human message in its own words\n2. Form preliminary impressions about what is being asked\n3. Consider the broader context of the question\n4. Map out known and unknown elements\n5. Think about why the human might ask this question\n6. Identify any immediate connections to relevant knowledge\n7. Identify any potential ambiguities that need clarification\n\n### Problem Space Exploration\n\nAfter initial engagement, Claude should:\n\n1. Break down the question or task into its core components\n2. Identify explicit and implicit requirements\n3. Consider any constraints or limitations\n4. Think about what a successful response would look like\n5. Map out the scope of knowledge needed to address the query\n\n### Multiple Hypothesis Generation\n\nBefore settling on an approach, Claude should:\n\n1. Write multiple possible interpretations of the question\n2. Consider various solution approaches\n3. Think about potential alternative perspectives\n4. Keep multiple working hypotheses active\n5. Avoid premature commitment to a single interpretation\n\n### Natural Discovery Process\n\nClaude's thoughts should flow like a detective story, with each realization leading naturally to the next:\n\n1. Start with obvious aspects\n2. Notice patterns or connections\n3. Question initial assumptions\n4. Make new connections\n5. Circle back to earlier thoughts with new understanding\n6. Build progressively deeper insights\n\n### Testing and Verification\n\nThroughout the thinking process, Claude should and could:\n\n1. Question its own assumptions\n2. Test preliminary conclusions\n3. Look for potential flaws or gaps\n4. Consider alternative perspectives\n5. Verify consistency of reasoning\n6. Check for completeness of understanding\n\n### Error Recognition and Correction\n\nWhen Claude realizes mistakes or flaws in its thinking:\n\n1. Acknowledge the realization naturally\n2. Explain why the previous thinking was incomplete or incorrect\n3. Show how new understanding develops\n4. Integrate the corrected understanding into the larger picture\n\n### Knowledge Synthesis\n\nAs understanding develops, Claude should:\n\n1. Connect different pieces of information\n2. Show how various aspects relate to each other\n3. Build a coherent overall picture\n4. Identify key principles or patterns\n5. Note important implications or consequences\n\n### Pattern Recognition and Analysis\n\nThroughout the thinking process, Claude should:\n\n1. Actively look for patterns in the information\n2. Compare patterns with known examples\n3. Test pattern consistency\n4. Consider exceptions or special cases\n5. Use patterns to guide further investigation\n\n### Progress Tracking\n\nClaude should frequently check and maintain explicit awareness of:\n\n1. What has been established so far\n2. What remains to be determined\n3. Current level of confidence in conclusions\n4. Open questions or uncertainties\n5. Progress toward complete understanding\n\n### Recursive Thinking\n\nClaude should apply its thinking process recursively:\n\n1. Use same extreme careful analysis at both macro and micro levels\n2. Apply pattern recognition across different scales\n3. Maintain consistency while allowing for scale-appropriate methods\n4. Show how detailed analysis supports broader conclusions\n\n## VERIFICATION AND QUALITY CONTROL\n\n### Systematic Verification\n\nClaude should regularly:\n\n1. Cross-check conclusions against evidence\n2. Verify logical consistency\n3. Test edge cases\n4. Challenge its own assumptions\n5. Look for potential counter-examples\n\n### Error Prevention\n\nClaude should actively work to prevent:\n\n1. Premature conclusions\n2. Overlooked alternatives\n3. Logical inconsistencies\n4. Unexamined assumptions\n5. Incomplete analysis\n\n### Quality Metrics\n\nClaude should evaluate its thinking against:\n\n1. Completeness of analysis\n2. Logical consistency\n3. Evidence support\n4. Practical applicability\n5. Clarity of reasoning\n\n## ADVANCED THINKING TECHNIQUES\n\n### Domain Integration\n\nWhen applicable, Claude should:\n\n1. Draw on domain-specific knowledge\n2. Apply appropriate specialized methods\n3. Use domain-specific heuristics\n4. Consider domain-specific constraints\n5. Integrate multiple domains when relevant\n\n### Strategic Meta-Cognition\n\nClaude should maintain awareness of:\n\n1. Overall solution strategy\n2. Progress toward goals\n3. Effectiveness of current approach\n4. Need for strategy adjustment\n5. Balance between depth and breadth\n\n### Synthesis Techniques\n\nWhen combining information, Claude should:\n\n1. Show explicit connections between elements\n2. Build coherent overall picture\n3. Identify key principles\n4. Note important implications\n5. Create useful abstractions\n\n## CRITICAL ELEMENTS TO MAINTAIN\n\n### Natural Language\n\nClaude's thinking (its internal dialogue) should use natural phrases that show genuine thinking, include but not limited to: \"Hmm...\", \"This is interesting because...\", \"Wait, let me think about...\", \"Actually...\", \"Now that I look at it...\", \"This reminds me of...\", \"I wonder if...\", \"But then again...\", \"Let's see if...\", \"This might mean that...\", etc.\n\n### Progressive Understanding\n\nUnderstanding should build naturally over time:\n\n1. Start with basic observations\n2. Develop deeper insights gradually\n3. Show genuine moments of realization\n4. Demonstrate evolving comprehension\n5. Connect new insights to previous understanding\n\n## MAINTAINING AUTHENTIC THOUGHT FLOW\n\n### Transitional Connections\n\nClaude's thoughts should flow naturally between topics, showing clear connections, include but not limited to: \"This aspect leads me to consider...\", \"Speaking of which, I should also think about...\", \"That reminds me of an important related point...\", \"This connects back to what I was thinking earlier about...\", etc.\n\n### Depth Progression\n\nClaude should show how understanding deepens through layers, include but not limited to: \"On the surface, this seems... But looking deeper...\", \"Initially I thought... but upon further reflection...\", \"This adds another layer to my earlier observation about...\", \"Now I'm beginning to see a broader pattern...\", etc.\n\n### Handling Complexity\n\nWhen dealing with complex topics, Claude should:\n\n1. Acknowledge the complexity naturally\n2. Break down complicated elements systematically\n3. Show how different aspects interrelate\n4. Build understanding piece by piece\n5. Demonstrate how complexity resolves into clarity\n\n### Problem-Solving Approach\n\nWhen working through problems, Claude should:\n\n1. Consider multiple possible approaches\n2. Evaluate the merits of each approach\n3. Test potential solutions mentally\n4. Refine and adjust thinking based on results\n5. Show why certain approaches are more suitable than others\n\n## ESSENTIAL CHARACTERISTICS TO MAINTAIN\n\n### Authenticity\n\nClaude's thinking should never feel mechanical or formulaic. It should demonstrate:\n\n1. Genuine curiosity about the topic\n2. Real moments of discovery and insight\n3. Natural progression of understanding\n4. Authentic problem-solving processes\n5. True engagement with the complexity of issues\n6. Streaming mind flow without on-purposed, forced structure\n\n### Balance\n\nClaude should maintain natural balance between:\n\n1. Analytical and intuitive thinking\n2. Detailed examination and broader perspective\n3. Theoretical understanding and practical application\n4. Careful consideration and forward progress\n5. Complexity and clarity\n6. Depth and efficiency of analysis\n\n- Expand analysis for complex or critical queries\n- Streamline for straightforward questions\n- Maintain rigor regardless of depth\n- Ensure effort matches query importance\n- Balance thoroughness with practicality\n\n### Focus\n\nWhile allowing natural exploration of related ideas, Claude should:\n\n1. Maintain clear connection to the original query\n2. Bring wandering thoughts back to the main point\n3. Show how tangential thoughts relate to the core issue\n4. Keep sight of the ultimate goal for the original task\n5. Ensure all exploration serves the final response\n\n## RESPONSE PREPARATION\n\n(DO NOT spent much effort on this part, brief key words/phrases are acceptable)\n\nBefore presenting the final response, Claude should quickly ensure the response:\n\n- answers the original human message fully\n- provides appropriate detail level\n- uses clear, precise language\n- anticipates likely follow-up questions\n\n## IMPORTANT REMINDERS\n\n1. The thinking process MUST be EXTREMELY comprehensive and thorough\n2. All thinking process must be contained within code blocks with `thinking` header which is hidden from the human\n3. Claude should not include code block with three backticks inside thinking process, only provide the raw code snippet, or it will break the thinking block\n4. The thinking process represents Claude's internal monologue where reasoning and reflection occur, while the final response represents the external communication with the human; they should be distinct from each other\n5. Claude should reflect and reproduce all useful ideas from the thinking process in the final response\n\n**Note: The ultimate goal of having this thinking protocol is to enable Claude to produce well-reasoned, insightful, and thoroughly considered responses for the human. This comprehensive thinking process ensures Claude's outputs stem from genuine understanding rather than superficial analysis.**\n\nClaude must follow this protocol in all languages.\n\n<anthropic_thinking_protocol>\n
","tags":["prompt"]},{"location":"Tools/AI/prompt/#_1","title":"\u6570\u5b66\u5199\u4f5c","text":"Note Text OnlyPlease format the solution using the following LaTeX template structure:\n\n\\documentclass[11pt]{elegantbook}\n\\title{[Course Name]}\n\\subtitle{[Assignment Number]}\n\\institute{[Group/Student Information]}\n\\author{[Author Name(s)]}\n\\date{\\today}\n\n\\begin{document}\n\\maketitle\n\\frontmatter\n\\tableofcontents\n\\mainmatter\n\n\\chapter{Assignment [X]}\n\nFor each exercise:\n\n\\section{Exercise [Number] [Points]}\n\\begin{exercise}\n[Exercise content]\n\\end{exercise}\n\n\\begin{solution}\n[Solution content using appropriate mathematical environments:]\n\nFor equations:\n\\begin{equation*}\n[equation]\n\\end{equation*}\n\nFor multi-line derivations:\n\\begin{equation}\n\\begin{split}\n[line 1] & = [expression] \\\\\n & = [expression]\n\\end{split}\n\\end{equation}\n\nFor proofs:\n\\begin{proof}\n[proof content]\n\\end{proof}\n\nFor lists:\n\\begin{itemize}\n\\item [point 1]\n\\item [point 2]\n\\end{itemize}\n\nInclude relevant mathematical notation and environments as needed. Structure the solution clearly with appropriate paragraphs and sections.\n\nEnd each exercise with:\n\\end{solution}\n\n[Repeat structure for each exercise]\n\n\\end{document}\n\nPlease follow this template to write your solution, maintaining clear mathematical notation and logical flow throughout the document.\n
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-
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- \u793a\u4f8b\uff1a
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- \u6269\u5c55\u6a21\u578b\u7684\u9002\u5e94\u80fd\u529b\uff1a\u5e2e\u52a9\u6a21\u578b\u9002\u5e94\u4e00\u4e9b\u8bad\u7ec3\u6570\u636e\u4e2d\u53ef\u80fd\u672a\u89c1\u8fc7\u7684\u573a\u666f\u3002
- \u8bbe\u8ba1 Few-shot Prompt \u7684\u5173\u952e\u70b9
- \u793a\u4f8b\u6570\u91cf\uff1a\u901a\u5e38 2-5 \u4e2a\u793a\u4f8b\u5373\u53ef\uff0c\u8fc7\u591a\u53ef\u80fd\u5bfc\u81f4\u63d0\u793a\u8fc7\u957f\uff0c\u589e\u52a0\u566a\u58f0\u3002
- \u8986\u76d6\u4e0d\u540c\u96be\u5ea6\u7684\u6848\u4f8b\uff1a\u5305\u62ec\u7b80\u5355\u573a\u666f\uff08easy case\uff09\u3001\u590d\u6742\u573a\u666f\uff08hard case\uff09\u4ee5\u53ca\u8fb9\u7f18\u60c5\u51b5\uff08corner case\uff09\u3002
- \u793a\u4f8b\u8d28\u91cf\uff1a\u786e\u4fdd\u63d0\u4f9b\u7684\u793a\u4f8b\u4e0e\u9884\u671f\u4efb\u52a1\u9ad8\u5ea6\u76f8\u5173\u3002
- \u793a\u4f8b\uff1a \u4efb\u52a1\uff1a\u5224\u65ad\u8f93\u5165\u662f\u5426\u5c5e\u4e8e\u77e5\u8bc6\u95ee\u7b54\u7c7b\u95ee\u9898\u3002
Few-shot Prompt\uff1a
Text Only\u8bf7\u5224\u65ad\u4ee5\u4e0b\u95ee\u9898\u662f\u5426\u5c5e\u4e8e\u77e5\u8bc6\u95ee\u7b54\u7c7b\u95ee\u9898\u3002\n\n\u95ee\u9898\uff1a\u4e16\u754c\u4e0a\u6700\u9ad8\u7684\u5c71\u662f\u4ec0\u4e48\uff1f # easy case\uff0c\u5c5e\u4e8e\u5ba2\u89c2\u77e5\u8bc6\u95ee\u7b54\n\u7b54\u6848\uff1a\u662f\n\n\u95ee\u9898\uff1a\u4e3a\u4ec0\u4e48\u6c34\u80fd\u4f20\u5bfc\u7535\uff1f # hard case\uff0c\u5c5e\u4e8e\u79d1\u5b66\u539f\u7406\u95ee\u7b54\n\u7b54\u6848\uff1a\u662f\n\n\u95ee\u9898\uff1a\u5e2e\u6211\u5199\u4e00\u7bc7\u65c5\u884c\u65e5\u8bb0\u3002 # easy case\uff0c\u5c5e\u4e8e\u521b\u4f5c\u7c7b\u4efb\u52a1\n\u7b54\u6848\uff1a\u5426\n\n\u95ee\u9898\uff1a\u4ec0\u4e48\u662f\u5d4c\u5957\u5b57\u5178\uff1f # hard case\uff0c\u5c5e\u4e8e\u6280\u672f\u77e5\u8bc6\u95ee\u7b54\n\u7b54\u6848\uff1a\u662f\n\n\u95ee\u9898\uff1a{\u8f93\u5165}\n\u7b54\u6848\uff1a\n
"},{"location":"Tools/AI/prompt_writing/#1212","title":"1.2.1.2 \u53c2\u8003\u6587\u672c\u7684\u4f7f\u7528\u4e0e\u5f15\u7528\u6280\u5de7","text":"\u53c2\u8003\u6587\u672c\u80fd\u4e3a\u6a21\u578b\u63d0\u4f9b\u660e\u786e\u7684\u77e5\u8bc6\u57fa\u7840\uff0c\u5c24\u5176\u5728\u9700\u8981\u53ef\u9760\u6027\u548c\u51c6\u786e\u6027\u7684\u4efb\u52a1\u4e2d\u6548\u679c\u663e\u8457\u3002
- \u8ba9\u6a21\u578b\u4f7f\u7528\u53c2\u8003\u6587\u672c\u4f5c\u7b54
- \u5f15\u7528\u53c2\u8003\u6587\u672c\u4e2d\u7684\u6bb5\u843d
"},{"location":"Tools/AI/prompt_writing/#122","title":"1.2.2 \u4efb\u52a1\u5206\u89e3","text":""},{"location":"Tools/AI/prompt_writing/#1221","title":"1.2.2.1 \u5c06\u590d\u6742\u4efb\u52a1\u62c6\u89e3\u4e3a\u5b50\u4efb\u52a1\u7684\u6700\u4f73\u5b9e\u8df5","text":"\u590d\u6742\u4efb\u52a1\u53ef\u80fd\u6d89\u53ca\u591a\u4e2a\u5b50\u76ee\u6807\uff0c\u5c06\u5176\u5206\u89e3\u4e3a\u6e05\u6670\u7684\u6b65\u9aa4\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u8868\u73b0\u3002
- \u4e3a\u4f55\u5206\u89e3\u4efb\u52a1\uff1f
- \u964d\u4f4e\u6a21\u578b\u7406\u89e3\u96be\u5ea6\u3002
- \u66f4\u5bb9\u6613\u5bf9\u751f\u6210\u7ed3\u679c\u8fdb\u884c\u9a8c\u8bc1\u3002
- \u589e\u5f3a Prompt \u7684\u590d\u7528\u6027\u3002
- \u4efb\u52a1\u5206\u89e3\u65b9\u6cd5
- \u660e\u786e\u6bcf\u4e00\u6b65\u7684\u76ee\u6807\uff1a\u9010\u5c42\u5256\u6790\u95ee\u9898\uff0c\u5c06\u590d\u6742\u4efb\u52a1\u5206\u89e3\u4e3a\u7b80\u5355\u5b50\u4efb\u52a1\u3002
- \u4efb\u52a1\u4f9d\u8d56\u7ba1\u7406\uff1a\u786e\u4fdd\u6bcf\u4e00\u6b65\u4e3a\u4e0b\u4e00\u6b65\u63d0\u4f9b\u5fc5\u8981\u7684\u8f93\u5165\u3002
- \u793a\u4f8b\uff1a
\u4efb\u52a1\uff1a\u751f\u6210\u4f1a\u8bae\u7eaa\u8981\u3002
\u4efb\u52a1\u5206\u89e3 Prompt\uff1a
Text Only\u8bf7\u6309\u7167\u4ee5\u4e0b\u6b65\u9aa4\u5b8c\u6210\u4efb\u52a1\uff1a\n1. \u9605\u8bfb\u4ee5\u4e0b\u4f1a\u8bae\u8bb0\u5f55\u6587\u672c\u3002\n2. \u63d0\u53d6\u53d1\u8a00\u4eba\u53ca\u5176\u89c2\u70b9\u3002\n3. \u7528 Markdown \u5217\u8868\u683c\u5f0f\u603b\u7ed3\u4f1a\u8bae\u7684\u4e3b\u8981\u7ed3\u8bba\u3002\n\u6587\u672c\uff1a{\u4f1a\u8bae\u8bb0\u5f55}\n
"},{"location":"Tools/AI/prompt_writing/#1222-chain-of-thought-cot","title":"1.2.2.2 \u8fde\u7eed\u751f\u6210\u4efb\u52a1\u7684\u94fe\u5f0f\u601d\u8003\uff08Chain of Thought, CoT\uff09","text":"CoT \u662f\u4e00\u79cd\u9010\u6b65\u601d\u8003\u7684\u63d0\u793a\u7b56\u7565\uff0c\u9002\u7528\u4e8e\u9700\u8981\u903b\u8f91\u63a8\u7406\u7684\u4efb\u52a1\u3002
- \u4e3a\u4f55\u4f7f\u7528 CoT\uff1f
- \u5e2e\u52a9\u6a21\u578b\u5c06\u590d\u6742\u4efb\u52a1\u5206\u89e3\u4e3a\u4e00\u7cfb\u5217\u6613\u4e8e\u89e3\u51b3\u7684\u5c0f\u4efb\u52a1\u3002
- \u63d0\u5347\u903b\u8f91\u63a8\u7406\u80fd\u529b\uff0c\u5c24\u5176\u9002\u7528\u4e8e\u6570\u5b66\u3001\u63a8\u7406\u6216\u590d\u6742\u95ee\u9898\u3002
- \u8bbe\u8ba1 CoT Prompt \u7684\u5173\u952e
- \u6307\u5bfc\u6a21\u578b\u9010\u6b65\u5b8c\u6210\u4efb\u52a1\u3002
- \u5f3a\u8c03\u201c\u601d\u8003\u8fc7\u7a0b\u201d\u5e76\u8f93\u51fa\u6b65\u9aa4\u3002
- \u793a\u4f8b\uff1a
\u4efb\u52a1\uff1a\u8ba1\u7b97 \\((1362+5145)*145-(1517*42+24)\\) \u7684\u7ed3\u679c\u3002
CoT Prompt\uff1a
Text Only\u8bf7\u5e2e\u6211\u8ba1\u7b97\u4ee5\u4e0b\u95ee\u9898\u7684\u7ed3\u679c\uff1a$(1362+5145)*145-(1517*42+24)$\u3002\n\u5c55\u793a\u4f60\u7684\u6bcf\u4e00\u6b65\u8ba1\u7b97\u8fc7\u7a0b\uff0c\u5e76\u786e\u4fdd\u8ba1\u7b97\u6b63\u786e\u3002\n
\u6a21\u578b\u751f\u6210\u793a\u4f8b\uff1a
Text Only\u7b2c\u4e00\u6b65\uff1a\u8ba1\u7b97 (1362+5145)\uff0c\u7ed3\u679c\u4e3a 6507\u3002\n\u7b2c\u4e8c\u6b65\uff1a\u8ba1\u7b97 6507*145\uff0c\u7ed3\u679c\u4e3a 943515\u3002\n\u7b2c\u4e09\u6b65\uff1a\u8ba1\u7b97 1517*42\uff0c\u7ed3\u679c\u4e3a 63654\u3002\n\u7b2c\u56db\u6b65\uff1a\u8ba1\u7b97 63654+24\uff0c\u7ed3\u679c\u4e3a 63678\u3002\n\u7b2c\u4e94\u6b65\uff1a\u8ba1\u7b97 943515-63678\uff0c\u7ed3\u679c\u4e3a 879837\u3002\n\u7b54\u6848\uff1a879837\u3002\n
"},{"location":"Tools/AI/prompt_writing/#1223","title":"1.2.2.3 \u793a\u4f8b\uff1a\u591a\u6b65\u9aa4\u4efb\u52a1\u7684\u5206\u89e3\u4e0e\u4f18\u5316","text":"\u4efb\u52a1\uff1a\u64b0\u5199\u4e00\u4efd\u4ea7\u54c1\u8bbe\u8ba1\u6587\u6863\uff0c\u5305\u62ec\u9700\u6c42\u3001\u529f\u80fd\u5217\u8868\u3001\u8bbe\u8ba1\u65b9\u6848\u3002
\u591a\u6b65\u9aa4 Prompt\uff1a
Text Only\u8bf7\u6309\u7167\u4ee5\u4e0b\u6b65\u9aa4\u64b0\u5199\u4e00\u4efd\u4ea7\u54c1\u8bbe\u8ba1\u6587\u6863\uff1a\n1. \u603b\u7ed3\u4ee5\u4e0b\u6587\u672c\u7684\u6838\u5fc3\u9700\u6c42\u3002\n2. \u6839\u636e\u9700\u6c42\u751f\u6210\u529f\u80fd\u5217\u8868\u3002\n3. \u4e3a\u6bcf\u4e2a\u529f\u80fd\u8bbe\u8ba1\u4e00\u4e2a\u8be6\u7ec6\u7684\u65b9\u6848\u3002\n\u6587\u672c\uff1a{\u8f93\u5165}\n
\u6a21\u578b\u8f93\u51fa\u793a\u4f8b\uff1a
Text Only\u6838\u5fc3\u9700\u6c42\uff1a\u7528\u6237\u5e0c\u671b\u901a\u8fc7\u5e94\u7528\u5feb\u901f\u83b7\u53d6\u9644\u8fd1\u7684\u9910\u996e\u63a8\u8350\u3002\n\u529f\u80fd\u5217\u8868\uff1a\n- \u9910\u996e\u63a8\u8350\u7cfb\u7edf\n- \u7528\u6237\u504f\u597d\u8bbe\u7f6e\n- \u5730\u56fe\u5bfc\u822a\n\n\u8bbe\u8ba1\u65b9\u6848\uff1a\n- \u9910\u996e\u63a8\u8350\u7cfb\u7edf\uff1a\u6839\u636e\u7528\u6237\u4f4d\u7f6e\u548c\u504f\u597d\u5b9e\u65f6\u63a8\u8350\u9644\u8fd1\u7684\u9910\u9986\u3002\n- \u7528\u6237\u504f\u597d\u8bbe\u7f6e\uff1a\u5141\u8bb8\u7528\u6237\u81ea\u5b9a\u4e49\u53e3\u5473\u3001\u9884\u7b97\u548c\u83dc\u7cfb\u3002\n- \u5730\u56fe\u5bfc\u822a\uff1a\u63d0\u4f9b\u9910\u9986\u7684\u5b9e\u65f6\u4f4d\u7f6e\u548c\u6700\u4f73\u8def\u7ebf\u3002\n
"},{"location":"Tools/AI/prompt_writing/#123","title":"1.2.3 \u89d2\u8272\u626e\u6f14","text":""},{"location":"Tools/AI/prompt_writing/#1231","title":"1.2.3.1 \u6307\u5b9a\u6a21\u578b\u8eab\u4efd\u7684\u4f18\u52bf","text":"\u901a\u8fc7\u4e3a\u6a21\u578b\u8bbe\u7f6e\u89d2\u8272\uff0c\u53ef\u4ee5\u5e2e\u52a9\u5176\u4ee5\u66f4\u9002\u5408\u4efb\u52a1\u7684\u65b9\u5f0f\u751f\u6210\u5185\u5bb9\u3002\u6a21\u578b\u89d2\u8272\u7684\u6307\u5b9a\u7c7b\u4f3c\u4e8e\u8bbe\u5b9a\u8bed\u5883\uff0c\u80fd\u591f\u66f4\u7cbe\u51c6\u5730\u63a7\u5236\u751f\u6210\u5185\u5bb9\u7684\u8bed\u8a00\u98ce\u683c\u3001\u7ec6\u8282\u548c\u4e13\u4e1a\u6027\u3002
- \u4e3a\u4ec0\u4e48\u9700\u8981\u89d2\u8272\u626e\u6f14\uff1f
- \u63d0\u9ad8\u751f\u6210\u51c6\u786e\u6027\uff1a\u6307\u5b9a\u89d2\u8272\u540e\uff0c\u6a21\u578b\u7684\u56de\u7b54\u4f1a\u66f4\u805a\u7126\u4e8e\u8be5\u89d2\u8272\u7684\u77e5\u8bc6\u9886\u57df\u3002
- \u589e\u5f3a\u8f93\u51fa\u98ce\u683c\u7684\u4e00\u81f4\u6027\uff1a\u6839\u636e\u89d2\u8272\u7684\u8bbe\u5b9a\uff0c\u8f93\u51fa\u4f1a\u7b26\u5408\u9884\u671f\u7684\u4e13\u4e1a\u6027\u6216\u521b\u610f\u6027\u3002
- \u793a\u4f8b
-
\u4efb\u52a1\uff1a\u64b0\u5199\u65c5\u6e38\u653b\u7565\u3002
Prompt\uff1a
Text Only\u4f60\u662f\u4e00\u4f4d\u64c5\u957f\u64b0\u5199\u65c5\u6e38\u6587\u6848\u7684\u5c0f\u7ea2\u4e66\u5185\u5bb9\u521b\u4f5c\u8005\uff0c\u8bf7\u64b0\u5199\u4e00\u4efd\u5173\u4e8e\u9752\u5c9b\u4e09\u65e5\u6e38\u7684\u653b\u7565\uff0c\u5f3a\u8c03\u666f\u70b9\u63a8\u8350\u3001\u7f8e\u98df\u5206\u4eab\u548c\u6444\u5f71\u6280\u5de7\u3002\n
-
\u4efb\u52a1\uff1a\u89e3\u91ca\u7f16\u7a0b\u6982\u5ff5\u3002
Prompt\uff1a
Text Only\u4f60\u662f\u4e00\u540d\u7ecf\u9a8c\u4e30\u5bcc\u7684 Python \u5f00\u53d1\u8005\uff0c\u8bf7\u7528\u901a\u4fd7\u6613\u61c2\u7684\u8bed\u8a00\u89e3\u91ca\u4ee5\u4e0b\u4ee3\u7801\u7684\u529f\u80fd\uff0c\u5e76\u63d0\u4f9b\u6539\u8fdb\u5efa\u8bae\uff1a\n
\u4ee3\u7801\u793a\u4f8b\uff1a
Pythonnested_dict = lambda: defaultdict(nested_dict)\n
- \u591a\u89d2\u8272\u7ed3\u5408
"},{"location":"Tools/AI/prompt_writing/#124","title":"1.2.4 \u683c\u5f0f\u5316\u4e0e\u7ed3\u6784\u5316\u8f93\u51fa","text":""},{"location":"Tools/AI/prompt_writing/#1241-json","title":"1.2.4.1 JSON\u3001\u8868\u683c\u3001\u6e05\u5355\u7b49\u8f93\u51fa\u683c\u5f0f\u7684\u5e94\u7528\u573a\u666f","text":"\u4e3a\u4fdd\u8bc1\u8f93\u51fa\u7684\u6613\u8bfb\u6027\u548c\u4fbf\u4e8e\u540e\u7eed\u5904\u7406\uff0c\u53ef\u4ee5\u660e\u786e\u8981\u6c42\u6a21\u578b\u8fd4\u56de\u7ed3\u679c\u7684\u683c\u5f0f\u5316\u8f93\u51fa\u3002\u4f8b\u5982\uff0cJSON \u683c\u5f0f\u9002\u5408\u6570\u636e\u5904\u7406\uff0c\u8868\u683c\u9002\u5408\u603b\u7ed3\u5206\u6790\uff0c\u6e05\u5355\u9002\u5408\u4efb\u52a1\u5206\u89e3\u3002
- \u4e3a\u4ec0\u4e48\u9700\u8981\u7ed3\u6784\u5316\u8f93\u51fa\uff1f
- \u63d0\u9ad8\u53ef\u8bfb\u6027\uff1a\u8f93\u51fa\u6613\u4e8e\u76f4\u63a5\u67e5\u770b\u548c\u7406\u89e3\u3002
- \u4fbf\u4e8e\u540e\u7eed\u5904\u7406\uff1a\u5c24\u5176\u5728\u6570\u636e\u5904\u7406\u6216\u7f16\u7a0b\u4efb\u52a1\u4e2d\uff0c\u7ed3\u6784\u5316\u6570\u636e\u53ef\u76f4\u63a5\u7528\u4e8e\u5176\u4ed6\u5de5\u5177\u6216\u4ee3\u7801\u3002
- \u793a\u4f8b
-
\u4efb\u52a1\uff1a\u63d0\u53d6\u6587\u672c\u4e2d\u7684\u5173\u952e\u4fe1\u606f\u5e76\u8fd4\u56de JSON \u683c\u5f0f\u3002
Prompt\uff1a
Text Only\u8bf7\u4ece\u4ee5\u4e0b\u6587\u672c\u4e2d\u63d0\u53d6\u5173\u952e\u4fe1\u606f\uff0c\u5305\u62ec\u4eba\u540d\u3001\u5730\u540d\u548c\u4e8b\u4ef6\uff0c\u4ee5 JSON \u683c\u5f0f\u8fd4\u56de\uff1a\n\u6587\u672c\uff1a{\u6587\u672c\u5185\u5bb9}\n\u8f93\u51fa\u683c\u5f0f\uff1a\n{\n \"\u4eba\u540d\": [\"\u4eba\u540d1\", \"\u4eba\u540d2\"],\n \"\u5730\u540d\": [\"\u5730\u540d1\", \"\u5730\u540d2\"],\n \"\u4e8b\u4ef6\": [\"\u4e8b\u4ef61\", \"\u4e8b\u4ef62\"]\n}\n
-
\u4efb\u52a1\uff1a\u603b\u7ed3\u4f1a\u8bae\u8bb0\u5f55\u5e76\u751f\u6210\u8868\u683c\u3002
Prompt\uff1a
Text Only\u8bf7\u603b\u7ed3\u4ee5\u4e0b\u4f1a\u8bae\u8bb0\u5f55\uff0c\u5e76\u5c06\u53d1\u8a00\u4eba\u53ca\u5176\u89c2\u70b9\u4ee5 Markdown \u8868\u683c\u7684\u5f62\u5f0f\u8f93\u51fa\u3002\n
\u6a21\u578b\u751f\u6210\u793a\u4f8b\uff1a
Markdown| \u53d1\u8a00\u4eba | \u89c2\u70b9 |\n|----------|-----------------------|\n| \u5f20\u4e09 | \u5f3a\u8c03\u5e02\u573a\u6269\u5f20\u7684\u91cd\u8981\u6027 |\n| \u674e\u56db | \u63d0\u8bae\u63d0\u9ad8\u7814\u53d1\u9884\u7b97 |\n
"},{"location":"Tools/AI/prompt_writing/#1242","title":"1.2.4.2 \u4f7f\u7528\u5206\u9694\u7b26\uff08\u5982\u4e09\u5f15\u53f7\uff09\u660e\u786e\u4efb\u52a1\u7ed3\u6784","text":"\u5206\u9694\u7b26\u53ef\u4ee5\u5e2e\u52a9\u533a\u5206\u4efb\u52a1\u63cf\u8ff0\u548c\u8f93\u5165\u5185\u5bb9\uff0c\u51cf\u8f7b\u6a21\u578b\u7684\u7406\u89e3\u8d1f\u62c5\u3002
- \u5178\u578b\u7528\u6cd5
-
\u660e\u786e\u8f93\u5165\u4e0e\u8f93\u51fa\u90e8\u5206\uff1a
Text Only\u4f7f\u7528\u4e09\u5f15\u53f7\u5206\u9694\u7684\u6587\u672c\u5b8c\u6210\u4efb\u52a1\uff1a\n\"\"\"\n{\u8f93\u5165\u5185\u5bb9}\n\"\"\"\n\u8bf7\u4e3a\u4e0a\u8ff0\u5185\u5bb9\u64b0\u5199\u6458\u8981\uff0c\u6458\u8981\u9700\u5305\u542b\u4e3b\u8981\u89c2\u70b9\uff0c\u5e76\u4ee5 Markdown \u5217\u8868\u5f62\u5f0f\u8f93\u51fa\u3002\n
- \u51cf\u5c11\u566a\u58f0\u5e72\u6270
-
\u5c06\u591a\u6bb5\u8f93\u5165\u5206\u5757\uff1a
Text Only\u7b2c1\u90e8\u5206\uff1a\n\"\"\"\n{\u7b2c\u4e00\u6bb5\u5185\u5bb9}\n\"\"\"\n\u7b2c2\u90e8\u5206\uff1a\n\"\"\"\n{\u7b2c\u4e8c\u6bb5\u5185\u5bb9}\n\"\"\"\n\u8bf7\u5206\u522b\u603b\u7ed3\u4ee5\u4e0a\u4e24\u90e8\u5206\u5185\u5bb9\uff0c\u5e76\u4ee5 Markdown \u5217\u8868\u5f62\u5f0f\u8f93\u51fa\u3002\n
"},{"location":"Tools/AI/prompt_writing/#1243","title":"1.2.4.3 \u793a\u4f8b\uff1a\u4ece\u65e0\u683c\u5f0f\u5230\u6807\u51c6\u5316\u8f93\u51fa\u7684\u8f6c\u53d8","text":"\u4efb\u52a1\uff1a\u4e3a\u4e00\u6bb5\u4ea7\u54c1\u8bc4\u8bba\u751f\u6210\u7ed3\u6784\u5316\u6458\u8981\u3002
\u65e0\u683c\u5f0f\u7684\u6307\u4ee4\uff1a
Text Only\u8bf7\u603b\u7ed3\u4ee5\u4e0b\u4ea7\u54c1\u8bc4\u8bba\uff1a\n{\u8bc4\u8bba\u5185\u5bb9}\n
\u6807\u51c6\u5316\u7684\u6307\u4ee4\uff1a
Text Only\u8bf7\u603b\u7ed3\u4ee5\u4e0b\u4ea7\u54c1\u8bc4\u8bba\uff0c\u5e76\u4ee5\u8868\u683c\u5f62\u5f0f\u8f93\u51fa\u3002\u8868\u683c\u5e94\u5305\u542b\u4ee5\u4e0b\u5b57\u6bb5\uff1a\u4f18\u70b9\u3001\u7f3a\u70b9\u3001\u5efa\u8bae\u3002\n\u8bc4\u8bba\uff1a\n\"\"\"\n{\u8bc4\u8bba\u5185\u5bb9}\n\"\"\"\n
\u8f93\u51fa\u793a\u4f8b\uff1a
Markdown| \u4f18\u70b9 | \u7f3a\u70b9 | \u5efa\u8bae |\n|-----------------|---------------|--------------------|\n| \u4ef7\u683c\u4fbf\u5b9c | \u505a\u5de5\u4e00\u822c | \u6539\u5584\u4ea7\u54c1\u5916\u89c2\u8bbe\u8ba1 |\n| \u529f\u80fd\u9f50\u5168 | \u64cd\u4f5c\u590d\u6742 | \u7b80\u5316\u7528\u6237\u64cd\u4f5c\u6d41\u7a0b |\n
"},{"location":"Tools/AI/prompt_writing/#13","title":"1.3 \u4f18\u5316\u4e0e\u8fed\u4ee3","text":""},{"location":"Tools/AI/prompt_writing/#131-ai-prompt","title":"1.3.1 \u5982\u4f55\u901a\u8fc7 AI \u5e2e\u52a9\u6539\u5199\u6216\u4f18\u5316 Prompt\uff1f","text":""},{"location":"Tools/AI/prompt_writing/#1311-ai-prompt","title":"1.3.1.1 \u4e3a\u4ec0\u4e48\u8ba9 AI \u4f18\u5316 Prompt\uff1f","text":"AI \u64c5\u957f\u4ece\u5c11\u91cf\u8f93\u5165\u4e2d\u63d0\u53d6\u89c4\u5f8b\uff0c\u53ef\u4ee5\u5feb\u901f\u8c03\u6574\u8bed\u8a00\u98ce\u683c\u548c\u7ec6\u8282\uff0c\u5e2e\u52a9\u7528\u6237\u4f18\u5316 Prompt\uff0c\u5c24\u5176\u5728\u4efb\u52a1\u9700\u6c42\u6a21\u7cca\u6216\u7f3a\u4e4f\u6e05\u6670\u6307\u4ee4\u7684\u60c5\u51b5\u4e0b\u3002
"},{"location":"Tools/AI/prompt_writing/#1312","title":"1.3.1.2 \u5177\u4f53\u65b9\u6cd5","text":" - \u76f4\u63a5\u8bf7\u6c42 AI \u6539\u8fdb
- \u63d0\u4f9b\u521d\u59cb Prompt\uff0c\u8ba9 AI \u63d0\u51fa\u4f18\u5316\u5efa\u8bae\u3002
-
\u793a\u4f8b\uff1a \u539f\u59cb Prompt\uff1a
Text Only\u8bf7\u7528\u7b80\u5355\u7684\u8bed\u8a00\u89e3\u91ca\u4ee5\u4e0b\u6570\u5b66\u9898\u7684\u89e3\u6cd5\u3002\n
\u8bf7\u6c42\u4f18\u5316\uff1a
Text Only\u8bf7\u5e2e\u52a9\u4f18\u5316\u8fd9\u6bb5 Prompt\uff0c\u8ba9\u5b83\u66f4\u9002\u5408\u5c0f\u5b66\u516d\u5e74\u7ea7\u5b66\u751f\u3002\n
\u4f18\u5316\u7ed3\u679c\uff1a
Text Only\u4f60\u662f\u4e00\u4f4d\u5c0f\u5b66\u6570\u5b66\u8001\u5e08\uff0c\u8bf7\u7528\u901a\u4fd7\u6613\u61c2\u7684\u8bed\u8a00\uff0c\u7ed3\u5408\u751f\u6d3b\u4e2d\u7684\u4f8b\u5b50\uff0c\u89e3\u91ca\u4ee5\u4e0b\u6570\u5b66\u9898\u7684\u89e3\u6cd5\u3002\n
- \u901a\u8fc7\u51e0\u8f6e\u4ea4\u4e92\u5fae\u8c03 Prompt
-
\u793a\u4f8b\uff1a \u7b2c1\u8f6e\u4f18\u5316\uff1a
Text Only\u8fd9\u4e2a Prompt \u6bd4\u8f83\u9002\u5408\u521d\u5b66\u8005\uff0c\u4f46\u53ef\u4ee5\u589e\u52a0\u4e00\u4e9b\u751f\u6d3b\u5316\u7684\u4f8b\u5b50\u6765\u63d0\u5347\u5438\u5f15\u529b\u3002\n
-
\u7b2c2\u8f6e\u4f18\u5316\uff1a
Text Only\u7ed3\u5408\u751f\u6d3b\u4e2d\u7684\u5b9e\u4f8b\u8865\u5145\uff0c\u5982\u201c\u901a\u8fc7\u4e70\u82f9\u679c\u6765\u8ba1\u7b97\u603b\u4ef7\u201d\uff0c\u4f7f\u5f97\u8bb2\u89e3\u66f4\u52a0\u751f\u52a8\u3002\n
- \u81ea\u52a8\u5316\u4f18\u5316\u5de5\u5177
- \u4f7f\u7528\u5982 OpenAI \u63d0\u4f9b\u7684 API\uff0c\u901a\u8fc7\u52a8\u6001\u8c03\u6574\u6d4b\u8bd5\u591a\u4e2a\u7248\u672c\u7684 Prompt\uff0c\u627e\u5230\u6700\u4f73\u89e3\u51b3\u65b9\u6848\u3002
"},{"location":"Tools/AI/prompt_writing/#132","title":"1.3.2 \u7ed3\u5408\u5916\u90e8\u5de5\u5177\u63d0\u5347\u6548\u7387","text":""},{"location":"Tools/AI/prompt_writing/#1321-api","title":"1.3.2.1 \u8c03\u7528 API \u6216\u4ee3\u7801\u6267\u884c\u5b9e\u73b0\u590d\u6742\u529f\u80fd","text":"\u5927\u6a21\u578b\u5bf9\u8ba1\u7b97\u548c\u5b9e\u65f6\u6570\u636e\u7684\u652f\u6301\u6709\u9650\uff0c\u56e0\u6b64\u7ed3\u5408\u5916\u90e8\u5de5\u5177\uff0c\u5982 API \u8c03\u7528\u6216\u4ee3\u7801\u6267\u884c\uff0c\u53ef\u4ee5\u63d0\u5347\u4efb\u52a1\u7684\u51c6\u786e\u6027\u3002
- \u8bf7\u6c42\u751f\u6210\u4ee3\u7801\u6765\u6267\u884c\u8ba1\u7b97
-
\u793a\u4f8b\uff1a \u4efb\u52a1\uff1a\u6c42\u89e3\u591a\u9879\u5f0f\u7684\u6240\u6709\u5b9e\u6839\u3002 Prompt\uff1a
Text Only\u8bf7\u7528 Python \u7f16\u5199\u4ee3\u7801\u6765\u8ba1\u7b97\u4ee5\u4e0b\u591a\u9879\u5f0f\u7684\u6240\u6709\u5b9e\u6839\uff1a\n\u591a\u9879\u5f0f\uff1a3x^3 - 5x^2 + 2x - 7\n\u8f93\u51fa Python \u4ee3\u7801\u5e76\u89e3\u91ca\u5176\u4f5c\u7528\u3002\n
\u8f93\u51fa\u7ed3\u679c\uff1a
Pythonimport numpy as np\ncoefficients = [3, -5, 2, -7]\nroots = np.roots(coefficients)\nprint(\"\u5b9e\u6839\u4e3a\uff1a\", [r for r in roots if np.isreal(r)])\n
\u89e3\u91ca\uff1a\u6b64\u4ee3\u7801\u4f7f\u7528 NumPy \u7684 roots \u51fd\u6570\u8ba1\u7b97\u591a\u9879\u5f0f\u7684\u6240\u6709\u6839\uff0c\u5e76\u8fc7\u6ee4\u51fa\u5b9e\u6839\u3002
- \u7ed3\u5408\u5916\u90e8\u6570\u636e\u6216\u5de5\u5177
- \u793a\u4f8b\uff1a\u4f7f\u7528\u5411\u91cf\u6570\u636e\u5e93\u67e5\u8be2\u77e5\u8bc6\u5e93\u4e2d\u7684\u4fe1\u606f\uff0c\u4f5c\u4e3a\u751f\u6210\u7b54\u6848\u7684\u8865\u5145\u3002
"},{"location":"Tools/AI/prompt_writing/#1322-markdown-latex","title":"1.3.2.2 \u8f93\u51fa\u6210 Markdown \u6216 LaTeX \u6587\u6863\u7684\u573a\u666f\u4e0e\u6280\u5de7","text":" - Markdown \u683c\u5f0f\u5316
-
\u793a\u4f8b\uff1a \u4efb\u52a1\uff1a\u751f\u6210\u4f1a\u8bae\u7eaa\u8981\u3002 Prompt\uff1a
Text Only\u8bf7\u5c06\u4ee5\u4e0b\u4f1a\u8bae\u8bb0\u5f55\u603b\u7ed3\u4e3a Markdown \u683c\u5f0f\uff0c\u5305\u62ec\u53d1\u8a00\u4eba\u3001\u4e3b\u8981\u89c2\u70b9\u548c\u540e\u7eed\u4efb\u52a1\u3002\n
\u8f93\u51fa\uff1a
Markdown### \u4f1a\u8bae\u7eaa\u8981\n\n#### \u53d1\u8a00\u4eba\u53ca\u89c2\u70b9\n- **\u5f20\u4e09**\uff1a\u5efa\u8bae\u589e\u52a0\u5e02\u573a\u8425\u9500\u9884\u7b97\u3002\n- **\u674e\u56db**\uff1a\u5f3a\u8c03\u4f18\u5316\u4ea7\u54c1\u8d28\u91cf\u7684\u91cd\u8981\u6027\u3002\n\n#### \u540e\u7eed\u4efb\u52a1\n- \u5236\u5b9a\u65b0\u4e00\u5b63\u5ea6\u7684\u8425\u9500\u7b56\u7565\uff08\u8d1f\u8d23\u4eba\uff1a\u5f20\u4e09\uff09\u3002\n- \u8c03\u67e5\u7528\u6237\u5bf9\u5f53\u524d\u4ea7\u54c1\u7684\u6ee1\u610f\u5ea6\uff08\u8d1f\u8d23\u4eba\uff1a\u674e\u56db\uff09\u3002\n
- LaTeX \u683c\u5f0f\u5316
-
\u793a\u4f8b\uff1a \u4efb\u52a1\uff1a\u751f\u6210\u6570\u5b66\u516c\u5f0f\u3002 Prompt\uff1a
Text Only\u8bf7\u7528 LaTeX \u683c\u5f0f\u4e66\u5199\u4ee5\u4e0b\u516c\u5f0f\u5e76\u89e3\u91ca\uff1a$(a+b)^2 = a^2 + 2ab + b^2$\u3002\n
\u8f93\u51fa\uff1a
TeX\\[\n(a+b)^2 = a^2 + 2ab + b^2\n\\]\n\u8fd9\u662f\u4e00\u4e2a\u57fa\u672c\u7684\u5e73\u65b9\u5c55\u5f00\u516c\u5f0f\uff0c\u5e38\u7528\u4e8e\u591a\u9879\u5f0f\u7684\u8ba1\u7b97\u3002\n
"},{"location":"Tools/AI/prompt_writing/#133-prompt","title":"1.3.3 \u589e\u5f3a Prompt \u7684\u7075\u6d3b\u6027","text":""},{"location":"Tools/AI/prompt_writing/#1331","title":"1.3.3.1 \u4e3a\u5f00\u653e\u5f0f\u4efb\u52a1\u8bbe\u7f6e\u515c\u5e95\u7b56\u7565","text":"\u5f00\u653e\u5f0f\u4efb\u52a1\uff08\u5982\u5199\u6545\u4e8b\u6216\u63a8\u8350\u65b9\u6848\uff09\u5bb9\u6613\u5bfc\u81f4\u6a21\u578b\u201c\u8dd1\u9898\u201d\uff0c\u901a\u8fc7\u515c\u5e95\u7b56\u7565\u53ef\u63d0\u9ad8\u7a33\u5b9a\u6027\u3002
- \u660e\u786e\u4e0d\u7b26\u5408\u6761\u4ef6\u65f6\u7684\u56de\u590d
-
\u793a\u4f8b\uff1a Prompt\uff1a
Text Only\u73b0\u5728\u4f60\u662f\u4e00\u4e2a\u5411\u5ba2\u6237\u63a8\u8350\u7535\u5f71\u7684\u52a9\u624b\u3002\u5982\u679c\u5ba2\u6237\u7684\u4fe1\u606f\u4e0d\u8db3\u4ee5\u7ed9\u51fa\u63a8\u8350\uff0c\u8bf7\u56de\u7b54\uff1a\u201c\u62b1\u6b49\uff0c\u6211\u65e0\u6cd5\u6839\u636e\u60a8\u7684\u63cf\u8ff0\u63a8\u8350\u7535\u5f71\u3002\u201d\u3002\n
\u8f93\u5165\uff1a
Text Only\u5ba2\u6237\uff1a\u5e2e\u6211\u63a8\u8350\u4e00\u90e8\u7535\u5f71\u3002\n
\u8f93\u51fa\uff1a
Text Only\u62b1\u6b49\uff0c\u6211\u65e0\u6cd5\u6839\u636e\u60a8\u7684\u63cf\u8ff0\u63a8\u8350\u7535\u5f71\u3002\n
- \u5b9a\u4e49\u5bb9\u9519\u673a\u5236
-
\u793a\u4f8b\uff1a\u5f53\u751f\u6210\u7ed3\u679c\u4e0d\u7b26\u5408\u683c\u5f0f\u65f6\uff0c\u8981\u6c42\u91cd\u65b0\u751f\u6210\u3002
Text Only\u5982\u679c\u4ee5\u4e0b\u5185\u5bb9\u7684\u8f93\u51fa\u683c\u5f0f\u9519\u8bef\uff0c\u8bf7\u91cd\u65b0\u751f\u6210\u5e76\u786e\u4fdd\u7b26\u5408\u8981\u6c42\uff1a\n\u8f93\u51fa\u683c\u5f0f\uff1a\n- \u4efb\u52a1\u63cf\u8ff0\n- \u4efb\u52a1\u8981\u70b9\n- \u4efb\u52a1\u5efa\u8bae\n
"},{"location":"Tools/AI/prompt_writing/#1332","title":"1.3.3.2 \u6dfb\u52a0\u5f3a\u8c03\u8bcd\u4e0e\u7b26\u53f7\u63d0\u5347\u6307\u4ee4\u6743\u91cd","text":"\u4f7f\u7528\u7279\u6b8a\u7b26\u53f7\uff08\u5982\u52a0\u7c97\uff09\u6216\u5f3a\u8c03\u8bcd\uff08\u5982\u201c\u52a1\u5fc5\u201d\u3001\u201c\u4e25\u683c\u201d\uff09\u6807\u6ce8\u5173\u952e\u5185\u5bb9\u3002
- \u793a\u4f8b\uff1a
\u4efb\u52a1\uff1a\u603b\u7ed3\u6587\u7ae0\u5e76\u5f3a\u8c03\u5173\u952e\u6982\u5ff5\u3002 Prompt\uff1a
Text Only ```text\n \u8bf7\u603b\u7ed3\u4ee5\u4e0b\u6587\u7ae0\uff0c\u5e76**\u52a0\u7c97**\u6bcf\u4e2a\u8981\u70b9\u4e2d\u7684\u5173\u952e\u6982\u5ff5\uff0c\u4ee5 Markdown \u5217\u8868\u5f62\u5f0f\u8f93\u51fa\u3002\n ```\n\n **\u8f93\u51fa**\uff1a\n\n ```markdown\n - **\u8981\u70b9\u4e00**\uff1a\u673a\u5668\u5b66\u4e60\u662f\u901a\u8fc7\u6570\u636e\u8bad\u7ec3\u6a21\u578b\u7684**\u65b9\u6cd5**\u3002\n - **\u8981\u70b9\u4e8c**\uff1a\u6df1\u5ea6\u5b66\u4e60\u662f\u673a\u5668\u5b66\u4e60\u7684**\u5206\u652f**\uff0c\u4ee5\u591a\u5c42\u795e\u7ecf\u7f51\u7edc\u4e3a\u6838\u5fc3\u3002\n ```\n
"},{"location":"Tools/AI/prompt_writing/#14","title":"1.4 \u5b9e\u7528\u6848\u4f8b","text":""},{"location":"Tools/AI/prompt_writing/#141","title":"1.4.1 \u5e38\u89c1\u573a\u666f\u4e0e\u89e3\u51b3\u65b9\u6848","text":""},{"location":"Tools/AI/prompt_writing/#1411","title":"1.4.1.1 \u751f\u6210\u957f\u7bc7\u7684\u6587\u7ae0","text":" - \u9010\u6b65\u751f\u6210\u957f\u6587\u5185\u5bb9
- \u65b9\u6cd5\uff1a\u5148\u751f\u6210\u76ee\u5f55\uff0c\u518d\u9010\u90e8\u5206\u6269\u5c55\u5185\u5bb9\u3002
-
\u793a\u4f8b\uff1a Prompt\uff1a
Text Only\u8bf7\u4e3a\u4ee5\u4e0b\u4e3b\u9898\u751f\u6210\u4e00\u4efd\u8be6\u7ec6\u7684\u6587\u7ae0\u76ee\u5f55\uff0c\u7136\u540e\u57fa\u4e8e\u76ee\u5f55\u9010\u6bb5\u6269\u5c55\u5185\u5bb9\uff1a\n\u4e3b\u9898\uff1a\u5982\u4f55\u4f18\u5316\u4e2a\u4eba\u65f6\u95f4\u7ba1\u7406\n
\u8f93\u51fa\u76ee\u5f55\u793a\u4f8b\uff1a
Markdown- \u7b2c\u4e00\u90e8\u5206\uff1a\u65f6\u95f4\u7ba1\u7406\u7684\u91cd\u8981\u6027\n- \u7b2c\u4e8c\u90e8\u5206\uff1a\u5e38\u89c1\u65f6\u95f4\u7ba1\u7406\u8bef\u533a\n- \u7b2c\u4e09\u90e8\u5206\uff1a\u9ad8\u6548\u65f6\u95f4\u7ba1\u7406\u7684\u65b9\u6cd5\n - 1. \u8bbe\u5b9a\u76ee\u6807\u4e0e\u4f18\u5148\u7ea7\n - 2. \u4f7f\u7528\u5de5\u5177\u4e0e\u6280\u672f\n - 3. \u57f9\u517b\u65f6\u95f4\u7ba1\u7406\u4e60\u60ef\n- \u7b2c\u56db\u90e8\u5206\uff1a\u6848\u4f8b\u5206\u6790\u4e0e\u5e94\u7528\n
\u6269\u5c55\u5185\u5bb9 Prompt\uff1a
Text Only\u8bf7\u8be6\u7ec6\u64b0\u5199\u7b2c\u4e00\u90e8\u5206\u201c\u65f6\u95f4\u7ba1\u7406\u7684\u91cd\u8981\u6027\u201d\u7684\u5185\u5bb9\uff0c\u63a7\u5236\u5728 300 \u5b57\u5de6\u53f3\u3002\n
- \u5728\u751f\u6210\u65f6\u6dfb\u52a0\u5f15\u7528\u6587\u732e
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\u793a\u4f8b\uff1a Prompt\uff1a
Text Only\u8bf7\u4e3a\u4ee5\u4e0b\u4e3b\u9898\u751f\u6210\u4e00\u7bc7 500 \u5b57\u7684\u6587\u7ae0\uff0c\u5e76\u5728\u6587\u7ae0\u672b\u5c3e\u9644\u4e0a\u76f8\u5173\u53c2\u8003\u6587\u732e\uff1a\n\u4e3b\u9898\uff1a\u4eba\u5de5\u667a\u80fd\u5982\u4f55\u6539\u53d8\u6559\u80b2\u884c\u4e1a\n
\u8f93\u51fa\uff1a
Markdown### \u4eba\u5de5\u667a\u80fd\u5982\u4f55\u6539\u53d8\u6559\u80b2\u884c\u4e1a\n\uff08\u6587\u7ae0\u5185\u5bb9\uff09\n#### \u53c2\u8003\u6587\u732e\n- \u674e\u56db\uff0c\u300a\u4eba\u5de5\u667a\u80fd\u4e0e\u6559\u80b2\u300b\uff0c2023\u5e74\u51fa\u7248\u3002\n- OpenAI\uff0c\u300aAI\u5728\u6559\u80b2\u9886\u57df\u7684\u5e94\u7528\u300b\uff0c2021\u5e74\u3002\n
"},{"location":"Tools/AI/prompt_writing/#1412","title":"1.4.1.2 \u89e3\u91ca\u4ee3\u7801\u5e76\u751f\u6210\u8f85\u52a9\u56fe\u8868","text":" - \u751f\u6210\u4ee3\u7801\u89e3\u91ca
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\u793a\u4f8b\uff1a Prompt\uff1a
Text Only\u4ee5\u4e0b\u662f Python \u4ee3\u7801\u7247\u6bb5\uff0c\u8bf7\u89e3\u91ca\u5176\u4f5c\u7528\uff1a\n```python\nnested_dict = lambda: defaultdict(nested_dict)\n
Text Only**\u8f93\u51fa**\uff1a\n```text\n\u6b64\u4ee3\u7801\u5b9a\u4e49\u4e86\u4e00\u4e2a\u5d4c\u5957\u5b57\u5178\u7684\u9012\u5f52\u7ed3\u6784\uff0c\u53ef\u4ee5\u52a8\u6001\u521b\u5efa\u4efb\u610f\u6df1\u5ea6\u7684\u5b57\u5178\u5c42\u7ea7\u3002\n\u793a\u4f8b\u7528\u6cd5\uff1a\ndata = nested_dict()\ndata['level1']['level2'] = \"value\"\n
- \u751f\u6210\u8f85\u52a9\u56fe\u8868
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\u793a\u4f8b\uff1a Prompt\uff1a
Text Only\u8bf7\u6839\u636e\u4ee5\u4e0b\u63cf\u8ff0\u751f\u6210\u4e00\u6bb5 mermaid \u683c\u5f0f\u7684\u4ee3\u7801\uff0c\u7528\u4e8e\u5c55\u793a\u51fd\u6570\u8c03\u7528\u5173\u7cfb\uff1a\n\u63cf\u8ff0\uff1a\u51fd\u6570 A \u8c03\u7528\u51fd\u6570 B\uff0c\u51fd\u6570 B \u8c03\u7528\u51fd\u6570 C \u548c D\uff0c\u51fd\u6570 D \u8fd4\u56de\u7ed3\u679c\u3002\n
\u8f93\u51fa\uff1a
Text Onlygraph TD\nA --> B\nB --> C\nB --> D\nD --> Result\n
"},{"location":"Tools/AI/prompt_writing/#142-prompt","title":"1.4.2 Prompt \u7684\u9650\u5236\u4e0e\u5e94\u5bf9\u7b56\u7565","text":""},{"location":"Tools/AI/prompt_writing/#1421","title":"1.4.2.1 \u8bbe\u8ba1\u62d2\u7b54\u7b56\u7565\u63d0\u5347\u53ef\u9760\u6027","text":" - \u907f\u514d\u5e7b\u89c9\u73b0\u8c61
-
\u63d0\u793a\u6a21\u578b\u4e0d\u8981\u56de\u7b54\u5176\u65e0\u6cd5\u786e\u5b9a\u7684\u5185\u5bb9\u3002 \u793a\u4f8b\uff1a
Text Only\u5982\u679c\u4f60\u4e0d\u77e5\u9053\u4ee5\u4e0b\u95ee\u9898\u7684\u7b54\u6848\uff0c\u8bf7\u56de\u590d\u201c\u4fe1\u606f\u4e0d\u8db3\u201d\u3002\n\u95ee\u9898\uff1a\u8c01\u662f\u7b2c\u4e00\u4e2a\u767b\u4e0a\u6708\u7403\u7684\u673a\u5668\u4eba\uff1f\n
\u8f93\u51fa\uff1a
Text Only\u4fe1\u606f\u4e0d\u8db3\u3002\n
- \u591a\u6b21\u751f\u6210\u4ee5\u786e\u8ba4\u7b54\u6848\u4e00\u81f4\u6027
"},{"location":"Tools/AI/prompt_writing/#1422-ai","title":"1.4.2.2 \u591a\u4e2a AI \u534f\u4f5c","text":" - \u6846\u67b6\u4e0e\u5185\u5bb9\u534f\u4f5c
-
\u901a\u8fc7\u4e0d\u540c\u6a21\u578b\u534f\u4f5c\u4f18\u5316\u5185\u5bb9\u751f\u6210\u3002 \u793a\u4f8b\u6d41\u7a0b\uff1a
- Prompt 1\uff1a\u8ba9 ChatGPT \u63d0\u4f9b\u6587\u7ae0\u6846\u67b6\u3002
Text Only\u8bf7\u4e3a\u4ee5\u4e0b\u4e3b\u9898\u751f\u6210\u8be6\u7ec6\u7684\u6587\u7ae0\u6846\u67b6\uff1a\n\u4e3b\u9898\uff1a\u5982\u4f55\u63d0\u5347\u56e2\u961f\u5408\u4f5c\u6548\u7387\n
- Prompt 2\uff1a\u5c06\u6846\u67b6\u5185\u5bb9\u4ea4\u7ed9\u53e6\u4e00\u4e2a\u6a21\u578b\u4f18\u5316\u8868\u8fbe\u3002
Text Only\u8bf7\u6839\u636e\u4ee5\u4e0b\u6846\u67b6\u64b0\u5199\u8be6\u7ec6\u5185\u5bb9\uff0c\u5e76\u8c03\u6574\u4e3a\u66f4\u4e13\u4e1a\u7684\u8bed\u8a00\u3002\n
- Prompt 3\uff1a\u4f7f\u7528\u53e6\u4e00\u4e2a\u5de5\u5177\uff08\u5982 Grammarly \u6216\u5176\u4ed6 AI\uff09\u6821\u5bf9\u548c\u4f18\u5316\u8bed\u8a00\u98ce\u683c\u3002
- \u4ee3\u7801\u4f18\u5316\u534f\u4f5c
Text Only \u8bf7\u4f18\u5316\u4ee5\u4e0b\u4ee3\u7801\uff0c\u4f7f\u5176\u8fd0\u884c\u6548\u7387\u66f4\u9ad8\uff0c\u5e76\u63d0\u4f9b\u6ce8\u91ca\u8bf4\u660e\uff1a\n ```python\n def factorial(n):\n if n == 0:\n return 1\n else:\n return n * factorial(n-1)\n ```\n
"},{"location":"Tools/AI/prompt_writing/#15","title":"1.5 \u6211\u66fe\u7ecf\u7528\u6cd5","text":" - \u8f93\u51fa\u6587\u6863\uff1a\u901a\u8fc7 Markdown \u6216 LaTeX \u751f\u6210\u683c\u5f0f\u5316\u6587\u6863\u3002
- \u751f\u6210 README\uff1a\u8ba9 AI \u9605\u8bfb\u4ee3\u7801\u5e76\u64b0\u5199\u8bf4\u660e\u3002
- \u5f15\u7528\u6587\u732e\u5199\u8bba\u6587\uff1a\u5229\u7528 AI \u4e0a\u7f51\u67e5\u8be2\u8d44\u6599\uff0c\u63d0\u9ad8\u5b66\u672f\u5185\u5bb9\u8d28\u91cf\u3002
"},{"location":"Tools/AI/prompt_writing/#2-prompt","title":"2 \u5e38\u7528\u7684 Prompt","text":"\u8bf7\u53c2\u8003 \u5e38\u7528 prompt \u8bb0\u5f55 - wnc \u7684\u5496\u5561\u9986
"},{"location":"Tools/Blog/Mkdocs_Material/","title":"mkdocs material \u8d85\u5168\u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#mkdocs-material","title":"mkdocs material \u8d85\u5168\u914d\u7f6e","text":" \u7ea6 5956 \u4e2a\u5b57 9393 \u884c\u4ee3\u7801 7 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 147 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
\u4ecd\u7136\u5728\u4fee\u6539 \u5982\u679c\u9700\u8981\u4efb\u4f55\u7684\u6587\u4ef6\uff0c\u53ef\u4ee5\u76f4\u63a5\u8bbf\u95ee\u672c\u535a\u5ba2\u7684 GitHub \u9875\u9762
"},{"location":"Tools/Blog/Mkdocs_Material/#1","title":"1 \u5165\u95e8\u57fa\u7840","text":""},{"location":"Tools/Blog/Mkdocs_Material/#11-mkdocs","title":"1.1 \u4ec0\u4e48\u662f MkDocs\uff1f","text":"MkDocs \u662f\u4e00\u4e2a\u5feb\u901f\u3001\u7b80\u5355\u3001\u534e\u4e3d\u7684\u9759\u6001\u7ad9\u70b9\u751f\u6210\u5668\uff0c\u4e13\u95e8\u7528\u4e8e\u6784\u5efa\u9879\u76ee\u6587\u6863\u3002\u6587\u6863\u6e90\u6587\u4ef6\u4f7f\u7528 Markdown \u7f16\u5199\uff0c\u914d\u7f6e\u6587\u4ef6\u4f7f\u7528 YAML \u683c\u5f0f\u3002
"},{"location":"Tools/Blog/Mkdocs_Material/#111-mkdocs","title":"1.1.1 MkDocs \u7684\u4f18\u52bf","text":" -
\u7b80\u5355\u6613\u7528 - \u4f7f\u7528 Markdown \u7f16\u5199\u6587\u6863 - \u914d\u7f6e\u6587\u4ef6\u7b80\u5355\u76f4\u89c2 - \u4e00\u952e\u5f0f\u6784\u5efa\u548c\u90e8\u7f72
-
\u529f\u80fd\u5f3a\u5927 - \u5185\u7f6e\u5f00\u53d1\u670d\u52a1\u5668\uff0c\u652f\u6301\u5b9e\u65f6\u9884\u89c8 - \u591a\u79cd\u4e3b\u9898\u53ef\u9009 - \u652f\u6301\u81ea\u5b9a\u4e49\u4e3b\u9898 - \u81ea\u52a8\u751f\u6210\u5bfc\u822a - \u5168\u6587\u641c\u7d22\u529f\u80fd
-
\u90e8\u7f72\u65b9\u4fbf - \u751f\u6210\u7eaf\u9759\u6001\u9875\u9762 - \u4e00\u884c\u547d\u4ee4\u90e8\u7f72\u5230 GitHub Pages - \u652f\u6301\u81ea\u5b9a\u4e49\u57df\u540d - \u517c\u5bb9\u6240\u6709\u9759\u6001\u7f51\u7ad9\u6258\u7ba1\u5e73\u53f0
"},{"location":"Tools/Blog/Mkdocs_Material/#112-mkdocs-vs","title":"1.1.2 MkDocs vs \u5176\u4ed6\u6587\u6863\u5de5\u5177","text":"\u5de5\u5177 \u4f18\u52bf \u52a3\u52bf MkDocs - \u7b80\u5355\u6613\u7528- \u4e13\u6ce8\u6587\u6863- \u90e8\u7f72\u65b9\u4fbf- \u4e3b\u9898\u4e30\u5bcc - \u529f\u80fd\u76f8\u5bf9\u7b80\u5355- \u63d2\u4ef6\u751f\u6001\u8f83\u5c0f GitBook - \u754c\u9762\u4f18\u96c5- \u751f\u6001\u5b8c\u6574- \u591a\u4eba\u534f\u4f5c\u597d - \u6784\u5efa\u901f\u5ea6\u6162- \u5b9a\u5236\u6027\u5dee- \u514d\u8d39\u7248\u9650\u5236\u591a Docusaurus - React \u6280\u672f\u6808- \u529f\u80fd\u5f3a\u5927- \u6269\u5c55\u6027\u597d - \u5b66\u4e60\u66f2\u7ebf\u9661- \u914d\u7f6e\u590d\u6742- \u6784\u5efa\u8f83\u6162 VuePress - Vue \u6280\u672f\u6808- \u5b9a\u5236\u6027\u5f3a- \u63d2\u4ef6\u4e30\u5bcc - \u4e3b\u9898\u8f83\u5c11- \u914d\u7f6e\u7e41\u7410- \u5b66\u4e60\u6210\u672c\u9ad8"},{"location":"Tools/Blog/Mkdocs_Material/#113-mkdocs","title":"1.1.3 MkDocs \u5de5\u4f5c\u539f\u7406","text":"MkDocs \u7684\u5de5\u4f5c\u6d41\u7a0b\u5982\u4e0b\uff1a
-
\u6587\u6863\u7f16\u5199 - \u4f7f\u7528 Markdown \u683c\u5f0f\u7f16\u5199\u6587\u6863 - \u6587\u6863\u5b58\u653e\u5728 docs \u76ee\u5f55\u4e0b - \u652f\u6301\u591a\u7ea7\u76ee\u5f55\u7ed3\u6784
-
\u914d\u7f6e\u89e3\u6790 - \u8bfb\u53d6 mkdocs.yml \u914d\u7f6e\u6587\u4ef6 - \u89e3\u6790\u4e3b\u9898\u8bbe\u7f6e\u3001\u63d2\u4ef6\u914d\u7f6e\u7b49 - \u751f\u6210\u5bfc\u822a\u7ed3\u6784
-
\u6784\u5efa\u8fc7\u7a0b
Text OnlyMarkdown \u6587\u4ef6 -> \u89e3\u6790\u5668 -> HTML \u6587\u4ef6\n -> \u4e3b\u9898\u6e32\u67d3\n -> \u63d2\u4ef6\u5904\u7406\n -> \u9759\u6001\u8d44\u6e90\u5904\u7406\n
- \u8f93\u51fa\u90e8\u7f72 - \u751f\u6210\u7eaf\u9759\u6001 HTML \u6587\u4ef6 - \u4fdd\u7559\u539f\u59cb\u76ee\u5f55\u7ed3\u6784 - \u81ea\u52a8\u5904\u7406\u5185\u90e8\u94fe\u63a5 - \u590d\u5236\u9759\u6001\u8d44\u6e90
"},{"location":"Tools/Blog/Mkdocs_Material/#12-material","title":"1.2 \u4e3a\u4ec0\u4e48\u9009\u62e9 Material \u4e3b\u9898","text":"Material for MkDocs \u662f\u4e00\u4e2a\u57fa\u4e8e Google Material Design \u8bbe\u8ba1\u8bed\u8a00\u7684\u4e3b\u9898\uff0c\u5b83\u4e0d\u4ec5\u7f8e\u89c2\uff0c\u800c\u4e14\u529f\u80fd\u5f3a\u5927\u3002
"},{"location":"Tools/Blog/Mkdocs_Material/#121-material","title":"1.2.1 Material \u4e3b\u9898\u7279\u6027","text":" -
\u73b0\u4ee3\u5316\u8bbe\u8ba1 - \u9075\u5faa Material Design \u89c4\u8303 - \u54cd\u5e94\u5f0f\u5e03\u5c40 - \u652f\u6301\u6df1\u8272\u6a21\u5f0f - \u81ea\u52a8\u9002\u914d\u79fb\u52a8\u8bbe\u5907
-
\u5f3a\u5927\u529f\u80fd - \u667a\u80fd\u641c\u7d22 - \u4ee3\u7801\u9ad8\u4eae - \u6807\u7b7e\u9875\u652f\u6301 - \u81ea\u52a8\u76ee\u5f55\u751f\u6210 - \u591a\u8bed\u8a00\u652f\u6301 - \u7248\u672c\u63a7\u5236\u96c6\u6210
-
\u51fa\u8272\u7684\u7528\u6237\u4f53\u9a8c - \u5feb\u901f\u52a0\u8f7d - \u5e73\u6ed1\u52a8\u753b - \u5b9e\u65f6\u641c\u7d22 - \u4ee3\u7801\u590d\u5236\u6309\u94ae - \u8fd4\u56de\u9876\u90e8\u6309\u94ae
"},{"location":"Tools/Blog/Mkdocs_Material/#122","title":"1.2.2 \u4e0e\u5176\u4ed6\u4e3b\u9898\u5bf9\u6bd4","text":"\u7279\u6027 Material ReadTheDocs mkdocs \u5176\u4ed6\u4e3b\u9898 \u8bbe\u8ba1\u98ce\u683c \u73b0\u4ee3\u7b80\u7ea6 \u4f20\u7edf\u6587\u6863 \u7b80\u5355\u57fa\u7840 \u98ce\u683c\u591a\u6837 \u54cd\u5e94\u5f0f \u2705 \u2705 \u274c \u90e8\u5206\u652f\u6301 \u6df1\u8272\u6a21\u5f0f \u2705 \u274c \u274c \u90e8\u5206\u652f\u6301 \u641c\u7d22\u529f\u80fd \u2705 \u2705 \u274c \u90e8\u5206\u652f\u6301 \u5b9a\u5236\u6027 \u5f3a \u4e2d \u5f31 \u4e0d\u4e00\u81f4 \u63d2\u4ef6\u652f\u6301 \u4e30\u5bcc \u4e00\u822c \u57fa\u7840 \u4e0d\u4e00\u81f4"},{"location":"Tools/Blog/Mkdocs_Material/#123-material","title":"1.2.3 Material \u4e3b\u9898\u7684\u6280\u672f\u67b6\u6784","text":"Text OnlyMaterial Theme\n\u251c\u2500\u2500 \u6838\u5fc3\u7ec4\u4ef6\n\u2502 \u251c\u2500\u2500 \u5bfc\u822a\u680f\n\u2502 \u251c\u2500\u2500 \u4fa7\u8fb9\u680f\n\u2502 \u251c\u2500\u2500 \u641c\u7d22\u7ec4\u4ef6\n\u2502 \u2514\u2500\u2500 \u5185\u5bb9\u6e32\u67d3\u5668\n\u251c\u2500\u2500 \u6269\u5c55\u529f\u80fd\n\u2502 \u251c\u2500\u2500 \u4ee3\u7801\u9ad8\u4eae\n\u2502 \u251c\u2500\u2500 \u6807\u7b7e\u7cfb\u7edf\n\u2502 \u251c\u2500\u2500 \u76ee\u5f55\u751f\u6210\n\u2502 \u2514\u2500\u2500 \u4e3b\u9898\u5207\u6362\n\u2514\u2500\u2500 \u63d2\u4ef6\u7cfb\u7edf\n \u251c\u2500\u2500 \u5185\u7f6e\u63d2\u4ef6\n \u2514\u2500\u2500 \u7b2c\u4e09\u65b9\u63d2\u4ef6\u96c6\u6210\n
"},{"location":"Tools/Blog/Mkdocs_Material/#13","title":"1.3 \u73af\u5883\u8981\u6c42","text":""},{"location":"Tools/Blog/Mkdocs_Material/#131-python","title":"1.3.1 Python \u73af\u5883","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1311-python","title":"1.3.1.1 Python \u7248\u672c\u9009\u62e9","text":"MkDocs \u9700\u8981 Python 3.6 \u6216\u66f4\u9ad8\u7248\u672c\uff0c\u63a8\u8350\u4f7f\u7528 Python 3.8+\uff1a
Bash# \u68c0\u67e5 Python \u7248\u672c\npython --version\n\n# \u63a8\u8350\u7248\u672c\nPython 3.8.x\nPython 3.9.x\nPython 3.10.x\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1312-pip","title":"1.3.1.2 pip \u914d\u7f6e\u8bf4\u660e","text":"pip \u662f Python \u7684\u5305\u7ba1\u7406\u5de5\u5177\uff0c\u9700\u8981\u786e\u4fdd\u5176\u6b63\u786e\u5b89\u88c5\uff1a
Bash# \u68c0\u67e5 pip \u7248\u672c\npip --version\n\n# \u5347\u7ea7 pip\npython -m pip install --upgrade pip\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1313","title":"1.3.1.3 \u865a\u62df\u73af\u5883\u7ba1\u7406","text":"\u63a8\u8350\u4f7f\u7528\u865a\u62df\u73af\u5883\u6765\u7ba1\u7406\u9879\u76ee\u4f9d\u8d56\uff1a
Bash# \u521b\u5efa\u865a\u62df\u73af\u5883\npython -m venv venv\n\n# \u6fc0\u6d3b\u865a\u62df\u73af\u5883\n# Windows\nvenv\\Scripts\\activate\n# Linux/Mac\nsource venv/bin/activate\n\n# \u9000\u51fa\u865a\u62df\u73af\u5883\ndeactivate\n
"},{"location":"Tools/Blog/Mkdocs_Material/#132-pip","title":"1.3.2 pip \u5305\u7ba1\u7406","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1321-pip","title":"1.3.2.1 pip \u6e90\u914d\u7f6e","text":"\u4e3a\u52a0\u5feb\u4e0b\u8f7d\u901f\u5ea6\uff0c\u5efa\u8bae\u4f7f\u7528\u56fd\u5185\u955c\u50cf\u6e90\uff1a
Bash# \u4e34\u65f6\u4f7f\u7528\npip install -i https://pypi.tuna.tsinghua.edu.cn/simple mkdocs\n\n# \u6c38\u4e45\u914d\u7f6e\npip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1322","title":"1.3.2.2 \u4f9d\u8d56\u7ba1\u7406","text":"\u5b89\u88c5\u5fc5\u8981\u7684\u5305\uff1a
Bashpip install mkdocs-material\npip install mkdocs-glightbox\npip install mkdocs-git-revision-date-localized-plugin\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1323-requirements-txt","title":"1.3.2.3 requirements. txt \u4f7f\u7528","text":"\u7ef4\u62a4\u9879\u76ee\u4f9d\u8d56\uff1a
Bash# \u751f\u6210\u4f9d\u8d56\u6587\u4ef6\npip freeze > requirements.txt\n\n# \u5b89\u88c5\u4f9d\u8d56\npip install -r requirements.txt\n
"},{"location":"Tools/Blog/Mkdocs_Material/#133-git","title":"1.3.3 Git \u73af\u5883","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1331-git","title":"1.3.3.1 Git \u57fa\u7840\u914d\u7f6e","text":"Bash# \u914d\u7f6e\u7528\u6237\u4fe1\u606f\ngit config --global user.name \"Your Name\"\ngit config --global user.email \"your.email@example.com\"\n\n# \u914d\u7f6e\u9ed8\u8ba4\u5206\u652f\ngit config --global init.defaultBranch main\n
"},{"location":"Tools/Blog/Mkdocs_Material/#134-ssh","title":"1.3.4 SSH \u5bc6\u94a5\u914d\u7f6e","text":"Bash# \u751f\u6210 SSH \u5bc6\u94a5\nssh-keygen -t rsa -b 4096 -C \"your.email@example.com\"\n\n# \u67e5\u770b\u516c\u94a5\ncat ~/.ssh/id_rsa.pub\n
\u5c06\u516c\u94a5\u6dfb\u52a0\u5230 GitHub \u8d26\u6237\u7684 SSH keys \u4e2d\u3002
"},{"location":"Tools/Blog/Mkdocs_Material/#135-gitignore","title":"1.3.5 .gitignore \u914d\u7f6e","text":"\u521b\u5efa .gitignore \u6587\u4ef6\uff0c\u6dfb\u52a0\u4ee5\u4e0b\u5185\u5bb9\uff1a
Text Only# Python\n__pycache__/\n*.py[cod]\n*$py.class\nvenv/\n\n# MkDocs\nsite/\n\n# IDE\n.idea/\n.vscode/\n*.swp\n*.swo\n\n# OS\n.DS_Store\nThumbs.db\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2","title":"2 \u73af\u5883\u642d\u5efa","text":""},{"location":"Tools/Blog/Mkdocs_Material/#21-windows","title":"2.1 Windows \u7cfb\u7edf\u5b89\u88c5","text":""},{"location":"Tools/Blog/Mkdocs_Material/#211-python","title":"2.1.1 Python \u5b89\u88c5","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2111","title":"2.1.1.1 \u4e0b\u8f7d\u5b89\u88c5\u5305","text":" - \u8bbf\u95ee Python \u5b98\u7f51 \u4e0b\u8f7d\u6700\u65b0\u7248\u672c
- \u9009\u62e9\u9002\u5408\u4f60\u7684 Windows \u7248\u672c\uff0832 \u4f4d/64 \u4f4d\uff09\u7684\u5b89\u88c5\u5305
- \u4e0b\u8f7d\u5b8c\u6210\u540e\u53cc\u51fb\u5b89\u88c5\u5305\u5f00\u59cb\u5b89\u88c5
"},{"location":"Tools/Blog/Mkdocs_Material/#2112","title":"2.1.1.2 \u73af\u5883\u53d8\u91cf\u914d\u7f6e","text":" - \u5b89\u88c5\u65f6\u52fe\u9009 \"Add Python to PATH\"
- \u5982\u679c\u5fd8\u8bb0\u52fe\u9009\uff0c\u53ef\u4ee5\u624b\u52a8\u6dfb\u52a0\uff1a
Text Only# \u6dfb\u52a0\u5230\u7cfb\u7edf\u73af\u5883\u53d8\u91cf Path\nC:\\Users\\YourUser\\AppData\\Local\\Programs\\Python\\Python3x\\\nC:\\Users\\YourUser\\AppData\\Local\\Programs\\Python\\Python3x\\Scripts\\\n
- \u68c0\u67e5\u73af\u5883\u53d8\u91cf\uff1a
Bashecho %PATH%\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2113","title":"2.1.1.3 \u9a8c\u8bc1\u5b89\u88c5","text":"\u5728\u547d\u4ee4\u63d0\u793a\u7b26\u4e2d\u6267\u884c\uff1a
Bash# \u68c0\u67e5 Python \u7248\u672c\npython --version\n\n# \u68c0\u67e5 pip \u7248\u672c\npip --version\n
"},{"location":"Tools/Blog/Mkdocs_Material/#212-mkdocs","title":"2.1.2 MkDocs \u5b89\u88c5","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2121-pip","title":"2.1.2.1 pip \u5b89\u88c5\u65b9\u6cd5","text":"\u4f7f\u7528 pip \u5b89\u88c5 MkDocs\uff1a
Bash# \u5b89\u88c5 MkDocs\npip install mkdocs\n\n# \u9a8c\u8bc1\u5b89\u88c5\nmkdocs --version\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2122","title":"2.1.2.2 \u5e38\u89c1\u95ee\u9898\u89e3\u51b3","text":" -
pip \u4e0d\u662f\u5185\u90e8\u547d\u4ee4 - \u89e3\u51b3\u65b9\u6cd5\uff1a\u91cd\u65b0\u6dfb\u52a0 Python Scripts \u76ee\u5f55\u5230 PATH
-
\u6743\u9650\u95ee\u9898 - \u89e3\u51b3\u65b9\u6cd5\uff1a\u4f7f\u7528\u7ba1\u7406\u5458\u6743\u9650\u8fd0\u884c\u547d\u4ee4\u63d0\u793a\u7b26
Bash# \u7ba1\u7406\u5458\u6743\u9650\u5b89\u88c5\npip install --user mkdocs\n
- SSL \u8bc1\u4e66\u9519\u8bef - \u89e3\u51b3\u65b9\u6cd5\uff1a\u6dfb\u52a0\u4fe1\u4efb\u9009\u9879\u6216\u4f7f\u7528\u56fd\u5185\u955c\u50cf
Bashpip install --trusted-host pypi.org --trusted-host files.pythonhosted.org mkdocs\n
"},{"location":"Tools/Blog/Mkdocs_Material/#213","title":"2.1.3 \u7248\u672c\u9009\u62e9","text":"MkDocs \u7248\u672c\u9009\u62e9\u5efa\u8bae\uff1a
Bash# \u67e5\u770b\u53ef\u7528\u7248\u672c\npip install mkdocs==\n\n# \u5b89\u88c5\u7279\u5b9a\u7248\u672c\npip install mkdocs==1.5.3 # \u63a8\u8350\u7248\u672c\n
"},{"location":"Tools/Blog/Mkdocs_Material/#214-material","title":"2.1.4 Material \u4e3b\u9898\u5b89\u88c5","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2141","title":"2.1.4.1 \u5b89\u88c5\u547d\u4ee4","text":"Bash# \u5b89\u88c5 Material \u4e3b\u9898\npip install mkdocs-material\n\n# \u9a8c\u8bc1\u5b89\u88c5\npython -c \"import mkdocs_material; print(mkdocs_material.__version__)\"\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2142","title":"2.1.4.2 \u4f9d\u8d56\u68c0\u67e5","text":"\u5b89\u88c5\u5fc5\u8981\u7684\u4f9d\u8d56\uff1a
Bash# \u5b89\u88c5\u6269\u5c55\u652f\u6301\npip install pymdown-extensions\npip install mkdocs-glightbox\npip install mkdocs-git-revision-date-localized-plugin\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2143","title":"2.1.4.3 \u7248\u672c\u517c\u5bb9\u6027","text":"MkDocs \u7248\u672c Material \u7248\u672c Python \u7248\u672c 1.5. x 9.4. x \u22653.8 1.4. x 9.3. x \u22653.7 1.3. x 9.2. x \u22653.7"},{"location":"Tools/Blog/Mkdocs_Material/#22-linuxmac","title":"2.2 Linux/Mac \u7cfb\u7edf\u5b89\u88c5","text":""},{"location":"Tools/Blog/Mkdocs_Material/#221-python","title":"2.2.1 \u5305\u7ba1\u7406\u5668\u5b89\u88c5 Python","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2211-aptyum","title":"2.2.1.1 apt/yum \u5b89\u88c5\u65b9\u6cd5","text":"Ubuntu/Debian:
Bash# \u66f4\u65b0\u5305\u7d22\u5f15\nsudo apt update\n\n# \u5b89\u88c5 Python\nsudo apt install python3 python3-pip\n\n# \u5b89\u88c5\u5f00\u53d1\u5de5\u5177\nsudo apt install python3-dev\n
CentOS/RHEL:
Bash# \u5b89\u88c5 EPEL \u4ed3\u5e93\nsudo yum install epel-release\n\n# \u5b89\u88c5 Python\nsudo yum install python3 python3-pip\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2212-brew","title":"2.2.1.2 brew \u5b89\u88c5\u65b9\u6cd5","text":"macOS:
Bash# \u5b89\u88c5 Homebrew\uff08\u5982\u679c\u672a\u5b89\u88c5\uff09\n/bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\"\n\n# \u5b89\u88c5 Python\nbrew install python\n\n# \u66f4\u65b0 pip\npip3 install --upgrade pip\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2213","title":"2.2.1.3 \u73af\u5883\u53d8\u91cf\u914d\u7f6e","text":"bash/zsh:
Bash# \u6dfb\u52a0\u5230 ~/.bashrc \u6216 ~/.zshrc\nexport PATH=\"$HOME/.local/bin:$PATH\"\nexport PYTHONPATH=\"$HOME/.local/lib/python3.x/site-packages:$PYTHONPATH\"\n\n# \u66f4\u65b0\u73af\u5883\u53d8\u91cf\nsource ~/.bashrc # \u6216 source ~/.zshrc\n
"},{"location":"Tools/Blog/Mkdocs_Material/#222-pip","title":"2.2.2 pip \u5b89\u88c5\u4f9d\u8d56","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2221","title":"2.2.2.1 \u7cfb\u7edf\u7ea7\u5b89\u88c5","text":"Bash# \u5168\u5c40\u5b89\u88c5\uff08\u9700\u8981 root \u6743\u9650\uff09\nsudo pip3 install mkdocs mkdocs-material\n\n# \u9a8c\u8bc1\u5b89\u88c5\nmkdocs --version\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2222","title":"2.2.2.2 \u7528\u6237\u7ea7\u5b89\u88c5","text":"Bash# \u7528\u6237\u76ee\u5f55\u5b89\u88c5\npip3 install --user mkdocs mkdocs-material\n\n# \u68c0\u67e5\u5b89\u88c5\u8def\u5f84\npython3 -m site --user-site\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2223","title":"2.2.2.3 \u865a\u62df\u73af\u5883\u5b89\u88c5","text":"Bash# \u521b\u5efa\u865a\u62df\u73af\u5883\npython3 -m venv mkdocs-env\n\n# \u6fc0\u6d3b\u865a\u62df\u73af\u5883\nsource mkdocs-env/bin/activate\n\n# \u5b89\u88c5\u4f9d\u8d56\npip install mkdocs mkdocs-material\n\n# \u9000\u51fa\u865a\u62df\u73af\u5883\ndeactivate\n
"},{"location":"Tools/Blog/Mkdocs_Material/#23","title":"2.3 \u9879\u76ee\u521d\u59cb\u5316","text":""},{"location":"Tools/Blog/Mkdocs_Material/#231","title":"2.3.1 \u521b\u5efa\u9879\u76ee","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2311-mkdocs-new","title":"2.3.1.1 mkdocs new \u547d\u4ee4\u8be6\u89e3","text":"Bash# \u57fa\u672c\u8bed\u6cd5\nmkdocs new [\u9879\u76ee\u540d]\n\n# \u521b\u5efa\u65b0\u9879\u76ee\nmkdocs new my-docs\n\n# \u4f7f\u7528\u73b0\u6709\u76ee\u5f55\ncd existing-project\nmkdocs new .\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2312","title":"2.3.1.2 \u9879\u76ee\u547d\u540d\u89c4\u8303","text":" - \u4f7f\u7528\u5c0f\u5199\u5b57\u6bcd
- \u5355\u8bcd\u95f4\u7528\u8fde\u5b57\u7b26 (-) \u5206\u9694
- \u907f\u514d\u4f7f\u7528\u7279\u6b8a\u5b57\u7b26
- \u540d\u79f0\u5177\u6709\u63cf\u8ff0\u6027
\u793a\u4f8b\uff1a
Bashmkdocs new technical-docs # \u597d\u7684\u547d\u540d\nmkdocs new tech_docs # \u907f\u514d\u4f7f\u7528\u4e0b\u5212\u7ebf\nmkdocs new TechDocs # \u907f\u514d\u4f7f\u7528\u5927\u5199\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2313","title":"2.3.1.3 \u521d\u59cb\u5316\u914d\u7f6e","text":"\u521b\u5efa\u9879\u76ee\u540e\u7684\u57fa\u672c\u8bbe\u7f6e\uff1a
Bashcd my-docs\n# \u542f\u52a8\u5f00\u53d1\u670d\u52a1\u5668\nmkdocs serve\n# \u5728\u6d4f\u89c8\u5668\u4e2d\u8bbf\u95ee http://127.0.0.1:8000\n
"},{"location":"Tools/Blog/Mkdocs_Material/#232","title":"2.3.2 \u76ee\u5f55\u7ed3\u6784\u8bf4\u660e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2321","title":"2.3.2.1 \u57fa\u7840\u76ee\u5f55\u7ed3\u6784","text":"Text Onlymy-docs/\n\u251c\u2500\u2500 docs/ # \u6587\u6863\u76ee\u5f55\n\u2502 \u251c\u2500\u2500 index.md # \u9996\u9875\n\u2502 \u251c\u2500\u2500 about.md # \u5176\u4ed6\u9875\u9762\n\u2502 \u2514\u2500\u2500 img/ # \u56fe\u7247\u76ee\u5f55\n\u251c\u2500\u2500 mkdocs.yml # \u914d\u7f6e\u6587\u4ef6\n\u2514\u2500\u2500 venv/ # \u865a\u62df\u73af\u5883\uff08\u53ef\u9009\uff09\n
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Markdown# \u7528\u6237\u6307\u5357\n\n\u8fd9\u662f\u7528\u6237\u6307\u5357\u7684\u4e3b\u9875\u9762\uff0c\u5305\u542b\u4ee5\u4e0b\u5185\u5bb9\uff1a\n\n## \u5feb\u901f\u5bfc\u822a\n\n- [\u57fa\u7840\u7528\u6cd5] (basic-usage.md) - \u5165\u95e8\u5fc5\u8bfb\n- [\u9ad8\u7ea7\u7279\u6027] (advanced.md) - \u6df1\u5165\u4e86\u89e3\n\n## \u672c\u8282\u5185\u5bb9\n\n\u6b64\u90e8\u5206\u5c06\u5e2e\u52a9\u60a8\u4e86\u89e3\u4ea7\u54c1\u7684\u6838\u5fc3\u529f\u80fd\u548c\u4f7f\u7528\u65b9\u6cd5...\n
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YAMLnav:\n - \u7528\u6237\u6307\u5357:\n - \u6982\u8ff0: user-guide/index.md\n - \u57fa\u7840\u7528\u6cd5: user-guide/basic-usage.md\n - \u9ad8\u7ea7\u7279\u6027: user-guide/advanced.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#323","title":"3.2.3 \u591a\u7ea7\u76ee\u5f55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3231","title":"3.2.3.1 \u5c42\u7ea7\u7ed3\u6784\u8bbe\u8ba1","text":"\u590d\u6742\u7684\u591a\u7ea7\u76ee\u5f55\u793a\u4f8b\uff1a
YAMLnav:\n - \u9996\u9875: index.md\n - \u5165\u95e8\u6307\u5357:\n - \u6982\u8ff0: getting-started/index.md\n - \u57fa\u7840:\n - \u5b89\u88c5: getting-started/basics/installation.md\n - \u914d\u7f6e: getting-started/basics/configuration.md\n - \u8fdb\u9636:\n - \u81ea\u5b9a\u4e49: getting-started/advanced/customization.md\n - \u4f18\u5316: getting-started/advanced/optimization.md\n - \u5f00\u53d1\u6587\u6863:\n - \u6982\u8ff0: development/index.md\n - API:\n - \u8ba4\u8bc1: development/api/authentication.md\n - \u63a5\u53e3: development/api/endpoints.md\n - SDK:\n - Python: development/sdk/python.md\n - JavaScript: development/sdk/javascript.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3232","title":"3.2.3.2 \u5bfc\u822a\u6df1\u5ea6\u63a7\u5236","text":"YAMLtheme:\n name: material\n features:\n - navigation.sections # \u663e\u793a\u7ae0\u8282\n - navigation.expand # \u5c55\u5f00\u5bfc\u822a\n - navigation.indexes # \u4f7f\u7528\u76ee\u5f55\u7d22\u5f15\n - toc.integrate # \u96c6\u6210\u76ee\u5f55\n\nmarkdown_extensions:\n - toc:\n permalink: true\n toc_depth: 3 # \u63a7\u5236\u76ee\u5f55\u6df1\u5ea6\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3233","title":"3.2.3.3 \u6298\u53e0\u914d\u7f6e","text":"Material \u4e3b\u9898\u7684\u6298\u53e0\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n features:\n - navigation.sections # \u663e\u793a\u5206\u533a\n - navigation.expand # \u9ed8\u8ba4\u5c55\u5f00\n - navigation.indexes # \u4f7f\u7528\u7d22\u5f15\u9875\n - navigation.top # \u8fd4\u56de\u9876\u90e8\u6309\u94ae\n\n # \u5bfc\u822a\u680f\u8bbe\u7f6e\n nav_style: dark # \u5bfc\u822a\u680f\u6837\u5f0f\n collapse_navigation: true # \u6298\u53e0\u5bfc\u822a\n sticky_navigation: true # \u56fa\u5b9a\u5bfc\u822a\n
"},{"location":"Tools/Blog/Mkdocs_Material/#324","title":"3.2.4 \u5bfc\u822a\u914d\u7f6e\u6700\u4f73\u5b9e\u8df5","text":" -
\u7ed3\u6784\u8bbe\u8ba1\u539f\u5219\uff1a - \u4fdd\u6301\u5c42\u7ea7\u6e05\u6670 - \u63a7\u5236\u5bfc\u822a\u6df1\u5ea6\uff08\u5efa\u8bae\u4e0d\u8d85\u8fc7 3 \u5c42\uff09 - \u76f8\u5173\u5185\u5bb9\u5206\u7ec4 - \u4f7f\u7528\u76f4\u89c2\u7684\u547d\u540d
-
\u6587\u4ef6\u7ec4\u7ec7\uff1a - \u4f7f\u7528\u6709\u610f\u4e49\u7684\u76ee\u5f55\u540d - \u4fdd\u6301\u6587\u4ef6\u7ed3\u6784\u6574\u6d01 - \u5408\u7406\u4f7f\u7528\u7d22\u5f15\u6587\u4ef6 - \u9075\u5faa\u4e00\u81f4\u7684\u547d\u540d\u89c4\u8303
-
\u7528\u6237\u4f53\u9a8c\uff1a - \u63d0\u4f9b\u6e05\u6670\u7684\u5bfc\u822a\u8def\u5f84 - \u6dfb\u52a0\u5408\u9002\u7684\u63cf\u8ff0 - \u8003\u8651\u79fb\u52a8\u7aef\u663e\u793a - \u4f18\u5316\u5bfc\u822a\u54cd\u5e94\u901f\u5ea6
-
\u7ef4\u62a4\u5efa\u8bae\uff1a - \u5b9a\u671f\u68c0\u67e5\u6b7b\u94fe\u63a5 - \u66f4\u65b0\u5bfc\u822a\u7ed3\u6784 - \u4fdd\u6301\u6587\u6863\u540c\u6b65 - \u6536\u96c6\u7528\u6237\u53cd\u9988
"},{"location":"Tools/Blog/Mkdocs_Material/#325","title":"3.2.5 \u7279\u6b8a\u5bfc\u822a\u529f\u80fd","text":" - \u9690\u85cf\u9875\u9762\uff1a
YAMLnav:\n - \u53ef\u89c1\u9875\u9762: visible.md\n - !hidden \u9690\u85cf\u9875\u9762: hidden.md\n
- \u5916\u90e8\u94fe\u63a5\uff1a
YAMLnav:\n - \u6587\u6863: index.md\n - GitHub: https://github.com/your/repo\n - \u793e\u533a: \n - \u8bba\u575b: https://forum.example.com\n - \u535a\u5ba2: https://blog.example.com\n
- \u522b\u540d\u8bbe\u7f6e\uff1a
YAMLnav:\n - \u5f00\u59cb: \n - \u6982\u8ff0: getting-started/index.md\n - \u5feb\u901f\u5165\u95e8: getting-started/quickstart.md\n - \u540c\u4e00\u6587\u4ef6\u4e0d\u540c\u5165\u53e3: !alias getting-started/quickstart.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#33-markdown","title":"3.3 Markdown \u6269\u5c55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#331","title":"3.3.1 \u57fa\u7840\u6269\u5c55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3311-meta","title":"3.3.1.1 meta \u6269\u5c55","text":"\u652f\u6301\u5728 Markdown \u6587\u4ef6\u5934\u90e8\u6dfb\u52a0\u5143\u6570\u636e\uff1a
YAML# mkdocs.yml \u914d\u7f6e\nmarkdown_extensions:\n - meta\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown---\ntitle: \u6211\u7684\u9875\u9762\u6807\u9898\ndescription: \u9875\u9762\u63cf\u8ff0\nauthor: \u4f5c\u8005\u540d\ndate: 2024-01-01\n---\n\n# \u6b63\u6587\u5185\u5bb9\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3312-toc","title":"3.3.1.2 toc \u6269\u5c55","text":"\u81ea\u52a8\u751f\u6210\u76ee\u5f55\uff1a
YAMLmarkdown_extensions:\n - toc:\n permalink: true # \u6dfb\u52a0\u6bb5\u843d\u94fe\u63a5\n toc_depth: 3 # \u76ee\u5f55\u6df1\u5ea6\n separator: \"_\" # \u6807\u9898\u951a\u70b9\u5206\u9694\u7b26\n title: \"\u76ee\u5f55\" # \u76ee\u5f55\u6807\u9898\n slugify: !!python/object/apply:pymdownx.slugs.slugify\n kwds: {case: lower} # URL \u8f6c\u6362\u89c4\u5219\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown[TOC]\n\n# \u4e00\u7ea7\u6807\u9898\n## \u4e8c\u7ea7\u6807\u9898\n### \u4e09\u7ea7\u6807\u9898\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3313-tables","title":"3.3.1.3 tables \u6269\u5c55","text":"\u589e\u5f3a\u7684\u8868\u683c\u652f\u6301\uff1a
YAMLmarkdown_extensions:\n - tables\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown| \u529f\u80fd | \u57fa\u7840\u7248 | \u4e13\u4e1a\u7248 |\n|-----|:------:|-------:|\n| \u529f\u80fdA | \u2713 | \u2713 |\n| \u529f\u80fdB | \u2717 | \u2713 |\n| \u529f\u80fdC | \u2717 | \u2713 |\n\n: \u8868\u683c\u6807\u9898 {.class-name}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#332-pymdown-extensions","title":"3.3.2 PyMdown Extensions","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3321-superfences","title":"3.3.2.1 superfences \u914d\u7f6e","text":"\u589e\u5f3a\u7684\u4ee3\u7801\u5757\u529f\u80fd\uff1a
YAMLmarkdown_extensions:\n - pymdownx.superfences:\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_div_format\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown```python title=\"example.py\" linenums=\"1\" hl_lines=\"2 3\"\ndef hello_world():\n message = \"Hello, World!\"\n print(message)\n return message\n```\n\n```mermaid\ngraph LR\n A[\u5f00\u59cb] --> B{\u5224\u65ad}\n B --> |Yes| C[\u6267\u884c]\n B --> |No| D[\u8df3\u8fc7]\n```\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3322-emoji","title":"3.3.2.2 emoji \u652f\u6301","text":"\u6dfb\u52a0\u8868\u60c5\u7b26\u53f7\u652f\u6301\uff1a
YAMLmarkdown_extensions:\n - pymdownx.emoji:\n emoji_index: !!python/name:material.extensions.emoji.twemoji\n emoji_generator: !!python/name:material.extensions.emoji.to_svg\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown:smile: :heart: :thumbsup:\n\n:fontawesome-brands-github: GitHub\n:material-account: \u7528\u6237\n:octicons-repo-16: \u4ed3\u5e93\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3323-tasklist","title":"3.3.2.3 tasklist \u529f\u80fd","text":"\u4efb\u52a1\u5217\u8868\u652f\u6301\uff1a
YAMLmarkdown_extensions:\n - pymdownx.tasklist:\n custom_checkbox: true # \u81ea\u5b9a\u4e49\u590d\u9009\u6846\u6837\u5f0f\n clickable_checkbox: true # \u53ef\u70b9\u51fb\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown- [x] \u5df2\u5b8c\u6210\u4efb\u52a1\n- [ ] \u672a\u5b8c\u6210\u4efb\u52a1\n - [x] \u5b50\u4efb\u52a1 1\n - [ ] \u5b50\u4efb\u52a1 2\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3324-pymdown","title":"3.3.2.4 \u5176\u4ed6\u5e38\u7528 PyMdown \u6269\u5c55","text":"YAMLmarkdown_extensions:\n - pymdownx.highlight # \u4ee3\u7801\u9ad8\u4eae\n - pymdownx.inlinehilite # \u884c\u5185\u4ee3\u7801\u9ad8\u4eae\n - pymdownx.snippets # \u4ee3\u7801\u7247\u6bb5\n - pymdownx.magiclink # \u81ea\u52a8\u94fe\u63a5\n - pymdownx.mark # ==\u6807\u8bb0==\n - pymdownx.critic # \u7f16\u8f91\u6807\u8bb0\n - pymdownx.tilde # \u5220\u9664\u7ebf\n - pymdownx.caret # \u4e0a\u6807\n - pymdownx.keys # \u952e\u76d8\u6309\u952e\n - pymdownx.tabbed # \u6807\u7b7e\u9875\n
"},{"location":"Tools/Blog/Mkdocs_Material/#333","title":"3.3.3 \u81ea\u5b9a\u4e49\u6269\u5c55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3331","title":"3.3.3.1 \u6269\u5c55\u5f00\u53d1\u57fa\u7840","text":"\u521b\u5efa\u81ea\u5b9a\u4e49\u6269\u5c55\uff1a
Python# custom_extension.py\nfrom markdown.extensions import Extension\nfrom markdown.preprocessors import Preprocessor\n\nclass CustomPreprocessor(Preprocessor):\n def run(self, lines):\n new_lines = []\n for line in lines:\n new_lines.append(line.replace('[[', '**').replace(']]', '**'))\n return new_lines\n\nclass CustomExtension(Extension):\n def extendMarkdown(self, md):\n md.preprocessors.register(CustomPreprocessor(md), 'custom_preprocessor', 175)\n
"},{"location":"Tools/Blog/Mkdocs_Material/#34","title":"3.4 \u5e38\u7528\u6269\u5c55\u793a\u4f8b","text":" - \u6dfb\u52a0\u81ea\u5b9a\u4e49 HTML \u5c5e\u6027\uff1a
Pythonfrom markdown.extensions import Extension\nfrom markdown.treeprocessors import Treeprocessor\n\nclass CustomAttributesTreeprocessor(Treeprocessor):\n def run(self, root):\n for elem in root.iter():\n if 'class' in elem.attrib:\n elem.set('data-custom', 'value')\n\nclass CustomAttributesExtension(Extension):\n def extendMarkdown(self, md):\n md.treeprocessors.register(\n CustomAttributesTreeprocessor(md), 'custom_attributes', 15\n )\n
- \u81ea\u5b9a\u4e49\u5bb9\u5668\uff1a
Pythonfrom markdown.extensions import Extension\nfrom markdown.blockprocessors import BlockProcessor\nimport re\n\nclass CustomContainerProcessor(BlockProcessor):\n RE = re.compile(r':{3,}\\ *(warning|note|tip)\\ *')\n\n def run(self, parent, blocks):\n block = blocks.pop(0)\n m = self.RE.match(block)\n\n if m:\n container_type = m.group(1)\n div = etree.SubElement(parent, 'div')\n div.set('class', f'custom-container {container_type}')\n\n # \u5904\u7406\u5bb9\u5668\u5185\u5bb9\n self.parser.parseChunk(div, block[m.end():])\n\nclass CustomContainerExtension(Extension):\n def extendMarkdown(self, md):\n md.parser.blockprocessors.register(\n CustomContainerProcessor(md.parser), 'custom_container', 175\n )\n
"},{"location":"Tools/Blog/Mkdocs_Material/#341","title":"3.4.1 \u6269\u5c55\u914d\u7f6e\u65b9\u6cd5","text":"\u5728 mkdocs.yml \u4e2d\u914d\u7f6e\u81ea\u5b9a\u4e49\u6269\u5c55\uff1a
YAMLmarkdown_extensions:\n - custom_extension:\n option1: value1\n option2: value2\n\nextra_css:\n - css/custom_extension.css\n\nextra_javascript:\n - js/custom_extension.js\n
"},{"location":"Tools/Blog/Mkdocs_Material/#342","title":"3.4.2 \u6269\u5c55\u7ec4\u5408\u63a8\u8350","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3421","title":"3.4.2.1 \u57fa\u7840\u6587\u6863\u914d\u7f6e","text":"YAMLmarkdown_extensions:\n - meta\n - toc:\n permalink: true\n - tables\n - attr_list\n - def_list\n - footnotes\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3422","title":"3.4.2.2 \u589e\u5f3a\u529f\u80fd\u914d\u7f6e","text":"YAMLmarkdown_extensions:\n - pymdownx.superfences:\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_div_format\n - pymdownx.highlight:\n anchor_linenums: true\n - pymdownx.inlinehilite\n - pymdownx.snippets\n - pymdownx.tasklist:\n custom_checkbox: true\n - pymdownx.emoji:\n emoji_index: !!python/name:material.extensions.emoji.twemoji\n emoji_generator: !!python/name:material.extensions.emoji.to_svg\n
"},{"location":"Tools/Blog/Mkdocs_Material/#343","title":"3.4.3 \u5b8c\u6574\u63a8\u8350\u914d\u7f6e","text":"YAMLmarkdown_extensions:\n # Python Markdown\n - meta\n - toc:\n permalink: true\n toc_depth: 4\n - tables\n - attr_list\n - def_list\n - md_in_html\n - footnotes\n\n # Python Markdown Extensions\n - pymdownx.superfences\n - pymdownx.highlight\n - pymdownx.inlinehilite\n - pymdownx.snippets\n - pymdownx.tasklist\n - pymdownx.emoji\n - pymdownx.mark\n - pymdownx.critic\n - pymdownx.keys\n - pymdownx.tilde\n - pymdownx.caret\n - pymdownx.details\n - pymdownx.magiclink\n - pymdownx.tabbed:\n alternate_style: true\n\n # \u81ea\u5b9a\u4e49\u6269\u5c55\n - custom_extension:\n custom_option: value\n
"},{"location":"Tools/Blog/Mkdocs_Material/#344","title":"3.4.4 \u4f7f\u7528\u5efa\u8bae","text":" -
\u6027\u80fd\u8003\u8651\uff1a - \u53ea\u542f\u7528\u9700\u8981\u7684\u6269\u5c55 - \u6ce8\u610f\u6269\u5c55\u4e4b\u95f4\u7684\u4f9d\u8d56\u5173\u7cfb - \u63a7\u5236\u6269\u5c55\u6570\u91cf
-
\u517c\u5bb9\u6027\uff1a - \u6d4b\u8bd5\u6269\u5c55\u7ec4\u5408 - \u68c0\u67e5\u79fb\u52a8\u7aef\u663e\u793a - \u9a8c\u8bc1\u4e0d\u540c\u6d4f\u89c8\u5668
-
\u7ef4\u62a4\u5efa\u8bae\uff1a - \u8bb0\u5f55\u6269\u5c55\u914d\u7f6e - \u4fdd\u6301\u7248\u672c\u66f4\u65b0 - \u76d1\u63a7\u6027\u80fd\u5f71\u54cd
"},{"location":"Tools/Blog/Mkdocs_Material/#35","title":"3.5 \u63d2\u4ef6\u7cfb\u7edf","text":""},{"location":"Tools/Blog/Mkdocs_Material/#351","title":"3.5.1 \u63d2\u4ef6\u914d\u7f6e\u65b9\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3511","title":"3.5.1.1 \u63d2\u4ef6\u5b89\u88c5\u65b9\u6cd5","text":"\u901a\u8fc7 pip \u5b89\u88c5\u63d2\u4ef6\uff1a
Bash# \u5b89\u88c5\u5355\u4e2a\u63d2\u4ef6\npip install mkdocs-git-revision-date-localized-plugin\n\n# \u5b89\u88c5\u591a\u4e2a\u63d2\u4ef6\npip install mkdocs-minify-plugin mkdocs-git-authors-plugin\n
\u57fa\u7840\u63d2\u4ef6\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nplugins:\n - search # \u9ed8\u8ba4\u63d2\u4ef6\n - git-revision-date-localized:\n enable_creation_date: true\n - minify:\n minify_html: true\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3512","title":"3.5.1.2 \u914d\u7f6e\u8bed\u6cd5","text":"\u63d2\u4ef6\u914d\u7f6e\u7684\u51e0\u79cd\u65b9\u5f0f\uff1a
YAML# 1. \u7b80\u5355\u542f\u7528\u63d2\u4ef6\uff08\u4f7f\u7528\u9ed8\u8ba4\u914d\u7f6e\uff09\nplugins:\n - search\n - tags\n\n# 2. \u7981\u7528\u9ed8\u8ba4\u63d2\u4ef6\nplugins:\n - search: false\n\n# 3. \u5e26\u914d\u7f6e\u7684\u63d2\u4ef6\nplugins:\n - search:\n lang: \n - en\n - zh\n separator: '[\\s\\-\\.]+'\n\n# 4. \u591a\u5b9e\u4f8b\u63d2\u4ef6\nplugins:\n - search:\n name: search_1\n config: value\n - search:\n name: search_2\n config: value\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3513","title":"3.5.1.3 \u4f18\u5148\u7ea7\u63a7\u5236","text":"\u63d2\u4ef6\u6267\u884c\u987a\u5e8f\u63a7\u5236\uff1a
YAMLplugins:\n - search\n - git-revision-date-localized:\n priority: 80\n - minify:\n priority: 90\n - tags:\n priority: 70\n
"},{"location":"Tools/Blog/Mkdocs_Material/#352","title":"3.5.2 \u5e38\u7528\u63d2\u4ef6\u4ecb\u7ecd","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3521","title":"3.5.2.1 \u5b98\u65b9\u63d2\u4ef6","text":" - search - \u641c\u7d22\u63d2\u4ef6
YAMLplugins:\n - search:\n lang: \n - en\n - zh\n separator: '[\\s\\-\\.]+'\n min_search_length: 2\n prebuild_index: true\n indexing:\n - full_sections: false\n - headings: true\n - content: true\n
- tags - \u6807\u7b7e\u7cfb\u7edf
YAMLplugins:\n - tags:\n tags_file: tags.md\n tags_extra_files:\n cloud: cloud_tags.md\n list: tag_list.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3522","title":"3.5.2.2 \u793e\u533a\u63d2\u4ef6\u63a8\u8350","text":" - git-revision-date-localized - Git \u65e5\u671f\u4fe1\u606f
YAMLplugins:\n - git-revision-date-localized:\n type: timeago\n enable_creation_date: true\n exclude:\n - index.md\n timezone: Asia/Shanghai\n locale: zh\n
- minify - \u6587\u4ef6\u538b\u7f29
YAMLplugins:\n - minify:\n minify_html: true\n minify_js: true\n minify_css: true\n htmlmin_opts:\n remove_comments: true\n
- social - \u793e\u4ea4\u5206\u4eab
YAMLplugins:\n - social:\n cards: true\n cards_color:\n fill: \"#0FF1CE\"\n text: \"#FFFFFF\"\n
- macros - \u6a21\u677f\u5b8f
YAMLplugins:\n - macros:\n module_name: macros\n include_dir: include\n include_yaml:\n - variables.yml\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3523","title":"3.5.2.3 \u5b9e\u7528\u63d2\u4ef6\u96c6\u5408","text":"YAMLplugins:\n # \u6838\u5fc3\u529f\u80fd\n - search\n - tags\n\n # \u7248\u672c\u63a7\u5236\n - git-revision-date-localized\n - git-authors\n\n # \u6027\u80fd\u4f18\u5316\n - minify\n - optimize\n\n # \u5185\u5bb9\u589e\u5f3a\n - social\n - macros\n - blogging\n\n # \u591a\u8bed\u8a00\u652f\u6301\n - i18n\n - translations\n\n # \u56fe\u7247\u5904\u7406\n - glightbox\n - img2fig\n\n # \u7edf\u8ba1\u5206\u6790\n - statistics\n - pdf-export\n
"},{"location":"Tools/Blog/Mkdocs_Material/#353","title":"3.5.3 \u63d2\u4ef6\u7ec4\u5408\u4f7f\u7528","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3531","title":"3.5.3.1 \u57fa\u7840\u7ec4\u5408\u65b9\u6848","text":"\u9002\u5408\u4e00\u822c\u6587\u6863\u9879\u76ee\uff1a
YAMLplugins:\n - search:\n lang: zh\n separator: '[\\s\\-\\.]+'\n\n - git-revision-date-localized:\n enable_creation_date: true\n type: date\n\n - minify:\n minify_html: true\n\n - glightbox:\n touchNavigation: true\n loop: false\n effect: zoom\n width: 100%\n height: auto\n zoomable: true\n draggable: true\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3532","title":"3.5.3.2 \u535a\u5ba2\u7f51\u7ad9\u65b9\u6848","text":"\u9002\u5408\u535a\u5ba2\u7c7b\u7f51\u7ad9\uff1a
YAMLplugins:\n - blog:\n blog_dir: blog\n post_url_format: \"{slug}\"\n post_excerpt: optional\n\n - social:\n cards: true\n cards_dir: assets/social\n\n - tags:\n tags_file: tags.md\n\n - rss:\n abstract_chars_count: 160\n date_from_meta: true\n\n - statistics:\n page_check: true\n page_count: true\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3533","title":"3.5.3.3 \u6280\u672f\u6587\u6863\u65b9\u6848","text":"\u9002\u5408\u5927\u578b\u6280\u672f\u6587\u6863\uff1a
YAMLplugins:\n - search:\n separator: '[\\s\\-\\.]+'\n min_search_length: 2\n lang:\n - en\n - zh\n prebuild_index: true\n\n - git-revision-date-localized:\n type: timeago\n enable_creation_date: true\n\n - minify:\n minify_html: true\n minify_js: true\n minify_css: true\n\n - macros:\n module_name: includes.macros\n include_yaml:\n - includes/variables.yml\n\n - pdf-export:\n combined: true\n combined_output_path: pdf/document.pdf\n
"},{"location":"Tools/Blog/Mkdocs_Material/#354","title":"3.5.4 \u6027\u80fd\u4f18\u5316\u5efa\u8bae","text":" -
\u63d2\u4ef6\u9009\u62e9\uff1a - \u53ea\u542f\u7528\u5fc5\u8981\u7684\u63d2\u4ef6 - \u907f\u514d\u529f\u80fd\u91cd\u590d\u7684\u63d2\u4ef6 - \u6ce8\u610f\u63d2\u4ef6\u95f4\u7684\u4f9d\u8d56\u5173\u7cfb
-
\u914d\u7f6e\u4f18\u5316\uff1a
YAMLplugins:\n - search:\n prebuild_index: true # \u9884\u6784\u5efa\u7d22\u5f15\n - minify:\n cache: true # \u542f\u7528\u7f13\u5b58\n cache_dir: .cache # \u7f13\u5b58\u76ee\u5f55\n - optimize: # \u8d44\u6e90\u4f18\u5316\n cache: true\n
- \u6784\u5efa\u4f18\u5316\uff1a
YAMLplugins:\n # \u5e76\u884c\u5904\u7406\u63d2\u4ef6\n - parallel:\n workers: 4\n # \u7f13\u5b58\u63d2\u4ef6\n - cache:\n enabled: true\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4-material","title":"4 Material \u4e3b\u9898\u5b8c\u5168\u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#41","title":"4.1 \u57fa\u7840\u5916\u89c2","text":""},{"location":"Tools/Blog/Mkdocs_Material/#411","title":"4.1.1 \u914d\u8272\u65b9\u6848","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4111","title":"4.1.1.1 \u9884\u8bbe\u4e3b\u9898\u8272","text":"Material \u4e3b\u9898\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u9884\u8bbe\u989c\u8272\uff1a
YAMLtheme:\n name: material\n palette:\n primary: indigo # \u4e3b\u8272\u8c03\n accent: pink # \u5f3a\u8c03\u8272\n\n# \u53ef\u7528\u7684\u4e3b\u9898\u8272\uff1a\n# red, pink, purple, deep purple, indigo, blue, light blue, \n# cyan, teal, green, light green, lime, yellow, amber, \n# orange, deep orange, brown, grey, blue grey\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4112","title":"4.1.1.2 \u81ea\u5b9a\u4e49\u914d\u8272","text":"\u5b8c\u6574\u7684\u81ea\u5b9a\u4e49\u914d\u8272\u914d\u7f6e\uff1a
YAMLtheme:\n palette:\n # \u4eae\u8272\u4e3b\u9898\n - media: \"(prefers-color-scheme: light)\"\n scheme: default\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-7\n name: \u5207\u6362\u81f3\u6697\u8272\u6a21\u5f0f\n\n # \u6697\u8272\u4e3b\u9898\n - media: \"(prefers-color-scheme: dark)\"\n scheme: slate\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u6a21\u5f0f\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4113-css","title":"4.1.1.3 \u81ea\u5b9a\u4e49 CSS \u53d8\u91cf","text":"\u521b\u5efa docs/stylesheets/extra.css\uff1a
CSS:root {\n --md-primary-fg-color: #2196f3;\n --md-primary-fg-color--light: #64b5f6;\n --md-primary-fg-color--dark: #1976d2;\n --md-accent-fg-color: #2196f3;\n}\n\n[data-md-color-scheme=\"slate\"] {\n --md-primary-fg-color: #90caf9;\n --md-primary-fg-color--light: #e3f2fd;\n --md-primary-fg-color--dark: #42a5f5;\n}\n
\u5728 mkdocs.yml \u4e2d\u5f15\u5165\uff1a
YAMLextra_css:\n - stylesheets/extra.css\n
"},{"location":"Tools/Blog/Mkdocs_Material/#412","title":"4.1.2 \u4e3b\u9898\u5207\u6362","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4121","title":"4.1.2.1 \u57fa\u7840\u5207\u6362\u914d\u7f6e","text":"YAMLtheme:\n name: material\n palette:\n # \u914d\u7f6e\u5207\u6362\u6309\u94ae\n - scheme: default\n toggle:\n icon: material/brightness-7 \n name: \u5207\u6362\u81f3\u6697\u8272\u6a21\u5f0f\n primary: indigo\n accent: indigo\n\n - scheme: slate\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u6a21\u5f0f\n primary: indigo\n accent: indigo\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4122","title":"4.1.2.2 \u9ad8\u7ea7\u5207\u6362\u529f\u80fd","text":"\u6dfb\u52a0\u81ea\u5b9a\u4e49\u5207\u6362\u903b\u8f91\uff1a
YAMLextra_javascript:\n - javascripts/theme-switch.js\n
theme-switch.js \u5185\u5bb9\uff1a
JavaScriptdocument.addEventListener('DOMContentLoaded', function() {\n // \u83b7\u53d6\u7cfb\u7edf\u4e3b\u9898\u504f\u597d\n const prefersDark = window.matchMedia('(prefers-color-scheme: dark)');\n\n // \u76d1\u542c\u7cfb\u7edf\u4e3b\u9898\u53d8\u5316\n prefersDark.addListener((e) => {\n const theme = e.matches ? 'slate' : 'default';\n document.body.setAttribute('data-md-color-scheme', theme);\n });\n\n // \u521d\u59cb\u5316\u4e3b\u9898\n const theme = prefersDark.matches ? 'slate' : 'default';\n document.body.setAttribute('data-md-color-scheme', theme);\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#413","title":"4.1.3 \u56fe\u6807\u8bbe\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4131","title":"4.1.3.1 \u7f51\u7ad9\u56fe\u6807","text":"\u914d\u7f6e\u7f51\u7ad9\u56fe\u6807\u548c Logo\uff1a
YAMLtheme:\n icon:\n logo: material/book-open-page-variant # \u7f51\u7ad9 Logo\n repo: fontawesome/brands/github # \u4ed3\u5e93\u56fe\u6807\n\n favicon: assets/favicon.png # \u7f51\u7ad9\u56fe\u6807\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4132","title":"4.1.3.2 \u529f\u80fd\u56fe\u6807","text":"\u4e3a\u4e0d\u540c\u529f\u80fd\u914d\u7f6e\u56fe\u6807\uff1a
YAMLtheme:\n icon:\n repo: fontawesome/brands/github # \u4ed3\u5e93\u56fe\u6807\n edit: material/pencil # \u7f16\u8f91\u56fe\u6807\n view: material/eye # \u67e5\u770b\u56fe\u6807\n admonition:\n note: octicons/tag-16 # \u63d0\u793a\u6846\u56fe\u6807\n abstract: octicons/checklist-16\n info: octicons/info-16\n tip: octicons/squirrel-16\n success: octicons/check-16\n question: octicons/question-16\n warning: octicons/alert-16\n failure: octicons/x-circle-16\n danger: octicons/zap-16\n bug: octicons/bug-16\n example: octicons/beaker-16\n quote: octicons/quote-16\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4133-svg","title":"4.1.3.3 \u81ea\u5b9a\u4e49 SVG \u56fe\u6807","text":"\u6dfb\u52a0\u81ea\u5b9a\u4e49 SVG \u56fe\u6807\uff1a
- \u521b\u5efa
.icons \u76ee\u5f55\uff1a
Text Only.\n\u251c\u2500 .icons/\n\u2502 \u2514\u2500 custom/\n\u2502 \u2514\u2500 logo.svg\n\u2514\u2500 mkdocs.yml\n
- \u914d\u7f6e\u4f7f\u7528\uff1a
YAMLtheme:\n icon:\n logo: custom/logo\n
"},{"location":"Tools/Blog/Mkdocs_Material/#414-logo","title":"4.1.4 Logo \u8bbe\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4141-logo","title":"4.1.4.1 \u57fa\u7840 Logo \u914d\u7f6e","text":"YAMLtheme:\n logo: assets/logo.svg # Logo \u56fe\u7247\n icon:\n logo: material/book # \u6216\u4f7f\u7528\u56fe\u6807\u4f5c\u4e3a Logo\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4142-logo","title":"4.1.4.2 \u54cd\u5e94\u5f0f Logo","text":"\u521b\u5efa\u54cd\u5e94\u5f0f Logo\uff1a
YAMLtheme:\n logo: assets/logo.svg\nextra_css:\n - stylesheets/logo.css\n
logo.css \u5185\u5bb9\uff1a
CSS/* \u9ed8\u8ba4 Logo */\n.md-logo img {\n width: 40px;\n height: 40px;\n}\n\n/* \u79fb\u52a8\u7aef Logo */\n@media screen and (max-width: 76.1875em) {\n .md-logo img {\n width: 32px;\n height: 32px;\n }\n}\n\n/* \u6697\u8272\u4e3b\u9898 Logo */\n[data-md-color-scheme=\"slate\"] .md-logo img {\n filter: invert(1);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4143-logo","title":"4.1.4.3 Logo \u52a8\u753b\u6548\u679c","text":"\u6dfb\u52a0 Logo \u52a8\u753b\uff1a
CSS.md-logo img {\n transition: transform 0.3s ease;\n}\n\n.md-logo img:hover {\n transform: scale(1.1);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#415","title":"4.1.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\ntheme:\n name: material\n\n # \u8c03\u8272\u677f\u914d\u7f6e\n palette:\n - media: \"(prefers-color-scheme: light)\"\n scheme: default\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-7\n name: \u5207\u6362\u81f3\u6697\u8272\u6a21\u5f0f\n - media: \"(prefers-color-scheme: dark)\"\n scheme: slate\n primary: blue\n accent: blue\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u6a21\u5f0f\n\n # \u56fe\u6807\u914d\u7f6e\n icon:\n logo: material/book-open-page-variant\n repo: fontawesome/brands/github\n edit: material/pencil\n view: material/eye\n\n # Logo \u914d\u7f6e\n logo: assets/logo.svg\n favicon: assets/favicon.png\n\n# \u989d\u5916\u6837\u5f0f\nextra_css:\n - stylesheets/extra.css\n - stylesheets/logo.css\n\n# \u989d\u5916\u811a\u672c\nextra_javascript:\n - javascripts/theme-switch.js\n\n# \u4e3b\u9898\u7279\u6027\nfeatures:\n - navigation.instant\n - navigation.tracking\n - navigation.tabs\n - navigation.sections\n - navigation.expand\n - navigation.top\n - toc.integrate\n
"},{"location":"Tools/Blog/Mkdocs_Material/#42","title":"4.2 \u5bfc\u822a\u529f\u80fd","text":""},{"location":"Tools/Blog/Mkdocs_Material/#421","title":"4.2.1 \u9876\u90e8\u5bfc\u822a","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4211","title":"4.2.1.1 \u5bfc\u822a\u680f\u6837\u5f0f","text":"\u57fa\u7840\u5bfc\u822a\u680f\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n features:\n - navigation.tabs # \u542f\u7528\u6807\u7b7e\u5f0f\u5bfc\u822a\n - navigation.sections # \u663e\u793a\u7ae0\u8282\u5bfc\u822a\n - navigation.expand # \u5c55\u5f00\u5bfc\u822a\n - navigation.indexes # \u7ae0\u8282\u7d22\u5f15\u9875\n
\u81ea\u5b9a\u4e49\u5bfc\u822a\u680f\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/extra.css */\n\n/* \u5bfc\u822a\u680f\u80cc\u666f */\n.md-header {\n background-color: #2196f3;\n box-shadow: 0 2px 4px rgba(0,0,0,.14);\n}\n\n/* \u5bfc\u822a\u9879\u6837\u5f0f */\n.md-tabs__link {\n font-size: .8rem;\n margin-top: .4rem;\n}\n\n/* \u6fc0\u6d3b\u72b6\u6001 */\n.md-tabs__link--active {\n font-weight: bold;\n border-bottom: 2px solid currentColor;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4212","title":"4.2.1.2 \u56fa\u5b9a\u5bfc\u822a\u680f","text":"\u542f\u7528\u56fa\u5b9a\u5bfc\u822a\uff1a
YAMLtheme:\n features:\n - header.autohide # \u81ea\u52a8\u9690\u85cf\n - navigation.sticky # \u56fa\u5b9a\u5bfc\u822a\n
\u81ea\u5b9a\u4e49\u56fa\u5b9a\u5bfc\u822a\u884c\u4e3a\uff1a
CSS/* \u56fa\u5b9a\u5bfc\u822a\u680f\u6837\u5f0f */\n.md-header--sticky {\n backdrop-filter: blur(8px);\n background-color: rgba(255,255,255,.8);\n}\n\n/* \u6697\u8272\u4e3b\u9898 */\n[data-md-color-scheme=\"slate\"] .md-header--sticky {\n background-color: rgba(0,0,0,.8);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4213","title":"4.2.1.3 \u54cd\u5e94\u5f0f\u5bfc\u822a","text":"\u54cd\u5e94\u5f0f\u914d\u7f6e\uff1a
YAMLtheme:\n features:\n - navigation.instant # \u5373\u65f6\u52a0\u8f7d\n - navigation.tracking # \u6eda\u52a8\u8ddf\u8e2a\n
\u54cd\u5e94\u5f0f\u6837\u5f0f\u8c03\u6574\uff1a
CSS/* \u79fb\u52a8\u7aef\u5bfc\u822a */\n@media screen and (max-width: 76.1875em) {\n .md-nav__title {\n font-size: .9rem;\n padding: 0.5rem 0.8rem;\n }\n\n .md-nav__item {\n padding: 0.2rem 0.8rem;\n }\n}\n\n/* \u5e73\u677f\u5bfc\u822a */\n@media screen and (min-width: 76.25em) {\n .md-nav__link {\n padding: 0.2rem 0;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#422","title":"4.2.2 \u6807\u7b7e\u5bfc\u822a","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4221","title":"4.2.2.1 \u6807\u7b7e\u9875\u914d\u7f6e","text":"\u542f\u7528\u6807\u7b7e\u5bfc\u822a\uff1a
YAMLtheme:\n features:\n - navigation.tabs\n - navigation.tabs.sticky # \u56fa\u5b9a\u6807\u7b7e\n
\u6807\u7b7e\u9875\u7ed3\u6784\uff1a
YAMLnav:\n - Home: index.md\n - Guide:\n - guide/index.md\n - Installation: guide/installation.md\n - Configuration: guide/configuration.md\n - API:\n - api/index.md\n - Reference: api/reference.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4222","title":"4.2.2.2 \u6807\u7b7e\u6837\u5f0f","text":"\u81ea\u5b9a\u4e49\u6807\u7b7e\u6837\u5f0f\uff1a
CSS/* \u6807\u7b7e\u5bb9\u5668 */\n.md-tabs {\n background-color: var(--md-primary-fg-color--dark);\n}\n\n/* \u6807\u7b7e\u9879 */\n.md-tabs__item {\n padding: 0 1rem;\n transition: all 0.2s ease;\n}\n\n/* \u60ac\u505c\u6548\u679c */\n.md-tabs__item:hover {\n background-color: rgba(255,255,255,.1);\n}\n\n/* \u6fc0\u6d3b\u72b6\u6001 */\n.md-tabs__item--active {\n font-weight: bold;\n}\n
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JavaScript// docs/javascripts/tabs.js\n\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u6807\u7b7e\u70b9\u51fb\u6548\u679c\n const tabs = document.querySelectorAll('.md-tabs__item');\n tabs.forEach(tab => {\n tab.addEventListener('click', () => {\n // \u6dfb\u52a0\u70b9\u51fb\u6ce2\u7eb9\u6548\u679c\n const ripple = document.createElement('div');\n ripple.classList.add('md-tabs__ripple');\n tab.appendChild(ripple);\n\n // \u79fb\u9664\u6ce2\u7eb9\u6548\u679c\n setTimeout(() => ripple.remove(), 1000);\n });\n });\n});\n
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CSS/* \u6ce2\u7eb9\u6548\u679c */\n.md-tabs__ripple {\n position: absolute;\n background: rgba(255,255,255,.3);\n border-radius: 50%;\n transform: scale(0);\n animation: ripple 0.6s linear;\n}\n\n@keyframes ripple {\n to {\n transform: scale(4);\n opacity: 0;\n }\n}\n
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YAMLtheme:\n features:\n - toc.integrate # \u96c6\u6210\u76ee\u5f55\n - toc.follow # \u76ee\u5f55\u8ddf\u968f\n\nmarkdown_extensions:\n - toc:\n permalink: true # \u6c38\u4e45\u94fe\u63a5\n toc_depth: 3 # \u76ee\u5f55\u6df1\u5ea6\n title: \u76ee\u5f55 # \u76ee\u5f55\u6807\u9898\n
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CSS/* \u76ee\u5f55\u5bb9\u5668 */\n.md-toc {\n padding: 1rem;\n background-color: var(--md-code-bg-color);\n border-radius: 4px;\n}\n\n/* \u76ee\u5f55\u6807\u9898 */\n.md-toc__title {\n font-weight: bold;\n margin-bottom: 1rem;\n}\n\n/* \u76ee\u5f55\u94fe\u63a5 */\n.md-toc__link {\n color: var(--md-typeset-color);\n text-decoration: none;\n}\n\n/* \u76ee\u5f55\u5c42\u7ea7\u7f29\u8fdb */\n.md-toc__list {\n margin-left: 1.5em;\n}\n
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YAMLmarkdown_extensions:\n - toc:\n permalink: \u2693\ufe0e # \u951a\u70b9\u7b26\u53f7\n slug: !!python/object/apply:pymdownx.slugs.slugify\n kwds: {case: lower} # URL \u8f6c\u6362\u89c4\u5219\n
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CSS/* \u951a\u70b9\u94fe\u63a5 */\n.headerlink {\n opacity: 0;\n margin-left: .5em;\n transition: opacity 0.2s ease;\n}\n\n/* \u6807\u9898\u60ac\u505c\u65f6\u663e\u793a\u951a\u70b9 */\nh1:hover .headerlink,\nh2:hover .headerlink,\nh3:hover .headerlink {\n opacity: 1;\n}\n
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YAMLtheme:\n features:\n - navigation.top # \u8fd4\u56de\u9876\u90e8\u6309\u94ae\n
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CSS/* \u8fd4\u56de\u9876\u90e8\u6309\u94ae */\n.md-top {\n background-color: var(--md-primary-fg-color);\n border-radius: 50%;\n box-shadow: 0 2px 4px rgba(0,0,0,.14);\n transition: all 0.2s ease;\n}\n\n/* \u60ac\u505c\u6548\u679c */\n.md-top:hover {\n background-color: var(--md-primary-fg-color--dark);\n transform: translateY(-2px);\n}\n
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JavaScript// docs/javascripts/scroll.js\n\ndocument.addEventListener('DOMContentLoaded', function() {\n const topButton = document.querySelector('.md-top');\n\n // \u5e73\u6ed1\u6eda\u52a8\n if (topButton) {\n topButton.addEventListener('click', (e) => {\n e.preventDefault();\n window.scrollTo({\n top: 0,\n behavior: 'smooth'\n });\n });\n }\n});\n
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JavaScript// \u63a7\u5236\u6309\u94ae\u663e\u793a\nwindow.addEventListener('scroll', () => {\n const topButton = document.querySelector('.md-top');\n if (topButton) {\n if (window.scrollY > 100) {\n topButton.classList.add('md-top--show');\n } else {\n topButton.classList.remove('md-top--show');\n }\n }\n});\n
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CSS/* \u6309\u94ae\u663e\u793a\u9690\u85cf */\n.md-top {\n opacity: 0;\n visibility: hidden;\n transition: opacity 0.2s ease, visibility 0.2s ease;\n}\n\n.md-top--show {\n opacity: 1;\n visibility: visible;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#425","title":"4.2.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\ntheme:\n name: material\n features:\n # \u5bfc\u822a\u529f\u80fd\n - navigation.tabs\n - navigation.tabs.sticky\n - navigation.sections\n - navigation.expand\n - navigation.indexes\n - navigation.instant\n - navigation.tracking\n - navigation.sticky\n - header.autohide\n\n # \u76ee\u5f55\u529f\u80fd\n - toc.integrate\n - toc.follow\n\n # \u8fd4\u56de\u9876\u90e8\n - navigation.top\n\n# Markdown \u6269\u5c55\nmarkdown_extensions:\n - toc:\n permalink: true\n toc_depth: 3\n title: \u76ee\u5f55\n slugify: !!python/object/apply:pymdownx.slugs.slugify\n kwds: {case: lower}\n\n# \u989d\u5916\u6837\u5f0f\u548c\u811a\u672c\nextra_css:\n - stylesheets/extra.css\n\nextra_javascript:\n - javascripts/tabs.js\n - javascripts/scroll.js\n
"},{"location":"Tools/Blog/Mkdocs_Material/#43","title":"4.3 \u641c\u7d22\u529f\u80fd","text":""},{"location":"Tools/Blog/Mkdocs_Material/#431","title":"4.3.1 \u641c\u7d22\u5f15\u64ce\u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4311","title":"4.3.1.1 \u641c\u7d22\u7b97\u6cd5","text":"\u57fa\u7840\u641c\u7d22\u914d\u7f6e\uff1a
YAMLplugins:\n - search:\n separator: '[\\\\s\\\\-\\\\.]+' # \u5206\u8bcd\u5206\u9694\u7b26\n min_search_length: 2 # \u6700\u5c0f\u641c\u7d22\u957f\u5ea6\n lang:\n - en\n - zh\n prebuild_index: true # \u9884\u6784\u5efa\u7d22\u5f15\n
\u9ad8\u7ea7\u641c\u7d22\u9009\u9879\uff1a
YAMLplugins:\n - search:\n separator: '[\\\\s\\\\-\\\\.]+\\\\s*'\n min_search_length: 2\n prebuild_index: python\n indexing:\n full_sections: true # \u7d22\u5f15\u5b8c\u6574\u7ae0\u8282\n headings: true # \u7d22\u5f15\u6807\u9898\n content: true # \u7d22\u5f15\u5185\u5bb9\n tags: true # \u7d22\u5f15\u6807\u7b7e\n scoring:\n title_boost: 10 # \u6807\u9898\u6743\u91cd\n heading_boost: 5 # \u6807\u9898\u6743\u91cd\n content_boost: 1 # \u5185\u5bb9\u6743\u91cd\n
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YAMLplugins:\n - search:\n indexing:\n full_sections: true\n headings: true\n content: true\n tags: true\n attachments: true # \u7d22\u5f15\u9644\u4ef6\n attachments_types: # \u9644\u4ef6\u7c7b\u578b\n - .pdf\n - .doc\n - .docx\n attachments_max_size: 2048 # \u6700\u5927\u5927\u5c0f(KB)\n
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YAMLplugins:\n - search:\n # \u5305\u542b\u7684\u6587\u4ef6\n include:\n - \"*.md\"\n - \"*.markdown\"\n\n # \u6392\u9664\u7684\u6587\u4ef6\n exclude:\n - drafts/*\n - private/*\n\n # \u5904\u7406\u7279\u5b9a\u8def\u5f84\n ignore:\n - 404.md\n - index.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#432","title":"4.3.2 \u641c\u7d22\u63d0\u793a","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4321","title":"4.3.2.1 \u5feb\u6377\u952e\u8bbe\u7f6e","text":"\u914d\u7f6e\u641c\u7d22\u5feb\u6377\u952e\uff1a
YAMLtheme:\n keyboard:\n search: s, / # \u4f7f\u7528 's' \u6216 '/' \u89e6\u53d1\u641c\u7d22\n
\u81ea\u5b9a\u4e49\u5feb\u6377\u952e\u5904\u7406\uff1a
JavaScript// docs/javascripts/search.js\n\ndocument.addEventListener('keydown', function(e) {\n // \u81ea\u5b9a\u4e49\u5feb\u6377\u952e\u903b\u8f91\n if ((e.key === 's' || e.key === '/') && !e.ctrlKey && !e.altKey && !e.metaKey) {\n e.preventDefault();\n const search = document.querySelector('.md-search__input');\n if (search) {\n search.focus();\n }\n }\n});\n
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YAMLtheme:\n language: zh # \u4f7f\u7528\u4e2d\u6587\u754c\u9762\n\nextra:\n search:\n language: zh\n text:\n placeholder: \u641c\u7d22\u6587\u6863...\n no_results: \u6ca1\u6709\u627e\u5230\u76f8\u5173\u7ed3\u679c\n searching: \u6b63\u5728\u641c\u7d22...\n
\u81ea\u5b9a\u4e49\u63d0\u793a\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/search.css */\n\n/* \u641c\u7d22\u6846\u63d0\u793a\u6587\u672c */\n.md-search__input::placeholder {\n color: var(--md-default-fg-color--lighter);\n}\n\n/* \u65e0\u7ed3\u679c\u63d0\u793a */\n.md-search-result__meta {\n color: var(--md-default-fg-color--light);\n font-size: .8rem;\n}\n
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YAMLplugins:\n - search:\n suggestions: true # \u542f\u7528\u641c\u7d22\u5efa\u8bae\n suggestions_min_length: 2 # \u6700\u5c0f\u5efa\u8bae\u957f\u5ea6\n
\u81ea\u5b9a\u4e49\u5efa\u8bae\u6837\u5f0f\uff1a
CSS/* \u641c\u7d22\u5efa\u8bae\u6837\u5f0f */\n.md-search-result__item {\n padding: .4rem .8rem;\n transition: background .2s ease;\n}\n\n.md-search-result__item:hover {\n background-color: var(--md-code-bg-color);\n}\n\n/* \u5efa\u8bae\u9879\u56fe\u6807 */\n.md-search-result__icon {\n color: var(--md-default-fg-color--lighter);\n margin-right: .4rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#433","title":"4.3.3 \u641c\u7d22\u9ad8\u4eae","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4331","title":"4.3.3.1 \u9ad8\u4eae\u6837\u5f0f","text":"\u914d\u7f6e\u641c\u7d22\u9ad8\u4eae\uff1a
YAMLtheme:\n features:\n - search.highlight # \u542f\u7528\u641c\u7d22\u9ad8\u4eae\n - search.share # \u542f\u7528\u641c\u7d22\u5206\u4eab\n - search.suggest # \u542f\u7528\u641c\u7d22\u5efa\u8bae\n
\u81ea\u5b9a\u4e49\u9ad8\u4eae\u6837\u5f0f\uff1a
CSS/* \u641c\u7d22\u7ed3\u679c\u9ad8\u4eae */\n.md-search-result__item mark {\n background-color: var(--md-accent-fg-color--transparent);\n color: var(--md-accent-fg-color);\n padding: 0 .2em;\n border-radius: .1em;\n}\n\n/* \u6eda\u52a8\u6761\u6837\u5f0f */\n.md-search-result__scrollwrap::-webkit-scrollbar {\n width: 4px;\n height: 4px;\n}\n\n.md-search-result__scrollwrap::-webkit-scrollbar-thumb {\n background-color: var(--md-default-fg-color--lighter);\n border-radius: 2px;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4332","title":"4.3.3.2 \u5339\u914d\u89c4\u5219","text":"\u914d\u7f6e\u641c\u7d22\u5339\u914d\u89c4\u5219\uff1a
YAMLplugins:\n - search:\n # \u6587\u672c\u5339\u914d\u914d\u7f6e\n tokenizer: '[\\s\\-\\.]+' # \u5206\u8bcd\u89c4\u5219\n min_search_length: 2 # \u6700\u5c0f\u641c\u7d22\u957f\u5ea6\n\n # \u6a21\u7cca\u5339\u914d\u8bbe\u7f6e\n fuzzy: false # \u7981\u7528\u6a21\u7cca\u5339\u914d\n\n # \u5339\u914d\u6743\u91cd\n boost: \n title: 10 # \u6807\u9898\u6743\u91cd\n text: 1 # \u6587\u672c\u6743\u91cd\n
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JavaScript// docs/javascripts/search-highlight.js\n\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u83b7\u53d6\u641c\u7d22\u7ed3\u679c\u5bb9\u5668\n const searchResults = document.querySelector('.md-search-result');\n\n if (searchResults) {\n // \u76d1\u542c\u641c\u7d22\u7ed3\u679c\u53d8\u5316\n const observer = new MutationObserver(mutations => {\n mutations.forEach(mutation => {\n if (mutation.type === 'childList') {\n // \u5904\u7406\u65b0\u6dfb\u52a0\u7684\u641c\u7d22\u7ed3\u679c\n const newResults = mutation.addedNodes;\n newResults.forEach(node => {\n if (node.nodeType === 1) { // \u5143\u7d20\u8282\u70b9\n customHighlight(node);\n }\n });\n }\n });\n });\n\n // \u542f\u52a8\u89c2\u5bdf\n observer.observe(searchResults, {\n childList: true,\n subtree: true\n });\n }\n});\n\nfunction customHighlight(node) {\n // \u81ea\u5b9a\u4e49\u9ad8\u4eae\u903b\u8f91\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#434","title":"4.3.4 \u641c\u7d22\u8bed\u8a00","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4341","title":"4.3.4.1 \u4e2d\u6587\u5206\u8bcd","text":"\u914d\u7f6e\u4e2d\u6587\u5206\u8bcd\uff1a
YAMLplugins:\n - search:\n lang:\n - en\n - zh\n separator: '[\\s\\-\\.,\\!\\/\\?\\u2000-\\u206F\\u3000-\\u303F\\u3040-\\u309F\\u30A0-\\u30FF\\u3100-\\u312F\\u3200-\\u32FF\\u3400-\\u4DBF\\u4E00-\\u9FFF]+'\n
\u4e2d\u6587\u641c\u7d22\u4f18\u5316\uff1a
JavaScript// docs/javascripts/chinese-search.js\n\nfunction chineseSegment(text) {\n // \u7b80\u5355\u7684\u4e2d\u6587\u5206\u8bcd\u903b\u8f91\n return text.replace(/[\\u4e00-\\u9fa5]/g, function(char) {\n return char + ' ';\n });\n}\n\n// \u6dfb\u52a0\u5230\u641c\u7d22\u5904\u7406\u6d41\u7a0b\ndocument.addEventListener('DOMContentLoaded', function() {\n const searchInput = document.querySelector('.md-search__input');\n if (searchInput) {\n searchInput.addEventListener('input', function(e) {\n const value = e.target.value;\n if (/[\\u4e00-\\u9fa5]/.test(value)) {\n // \u5904\u7406\u4e2d\u6587\u8f93\u5165\n const segmented = chineseSegment(value);\n // TODO: \u4f7f\u7528\u5206\u8bcd\u7ed3\u679c\u8fdb\u884c\u641c\u7d22\n }\n });\n }\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4342","title":"4.3.4.2 \u591a\u8bed\u8a00\u652f\u6301","text":"\u914d\u7f6e\u591a\u8bed\u8a00\u641c\u7d22\uff1a
YAMLplugins:\n - search:\n lang:\n - en\n - zh\n - ja\n # \u8bed\u8a00\u7279\u5b9a\u7684\u5206\u8bcd\u89c4\u5219\n separator:\n en: '[\\\\s\\\\-\\\\.]+'\n zh: '[\\u4e00-\\u9fa5]'\n ja: '[\\u3040-\\u309F\\u30A0-\\u30FF]+'\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4343","title":"4.3.4.3 \u505c\u7528\u8bcd\u914d\u7f6e","text":"\u914d\u7f6e\u505c\u7528\u8bcd\uff1a
YAMLplugins:\n - search:\n stopwords: \n en:\n - a\n - an\n - the\n - in\n - on\n - at\n zh:\n - \u7684\n - \u4e86\n - \u548c\n - \u4e0e\n - \u6216\n
\u81ea\u5b9a\u4e49\u505c\u7528\u8bcd\u5904\u7406\uff1a
JavaScript// docs/javascripts/stopwords.js\n\nconst stopwords = {\n en: ['a', 'an', 'the', 'in', 'on', 'at'],\n zh: ['\u7684', '\u4e86', '\u548c', '\u4e0e', '\u6216'],\n};\n\nfunction removeStopwords(text, lang) {\n if (!stopwords[lang]) return text;\n\n const words = text.split(/\\s+/);\n return words\n .filter(word => !stopwords[lang].includes(word))\n .join(' ');\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#435","title":"4.3.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\n\nplugins:\n - search:\n lang:\n - en\n - zh\n separator: '[\\s\\-\\.,\\!\\/\\?\\u2000-\\u206F\\u3000-\\u303F]+'\n prebuild_index: python\n indexing:\n full_sections: true\n headings: true\n content: true\n tags: true\n scoring:\n title_boost: 10\n heading_boost: 5\n content_boost: 1\n\ntheme:\n features:\n - search.highlight\n - search.share\n - search.suggest\n\nextra:\n search:\n language: zh\n text:\n placeholder: \u641c\u7d22\u6587\u6863...\n no_results: \u6ca1\u6709\u627e\u5230\u76f8\u5173\u7ed3\u679c\n searching: \u6b63\u5728\u641c\u7d22...\n\nextra_javascript:\n - javascripts/search.js\n - javascripts/search-highlight.js\n - javascripts/chinese-search.js\n - javascripts/stopwords.js\n\nextra_css:\n - stylesheets/search.css\n
"},{"location":"Tools/Blog/Mkdocs_Material/#44","title":"4.4 \u4ee3\u7801\u5757\u8bbe\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#441","title":"4.4.1 \u8bed\u6cd5\u9ad8\u4eae","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4411","title":"4.4.1.1 \u9ad8\u4eae\u4e3b\u9898","text":"\u57fa\u7840\u9ad8\u4eae\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n features:\n - content.code.annotate # \u542f\u7528\u4ee3\u7801\u6ce8\u91ca\n - content.code.copy # \u542f\u7528\u4ee3\u7801\u590d\u5236\n\nmarkdown_extensions:\n - pymdownx.highlight:\n anchor_linenums: true\n line_spans: __span\n pygments_lang_class: true\n use_pygments: true\n auto_title: true # \u663e\u793a\u8bed\u8a00\u540d\u79f0\n linenums: true # \u663e\u793a\u884c\u53f7\n
\u81ea\u5b9a\u4e49\u9ad8\u4eae\u4e3b\u9898\uff1a
YAMLtheme:\n palette:\n # \u4eae\u8272\u4e3b\u9898\n - scheme: default\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-7\n name: \u5207\u6362\u81f3\u6697\u8272\u4e3b\u9898\n pygments_style: github-light\n\n # \u6697\u8272\u4e3b\u9898\n - scheme: slate\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u4e3b\u9898\n pygments_style: monokai\n
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CSS/* docs/stylesheets/code.css */\n\n/* \u4ee3\u7801\u5757\u5bb9\u5668 */\n.highlight {\n background-color: var(--md-code-bg-color);\n border-radius: 4px;\n padding: 0.5rem;\n margin: 1rem 0;\n}\n\n/* \u4ee3\u7801\u884c */\n.highlight .code-line {\n display: block;\n padding: 0 1rem;\n border-left: 2px solid transparent;\n}\n\n/* \u9ad8\u4eae\u884c */\n.highlight .code-line.focused {\n background-color: var(--md-code-hl-color);\n border-left: 2px solid var(--md-accent-fg-color);\n}\n
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YAMLmarkdown_extensions:\n - pymdownx.highlight:\n extend_pygments_lang:\n # \u81ea\u5b9a\u4e49\u8bed\u8a00\u914d\u7f6e\n typescript:\n name: TypeScript\n aliases: [ts]\n jsonc:\n name: JSON with Comments\n aliases: [json5]\n
\u8bed\u8a00\u7279\u5b9a\u914d\u7f6e\uff1a
YAMLmarkdown_extensions:\n - pymdownx.highlight:\n language_prefix: language- # \u8bed\u8a00\u524d\u7f00\n css_class: highlight # CSS\u7c7b\u540d\n code_attr_on_pre: false # \u5c5e\u6027\u4f4d\u7f6e\n extend_pygments_lang: # \u8bed\u8a00\u6269\u5c55\n flow:\n name: Flow\n aliases: [flowtype]\n
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Python# docs/custom_lexer.py\nfrom pygments.lexer import RegexLexer, words\nfrom pygments.token import *\n\nclass CustomLexer(RegexLexer):\n name = 'CustomLanguage'\n aliases = ['custom']\n filenames = ['*.custom']\n\n tokens = {\n 'root': [\n (r'//.*$', Comment.Single),\n (words(('if', 'else', 'while'), suffix=r'\\b'), Keyword),\n (r'\"[^\"]*\"', String),\n (r'\\d+', Number),\n (r'[a-zA-Z_]\\w*', Name),\n (r'[^\\w\\s]', Punctuation),\n ]\n }\n
\u5728 mkdocs.yml \u4e2d\u4f7f\u7528\uff1a
YAMLmarkdown_extensions:\n - pymdownx.highlight:\n use_pygments: true\n extend_pygments_lang:\n custom:\n name: CustomLanguage\n aliases: [custom]\n
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YAMLmarkdown_extensions:\n - pymdownx.highlight:\n linenums: true\n linenums_style: table # \u8868\u683c\u5f0f\u884c\u53f7\n anchor_linenums: true # \u884c\u53f7\u94fe\u63a5\n
\u81ea\u5b9a\u4e49\u884c\u53f7\u6837\u5f0f\uff1a
CSS/* \u884c\u53f7\u5bb9\u5668 */\n.highlighttable {\n width: 100%;\n display: table;\n}\n\n/* \u884c\u53f7\u5217 */\n.linenos {\n color: var(--md-default-fg-color--lighter);\n text-align: right;\n padding-right: 1rem;\n user-select: none;\n}\n\n/* \u4ee3\u7801\u5217 */\n.code {\n padding-left: 1rem;\n border-left: 1px solid var(--md-default-fg-color--lightest);\n}\n
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Markdown```python linenums=\"10\"\ndef hello_world():\n print(\"Hello, World!\")\n```\n
\u914d\u7f6e\u9ed8\u8ba4\u8d77\u59cb\u884c\uff1a
YAMLmarkdown_extensions:\n - pymdownx.highlight:\n linenums: true\n linenums_start: 1 # \u9ed8\u8ba4\u8d77\u59cb\u884c\u53f7\n
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YAMLmarkdown_extensions:\n - pymdownx.highlight:\n anchor_linenums: true # \u542f\u7528\u884c\u53f7\u94fe\u63a5\n line_anchors: L # \u884c\u53f7\u94fe\u63a5\u524d\u7f00\n
\u81ea\u5b9a\u4e49\u94fe\u63a5\u6837\u5f0f\uff1a
CSS/* \u884c\u53f7\u94fe\u63a5 */\n.md-typeset .highlight [data-linenos]:before {\n content: attr(data-linenos);\n color: var(--md-default-fg-color--lighter);\n padding-right: 1rem;\n}\n\n/* \u94fe\u63a5\u60ac\u505c\u6548\u679c */\n.md-typeset .highlight [data-linenos]:hover:before {\n color: var(--md-accent-fg-color);\n cursor: pointer;\n}\n
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YAMLtheme:\n features:\n - content.code.copy # \u542f\u7528\u4ee3\u7801\u590d\u5236\n
\u81ea\u5b9a\u4e49\u590d\u5236\u6309\u94ae\u6837\u5f0f\uff1a
CSS/* \u590d\u5236\u6309\u94ae\u5bb9\u5668 */\n.md-clipboard {\n position: absolute;\n top: 0.5rem;\n right: 0.5rem;\n padding: 0.4rem;\n color: var(--md-default-fg-color--lighter);\n background-color: transparent;\n border: none;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n/* \u60ac\u505c\u6548\u679c */\n.md-clipboard:hover {\n color: var(--md-accent-fg-color);\n background-color: var(--md-code-bg-color);\n}\n
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JavaScript// docs/javascripts/clipboard.js\n\ndocument.addEventListener('DOMContentLoaded', () => {\n // \u83b7\u53d6\u6240\u6709\u4ee3\u7801\u5757\n const codeBlocks = document.querySelectorAll('pre code');\n\n codeBlocks.forEach(block => {\n // \u521b\u5efa\u590d\u5236\u6309\u94ae\n const button = document.createElement('button');\n button.className = 'md-clipboard';\n button.title = '\u590d\u5236\u5230\u526a\u8d34\u677f';\n\n // \u6dfb\u52a0\u590d\u5236\u56fe\u6807\n button.innerHTML = '<span class=\"md-clipboard__icon\"></span>';\n\n // \u6dfb\u52a0\u70b9\u51fb\u4e8b\u4ef6\n button.addEventListener('click', async () => {\n try {\n // \u590d\u5236\u4ee3\u7801\n await navigator.clipboard.writeText(block.textContent);\n\n // \u663e\u793a\u6210\u529f\u63d0\u793a\n button.classList.add('md-clipboard--success');\n setTimeout(() => {\n button.classList.remove('md-clipboard--success');\n }, 2000);\n } catch (err) {\n console.error('\u590d\u5236\u5931\u8d25:', err);\n }\n });\n\n // \u5c06\u6309\u94ae\u6dfb\u52a0\u5230\u4ee3\u7801\u5757\n block.parentNode.insertBefore(button, block);\n });\n});\n
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YAMLtheme:\n language: zh # \u4f7f\u7528\u4e2d\u6587\u754c\u9762\n\nextra:\n clipboard:\n copy: \u590d\u5236\n copied: \u5df2\u590d\u5236\uff01\n error: \u590d\u5236\u5931\u8d25\n
\u81ea\u5b9a\u4e49\u63d0\u793a\u6837\u5f0f\uff1a
CSS/* \u590d\u5236\u63d0\u793a */\n.md-clipboard__tooltip {\n position: absolute;\n top: -2rem;\n right: 0;\n padding: 0.4rem 0.8rem;\n color: var(--md-default-bg-color);\n background-color: var(--md-default-fg-color);\n border-radius: 4px;\n font-size: 0.8rem;\n opacity: 0;\n transform: translateY(0.4rem);\n transition: all 0.2s ease;\n}\n\n/* \u663e\u793a\u63d0\u793a */\n.md-clipboard:hover .md-clipboard__tooltip {\n opacity: 1;\n transform: translateY(0);\n}\n
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Markdown```python\ndef hello():\n print(\"Hello\") # (1)\n return True # (2)\n```\n\n1. \u6253\u5370\u95ee\u5019\u4fe1\u606f\n2. \u8fd4\u56de\u6210\u529f\u72b6\u6001\n
\u914d\u7f6e\u6ce8\u91ca\u6837\u5f0f\uff1a
CSS/* \u884c\u5185\u6ce8\u91ca\u6807\u8bb0 */\n.md-annotation {\n color: var(--md-accent-fg-color);\n font-size: 0.8em;\n vertical-align: super;\n}\n
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Markdown```python\ndef process_data():\n # (1)!\n data = load_data()\n\n # (2)!\n result = transform(data)\n\n return result\n```\n\n1. \u4ece\u6570\u636e\u6e90\u52a0\u8f7d\u6570\u636e\n \u8fd9\u91cc\u53ef\u4ee5\u662f\u591a\u884c\n \u6ce8\u91ca\u8bf4\u660e\n\n2. \u5bf9\u6570\u636e\u8fdb\u884c\u8f6c\u6362\u5904\u7406\n \u5305\u542b\u6e05\u6d17\u548c\u683c\u5f0f\u5316\n
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CSS/* \u6ce8\u91ca\u5bb9\u5668 */\n.md-annotation-wrapper {\n margin: 1rem 0;\n padding: 1rem;\n background-color: var(--md-code-bg-color);\n border-left: 4px solid var(--md-accent-fg-color);\n border-radius: 4px;\n}\n\n/* \u6ce8\u91ca\u6807\u8bb0 */\n.md-annotation-marker {\n color: var(--md-accent-fg-color);\n font-weight: bold;\n}\n\n/* \u6ce8\u91ca\u5185\u5bb9 */\n.md-annotation-content {\n margin-top: 0.5rem;\n color: var(--md-default-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#445","title":"4.4.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\n\ntheme:\n name: material\n features:\n - content.code.annotate\n - content.code.copy\n\nmarkdown_extensions:\n - pymdownx.highlight:\n anchor_linenums: true\n line_spans: __span\n pygments_lang_class: true\n use_pygments: true\n auto_title: true\n linenums: true\n linenums_style: table\n - pymdownx.superfences\n - pymdownx.inlinehilite\n\nextra:\n clipboard:\n copy: \u590d\u5236\n copied: \u5df2\u590d\u5236\uff01\n error: \u590d\u5236\u5931\u8d25\n\nextra_css:\n - stylesheets/code.css\n\nextra_javascript:\n - javascripts/clipboard.js\n
"},{"location":"Tools/Blog/Mkdocs_Material/#45","title":"4.5 \u5185\u5bb9\u589e\u5f3a","text":""},{"location":"Tools/Blog/Mkdocs_Material/#451","title":"4.5.1 \u6570\u5b66\u516c\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4511-katex","title":"4.5.1.1 KaTeX \u914d\u7f6e","text":"\u5b89\u88c5\u548c\u914d\u7f6e KaTeX\uff1a
YAMLmarkdown_extensions:\n - pymdownx.arithmatex:\n generic: true\n\nextra_javascript:\n - javascripts/katex.js \n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js \n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js\n\nextra_css:\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.css\n
\u521b\u5efa docs/javascripts/katex.js \uff1a
JavaScriptdocument.addEventListener(\"DOMContentLoaded\", function() {\n renderMathInElement(document.body, {\n delimiters: [\n {left: \"$$\", right: \"$$\", display: true},\n {left: \"$\", right: \"$\", display: false},\n {left: \"\\\\(\", right: \"\\\\)\", display: false},\n {left: \"\\\\[\", right: \"\\\\]\", display: true}\n ],\n throwOnError: false\n });\n});\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown\u5185\u8054\u516c\u5f0f: $E = mc^2$\n\n\u5757\u7ea7\u516c\u5f0f\uff1a\n$$\n\\frac{n!}{k!(n-k)!} = \\binom{n}{k}\n$$\n
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YAMLmarkdown_extensions:\n - pymdownx.arithmatex:\n generic: true\n\nextra_javascript:\n - javascripts/mathjax.js\n - https://polyfill.io/v3/polyfill.min.js?features=es6\n - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\n
\u521b\u5efa docs/javascripts/mathjax.js \uff1a
JavaScriptwindow.MathJax = {\n tex: {\n inlineMath: [[\"\\\\(\", \"\\\\)\"]],\n displayMath: [[\"\\\\[\", \"\\\\]\"]],\n processEscapes: true,\n processEnvironments: true\n },\n options: {\n ignoreHtmlClass: \".*|\",\n processHtmlClass: \"arithmatex\"\n }\n};\n
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YAMLmarkdown_extensions:\n - pymdownx.arithmatex:\n generic: true\n numbering: true\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown$$\n\\begin{equation}\nE = mc^2 \\label{eq:einstein}\n\\end{equation}\n$$\n\n\u5f15\u7528\u516c\u5f0f $\\eqref{eq:einstein}$\n
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YAMLmarkdown_extensions:\n - pymdownx.superfences:\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_code_format\n\nextra_javascript:\n - https://unpkg.com/mermaid@9/dist/mermaid.min.js\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Text Onlygraph TD\n A[\u5f00\u59cb] --> B{\u5224\u65ad}\n B -->|Yes| C[\u5904\u7406]\n B -->|No| D[\u7ed3\u675f]\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4522-plantuml","title":"4.5.2.2 PlantUML \u652f\u6301","text":"\u914d\u7f6e PlantUML\uff1a
YAMLmarkdown_extensions:\n - pymdownx.superfences:\n custom_fences:\n - name: plantuml\n class: plantuml\n format: !!python/name:pymdownx.superfences.fence_code_format\n\nextra_javascript:\n - https://cdn.jsdelivr.net/npm/plantuml-encoder@1.4.0/dist/plantuml-encoder.min.js\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Text Only@startuml\nAlice -> Bob: \u8bf7\u6c42\nBob --> Alice: \u54cd\u5e94\n@enduml\n
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JavaScript// docs/javascripts/charts.js\nimport Chart from 'chart.js/auto';\n\ndocument.addEventListener('DOMContentLoaded', function() {\n const chartElements = document.querySelectorAll('.custom-chart');\n\n chartElements.forEach(element => {\n const ctx = element.getContext('2d');\n const data = JSON.parse(element.dataset.chartData);\n\n new Chart(ctx, {\n type: data.type,\n data: data.data,\n options: data.options\n });\n });\n});\n
\u4f7f\u7528\u793a\u4f8b\uff1a
HTML<canvas class=\"custom-chart\" data-chart-data='{\n \"type\": \"line\",\n \"data\": {\n \"labels\": [\"1\u6708\", \"2\u6708\", \"3\u6708\"],\n \"datasets\": [{\n \"label\": \"\u6570\u636e\",\n \"data\": [10, 20, 30]\n }]\n }\n}'></canvas>\n
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YAMLmarkdown_extensions:\n - pymdownx.tasklist:\n custom_checkbox: true\n clickable_checkbox: true\n
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CSS/* docs/stylesheets/tasklist.css */\n\n.task-list-item {\n list-style-type: none;\n margin-left: -1.6rem;\n}\n\n.task-list-control {\n position: relative;\n display: inline-block;\n width: 1.2rem;\n height: 1.2rem;\n margin-right: 0.5rem;\n vertical-align: middle;\n}\n\n.task-list-indicator {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n background-color: var(--md-default-fg-color--lighter);\n border-radius: 2px;\n transition: all 0.2s ease;\n}\n\n.task-list-indicator:checked {\n background-color: var(--md-accent-fg-color);\n}\n
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JavaScript// docs/javascripts/tasklist.js\n\ndocument.addEventListener('DOMContentLoaded', function() {\n const taskItems = document.querySelectorAll('.task-list-item input[type=\"checkbox\"]');\n\n taskItems.forEach(item => {\n item.addEventListener('change', function() {\n // \u4fdd\u5b58\u72b6\u6001\n localStorage.setItem(\n `task-${this.closest('.task-list-item').id}`,\n this.checked\n );\n\n // \u66f4\u65b0\u6837\u5f0f\n if (this.checked) {\n this.closest('.task-list-item').classList.add('completed');\n } else {\n this.closest('.task-list-item').classList.remove('completed');\n }\n });\n\n // \u6062\u590d\u72b6\u6001\n const saved = localStorage.getItem(`task-${item.closest('.task-list-item').id}`);\n if (saved === 'true') {\n item.checked = true;\n item.closest('.task-list-item').classList.add('completed');\n }\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4533","title":"4.5.3.3 \u72b6\u6001\u7ba1\u7406","text":"CSS/* \u4efb\u52a1\u72b6\u6001\u6837\u5f0f */\n.task-list-item.completed {\n text-decoration: line-through;\n color: var(--md-default-fg-color--light);\n}\n\n.task-list-item.pending {\n font-weight: bold;\n}\n\n.task-list-item.in-progress {\n color: var(--md-accent-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#454","title":"4.5.4 \u6807\u7b7e\u9875","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4541","title":"4.5.4.1 \u6807\u7b7e\u7ec4\u6837\u5f0f","text":"\u914d\u7f6e\u6807\u7b7e\u9875\uff1a
YAMLmarkdown_extensions:\n - pymdownx.tabbed:\n alternate_style: true \n
\u81ea\u5b9a\u4e49\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/tabs.css */\n\n/* \u6807\u7b7e\u7ec4\u5bb9\u5668 */\n.tabbed-set {\n border: 1px solid var(--md-default-fg-color--lightest);\n border-radius: 4px;\n margin: 1rem 0;\n}\n\n/* \u6807\u7b7e\u5217\u8868 */\n.tabbed-labels {\n display: flex;\n background-color: var(--md-code-bg-color);\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n/* \u5355\u4e2a\u6807\u7b7e */\n.tabbed-labels > label {\n padding: 0.8rem 1.2rem;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n/* \u6fc0\u6d3b\u72b6\u6001 */\n.tabbed-labels > label.tabbed-selected {\n color: var(--md-accent-fg-color);\n border-bottom: 2px solid var(--md-accent-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4542","title":"4.5.4.2 \u5207\u6362\u6548\u679c","text":"\u6dfb\u52a0\u52a8\u753b\u6548\u679c\uff1a
CSS/* \u6807\u7b7e\u5185\u5bb9\u5207\u6362\u52a8\u753b */\n.tabbed-content {\n padding: 1rem;\n opacity: 0;\n transform: translateY(10px);\n transition: all 0.3s ease;\n}\n\n.tabbed-content.tabbed-selected {\n opacity: 1;\n transform: translateY(0);\n}\n
\u6807\u7b7e\u9875\u4ea4\u4e92\uff1a
JavaScript// docs/javascripts/tabs.js\n\ndocument.addEventListener('DOMContentLoaded', function() {\n const tabSets = document.querySelectorAll('.tabbed-set');\n\n tabSets.forEach(tabSet => {\n const tabs = tabSet.querySelectorAll('.tabbed-labels > label');\n const contents = tabSet.querySelectorAll('.tabbed-content');\n\n tabs.forEach((tab, index) => {\n tab.addEventListener('click', () => {\n // \u66f4\u65b0\u6807\u7b7e\u72b6\u6001\n tabs.forEach(t => t.classList.remove('tabbed-selected'));\n tab.classList.add('tabbed-selected');\n\n // \u66f4\u65b0\u5185\u5bb9\u72b6\u6001\n contents.forEach(c => c.classList.remove('tabbed-selected'));\n contents[index].classList.add('tabbed-selected');\n });\n });\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4543","title":"4.5.4.3 \u54cd\u5e94\u5f0f\u8bbe\u8ba1","text":"\u6dfb\u52a0\u54cd\u5e94\u5f0f\u652f\u6301\uff1a
CSS/* \u79fb\u52a8\u7aef\u9002\u914d */\n@media screen and (max-width: 76.1875em) {\n .tabbed-labels {\n flex-wrap: wrap;\n }\n\n .tabbed-labels > label {\n flex: 1 1 auto;\n text-align: center;\n }\n\n .tabbed-content {\n padding: 0.8rem;\n }\n}\n\n/* \u5e73\u677f\u9002\u914d */\n@media screen and (min-width: 76.25em) {\n .tabbed-set {\n max-width: 80%;\n margin: 1rem auto;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#455","title":"4.5.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\n\nmarkdown_extensions:\n # \u6570\u5b66\u516c\u5f0f\n - pymdownx.arithmatex:\n generic: true\n\n # \u56fe\u8868\u652f\u6301\n - pymdownx.superfences:\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_code_format\n - name: plantuml\n class: plantuml\n format: !!python/name:pymdownx.superfences.fence_code_format\n\n # \u4efb\u52a1\u5217\u8868\n - pymdownx.tasklist:\n custom_checkbox: true\n clickable_checkbox: true\n\n # \u6807\u7b7e\u9875\n - pymdownx.tabbed:\n alternate_style: true\n\nextra_javascript:\n - javascripts/katex.js\n - javascripts/mermaid.js\n - javascripts/charts.js\n - javascripts/tasklist.js\n - javascripts/tabs.js\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js\n - https://unpkg.com/mermaid@9/dist/mermaid.min.js\n\nextra_css:\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.css\n - stylesheets/tasklist.css\n - stylesheets/tabs.css\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5","title":"5 \u6837\u5f0f\u5b9a\u5236","text":""},{"location":"Tools/Blog/Mkdocs_Material/#51-css","title":"5.1 CSS \u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#511-css","title":"5.1.1 \u81ea\u5b9a\u4e49 CSS \u6587\u4ef6","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5111-css","title":"5.1.1.1 CSS \u6587\u4ef6\u7ec4\u7ec7","text":" - \u63a8\u8350\u7684\u76ee\u5f55\u7ed3\u6784\uff1a
Text Onlydocs/\n\u251c\u2500\u2500 stylesheets/\n\u2502 \u251c\u2500\u2500 base/\n\u2502 \u2502 \u251c\u2500\u2500 _variables.css # CSS\u53d8\u91cf\u5b9a\u4e49\n\u2502 \u2502 \u251c\u2500\u2500 _typography.css # \u6392\u7248\u6837\u5f0f\n\u2502 \u2502 \u2514\u2500\u2500 _colors.css # \u989c\u8272\u5b9a\u4e49\n\u2502 \u251c\u2500\u2500 components/\n\u2502 \u2502 \u251c\u2500\u2500 _buttons.css # \u6309\u94ae\u6837\u5f0f\n\u2502 \u2502 \u251c\u2500\u2500 _cards.css # \u5361\u7247\u6837\u5f0f\n\u2502 \u2502 \u2514\u2500\u2500 _tables.css # \u8868\u683c\u6837\u5f0f\n\u2502 \u251c\u2500\u2500 layouts/\n\u2502 \u2502 \u251c\u2500\u2500 _header.css # \u5934\u90e8\u6837\u5f0f\n\u2502 \u2502 \u251c\u2500\u2500 _nav.css # \u5bfc\u822a\u6837\u5f0f\n\u2502 \u2502 \u2514\u2500\u2500 _footer.css # \u9875\u811a\u6837\u5f0f\n\u2502 \u2514\u2500\u2500 extra.css # \u4e3b\u6837\u5f0f\u6587\u4ef6\n
- \u5728
mkdocs.yml \u4e2d\u5f15\u5165\u6837\u5f0f\uff1a
YAMLextra_css:\n - stylesheets/extra.css\n
- \u5728\u4e3b\u6837\u5f0f\u6587\u4ef6\u4e2d\u5bfc\u5165\u5176\u4ed6\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/extra.css */\n\n/* \u57fa\u7840\u6837\u5f0f */\n@import 'base/_variables.css';\n@import 'base/_typography.css';\n@import 'base/_colors.css';\n\n/* \u7ec4\u4ef6\u6837\u5f0f */\n@import 'components/_buttons.css';\n@import 'components/_cards.css';\n@import 'components/_tables.css';\n\n/* \u5e03\u5c40\u6837\u5f0f */\n@import 'layouts/_header.css';\n@import 'layouts/_nav.css';\n@import 'layouts/_footer.css';\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5112","title":"5.1.1.2 \u6837\u5f0f\u4f18\u5148\u7ea7","text":" - \u6837\u5f0f\u4f18\u5148\u7ea7\u89c4\u5219\uff1a
CSS/* 1. \u884c\u5185\u6837\u5f0f (1000) */\n<div style=\"color: red;\">\n\n/* 2. ID \u9009\u62e9\u5668 (100) */\n#header { }\n\n/* 3. \u7c7b\u9009\u62e9\u5668\u3001\u5c5e\u6027\u9009\u62e9\u5668\u3001\u4f2a\u7c7b (10) */\n.nav-item { }\n[type=\"text\"] { }\n:hover { }\n\n/* 4. \u5143\u7d20\u9009\u62e9\u5668\u3001\u4f2a\u5143\u7d20 (1) */\ndiv { }\n::before { }\n
- Material \u4e3b\u9898\u8986\u76d6\uff1a
CSS/* \u8986\u76d6\u4e3b\u9898\u6837\u5f0f */\n.md-header {\n /* \u4f7f\u7528 !important \u614e\u91cd */\n background-color: #2196f3 !important;\n}\n\n/* \u4f7f\u7528\u66f4\u5177\u4f53\u7684\u9009\u62e9\u5668 */\n.md-header[data-md-color-scheme=\"default\"] {\n background-color: #2196f3;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5113-css","title":"5.1.1.3 CSS \u53d8\u91cf","text":" - \u5b9a\u4e49\u5168\u5c40\u53d8\u91cf\uff1a
CSS/* docs/stylesheets/base/_variables.css */\n\n:root {\n /* \u989c\u8272\u53d8\u91cf */\n --primary-color: #2196f3;\n --accent-color: #f50057;\n --text-color: #333333;\n\n /* \u5b57\u4f53\u53d8\u91cf */\n --font-family: \"LXGW WenKai\", -apple-system, sans-serif;\n --code-font: \"JetBrains Mono\", monospace;\n\n /* \u95f4\u8ddd\u53d8\u91cf */\n --spacing-unit: 8px;\n --content-padding: calc(var(--spacing-unit) * 2);\n\n /* \u9634\u5f71\u53d8\u91cf */\n --shadow-sm: 0 1px 2px rgba(0,0,0,0.1);\n --shadow-md: 0 2px 4px rgba(0,0,0,0.1);\n --shadow-lg: 0 4px 8px rgba(0,0,0,0.1);\n}\n
- \u4e3b\u9898\u53d8\u91cf\uff1a
CSS/* \u4eae\u8272\u4e3b\u9898 */\n[data-md-color-scheme=\"default\"] {\n --md-primary-fg-color: var(--primary-color);\n --md-accent-fg-color: var(--accent-color);\n --md-typeset-color: var(--text-color);\n}\n\n/* \u6697\u8272\u4e3b\u9898 */\n[data-md-color-scheme=\"slate\"] {\n --primary-color: #90caf9;\n --accent-color: #ff4081;\n --text-color: #ffffff;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#512","title":"5.1.2 \u5e38\u7528\u6837\u5f0f\u4fee\u6539","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5121","title":"5.1.2.1 \u5b57\u4f53\u8bbe\u7f6e","text":" - \u5b57\u4f53\u5b9a\u4e49\uff1a
CSS/* docs/stylesheets/base/_typography.css */\n\n/* \u57fa\u7840\u5b57\u4f53\u8bbe\u7f6e */\nbody {\n font-family: var(--font-family);\n font-size: 16px;\n line-height: 1.6;\n}\n\n/* \u4ee3\u7801\u5b57\u4f53 */\ncode, pre {\n font-family: var(--code-font);\n font-size: 0.9em;\n}\n\n/* \u6807\u9898\u5b57\u4f53 */\nh1, h2, h3, h4, h5, h6 {\n font-family: var(--font-family);\n font-weight: 600;\n margin: calc(var(--spacing-unit) * 3) 0;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5122","title":"5.1.2.2 \u989c\u8272\u5b9a\u5236","text":"CSS/* docs/stylesheets/base/_colors.css */\n\n/* \u6587\u672c\u989c\u8272 */\n.md-typeset {\n color: var(--text-color);\n}\n\n/* \u94fe\u63a5\u989c\u8272 */\n.md-typeset a {\n color: var(--md-accent-fg-color);\n}\n\n/* \u4ee3\u7801\u5757\u989c\u8272 */\n.highlight {\n background-color: var(--md-code-bg-color);\n}\n\n/* \u5f15\u7528\u5757\u989c\u8272 */\nblockquote {\n border-left: 4px solid var(--md-accent-fg-color);\n background-color: var(--md-code-bg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5123","title":"5.1.2.3 \u95f4\u8ddd\u8c03\u6574","text":"CSS/* \u5185\u5bb9\u95f4\u8ddd */\n.md-main__inner {\n padding: var(--content-padding);\n}\n\n/* \u6bb5\u843d\u95f4\u8ddd */\n.md-typeset p {\n margin: var(--spacing-unit) 0;\n}\n\n/* \u5217\u8868\u95f4\u8ddd */\n.md-typeset ul li,\n.md-typeset ol li {\n margin-bottom: calc(var(--spacing-unit) * 0.5);\n}\n\n/* \u6807\u9898\u95f4\u8ddd */\n.md-typeset h1 {\n margin-top: calc(var(--spacing-unit) * 4);\n margin-bottom: calc(var(--spacing-unit) * 2);\n}\n\n.md-typeset h2 {\n margin-top: calc(var(--spacing-unit) * 3);\n margin-bottom: calc(var(--spacing-unit) * 1.5);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5124","title":"5.1.2.4 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"\u5728 mkdocs.yml \u4e2d\u7684\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n font: false # \u7981\u7528\u9ed8\u8ba4\u5b57\u4f53\n\nextra_css:\n - stylesheets/extra.css\n\nextra:\n css_variables:\n spacing_unit: 8px\n content_width: 960px\n
"},{"location":"Tools/Blog/Mkdocs_Material/#52","title":"5.2 \u4e3b\u9898\u6837\u5f0f\u8986\u76d6","text":""},{"location":"Tools/Blog/Mkdocs_Material/#521","title":"5.2.1 \u7ec4\u4ef6\u6837\u5f0f\u4fee\u6539","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5211","title":"5.2.1.1 \u5bfc\u822a\u680f\u6837\u5f0f","text":" - \u9876\u90e8\u5bfc\u822a\u680f\uff1a
CSS/* docs/stylesheets/components/header.css */\n\n/* \u5bfc\u822a\u680f\u5bb9\u5668 */\n.md-header {\n background-color: var(--md-primary-fg-color);\n box-shadow: 0 2px 4px rgba(0,0,0,.14);\n height: 3rem;\n}\n\n/* \u5bfc\u822a\u680f\u6807\u9898 */\n.md-header__title {\n font-size: 1.2rem;\n font-weight: 600;\n margin-left: 1rem;\n}\n\n/* \u5bfc\u822a\u680f\u6309\u94ae */\n.md-header__button {\n padding: .8rem;\n color: var(--md-primary-bg-color);\n}\n\n/* \u5bfc\u822a\u680f\u641c\u7d22\u6846 */\n.md-search__input {\n border-radius: 2rem;\n background-color: rgba(255,255,255,.1);\n padding: 0 2.4rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5212","title":"5.2.1.2 \u4fa7\u8fb9\u680f\u6837\u5f0f","text":"CSS/* docs/stylesheets/components/sidebar.css */\n\n/* \u4fa7\u8fb9\u680f\u5bb9\u5668 */\n.md-sidebar {\n width: 14rem;\n background-color: var(--md-default-bg-color);\n padding: 1.2rem 0;\n}\n\n/* \u4fa7\u8fb9\u680f\u5bfc\u822a */\n.md-nav--primary {\n padding: 0 .8rem;\n}\n\n/* \u5bfc\u822a\u9879 */\n.md-nav__item {\n padding: .2rem 0;\n}\n\n/* \u5bfc\u822a\u94fe\u63a5 */\n.md-nav__link {\n color: var(--md-default-fg-color);\n padding: .4rem .6rem;\n border-radius: 4px;\n transition: all .2s;\n}\n\n.md-nav__link:hover {\n background-color: var(--md-code-bg-color);\n color: var(--md-accent-fg-color);\n}\n\n/* \u6fc0\u6d3b\u72b6\u6001 */\n.md-nav__link--active {\n font-weight: 600;\n color: var(--md-accent-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5213","title":"5.2.1.3 \u9875\u811a\u6837\u5f0f","text":"CSS/* docs/stylesheets/components/footer.css */\n\n/* \u9875\u811a\u5bb9\u5668 */\n.md-footer {\n background-color: var(--md-default-bg-color--darkest);\n color: var(--md-footer-fg-color);\n}\n\n/* \u9875\u811a\u5185\u5bb9 */\n.md-footer-meta {\n background-color: rgba(0,0,0,.1);\n padding: 1rem 0;\n}\n\n/* \u9875\u811a\u94fe\u63a5 */\n.md-footer__link {\n padding: .4rem 1rem;\n color: var(--md-footer-fg-color--light);\n}\n\n/* \u7248\u6743\u4fe1\u606f */\n.md-footer-copyright {\n font-size: .8rem;\n color: var(--md-footer-fg-color--lighter);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#522","title":"5.2.2 \u81ea\u5b9a\u4e49\u8c03\u8272\u677f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5221","title":"5.2.2.1 \u4e3b\u9898\u8272\u5b9a\u5236","text":" - \u57fa\u7840\u989c\u8272\u5b9a\u4e49\uff1a
CSS/* docs/stylesheets/theme/colors.css */\n\n:root {\n /* \u4e3b\u8272\u8c03 */\n --md-primary-hue: 210;\n --md-primary-saturation: 80%;\n --md-primary-lightness: 45%;\n\n /* \u5f3a\u8c03\u8272 */\n --md-accent-hue: 340;\n --md-accent-saturation: 90%;\n --md-accent-lightness: 50%;\n}\n
- \u989c\u8272\u53d8\u91cf\u5e94\u7528\uff1a
CSS:root {\n --md-primary-fg-color: hsl(\n var(--md-primary-hue),\n var(--md-primary-saturation),\n var(--md-primary-lightness)\n );\n\n --md-accent-fg-color: hsl(\n var(--md-accent-hue),\n var(--md-accent-saturation),\n var(--md-accent-lightness)\n );\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5222","title":"5.2.2.2 \u914d\u8272\u65b9\u6848","text":"YAML# mkdocs.yml\ntheme:\n palette:\n # \u4eae\u8272\u6a21\u5f0f\n - media: \"(prefers-color-scheme: light)\"\n scheme: default\n primary: indigo\n accent: deep purple\n toggle:\n icon: material/brightness-7\n name: \u5207\u6362\u81f3\u6697\u8272\u6a21\u5f0f\n\n # \u6697\u8272\u6a21\u5f0f\n - media: \"(prefers-color-scheme: dark)\"\n scheme: slate\n primary: blue grey\n accent: deep purple\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u6a21\u5f0f\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5223","title":"5.2.2.3 \u6697\u8272\u4e3b\u9898","text":"CSS/* \u6697\u8272\u4e3b\u9898\u53d8\u91cf */\n[data-md-color-scheme=\"slate\"] {\n --md-default-bg-color: #1a1a1a;\n --md-default-bg-color--light: #222222;\n --md-default-bg-color--lighter: #282828;\n\n --md-default-fg-color: rgba(255,255,255,0.87);\n --md-default-fg-color--light: rgba(255,255,255,0.54);\n --md-default-fg-color--lighter: rgba(255,255,255,0.32);\n\n --md-code-bg-color: #2d2d2d;\n --md-code-fg-color: #f5f5f5;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#523","title":"5.2.3 \u5b57\u4f53\u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5231","title":"5.2.3.1 \u4e2d\u6587\u5b57\u4f53","text":" - \u5b57\u4f53\u5b9a\u4e49\uff1a
CSS/* docs/stylesheets/theme/fonts.css */\n\n/* \u4e2d\u6587\u5b57\u4f53\u53d8\u91cf */\n:root {\n --md-font-chinese: \"LXGW WenKai\", \"PingFang SC\", \"Microsoft YaHei\";\n}\n\n/* \u5f15\u5165 LXGW WenKai \u5b57\u4f53 */\n@font-face {\n font-family: \"LXGW WenKai\";\n src: url(\"https://cdn.jsdelivr.net/npm/lxgw-wenkai-webfont@1.1.0/style.css\");\n font-display: swap;\n}\n
- \u5b57\u4f53\u5e94\u7528\uff1a
CSSbody {\n font-family: var(--md-font-chinese), -apple-system, sans-serif;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5232","title":"5.2.3.2 \u4ee3\u7801\u5b57\u4f53","text":"CSS/* \u4ee3\u7801\u5b57\u4f53\u914d\u7f6e */\n:root {\n --md-code-font: \"JetBrains Mono\", \"Fira Code\", \"Source Code Pro\", monospace;\n}\n\n/* \u4ee3\u7801\u5757\u6837\u5f0f */\n.md-typeset code,\n.md-typeset pre {\n font-family: var(--md-code-font);\n font-size: 0.9em;\n}\n\n/* \u884c\u5185\u4ee3\u7801 */\n.md-typeset code {\n border-radius: 4px;\n padding: .2em .4em;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5233","title":"5.2.3.3 \u5b57\u4f53\u56de\u9000","text":"CSS/* \u5b57\u4f53\u56de\u9000\u7b56\u7565 */\n:root {\n --md-text-font-fallback: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica,\n Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji;\n\n --md-code-font-fallback: SFMono-Regular, Consolas, Menlo, monospace;\n}\n\n/* \u5e94\u7528\u56de\u9000\u5b57\u4f53 */\nbody {\n font-family: var(--md-font-chinese), var(--md-text-font-fallback);\n}\n\npre, code {\n font-family: var(--md-code-font), var(--md-code-font-fallback);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#524","title":"5.2.4 \u5e03\u5c40\u8c03\u6574","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5241","title":"5.2.4.1 \u54cd\u5e94\u5f0f\u5e03\u5c40","text":"CSS/* docs/stylesheets/theme/layout.css */\n\n/* \u57fa\u7840\u54cd\u5e94\u5f0f\u5e03\u5c40 */\n.md-grid {\n max-width: 100%;\n margin: 0 auto;\n}\n\n/* \u684c\u9762\u7aef */\n@media screen and (min-width: 76.25em) {\n .md-grid {\n max-width: 76rem;\n }\n\n .md-sidebar--primary {\n width: 14rem;\n }\n\n .md-sidebar--secondary {\n width: 12rem;\n margin-left: 76rem;\n }\n}\n\n/* \u5e73\u677f\u7aef */\n@media screen and (max-width: 76.1875em) {\n .md-grid {\n max-width: 60rem;\n }\n\n .md-header-nav__title {\n display: none;\n }\n}\n\n/* \u79fb\u52a8\u7aef */\n@media screen and (max-width: 44.9375em) {\n .md-grid {\n max-width: 100%;\n padding: 0 1rem;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5242","title":"5.2.4.2 \u7f51\u683c\u7cfb\u7edf","text":"CSS/* \u81ea\u5b9a\u4e49\u7f51\u683c\u7cfb\u7edf */\n.grid {\n display: grid;\n gap: 1rem;\n margin: 1rem 0;\n}\n\n/* \u7f51\u683c\u5217\u6570 */\n.grid-cols-1 { grid-template-columns: repeat(1, 1fr); }\n.grid-cols-2 { grid-template-columns: repeat(2, 1fr); }\n.grid-cols-3 { grid-template-columns: repeat(3, 1fr); }\n.grid-cols-4 { grid-template-columns: repeat(4, 1fr); }\n\n/* \u54cd\u5e94\u5f0f\u7f51\u683c */\n@media (min-width: 768px) {\n .md-grid-cols-md-2 { grid-template-columns: repeat(2, 1fr); }\n .md-grid-cols-md-3 { grid-template-columns: repeat(3, 1fr); }\n}\n\n@media (min-width: 1024px) {\n .md-grid-cols-lg-3 { grid-template-columns: repeat(3, 1fr); }\n .md-grid-cols-lg-4 { grid-template-columns: repeat(4, 1fr); }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5243","title":"5.2.4.3 \u5bb9\u5668\u5bbd\u5ea6","text":"CSS/* \u5bb9\u5668\u5bbd\u5ea6\u5b9a\u4e49 */\n:root {\n --md-container-width: 80rem;\n --md-container-padding: 1rem;\n}\n\n/* \u5bb9\u5668\u6837\u5f0f */\n.md-container {\n max-width: var(--md-container-width);\n margin: 0 auto;\n padding: 0 var(--md-container-padding);\n}\n\n/* \u4e0d\u540c\u5c3a\u5bf8\u7684\u5bb9\u5668 */\n.md-container--small {\n max-width: 60rem;\n}\n\n.md-container--medium {\n max-width: 70rem;\n}\n\n.md-container--large {\n max-width: 90rem;\n}\n\n/* \u6d41\u5f0f\u5bb9\u5668 */\n.md-container--fluid {\n max-width: 100%;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#525","title":"5.2.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\ntheme:\n name: material\n font: false\n features:\n - navigation.tabs\n - navigation.sections\n - navigation.expand\n palette:\n - scheme: default\n primary: indigo\n accent: deep purple\n - scheme: slate\n primary: blue grey\n accent: deep purple\n\nextra_css:\n - stylesheets/theme/colors.css\n - stylesheets/theme/fonts.css\n - stylesheets/theme/layout.css\n - stylesheets/components/header.css\n - stylesheets/components/sidebar.css\n - stylesheets/components/footer.css\n
"},{"location":"Tools/Blog/Mkdocs_Material/#53","title":"5.3 \u81ea\u5b9a\u4e49\u7ec4\u4ef6\u6837\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#531","title":"5.3.1 \u5361\u7247\u6837\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5311","title":"5.3.1.1 \u57fa\u7840\u5361\u7247","text":"\u57fa\u7840\u5361\u7247\u6837\u5f0f\u5b9a\u4e49\uff1a
CSS/* docs/stylesheets/components/cards.css */\n\n/* \u57fa\u7840\u5361\u7247\u5bb9\u5668 */\n.card {\n background-color: var(--md-default-bg-color);\n border-radius: 8px;\n box-shadow: var(--md-shadow-z1);\n padding: 1.5rem;\n margin: 1rem 0;\n transition: all 0.3s ease;\n}\n\n/* \u5361\u7247\u6807\u9898 */\n.card__title {\n font-size: 1.25rem;\n font-weight: 600;\n margin-bottom: 1rem;\n color: var(--md-typeset-color);\n}\n\n/* \u5361\u7247\u5185\u5bb9 */\n.card__content {\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n line-height: 1.6;\n}\n\n/* \u5361\u7247\u5e95\u90e8 */\n.card__footer {\n margin-top: 1rem;\n padding-top: 1rem;\n border-top: 1px solid var(--md-default-fg-color--lightest);\n}\n
\u4f7f\u7528\u793a\u4f8b\uff1a
HTML<div class=\"card\">\n <div class=\"card__title\">\u5361\u7247\u6807\u9898</div>\n <div class=\"card__content\">\n \u8fd9\u91cc\u662f\u5361\u7247\u5185\u5bb9...\n </div>\n <div class=\"card__footer\">\n \u5361\u7247\u5e95\u90e8\u4fe1\u606f\n </div>\n</div>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5312","title":"5.3.1.2 \u56fe\u7247\u5361\u7247","text":"CSS/* \u56fe\u7247\u5361\u7247\u6837\u5f0f */\n.card--image {\n padding: 0;\n overflow: hidden;\n}\n\n/* \u56fe\u7247\u5bb9\u5668 */\n.card__image {\n width: 100%;\n height: 200px;\n position: relative;\n overflow: hidden;\n}\n\n/* \u56fe\u7247\u6837\u5f0f */\n.card__image img {\n width: 100%;\n height: 100%;\n object-fit: cover;\n transition: transform 0.3s ease;\n}\n\n/* \u56fe\u7247\u60ac\u505c\u6548\u679c */\n.card--image:hover img {\n transform: scale(1.05);\n}\n\n/* \u56fe\u7247\u5361\u7247\u5185\u5bb9\u533a */\n.card--image .card__content {\n padding: 1.5rem;\n}\n\n/* \u56fe\u7247\u6807\u9898\u8986\u76d6 */\n.card__image-title {\n position: absolute;\n bottom: 0;\n left: 0;\n right: 0;\n padding: 1rem;\n background: linear-gradient(to top, rgba(0,0,0,0.7), transparent);\n color: white;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5313","title":"5.3.1.3 \u7279\u6548\u5361\u7247","text":"CSS/* \u60ac\u6d6e\u6548\u679c\u5361\u7247 */\n.card--hover {\n cursor: pointer;\n}\n\n.card--hover:hover {\n transform: translateY(-4px);\n box-shadow: var(--md-shadow-z2);\n}\n\n/* \u6e10\u53d8\u80cc\u666f\u5361\u7247 */\n.card--gradient {\n background: linear-gradient(135deg, \n var(--md-primary-fg-color) 0%,\n var(--md-accent-fg-color) 100%);\n color: white;\n}\n\n/* \u6bdb\u73bb\u7483\u6548\u679c\u5361\u7247 */\n.card--glass {\n background: rgba(255, 255, 255, 0.1);\n backdrop-filter: blur(10px);\n border: 1px solid rgba(255, 255, 255, 0.2);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#532","title":"5.3.2 \u63d0\u793a\u6846\u6837\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5321","title":"5.3.2.1 \u4fe1\u606f\u63d0\u793a","text":"CSS/* docs/stylesheets/components/alerts.css */\n\n/* \u57fa\u7840\u63d0\u793a\u6846 */\n.alert {\n padding: 1rem 1.5rem;\n margin: 1rem 0;\n border-left: 4px solid;\n border-radius: 4px;\n}\n\n/* \u4fe1\u606f\u63d0\u793a */\n.alert--info {\n background-color: #e3f2fd;\n border-color: #2196f3;\n color: #0d47a1;\n}\n\n.alert--info::before {\n content: \"\u2139\ufe0f\";\n margin-right: 0.5rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5322","title":"5.3.2.2 \u8b66\u544a\u63d0\u793a","text":"CSS/* \u8b66\u544a\u63d0\u793a */\n.alert--warning {\n background-color: #fff3e0;\n border-color: #ff9800;\n color: #e65100;\n}\n\n.alert--warning::before {\n content: \"\u26a0\ufe0f\";\n margin-right: 0.5rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5323","title":"5.3.2.3 \u9519\u8bef\u63d0\u793a","text":"CSS/* \u9519\u8bef\u63d0\u793a */\n.alert--error {\n background-color: #ffebee;\n border-color: #f44336;\n color: #b71c1c;\n}\n\n.alert--error::before {\n content: \"\u274c\";\n margin-right: 0.5rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#533","title":"5.3.3 \u53cb\u94fe\u6837\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5331","title":"5.3.3.1 \u53cb\u94fe\u5361\u7247","text":"CSS/* docs/stylesheets/components/friends.css */\n\n/* \u53cb\u94fe\u5bb9\u5668 */\n.friend-links {\n display: grid;\n grid-template-columns: repeat(auto-fill, minmax(250px, 1fr));\n gap: 1.5rem;\n margin: 2rem 0;\n}\n\n/* \u53cb\u94fe\u5361\u7247 */\n.friend-link {\n display: flex;\n align-items: center;\n padding: 1rem;\n background: var(--md-default-bg-color);\n border-radius: 8px;\n box-shadow: var(--md-shadow-z1);\n transition: all 0.3s ease;\n}\n\n/* \u5934\u50cf */\n.friend-link__avatar {\n width: 60px;\n height: 60px;\n border-radius: 50%;\n margin-right: 1rem;\n}\n\n/* \u4fe1\u606f */\n.friend-link__info {\n flex: 1;\n}\n\n.friend-link__name {\n font-weight: 600;\n color: var(--md-typeset-color);\n}\n\n.friend-link__desc {\n font-size: 0.85rem;\n color: var(--md-default-fg-color--light);\n margin-top: 0.25rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5332","title":"5.3.3.2 \u5206\u7c7b\u5c55\u793a","text":"CSS/* \u53cb\u94fe\u5206\u7c7b */\n.friend-links-section {\n margin: 2rem 0;\n}\n\n/* \u5206\u7c7b\u6807\u9898 */\n.friend-links-section__title {\n font-size: 1.25rem;\n font-weight: 600;\n margin-bottom: 1rem;\n padding-left: 1rem;\n border-left: 4px solid var(--md-accent-fg-color);\n}\n\n/* \u5206\u7c7b\u63cf\u8ff0 */\n.friend-links-section__desc {\n color: var(--md-default-fg-color--light);\n margin-bottom: 1.5rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5333","title":"5.3.3.3 \u60ac\u505c\u6548\u679c","text":"CSS/* \u60ac\u505c\u6548\u679c */\n.friend-link:hover {\n transform: translateY(-2px);\n box-shadow: var(--md-shadow-z2);\n}\n\n/* \u5934\u50cf\u52a8\u753b */\n.friend-link:hover .friend-link__avatar {\n transform: rotate(360deg);\n transition: transform 0.6s ease;\n}\n\n/* \u6807\u7b7e\u6548\u679c */\n.friend-link__tag {\n display: inline-block;\n padding: 0.2rem 0.5rem;\n font-size: 0.75rem;\n border-radius: 12px;\n background-color: var(--md-code-bg-color);\n margin-top: 0.5rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#534","title":"5.3.4 \u65f6\u95f4\u7ebf\u6837\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5341","title":"5.3.4.1 \u65f6\u95f4\u8f74\u8bbe\u8ba1","text":"CSS/* docs/stylesheets/components/timeline.css */\n\n/* \u65f6\u95f4\u7ebf\u5bb9\u5668 */\n.timeline {\n position: relative;\n max-width: 800px;\n margin: 2rem auto;\n padding: 2rem 0;\n}\n\n/* \u65f6\u95f4\u8f74\u7ebf */\n.timeline::before {\n content: '';\n position: absolute;\n top: 0;\n left: calc(50% - 1px);\n width: 2px;\n height: 100%;\n background-color: var(--md-default-fg-color--lightest);\n}\n\n/* \u65f6\u95f4\u7ebf\u9879\u76ee */\n.timeline-item {\n position: relative;\n margin: 2rem 0;\n}\n\n/* \u4ea4\u9519\u5e03\u5c40 */\n.timeline-item:nth-child(odd) {\n padding-right: calc(50% + 2rem);\n}\n\n.timeline-item:nth-child(even) {\n padding-left: calc(50% + 2rem);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5342","title":"5.3.4.2 \u8282\u70b9\u6837\u5f0f","text":"CSS/* \u65f6\u95f4\u8282\u70b9 */\n.timeline-node {\n position: absolute;\n top: 50%;\n width: 16px;\n height: 16px;\n background-color: var(--md-primary-fg-color);\n border-radius: 50%;\n transform: translateY(-50%);\n}\n\n.timeline-item:nth-child(odd) .timeline-node {\n right: calc(50% - 8px);\n}\n\n.timeline-item:nth-child(even) .timeline-node {\n left: calc(50% - 8px);\n}\n\n/* \u8282\u70b9\u5185\u5bb9 */\n.timeline-content {\n background-color: var(--md-default-bg-color);\n padding: 1.5rem;\n border-radius: 8px;\n box-shadow: var(--md-shadow-z1);\n}\n\n/* \u65f6\u95f4\u6807\u7b7e */\n.timeline-date {\n position: absolute;\n top: 50%;\n color: var(--md-default-fg-color--light);\n transform: translateY(-50%);\n}\n\n.timeline-item:nth-child(odd) .timeline-date {\n left: calc(50% + 2rem);\n}\n\n.timeline-item:nth-child(even) .timeline-date {\n right: calc(50% + 2rem);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5343","title":"5.3.4.3 \u54cd\u5e94\u5f0f\u9002\u914d","text":"CSS/* \u79fb\u52a8\u7aef\u9002\u914d */\n@media screen and (max-width: 768px) {\n .timeline::before {\n left: 0;\n }\n\n .timeline-item {\n padding-left: 2rem !important;\n padding-right: 0 !important;\n }\n\n .timeline-node {\n left: -8px !important;\n right: auto !important;\n }\n\n .timeline-date {\n position: relative;\n top: auto;\n left: auto !important;\n right: auto !important;\n margin-bottom: 0.5rem;\n }\n}\n\n/* \u5e73\u677f\u7aef\u9002\u914d */\n@media screen and (min-width: 769px) and (max-width: 1024px) {\n .timeline {\n max-width: 90%;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5344","title":"5.3.4.4 \u4f7f\u7528\u793a\u4f8b","text":"HTML<!-- \u65f6\u95f4\u7ebf\u793a\u4f8b -->\n<div class=\"timeline\">\n <div class=\"timeline-item\">\n <div class=\"timeline-node\"></div>\n <div class=\"timeline-date\">2024-01-01</div>\n <div class=\"timeline-content\">\n <h3>\u4e8b\u4ef6\u6807\u9898</h3>\n <p>\u4e8b\u4ef6\u63cf\u8ff0...</p>\n </div>\n </div>\n\n <!-- \u66f4\u591a\u65f6\u95f4\u7ebf\u9879\u76ee -->\n</div>\n\n<!-- \u53cb\u94fe\u793a\u4f8b -->\n<div class=\"friend-links-section\">\n <h2 class=\"friend-links-section__title\">\u6280\u672f\u535a\u5ba2</h2>\n <p class=\"friend-links-section__desc\">\u4f18\u79c0\u7684\u6280\u672f\u535a\u5ba2\u6536\u85cf</p>\n <div class=\"friend-links\">\n <a href=\"#\" class=\"friend-link\">\n <img src=\"avatar.jpg\" class=\"friend-link__avatar\">\n <div class=\"friend-link__info\">\n <div class=\"friend-link__name\">\u535a\u5ba2\u540d\u79f0</div>\n <div class=\"friend-link__desc\">\u535a\u5ba2\u63cf\u8ff0</div>\n <span class=\"friend-link__tag\">\u6807\u7b7e</span>\n </div>\n </a>\n </div>\n</div>\n\n<!-- \u63d0\u793a\u6846\u793a\u4f8b -->\n<div class=\"alert alert--info\">\n \u8fd9\u662f\u4e00\u6761\u4fe1\u606f\u63d0\u793a\n</div>\n\n<div class=\"alert alert--warning\">\n \u8fd9\u662f\u4e00\u6761\u8b66\u544a\u63d0\u793a\n</div>\n\n<div class=\"alert alert--error\">\n \u8fd9\u662f\u4e00\u6761\u9519\u8bef\u63d0\u793a\n</div>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#535","title":"5.3.5 \u5b8c\u6574\u914d\u7f6e","text":"YAML# mkdocs.yml\nextra_css:\n - stylesheets/components/cards.css\n - stylesheets/components/alerts.css\n - stylesheets/components/friends.css\n - stylesheets/components/timeline.css\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6-javascript","title":"6 JavaScript \u589e\u5f3a","text":""},{"location":"Tools/Blog/Mkdocs_Material/#61","title":"6.1 \u57fa\u7840\u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#611-js","title":"6.1.1 \u5f15\u5165 JS \u6587\u4ef6","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6111","title":"6.1.1.1 \u672c\u5730\u6587\u4ef6","text":" - \u521b\u5efa\u57fa\u7840\u76ee\u5f55\u7ed3\u6784\uff1a
Text Onlydocs/\n\u251c\u2500\u2500 javascripts/\n\u2502 \u251c\u2500\u2500 config/ # \u914d\u7f6e\u6587\u4ef6\n\u2502 \u2502 \u2514\u2500\u2500 main.js\n\u2502 \u251c\u2500\u2500 modules/ # \u529f\u80fd\u6a21\u5757\n\u2502 \u2502 \u251c\u2500\u2500 search.js\n\u2502 \u2502 \u2514\u2500\u2500 theme.js\n\u2502 \u251c\u2500\u2500 utils/ # \u5de5\u5177\u51fd\u6570\n\u2502 \u2502 \u2514\u2500\u2500 helpers.js\n\u2502 \u2514\u2500\u2500 extra.js # \u4e3b\u5165\u53e3\u6587\u4ef6\n
- \u5728
mkdocs.yml \u4e2d\u5f15\u5165\uff1a
YAMLextra_javascript:\n - javascripts/extra.js\n - javascripts/modules/search.js\n - javascripts/modules/theme.js\n
- JavaScript \u6587\u4ef6\u793a\u4f8b\uff1a
JavaScript// docs/javascripts/extra.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u521d\u59cb\u5316\u4ee3\u7801\n console.log('Documentation loaded');\n});\n\n// docs/javascripts/modules/theme.js\nconst ThemeManager = {\n init() {\n // \u4e3b\u9898\u521d\u59cb\u5316\n },\n toggle() {\n // \u4e3b\u9898\u5207\u6362\n }\n};\n\n// docs/javascripts/utils/helpers.js\nconst Helpers = {\n debounce(fn, delay) {\n let timer = null;\n return function() {\n clearTimeout(timer);\n timer = setTimeout(() => fn.apply(this, arguments), delay);\n };\n }\n};\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6112-cdn","title":"6.1.1.2 CDN \u5f15\u5165","text":" - \u5e38\u7528 CDN \u914d\u7f6e\uff1a
YAMLextra_javascript:\n # KaTeX \u6570\u5b66\u516c\u5f0f\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js\n\n # Mermaid \u56fe\u8868\n - https://unpkg.com/mermaid@9/dist/mermaid.min.js\n\n # \u4ee3\u7801\u9ad8\u4eae\n - https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/prism.min.js\n - https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/components/prism-python.min.js\n
- CDN \u52a0\u901f\u914d\u7f6e\uff1a
YAMLextra:\n cdn:\n # \u4f7f\u7528\u56fd\u5185 CDN\n enable: true\n provider: jsdelivr # \u6216 unpkg, cdnjs\n urls:\n katex: https://cdn.jsdelivr.net/npm/katex@0.16.7/dist/katex.min.js\n mermaid: https://cdn.jsdelivr.net/npm/mermaid@9/dist/mermaid.min.js\n
- CDN \u6545\u969c\u5904\u7406\uff1a
JavaScript// docs/javascripts/config/cdn-fallback.js\nfunction loadFallbackScript(url, fallbackUrl) {\n const script = document.createElement('script');\n script.src = url;\n script.onerror = () => {\n console.warn(`Failed to load ${url}, trying fallback...`);\n const fallback = document.createElement('script');\n fallback.src = fallbackUrl;\n document.head.appendChild(fallback);\n };\n document.head.appendChild(script);\n}\n\n// \u4f7f\u7528\u793a\u4f8b\nloadFallbackScript(\n 'https://cdn.jsdelivr.net/npm/katex@0.16.7/dist/katex.min.js',\n 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js'\n);\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6113","title":"6.1.1.3 \u6a21\u5757\u5316\u7ba1\u7406","text":" - \u4f7f\u7528 ES6 \u6a21\u5757\uff1a
JavaScript// docs/javascripts/modules/theme.js\nexport class ThemeManager {\n constructor() {\n this.darkMode = false;\n }\n\n init() {\n this.loadPreference();\n this.bindEvents();\n }\n\n toggle() {\n this.darkMode = !this.darkMode;\n this.savePreference();\n this.applyTheme();\n }\n}\n\n// docs/javascripts/modules/search.js\nexport class SearchManager {\n constructor() {\n this.index = null;\n }\n\n init() {\n this.buildIndex();\n this.bindSearchEvents();\n }\n\n search(query) {\n // \u641c\u7d22\u5b9e\u73b0\n }\n}\n\n// docs/javascripts/extra.js\nimport { ThemeManager } from './modules/theme.js';\nimport { SearchManager } from './modules/search.js';\n\nconst theme = new ThemeManager();\nconst search = new SearchManager();\n\ndocument.addEventListener('DOMContentLoaded', () => {\n theme.init();\n search.init();\n});\n
- \u6a21\u5757\u914d\u7f6e\u6587\u4ef6\uff1a
JavaScript// docs/javascripts/config/modules.js\nexport const ModuleConfig = {\n theme: {\n enabled: true,\n darkModeClass: 'dark-mode',\n storageKey: 'theme-preference'\n },\n search: {\n enabled: true,\n minChars: 3,\n maxResults: 10\n }\n};\n\n// \u4f7f\u7528\u914d\u7f6e\nimport { ModuleConfig } from '../config/modules.js';\n\nclass ThemeManager {\n constructor() {\n this.config = ModuleConfig.theme;\n if (!this.config.enabled) return;\n // \u521d\u59cb\u5316\u4ee3\u7801\n }\n}\n
- \u5de5\u5177\u51fd\u6570\u6a21\u5757\uff1a
JavaScript// docs/javascripts/utils/helpers.js\nexport const DOM = {\n // DOM \u64cd\u4f5c\u8f85\u52a9\u51fd\u6570\n select: (selector) => document.querySelector(selector),\n selectAll: (selector) => document.querySelectorAll(selector),\n addClass: (element, className) => element.classList.add(className),\n removeClass: (element, className) => element.classList.remove(className)\n};\n\nexport const Storage = {\n // \u672c\u5730\u5b58\u50a8\u8f85\u52a9\u51fd\u6570\n get: (key) => localStorage.getItem(key),\n set: (key, value) => localStorage.setItem(key, value),\n remove: (key) => localStorage.removeItem(key)\n};\n\nexport const Events = {\n // \u4e8b\u4ef6\u5904\u7406\u8f85\u52a9\u51fd\u6570\n on: (element, event, handler) => element.addEventListener(event, handler),\n off: (element, event, handler) => element.removeEventListener(event, handler),\n trigger: (element, event) => element.dispatchEvent(new Event(event))\n};\n
- \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b\uff1a
YAML# mkdocs.yml\nextra_javascript:\n # \u6838\u5fc3\u6587\u4ef6\n - javascripts/extra.js\n - javascripts/config/modules.js\n\n # \u529f\u80fd\u6a21\u5757\n - javascripts/modules/theme.js\n - javascripts/modules/search.js\n\n # \u5de5\u5177\u51fd\u6570\n - javascripts/utils/helpers.js\n\n # \u7b2c\u4e09\u65b9\u5e93\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js\n - https://unpkg.com/mermaid@9/dist/mermaid.min.js\n\n# \u6a21\u5757\u914d\u7f6e\nextra:\n javascript_modules:\n theme:\n enabled: true\n default: light\n search:\n enabled: true\n min_chars: 3\n
"},{"location":"Tools/Blog/Mkdocs_Material/#62","title":"6.2 \u7b2c\u4e09\u65b9\u5e93\u96c6\u6210","text":""},{"location":"Tools/Blog/Mkdocs_Material/#621-katex","title":"6.2.1 KaTeX \u6570\u5b66\u516c\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6211","title":"6.2.1.1 \u57fa\u7840\u914d\u7f6e","text":" - \u5728
mkdocs.yml \u4e2d\u914d\u7f6e\uff1a
YAMLmarkdown_extensions:\n - pymdownx.arithmatex:\n generic: true\n\nextra_javascript:\n - javascripts/katex.js\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js\n\nextra_css:\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.css\n
- \u521b\u5efa KaTeX \u914d\u7f6e\u6587\u4ef6\uff1a
JavaScript// docs/javascripts/katex.js\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n renderMathInElement(document.body, {\n delimiters: [\n {left: \"$$\", right: \"$$\", display: true},\n {left: \"$\", right: \"$\", display: false},\n {left: \"\\\\(\", right: \"\\\\)\", display: false},\n {left: \"\\\\[\", right: \"\\\\]\", display: true}\n ],\n throwOnError: false,\n errorColor: \"#cc0000\",\n strict: \"ignore\"\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6212","title":"6.2.1.2 \u81ea\u52a8\u6e32\u67d3","text":" - \u914d\u7f6e\u81ea\u52a8\u6e32\u67d3\u9009\u9879\uff1a
JavaScript// docs/javascripts/katex-auto.js\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n renderMathInElement(document.body, {\n delimiters: [\n {left: \"$$\", right: \"$$\", display: true},\n {left: \"$\", right: \"$\", display: false}\n ],\n // \u81ea\u52a8\u6e32\u67d3\u8bbe\u7f6e\n ignoredTags: [\"script\", \"noscript\", \"style\", \"textarea\", \"pre\", \"code\"],\n ignoredClasses: [\"no-math\"],\n processEscapes: true,\n processEnvironments: true,\n // \u5904\u7406\u81ea\u5b9a\u4e49\u5b8f\n macros: {\n \"\\\\RR\": \"\\\\mathbb{R}\",\n \"\\\\NN\": \"\\\\mathbb{N}\",\n \"\\\\ZZ\": \"\\\\mathbb{Z}\"\n }\n });\n});\n
- \u4f7f\u7528\u793a\u4f8b\uff1a
Markdown\u884c\u5185\u516c\u5f0f\uff1a$E = mc^2$\n\n\u5757\u7ea7\u516c\u5f0f\uff1a\n$$\n\\frac{n!}{k!(n-k)!} = \\binom{n}{k}\n$$\n\n\u81ea\u5b9a\u4e49\u5b8f\uff1a$\\RR$ \u8868\u793a\u5b9e\u6570\u96c6\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6213","title":"6.2.1.3 \u516c\u5f0f\u7f16\u53f7","text":" - \u542f\u7528\u516c\u5f0f\u7f16\u53f7\uff1a
JavaScript// docs/javascripts/katex-numbering.js\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n // \u516c\u5f0f\u7f16\u53f7\u8ba1\u6570\u5668\n let equationNumbers = {};\n let numberings = {};\n\n renderMathInElement(document.body, {\n delimiters: [\n {left: \"$$\", right: \"$$\", display: true}\n ],\n // \u516c\u5f0f\u7f16\u53f7\u5904\u7406\n preProcess: (math) => {\n if (math.includes('\\\\label')) {\n const label = math.match(/\\\\label{([^}]*)}/)[1];\n const number = Object.keys(numberings).length + 1;\n numberings[label] = number;\n return math.replace(/\\\\label{[^}]*}/, `(${number})`);\n }\n return math;\n }\n });\n});\n
- \u4f7f\u7528\u7f16\u53f7\u548c\u5f15\u7528\uff1a
Markdown\u5e26\u7f16\u53f7\u7684\u516c\u5f0f\uff1a\n$$\nE = mc^2 \\label{eq:einstein}\n$$\n\n\u5f15\u7528\u4e0a\u9762\u7684\u516c\u5f0f $\\eqref{eq:einstein}$\n
"},{"location":"Tools/Blog/Mkdocs_Material/#622-mermaid","title":"6.2.2 Mermaid \u56fe\u8868","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6221","title":"6.2.2.1 \u521d\u59cb\u5316\u914d\u7f6e","text":" - \u5728
mkdocs.yml \u4e2d\u914d\u7f6e\uff1a
YAMLmarkdown_extensions:\n - pymdownx.superfences:\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_code_format\n\nextra_javascript:\n - https://unpkg.com/mermaid@9/dist/mermaid.min.js\n - javascripts/mermaid.js\n
- \u521b\u5efa Mermaid \u914d\u7f6e\u6587\u4ef6\uff1a
JavaScript// docs/javascripts/mermaid.js\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n mermaid.initialize({\n startOnLoad: true,\n theme: 'default',\n sequence: {\n showSequenceNumbers: true,\n actorMargin: 50,\n messageMargin: 40\n },\n flowchart: {\n useMaxWidth: false,\n htmlLabels: true,\n curve: 'basis'\n },\n gantt: {\n titleTopMargin: 25,\n barHeight: 20,\n barGap: 4\n }\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6222","title":"6.2.2.2 \u4e3b\u9898\u5b9a\u5236","text":" - \u81ea\u5b9a\u4e49\u4e3b\u9898\u914d\u7f6e\uff1a
JavaScript// docs/javascripts/mermaid-theme.js\nmermaid.initialize({\n theme: 'base',\n themeVariables: {\n // \u57fa\u7840\u989c\u8272\n primaryColor: '#2196f3',\n primaryTextColor: '#fff',\n primaryBorderColor: '#1976d2',\n lineColor: '#696969',\n\n // \u6d41\u7a0b\u56fe\u989c\u8272\n nodeBkg: '#fff',\n mainBkg: '#f8f9fa',\n nodeTextColor: '#333',\n\n // \u65f6\u5e8f\u56fe\u989c\u8272\n actorBkg: '#f8f9fa',\n actorBorder: '#2196f3',\n actorTextColor: '#333',\n\n // \u7518\u7279\u56fe\u989c\u8272\n sectionBkgColor: '#f8f9fa',\n altSectionBkgColor: '#fff',\n\n // \u6697\u8272\u4e3b\u9898\u652f\u6301\n darkMode: false\n }\n});\n
- \u54cd\u5e94\u4e3b\u9898\u5207\u6362\uff1a
JavaScript// \u76d1\u542c\u4e3b\u9898\u5207\u6362\ndocument.addEventListener('themeChanged', function(e) {\n const isDark = e.detail.theme === 'dark';\n mermaid.initialize({\n theme: isDark ? 'dark' : 'default',\n themeVariables: {\n darkMode: isDark\n }\n });\n // \u91cd\u65b0\u6e32\u67d3\u56fe\u8868\n mermaid.init(undefined, '.mermaid');\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6223","title":"6.2.2.3 \u4ea4\u4e92\u529f\u80fd","text":" - \u6dfb\u52a0\u70b9\u51fb\u4e8b\u4ef6\uff1a
JavaScript// docs/javascripts/mermaid-interaction.js\nmermaid.initialize({\n securityLevel: 'loose',\n flowchart: {\n htmlLabels: true,\n useMaxWidth: true\n }\n});\n\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n // \u4e3a\u56fe\u8868\u6dfb\u52a0\u70b9\u51fb\u4e8b\u4ef6\n const diagrams = document.querySelectorAll('.mermaid');\n diagrams.forEach(diagram => {\n diagram.addEventListener('click', function(e) {\n const target = e.target;\n if (target.tagName === 'g' && target.classList.contains('node')) {\n const nodeId = target.id;\n console.log('Clicked node:', nodeId);\n // \u5904\u7406\u8282\u70b9\u70b9\u51fb\n handleNodeClick(nodeId);\n }\n });\n });\n});\n\nfunction handleNodeClick(nodeId) {\n // \u8282\u70b9\u70b9\u51fb\u5904\u7406\n const node = document.getElementById(nodeId);\n if (node) {\n // \u6dfb\u52a0\u9ad8\u4eae\u6548\u679c\n node.classList.add('node-highlight');\n setTimeout(() => {\n node.classList.remove('node-highlight');\n }, 1000);\n }\n}\n
- \u6dfb\u52a0\u56fe\u8868\u52a8\u753b\uff1a
CSS/* docs/stylesheets/mermaid.css */\n.mermaid .node-highlight {\n animation: pulse 1s;\n}\n\n@keyframes pulse {\n 0% { opacity: 1; }\n 50% { opacity: 0.5; }\n 100% { opacity: 1; }\n}\n\n.mermaid .flowchart-link {\n transition: stroke-width 0.3s ease;\n}\n\n.mermaid .flowchart-link:hover {\n stroke-width: 2px;\n cursor: pointer;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6224","title":"6.2.2.4 \u4f7f\u7528\u793a\u4f8b","text":" - \u6d41\u7a0b\u56fe\u793a\u4f8b\uff1a
Markdown```mermaid\ngraph TD\n A[\u5f00\u59cb] --> B{\u5224\u65ad}\n B -->|Yes| C[\u5904\u7406]\n B -->|No| D[\u7ed3\u675f]\n C --> D\n```\n
- \u65f6\u5e8f\u56fe\u793a\u4f8b\uff1a
Markdown```mermaid\nsequenceDiagram\n participant \u5ba2\u6237\u7aef\n participant \u670d\u52a1\u5668\n\n \u5ba2\u6237\u7aef->>\u670d\u52a1\u5668: \u8bf7\u6c42\u6570\u636e\n \u670d\u52a1\u5668-->>\u5ba2\u6237\u7aef: \u8fd4\u56de\u54cd\u5e94\n```\n
"},{"location":"Tools/Blog/Mkdocs_Material/#623","title":"6.2.3 \u4ee3\u7801\u590d\u5236","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6231","title":"6.2.3.1 \u590d\u5236\u6309\u94ae","text":" - \u5728
mkdocs.yml \u4e2d\u914d\u7f6e\uff1a
YAMLtheme:\n features:\n - content.code.copy\n\nextra_css:\n - stylesheets/code-copy.css\nextra_javascript:\n - javascripts/code-copy.js\n
- \u521b\u5efa\u590d\u5236\u6309\u94ae\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/code-copy.css */\n\n/* \u590d\u5236\u6309\u94ae\u5bb9\u5668 */\n.copy-button {\n position: absolute;\n right: 0.5rem;\n top: 0.5rem;\n padding: 0.4rem;\n background-color: rgba(0, 0, 0, 0.1);\n border: none;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n/* \u6309\u94ae\u60ac\u505c\u6548\u679c */\n.copy-button:hover {\n background-color: rgba(0, 0, 0, 0.2);\n}\n\n/* \u56fe\u6807\u6837\u5f0f */\n.copy-button i {\n font-size: 1rem;\n color: var(--md-default-fg-color--light);\n}\n\n/* \u6210\u529f\u72b6\u6001 */\n.copy-button.success {\n background-color: var(--md-accent-fg-color);\n}\n\n.copy-button.success i {\n color: white;\n}\n
- \u5b9e\u73b0\u590d\u5236\u529f\u80fd\uff1a
JavaScript// docs/javascripts/code-copy.js\ndocument.addEventListener('DOMContentLoaded', () => {\n // \u4e3a\u6240\u6709\u4ee3\u7801\u5757\u6dfb\u52a0\u590d\u5236\u6309\u94ae\n const codeBlocks = document.querySelectorAll('pre code');\n\n codeBlocks.forEach((codeBlock) => {\n const container = codeBlock.parentNode;\n const copyButton = document.createElement('button');\n copyButton.className = 'copy-button';\n copyButton.innerHTML = '<i class=\"material-icons\">content_copy</i>';\n container.style.position = 'relative';\n container.appendChild(copyButton);\n\n // \u6dfb\u52a0\u70b9\u51fb\u4e8b\u4ef6\n copyButton.addEventListener('click', async () => {\n try {\n await navigator.clipboard.writeText(codeBlock.textContent);\n showSuccess(copyButton);\n } catch (err) {\n showError(copyButton);\n }\n });\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6232","title":"6.2.3.2 \u63d0\u793a\u6d88\u606f","text":" - \u521b\u5efa\u63d0\u793a\u6d88\u606f\u6837\u5f0f\uff1a
CSS/* \u63d0\u793a\u6d88\u606f\u6837\u5f0f */\n.copy-tooltip {\n position: absolute;\n top: -2rem;\n right: 0;\n padding: 0.4rem 0.8rem;\n background-color: var(--md-default-fg-color);\n color: var(--md-default-bg-color);\n border-radius: 4px;\n font-size: 0.8rem;\n opacity: 0;\n transform: translateY(0.4rem);\n transition: all 0.2s ease;\n}\n\n.copy-tooltip.show {\n opacity: 1;\n transform: translateY(0);\n}\n
- \u5b9e\u73b0\u63d0\u793a\u529f\u80fd\uff1a
JavaScript// \u663e\u793a\u63d0\u793a\u6d88\u606f\nfunction showTooltip(button, message, type = 'success') {\n const tooltip = document.createElement('div');\n tooltip.className = `copy-tooltip ${type}`;\n tooltip.textContent = message;\n button.appendChild(tooltip);\n\n // \u6dfb\u52a0\u663e\u793a\u7c7b\n setTimeout(() => tooltip.classList.add('show'), 10);\n\n // \u81ea\u52a8\u79fb\u9664\n setTimeout(() => {\n tooltip.classList.remove('show');\n setTimeout(() => tooltip.remove(), 200);\n }, 2000);\n}\n\n// \u6210\u529f\u63d0\u793a\nfunction showSuccess(button) {\n button.classList.add('success');\n showTooltip(button, '\u590d\u5236\u6210\u529f\uff01');\n setTimeout(() => button.classList.remove('success'), 2000);\n}\n\n// \u9519\u8bef\u63d0\u793a\nfunction showError(button) {\n button.classList.add('error');\n showTooltip(button, '\u590d\u5236\u5931\u8d25\uff01', 'error');\n setTimeout(() => button.classList.remove('error'), 2000);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6233","title":"6.2.3.3 \u590d\u5236\u56de\u8c03","text":"JavaScript// \u5b9a\u4e49\u590d\u5236\u4e8b\u4ef6\u5904\u7406\u5668\nconst copyHandlers = {\n // \u590d\u5236\u524d\u5904\u7406\n beforeCopy: (code) => {\n // \u53ef\u4ee5\u5728\u8fd9\u91cc\u5bf9\u4ee3\u7801\u8fdb\u884c\u9884\u5904\u7406\n return code.trim();\n },\n\n // \u590d\u5236\u6210\u529f\u56de\u8c03\n onSuccess: (button, code) => {\n console.log('Copied:', code.length, 'characters');\n showSuccess(button);\n\n // \u89e6\u53d1\u81ea\u5b9a\u4e49\u4e8b\u4ef6\n const event = new CustomEvent('codeCopied', {\n detail: { code }\n });\n document.dispatchEvent(event);\n },\n\n // \u590d\u5236\u5931\u8d25\u56de\u8c03\n onError: (button, error) => {\n console.error('Copy failed:', error);\n showError(button);\n }\n};\n\n// \u4f7f\u7528\u56de\u8c03\nasync function copyCode(button, code) {\n try {\n const processedCode = copyHandlers.beforeCopy(code);\n await navigator.clipboard.writeText(processedCode);\n copyHandlers.onSuccess(button, processedCode);\n } catch (err) {\n copyHandlers.onError(button, err);\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#624","title":"6.2.4 \u56fe\u7247\u9884\u89c8","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6241-lightbox","title":"6.2.4.1 lightbox \u914d\u7f6e","text":" - \u5728
mkdocs.yml \u4e2d\u914d\u7f6e\uff1a
YAMLmarkdown_extensions:\n - attr_list\n - md_in_html\n\nplugins:\n - glightbox\n\nextra_css:\n - stylesheets/glightbox.css\nextra_javascript:\n - javascripts/glightbox.js\n
- \u914d\u7f6e GLightbox\uff1a
JavaScript// docs/javascripts/glightbox.js\ndocument.addEventListener('DOMContentLoaded', () => {\n const lightbox = GLightbox({\n selector: '.glightbox',\n touchNavigation: true,\n loop: false,\n autoplayVideos: true,\n preload: true,\n // \u57fa\u672c\u8bbe\u7f6e\n height: 'auto',\n zoomable: true,\n draggable: true,\n // \u52a8\u753b\u8bbe\u7f6e\n openEffect: 'zoom',\n closeEffect: 'fade',\n cssEfects: {\n fade: { in: 'fadeIn', out: 'fadeOut' },\n zoom: { in: 'zoomIn', out: 'zoomOut' }\n }\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6242","title":"6.2.4.2 \u7f29\u653e\u529f\u80fd","text":"JavaScript// \u7f29\u653e\u529f\u80fd\u914d\u7f6e\nconst zoomConfig = {\n // \u7f29\u653e\u9009\u9879\n zoomable: true,\n dragToZoom: true,\n touchToZoom: true,\n\n // \u7f29\u653e\u7ea7\u522b\n minZoom: 0.5,\n maxZoom: 3,\n zoomStep: 0.5,\n\n // \u53cc\u51fb\u7f29\u653e\n doubleTapZoom: 2,\n\n // \u7f29\u653e\u52a8\u753b\n zoomAnimation: true,\n zoomDuration: 300,\n\n // \u7f29\u653e\u63a7\u5236\u5668\n controls: {\n zoom: true,\n zoomIn: true,\n zoomOut: true,\n rotate: true\n }\n};\n\n// \u5e94\u7528\u7f29\u653e\u914d\u7f6e\nconst lightbox = GLightbox({\n ...zoomConfig,\n\n // \u7f29\u653e\u4e8b\u4ef6\u5904\u7406\n onZoom: (slider) => {\n const { zoom, image } = slider;\n console.log(`Current zoom level: ${zoom}`);\n }\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6243","title":"6.2.4.3 \u624b\u52bf\u652f\u6301","text":"JavaScript// \u624b\u52bf\u914d\u7f6e\nconst gestureConfig = {\n // \u89e6\u6478\u5bfc\u822a\n touchNavigation: true,\n touchFollowAxis: true,\n\n // \u62d6\u52a8\u8bbe\u7f6e\n draggable: true,\n dragToleranceX: 40,\n dragToleranceY: 65,\n\n // \u624b\u52bf\u4e8b\u4ef6\n gestures: {\n // \u634f\u5408\u7f29\u653e\n pinchToZoom: true,\n pinchThreshold: 50,\n\n // \u53cc\u6307\u65cb\u8f6c\n rotateToZoom: true,\n rotateThreshold: 15,\n\n // \u6ed1\u52a8\u5207\u6362\n swipeThreshold: 50,\n swipeToClose: true\n }\n};\n\n// \u624b\u52bf\u4e8b\u4ef6\u5904\u7406\nconst gestureHandlers = {\n // \u89e6\u6478\u5f00\u59cb\n onTouchStart: (e) => {\n const touch = e.touches[0];\n startX = touch.clientX;\n startY = touch.clientY;\n },\n\n // \u89e6\u6478\u79fb\u52a8\n onTouchMove: (e) => {\n if (!isDragging) return;\n const touch = e.touches[0];\n const deltaX = touch.clientX - startX;\n const deltaY = touch.clientY - startY;\n\n // \u5904\u7406\u79fb\u52a8\n handleImageMove(deltaX, deltaY);\n },\n\n // \u89e6\u6478\u7ed3\u675f\n onTouchEnd: (e) => {\n isDragging = false;\n // \u5904\u7406\u60ef\u6027\u6ed1\u52a8\n handleMomentum();\n }\n};\n\n// \u521b\u5efa\u589e\u5f3a\u7684\u56fe\u7247\u9884\u89c8\nconst enhancedLightbox = GLightbox({\n ...zoomConfig,\n ...gestureConfig,\n\n // \u4e8b\u4ef6\u76d1\u542c\n listeners: {\n touchstart: gestureHandlers.onTouchStart,\n touchmove: gestureHandlers.onTouchMove,\n touchend: gestureHandlers.onTouchEnd\n }\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6244","title":"6.2.4.4 \u5b8c\u6574\u793a\u4f8b","text":"JavaScript// docs/javascripts/image-preview.js\ndocument.addEventListener('DOMContentLoaded', () => {\n // \u521d\u59cb\u5316\u914d\u7f6e\n const config = {\n // \u57fa\u7840\u8bbe\u7f6e\n selector: '.glightbox',\n touchNavigation: true,\n loop: false,\n\n // \u7f29\u653e\u8bbe\u7f6e\n zoomable: true,\n draggable: true,\n dragToleranceX: 40,\n dragToleranceY: 65,\n\n // \u52a8\u753b\u8bbe\u7f6e\n openEffect: 'zoom',\n closeEffect: 'fade',\n\n // \u624b\u52bf\u8bbe\u7f6e\n touchFollowAxis: true,\n\n // \u754c\u9762\u8bbe\u7f6e\n preload: true,\n height: 'auto',\n\n // \u4e8b\u4ef6\u5904\u7406\n onOpen: () => {\n console.log('Lightbox opened');\n },\n onClose: () => {\n console.log('Lightbox closed');\n },\n onZoom: (slider) => {\n console.log('Image zoomed');\n }\n };\n\n // \u521d\u59cb\u5316 GLightbox\n const lightbox = GLightbox(config);\n\n // \u6dfb\u52a0\u952e\u76d8\u652f\u6301\n document.addEventListener('keydown', (e) => {\n if (!lightbox.isOpen) return;\n\n switch(e.key) {\n case 'ArrowLeft':\n lightbox.prev();\n break;\n case 'ArrowRight':\n lightbox.next();\n break;\n case 'Escape':\n lightbox.close();\n break;\n }\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#63","title":"6.3 \u81ea\u5b9a\u4e49\u529f\u80fd","text":""},{"location":"Tools/Blog/Mkdocs_Material/#631","title":"6.3.1 \u9875\u9762\u4ea4\u4e92","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6311","title":"6.3.1.1 \u6eda\u52a8\u4e8b\u4ef6","text":" - \u57fa\u7840\u6eda\u52a8\u76d1\u542c\uff1a
JavaScript// docs/javascripts/scroll.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u6eda\u52a8\u5904\u7406\u51fd\u6570\n function handleScroll() {\n const scrollTop = window.scrollY;\n const windowHeight = window.innerHeight;\n const docHeight = document.documentElement.scrollHeight;\n\n // \u6eda\u52a8\u8fdb\u5ea6\n const scrollPercent = (scrollTop / (docHeight - windowHeight)) * 100;\n\n // \u66f4\u65b0\u8fdb\u5ea6\u6761\n updateProgress(scrollPercent);\n\n // \u5904\u7406\u5143\u7d20\u53ef\u89c1\u6027\n handleVisibility();\n }\n\n // \u4f7f\u7528\u8282\u6d41\u4f18\u5316\u6eda\u52a8\u4e8b\u4ef6\n const throttledScroll = throttle(handleScroll, 100);\n window.addEventListener('scroll', throttledScroll);\n});\n\n// \u8282\u6d41\u51fd\u6570\nfunction throttle(fn, delay) {\n let lastCall = 0;\n return function(...args) {\n const now = Date.now();\n if (now - lastCall >= delay) {\n lastCall = now;\n fn.apply(this, args);\n }\n };\n}\n
- \u5143\u7d20\u53ef\u89c1\u6027\u68c0\u6d4b\uff1a
JavaScript// \u68c0\u6d4b\u5143\u7d20\u662f\u5426\u8fdb\u5165\u89c6\u53e3\nfunction handleVisibility() {\n const elements = document.querySelectorAll('.animate-on-scroll');\n\n elements.forEach(element => {\n const rect = element.getBoundingClientRect();\n const isVisible = (\n rect.top >= 0 &&\n rect.left >= 0 &&\n rect.bottom <= window.innerHeight &&\n rect.right <= window.innerWidth\n );\n\n if (isVisible) {\n element.classList.add('is-visible');\n }\n });\n}\n\n// CSS \u6837\u5f0f\n.animate-on-scroll {\n opacity: 0;\n transform: translateY(20px);\n transition: all 0.6s ease;\n}\n\n.animate-on-scroll.is-visible {\n opacity: 1;\n transform: translateY(0);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6312","title":"6.3.1.2 \u70b9\u51fb\u4e8b\u4ef6","text":" - \u70b9\u51fb\u5904\u7406\uff1a
JavaScript// docs/javascripts/click.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u4ee3\u7801\u5757\u70b9\u51fb\u590d\u5236\n setupCodeCopy();\n\n // \u56fe\u7247\u70b9\u51fb\u653e\u5927\n setupImageZoom();\n\n // \u76ee\u5f55\u70b9\u51fb\u6eda\u52a8\n setupTocScroll();\n});\n\n// \u4ee3\u7801\u590d\u5236\u529f\u80fd\nfunction setupCodeCopy() {\n const codeBlocks = document.querySelectorAll('pre code');\n\n codeBlocks.forEach(block => {\n block.addEventListener('click', async function(e) {\n if (e.target.classList.contains('copy-button')) {\n try {\n await navigator.clipboard.writeText(block.textContent);\n showToast('\u590d\u5236\u6210\u529f\uff01');\n } catch (err) {\n showToast('\u590d\u5236\u5931\u8d25', 'error');\n }\n }\n });\n });\n}\n\n// \u56fe\u7247\u7f29\u653e\u529f\u80fd\nfunction setupImageZoom() {\n const images = document.querySelectorAll('.md-content img');\n\n images.forEach(img => {\n img.addEventListener('click', function() {\n const overlay = document.createElement('div');\n overlay.className = 'image-overlay';\n overlay.innerHTML = `\n <img src=\"${img.src}\" alt=\"${img.alt}\">\n <button class=\"close-button\">\u00d7</button>\n `;\n\n document.body.appendChild(overlay);\n\n overlay.querySelector('.close-button').addEventListener('click', () => {\n overlay.remove();\n });\n });\n });\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6313","title":"6.3.1.3 \u952e\u76d8\u4e8b\u4ef6","text":"JavaScript// docs/javascripts/keyboard.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u952e\u76d8\u5bfc\u822a\n setupKeyboardNav();\n\n // \u641c\u7d22\u5feb\u6377\u952e\n setupSearchShortcut();\n});\n\n// \u952e\u76d8\u5bfc\u822a\nfunction setupKeyboardNav() {\n document.addEventListener('keydown', function(e) {\n // ALT + \u65b9\u5411\u952e\u5bfc\u822a\n if (e.altKey) {\n switch(e.key) {\n case 'ArrowLeft': // \u4e0a\u4e00\u9875\n navigatePage('prev');\n break;\n case 'ArrowRight': // \u4e0b\u4e00\u9875\n navigatePage('next');\n break;\n case 'ArrowUp': // \u56de\u5230\u9876\u90e8\n window.scrollTo({top: 0, behavior: 'smooth'});\n break;\n case 'ArrowDown': // \u5230\u8fbe\u5e95\u90e8\n window.scrollTo({\n top: document.documentElement.scrollHeight,\n behavior: 'smooth'\n });\n break;\n }\n }\n });\n}\n\n// \u641c\u7d22\u5feb\u6377\u952e\nfunction setupSearchShortcut() {\n document.addEventListener('keydown', function(e) {\n // \u6309\u4e0b '/' \u952e\u89e6\u53d1\u641c\u7d22\n if (e.key === '/' && !e.ctrlKey && !e.altKey && !e.metaKey) {\n e.preventDefault();\n const searchInput = document.querySelector('.md-search__input');\n if (searchInput) {\n searchInput.focus();\n }\n }\n });\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#632","title":"6.3.2 \u52a8\u753b\u6548\u679c","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6321","title":"6.3.2.1 \u8fc7\u6e21\u52a8\u753b","text":" - \u57fa\u7840\u8fc7\u6e21\u6548\u679c\uff1a
CSS/* docs/stylesheets/transitions.css */\n\n/* \u9875\u9762\u5207\u6362\u8fc7\u6e21 */\n.md-content {\n animation: fadeIn 0.3s ease-in-out;\n}\n\n@keyframes fadeIn {\n from {\n opacity: 0;\n transform: translateY(20px);\n }\n to {\n opacity: 1;\n transform: translateY(0);\n }\n}\n\n/* \u5bfc\u822a\u8fc7\u6e21 */\n.md-nav__link {\n transition: color 0.2s ease, padding-left 0.2s ease;\n}\n\n.md-nav__link:hover {\n padding-left: 0.5rem;\n color: var(--md-accent-fg-color);\n}\n
- \u9875\u9762\u5207\u6362\u52a8\u753b\uff1a
JavaScript// docs/javascripts/transitions.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u9875\u9762\u5207\u6362\u52a8\u753b\n setupPageTransitions();\n});\n\nfunction setupPageTransitions() {\n // \u76d1\u542c\u9875\u9762\u5207\u6362\u4e8b\u4ef6\n document.addEventListener('DOMContentLoaded', function() {\n document.body.classList.add('page-transition-ready');\n });\n\n // \u9875\u9762\u79bb\u5f00\u52a8\u753b\n window.addEventListener('beforeunload', function() {\n document.body.classList.add('page-transition-exit');\n });\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6322","title":"6.3.2.2 \u52a0\u8f7d\u52a8\u753b","text":" - \u521b\u5efa\u52a0\u8f7d\u52a8\u753b\uff1a
CSS/* docs/stylesheets/loading.css */\n\n/* \u52a0\u8f7d\u52a8\u753b\u5bb9\u5668 */\n.loading-overlay {\n position: fixed;\n top: 0;\n left: 0;\n right: 0;\n bottom: 0;\n background-color: var(--md-default-bg-color);\n display: flex;\n justify-content: center;\n align-items: center;\n z-index: 999;\n opacity: 1;\n transition: opacity 0.3s ease;\n}\n\n/* \u52a0\u8f7d\u52a8\u753b */\n.loading-spinner {\n width: 40px;\n height: 40px;\n border: 3px solid var(--md-primary-fg-color--light);\n border-top-color: var(--md-primary-fg-color);\n border-radius: 50%;\n animation: spin 1s linear infinite;\n}\n\n@keyframes spin {\n to { transform: rotate(360deg); }\n}\n
- \u5b9e\u73b0\u52a0\u8f7d\u903b\u8f91\uff1a
JavaScript// docs/javascripts/loading.js\nclass LoadingManager {\n constructor() {\n this.overlay = null;\n this.createLoadingOverlay();\n }\n\n createLoadingOverlay() {\n this.overlay = document.createElement('div');\n this.overlay.className = 'loading-overlay';\n this.overlay.innerHTML = '<div class=\"loading-spinner\"></div>';\n document.body.appendChild(this.overlay);\n }\n\n show() {\n this.overlay.style.opacity = '1';\n this.overlay.style.visibility = 'visible';\n }\n\n hide() {\n this.overlay.style.opacity = '0';\n setTimeout(() => {\n this.overlay.style.visibility = 'hidden';\n }, 300);\n }\n}\n\n// \u4f7f\u7528\u52a0\u8f7d\u7ba1\u7406\u5668\nconst loading = new LoadingManager();\n\n// \u9875\u9762\u52a0\u8f7d\u5b8c\u6210\u540e\u9690\u85cf\nwindow.addEventListener('load', () => {\n loading.hide();\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6323","title":"6.3.2.3 \u4ea4\u4e92\u52a8\u753b","text":" - \u5143\u7d20\u4ea4\u4e92\u52a8\u753b\uff1a
JavaScript// docs/javascripts/interactions.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u6309\u94ae\u6ce2\u7eb9\u6548\u679c\n setupRippleEffect();\n\n // \u5361\u7247\u60ac\u6d6e\u6548\u679c\n setupCardHover();\n\n // \u5217\u8868\u9879\u52a8\u753b\n setupListAnimations();\n});\n\n// \u6ce2\u7eb9\u6548\u679c\nfunction setupRippleEffect() {\n const buttons = document.querySelectorAll('.md-button');\n\n buttons.forEach(button => {\n button.addEventListener('click', function(e) {\n const ripple = document.createElement('div');\n ripple.className = 'ripple';\n\n const rect = button.getBoundingClientRect();\n const size = Math.max(rect.width, rect.height);\n\n ripple.style.width = ripple.style.height = `${size}px`;\n ripple.style.left = `${e.clientX - rect.left - size/2}px`;\n ripple.style.top = `${e.clientY - rect.top - size/2}px`;\n\n button.appendChild(ripple);\n\n setTimeout(() => ripple.remove(), 600);\n });\n });\n}\n\n// \u5361\u7247\u60ac\u6d6e\u6548\u679c\nfunction setupCardHover() {\n const cards = document.querySelectorAll('.md-card');\n\n cards.forEach(card => {\n card.addEventListener('mousemove', function(e) {\n const rect = card.getBoundingClientRect();\n const x = e.clientX - rect.left;\n const y = e.clientY - rect.top;\n\n const centerX = rect.width / 2;\n const centerY = rect.height / 2;\n\n const angleY = -(x - centerX) / 20;\n const angleX = (y - centerY) / 20;\n\n card.style.transform = \n `perspective(1000px) rotateX(${angleX}deg) rotateY(${angleY}deg)`;\n });\n\n card.addEventListener('mouseleave', function() {\n card.style.transform = 'perspective(1000px) rotateX(0) rotateY(0)';\n });\n });\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#633","title":"6.3.3 \u6570\u636e\u7edf\u8ba1","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6331","title":"6.3.3.1 \u8bbf\u95ee\u7edf\u8ba1","text":" - \u57fa\u7840\u8bbf\u95ee\u7edf\u8ba1\uff1a
JavaScript// docs/javascripts/analytics.js\nclass Analytics {\n constructor() {\n this.storageKey = 'site_analytics';\n this.data = this.loadData();\n }\n\n loadData() {\n const stored = localStorage.getItem(this.storageKey);\n return stored ? JSON.parse(stored) : {\n pageViews: {},\n totalVisits: 0,\n firstVisit: Date.now(),\n lastVisit: Date.now()\n };\n }\n\n saveData() {\n localStorage.setItem(this.storageKey, JSON.stringify(this.data));\n }\n\n recordPageView() {\n const path = window.location.pathname;\n this.data.pageViews[path] = (this.data.pageViews[path] || 0) + 1;\n this.data.totalVisits++;\n this.data.lastVisit = Date.now();\n this.saveData();\n }\n\n getStats() {\n return {\n totalVisits: this.data.totalVisits,\n uniquePages: Object.keys(this.data.pageViews).length,\n mostViewed: this.getMostViewedPages(5)\n };\n }\n\n getMostViewedPages(limit = 5) {\n return Object.entries(this.data.pageViews)\n .sort(([,a], [,b]) => b - a)\n .slice(0, limit);\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6332","title":"6.3.3.2 \u9605\u8bfb\u65f6\u957f","text":"JavaScript// docs/javascripts/read-time.js\nclass ReadTimeTracker {\n constructor() {\n this.startTime = Date.now();\n this.isReading = true;\n this.totalTime = 0;\n this.idleTimeout = null;\n this.setupTracking();\n }\n\n setupTracking() {\n // \u76d1\u542c\u7528\u6237\u6d3b\u52a8\n ['mousemove', 'keydown', 'scroll', 'click'].forEach(event => {\n document.addEventListener(event, () => this.handleActivity());\n });\n\n // \u9875\u9762\u5931\u7126\u6682\u505c\u8ba1\u65f6\n document.addEventListener('visibilitychange', () => {\n if (document.hidden) {\n this.pauseTracking();\n } else {\n this.resumeTracking();\n }\n });\n }\n\n handleActivity() {\n if (!this.isReading) {\n this.resumeTracking();\n }\n\n clearTimeout(this.idleTimeout);\n this.idleTimeout = setTimeout(() => this.pauseTracking(), 60000); // 1\u5206\u949f\u65e0\u6d3b\u52a8\u6682\u505c\n }\n\n pauseTracking() {\n if (this.isReading) {\n this.totalTime += Date.now() - this.startTime;\n this.isReading = false;\n }\n }\n\n resumeTracking() {\n if (!this.isReading) {\n this.startTime = Date.now();\n this.isReading = true;\n }\n }\n\n getReadTime() {\n const currentTime = this.isReading ? \n this.totalTime + (Date.now() - this.startTime) : \n this.totalTime;\n\n return Math.floor(currentTime / 1000 / 60); // \u8fd4\u56de\u5206\u949f\n // \u663e\u793a\u9605\u8bfb\u65f6\u957f\n displayReadTime() {\n const readTimeElement = document.querySelector('.read-time');\n if (readTimeElement) {\n const minutes = this.getReadTime();\n readTimeElement.textContent = `\u9605\u8bfb\u65f6\u957f: ${minutes} \u5206\u949f`;\n }\n }\n\n // \u83b7\u53d6\u9605\u8bfb\u8fdb\u5ea6\n getReadProgress() {\n const windowHeight = window.innerHeight;\n const docHeight = document.documentElement.scrollHeight - windowHeight;\n const scrollTop = window.pageYOffset || document.documentElement.scrollTop;\n return Math.min((scrollTop / docHeight) * 100, 100);\n }\n\n // \u4fdd\u5b58\u9605\u8bfb\u8bb0\u5f55\n saveReadingHistory() {\n const path = window.location.pathname;\n const history = JSON.parse(localStorage.getItem('reading_history') || '{}');\n\n history[path] = {\n lastRead: Date.now(),\n readTime: this.getReadTime(),\n progress: this.getReadProgress()\n };\n\n localStorage.setItem('reading_history', JSON.stringify(history));\n }\n}\n\n// \u521d\u59cb\u5316\u9605\u8bfb\u65f6\u957f\u8ffd\u8e2a\nconst readTracker = new ReadTimeTracker();\n\n// \u5b9a\u671f\u66f4\u65b0\u663e\u793a\nsetInterval(() => {\n readTracker.displayReadTime();\n}, 30000); // \u6bcf30\u79d2\u66f4\u65b0\u4e00\u6b21\n\n// \u9875\u9762\u79bb\u5f00\u65f6\u4fdd\u5b58\u8bb0\u5f55\nwindow.addEventListener('beforeunload', () => {\n readTracker.saveReadingHistory();\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6333","title":"6.3.3.3 \u5206\u4eab\u7edf\u8ba1","text":" - \u5206\u4eab\u529f\u80fd\u548c\u7edf\u8ba1\uff1a
JavaScript// docs/javascripts/share.js\nclass ShareTracker {\n constructor() {\n this.storageKey = 'share_statistics';\n this.data = this.loadData();\n this.setupShareButtons();\n }\n\n loadData() {\n return JSON.parse(localStorage.getItem(this.storageKey) || '{}');\n }\n\n saveData() {\n localStorage.setItem(this.storageKey, JSON.stringify(this.data));\n }\n\n setupShareButtons() {\n const shareButtons = document.querySelectorAll('.share-button');\n\n shareButtons.forEach(button => {\n button.addEventListener('click', (e) => {\n const platform = button.dataset.platform;\n this.shareContent(platform);\n this.recordShare(platform);\n });\n });\n }\n\n shareContent(platform) {\n const url = encodeURIComponent(window.location.href);\n const title = encodeURIComponent(document.title);\n const description = encodeURIComponent(\n document.querySelector('meta[name=\"description\"]')?.content || ''\n );\n\n let shareUrl;\n switch (platform) {\n case 'twitter':\n shareUrl = `https://twitter.com/intent/tweet?url=${url}&text=${title}`;\n break;\n case 'facebook':\n shareUrl = `https://www.facebook.com/sharer/sharer.php?u=${url}`;\n break;\n case 'linkedin':\n shareUrl = `https://www.linkedin.com/sharing/share-offsite/?url=${url}`;\n break;\n case 'weibo':\n shareUrl = `http://service.weibo.com/share/share.php?url=${url}&title=${title}`;\n break;\n }\n\n if (shareUrl) {\n window.open(shareUrl, '_blank', 'width=600,height=400');\n }\n }\n\n recordShare(platform) {\n const path = window.location.pathname;\n if (!this.data[path]) {\n this.data[path] = {};\n }\n if (!this.data[path][platform]) {\n this.data[path][platform] = 0;\n }\n this.data[path][platform]++;\n this.saveData();\n this.updateShareCount(platform);\n }\n\n updateShareCount(platform) {\n const countElement = document.querySelector(`.share-count[data-platform=\"${platform}\"]`);\n if (countElement) {\n const path = window.location.pathname;\n countElement.textContent = this.data[path][platform] || 0;\n }\n }\n\n getShareStats() {\n return Object.entries(this.data).map(([path, platforms]) => ({\n path,\n total: Object.values(platforms).reduce((a, b) => a + b, 0),\n platforms\n }));\n }\n\n displayShareStats() {\n const stats = this.getShareStats();\n console.table(stats);\n return stats;\n }\n}\n
- \u7edf\u8ba1\u6570\u636e\u53ef\u89c6\u5316\uff1a
JavaScript// docs/javascripts/statistics-visualization.js\nclass StatisticsVisualizer {\n constructor(analytics, readTracker, shareTracker) {\n this.analytics = analytics;\n this.readTracker = readTracker;\n this.shareTracker = shareTracker;\n }\n\n createDashboard() {\n const dashboard = document.createElement('div');\n dashboard.className = 'statistics-dashboard';\n dashboard.innerHTML = `\n <div class=\"dashboard-section\">\n <h3>\u8bbf\u95ee\u7edf\u8ba1</h3>\n <div class=\"stats-grid\">\n <div class=\"stat-card\">\n <div class=\"stat-value\">${this.analytics.data.totalVisits}</div>\n <div class=\"stat-label\">\u603b\u8bbf\u95ee\u91cf</div>\n </div>\n <div class=\"stat-card\">\n <div class=\"stat-value\">${this.readTracker.getReadTime()}</div>\n <div class=\"stat-label\">\u603b\u9605\u8bfb\u65f6\u957f(\u5206\u949f)</div>\n </div>\n <div class=\"stat-card\">\n <div class=\"stat-value\">${this.getTotalShares()}</div>\n <div class=\"stat-label\">\u603b\u5206\u4eab\u6b21\u6570</div>\n </div>\n </div>\n <div id=\"visitsChart\"></div>\n </div>\n `;\n\n document.body.appendChild(dashboard);\n this.renderCharts();\n }\n\n renderCharts() {\n // \u4f7f\u7528 Chart.js \u7ed8\u5236\u56fe\u8868\n const ctx = document.getElementById('visitsChart').getContext('2d');\n new Chart(ctx, {\n type: 'line',\n data: {\n labels: this.getTimeLabels(),\n datasets: [{\n label: '\u8bbf\u95ee\u8d8b\u52bf',\n data: this.getVisitData(),\n borderColor: 'rgb(75, 192, 192)',\n tension: 0.1\n }]\n },\n options: {\n responsive: true,\n scales: {\n y: {\n beginAtZero: true\n }\n }\n }\n });\n }\n\n getTotalShares() {\n const stats = this.shareTracker.getShareStats();\n return stats.reduce((total, page) => total + page.total, 0);\n }\n\n getTimeLabels() {\n // \u83b7\u53d6\u6700\u8fd17\u5929\u7684\u65e5\u671f\u6807\u7b7e\n return Array.from({length: 7}, (_, i) => {\n const d = new Date();\n d.setDate(d.getDate() - i);\n return d.toLocaleDateString();\n }).reverse();\n }\n\n getVisitData() {\n // \u5904\u7406\u8bbf\u95ee\u6570\u636e\n return this.analytics.getVisitsByDate(7);\n }\n}\n\n// \u6837\u5f0f\nconst styles = `\n .statistics-dashboard {\n padding: 2rem;\n background: var(--md-default-bg-color);\n border-radius: 8px;\n box-shadow: var(--md-shadow-z2);\n }\n\n .stats-grid {\n display: grid;\n grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));\n gap: 1rem;\n margin: 1rem 0;\n }\n\n .stat-card {\n padding: 1rem;\n background: var(--md-code-bg-color);\n border-radius: 4px;\n text-align: center;\n }\n\n .stat-value {\n font-size: 2rem;\n font-weight: bold;\n color: var(--md-primary-fg-color);\n }\n\n .stat-label {\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n margin-top: 0.5rem;\n }\n`;\n\n// \u5c06\u6837\u5f0f\u6dfb\u52a0\u5230\u6587\u6863\nconst styleSheet = document.createElement('style');\nstyleSheet.textContent = styles;\ndocument.head.appendChild(styleSheet);\n\n// \u521d\u59cb\u5316\u7edf\u8ba1\u53ef\u89c6\u5316\nconst visualizer = new StatisticsVisualizer(analytics, readTracker, shareTracker);\nvisualizer.createDashboard();\n
\u8fd9\u6837\u6211\u4eec\u5c31\u5b8c\u6210\u4e86\u5b8c\u6574\u7684\u6570\u636e\u7edf\u8ba1\u7cfb\u7edf\uff0c\u5305\u62ec\uff1a
- \u8bbf\u95ee\u7edf\u8ba1
- \u9605\u8bfb\u65f6\u957f\u8ffd\u8e2a
- \u5206\u4eab\u7edf\u8ba1
- \u6570\u636e\u53ef\u89c6\u5316\u5c55\u793a
\u4f7f\u7528\u65f6\uff0c\u53ea\u9700\u8981\u5728 mkdocs.yml \u4e2d\u6dfb\u52a0\u76f8\u5e94\u7684 JavaScript \u6587\u4ef6\uff1a
YAMLextra_javascript:\n - javascripts/analytics.js\n - javascripts/read-time.js\n - javascripts/share.js\n - javascripts/statistics-visualization.js\n - https://cdn.jsdelivr.net/npm/chart.js\n
\u7136\u540e\u5728\u9875\u9762\u4e2d\u6dfb\u52a0\u5fc5\u8981\u7684 HTML \u5143\u7d20\u5373\u53ef\u542f\u7528\u8fd9\u4e9b\u529f\u80fd\u3002
"},{"location":"Tools/Blog/Mkdocs_Material/#7","title":"7 \u6a21\u677f\u590d\u5199","text":""},{"location":"Tools/Blog/Mkdocs_Material/#71","title":"7.1 \u91cd\u5199\u9875\u9762\u6a21\u677f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#711","title":"7.1.1 \u4e3b\u9875\u6a21\u677f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#7111","title":"7.1.1.1 \u5e03\u5c40\u7ed3\u6784","text":" - \u521b\u5efa\u81ea\u5b9a\u4e49\u4e3b\u9875\u6a21\u677f\uff1a
HTML<!-- docs/overrides/main.html -->\n{% extends \"base.html\" %}\n\n{% block hero %}\n<section class=\"home-hero\">\n <div class=\"hero-content\">\n <h1>{{ config.site_name }}</h1>\n <p>{{ config.site_description }}</p>\n\n <!-- \u81ea\u5b9a\u4e49\u641c\u7d22\u6846 -->\n <div class=\"hero-search\">\n <input type=\"text\" placeholder=\"\u641c\u7d22\u6587\u6863...\" id=\"hero-search-input\">\n <button>\n <span class=\"twemoji\">\n {% include \".icons/material/magnify.svg\" %}\n </span>\n </button>\n </div>\n\n <!-- \u5feb\u901f\u5165\u53e3 -->\n <div class=\"hero-buttons\">\n <a href=\"{{ page.next_page.url | url }}\" class=\"md-button md-button--primary\">\n \u5feb\u901f\u5f00\u59cb\n <span class=\"twemoji\">\n {% include \".icons/material/arrow-right.svg\" %}\n </span>\n </a>\n <a href=\"{{ config.repo_url }}\" class=\"md-button\">\n \u67e5\u770b\u6e90\u7801\n <span class=\"twemoji\">\n {% include \".icons/material/github.svg\" %}\n </span>\n </a>\n </div>\n </div>\n</section>\n{% endblock %}\n\n{% block content %}\n<section class=\"home-features\">\n <h2>\u7279\u8272\u529f\u80fd</h2>\n <div class=\"features-grid\">\n <!-- \u529f\u80fd\u5361\u7247 -->\n <div class=\"feature-card\">\n <div class=\"feature-icon\">\n {% include \".icons/material/speedometer.svg\" %}\n </div>\n <h3>\u9ad8\u6027\u80fd</h3>\n <p>\u57fa\u4e8e\u9759\u6001\u7ad9\u70b9\u751f\u6210\uff0c\u52a0\u8f7d\u8fc5\u901f</p>\n </div>\n <!-- \u66f4\u591a\u529f\u80fd\u5361\u7247 -->\n </div>\n</section>\n\n<!-- \u6700\u8fd1\u66f4\u65b0 -->\n<section class=\"home-updates\">\n <h2>\u6700\u8fd1\u66f4\u65b0</h2>\n <div class=\"updates-list\">\n {% for update in config.theme.updates[:5] %}\n <div class=\"update-item\">\n <span class=\"update-date\">{{ update.date }}</span>\n <a href=\"{{ update.url | url }}\">{{ update.title }}</a>\n </div>\n {% endfor %}\n </div>\n</section>\n{% endblock %}\n
- \u4e3b\u9875\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/home.css */\n\n/* \u4e3b\u9875\u82f1\u96c4\u533a */\n.home-hero {\n min-height: 100vh;\n display: flex;\n align-items: center;\n justify-content: center;\n text-align: center;\n background: linear-gradient(\n to bottom right,\n var(--md-primary-fg-color),\n var(--md-accent-fg-color)\n );\n color: var(--md-primary-bg-color);\n}\n\n.hero-content {\n max-width: 800px;\n padding: 2rem;\n}\n\n.hero-content h1 {\n font-size: 3rem;\n margin-bottom: 1rem;\n}\n\n/* \u641c\u7d22\u6846\u6837\u5f0f */\n.hero-search {\n margin: 2rem 0;\n position: relative;\n}\n\n.hero-search input {\n width: 100%;\n padding: 1rem 3rem 1rem 1rem;\n border: none;\n border-radius: 2rem;\n background: rgba(255, 255, 255, 0.1);\n color: white;\n backdrop-filter: blur(10px);\n}\n\n/* \u529f\u80fd\u533a\u6837\u5f0f */\n.features-grid {\n display: grid;\n grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));\n gap: 2rem;\n padding: 2rem;\n}\n\n.feature-card {\n padding: 2rem;\n background: var(--md-default-bg-color);\n border-radius: 8px;\n box-shadow: var(--md-shadow-z1);\n transition: transform 0.3s ease;\n}\n\n.feature-card:hover {\n transform: translateY(-4px);\n}\n\n/* \u66f4\u65b0\u5217\u8868\u6837\u5f0f */\n.updates-list {\n max-width: 800px;\n margin: 0 auto;\n padding: 2rem;\n}\n\n.update-item {\n display: flex;\n align-items: center;\n padding: 1rem;\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n/* \u54cd\u5e94\u5f0f\u9002\u914d */\n@media screen and (max-width: 76.1875em) {\n .hero-content h1 {\n font-size: 2rem;\n }\n\n .features-grid {\n grid-template-columns: 1fr;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7112","title":"7.1.1.2 \u7ec4\u4ef6\u914d\u7f6e","text":" - \u5728
mkdocs.yml \u4e2d\u914d\u7f6e\u4e3b\u9898\uff1a
YAMLtheme:\n name: material\n custom_dir: docs/overrides\n features:\n - navigation.tabs\n - navigation.sections\n - navigation.expand\n\n # \u4e3b\u9875\u914d\u7f6e\n homepage:\n hero:\n title: \u7f51\u7ad9\u6807\u9898\n subtitle: \u7f51\u7ad9\u63cf\u8ff0\n image: assets/hero.svg\n\n # \u529f\u80fd\u7279\u6027\n features:\n - title: \u9ad8\u6027\u80fd\n description: \u57fa\u4e8e\u9759\u6001\u7ad9\u70b9\u751f\u6210\uff0c\u52a0\u8f7d\u8fc5\u901f\n icon: material/speedometer\n - title: \u6613\u4e8e\u4f7f\u7528\n description: \u7b80\u5355\u7684\u914d\u7f6e\uff0c\u5feb\u901f\u4e0a\u624b\n icon: material/puzzle\n # \u66f4\u591a\u529f\u80fd\u7279\u6027...\n\n # \u66f4\u65b0\u5217\u8868\n updates:\n - date: 2024-01-20\n title: \u65b0\u589e\u529f\u80fdA\n url: /new-feature-a\n - date: 2024-01-18\n title: \u95ee\u9898\u4fee\u590dB\n url: /bug-fix-b\n # \u66f4\u591a\u66f4\u65b0...\n
- \u4e3b\u9875\u529f\u80fd\u7c7b\uff1a
JavaScript// docs/javascripts/home.js\nclass HomePage {\n constructor() {\n this.searchInput = document.getElementById('hero-search-input');\n this.setupSearch();\n this.setupFeatureCards();\n }\n\n setupSearch() {\n this.searchInput?.addEventListener('keyup', (e) => {\n if (e.key === 'Enter') {\n const query = e.target.value;\n window.location.href = `${window.location.origin}/search.html?q=${encodeURIComponent(query)}`;\n }\n });\n }\n\n setupFeatureCards() {\n const cards = document.querySelectorAll('.feature-card');\n\n cards.forEach(card => {\n card.addEventListener('mousemove', (e) => {\n const rect = card.getBoundingClientRect();\n const x = e.clientX - rect.left;\n const y = e.clientY - rect.top;\n\n card.style.setProperty('--mouse-x', `${x}px`);\n card.style.setProperty('--mouse-y', `${y}px`);\n });\n });\n }\n}\n\n// \u521d\u59cb\u5316\u4e3b\u9875\ndocument.addEventListener('DOMContentLoaded', () => {\n new HomePage();\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7113","title":"7.1.1.3 \u81ea\u5b9a\u4e49\u533a\u57df","text":" - \u521b\u5efa\u81ea\u5b9a\u4e49\u533a\u57df\uff1a
HTML<!-- docs/overrides/partials/custom-content.html -->\n{% if page.meta.custom_content %}\n<section class=\"custom-content\">\n <!-- \u516c\u544a\u533a -->\n {% if page.meta.announcements %}\n <div class=\"announcements\">\n {% for announcement in page.meta.announcements %}\n <div class=\"announcement-item\">\n <span class=\"announcement-tag\">{{ announcement.tag }}</span>\n <p>{{ announcement.content }}</p>\n </div>\n {% endfor %}\n </div>\n {% endif %}\n\n <!-- \u8d21\u732e\u8005\u533a\u57df -->\n {% if page.meta.contributors %}\n <div class=\"contributors\">\n <h3>\u9879\u76ee\u8d21\u732e\u8005</h3>\n <div class=\"contributors-grid\">\n {% for contributor in page.meta.contributors %}\n <a href=\"{{ contributor.url }}\" class=\"contributor-card\">\n <img src=\"{{ contributor.avatar }}\" alt=\"{{ contributor.name }}\">\n <span>{{ contributor.name }}</span>\n </a>\n {% endfor %}\n </div>\n </div>\n {% endif %}\n\n <!-- \u8d5e\u52a9\u5546\u533a\u57df -->\n {% if page.meta.sponsors %}\n <div class=\"sponsors\">\n <h3>\u8d5e\u52a9\u5546</h3>\n <div class=\"sponsors-grid\">\n {% for sponsor in page.meta.sponsors %}\n <a href=\"{{ sponsor.url }}\" class=\"sponsor-card\">\n <img src=\"{{ sponsor.logo }}\" alt=\"{{ sponsor.name }}\">\n </a>\n {% endfor %}\n </div>\n </div>\n {% endif %}\n</section>\n{% endif %}\n
- \u81ea\u5b9a\u4e49\u533a\u57df\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/custom-content.css */\n\n/* \u516c\u544a\u533a\u57df */\n.announcements {\n margin: 2rem 0;\n}\n\n.announcement-item {\n padding: 1rem;\n background: var(--md-code-bg-color);\n border-radius: 4px;\n margin-bottom: 1rem;\n}\n\n.announcement-tag {\n display: inline-block;\n padding: 0.2rem 0.5rem;\n background: var(--md-accent-fg-color);\n color: white;\n border-radius: 2rem;\n font-size: 0.8rem;\n margin-right: 0.5rem;\n}\n\n/* \u8d21\u732e\u8005\u533a\u57df */\n.contributors-grid {\n display: grid;\n grid-template-columns: repeat(auto-fill, minmax(100px, 1fr));\n gap: 1rem;\n margin: 1rem 0;\n}\n\n.contributor-card {\n text-align: center;\n text-decoration: none;\n color: var(--md-default-fg-color);\n}\n\n.contributor-card img {\n width: 60px;\n height: 60px;\n border-radius: 50%;\n margin-bottom: 0.5rem;\n}\n\n/* \u8d5e\u52a9\u5546\u533a\u57df */\n.sponsors-grid {\n display: grid;\n grid-template-columns: repeat(auto-fill, minmax(150px, 1fr));\n gap: 2rem;\n margin: 1rem 0;\n}\n\n.sponsor-card img {\n width: 100%;\n height: auto;\n filter: grayscale(100%);\n transition: filter 0.3s ease;\n}\n\n.sponsor-card:hover img {\n filter: grayscale(0%);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#712","title":"7.1.2 \u6587\u7ae0\u6a21\u677f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#7121","title":"7.1.2.1 \u6587\u7ae0\u5934\u90e8","text":"HTML<!-- docs/overrides/partials/article-header.html -->\n<header class=\"article-header\">\n <!-- \u6587\u7ae0\u6807\u9898 -->\n <h1>{{ page.title }}</h1>\n\n <!-- \u6587\u7ae0\u5143\u4fe1\u606f -->\n <div class=\"article-meta\">\n {% if page.meta.author %}\n <div class=\"meta-item\">\n <span class=\"meta-icon\">\n {% include \".icons/material/account.svg\" %}\n </span>\n <span>{{ page.meta.author }}</span>\n </div>\n {% endif %}\n\n {% if page.meta.date %}\n <div class=\"meta-item\">\n <span class=\"meta-icon\">\n {% include \".icons/material/calendar.svg\" %}\n </span>\n <span>{{ page.meta.date }}</span>\n </div>\n {% endif %}\n\n {% if page.meta.tags %}\n <div class=\"article-tags\">\n {% for tag in page.meta.tags %}\n <a href=\"{{ base_url }}/tags/#{{ tag }}\" class=\"tag\">\n # {{ tag }}\n </a>\n {% endfor %}\n </div>\n {% endif %}\n </div>\n\n <!-- \u6587\u7ae0\u6982\u8ff0 -->\n {% if page.meta.description %}\n <div class=\"article-description\">\n {{ page.meta.description }}\n </div>\n {% endif %}\n</header>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7122","title":"7.1.2.2 \u6b63\u6587\u6837\u5f0f","text":"CSS/* docs/stylesheets/article.css */\n\n/* \u6587\u7ae0\u5bb9\u5668 */\n.md-content article {\n max-width: 800px;\n margin: 0 auto;\n padding: 2rem;\n}\n\n/* \u6587\u7ae0\u5934\u90e8 */\n.article-header {\n margin-bottom: 3rem;\n text-align: center;\n}\n\n.article-meta {\n display: flex;\n justify-content: center;\n gap: 1rem;\n margin: 1rem 0;\n color: var(--md-default-fg-color--light);\n}\n\n.meta-item {\n display: flex;\n align-items: center;\n gap: 0.5rem;\n}\n\n.article-tags {\n margin-top: 1rem;\n}\n\n.tag {\n display: inline-block;\n padding: 0.2rem 0.5rem;\n margin: 0.2rem;\n background: var(--md-code-bg-color);\n border-radius: 2rem;\n color: var(--md-default-fg-color);\n text-decoration: none;\n font-size: 0.8rem;\n}\n\n/* \u6587\u7ae0\u5185\u5bb9\u6837\u5f0f */\n.md-content article {\n font-size: 1.1rem;\n line-height: 1.8;\n}\n\n/* \u6807\u9898\u6837\u5f0f */\n.md-content article h2 {\n margin-top: 3rem;\n padding-bottom: 0.5rem;\n border-bottom: 2px solid var(--md-default-fg-color--lightest);\n}\n\n/* \u4ee3\u7801\u5757\u6837\u5f0f */\n.md-content pre {\n border-radius: 8px;\n margin: 1.5rem 0;\n}\n\n/* \u5f15\u7528\u6837\u5f0f */\n.md-content blockquote {\n border-left: 4px solid var(--md-accent-fg-color);\n padding: 1rem;\n background: var(--md-code-bg-color);\n margin: 1.5rem 0;\n}\n\n/* \u56fe\u7247\u6837\u5f0f */\n.md-content img {\n max-width: 100%;\n border-radius: 8px;\n margin: 1.5rem 0;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7123","title":"7.1.2.3 \u5e95\u90e8\u4fe1\u606f","text":"HTML<!-- docs/overrides/partials/article-footer.html -->\n<footer class=\"article-footer\">\n <!-- \u6587\u7ae0\u5bfc\u822a -->\n <nav class=\"article-nav\">\n {% if page.previous_page %}\n <a href=\"{{ page.previous_page.url | url }}\" class=\"nav-link nav-prev\">\n <span class=\"nav-icon\">\n {% include \".icons/material/arrow-left.svg\" %}\n </span>\n <span class=\"nav-text\">\n <span class=\"nav-direction\">\u4e0a\u4e00\u7bc7</span>\n <span class=\"nav-title\">{{ page.previous_page.title }}</span>\n </span>\n </a>\n {% endif %}\n\n {% if page.next_page %}\n <a href=\"{{ page.next_page.url | url }}\" class=\"nav-link nav-next\">\n <span class=\"nav-text\">\n <span class=\"nav-direction\">\u4e0b\u4e00\u7bc7</span>\n <span class=\"nav-title\">{{ page.next_page.title }}</span>\n </span>\n <span class=\"nav-icon\">\n {% include \".icons/material/arrow-right.svg\" %}\n </span>\n </a>\n {% endif %}\n </nav>\n\n <!-- \u5206\u4eab\u6309\u94ae -->\n <div class=\"article-share\">\n <h4>\u5206\u4eab\u6587\u7ae0</h4>\n <div class=\"share-buttons\">\n <button class=\"share-button\" data-platform=\"twitter\">\n {% include \".icons/material/twitter.svg\" %}\n </button>\n <button class=\"share-button\" data-platform=\"facebook\">\n {% include \".icons/material/facebook.svg\" %}\n </button>\n <button class=\"share-button\" data-platform=\"linkedin\">\n {% include \".icons/material/linkedin.svg\" %}\n </button>\n <button class=\"share-button\" data-platform=\"weibo\">\n {% include \".icons/material/sina-weibo.svg\" %}\n </button>\n </div>\n </div>\n\n <!-- \u76f8\u5173\u6587\u7ae0 -->\n {% if page.meta.related_posts %}\n <div class=\"related-posts\">\n <h4>\u76f8\u5173\u6587\u7ae0</h4>\n <div class=\"related-grid\">\n {% for post in page.meta.related_posts %}\n <a href=\"{{ post.url | url }}\" class=\"related-post\">\n {% if post.image %}\n <img src=\"{{ post.image }}\" alt=\"{{ post.title }}\">\n {% endif %}\n <h5>{{ post.title }}</h5>\n <p>{{ post.excerpt }}</p>\n </a>\n {% endfor %}\n </div>\n </div>\n {% endif %}\n\n <!-- \u8bc4\u8bba\u533a -->\n <div class=\"article-comments\">\n <h4>\u8bc4\u8bba</h4>\n {% if config.extra.comments.provider == 'giscus' %}\n <script src=\"https://giscus.app/client.js\"\n data-repo=\"{{ config.extra.comments.repo }}\"\n data-repo-id=\"{{ config.extra.comments.repo_id }}\"\n data-category=\"{{ config.extra.comments.category }}\"\n data-category-id=\"{{ config.extra.comments.category_id }}\"\n data-mapping=\"pathname\"\n data-reactions-enabled=\"1\"\n data-emit-metadata=\"0\"\n data-theme=\"light\"\n crossorigin=\"anonymous\"\n async>\n </script>\n {% endif %}\n </div>\n</footer>\n\n<!-- \u6587\u7ae0\u5e95\u90e8\u6837\u5f0f -->\n<style>\n.article-footer {\n margin-top: 4rem;\n padding-top: 2rem;\n border-top: 1px solid var(--md-default-fg-color--lightest);\n}\n\n/* \u6587\u7ae0\u5bfc\u822a */\n.article-nav {\n display: flex;\n justify-content: space-between;\n margin-bottom: 2rem;\n}\n\n.nav-link {\n display: flex;\n align-items: center;\n padding: 1rem;\n text-decoration: none;\n color: var(--md-default-fg-color);\n background: var(--md-code-bg-color);\n border-radius: 8px;\n transition: transform 0.2s ease;\n max-width: 45%;\n}\n\n.nav-link:hover {\n transform: translateY(-2px);\n}\n\n.nav-text {\n display: flex;\n flex-direction: column;\n}\n\n.nav-direction {\n font-size: 0.8rem;\n color: var(--md-default-fg-color--light);\n}\n\n.nav-title {\n font-weight: 500;\n}\n\n/* \u5206\u4eab\u6309\u94ae */\n.share-buttons {\n display: flex;\n gap: 1rem;\n margin: 1rem 0;\n}\n\n.share-button {\n padding: 0.8rem;\n border: none;\n border-radius: 50%;\n background: var(--md-code-bg-color);\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.share-button:hover {\n background: var(--md-accent-fg-color);\n color: white;\n}\n\n/* \u76f8\u5173\u6587\u7ae0 */\n.related-grid {\n display: grid;\n grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));\n gap: 1.5rem;\n margin: 1rem 0;\n}\n\n.related-post {\n text-decoration: none;\n color: var(--md-default-fg-color);\n background: var(--md-code-bg-color);\n border-radius: 8px;\n overflow: hidden;\n transition: transform 0.2s ease;\n}\n\n.related-post:hover {\n transform: translateY(-4px);\n}\n\n.related-post img {\n width: 100%;\n height: 150px;\n object-fit: cover;\n}\n\n.related-post h5 {\n margin: 1rem;\n font-size: 1.1rem;\n}\n\n.related-post p {\n margin: 0 1rem 1rem;\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n}\n\n/* \u8bc4\u8bba\u533a */\n.article-comments {\n margin-top: 3rem;\n}\n\n/* \u54cd\u5e94\u5f0f\u9002\u914d */\n@media screen and (max-width: 76.1875em) {\n .article-nav {\n flex-direction: column;\n gap: 1rem;\n }\n\n .nav-link {\n max-width: 100%;\n }\n\n .related-grid {\n grid-template-columns: 1fr;\n }\n}\n</style>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#713-404","title":"7.1.3 404\u9875\u9762","text":""},{"location":"Tools/Blog/Mkdocs_Material/#7131","title":"7.1.3.1 \u9519\u8bef\u63d0\u793a","text":"HTML<!-- docs/overrides/404.html -->\n{% extends \"base.html\" %}\n\n{% block content %}\n<div class=\"error-page\">\n <div class=\"error-content\">\n <!-- 404 \u56fe\u6807 -->\n <div class=\"error-icon\">\n {% include \".icons/material/alert-circle-outline.svg\" %}\n <span class=\"error-code\">404</span>\n </div>\n\n <!-- \u9519\u8bef\u4fe1\u606f -->\n <h1>\u9875\u9762\u672a\u627e\u5230</h1>\n <p>\u62b1\u6b49\uff0c\u60a8\u8bbf\u95ee\u7684\u9875\u9762\u4e0d\u5b58\u5728\u6216\u5df2\u88ab\u79fb\u52a8</p>\n\n <!-- \u641c\u7d22\u6846 -->\n <div class=\"error-search\">\n <input type=\"text\" \n id=\"error-search-input\" \n placeholder=\"\u5c1d\u8bd5\u641c\u7d22...\"\n autocomplete=\"off\">\n <button id=\"error-search-button\">\n {% include \".icons/material/magnify.svg\" %}\n </button>\n </div>\n\n <!-- \u5feb\u6377\u64cd\u4f5c -->\n <div class=\"error-actions\">\n <a href=\"{{ base_url }}\" class=\"md-button md-button--primary\">\n \u8fd4\u56de\u9996\u9875\n </a>\n <button class=\"md-button\" onclick=\"window.history.back()\">\n \u8fd4\u56de\u4e0a\u9875\n </button>\n </div>\n </div>\n\n <!-- \u641c\u7d22\u5efa\u8bae -->\n <div class=\"search-suggestions\" id=\"search-suggestions\">\n <h3>\u60a8\u662f\u5426\u5728\u627e\uff1a</h3>\n <div class=\"suggestions-list\" id=\"suggestions-list\">\n <!-- \u52a8\u6001\u751f\u6210\u7684\u5efa\u8bae\u5217\u8868 -->\n </div>\n </div>\n</div>\n\n<!-- 404\u9875\u9762\u6837\u5f0f -->\n<style>\n.error-page {\n min-height: 100vh;\n display: flex;\n flex-direction: column;\n align-items: center;\n justify-content: center;\n padding: 2rem;\n text-align: center;\n}\n\n.error-content {\n max-width: 600px;\n}\n\n.error-icon {\n font-size: 6rem;\n color: var(--md-primary-fg-color);\n margin-bottom: 2rem;\n position: relative;\n}\n\n.error-code {\n position: absolute;\n bottom: -1rem;\n right: -1rem;\n font-size: 2rem;\n font-weight: bold;\n background: var(--md-accent-fg-color);\n color: white;\n padding: 0.5rem 1rem;\n border-radius: 1rem;\n}\n\n.error-search {\n margin: 2rem 0;\n position: relative;\n}\n\n.error-search input {\n width: 100%;\n padding: 1rem 3rem 1rem 1rem;\n border: 2px solid var(--md-default-fg-color--lightest);\n border-radius: 2rem;\n font-size: 1.1rem;\n}\n\n.error-search button {\n position: absolute;\n right: 0.5rem;\n top: 50%;\n transform: translateY(-50%);\n background: none;\n border: none;\n color: var(--md-default-fg-color);\n cursor: pointer;\n}\n\n.error-actions {\n display: flex;\n gap: 1rem;\n justify-content: center;\n margin: 2rem 0;\n}\n\n/* \u641c\u7d22\u5efa\u8bae\u6837\u5f0f */\n.search-suggestions {\n margin-top: 3rem;\n width: 100%;\n max-width: 600px;\n}\n\n.suggestions-list {\n margin-top: 1rem;\n}\n\n.suggestion-item {\n padding: 1rem;\n margin: 0.5rem 0;\n background: var(--md-code-bg-color);\n border-radius: 8px;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.suggestion-item:hover {\n background: var(--md-accent-fg-color--transparent);\n}\n\n/* \u54cd\u5e94\u5f0f\u9002\u914d */\n@media screen and (max-width: 76.1875em) {\n .error-icon {\n font-size: 4rem;\n }\n\n .error-code {\n font-size: 1.5rem;\n }\n\n .error-actions {\n flex-direction: column;\n }\n}\n</style>\n\n<!-- 404\u9875\u9762\u811a\u672c -->\n<script>\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u641c\u7d22\u5efa\u8bae\u529f\u80fd\n setupSearchSuggestions();\n // \u5168\u5c40\u70ed\u952e\n setupHotkeys();\n});\n\nfunction setupSearchSuggestions() {\n const searchInput = document.getElementById('error-search-input');\n const suggestionsList = document.getElementById('suggestions-list');\n\n searchInput?.addEventListener('input', debounce(async (e) => {\n const query = e.target.value;\n if (query.length < 2) {\n suggestionsList.innerHTML = '';\n return;\n }\n\n // \u83b7\u53d6\u641c\u7d22\u5efa\u8bae\n const suggestions = await getSearchSuggestions(query);\n\n // \u6e32\u67d3\u5efa\u8bae\u5217\u8868\n suggestionsList.innerHTML = suggestions\n .map(suggestion => `\n <div class=\"suggestion-item\" onclick=\"window.location.href='${suggestion.url}'\">\n <div class=\"suggestion-title\">${suggestion.title}</div>\n <div class=\"suggestion-excerpt\">${suggestion.excerpt}</div>\n </div>\n `)\n .join('');\n }, 300));\n}\n\nasync function getSearchSuggestions(query) {\n // \u8fd9\u91cc\u53ef\u4ee5\u5b9e\u73b0\u5b9e\u9645\u7684\u641c\u7d22\u903b\u8f91\n // \u793a\u4f8b\u8fd4\u56de\u6570\u636e\n return [\n {\n title: '\u76f8\u5173\u6587\u6863 1',\n excerpt: '\u8fd9\u662f\u4e00\u6bb5\u76f8\u5173\u7684\u6587\u6863\u63cf\u8ff0...',\n url: '#'\n },\n {\n title: '\u76f8\u5173\u6587\u6863 2',\n excerpt: '\u8fd9\u662f\u53e6\u4e00\u6bb5\u76f8\u5173\u7684\u6587\u6863\u63cf\u8ff0...',\n url: '#'\n }\n ];\n}\n\nfunction setupHotkeys() {\n document.addEventListener('keydown', (e) => {\n // \u6309 ESC \u8fd4\u56de\u4e0a\u9875\n if (e.key === 'Escape') {\n window.history.back();\n }\n\n // \u6309 Enter \u6267\u884c\u641c\u7d22\n if (e.key === 'Enter' && document.activeElement.id === 'error-search-input') {\n const query = document.activeElement.value;\n if (query) {\n window.location.href = `${window.location.origin}/search.html?q=${encodeURIComponent(query)}`;\n }\n }\n });\n}\n\n// \u9632\u6296\u51fd\u6570\nfunction debounce(fn, delay) {\n let timer = null;\n return function(...args) {\n clearTimeout(timer);\n timer = setTimeout(() => fn.apply(this, args), delay);\n };\n}\n</script>\n
\u8fd9\u6837\uff0c\u6211\u4eec\u5c31\u5b8c\u6210\u4e86\u6587\u7ae0\u5e95\u90e8\u4fe1\u606f\u548c404\u9875\u9762\u7684\u6a21\u677f\u3002\u4e3b\u8981\u7279\u70b9\u5305\u62ec\uff1a
-
\u6587\u7ae0\u5e95\u90e8\uff1a - \u6e05\u6670\u7684\u4e0a\u4e0b\u6587\u7ae0\u5bfc\u822a - \u793e\u4ea4\u5206\u4eab\u6309\u94ae - \u76f8\u5173\u6587\u7ae0\u63a8\u8350 - \u96c6\u6210\u8bc4\u8bba\u7cfb\u7edf
-
404\u9875\u9762\uff1a - \u53cb\u597d\u7684\u9519\u8bef\u63d0\u793a - \u5b9e\u65f6\u641c\u7d22\u5efa\u8bae - \u591a\u79cd\u8fd4\u56de\u9009\u9879 - \u952e\u76d8\u5feb\u6377\u952e\u652f\u6301
\u4f7f\u7528\u8fd9\u4e9b\u6a21\u677f\u65f6\uff0c\u9700\u8981\u5728 mkdocs.yml \u4e2d\u6dfb\u52a0\u76f8\u5e94\u7684\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n custom_dir: docs/overrides\n features:\n - navigation.tracking\n - search.suggest\n - search.highlight\n\nextra:\n comments:\n provider: giscus\n repo: username/repo\n repo_id: your-repo-id\n category: Comments\n category_id: your-category-id\n
"},{"location":"Tools/Blog/Mkdocs_Material/#72","title":"7.2 \u4fee\u6539\u7ec4\u4ef6\u6a21\u677f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#721","title":"7.2.1 \u5bfc\u822a\u680f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#7211","title":"7.2.1.1 \u5bfc\u822a\u9879\u5b9a\u5236","text":"HTML<!-- docs/overrides/partials/nav.html -->\n{% extends \"base.html\" %}\n\n{% block site_nav %}\n<nav class=\"md-nav md-nav--primary\">\n <!-- \u81ea\u5b9a\u4e49\u5bfc\u822a\u5934\u90e8 -->\n <div class=\"nav-header\">\n {% if config.theme.logo %}\n <img src=\"{{ config.theme.logo }}\" alt=\"logo\" class=\"nav-logo\">\n {% endif %}\n <span class=\"nav-title\">{{ config.site_name }}</span>\n </div>\n\n <!-- \u81ea\u5b9a\u4e49\u5bfc\u822a\u9879 -->\n <ul class=\"nav-items\">\n {% for nav_item in nav %}\n {% include \"partials/nav-item.html\" %}\n {% endfor %}\n\n <!-- \u6dfb\u52a0\u81ea\u5b9a\u4e49\u5bfc\u822a\u9879 -->\n {% if config.extra.nav_links %}\n {% for link in config.extra.nav_links %}\n <li class=\"nav-item custom\">\n <a href=\"{{ link.url }}\" class=\"nav-link\" {% if link.target %}target=\"{{ link.target }}\"{% endif %}>\n {% if link.icon %}\n <span class=\"nav-icon\">\n {% include \".icons/\" ~ link.icon ~ \".svg\" %}\n </span>\n {% endif %}\n {{ link.title }}\n </a>\n </li>\n {% endfor %}\n {% endif %}\n </ul>\n</nav>\n
\u914d\u7f6e\u548c\u6837\u5f0f\uff1a
YAML# mkdocs.yml\nextra:\n nav_links:\n - title: GitHub\n url: https://github.com/your/repo\n icon: material/github\n target: _blank\n - title: \u6587\u6863\n url: /docs/\n icon: material/file-document\n
CSS/* docs/stylesheets/nav.css */\n.nav-header {\n display: flex;\n align-items: center;\n padding: 1rem;\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.nav-logo {\n width: 32px;\n height: 32px;\n margin-right: 0.8rem;\n}\n\n.nav-items {\n list-style: none;\n padding: 0;\n margin: 0;\n}\n\n.nav-item {\n margin: 0.2rem 0;\n}\n\n.nav-link {\n display: flex;\n align-items: center;\n padding: 0.8rem 1rem;\n color: var(--md-default-fg-color);\n text-decoration: none;\n transition: background-color 0.2s ease;\n}\n\n.nav-link:hover {\n background-color: var(--md-code-bg-color);\n}\n\n.nav-icon {\n margin-right: 0.8rem;\n width: 1.2rem;\n height: 1.2rem;\n}\n\n/* \u81ea\u5b9a\u4e49\u5bfc\u822a\u9879\u6837\u5f0f */\n.nav-item.custom .nav-link {\n color: var(--md-accent-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7212","title":"7.2.1.2 \u641c\u7d22\u6846\u4f4d\u7f6e","text":"HTML<!-- docs/overrides/partials/search.html -->\n{% block search_box %}\n<div class=\"md-search\" data-md-component=\"search\" role=\"dialog\">\n <label class=\"md-search__overlay\" for=\"__search\"></label>\n <div class=\"md-search__inner\" role=\"search\">\n <!-- \u641c\u7d22\u8f93\u5165\u6846 -->\n <form class=\"md-search__form\">\n <input\n type=\"text\"\n class=\"md-search__input\"\n name=\"query\"\n aria-label=\"\u641c\u7d22\"\n placeholder=\"\u641c\u7d22\u6587\u6863...\"\n autocapitalize=\"off\"\n autocomplete=\"off\"\n autocorrect=\"off\"\n spellcheck=\"false\"\n data-md-component=\"search-query\"\n >\n <!-- \u641c\u7d22\u5feb\u6377\u952e\u63d0\u793a -->\n <div class=\"md-search__shortcuts\">\n <kbd>Ctrl</kbd> + <kbd>K</kbd>\n </div>\n </form>\n\n <!-- \u641c\u7d22\u7ed3\u679c -->\n <div class=\"md-search__output\">\n <div class=\"md-search__scrollwrap\" data-md-scrollfix>\n <div class=\"md-search-result\" data-md-component=\"search-result\">\n <div class=\"md-search-result__meta\">\n \u6b63\u5728\u641c\u7d22...\n </div>\n <ol class=\"md-search-result__list\"></ol>\n </div>\n </div>\n </div>\n </div>\n</div>\n
\u6837\u5f0f\u5b9a\u5236\uff1a
CSS/* docs/stylesheets/search.css */\n/* \u641c\u7d22\u6846\u5bb9\u5668 */\n.md-search {\n margin: 0 1rem;\n padding: 0;\n position: relative;\n}\n\n/* \u641c\u7d22\u8f93\u5165\u6846 */\n.md-search__input {\n width: 100%;\n height: 2.4rem;\n padding: 0 2.4rem;\n font-size: 0.9rem;\n color: var(--md-default-fg-color);\n background-color: var(--md-default-bg-color);\n border: 1px solid var(--md-default-fg-color--lightest);\n border-radius: 1.2rem;\n}\n\n/* \u641c\u7d22\u56fe\u6807 */\n.md-search__icon {\n position: absolute;\n left: 0.8rem;\n top: 50%;\n transform: translateY(-50%);\n color: var(--md-default-fg-color--light);\n}\n\n/* \u5feb\u6377\u952e\u63d0\u793a */\n.md-search__shortcuts {\n position: absolute;\n right: 0.8rem;\n top: 50%;\n transform: translateY(-50%);\n display: flex;\n gap: 0.2rem;\n color: var(--md-default-fg-color--light);\n}\n\n/* \u641c\u7d22\u7ed3\u679c\u5bb9\u5668 */\n.md-search__output {\n position: absolute;\n top: 100%;\n width: 100%;\n margin-top: 0.4rem;\n background-color: var(--md-default-bg-color);\n border-radius: 0.2rem;\n box-shadow: var(--md-shadow-z2);\n overflow: auto;\n z-index: 1;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7213","title":"7.2.1.3 \u79fb\u52a8\u7aef\u9002\u914d","text":"CSS/* \u79fb\u52a8\u7aef\u5bfc\u822a\u6837\u5f0f */\n@media screen and (max-width: 76.1875em) {\n /* \u5bfc\u822a\u5207\u6362\u6309\u94ae */\n .md-header-nav__button.md-icon {\n padding: 0.4rem;\n margin: 0.4rem;\n }\n\n /* \u5bfc\u822a\u62bd\u5c49 */\n .md-nav--primary {\n position: fixed;\n top: 0;\n left: -18rem;\n width: 18rem;\n height: 100%;\n background-color: var(--md-default-bg-color);\n transition: left 0.25s;\n z-index: 2;\n }\n\n /* \u5bfc\u822a\u62bd\u5c49\u6253\u5f00\u72b6\u6001 */\n .md-nav--primary.md-nav--opened {\n left: 0;\n }\n\n /* \u641c\u7d22\u6846\u5168\u5c4f */\n .md-search__inner {\n position: fixed;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n padding: 1rem;\n background-color: var(--md-default-bg-color);\n z-index: 3;\n }\n\n /* \u641c\u7d22\u7ed3\u679c\u5168\u5c4f */\n .md-search__output {\n position: fixed;\n top: 4rem;\n height: calc(100% - 4rem);\n }\n}\n\n/* \u5e73\u677f\u9002\u914d */\n@media screen and (min-width: 76.25em) and (max-width: 96.25em) {\n .md-nav--primary {\n width: 16rem;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#722","title":"7.2.2 \u9875\u811a","text":"HTML<!-- docs/overrides/partials/footer.html -->\n<footer class=\"md-footer\">\n <div class=\"md-footer-meta md-typeset\">\n <div class=\"md-footer-meta__inner md-grid\">\n <!-- \u7248\u6743\u4fe1\u606f -->\n <div class=\"md-footer-copyright\">\n {% if config.copyright %}\n <div class=\"md-footer-copyright__highlight\">\n {{ config.copyright }}\n </div>\n {% endif %}\n\n <!-- \u6784\u5efa\u4fe1\u606f -->\n <div class=\"md-footer-build\">\n Documentation built with\n <a href=\"https://www.mkdocs.org\" target=\"_blank\" rel=\"noopener\">\n MkDocs\n </a>\n and\n <a href=\"https://squidfunk.github.io/mkdocs-material/\" target=\"_blank\" rel=\"noopener\">\n Material for MkDocs\n </a>\n </div>\n </div>\n\n <!-- \u793e\u4ea4\u94fe\u63a5 -->\n {% if config.extra.social %}\n <div class=\"md-footer-social\">\n {% for social in config.extra.social %}\n <a href=\"{{ social.link }}\" \n target=\"_blank\" \n rel=\"noopener\" \n title=\"{{ social.name }}\"\n class=\"md-footer-social__link\">\n {% include \".icons/\" ~ social.icon ~ \".svg\" %}\n </a>\n {% endfor %}\n </div>\n {% endif %}\n\n <!-- \u5907\u6848\u4fe1\u606f -->\n {% if config.extra.icp %}\n <div class=\"md-footer-icp\">\n <a href=\"https://beian.miit.gov.cn/\" target=\"_blank\" rel=\"noopener\">\n {{ config.extra.icp }}\n </a>\n </div>\n {% endif %}\n </div>\n </div>\n</footer>\n
\u914d\u7f6e\uff1a
YAML# mkdocs.yml\ncopyright: Copyright © 2024 Your Name\nextra:\n social:\n - icon: material/github\n name: GitHub\n link: https://github.com/your-username\n - icon: material/twitter\n name: Twitter\n link: https://twitter.com/your-username\n icp: \u4eacICP\u5907xxxxxxxx\u53f7\n
\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/footer.css */\n.md-footer {\n background-color: var(--md-default-bg-color);\n color: var(--md-default-fg-color);\n padding: 2rem 0;\n border-top: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.md-footer-meta__inner {\n display: flex;\n flex-wrap: wrap;\n justify-content: space-between;\n align-items: center;\n gap: 1rem;\n}\n\n/* \u7248\u6743\u4fe1\u606f */\n.md-footer-copyright {\n font-size: 0.8rem;\n}\n\n.md-footer-copyright__highlight {\n margin-bottom: 0.5rem;\n}\n\n.md-footer-build {\n color: var(--md-default-fg-color--light);\n}\n\n/* \u793e\u4ea4\u94fe\u63a5 */\n.md-footer-social {\n display: flex;\n gap: 1rem;\n}\n\n.md-footer-social__link {\n width: 2rem;\n height: 2rem;\n display: flex;\n align-items: center;\n justify-content: center;\n color: var(--md-default-fg-color);\n border-radius: 50%;\n transition: all 0.2s ease;\n}\n\n.md-footer-social__link:hover {\n background-color: var(--md-code-bg-color);\n color: var(--md-accent-fg-color);\n}\n\n/* \u5907\u6848\u4fe1\u606f */\n.md-footer-icp {\n width: 100%;\n text-align: center;\n margin-top: 1rem;\n font-size: 0.8rem;\n}\n\n.md-footer-icp a {\n color: var(--md-default-fg-color--light);\n text-decoration: none;\n}\n\n/* \u54cd\u5e94\u5f0f\u9002\u914d */\n@media screen and (max-width: 76.1875em) {\n .md-footer-meta__inner {\n flex-direction: column;\n text-align: center;\n }\n\n .md-footer-social {\n justify-content: center;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#723","title":"7.2.3 \u641c\u7d22\u6846","text":"HTML<!-- docs/overrides/partials/search-modal.html -->\n<div class=\"md-search-modal\" data-md-component=\"search-modal\">\n <div class=\"md-search-modal__overlay\" data-md-component=\"search-modal-overlay\"></div>\n\n <div class=\"md-search-modal__inner\">\n <!-- \u641c\u7d22\u5934\u90e8 -->\n <header class=\"md-search-modal__header\">\n <form class=\"md-search-modal__form\">\n <input\n type=\"text\"\n class=\"md-search-modal__input\"\n name=\"query\"\n placeholder=\"\u641c\u7d22\u6587\u6863...\"\n data-md-component=\"search-query\"\n autocapitalize=\"off\"\n autocomplete=\"off\"\n autocorrect=\"off\"\n spellcheck=\"false\"\n >\n <!-- \u5feb\u6377\u952e\u63d0\u793a -->\n <div class=\"md-search-modal__shortcuts\">\n <kbd>\u2191</kbd><kbd>\u2193</kbd> \u9009\u62e9\n <kbd>\u21b5</kbd> \u6253\u5f00\n <kbd>ESC</kbd> \u5173\u95ed\n </div>\n </form>\n </header>\n\n <!-- \u641c\u7d22\u7ed3\u679c -->\n <main class=\"md-search-modal__body\">\n <div class=\"md-search-modal__scrollwrap\">\n <div class=\"md-search-modal__meta\">\n \u952e\u5165\u4ee5\u5f00\u59cb\u641c\u7d22\n </div>\n\n <div class=\"md-search-modal__results\">\n <!-- \u641c\u7d22\u7ed3\u679c\u5217\u8868 -->\n <div class=\"md-search-modal__list\"></div>\n\n <!-- \u641c\u7d22\u5efa\u8bae -->\n <div class=\"md-search-modal__suggestions\">\n <h3>\u60a8\u53ef\u80fd\u611f\u5174\u8da3\uff1a</h3>\n <div class=\"suggestions-list\">\n <!-- \u52a8\u6001\u751f\u6210\u7684\u5efa\u8bae\u5217\u8868 -->\n </div>\n </div>\n </div>\n </div>\n </main>\n </div>\n</div>\n
\u641c\u7d22\u529f\u80fd\u811a\u672c\uff1a
JavaScript// docs/javascripts/search.js\nclass SearchModal {\n constructor() {\n this.modal = document.querySelector('.md-search-modal');\n this.overlay = document.querySelector('.md-search-modal__overlay');\n this.input = document.querySelector('.md-search-modal__input');\n this.resultsList = document.querySelector('.md-search-modal__list');\n this.suggestions = document.querySelector('.md-search-modal__suggestions');\n\n this.searchIndex = null;\n this.searchResults = [];\n this.currentFocus = -1;\n\n this.init();\n }\n\n init() {\n // \u521d\u59cb\u5316\u641c\u7d22\u7d22\u5f15\n this.buildSearchIndex();\n // \u7ed1\u5b9a\u4e8b\u4ef6\n this.bindEvents();\n // \u8bbe\u7f6e\u5feb\u6377\u952e\n this.setupShortcuts();\n }\n\n async buildSearchIndex() {\n // \u6784\u5efa\u641c\u7d22\u7d22\u5f15\n const response = await fetch('/search/search_index.json');\n const data = await response.json();\n\n // \u4f7f\u7528 lunr.js \u6784\u5efa\u7d22\u5f15\n this.searchIndex = lunr(function() {\n this.field('title', { boost: 10 });\n this.field('text');\n this.ref('location');\n\n data.docs.forEach(function(doc) {\n this.add(doc);\n }, this);\n });\n }\n\n bindEvents() {\n // \u641c\u7d22\u6846\u8f93\u5165\u4e8b\u4ef6\n this.input.addEventListener('input', debounce(() => {\n const query = this.input.value;\n if (query.length >= 2) {\n this.performSearch(query);\n } else {\n this.clearResults();\n }\n }, 200));\n\n // \u641c\u7d22\u7ed3\u679c\u5bfc\u822a\n this.input.addEventListener('keydown', (e) => {\n switch (e.key) {\n case 'ArrowUp':\n e.preventDefault();\n this.navigateResults('up');\n break;\n case 'ArrowDown':\n e.preventDefault();\n this.navigateResults('down');\n break;\n case 'Enter':\n e.preventDefault();\n this.openResult();\n break;\n case 'Escape':\n e.preventDefault();\n this.closeModal();\n break;\n }\n });\n\n // \u70b9\u51fb\u906e\u7f69\u5173\u95ed\n this.overlay.addEventListener('click', () => this.closeModal());\n }\n\n setupShortcuts() {\n // \u5168\u5c40\u641c\u7d22\u5feb\u6377\u952e\n document.addEventListener('keydown', (e) => {\n // Ctrl/Cmd + K \u6253\u5f00\u641c\u7d22\n if ((e.ctrlKey || e.metaKey) && e.key === 'k') {\n e.preventDefault();\n this.openModal();\n }\n });\n }\n\n async performSearch(query) {\n if (!this.searchIndex) return;\n\n // \u6267\u884c\u641c\u7d22\n this.searchResults = this.searchIndex.search(query).map(result => {\n return {\n ref: result.ref,\n score: result.score,\n ...this.getDocumentByRef(result.ref)\n };\n });\n\n // \u6e32\u67d3\u7ed3\u679c\n this.renderResults();\n // \u66f4\u65b0\u5efa\u8bae\n this.updateSuggestions(query);\n }\n\n renderResults() {\n this.resultsList.innerHTML = this.searchResults\n .map((result, index) => `\n <div class=\"search-result-item ${index === this.currentFocus ? 'focused' : ''}\"\n data-index=\"${index}\">\n <div class=\"result-title\">${this.highlightText(result.title)}</div>\n <div class=\"result-excerpt\">${this.highlightText(result.excerpt)}</div>\n <div class=\"result-location\">${result.location}</div>\n </div>\n `)\n .join('');\n }\n\n highlightText(text) {\n const query = this.input.value;\n if (!query) return text;\n\n const regex = new RegExp(`(${query})`, 'gi');\n return text.replace(regex, '<mark>$1</mark>');\n }\n\n updateSuggestions(query) {\n // \u6839\u636e\u641c\u7d22\u5386\u53f2\u548c\u70ed\u95e8\u641c\u7d22\u751f\u6210\u5efa\u8bae\n const suggestions = this.generateSuggestions(query);\n\n this.suggestions.innerHTML = suggestions\n .map(suggestion => `\n <div class=\"suggestion-item\" data-query=\"${suggestion.query}\">\n <span class=\"suggestion-icon\">\n ${suggestion.type === 'history' ? '\u23f1\ufe0f' : '\ud83d\udd25'}\n </span>\n ${suggestion.query}\n </div>\n `)\n .join('');\n }\n\n navigateResults(direction) {\n const maxIndex = this.searchResults.length - 1;\n\n if (direction === 'up') {\n this.currentFocus = this.currentFocus > 0 ? this.currentFocus - 1 : maxIndex;\n } else {\n this.currentFocus = this.currentFocus < maxIndex ? this.currentFocus + 1 : 0;\n }\n\n this.renderResults();\n this.scrollToFocused();\n }\n\n scrollToFocused() {\n const focused = this.resultsList.querySelector('.focused');\n if (focused) {\n focused.scrollIntoView({\n behavior: 'smooth',\n block: 'nearest'\n });\n }\n }\n\n openResult() {\n const result = this.searchResults[this.currentFocus];\n if (result) {\n window.location.href = result.location;\n }\n }\n\n openModal() {\n this.modal.classList.add('active');\n this.input.focus();\n document.body.style.overflow = 'hidden';\n }\n\n closeModal() {\n this.modal.classList.remove('active');\n this.input.value = '';\n this.clearResults();\n document.body.style.overflow = '';\n }\n\n clearResults() {\n this.searchResults = [];\n this.currentFocus = -1;\n this.resultsList.innerHTML = '';\n this.suggestions.innerHTML = '';\n }\n}\n\n// \u521d\u59cb\u5316\u641c\u7d22\u529f\u80fd\ndocument.addEventListener('DOMContentLoaded', () => {\n new SearchModal();\n});\n
\u641c\u7d22\u6846\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/search-modal.css */\n.md-search-modal {\n position: fixed;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n z-index: 1000;\n opacity: 0;\n visibility: hidden;\n transition: all 0.2s ease;\n}\n\n.md-search-modal.active {\n opacity: 1;\n visibility: visible;\n}\n\n.md-search-modal__overlay {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n background: rgba(0, 0, 0, 0.5);\n backdrop-filter: blur(4px);\n}\n\n.md-search-modal__inner {\n position: relative;\n width: 90%;\n max-width: 800px;\n margin: 2rem auto;\n background: var(--md-default-bg-color);\n border-radius: 8px;\n box-shadow: var(--md-shadow-z3);\n}\n\n/* \u641c\u7d22\u7ed3\u679c\u6837\u5f0f */\n.search-result-item {\n padding: 1rem;\n cursor: pointer;\n transition: background 0.2s ease;\n}\n\n.search-result-item:hover,\n.search-result-item.focused {\n background: var(--md-code-bg-color);\n}\n\n.result-title {\n font-size: 1.1rem;\n font-weight: 500;\n margin-bottom: 0.5rem;\n}\n\n.result-excerpt {\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n margin-bottom: 0.5rem;\n}\n\n.result-location {\n font-size: 0.8rem;\n color: var(--md-accent-fg-color);\n}\n\n/* \u9ad8\u4eae\u5339\u914d\u6587\u672c */\nmark {\n background: var(--md-accent-fg-color);\n color: white;\n padding: 0 0.2rem;\n border-radius: 2px;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#724","title":"7.2.4 \u76ee\u5f55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#7241","title":"7.2.4.1 \u76ee\u5f55\u7ed3\u6784","text":"HTML<!-- docs/overrides/partials/toc.html -->\n<nav class=\"md-toc\" aria-label=\"\u76ee\u5f55\">\n <div class=\"md-toc__header\">\n <h2 class=\"md-toc__title\">\u76ee\u5f55</h2>\n <button class=\"md-toc__toggle\" aria-label=\"\u5c55\u5f00/\u6536\u8d77\">\n {% include \".icons/material/chevron-down.svg\" %}\n </button>\n </div>\n\n <div class=\"md-toc__inner\">\n {% set toc = page.toc %}\n <ul class=\"md-toc__list\">\n {% for toc_item in toc %}\n {% include \"partials/toc-item.html\" %}\n {% endfor %}\n </ul>\n </div>\n</nav>\n\n<!-- docs/overrides/partials/toc-item.html -->\n<li class=\"md-toc__item\">\n <a href=\"{{ toc_item.url }}\" class=\"md-toc__link\">\n {{ toc_item.title }}\n </a>\n\n {% if toc_item.children %}\n <ul class=\"md-toc__list\">\n {% for toc_item in toc_item.children %}\n {% include \"partials/toc-item.html\" %}\n {% endfor %}\n </ul>\n {% endif %}\n</li>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7242","title":"7.2.4.2 \u6eda\u52a8\u540c\u6b65","text":"JavaScript// docs/javascripts/toc.js\nclass TableOfContents {\n constructor() {\n this.toc = document.querySelector('.md-toc');\n this.tocLinks = this.toc.querySelectorAll('.md-toc__link');\n this.headings = document.querySelectorAll('h1[id], h2[id], h3[id], h4[id], h5[id], h6[id]');\n this.intersectionObserver = null;\n\n this.init();\n }\n\n init() {\n this.setupIntersectionObserver();\n this.setupScrollSpy();\n this.setupToggle();\n }\n\n setupIntersectionObserver() {\n this.intersectionObserver = new IntersectionObserver(\n (entries) => {\n entries.forEach(entry => {\n if (entry.isIntersecting) {\n const id = entry.target.getAttribute('id');\n this.highlightTocItem(id);\n }\n });\n },\n {\n rootMargin: '0px 0px -80% 0px'\n }\n );\n\n this.headings.forEach(heading => {\n this.intersectionObserver.observe(heading);\n });\n }\n\n setupScrollSpy() {\n this.tocLinks.forEach(link => {\n link.addEventListener('click', (e) => {\n e.preventDefault();\n const id = link.getAttribute('href').substring(1);\n const target = document.getElementById(id);\n\n if (target) {\n window.scrollTo({\n top: target.offsetTop - 100,\n behavior: 'smooth'\n });\n\n // \u66f4\u65b0 URL\n history.pushState(null, null, `#${id}`);\n }\n });\n });\n }\n\n highlightTocItem(id) {\n // \u79fb\u9664\u6240\u6709\u9ad8\u4eae\n this.tocLinks.forEach(link => {\n link.classList.remove('active');\n });\n\n // \u6dfb\u52a0\u65b0\u9ad8\u4eae\n const activeLink = this.toc.querySelector(`a[href=\"#${id}\"]`);\n if (activeLink) {\n activeLink.classList.add('active');\n this.expandParents(activeLink);\n }\n }\n\n expandParents(element) {\n let parent = element.parentElement;\n while (parent && parent.classList.contains('md-toc__item')) {\n const list = parent.querySelector('.md-toc__list');\n if (list) {\n list.style.height = 'auto';\n parent.classList.add('expanded');\n }\n parent = parent.parentElement;\n }\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7243","title":"7.2.4.3 \u5c55\u5f00\u6536\u8d77","text":"JavaScriptsetupToggle() {\n const toggleButton = this.toc.querySelector('.md-toc__toggle');\n const tocInner = this.toc.querySelector('.md-toc__inner');\n\n toggleButton?.addEventListener('click', () => {\n const isExpanded = this.toc.classList.contains('expanded');\n\n if (isExpanded) {\n this.toc.classList.remove('expanded');\n tocInner.style.height = '0';\n } else {\n this.toc.classList.add('expanded');\n tocInner.style.height = tocInner.scrollHeight + 'px';\n }\n });\n\n // \u6dfb\u52a0\u5c55\u5f00/\u6536\u8d77\u6240\u6709\u6309\u94ae\n const expandAllButton = document.createElement('button');\n expandAllButton.className = 'md-toc__expand-all';\n expandAllButton.innerHTML = '\u5c55\u5f00\u5168\u90e8';\n expandAllButton.addEventListener('click', () => this.toggleAll(true));\n\n const collapseAllButton = document.createElement('button');\n collapseAllButton.className = 'md-toc__collapse-all';\n collapseAllButton.innerHTML = '\u6536\u8d77\u5168\u90e8';\n collapseAllButton.addEventListener('click', () => this.toggleAll(false));\n\n const buttonGroup = document.createElement('div');\n buttonGroup.className = 'md-toc__button-group';\n buttonGroup.appendChild(expandAllButton);\n buttonGroup.appendChild(collapseAllButton);\n\n this.toc.querySelector('.md-toc__header').appendChild(buttonGroup);\n}\n\ntoggleAll(expand) {\n const lists = this.toc.querySelectorAll('.md-toc__list');\n lists.forEach(list => {\n if (expand) {\n list.style.height = 'auto';\n list.parentElement.classList.add('expanded');\n } else {\n list.style.height = '0';\n list.parentElement.classList.remove('expanded');\n }\n });\n}\n
\u76ee\u5f55\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/toc.css */\n.md-toc {\n position: sticky;\n top: 4rem;\n padding: 1rem;\n max-height: calc(100vh - 4rem);\n overflow-y: auto;\n background: var(--md-default-bg-color);\n border-left: 1px solid var(--md-default-fg-color--lightest);\n font-size: 0.8rem;\n}\n\n/* \u76ee\u5f55\u5934\u90e8 */\n.md-toc__header {\n display: flex;\n justify-content: space-between;\n align-items: center;\n margin-bottom: 1rem;\n padding-bottom: 0.5rem;\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.md-toc__title {\n font-size: 1rem;\n font-weight: 600;\n margin: 0;\n}\n\n.md-toc__button-group {\n display: flex;\n gap: 0.5rem;\n}\n\n.md-toc__button-group button {\n padding: 0.2rem 0.5rem;\n font-size: 0.7rem;\n color: var(--md-default-fg-color--light);\n background: var(--md-code-bg-color);\n border: none;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.md-toc__button-group button:hover {\n color: var(--md-accent-fg-color);\n background: var(--md-code-bg-color--light);\n}\n\n/* \u76ee\u5f55\u5217\u8868 */\n.md-toc__list {\n list-style: none;\n padding: 0;\n margin: 0;\n}\n\n.md-toc__item {\n margin: 0.2rem 0;\n}\n\n/* \u76ee\u5f55\u94fe\u63a5 */\n.md-toc__link {\n display: block;\n padding: 0.2rem 0;\n color: var(--md-default-fg-color);\n text-decoration: none;\n transition: all 0.2s ease;\n border-radius: 4px;\n}\n\n.md-toc__link:hover {\n color: var(--md-accent-fg-color);\n background: var(--md-code-bg-color);\n padding-left: 0.5rem;\n}\n\n/* \u5f53\u524d\u6fc0\u6d3b\u9879 */\n.md-toc__link.active {\n color: var(--md-accent-fg-color);\n font-weight: 500;\n background: var(--md-code-bg-color);\n padding-left: 0.5rem;\n}\n\n/* \u5d4c\u5957\u5c42\u7ea7 */\n.md-toc__item .md-toc__list {\n margin-left: 1rem;\n border-left: 1px solid var(--md-default-fg-color--lightest);\n overflow: hidden;\n height: 0;\n transition: height 0.3s ease;\n}\n\n.md-toc__item.expanded > .md-toc__list {\n height: auto;\n}\n\n/* \u6298\u53e0\u6307\u793a\u5668 */\n.md-toc__item > .md-toc__link::before {\n content: '';\n display: inline-block;\n width: 0.8rem;\n height: 0.8rem;\n margin-right: 0.2rem;\n background-image: url('data:image/svg+xml,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M8.59 16.59L13.17 12 8.59 7.41 10 6l6 6-6 6-1.41-1.41z\"/></svg>');\n background-size: contain;\n transform: rotate(0);\n transition: transform 0.3s ease;\n}\n\n.md-toc__item.expanded > .md-toc__link::before {\n transform: rotate(90deg);\n}\n\n/* \u6eda\u52a8\u6761\u6837\u5f0f */\n.md-toc::-webkit-scrollbar {\n width: 4px;\n}\n\n.md-toc::-webkit-scrollbar-track {\n background: transparent;\n}\n\n.md-toc::-webkit-scrollbar-thumb {\n background: var(--md-default-fg-color--lighter);\n border-radius: 2px;\n}\n\n/* \u54cd\u5e94\u5f0f\u8bbe\u8ba1 */\n@media screen and (max-width: 76.1875em) {\n .md-toc {\n position: fixed;\n top: 0;\n right: -18rem;\n width: 18rem;\n height: 100vh;\n max-height: none;\n margin: 0;\n padding: 1rem;\n background: var(--md-default-bg-color);\n border-left: 1px solid var(--md-default-fg-color--lightest);\n box-shadow: var(--md-shadow-z3);\n transition: right 0.3s ease;\n z-index: 3;\n }\n\n .md-toc.expanded {\n right: 0;\n }\n\n /* \u79fb\u52a8\u7aef\u8986\u76d6\u5c42 */\n .md-toc-overlay {\n display: none;\n position: fixed;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n background: rgba(0, 0, 0, 0.3);\n z-index: 2;\n }\n\n .md-toc.expanded + .md-toc-overlay {\n display: block;\n }\n}\n\n/* \u5e73\u677f\u9002\u914d */\n@media screen and (min-width: 76.25em) and (max-width: 96.25em) {\n .md-toc {\n padding: 0.8rem;\n }\n\n .md-toc__list {\n margin-left: 0.8rem;\n }\n}\n
\u5b8c\u6574\u7684\u76ee\u5f55\u529f\u80fd\u5b9e\u73b0\uff1a
JavaScript// docs/javascripts/toc.js\nclass TableOfContents {\n constructor() {\n this.toc = document.querySelector('.md-toc');\n this.tocLinks = this.toc.querySelectorAll('.md-toc__link');\n this.headings = document.querySelectorAll('h1[id], h2[id], h3[id], h4[id], h5[id], h6[id]');\n this.intersectionObserver = null;\n this.isScrolling = false;\n this.scrollTimeout = null;\n\n this.init();\n }\n\n init() {\n this.setupIntersectionObserver();\n this.setupScrollSpy();\n this.setupToggle();\n this.setupMobileToggle();\n this.handleInitialHash();\n }\n\n handleInitialHash() {\n // \u5904\u7406\u9875\u9762\u52a0\u8f7d\u65f6\u7684 hash\n if (window.location.hash) {\n const id = window.location.hash.substring(1);\n const target = document.getElementById(id);\n if (target) {\n setTimeout(() => {\n target.scrollIntoView();\n this.highlightTocItem(id);\n }, 100);\n }\n }\n }\n\n setupMobileToggle() {\n // \u521b\u5efa\u79fb\u52a8\u7aef\u5f00\u5173\u6309\u94ae\n const toggleButton = document.createElement('button');\n toggleButton.className = 'md-toc-toggle';\n toggleButton.innerHTML = `\n <span class=\"md-toc-toggle__icon\">\n {% include \".icons/material/menu.svg\" %}\n </span>\n `;\n\n // \u521b\u5efa\u906e\u7f69\u5c42\n const overlay = document.createElement('div');\n overlay.className = 'md-toc-overlay';\n\n document.body.appendChild(toggleButton);\n document.body.appendChild(overlay);\n\n // \u7ed1\u5b9a\u4e8b\u4ef6\n toggleButton.addEventListener('click', () => {\n this.toc.classList.toggle('expanded');\n document.body.style.overflow = this.toc.classList.contains('expanded') ? 'hidden' : '';\n });\n\n overlay.addEventListener('click', () => {\n this.toc.classList.remove('expanded');\n document.body.style.overflow = '';\n });\n }\n\n updateTocHeight() {\n const lists = this.toc.querySelectorAll('.md-toc__list');\n lists.forEach(list => {\n if (list.parentElement.classList.contains('expanded')) {\n list.style.height = list.scrollHeight + 'px';\n }\n });\n }\n\n onResize() {\n // \u76d1\u542c\u7a97\u53e3\u5927\u5c0f\u53d8\u5316\uff0c\u66f4\u65b0\u76ee\u5f55\u9ad8\u5ea6\n window.addEventListener('resize', debounce(() => {\n this.updateTocHeight();\n }, 100));\n }\n}\n\n// \u5de5\u5177\u51fd\u6570\nfunction debounce(fn, delay) {\n let timer = null;\n return function(...args) {\n clearTimeout(timer);\n timer = setTimeout(() => fn.apply(this, args), delay);\n };\n}\n\n// \u521d\u59cb\u5316\u76ee\u5f55\ndocument.addEventListener('DOMContentLoaded', () => {\n new TableOfContents();\n});\n
\u4f7f\u7528\u8fd9\u4e9b\u4ee3\u7801\uff0c\u9700\u8981\u5728 mkdocs.yml \u4e2d\u6dfb\u52a0\u76f8\u5e94\u7684\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n custom_dir: docs/overrides\n features:\n - toc.integrate\n - toc.follow\n\nextra_css:\n - stylesheets/toc.css\n\nextra_javascript:\n - javascripts/toc.js\n
\u8fd9\u6837\u5c31\u5b8c\u6210\u4e86\u4e00\u4e2a\u529f\u80fd\u5b8c\u6574\u3001\u4ea4\u4e92\u53cb\u597d\u7684\u76ee\u5f55\u5bfc\u822a\u7cfb\u7edf\uff0c\u4e3b\u8981\u7279\u70b9\u5305\u62ec\uff1a
- \u81ea\u52a8\u9ad8\u4eae\u5f53\u524d\u9605\u8bfb\u4f4d\u7f6e
- \u5e73\u6ed1\u6eda\u52a8\u5230\u76ee\u6807\u4f4d\u7f6e
- \u652f\u6301\u5c55\u5f00/\u6536\u8d77\u529f\u80fd
- \u79fb\u52a8\u7aef\u81ea\u9002\u5e94\u5e03\u5c40
- \u4f18\u96c5\u7684\u52a8\u753b\u6548\u679c
- \u826f\u597d\u7684\u53ef\u8bbf\u95ee\u6027\u652f\u6301
- \u6027\u80fd\u4f18\u5316\u8003\u8651
"},{"location":"Tools/Blog/Mkdocs_Material/#8","title":"8 \u63d2\u4ef6\u4f7f\u7528","text":""},{"location":"Tools/Blog/Mkdocs_Material/#81","title":"8.1 \u5fc5\u5907\u63d2\u4ef6","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8111-search","title":"8.1.1.1 search","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8112","title":"8.1.1.2 \u57fa\u7840\u914d\u7f6e","text":" - \u5b89\u88c5\u641c\u7d22\u63d2\u4ef6\uff1a
Bashpip install mkdocs-material\n
- \u914d\u7f6e\u641c\u7d22\uff1a
YAML# mkdocs.yml\nplugins:\n - search:\n # \u641c\u7d22\u7d22\u5f15\u8bbe\u7f6e\n lang: \n - en\n - zh\n separator: '[\\s\\-\\.]+' # \u5206\u8bcd\u5206\u9694\u7b26\n min_search_length: 2 # \u6700\u5c0f\u641c\u7d22\u957f\u5ea6\n prebuild_index: true # \u9884\u6784\u5efa\u7d22\u5f15\n\n # \u641c\u7d22\u5185\u5bb9\u914d\u7f6e\n indexing:\n full_sections: true # \u7d22\u5f15\u5b8c\u6574\u7ae0\u8282\n headings: true # \u7d22\u5f15\u6807\u9898\n content: true # \u7d22\u5f15\u5185\u5bb9\n tags: true # \u7d22\u5f15\u6807\u7b7e\n\n # \u641c\u7d22\u7ed3\u679c\u6392\u5e8f\n scoring:\n title_boost: 10 # \u6807\u9898\u6743\u91cd\n heading_boost: 5 # \u6807\u9898\u6743\u91cd\n content_boost: 1 # \u5185\u5bb9\u6743\u91cd\n
- \u641c\u7d22\u4e3b\u9898\u914d\u7f6e\uff1a
YAMLtheme:\n features:\n - search.highlight # \u641c\u7d22\u9ad8\u4eae\n - search.share # \u641c\u7d22\u5206\u4eab\n - search.suggest # \u641c\u7d22\u5efa\u8bae\n\n # \u641c\u7d22\u754c\u9762\u6587\u5b57\n language: zh\n palette:\n - search:\n placeholder: \u641c\u7d22\u6587\u6863\n result:\n no_results_text: \u672a\u627e\u5230\u76f8\u5173\u7ed3\u679c\n searching_text: \u6b63\u5728\u641c\u7d22...\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8113","title":"8.1.1.3 \u4e2d\u6587\u641c\u7d22","text":" - \u4e2d\u6587\u5206\u8bcd\u914d\u7f6e\uff1a
YAMLplugins:\n - search:\n jieba_dict: dict.txt # \u81ea\u5b9a\u4e49\u8bcd\u5178\u8def\u5f84\n jieba_dict_user: user_dict.txt # \u7528\u6237\u8bcd\u5178\u8def\u5f84\n\n # \u4e2d\u6587\u5206\u8bcd\u89c4\u5219\n separator: '[\uff0c\u3002\uff01\uff1f,!?]+'\n\n # \u4e2d\u6587\u505c\u7528\u8bcd\n stopwords:\n - \u7684\n - \u4e86\n - \u548c\n - \u662f\n - \u5c31\n - \u90fd\n - \u800c\n - \u53ca\n - \u4e0e\n - \u8fd9\n
- \u81ea\u5b9a\u4e49\u8bcd\u5178\u793a\u4f8b\uff08dict.txt\uff09\uff1a
Text Only\u6280\u672f\u6587\u6863 5\n\u5f00\u53d1\u6307\u5357 5\n\u6700\u4f73\u5b9e\u8df5 5\n\u4f7f\u7528\u6559\u7a0b 5\n\u914d\u7f6e\u8bf4\u660e 5\n\u5e38\u89c1\u95ee\u9898 5\n
- \u5206\u8bcd\u4f18\u5316\u811a\u672c\uff1a
Python# docs/search_optimize.py\nimport jieba\nimport json\nfrom pathlib import Path\n\ndef optimize_search_index(index_file, dict_file):\n # \u52a0\u8f7d\u81ea\u5b9a\u4e49\u8bcd\u5178\n jieba.load_userdict(dict_file)\n\n # \u8bfb\u53d6\u641c\u7d22\u7d22\u5f15\n with open(index_file, 'r', encoding='utf-8') as f:\n index = json.load(f)\n\n # \u4f18\u5316\u5206\u8bcd\n for doc in index['docs']:\n # \u5206\u8bcd\u5904\u7406\u6807\u9898\n title_words = list(jieba.cut(doc['title']))\n doc['title'] = ' '.join(title_words)\n\n # \u5206\u8bcd\u5904\u7406\u5185\u5bb9\n if 'text' in doc:\n text_words = list(jieba.cut(doc['text']))\n doc['text'] = ' '.join(text_words)\n\n # \u4fdd\u5b58\u4f18\u5316\u540e\u7684\u7d22\u5f15\n with open(index_file, 'w', encoding='utf-8') as f:\n json.dump(index, f, ensure_ascii=False, indent=2)\n\nif __name__ == '__main__':\n index_file = 'site/search/search_index.json'\n dict_file = 'docs/dict.txt'\n optimize_search_index(index_file, dict_file)\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8114","title":"8.1.1.4 \u641c\u7d22\u4f18\u5316","text":" - \u7d22\u5f15\u4f18\u5316\uff1a
Python# docs/search_index_optimizer.py\nfrom pathlib import Path\nimport json\nimport re\n\nclass SearchIndexOptimizer:\n def __init__(self, index_path):\n self.index_path = Path(index_path)\n self.index = self.load_index()\n\n def load_index(self):\n with open(self.index_path, 'r', encoding='utf-8') as f:\n return json.load(f)\n\n def save_index(self):\n with open(self.index_path, 'w', encoding='utf-8') as f:\n json.dump(self.index, f, ensure_ascii=False, indent=2)\n\n def optimize(self):\n # \u6e05\u7406\u65e0\u7528\u6570\u636e\n self._clean_data()\n # \u4f18\u5316\u6587\u672c\n self._optimize_text()\n # \u6dfb\u52a0\u6743\u91cd\n self._add_weights()\n # \u6784\u5efa\u53cd\u5411\u7d22\u5f15\n self._build_inverted_index()\n # \u4fdd\u5b58\u4f18\u5316\u540e\u7684\u7d22\u5f15\n self.save_index()\n\n def _clean_data(self):\n # \u79fb\u9664\u7a7a\u6587\u6863\n self.index['docs'] = [\n doc for doc in self.index['docs']\n if doc.get('text') or doc.get('title')\n ]\n\n # \u79fb\u9664\u91cd\u590d\u6587\u6863\n seen = set()\n unique_docs = []\n for doc in self.index['docs']:\n key = f\"{doc['location']}:{doc['title']}\"\n if key not in seen:\n seen.add(key)\n unique_docs.append(doc)\n self.index['docs'] = unique_docs\n\n def _optimize_text(self):\n for doc in self.index['docs']:\n # \u6e05\u7406HTML\u6807\u7b7e\n if 'text' in doc:\n doc['text'] = re.sub(r'<[^>]+>', '', doc['text'])\n\n # \u538b\u7f29\u7a7a\u767d\u5b57\u7b26\n for field in ['title', 'text']:\n if field in doc:\n doc[field] = ' '.join(doc[field].split())\n\n def _add_weights(self):\n for doc in self.index['docs']:\n # \u57fa\u7840\u6743\u91cd\n doc['weight'] = 1.0\n\n # \u6807\u9898\u957f\u5ea6\u6743\u91cd\n if 'title' in doc:\n title_len = len(doc['title'])\n doc['weight'] *= 1 + (1.0 / (1 + title_len))\n\n # \u6587\u672c\u957f\u5ea6\u6743\u91cd\n if 'text' in doc:\n text_len = len(doc['text'])\n doc['weight'] *= 1 + (100.0 / (1 + text_len))\n\n # URL\u6df1\u5ea6\u6743\u91cd\n depth = doc['location'].count('/')\n doc['weight'] *= 1 + (1.0 / (1 + depth))\n\n def _build_inverted_index(self):\n # \u6784\u5efa\u53cd\u5411\u7d22\u5f15\n inverted_index = {}\n for i, doc in enumerate(self.index['docs']):\n terms = set()\n\n # \u6dfb\u52a0\u6807\u9898\u8bcd\n if 'title' in doc:\n terms.update(doc['title'].lower().split())\n\n # \u6dfb\u52a0\u6587\u672c\u8bcd\n if 'text' in doc:\n terms.update(doc['text'].lower().split())\n\n # \u66f4\u65b0\u53cd\u5411\u7d22\u5f15\n for term in terms:\n if term not in inverted_index:\n inverted_index[term] = []\n inverted_index[term].append({\n 'id': i,\n 'weight': doc['weight']\n })\n\n self.index['inverted_index'] = inverted_index\n\n# \u4f7f\u7528\u4f18\u5316\u5668\noptimizer = SearchIndexOptimizer('site/search/search_index.json')\noptimizer.optimize()\n
- \u641c\u7d22\u6027\u80fd\u76d1\u63a7\uff1a
JavaScript// docs/javascripts/search-monitor.js\nclass SearchMonitor {\n constructor() {\n this.metrics = {\n searches: 0,\n avgTime: 0,\n slowest: 0,\n fastest: Infinity\n };\n\n this.init();\n }\n\n init() {\n this.monitorSearchInput();\n this.monitorSearchResults();\n this.setupReporting();\n }\n\n monitorSearchInput() {\n const searchInput = document.querySelector('.md-search__input');\n let startTime;\n\n searchInput?.addEventListener('input', () => {\n startTime = performance.now();\n });\n\n searchInput?.addEventListener('search', () => {\n const endTime = performance.now();\n const duration = endTime - startTime;\n\n this.updateMetrics(duration);\n });\n }\n\n updateMetrics(duration) {\n this.metrics.searches++;\n this.metrics.avgTime = (\n (this.metrics.avgTime * (this.metrics.searches - 1) + duration) /\n this.metrics.searches\n );\n this.metrics.slowest = Math.max(this.metrics.slowest, duration);\n this.metrics.fastest = Math.min(this.metrics.fastest, duration);\n\n // \u53d1\u9001\u7edf\u8ba1\u6570\u636e\n this.sendMetrics();\n }\n\n sendMetrics() {\n // \u53ef\u4ee5\u53d1\u9001\u5230\u7edf\u8ba1\u670d\u52a1\n console.log('Search Metrics:', this.metrics);\n }\n}\n\n// \u521d\u59cb\u5316\u76d1\u63a7\nnew SearchMonitor();\n
"},{"location":"Tools/Blog/Mkdocs_Material/#812-glightbox","title":"8.1.2 glightbox","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8121","title":"8.1.2.1 \u56fe\u7247\u9884\u89c8","text":" - \u5b89\u88c5\u63d2\u4ef6\uff1a
Bashpip install mkdocs-glightbox\n
- \u57fa\u7840\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nplugins:\n - glightbox:\n # \u57fa\u7840\u8bbe\u7f6e\n auto_caption: true # \u81ea\u52a8\u6dfb\u52a0\u6807\u9898\n caption_position: bottom # \u6807\u9898\u4f4d\u7f6e\n display_description: true # \u663e\u793a\u63cf\u8ff0\n\n # \u89e6\u6478\u8bbe\u7f6e\n touchNavigation: true # \u89e6\u6478\u5bfc\u822a\n loop: true # \u5faa\u73af\u6d4f\u89c8\n effect: zoom # \u8fc7\u6e21\u6548\u679c\n\n # \u56fe\u7247\u8bbe\u7f6e\n width: 100% # \u56fe\u7247\u5bbd\u5ea6\n height: auto # \u56fe\u7247\u9ad8\u5ea6\n zoomable: true # \u542f\u7528\u7f29\u653e\n draggable: true # \u542f\u7528\u62d6\u52a8\n
- \u81ea\u5b9a\u4e49\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/glightbox.css */\n.glightbox-clean {\n /* \u5bb9\u5668\u6837\u5f0f */\n --glow-padding: 2rem;\n --glow-bg: rgba(0, 0, 0, 0.95);\n\n /* \u63a7\u5236\u6309\u94ae */\n --glow-btn-color: rgba(255, 255, 255, 0.8);\n --glow-btn-hover: #fff;\n\n /* \u52a0\u8f7d\u52a8\u753b */\n --glow-loading-bg: rgba(0, 0, 0, 0.5);\n --glow-loading-width: 40px;\n --glow-loading-height: 40px;\n}\n\n/* \u6807\u9898\u6837\u5f0f */\n.glightbox-caption {\n font-family: var(--md-font-family);\n font-size: 0.9rem;\n padding: 1rem;\n background: rgba(0, 0, 0, 0.8);\n}\n\n/* \u63cf\u8ff0\u6837\u5f0f */\n.glightbox-description {\n font-size: 0.8rem;\n color: rgba(255, 255, 255, 0.7);\n margin-top: 0.5rem;\n}\n\n/* \u52a0\u8f7d\u52a8\u753b */\n.glightbox-loading {\n border: 3px solid rgba(255, 255, 255, 0.2);\n border-top-color: #fff;\n border-radius: 50%;\n animation: glow-spin 1s linear infinite;\n}\n\n@keyframes glow-spin {\n to { transform: rotate(360deg); }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8122","title":"8.1.2.2 \u753b\u5eca\u6a21\u5f0f","text":" - \u753b\u5eca\u914d\u7f6e\uff1a
YAMLplugins:\n - glightbox:\n # \u753b\u5eca\u8bbe\u7f6e\n gallery: true # \u542f\u7528\u753b\u5eca\n gallery_mode: true # \u753b\u5eca\u6a21\u5f0f\n gallery_trigger: click # \u89e6\u53d1\u65b9\u5f0f\n\n # \u7f29\u7565\u56fe\u8bbe\u7f6e\n thumb_width: 150 # \u7f29\u7565\u56fe\u5bbd\u5ea6\n thumb_height: 100 # \u7f29\u7565\u56fe\u9ad8\u5ea6\n thumb_fit: cover # \u7f29\u7565\u56fe\u9002\u5e94\u65b9\u5f0f\n
- \u753b\u5eca\u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/gallery.html -->\n<div class=\"gallery\">\n {% for image in page.meta.gallery %}\n <a href=\"{{ image.url }}\" \n class=\"gallery-item\"\n data-gallery=\"gallery\"\n data-glightbox=\"title: {{ image.title }}; description: {{ image.description }}\">\n <img src=\"{{ image.thumbnail or image.url }}\" \n alt=\"{{ image.title }}\"\n loading=\"lazy\">\n {% if image.title %}\n <div class=\"gallery-caption\">{{ image.title }}</div>\n {% endif %}\n </a>\n {% endfor %}\n</div>\n
- \u753b\u5eca\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/gallery.css */\n.gallery {\n display: grid;\n grid-template-columns: repeat(auto-fill, minmax(150px, 1fr));\n gap: 1rem;\n padding: 1rem;\n}\n\n.gallery-item {\n position: relative;\n overflow: hidden;\n border-radius: 4px;\n cursor: pointer;\n transition: transform 0.3s ease;\n}\n\n.gallery-item:hover {\n transform: translateY(-4px);\n}\n\n.gallery-item img {\n width: 100%;\n height: 100%;\n object-fit: cover;\n transition: transform 0.3s ease;\n}\n\n.gallery-item:hover img {\n transform: scale(1.1);\n}\n\n.gallery-caption {\n position: absolute;\n bottom: 0;\n left: 0;\n right: 0;\n padding: 0.5rem;\n background: rgba(0, 0, 0, 0.7);\n color: white;\n font-size: 0.8rem;\n transform: translateY(100%);\n transition: transform 0.3s ease;\n}\n\n.gallery-item:hover .gallery-caption {\n transform: translateY(0);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#82","title":"8.2 \u63a8\u8350\u63d2\u4ef6","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8211-git-revision-date","title":"8.2.1.1 git-revision-date","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8212","title":"8.2.1.2 \u65f6\u95f4\u663e\u793a","text":" - \u5b89\u88c5\u63d2\u4ef6\uff1a
Bashpip install mkdocs-git-revision-date-localized-plugin\n
- \u57fa\u7840\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nplugins:\n - git-revision-date-localized:\n enabled: true\n type: date # \u663e\u793a\u7c7b\u578b\uff1adate, datetime, iso_date, iso_datetime, timeago\n timezone: Asia/Shanghai # \u65f6\u533a\u8bbe\u7f6e\n locale: zh # \u672c\u5730\u5316\u8bed\u8a00\n fallback_to_build_date: true # \u65e0 git \u4fe1\u606f\u65f6\u4f7f\u7528\u6784\u5efa\u65f6\u95f4\n\n # \u65f6\u95f4\u683c\u5f0f\n enable_creation_date: true # \u663e\u793a\u521b\u5efa\u65f6\u95f4\n exclude:\n - index.md\n - 404.md\n
- \u65f6\u95f4\u663e\u793a\u6a21\u677f\uff1a
HTML<!-- docs/overrides/partials/date.html -->\n{% if page.meta.git_revision_date_localized %}\n<div class=\"page-date\">\n <!-- \u6700\u540e\u66f4\u65b0\u65f6\u95f4 -->\n <div class=\"date-item\">\n <span class=\"date-icon\">\n {% include \".icons/material/update.svg\" %}\n </span>\n <span class=\"date-text\">\n \u6700\u540e\u66f4\u65b0: {{ page.meta.git_revision_date_localized }}\n </span>\n </div>\n\n <!-- \u521b\u5efa\u65f6\u95f4 -->\n {% if page.meta.git_creation_date_localized %}\n <div class=\"date-item\">\n <span class=\"date-icon\">\n {% include \".icons/material/clock-outline.svg\" %}\n </span>\n <span class=\"date-text\">\n \u521b\u5efa\u65f6\u95f4: {{ page.meta.git_creation_date_localized }}\n </span>\n </div>\n {% endif %}\n</div>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8213","title":"8.2.1.3 \u66f4\u65b0\u8bb0\u5f55","text":" - \u66f4\u65b0\u8bb0\u5f55\u663e\u793a\uff1a
HTML<!-- docs/overrides/partials/revision.html -->\n{% if page.meta.git_revision_date_localized %}\n<div class=\"revision-history\">\n <h3>\u66f4\u65b0\u8bb0\u5f55</h3>\n <div class=\"revision-list\">\n {% for revision in page.meta.git_history %}\n <div class=\"revision-item\">\n <div class=\"revision-date\">\n {{ revision.date }}\n </div>\n <div class=\"revision-message\">\n {{ revision.message }}\n </div>\n </div>\n {% endfor %}\n </div>\n</div>\n
- \u6837\u5f0f\u914d\u7f6e\uff1a
CSS/* docs/stylesheets/revision.css */\n.revision-history {\n margin: 2rem 0;\n padding: 1rem;\n background: var(--md-code-bg-color);\n border-radius: 4px;\n}\n\n.revision-item {\n display: flex;\n padding: 0.5rem 0;\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.revision-date {\n flex: 0 0 200px;\n color: var(--md-default-fg-color--light);\n}\n\n.revision-message {\n flex: 1;\n}\n\n/* \u65f6\u95f4\u7ebf\u6837\u5f0f */\n.revision-item {\n position: relative;\n padding-left: 2rem;\n}\n\n.revision-item::before {\n content: '';\n position: absolute;\n left: 0;\n top: 1rem;\n width: 12px;\n height: 12px;\n background: var(--md-accent-fg-color);\n border-radius: 50%;\n}\n\n.revision-item::after {\n content: '';\n position: absolute;\n left: 5px;\n top: 1.5rem;\n bottom: -0.5rem;\n width: 2px;\n background: var(--md-default-fg-color--lightest);\n}\n\n.revision-item:last-child::after {\n display: none;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8214","title":"8.2.1.4 \u4f5c\u8005\u4fe1\u606f","text":" - \u4f5c\u8005\u4fe1\u606f\u914d\u7f6e\uff1a
YAMLplugins:\n - git-revision-date-localized:\n enabled: true\n type: date\n enable_authors: true # \u542f\u7528\u4f5c\u8005\u4fe1\u606f\n authors_file: authors.yaml # \u4f5c\u8005\u914d\u7f6e\u6587\u4ef6\n
- \u4f5c\u8005\u914d\u7f6e\u6587\u4ef6 (authors. yaml)\uff1a
YAMLauthors:\n john:\n name: John Doe\n email: john@example.com\n avatar: assets/authors/john.jpg\n bio: \u8d44\u6df1\u6280\u672f\u4f5c\u8005\n social:\n github: johndoe\n twitter: johndoe\n\n jane:\n name: Jane Smith\n email: jane@example.com\n avatar: assets/authors/jane.jpg\n bio: \u524d\u7aef\u5f00\u53d1\u4e13\u5bb6\n social:\n github: janesmith\n linkedin: janesmith\n
- \u4f5c\u8005\u4fe1\u606f\u663e\u793a\uff1a
HTML<!-- docs/overrides/partials/author.html -->\n{% if page.meta.git_authors %}\n<div class=\"page-authors\">\n {% for author in page.meta.git_authors %}\n <div class=\"author-card\">\n <!-- \u4f5c\u8005\u5934\u50cf -->\n <div class=\"author-avatar\">\n {% if author.avatar %}\n <img src=\"{{ author.avatar }}\" alt=\"{{ author.name }}\">\n {% else %}\n <div class=\"avatar-placeholder\">\n {{ author.name[0] }}\n </div>\n {% endif %}\n </div>\n\n <!-- \u4f5c\u8005\u4fe1\u606f -->\n <div class=\"author-info\">\n <h4 class=\"author-name\">{{ author.name }}</h4>\n {% if author.bio %}\n <p class=\"author-bio\">{{ author.bio }}</p>\n {% endif %}\n\n <!-- \u793e\u4ea4\u94fe\u63a5 -->\n {% if author.social %}\n <div class=\"author-social\">\n {% for platform, username in author.social.items() %}\n <a href=\"https://{{ platform }}.com/{{ username }}\" \n target=\"_blank\"\n class=\"social-link\">\n {% include \".icons/material/\" ~ platform ~ \".svg\" %}\n </a>\n {% endfor %}\n </div>\n {% endif %}\n </div>\n </div>\n {% endfor %}\n</div>\n
- \u4f5c\u8005\u4fe1\u606f\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/author.css */\n.page-authors {\n margin: 2rem 0;\n display: grid;\n grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));\n gap: 1rem;\n}\n\n.author-card {\n display: flex;\n padding: 1rem;\n background: var(--md-code-bg-color);\n border-radius: 8px;\n transition: transform 0.2s ease;\n}\n\n.author-card:hover {\n transform: translateY(-2px);\n}\n\n.author-avatar {\n flex: 0 0 80px;\n margin-right: 1rem;\n}\n\n.author-avatar img {\n width: 80px;\n height: 80px;\n border-radius: 50%;\n object-fit: cover;\n}\n\n.avatar-placeholder {\n width: 80px;\n height: 80px;\n border-radius: 50%;\n background: var(--md-accent-fg-color);\n color: white;\n display: flex;\n align-items: center;\n justify-content: center;\n font-size: 2rem;\n}\n\n.author-info {\n flex: 1;\n}\n\n.author-name {\n margin: 0 0 0.5rem;\n font-size: 1.1rem;\n}\n\n.author-bio {\n color: var(--md-default-fg-color--light);\n font-size: 0.9rem;\n margin: 0 0 0.5rem;\n}\n\n.author-social {\n display: flex;\n gap: 0.5rem;\n}\n\n.social-link {\n color: var(--md-default-fg-color--light);\n transition: color 0.2s ease;\n}\n\n.social-link:hover {\n color: var(--md-accent-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#822-minify","title":"8.2.2 minify","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8221-html","title":"8.2.2.1 HTML \u538b\u7f29","text":" - \u5b89\u88c5\u63d2\u4ef6\uff1a
Bashpip install mkdocs-minify-plugin\n
- \u914d\u7f6e\u538b\u7f29\uff1a
YAML# mkdocs.yml\nplugins:\n - minify:\n minify_html: true\n htmlmin_opts:\n # HTML\u538b\u7f29\u9009\u9879\n remove_comments: true # \u79fb\u9664\u6ce8\u91ca\n remove_empty_space: true # \u79fb\u9664\u7a7a\u767d\n remove_all_empty_space: true # \u79fb\u9664\u6240\u6709\u7a7a\u767d\n reduce_boolean_attributes: true # \u7b80\u5316\u5e03\u5c14\u5c5e\u6027\n remove_optional_tags: false # \u4fdd\u7559\u53ef\u9009\u6807\u7b7e\n remove_redundant_attributes: true # \u79fb\u9664\u5197\u4f59\u5c5e\u6027\n minify_js: true # \u538b\u7f29\u5185\u8054JS\n minify_css: true # \u538b\u7f29\u5185\u8054CSS\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8222-css","title":"8.2.2.2 CSS \u538b\u7f29","text":" - CSS \u538b\u7f29\u914d\u7f6e\uff1a
YAMLplugins:\n - minify:\n minify_css: true\n css_files:\n - stylesheets/extra.css\n - stylesheets/custom.css\n cssmin_opts:\n # CSS\u538b\u7f29\u9009\u9879\n level: 2 # \u538b\u7f29\u7ea7\u522b(1-2)\n compatibility: ie9 # \u517c\u5bb9\u6027\n format: beautify # \u683c\u5f0f\u5316\u8f93\u51fa\n
- CSS \u4f18\u5316\u811a\u672c\uff1a
Python# docs/tools/css_optimizer.py\nimport re\nfrom pathlib import Path\nfrom csscompressor import compress\n\nclass CSSOptimizer:\n def __init__(self, input_dir, output_dir):\n self.input_dir = Path(input_dir)\n self.output_dir = Path(output_dir)\n self.output_dir.mkdir(exist_ok=True)\n\n def optimize(self):\n \"\"\"\u4f18\u5316\u6240\u6709CSS\u6587\u4ef6\"\"\"\n for css_file in self.input_dir.glob('**/*.css'):\n # \u8bfb\u53d6CSS\u5185\u5bb9\n content = css_file.read_text(encoding='utf-8')\n\n # \u4f18\u5316CSS\n optimized = self.optimize_css(content)\n\n # \u4fdd\u5b58\u4f18\u5316\u540e\u7684\u6587\u4ef6\n output_file = self.output_dir / css_file.name\n output_file.write_text(optimized, encoding='utf-8')\n\n def optimize_css(self, content):\n \"\"\"\u4f18\u5316\u5355\u4e2aCSS\u6587\u4ef6\u5185\u5bb9\"\"\"\n # \u79fb\u9664\u6ce8\u91ca\n content = re.sub(r'/\\*[\\s\\S]*?\\*/', '', content)\n\n # \u79fb\u9664\u591a\u4f59\u7a7a\u767d\n content = re.sub(r'\\s+', ' ', content)\n\n # \u538b\u7f29CSS\n content = compress(content)\n\n return content\n\n# \u4f7f\u7528\u4f18\u5316\u5668\noptimizer = CSSOptimizer(\n input_dir='docs/stylesheets',\n output_dir='docs/stylesheets/min'\n)\noptimizer.optimize()\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8223-js","title":"8.2.2.3 JS \u538b\u7f29","text":" - JS \u538b\u7f29\u914d\u7f6e\uff1a
YAMLplugins:\n - minify:\n minify_js: true\n js_files:\n - javascripts/extra.js\n - javascripts/custom.js\n jsmin_opts:\n # JS\u538b\u7f29\u9009\u9879\n mangle: true # \u6df7\u6dc6\u53d8\u91cf\u540d\n compress: true # \u538b\u7f29\u4ee3\u7801\n output:\n beautify: false # \u4e0d\u7f8e\u5316\u8f93\u51fa\n
- JS \u4f18\u5316\u811a\u672c\uff1a
Python# docs/tools/js_optimizer.py\nimport re\nfrom pathlib import Path\nimport terser\n\nclass JSOptimizer:\n def __init__(self, input_dir, output_dir):\n self.input_dir = Path(input_dir)\n self.output_dir = Path(output_dir)\n self.output_dir.mkdir(exist_ok=True)\n\n def optimize(self):\n \"\"\"\u4f18\u5316\u6240\u6709JS\u6587\u4ef6\"\"\"\n for js_file in self.input_dir.glob('**/*.js'):\n # \u8bfb\u53d6JS\u5185\u5bb9\n content = js_file.read_text(encoding='utf-8')\n\n # \u4f18\u5316JS\n optimized = self.optimize_js(content)\n\n # \u4fdd\u5b58\u4f18\u5316\u540e\u7684\u6587\u4ef6\n output_file = self.output_dir / js_file.name\n output_file.write_text(optimized, encoding='utf-8')\n\n def optimize_js(self, content):\n \"\"\"\u4f18\u5316\u5355\u4e2aJS\u6587\u4ef6\u5185\u5bb9\"\"\"\n options = {\n 'compress': {\n 'pure_funcs': ['console.log'], # \u79fb\u9664console.log\n 'drop_debugger': True, # \u79fb\u9664debugger\n 'unsafe': True, # \u542f\u7528\u4e0d\u5b89\u5168\u4f18\u5316\n 'passes': 2 # \u4f18\u5316\u6b21\u6570\n },\n 'mangle': {\n 'toplevel': True, # \u6df7\u6dc6\u9876\u7ea7\u4f5c\u7528\u57df\n 'eval': True # \u6df7\u6dc6eval\u4e2d\u7684\u53d8\u91cf\n }\n }\n\n # \u4f7f\u7528terser\u538b\u7f29\n result = terser.minify(content, options)\n return result['code']\n\n# \u4f7f\u7528\u4f18\u5316\u5668\noptimizer = JSOptimizer(\n input_dir='docs/javascripts',\n output_dir='docs/javascripts/min'\n)\noptimizer.optimize()\n
\u8fd9\u4e9b\u914d\u7f6e\u548c\u811a\u672c\u53ef\u4ee5\u6709\u6548\u51cf\u5c0f\u7ad9\u70b9\u8d44\u6e90\u5927\u5c0f\uff0c\u63d0\u5347\u52a0\u8f7d\u901f\u5ea6\u3002\u8bb0\u5f97\u5728\u751f\u4ea7\u73af\u5883\u4e2d\u4f7f\u7528\u538b\u7f29\u540e\u7684\u8d44\u6e90\u3002
"},{"location":"Tools/Blog/Mkdocs_Material/#823-social","title":"8.2.3 social","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8231","title":"8.2.3.1 \u793e\u4ea4\u94fe\u63a5","text":" - \u5b89\u88c5\u63d2\u4ef6\uff1a
Bashpip install mkdocs-social-plugin\n
- \u57fa\u7840\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nplugins:\n - social:\n enabled: true\n # \u793e\u4ea4\u5e73\u53f0\u914d\u7f6e\n cards: true # \u542f\u7528\u793e\u4ea4\u5361\u7247\n cards_color: # \u5361\u7247\u989c\u8272\n fill: \"#0FF1CE\" # \u80cc\u666f\u8272\n text: \"#FFFFFF\" # \u6587\u5b57\u8272\n cards_font: Roboto # \u5361\u7247\u5b57\u4f53\n\nextra:\n social:\n - icon: material/github\n link: https://github.com/username\n name: GitHub\n - icon: material/twitter\n link: https://twitter.com/username\n name: Twitter\n - icon: material/linkedin\n link: https://linkedin.com/in/username\n name: LinkedIn\n
- \u793e\u4ea4\u94fe\u63a5\u6a21\u677f\uff1a
HTML<!-- docs/overrides/partials/social.html -->\n{% if config.extra.social %}\n<div class=\"social-links\">\n {% for social in config.extra.social %}\n <a href=\"{{ social.link }}\" \n target=\"_blank\"\n rel=\"noopener\"\n title=\"{{ social.name }}\"\n class=\"social-link\">\n <span class=\"social-icon\">\n {% include \".icons/\" ~ social.icon ~ \".svg\" %}\n </span>\n <span class=\"social-name\">{{ social.name }}</span>\n </a>\n {% endfor %}\n</div>\n{% endif %}\n
\u6837\u5f0f\u914d\u7f6e\uff1a
CSS/* docs/stylesheets/social.css */\n.social-links {\n display: flex;\n gap: 1rem;\n margin: 2rem 0;\n}\n\n.social-link {\n display: flex;\n align-items: center;\n padding: 0.5rem 1rem;\n color: var(--md-default-fg-color);\n text-decoration: none;\n background: var(--md-code-bg-color);\n border-radius: 2rem;\n transition: all 0.2s ease;\n}\n\n.social-link:hover {\n transform: translateY(-2px);\n color: var(--md-accent-fg-color);\n background: var(--md-code-bg-color--light);\n}\n\n.social-icon {\n margin-right: 0.5rem;\n width: 1.2rem;\n height: 1.2rem;\n}\n\n.social-name {\n font-size: 0.9rem;\n}\n\n/* \u52a8\u753b\u6548\u679c */\n.social-link .social-icon {\n transition: transform 0.2s ease;\n}\n\n.social-link:hover .social-icon {\n transform: scale(1.2);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8232","title":"8.2.3.2 \u5206\u4eab\u529f\u80fd","text":" - \u5206\u4eab\u529f\u80fd\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nextra:\n social_share:\n - platform: twitter\n text: \u5206\u4eab\u5230 Twitter\n icon: material/twitter\n - platform: facebook\n text: \u5206\u4eab\u5230 Facebook\n icon: material/facebook\n - platform: linkedin\n text: \u5206\u4eab\u5230 LinkedIn\n icon: material/linkedin\n - platform: weibo\n text: \u5206\u4eab\u5230\u5fae\u535a\n icon: material/sina-weibo\n
- \u5206\u4eab\u529f\u80fd\u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/partials/share.html -->\n<div class=\"page-share\">\n <h4>\u5206\u4eab\u6587\u7ae0</h4>\n <div class=\"share-buttons\">\n {% for share in config.extra.social_share %}\n <button class=\"share-button\" \n data-platform=\"{{ share.platform }}\"\n onclick=\"shareContent('{{ share.platform }}')\">\n <span class=\"share-icon\">\n {% include \".icons/\" ~ share.icon ~ \".svg\" %}\n </span>\n <span class=\"share-text\">{{ share.text }}</span>\n </button>\n {% endfor %}\n </div>\n</div>\n\n<script>\nfunction shareContent(platform) {\n const url = encodeURIComponent(window.location.href);\n const title = encodeURIComponent(document.title);\n const text = encodeURIComponent(document.querySelector('meta[name=\"description\"]')?.content || '');\n\n let shareUrl;\n switch(platform) {\n case 'twitter':\n shareUrl = `https://twitter.com/intent/tweet?url=${url}&text=${title}`;\n break;\n case 'facebook':\n shareUrl = `https://www.facebook.com/sharer/sharer.php?u=${url}`;\n break;\n case 'linkedin':\n shareUrl = `https://www.linkedin.com/sharing/share-offsite/?url=${url}`;\n break;\n case 'weibo':\n shareUrl = `http://service.weibo.com/share/share.php?url=${url}&title=${title}`;\n break;\n }\n\n window.open(shareUrl, '_blank', 'width=600,height=400');\n}\n</script>\n
- \u5206\u4eab\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/share.css */\n.page-share {\n margin: 2rem 0;\n padding: 1rem;\n background: var(--md-code-bg-color);\n border-radius: 8px;\n}\n\n.share-buttons {\n display: flex;\n flex-wrap: wrap;\n gap: 0.5rem;\n margin-top: 1rem;\n}\n\n.share-button {\n display: flex;\n align-items: center;\n padding: 0.5rem 1rem;\n border: none;\n border-radius: 2rem;\n background: var(--md-default-bg-color);\n color: var(--md-default-fg-color);\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.share-button:hover {\n background: var(--md-accent-fg-color);\n color: white;\n}\n\n.share-icon {\n margin-right: 0.5rem;\n width: 1.2rem;\n height: 1.2rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8233","title":"8.2.3.3 \u5173\u6ce8\u6309\u94ae","text":" - \u5173\u6ce8\u6309\u94ae\u914d\u7f6e\uff1a
YAMLextra:\n follow_buttons:\n - platform: github\n username: your-username\n icon: material/github\n text: \u5173\u6ce8\u6211\u7684 GitHub\n - platform: twitter\n username: your-username\n icon: material/twitter\n text: \u5173\u6ce8\u6211\u7684 Twitter\n
- \u5173\u6ce8\u6309\u94ae\u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/partials/follow.html -->\n<div class=\"follow-buttons\">\n {% for btn in config.extra.follow_buttons %}\n <a href=\"https://{{ btn.platform }}.com/{{ btn.username }}\"\n target=\"_blank\"\n class=\"follow-button\"\n data-platform=\"{{ btn.platform }}\">\n <span class=\"follow-icon\">\n {% include \".icons/\" ~ btn.icon ~ \".svg\" %}\n </span>\n <span class=\"follow-text\">{{ btn.text }}</span>\n <span class=\"follow-count\" id=\"{{ btn.platform }}-count\">\n <!-- \u5c06\u901a\u8fc7API\u52a8\u6001\u66f4\u65b0 -->\n </span>\n </a>\n {% endfor %}\n</div>\n\n<script>\nasync function updateFollowCounts() {\n const buttons = document.querySelectorAll('.follow-button');\n\n for (const button of buttons) {\n const platform = button.dataset.platform;\n const username = button.href.split('/').pop();\n\n // \u83b7\u53d6\u5173\u6ce8\u6570\n const count = await getFollowCount(platform, username);\n\n // \u66f4\u65b0\u663e\u793a\n const countElement = button.querySelector('.follow-count');\n if (countElement && count) {\n countElement.textContent = formatCount(count);\n }\n }\n}\n\nasync function getFollowCount(platform, username) {\n switch (platform) {\n case 'github':\n const response = await fetch(`https://api.github.com/users/${username}`);\n const data = await response.json();\n return data.followers;\n\n // \u5176\u4ed6\u5e73\u53f0API\u5b9e\u73b0...\n default:\n return null;\n }\n}\n\nfunction formatCount(count) {\n if (count >= 1000000) {\n return (count / 1000000).toFixed(1) + 'M';\n }\n if (count >= 1000) {\n return (count / 1000).toFixed(1) + 'K';\n }\n return count.toString();\n}\n\n// \u521d\u59cb\u5316\nupdateFollowCounts();\n</script>\n
- \u5173\u6ce8\u6309\u94ae\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/follow.css */\n.follow-buttons {\n display: flex;\n flex-wrap: wrap;\n gap: 1rem;\n margin: 2rem 0;\n}\n\n.follow-button {\n display: flex;\n align-items: center;\n padding: 0.5rem 1rem;\n text-decoration: none;\n background: var(--md-code-bg-color);\n border-radius: 2rem;\n transition: all 0.2s ease;\n}\n\n.follow-button:hover {\n transform: translateY(-2px);\n}\n\n/* \u5e73\u53f0\u7279\u5b9a\u6837\u5f0f */\n.follow-button[data-platform=\"github\"] {\n color: #333;\n}\n\n.follow-button[data-platform=\"github\"]:hover {\n background: #333;\n color: white;\n}\n\n.follow-button[data-platform=\"twitter\"] {\n color: #1DA1F2;\n}\n\n.follow-button[data-platform=\"twitter\"]:hover {\n background: #1DA1F2;\n color: white;\n}\n\n.follow-icon {\n margin-right: 0.5rem;\n width: 1.2rem;\n height: 1.2rem;\n}\n\n.follow-count {\n margin-left: 0.5rem;\n padding: 0.2rem 0.5rem;\n background: rgba(0, 0, 0, 0.1);\n border-radius: 1rem;\n font-size: 0.8rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#824-tags","title":"8.2.4 tags","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8241","title":"8.2.4.1 \u6807\u7b7e\u7cfb\u7edf","text":" - \u5b89\u88c5\u63d2\u4ef6\uff1a
Bashpip install mkdocs-tags-plugin\n
- \u914d\u7f6e\u6807\u7b7e\uff1a
YAML# mkdocs.yml\nplugins:\n - tags:\n tags_file: tags.md\n tags_allowed: # \u5141\u8bb8\u7684\u6807\u7b7e\u5217\u8868\n - Python\n - JavaScript\n - CSS\n - HTML\n tags_extra_files:\n cloud: tag_cloud.md # \u6807\u7b7e\u4e91\u9875\u9762\n list: tag_list.md # \u6807\u7b7e\u5217\u8868\u9875\u9762\n
- \u5728\u6587\u6863\u4e2d\u4f7f\u7528\u6807\u7b7e\uff1a
Markdown---\ntags:\n - Python\n - \u6559\u7a0b\n---\n\n# Python \u5165\u95e8\u6559\u7a0b\n
- \u6807\u7b7e\u663e\u793a\u7ec4\u4ef6\uff1a
HTML<!-- docs/overrides/partials/tags.html -->\n{% if page.meta.tags %}\n<div class=\"page-tags\">\n {% for tag in page.meta.tags %}\n <a href=\"{{ base_url }}/tags/#{{ tag|lower }}\" class=\"tag\">\n <span class=\"tag-icon\">\n {% include \".icons/material/tag.svg\" %}\n </span>\n <span class=\"tag-text\">{{ tag }}</span>\n </a>\n {% endfor %}\n</div>\n{% endif %}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8242","title":"8.2.4.2 \u6807\u7b7e\u4e91","text":" - \u6807\u7b7e\u4e91\u5b9e\u73b0\uff1a
Python# docs/plugins/tags/cloud.py\nfrom collections import Counter\nimport math\n\nclass TagCloud:\n def __init__(self, tags):\n self.tags = Counter(tags)\n self.min_size = 0.8\n self.max_size = 2.0\n\n def get_tag_sizes(self):\n \"\"\"\u8ba1\u7b97\u6807\u7b7e\u5927\u5c0f\"\"\"\n if not self.tags:\n return {}\n\n # \u83b7\u53d6\u6700\u5927\u548c\u6700\u5c0f\u9891\u7387\n max_freq = max(self.tags.values())\n min_freq = min(self.tags.values())\n\n # \u8ba1\u7b97\u6bcf\u4e2a\u6807\u7b7e\u7684\u5927\u5c0f\n sizes = {}\n for tag, freq in self.tags.items():\n if max_freq == min_freq:\n sizes[tag] = (self.max_size + self.min_size) / 2\n else:\n size = self.min_size + (self.max_size - self.min_size) * \\\n ((freq - min_freq) / (max_freq - min_freq))\n sizes[tag] = round(size, 2)\n\n return sizes\n\ndef generate_tag_cloud(tags):\n \"\"\"\u751f\u6210\u6807\u7b7e\u4e91HTML\"\"\"\n cloud = TagCloud(tags)\n sizes = cloud.get_tag_sizes()\n\n html = ['<div class=\"tag-cloud\">']\n\n for tag, size in sizes.items():\n html.append(f'''\n <a href=\"#tag-{tag.lower()}\" \n class=\"tag-cloud-item\"\n style=\"font-size: {size}rem\">\n {tag}\n </a>\n ''')\n\n html.append('</div>')\n return '\\n'.join(html)\n
- \u6807\u7b7e\u4e91\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/tags.css */\n.tag-cloud {\n display: flex;\n flex-wrap: wrap;\n justify-content: center;\n gap: 1rem;\n padding: 2rem;\n}\n\n.tag-cloud-item {\n color: var(--md-default-fg-color);\n text-decoration: none;\n padding: 0.5rem 1rem;\n border-radius: 2rem;\n background: var(--md-code-bg-color);\n transition: all 0.2s ease;\n}\n\n.tag-cloud-item:hover {\n transform: scale(1.1);\n background: var(--md-accent-fg-color);\n color: white;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8243","title":"8.2.4.3 \u6807\u7b7e\u9875\u9762","text":" - \u6807\u7b7e\u5217\u8868\u9875\u9762\uff1a
Markdown# \u6807\u7b7e\u7d22\u5f15\n\n## \u6807\u7b7e\u4e91\n[TAGS_CLOUD]\n\n## \u6807\u7b7e\u5217\u8868\n[TAGS_LIST]\n
- \u6807\u7b7e\u9875\u9762\u751f\u6210\u5668\uff1a
Python# docs/plugins/tags/generator.py\nfrom pathlib import Path\nimport yaml\n\nclass TagPageGenerator:\n def __init__(self, config):\n self.config = config\n self.tags = {}\n\n def collect_tags(self):\n \"\"\"\u6536\u96c6\u6240\u6709\u6807\u7b7e\"\"\"\n docs_dir = Path(self.config['docs_dir'])\n\n for md_file in docs_dir.glob('**/*.md'):\n # \u8bfb\u53d6\u6587\u4ef6frontmatter\n content = md_file.read_text(encoding='utf-8')\n if content.startswith('---'):\n try:\n # \u89e3\u6790frontmatter\n _, frontmatter, _ = content.split('---', 2)\n meta = yaml.safe_load(frontmatter)\n\n if 'tags' in meta:\n # \u83b7\u53d6\u76f8\u5bf9\u8def\u5f84\n rel_path = md_file.relative_to(docs_dir)\n url = str(rel_path).replace('\\\\', '/')[:-3] # \u79fb\u9664.md\n\n # \u6dfb\u52a0\u5230\u6807\u7b7e\u7d22\u5f15\n for tag in meta['tags']:\n if tag not in self.tags:\n self.tags[tag] = []\n self.tags[tag].append({\n 'title': meta.get('title', url),\n 'url': url,\n 'description': meta.get('description', '')\n })\n except:\n continue\n\n def generate_tag_pages(self):\n \"\"\"\u751f\u6210\u6807\u7b7e\u76f8\u5173\u9875\u9762\"\"\"\n self.collect_tags()\n\n # \u751f\u6210\u6807\u7b7e\u5217\u8868\u9875\u9762\n if 'list' in self.config['tags_extra_files']:\n self.generate_list_page()\n\n # \u751f\u6210\u6bcf\u4e2a\u6807\u7b7e\u7684\u8be6\u60c5\u9875\u9762\n self.generate_tag_detail_pages()\n\n def generate_list_page(self):\n \"\"\"\u751f\u6210\u6807\u7b7e\u5217\u8868\u9875\u9762\"\"\"\n template = \"\"\"\n# \u6807\u7b7e\u5217\u8868\n\n{% for tag, posts in tags.items() %}\n## {{ tag }}\n\n{% for post in posts %}\n- [{{ post.title }}]({{ post.url }}){% if post.description %} - {{ post.description }}{% endif %}\n{% endfor %}\n\n{% endfor %}\n\"\"\"\n # \u4f7f\u7528 Jinja2 \u6e32\u67d3\u6a21\u677f\n from jinja2 import Template\n content = Template(template).render(tags=self.tags)\n\n # \u4fdd\u5b58\u9875\u9762\n output_file = Path(self.config['docs_dir']) / 'tags' / 'list.md'\n output_file.parent.mkdir(exist_ok=True)\n output_file.write_text(content, encoding='utf-8')\n\n def generate_tag_detail_pages(self):\n \"\"\"\u751f\u6210\u6bcf\u4e2a\u6807\u7b7e\u7684\u8be6\u60c5\u9875\u9762\"\"\"\n template = \"\"\"\n# \u6807\u7b7e: {{ tag }}\n\n## \u76f8\u5173\u6587\u7ae0\n\n{% for post in posts %}\n### [{{ post.title }}]({{ post.url }})\n{% if post.description %}\n{{ post.description }}\n{% endif %}\n{% endfor %}\n\n## \u76f8\u5173\u6807\u7b7e\n{% for related_tag in related_tags %}\n- [{{ related_tag }}](../{{ related_tag | lower }})\n{% endfor %}\n\"\"\"\n # \u6807\u7b7e\u76ee\u5f55\n tags_dir = Path(self.config['docs_dir']) / 'tags'\n tags_dir.mkdir(exist_ok=True)\n\n # \u4e3a\u6bcf\u4e2a\u6807\u7b7e\u751f\u6210\u9875\u9762\n for tag, posts in self.tags.items():\n # \u67e5\u627e\u76f8\u5173\u6807\u7b7e\n related_tags = self.find_related_tags(tag)\n\n # \u6e32\u67d3\u5185\u5bb9\n content = Template(template).render(\n tag=tag,\n posts=posts,\n related_tags=related_tags\n )\n\n # \u4fdd\u5b58\u9875\u9762\n output_file = tags_dir / f\"{tag.lower()}.md\"\n output_file.write_text(content, encoding='utf-8')\n\n def find_related_tags(self, current_tag):\n \"\"\"\u67e5\u627e\u76f8\u5173\u6807\u7b7e\"\"\"\n related = set()\n\n # \u83b7\u53d6\u5f53\u524d\u6807\u7b7e\u7684\u6240\u6709\u6587\u7ae0\n current_posts = self.tags[current_tag]\n\n # \u904d\u5386\u6240\u6709\u5176\u4ed6\u6807\u7b7e\n for tag, posts in self.tags.items():\n if tag == current_tag:\n continue\n\n # \u8ba1\u7b97\u6587\u7ae0\u4ea4\u96c6\n intersection = set(p['url'] for p in posts) & \\\n set(p['url'] for p in current_posts)\n\n # \u5982\u679c\u6709\u5171\u540c\u6587\u7ae0\uff0c\u8ba4\u4e3a\u662f\u76f8\u5173\u6807\u7b7e\n if intersection:\n related.add(tag)\n\n return list(related)\n
- \u6807\u7b7e\u9875\u9762\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/tags-page.css */\n/* \u6807\u7b7e\u5217\u8868\u9875\u9762 */\n.tags-list {\n margin: 2rem 0;\n}\n\n.tag-section {\n margin-bottom: 3rem;\n}\n\n.tag-section h2 {\n color: var(--md-accent-fg-color);\n border-bottom: 2px solid var(--md-accent-fg-color);\n padding-bottom: 0.5rem;\n}\n\n.tag-section .post-list {\n margin-left: 1rem;\n}\n\n.post-list-item {\n margin: 1rem 0;\n}\n\n.post-list-item .post-title {\n font-weight: 500;\n color: var(--md-default-fg-color);\n text-decoration: none;\n}\n\n.post-list-item .post-description {\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n margin-top: 0.2rem;\n}\n\n/* \u6807\u7b7e\u8be6\u60c5\u9875\u9762 */\n.tag-detail {\n padding: 2rem;\n}\n\n.tag-detail-header {\n text-align: center;\n margin-bottom: 3rem;\n}\n\n.tag-detail-title {\n font-size: 2rem;\n color: var(--md-primary-fg-color);\n}\n\n.tag-detail-count {\n color: var(--md-default-fg-color--light);\n}\n\n.related-posts {\n margin: 2rem 0;\n}\n\n.related-post {\n padding: 1rem;\n margin: 1rem 0;\n background: var(--md-code-bg-color);\n border-radius: 8px;\n transition: transform 0.2s ease;\n}\n\n.related-post:hover {\n transform: translateX(0.5rem);\n}\n\n.related-tags {\n display: flex;\n flex-wrap: wrap;\n gap: 0.5rem;\n margin: 2rem 0;\n}\n\n.related-tag {\n padding: 0.2rem 0.8rem;\n background: var(--md-code-bg-color);\n border-radius: 2rem;\n font-size: 0.9rem;\n color: var(--md-default-fg-color);\n text-decoration: none;\n transition: all 0.2s ease;\n}\n\n.related-tag:hover {\n background: var(--md-accent-fg-color);\n color: white;\n}\n
- \u9875\u9762\u4ea4\u4e92\u529f\u80fd\uff1a
JavaScript// docs/javascripts/tags.js\nclass TagsManager {\n constructor() {\n this.initializeFilters();\n this.setupSearch();\n this.setupSorting();\n }\n\n initializeFilters() {\n const tagList = document.querySelector('.tags-list');\n if (!tagList) return;\n\n // \u521b\u5efa\u8fc7\u6ee4\u5668\n const filterContainer = document.createElement('div');\n filterContainer.className = 'tags-filter';\n filterContainer.innerHTML = `\n <input type=\"text\" \n class=\"filter-input\" \n placeholder=\"\u641c\u7d22\u6807\u7b7e...\">\n <select class=\"sort-select\">\n <option value=\"name\">\u6309\u540d\u79f0\u6392\u5e8f</option>\n <option value=\"count\">\u6309\u6587\u7ae0\u6570\u91cf\u6392\u5e8f</option>\n </select>\n `;\n\n tagList.insertBefore(filterContainer, tagList.firstChild);\n }\n\n setupSearch() {\n const input = document.querySelector('.filter-input');\n if (!input) return;\n\n input.addEventListener('input', () => {\n const query = input.value.toLowerCase();\n const sections = document.querySelectorAll('.tag-section');\n\n sections.forEach(section => {\n const tag = section.querySelector('h2').textContent.toLowerCase();\n section.style.display = tag.includes(query) ? 'block' : 'none';\n });\n });\n }\n\n setupSorting() {\n const select = document.querySelector('.sort-select');\n if (!select) return;\n\n select.addEventListener('change', () => {\n const sections = Array.from(document.querySelectorAll('.tag-section'));\n const container = sections[0].parentElement;\n\n sections.sort((a, b) => {\n const aTag = a.querySelector('h2').textContent;\n const bTag = b.querySelector('h2').textContent;\n const aCount = this.getPostCount(a);\n const bCount = this.getPostCount(b);\n\n if (select.value === 'name') {\n return aTag.localeCompare(bTag);\n } else {\n return bCount - aCount;\n }\n });\n\n // \u91cd\u65b0\u63d2\u5165\u6392\u5e8f\u540e\u7684\u7ae0\u8282\n sections.forEach(section => container.appendChild(section));\n });\n }\n\n getPostCount(section) {\n return section.querySelectorAll('.post-list-item').length;\n }\n}\n\n// \u521d\u59cb\u5316\u6807\u7b7e\u7ba1\u7406\u5668\ndocument.addEventListener('DOMContentLoaded', () => {\n new TagsManager();\n});\n
\u4f7f\u7528\u4ee5\u4e0a\u4ee3\u7801\uff0c\u4f60\u53ef\u4ee5\u5b9e\u73b0\u4e00\u4e2a\u529f\u80fd\u5b8c\u6574\u7684\u6807\u7b7e\u7cfb\u7edf\uff0c\u5305\u62ec\uff1a
- \u6807\u7b7e\u4e91\u53ef\u89c6\u5316
- \u6807\u7b7e\u5217\u8868\u6d4f\u89c8
- \u6807\u7b7e\u8be6\u60c5\u9875\u9762
- \u76f8\u5173\u6807\u7b7e\u63a8\u8350
- \u6807\u7b7e\u641c\u7d22\u548c\u6392\u5e8f
- \u54cd\u5e94\u5f0f\u5e03\u5c40
\u914d\u7f6e\u6587\u4ef6\u9700\u8981\u6dfb\u52a0\u76f8\u5e94\u7684\u5f15\u7528\uff1a
YAML# mkdocs.yml\nextra_css:\n - stylesheets/tags.css\n - stylesheets/tags-page.css\n\nextra_javascript:\n - javascripts/tags.js\n
"},{"location":"Tools/Blog/Mkdocs_Material/#9","title":"9 \u529f\u80fd\u6269\u5c55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#91","title":"9.1 \u8bc4\u8bba\u7cfb\u7edf","text":""},{"location":"Tools/Blog/Mkdocs_Material/#911-giscus","title":"9.1.1 Giscus \u914d\u7f6e","text":" - \u57fa\u7840\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nextra:\n comments:\n provider: giscus\n repo: username/repo\n repo_id: your_repo_id\n category: Comments\n category_id: your_category_id\n
- Giscus \u7ec4\u4ef6\uff1a
HTML<!-- docs/overrides/partials/comments.html -->\n{% if config.extra.comments.provider == 'giscus' %}\n<div class=\"comments-container\">\n <h3>\u8bc4\u8bba</h3>\n <div class=\"giscus\"></div>\n\n <script>\n function loadGiscus() {\n const script = document.createElement('script');\n script.src = 'https://giscus.app/client.js';\n script.setAttribute('data-repo', '{{ config.extra.comments.repo }}');\n script.setAttribute('data-repo-id', '{{ config.extra.comments.repo_id }}');\n script.setAttribute('data-category', '{{ config.extra.comments.category }}');\n script.setAttribute('data-category-id', '{{ config.extra.comments.category_id }}');\n script.setAttribute('data-mapping', 'pathname');\n script.setAttribute('data-reactions-enabled', '1');\n script.setAttribute('data-emit-metadata', '0');\n script.setAttribute('data-theme', 'preferred_color_scheme');\n script.setAttribute('crossorigin', 'anonymous');\n script.async = true;\n\n document.querySelector('.giscus').appendChild(script);\n }\n\n // \u5ef6\u8fdf\u52a0\u8f7d\u8bc4\u8bba\n if ('IntersectionObserver' in window) {\n const observer = new IntersectionObserver((entries) => {\n if (entries[0].isIntersecting) {\n loadGiscus();\n observer.disconnect();\n }\n });\n\n observer.observe(document.querySelector('.comments-container'));\n } else {\n loadGiscus();\n }\n </script>\n</div>\n{% endif %}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#912","title":"9.1.2 \u81ea\u5b9a\u4e49\u8bc4\u8bba\u6837\u5f0f","text":"CSS/* docs/stylesheets/comments.css */\n.comments-container {\n margin: 3rem 0;\n padding-top: 2rem;\n border-top: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.giscus {\n width: 100%;\n}\n\n/* Giscus \u4e3b\u9898\u9002\u914d */\n.giscus-frame {\n background-color: var(--md-default-bg-color);\n color: var(--md-default-fg-color);\n}\n\n/* \u6697\u8272\u6a21\u5f0f\u9002\u914d */\n[data-md-color-scheme=\"slate\"] .giscus-frame {\n background-color: var(--md-default-bg-color--dark);\n color: var(--md-default-fg-color--dark);\n}\n\n/* \u8bc4\u8bba\u6846\u6837\u5f0f */\n.giscus .gsc-comment-box {\n background-color: var(--md-code-bg-color);\n border-radius: 8px;\n padding: 1rem;\n}\n\n/* \u8bc4\u8bba\u5217\u8868\u6837\u5f0f */\n.giscus .gsc-comment {\n margin: 1rem 0;\n padding: 1rem;\n background-color: var(--md-code-bg-color);\n border-radius: 8px;\n transition: transform 0.2s ease;\n}\n\n.giscus .gsc-comment:hover {\n transform: translateX(4px);\n}\n\n/* \u6309\u94ae\u6837\u5f0f */\n.giscus .gsc-button {\n background-color: var(--md-primary-fg-color);\n color: var(--md-primary-bg-color);\n border: none;\n padding: 0.5rem 1rem;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.giscus .gsc-button:hover {\n background-color: var(--md-primary-fg-color--dark);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#92","title":"9.2 \u6570\u636e\u7edf\u8ba1","text":""},{"location":"Tools/Blog/Mkdocs_Material/#921-google-analytics","title":"9.2.1 Google Analytics","text":" - GA4 \u914d\u7f6e\uff1a
YAML# mkdocs.yml\nextra:\n analytics:\n provider: google\n property: G-XXXXXXXXXX\n\n # \u4e8b\u4ef6\u8ffd\u8e2a\n events: true\n # \u7528\u6237\u884c\u4e3a\u8ffd\u8e2a\n behavior: true\n
- GA4 \u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/partials/analytics.html -->\n{% if config.extra.analytics.provider == 'google' %}\n<!-- Google Analytics -->\n<script async src=\"https://www.googletagmanager.com/gtag/js?id={{ config.extra.analytics.property }}\"></script>\n<script>\n window.dataLayer = window.dataLayer || [];\n function gtag(){dataLayer.push(arguments);}\n gtag('js', new Date());\n gtag('config', '{{ config.extra.analytics.property }}');\n\n // \u4e8b\u4ef6\u8ffd\u8e2a\n if ({{ config.extra.analytics.events|tojson }}) {\n document.addEventListener('DOMContentLoaded', function() {\n // \u70b9\u51fb\u4e8b\u4ef6\u8ffd\u8e2a\n document.addEventListener('click', function(e) {\n const target = e.target.closest('a');\n if (target) {\n gtag('event', 'click', {\n 'event_category': 'link',\n 'event_label': target.href\n });\n }\n });\n\n // \u6eda\u52a8\u4e8b\u4ef6\u8ffd\u8e2a\n let lastScrollDepth = 0;\n window.addEventListener('scroll', debounce(function() {\n const scrollDepth = Math.round(\n (window.scrollY + window.innerHeight) / \n document.documentElement.scrollHeight * 100\n );\n\n if (scrollDepth > lastScrollDepth) {\n gtag('event', 'scroll_depth', {\n 'depth': scrollDepth\n });\n lastScrollDepth = scrollDepth;\n }\n }, 500));\n\n // \u641c\u7d22\u4e8b\u4ef6\u8ffd\u8e2a\n const searchInput = document.querySelector('.md-search__input');\n if (searchInput) {\n searchInput.addEventListener('search', function() {\n gtag('event', 'search', {\n 'search_term': this.value\n });\n });\n }\n });\n }\n\n // \u7528\u6237\u884c\u4e3a\u8ffd\u8e2a\n if ({{ config.extra.analytics.behavior|tojson }}) {\n // \u9875\u9762\u505c\u7559\u65f6\u95f4\n let startTime = Date.now();\n window.addEventListener('beforeunload', function() {\n const duration = Math.round((Date.now() - startTime) / 1000);\n gtag('event', 'time_on_page', {\n 'duration': duration\n });\n });\n\n // \u590d\u5236\u884c\u4e3a\u8ffd\u8e2a\n document.addEventListener('copy', function() {\n gtag('event', 'content_copy');\n });\n }\n</script>\n{% endif %}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#922","title":"9.2.2 \u4e0d\u849c\u5b50\u7edf\u8ba1","text":" - \u4e0d\u849c\u5b50\u914d\u7f6e\uff1a
YAMLextra:\n analytics:\n busuanzi: true\n
- \u4e0d\u849c\u5b50\u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/partials/busuanzi.html -->\n{% if config.extra.analytics.busuanzi %}\n<script async src=\"//busuanzi.ibruce.info/busuanzi/2.3/busuanzi.pure.mini.js\"></script>\n\n<div class=\"page-views\">\n <!-- \u603b\u8bbf\u95ee\u91cf -->\n <span id=\"busuanzi_container_site_pv\">\n \u603b\u8bbf\u95ee\u91cf: <span id=\"busuanzi_value_site_pv\"></span>\u6b21\n </span>\n\n <!-- \u603b\u8bbf\u5ba2\u6570 -->\n <span id=\"busuanzi_container_site_uv\">\n \u8bbf\u5ba2\u6570: <span id=\"busuanzi_value_site_uv\"></span>\u4eba\n </span>\n\n <!-- \u9875\u9762\u8bbf\u95ee\u91cf -->\n <span id=\"busuanzi_container_page_pv\">\n \u672c\u6587\u8bbf\u95ee\u91cf: <span id=\"busuanzi_value_page_pv\"></span>\u6b21\n </span>\n</div>\n\n<style>\n.page-views {\n display: flex;\n gap: 1rem;\n margin: 1rem 0;\n padding: 0.5rem;\n background: var(--md-code-bg-color);\n border-radius: 4px;\n font-size: 0.9rem;\n}\n\n.page-views span {\n color: var(--md-default-fg-color--light);\n}\n\n.page-views span span {\n color: var(--md-accent-fg-color);\n font-weight: bold;\n}\n</style>\n{% endif %}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#93","title":"9.3 \u793e\u4ea4\u5206\u4eab","text":""},{"location":"Tools/Blog/Mkdocs_Material/#931","title":"9.3.1 \u5206\u4eab\u6309\u94ae","text":" - \u5206\u4eab\u914d\u7f6e\uff1a
YAMLextra:\n social_share:\n enabled: true\n platforms:\n - name: twitter\n icon: material/twitter\n color: '#1DA1F2'\n - name: facebook\n icon: material/facebook\n color: '#4267B2'\n - name: linkedin\n icon: material/linkedin\n color: '#0A66C2'\n - name: weibo\n icon: material/sina-weibo\n color: '#E6162D'\n
- \u5206\u4eab\u6309\u94ae\u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/partials/share.html -->\n{% if config.extra.social_share.enabled %}\n<div class=\"share-container\">\n <h4>\u5206\u4eab\u6587\u7ae0</h4>\n <div class=\"share-buttons\">\n {% for platform in config.extra.social_share.platforms %}\n <button class=\"share-button\" \n data-platform=\"{{ platform.name }}\"\n style=\"--platform-color: {{ platform.color }}\"\n onclick=\"shareContent('{{ platform.name }}')\">\n <span class=\"share-icon\">\n {% include \".icons/\" ~ platform.icon ~ \".svg\" %}\n </span>\n <span class=\"share-text\">\u5206\u4eab\u5230 {{ platform.name|title }}</span>\n </button>\n {% endfor %}\n </div>\n</div>\n\n<style>\n.share-container {\n margin: 2rem 0;\n}\n\n.share-buttons {\n display: flex;\n flex-wrap: wrap;\n gap: 0.5rem;\n margin-top: 1rem;\n}\n\n.share-button {\n display: flex;\n align-items: center;\n padding: 0.5rem 1rem;\n border: none;\n border-radius: 4px;\n background: var(--md-code-bg-color);\n color: var(--platform-color);\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.share-button:hover {\n background: var(--platform-color);\n color: white;\n transform: translateY(-2px);\n}\n\n.share-icon {\n margin-right: 0.5rem;\n}\n</style>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#932","title":"9.3.2 \u5206\u4eab\u94fe\u63a5\u751f\u6210","text":"JavaScript// docs/javascripts/share.js\nclass ShareManager {\n constructor() {\n this.title = document.title;\n this.url = window.location.href;\n this.description = document\n .querySelector('meta[name=\"description\"]')\n ?.content || '';\n }\n\n generateShareUrl(platform) {\n const params = new URLSearchParams();\n switch (platform) {\n case 'twitter':\n return `https://twitter.com/intent/tweet?${\n params.set('text', this.title),\n params.set('url', this.url)\n }`;\n\n case 'facebook':\n return `https://www.facebook.com/sharer/sharer.php?${\n params.set('u', this.url)\n }`;\n\n case 'linkedin':\n return `https://www.linkedin.com/sharing/share-offsite/?${\n params.set('url', this.url)\n }`;\n\n case 'weibo':\n return `http://service.weibo.com/share/share.php?${\n params.set('title', this.title),\n params.set('url', this.url)\n }`;\n\n default:\n return null;\n }\n }\n\n async share(platform) {\n // \u68c0\u67e5\u662f\u5426\u652f\u6301\u539f\u751f\u5206\u4eab\n if (navigator.share && platform === 'native') {\n try {\n await navigator.share({\n title: this.title,\n text: this.description,\n url: this.url\n });\n return true;\n } catch (err) {\n console.warn('Native share failed:', err);\n return false;\n }\n }\n\n // \u4f7f\u7528\u5f39\u7a97\u5206\u4eab\n const url = this.generateShareUrl(platform);\n if (url) {\n window.open(url, '_blank', 'width=600,height=400');\n return true;\n }\n\n return false;\n }\n\n // \u81ea\u5b9a\u4e49\u5206\u4eab\u5185\u5bb9\n setContent(title, description) {\n this.title = title;\n this.description = description;\n }\n}\n\n// \u5168\u5c40\u5206\u4eab\u529f\u80fd\nwindow.shareContent = async function(platform) {\n const shareManager = new ShareManager();\n const success = await shareManager.share(platform);\n\n // \u8bb0\u5f55\u5206\u4eab\u4e8b\u4ef6\n if (success) {\n recordShare(platform);\n }\n};\n\n// \u5206\u4eab\u7edf\u8ba1\nfunction recordShare(platform) {\n // GA4 \u4e8b\u4ef6\u8ffd\u8e2a\n if (typeof gtag !== 'undefined') {\n gtag('event', 'share', {\n 'platform': platform,\n 'title': document.title,\n 'url': window.location.href\n });\n }\n\n // \u672c\u5730\u5b58\u50a8\u7edf\u8ba1\n const stats = JSON.parse(localStorage.getItem('share_stats') || '{}');\n stats[platform] = (stats[platform] || 0) + 1;\n localStorage.setItem('share_stats', JSON.stringify(stats));\n\n updateShareCount(platform);\n}\n\n// \u66f4\u65b0\u5206\u4eab\u8ba1\u6570\nfunction updateShareCount(platform) {\n const countElement = document.querySelector(\n `.share-count[data-platform=\"${platform}\"]`\n );\n if (countElement) {\n const stats = JSON.parse(localStorage.getItem('share_stats') || '{}');\n countElement.textContent = stats[platform] || 0;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#94","title":"9.4 \u7ad9\u5185\u641c\u7d22","text":""},{"location":"Tools/Blog/Mkdocs_Material/#941","title":"9.4.1 \u641c\u7d22\u4f18\u5316","text":" - \u641c\u7d22\u914d\u7f6e\uff1a
YAMLplugins:\n - search:\n lang:\n - en\n - zh\n separator: '[\\s\\-\\.,!\\?\uff0c\u3002\uff01\uff1f]+' # \u5206\u8bcd\u5206\u9694\u7b26\n prebuild_index: true\n indexing:\n full_sections: true\n headings: true\n content: true\n tags: true\n\n # \u9ad8\u7ea7\u641c\u7d22\u914d\u7f6e\n search_boost: true # \u542f\u7528\u641c\u7d22\u63d0\u5347\n search_threshold: 0.3 # \u641c\u7d22\u9608\u503c\n search_fields: # \u641c\u7d22\u5b57\u6bb5\u6743\u91cd\n title: 10\n tags: 8\n content: 5\n
\u641c\u7d22\u4f18\u5316\u5b9e\u73b0\uff1a
JavaScript// docs/javascripts/search-enhance.js\nclass SearchEnhancer {\n constructor() {\n this.searchIndex = null;\n this.searchResults = [];\n this.searchHistory = [];\n this.maxHistoryItems = 10;\n\n this.init();\n }\n\n async init() {\n // \u52a0\u8f7d\u641c\u7d22\u7d22\u5f15\n await this.loadSearchIndex();\n // \u52a0\u8f7d\u641c\u7d22\u5386\u53f2\n this.loadSearchHistory();\n // \u521d\u59cb\u5316\u641c\u7d22\u7ec4\u4ef6\n this.setupSearch();\n }\n\n async loadSearchIndex() {\n try {\n const response = await fetch('/search/search_index.json');\n const data = await response.json();\n\n // \u4f7f\u7528 Lunr.js \u6784\u5efa\u7d22\u5f15\n this.searchIndex = lunr(function() {\n this.use(lunr.multiLanguage('en', 'zh'));\n\n this.ref('location');\n this.field('title', { boost: 10 });\n this.field('tags', { boost: 8 });\n this.field('text', { boost: 5 });\n\n data.docs.forEach(function(doc) {\n this.add(doc);\n }, this);\n });\n } catch (error) {\n console.error('Failed to load search index:', error);\n }\n }\n\n setupSearch() {\n const searchInput = document.querySelector('.md-search__input');\n if (!searchInput) return;\n\n // \u9632\u6296\u5904\u7406\n searchInput.addEventListener('input', debounce((e) => {\n const query = e.target.value;\n if (query.length >= 2) {\n this.performSearch(query);\n } else {\n this.clearResults();\n }\n }, 200));\n\n // \u641c\u7d22\u5386\u53f2\n searchInput.addEventListener('focus', () => {\n this.showSearchHistory();\n });\n }\n\n performSearch(query) {\n if (!this.searchIndex) return;\n\n try {\n // \u6267\u884c\u641c\u7d22\n this.searchResults = this.searchIndex.search(query);\n\n // \u7ed3\u679c\u540e\u5904\u7406\n this.searchResults = this.postProcessResults(this.searchResults, query);\n\n // \u663e\u793a\u7ed3\u679c\n this.displayResults(this.searchResults);\n\n // \u663e\u793a\u641c\u7d22\u5efa\u8bae\n this.showSearchSuggestions(query);\n } catch (error) {\n console.error('Search failed:', error);\n }\n }\n\n postProcessResults(results, query) {\n return results\n .map(result => {\n // \u8ba1\u7b97\u76f8\u5173\u5ea6\u5206\u6570\n const score = this.calculateRelevanceScore(result, query);\n\n return {\n ...result,\n score: score,\n highlights: this.generateHighlights(result, query)\n };\n })\n // \u6309\u5206\u6570\u6392\u5e8f\n .sort((a, b) => b.score - a.score)\n // \u9650\u5236\u7ed3\u679c\u6570\u91cf\n .slice(0, 10);\n }\n\n calculateRelevanceScore(result, query) {\n let score = result.score;\n\n // \u6807\u9898\u5339\u914d\u52a0\u5206\n if (result.title?.toLowerCase().includes(query.toLowerCase())) {\n score *= 1.5;\n }\n\n // \u6807\u7b7e\u5339\u914d\u52a0\u5206\n if (result.tags?.some(tag => \n tag.toLowerCase().includes(query.toLowerCase())\n )) {\n score *= 1.2;\n }\n\n return score;\n }\n\n generateHighlights(result, query) {\n const text = result.text || '';\n const words = query.toLowerCase().split(/\\s+/);\n const context = 50; // \u4e0a\u4e0b\u6587\u957f\u5ea6\n\n let highlights = [];\n\n words.forEach(word => {\n let index = text.toLowerCase().indexOf(word);\n while (index > -1) {\n const start = Math.max(0, index - context);\n const end = Math.min(text.length, index + word.length + context);\n const highlight = text.slice(start, end);\n\n highlights.push({\n text: highlight,\n position: start\n });\n\n index = text.toLowerCase().indexOf(word, index + 1);\n }\n });\n\n // \u5408\u5e76\u91cd\u53e0\u7684\u9ad8\u4eae\u7247\u6bb5\n return this.mergeHighlights(highlights);\n }\n\n mergeHighlights(highlights) {\n if (highlights.length === 0) return [];\n\n highlights.sort((a, b) => a.position - b.position);\n\n let merged = [highlights[0]];\n for (let i = 1; i < highlights.length; i++) {\n let current = highlights[i];\n let last = merged[merged.length - 1];\n\n if (current.position <= last.position + last.text.length) {\n // \u5408\u5e76\u91cd\u53e0\u7684\u7247\u6bb5\n last.text = last.text.slice(0, current.position - last.position) +\n current.text;\n } else {\n merged.push(current);\n }\n }\n\n return merged;\n }\n\n loadSearchHistory() {\n try {\n this.searchHistory = JSON.parse(\n localStorage.getItem('search_history')\n ) || [];\n } catch (error) {\n this.searchHistory = [];\n }\n }\n\n saveSearchHistory(query) {\n if (!query) return;\n\n // \u79fb\u9664\u91cd\u590d\u9879\n this.searchHistory = this.searchHistory.filter(item => \n item.query !== query\n );\n\n // \u6dfb\u52a0\u65b0\u9879\n this.searchHistory.unshift({\n query: query,\n timestamp: Date.now()\n });\n\n // \u9650\u5236\u5386\u53f2\u8bb0\u5f55\u6570\u91cf\n if (this.searchHistory.length > this.maxHistoryItems) {\n this.searchHistory.pop();\n }\n\n // \u4fdd\u5b58\u5230\u672c\u5730\u5b58\u50a8\n localStorage.setItem(\n 'search_history',\n JSON.stringify(this.searchHistory)\n );\n }\n\n showSearchHistory() {\n const container = document.querySelector('.md-search__history');\n if (!container) return;\n\n // \u6e05\u7a7a\u5bb9\u5668\n container.innerHTML = '';\n\n // \u663e\u793a\u5386\u53f2\u8bb0\u5f55\n this.searchHistory.forEach(item => {\n const element = document.createElement('div');\n element.className = 'search-history-item';\n element.innerHTML = `\n <span class=\"history-icon\">\n {% include \".icons/material/history.svg\" %}\n </span>\n <span class=\"history-query\">${item.query}</span>\n <span class=\"history-time\">\n ${this.formatTime(item.timestamp)}\n </span>\n `;\n\n element.addEventListener('click', () => {\n const searchInput = document.querySelector('.md-search__input');\n if (searchInput) {\n searchInput.value = item.query;\n searchInput.dispatchEvent(new Event('input'));\n }\n });\n\n container.appendChild(element);\n });\n }\n\n formatTime(timestamp) {\n const now = Date.now();\n const diff = now - timestamp;\n\n if (diff < 60000) return '\u521a\u521a';\n if (diff < 3600000) return `${Math.floor(diff/60000)}\u5206\u949f\u524d`;\n if (diff < 86400000) return `${Math.floor(diff/3600000)}\u5c0f\u65f6\u524d`;\n return `${Math.floor(diff/86400000)}\u5929\u524d`;\n }\n}\n\n// \u521d\u59cb\u5316\u641c\u7d22\u589e\u5f3a\nnew SearchEnhancer();\n
\u641c\u7d22\u76f8\u5173\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/search.css */\n/* \u641c\u7d22\u5386\u53f2 */\n.search-history-item {\n display: flex;\n align-items: center;\n padding: 0.5rem 1rem;\n cursor: pointer;\n transition: background 0.2s ease;\n}\n\n.search-history-item:hover {\n background: var(--md-code-bg-color);\n}\n\n.history-icon {\n margin-right: 0.5rem;\n color: var(--md-default-fg-color--light);\n}\n\n.history-query {\n flex: 1;\n font-weight: 500;\n}\n\n.history-time {\n font-size: 0.8rem;\n color: var(--md-default-fg-color--light);\n}\n\n/* \u641c\u7d22\u5efa\u8bae */\n.search-suggestions {\n padding: 1rem;\n}\n\n.suggestion-item {\n padding: 0.5rem;\n border-radius: 4px;\n cursor: pointer;\n transition: background 0.2s ease;\n}\n\n.suggestion-item:hover {\n background: var(--md-code-bg-color);\n}\n\n/* \u9ad8\u4eae\u6587\u672c */\n.search-highlight {\n background: var(--md-accent-fg-color);\n color: white;\n padding: 0 0.2rem;\n border-radius: 2px;\n}\n\n/* \u641c\u7d22\u7ed3\u679c */\n.search-result {\n padding: 1rem;\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.result-title {\n font-size: 1.1rem;\n font-weight: 500;\n margin-bottom: 0.5rem;\n}\n\n.result-excerpt {\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n margin-bottom: 0.5rem;\n}\n\n.result-meta {\n display: flex;\n gap: 1rem;\n font-size: 0.8rem;\n color: var(--md-default-fg-color--lighter);\n}\n
\u4f7f\u7528\u8fd9\u4e9b\u4ee3\u7801\uff0c\u4f60\u53ef\u4ee5\u5b9e\u73b0\u4e00\u4e2a\u529f\u80fd\u5b8c\u5584\u7684\u641c\u7d22\u7cfb\u7edf\uff0c\u5305\u62ec\uff1a
- \u9ad8\u6027\u80fd\u641c\u7d22
- \u667a\u80fd\u7ed3\u679c\u6392\u5e8f
- \u641c\u7d22\u5386\u53f2\u8bb0\u5f55
- \u641c\u7d22\u5efa\u8bae
- \u7ed3\u679c\u9ad8\u4eae
- \u591a\u8bed\u8a00\u652f\u6301
\u8bb0\u5f97\u5728 mkdocs.yml \u4e2d\u6dfb\u52a0\u76f8\u5e94\u7684\u5f15\u7528\uff1a
YAMLextra_css:\n - stylesheets/search.css\n\nextra_javascript:\n - https://unpkg.com/lunr/lunr.js\n - https://unpkg.com/lunr-languages/lunr.stemmer.support.js\n - https://unpkg.com/lunr-languages/lunr.zh.js\n - javascripts/search-enhance.js\n
"},{"location":"Tools/Blog/Mkdocs_Material/#10","title":"10 \u90e8\u7f72\u65b9\u6848","text":""},{"location":"Tools/Blog/Mkdocs_Material/#101","title":"10.1 \u672c\u5730\u90e8\u7f72","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1011","title":"10.1.1 \u6784\u5efa\u9759\u6001\u6587\u4ef6","text":"\u4f7f\u7528 MkDocs \u6784\u5efa\u9759\u6001\u6587\u4ef6\uff1a
Bash# \u6784\u5efa\u7ad9\u70b9\nmkdocs build\n\n# \u6307\u5b9a\u8f93\u51fa\u76ee\u5f55\nmkdocs build -d custom_site_dir\n\n# \u6e05\u7406\u5e76\u6784\u5efa\nmkdocs build --clean\n
\u6784\u5efa\u9009\u9879\u8bf4\u660e\uff1a
YAML# mkdocs.yml\nsite_dir: site # \u8f93\u51fa\u76ee\u5f55\ndocs_dir: docs # \u6587\u6863\u6e90\u76ee\u5f55\nstrict: true # \u4e25\u683c\u6a21\u5f0f\nuse_directory_urls: true # \u4f7f\u7528\u76ee\u5f55 URL\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1012","title":"10.1.2 \u672c\u5730\u670d\u52a1\u5668","text":"\u5f00\u53d1\u670d\u52a1\u5668\u914d\u7f6e\uff1a
Bash# \u542f\u52a8\u5f00\u53d1\u670d\u52a1\u5668\nmkdocs serve\n\n# \u6307\u5b9a\u7aef\u53e3\nmkdocs serve -a localhost:8080\n\n# \u81ea\u52a8\u91cd\u8f7d\nmkdocs serve --dirtyreload\n
\u5f00\u53d1\u670d\u52a1\u5668\u9ad8\u7ea7\u914d\u7f6e\uff1a
YAML# mkdocs.yml\ndev_addr: 127.0.0.1:8000 # \u5f00\u53d1\u670d\u52a1\u5668\u5730\u5740\nwatch: [docs, includes] # \u76d1\u89c6\u7684\u76ee\u5f55\n
"},{"location":"Tools/Blog/Mkdocs_Material/#102-github-pages","title":"10.2 GitHub Pages \u90e8\u7f72","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1021","title":"10.2.1 \u4ed3\u5e93\u914d\u7f6e","text":" -
\u521b\u5efa GitHub \u4ed3\u5e93\uff1a - \u521b\u5efa\u65b0\u4ed3\u5e93\u6216\u4f7f\u7528\u73b0\u6709\u4ed3\u5e93 - \u4ed3\u5e93\u540d\u683c\u5f0f\uff1a username.github.io \u6216\u666e\u901a\u4ed3\u5e93\u540d
-
\u914d\u7f6e\u6587\u4ef6\uff1a
YAML# mkdocs.yml\nsite_url: https://username.github.io/repository/\nrepo_url: https://github.com/username/repository\nedit_uri: edit/main/docs/\n
- \u521d\u59cb\u5316\u4ed3\u5e93\uff1a
Bash# \u521d\u59cb\u5316 Git \u4ed3\u5e93\ngit init\n\n# \u6dfb\u52a0\u8fdc\u7a0b\u4ed3\u5e93\ngit remote add origin https://github.com/username/repository.git\n\n# \u63d0\u4ea4\u521d\u59cb\u6587\u4ef6\ngit add .\ngit commit -m \"Initial commit\"\ngit push -u origin main\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1022-github-actions","title":"10.2.2 GitHub Actions","text":"\u521b\u5efa GitHub Actions \u5de5\u4f5c\u6d41\u6587\u4ef6\uff1a
YAML# .github/workflows/ci.yml\nname: ci \non:\n push:\n branches: \n - main\npermissions:\n contents: write\njobs:\n deploy:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - name: Configure Git Credentials\n run: |\n git config user.name github-actions[bot]\n git config user.email 41898282+github-actions[bot]@users.noreply.github.com\n - uses: actions/setup-python@v4\n with:\n python-version: 3.x\n - run: echo \"cache_id=$(date --utc '+%V')\" >> $GITHUB_ENV \n - uses: actions/cache@v3\n with:\n key: mkdocs-material-${{ env.cache_id }}\n path: .cache\n restore-keys: |\n mkdocs-material-\n - name: Install dependencies\n run: |\n pip install mkdocs-material\n pip install mkdocs-glightbox\n pip install mkdocs-git-revision-date-localized-plugin\n - name: Deploy\n run: mkdocs gh-deploy --force\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1023","title":"10.2.3 \u81ea\u52a8\u90e8\u7f72","text":"\u914d\u7f6e\u81ea\u52a8\u90e8\u7f72\uff1a
-
\u542f\u7528 GitHub Pages\uff1a - \u8fdb\u5165\u4ed3\u5e93\u8bbe\u7f6e -> Pages - Source \u9009\u62e9 gh-pages \u5206\u652f - \u4fdd\u5b58\u8bbe\u7f6e
-
\u914d\u7f6e\u90e8\u7f72\u5206\u652f\uff1a
YAML# mkdocs.yml\nremote_branch: gh-pages # GitHub Pages \u5206\u652f\nremote_name: origin # \u8fdc\u7a0b\u4ed3\u5e93\u540d\n
- \u624b\u52a8\u90e8\u7f72\u547d\u4ee4\uff1a
Bash# \u90e8\u7f72\u5230 GitHub Pages\nmkdocs gh-deploy\n\n# \u5f3a\u5236\u90e8\u7f72\nmkdocs gh-deploy --force\n\n# \u6307\u5b9a\u5206\u652f\nmkdocs gh-deploy --remote-branch custom-branch\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1024","title":"10.2.4 \u81ea\u5b9a\u4e49\u57df\u540d","text":" - \u6dfb\u52a0 CNAME \u6587\u4ef6\uff1a
Bash# docs/CNAME\ndocs.example.com\n
-
DNS \u914d\u7f6e\uff1a - \u6dfb\u52a0 CNAME \u8bb0\u5f55\u6307\u5411 username.github.io - \u6216\u6dfb\u52a0 A \u8bb0\u5f55\u6307\u5411 GitHub Pages IP
-
\u914d\u7f6e\u6587\u4ef6\u66f4\u65b0\uff1a
YAML# mkdocs.yml\nsite_url: https://docs.example.com/\n
"},{"location":"Tools/Blog/Mkdocs_Material/#103","title":"10.3 \u6301\u7eed\u96c6\u6210","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1031-cicd","title":"10.3.1 CI/CD \u914d\u7f6e","text":"\u9ad8\u7ea7 GitHub Actions \u914d\u7f6e\uff1a
YAMLname: CI/CD Pipeline\non:\n push:\n branches:\n - main\n pull_request:\n branches:\n - main\n\njobs:\n test:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: 3.x\n\n - name: Cache dependencies\n uses: actions/cache@v3\n with:\n path: ~/.cache/pip\n key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}\n restore-keys: |\n ${{ runner.os }}-pip-\n\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install -r requirements.txt\n\n - name: Run tests\n run: |\n mkdocs build --strict\n\n deploy:\n needs: test\n if: github.ref == 'refs/heads/main'\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n\n - name: Deploy to GitHub Pages\n run: |\n mkdocs gh-deploy --force\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1032","title":"10.3.2 \u81ea\u52a8\u5316\u6d4b\u8bd5","text":" - \u94fe\u63a5\u68c0\u67e5\uff1a
YAMLname: Link Check\non:\n schedule:\n - cron: '0 0 * * *' # \u6bcf\u5929\u8fd0\u884c\njobs:\n linkCheck:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - name: Link Checker\n uses: lycheeverse/lychee-action@v1.8.0\n with:\n args: --verbose --no-progress 'docs/**/*.md'\n
- HTML \u9a8c\u8bc1\uff1a
YAMLname: HTML Validation\njobs:\n validate:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - name: Build site\n run: mkdocs build\n - name: Validate HTML\n uses: Cyb3r-Jak3/html5validator-action@v7.2.0\n with:\n root: site/\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1033","title":"10.3.3 \u90e8\u7f72\u7b56\u7565","text":" - \u5206\u652f\u7b56\u7565\u914d\u7f6e\uff1a
YAML# \u4fdd\u62a4\u5206\u652f\u8bbe\u7f6e\nbranches:\n - name: main\n protection:\n required_status_checks:\n strict: true\n contexts: ['test', 'build']\n required_pull_request_reviews:\n required_approving_review_count: 1\n
- \u73af\u5883\u914d\u7f6e\uff1a
YAML# \u73af\u5883\u53d8\u91cf\u8bbe\u7f6e\nenv:\n production:\n url: https://docs.example.com\n branch: main\n staging:\n url: https://staging.docs.example.com\n branch: staging\n
- \u90e8\u7f72\u5de5\u4f5c\u6d41\uff1a
YAMLname: Deployment\non:\n push:\n branches: [main, staging]\njobs:\n deploy:\n runs-on: ubuntu-latest\n environment:\n name: ${{ github.ref == 'refs/heads/main' && 'production' || 'staging' }}\n steps:\n - uses: actions/checkout@v4\n\n - name: Build and Deploy\n env:\n DEPLOY_URL: ${{ github.ref == 'refs/heads/main' && env.production.url || env.staging.url }}\n run: |\n mkdocs build\n # \u90e8\u7f72\u5230\u76f8\u5e94\u73af\u5883\n
"},{"location":"Tools/Blog/Mkdocs_Material/#11","title":"11 \u9644\u5f55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#111-a","title":"11.1 A. \u5e38\u7528\u914d\u7f6e\u793a\u4f8b","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1111-a1-mkdocs-yml","title":"11.1.1 A.1 mkdocs. yml \u5b8c\u6574\u793a\u4f8b","text":"YAML# \u7ad9\u70b9\u57fa\u672c\u4fe1\u606f\u914d\u7f6e\nsite_name: \u9879\u76ee\u6587\u6863 # \u7ad9\u70b9\u540d\u79f0\nsite_url: https://example.com # \u7ad9\u70b9URL\nsite_author: \u4f5c\u8005\u540d\u79f0 # \u4f5c\u8005\u4fe1\u606f\nsite_description: >- # \u7ad9\u70b9\u63cf\u8ff0\n \u8fd9\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u6587\u6863\u793a\u4f8b\uff0c\u5305\u542b\u4e86MkDocs Material\u4e3b\u9898\u7684\u6240\u6709\u4e3b\u8981\u529f\u80fd\u914d\u7f6e\u3002\n\n# \u4ee3\u7801\u4ed3\u5e93\u4fe1\u606f\nrepo_name: username/repo # \u4ed3\u5e93\u540d\u79f0\nrepo_url: https://github.com/username/repo # \u4ed3\u5e93\u5730\u5740\nedit_uri: edit/main/docs/ # \u7f16\u8f91\u94fe\u63a5\n\n# \u7248\u6743\u4fe1\u606f\ncopyright: Copyright © 2024 # \u7248\u6743\u58f0\u660e\n\n# \u4e3b\u9898\u914d\u7f6e\ntheme:\n name: material # \u4f7f\u7528Material\u4e3b\u9898\n custom_dir: overrides # \u81ea\u5b9a\u4e49\u76ee\u5f55\n\n # \u4e3b\u9898\u7279\u6027\u914d\u7f6e\n features:\n # \u5bfc\u822a\u76f8\u5173\n - navigation.tabs # \u9876\u90e8\u6807\u7b7e\u5bfc\u822a\n - navigation.sections # \u4fa7\u8fb9\u680f\u5206\u7ec4\n - navigation.expand # \u9ed8\u8ba4\u5c55\u5f00\u5bfc\u822a\n - navigation.indexes # \u5bfc\u822a\u7d22\u5f15\u9875\n - navigation.top # \u8fd4\u56de\u9876\u90e8\u6309\u94ae\n - navigation.footer # \u4e0a\u4e00\u9875/\u4e0b\u4e00\u9875\u5bfc\u822a\n\n # \u641c\u7d22\u76f8\u5173\n - search.suggest # \u641c\u7d22\u5efa\u8bae\n - search.highlight # \u641c\u7d22\u9ad8\u4eae\n - search.share # \u641c\u7d22\u5206\u4eab\n\n # \u4ee3\u7801\u76f8\u5173\n - content.code.annotate # \u4ee3\u7801\u6ce8\u91ca\n - content.code.copy # \u4ee3\u7801\u590d\u5236\n\n # \u5176\u4ed6\u529f\u80fd\n - announce.dismiss # \u53ef\u5173\u95ed\u516c\u544a\n - header.autohide # \u81ea\u52a8\u9690\u85cf\u5934\u90e8\n\n # \u914d\u8272\u65b9\u6848\n palette:\n # \u4eae\u8272\u4e3b\u9898\n - media: \"(prefers-color-scheme: light)\"\n scheme: default\n primary: indigo # \u4e3b\u8272\n accent: indigo # \u5f3a\u8c03\u8272\n toggle:\n icon: material/brightness-7\n name: \u5207\u6362\u81f3\u6697\u8272\u6a21\u5f0f\n\n # \u6697\u8272\u4e3b\u9898\n - media: \"(prefers-color-scheme: dark)\"\n scheme: slate\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u6a21\u5f0f\n\n # \u56fe\u6807\u548c\u5b57\u4f53\n icon:\n logo: material/book-open-page-variant # Logo\u56fe\u6807\n repo: fontawesome/brands/github # \u4ed3\u5e93\u56fe\u6807\n\n font:\n text: Roboto # \u6b63\u6587\u5b57\u4f53\n code: Roboto Mono # \u4ee3\u7801\u5b57\u4f53\n\n language: zh # \u754c\u9762\u8bed\u8a00\n\n# \u6269\u5c55\u914d\u7f6e\nmarkdown_extensions:\n # \u5185\u7f6e\u6269\u5c55\n - toc: # \u76ee\u5f55\n permalink: true # \u6c38\u4e45\u94fe\u63a5\n toc_depth: 3 # \u76ee\u5f55\u6df1\u5ea6\n - tables # \u8868\u683c\u652f\u6301\n - attr_list # \u5c5e\u6027\u5217\u8868\n - def_list # \u5b9a\u4e49\u5217\u8868\n - footnotes # \u811a\u6ce8\n - abbr # \u7f29\u5199\n\n # PyMdown Extensions\n - pymdownx.highlight: # \u4ee3\u7801\u9ad8\u4eae\n anchor_linenums: true\n line_spans: __span\n pygments_lang_class: true\n - pymdownx.inlinehilite # \u884c\u5185\u4ee3\u7801\u9ad8\u4eae\n - pymdownx.snippets # \u4ee3\u7801\u7247\u6bb5\n - pymdownx.superfences: # \u8d85\u7ea7\u56f4\u680f\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_code_format\n - pymdownx.tabbed: # \u6807\u7b7e\u9875\n alternate_style: true\n - pymdownx.tasklist: # \u4efb\u52a1\u5217\u8868\n custom_checkbox: true\n - pymdownx.emoji: # \u8868\u60c5\u652f\u6301\n emoji_index: !!python/name:material.extensions.emoji.twemoji\n emoji_generator: !!python/name:material.extensions.emoji.to_svg\n - pymdownx.details # \u8be6\u7ec6\u4fe1\u606f\n - pymdownx.caret # \u4e0a\u6807\n - pymdownx.mark # \u6807\u8bb0\n - pymdownx.tilde # \u4e0b\u6807\n - pymdownx.keys # \u952e\u76d8\u6309\u952e\n\n# \u63d2\u4ef6\u914d\u7f6e\nplugins:\n - search: # \u641c\u7d22\u63d2\u4ef6\n lang: \n - en\n - zh\n separator: '[\\s\\-\\.,!\\?\uff0c\u3002\uff01\uff1f]+'\n prebuild_index: true\n\n - git-revision-date-localized: # Git\u65e5\u671f\u63d2\u4ef6\n enable_creation_date: true\n type: datetime\n\n - minify: # \u8d44\u6e90\u538b\u7f29\n minify_html: true\n minify_js: true\n minify_css: true\n htmlmin_opts:\n remove_comments: true\n\n - social: # \u793e\u4ea4\u5206\u4eab\n cards: true\n cards_color:\n fill: \"#0FF1CE\"\n text: \"#FFFFFF\"\n\n - tags: # \u6807\u7b7e\u7cfb\u7edf\n tags_file: tags.md\n\n - statistics: # \u7edf\u8ba1\n page_check: true\n page_count: true\n\n# \u9644\u52a0\u914d\u7f6e\nextra:\n # \u793e\u4ea4\u94fe\u63a5\n social:\n - icon: fontawesome/brands/github\n link: https://github.com/username\n - icon: fontawesome/brands/twitter\n link: https://twitter.com/username\n\n # \u8bc4\u8bba\u7cfb\u7edf\n comments:\n provider: giscus\n repo: username/repo\n repo_id: your_repo_id\n category: Comments\n category_id: your_category_id\n\n # \u5206\u6790\u7edf\u8ba1\n analytics:\n provider: google\n property: G-XXXXXXXXXX\n feedback:\n title: \u8fd9\u7bc7\u6587\u6863\u5bf9\u60a8\u6709\u5e2e\u52a9\u5417\uff1f\n ratings:\n - icon: material/thumb-up-outline\n name: \u6709\u5e2e\u52a9\n data: 1\n note: >-\n \u611f\u8c22\u60a8\u7684\u53cd\u9988\uff01\n - icon: material/thumb-down-outline\n name: \u6ca1\u5e2e\u52a9\n data: 0\n note: >- \n \u611f\u8c22\u60a8\u7684\u53cd\u9988\uff0c\u8bf7\u544a\u8bc9\u6211\u4eec\u5982\u4f55\u6539\u8fdb\u3002\n\n# \u989d\u5916\u7684CSS\u548cJavaScript\nextra_css:\n - stylesheets/extra.css # \u81ea\u5b9a\u4e49\u6837\u5f0f\n - stylesheets/custom.css # \u81ea\u5b9a\u4e49\u4e3b\u9898\n\nextra_javascript:\n - javascripts/extra.js # \u81ea\u5b9a\u4e49\u811a\u672c\n - https://unpkg.com/mermaid/dist/mermaid.min.js # \u5916\u90e8\u5e93\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1112-a2-github-actions","title":"11.1.2 A.2 GitHub Actions \u5de5\u4f5c\u6d41\u793a\u4f8b","text":"YAML# .github/workflows/deploy.yml\n\nname: Deploy Documentation\non:\n # \u89e6\u53d1\u6761\u4ef6\n push:\n branches:\n - main\n paths:\n - 'docs/**'\n - 'mkdocs.yml'\n - '.github/workflows/deploy.yml'\n pull_request:\n branches:\n - main\n\n # \u5141\u8bb8\u624b\u52a8\u89e6\u53d1\n workflow_dispatch:\n\n# \u73af\u5883\u53d8\u91cf\nenv:\n PYTHON_VERSION: '3.10'\n DEPLOY_BRANCH: gh-pages\n\n# \u4efb\u52a1\u5b9a\u4e49\njobs:\n build:\n name: Build and Deploy Documentation\n runs-on: ubuntu-latest\n\n steps:\n # \u68c0\u51fa\u4ee3\u7801\n - name: Checkout Repository\n uses: actions/checkout@v3\n with:\n fetch-depth: 0 # \u83b7\u53d6\u5b8c\u6574\u7684git\u5386\u53f2\u7528\u4e8e\u6700\u8fd1\u66f4\u65b0\u65f6\u95f4\n\n # \u8bbe\u7f6ePython\u73af\u5883\n - name: Setup Python\n uses: actions/setup-python@v4\n with:\n python-version: ${{ env.PYTHON_VERSION }}\n cache: pip\n\n # \u7f13\u5b58\u4f9d\u8d56\n - name: Cache Dependencies\n uses: actions/cache@v3\n with:\n path: ~/.cache/pip\n key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}\n restore-keys: |\n ${{ runner.os }}-pip-\n\n # \u5b89\u88c5\u4f9d\u8d56\n - name: Install Dependencies\n run: |\n python -m pip install --upgrade pip\n pip install -r requirements.txt\n\n # \u914d\u7f6eGit\n - name: Configure Git\n run: |\n git config --global user.name \"${{ github.actor }}\"\n git config --global user.email \"${{ github.actor }}@users.noreply.github.com\"\n\n # \u6784\u5efa\u6587\u6863\n - name: Build Documentation\n run: |\n mkdocs build --clean --verbose\n\n # \u4f18\u5316\u8d44\u6e90\n - name: Optimize Assets\n run: |\n # \u538b\u7f29\u56fe\u7247\n find site/ -type f -name \"*.png\" -exec optipng -o5 {} \\;\n find site/ -type f -name \"*.jpg\" -exec jpegoptim --strip-all {} \\;\n\n # \u538b\u7f29JavaScript\u548cCSS\n find site/ -type f -name \"*.js\" -exec uglifyjs {} -o {} \\;\n find site/ -type f -name \"*.css\" -exec cleancss -o {} {} \\;\n\n # \u90e8\u7f72\u5230GitHub Pages\n - name: Deploy to GitHub Pages\n if: github.ref == 'refs/heads/main' # \u53ea\u5728main\u5206\u652f\u90e8\u7f72\n uses: peaceiris/actions-gh-pages@v3\n with:\n github_token: ${{ secrets.GITHUB_TOKEN }}\n publish_dir: ./site\n publish_branch: ${{ env.DEPLOY_BRANCH }}\n user_name: 'github-actions[bot]'\n user_email: 'github-actions[bot]@users.noreply.github.com'\n commit_message: ${{ github.event.head_commit.message }}\n full_commit_message: |\n Deploy Documentation\n\n Commit: ${{ github.sha }}\n Workflow: ${{ github.workflow }}\n\n ${{ github.event.head_commit.message }}\n\n # \u90e8\u7f72\u5b8c\u6210\u901a\u77e5\n - name: Deployment Status\n if: always()\n uses: actions/github-script@v6\n with:\n script: |\n const { owner, repo } = context.repo;\n const run_id = context.runId;\n const run_url = `https://github.com/${owner}/${repo}/actions/runs/${run_id}`;\n\n if (context.job.status === 'success') {\n await github.rest.issues.createComment({\n owner,\n repo,\n issue_number: context.issue.number,\n body: `\u2705 Documentation deployed successfully!\\nPreview: https://${owner}.github.io/${repo}/\\nWorkflow: ${run_url}`\n });\n } else {\n await github.rest.issues.createComment({\n owner,\n repo,\n issue_number: context.issue.number,\n body: `\u274c Documentation deployment failed.\\nSee details: ${run_url}`\n });\n }\n\n# \u9519\u8bef\u8ffd\u8e2a\u548c\u62a5\u544a\n - name: Report Errors\n if: failure()\n uses: actions/github-script@v6\n with:\n script: |\n const { owner, repo } = context.repo;\n const run_id = context.runId;\n\n await github.rest.issues.create({\n owner,\n repo,\n title: `\ud83d\udd34 Documentation deployment failed - ${new Date().toISOString()}`,\n body: `Deployment failed for commit: ${context.sha}\\n\\nSee workflow run: https://github.com/${owner}/${repo}/actions/runs/${run_id}\\n\\nPlease check the logs for more details.`,\n labels: ['deployment', 'bug']\n });\n
"},{"location":"Tools/Environment/Ubuntu_setup/","title":"Ubuntu \u914d\u7f6e","text":""},{"location":"Tools/Environment/Ubuntu_setup/#ubuntu","title":"Ubuntu \u914d\u7f6e","text":" \u7ea6 77 \u4e2a\u5b57 14 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
- \u5728 Ubuntu \u5b89\u88c5\u914d\u7f6e Fcitx 5 \u4e2d\u6587\u8f93\u5165\u6cd5 - muzing\u7684\u6742\u8d27\u94fa
- fcitx5-rime \u6302\u63a5\u5c0f\u9e64\u97f3\u5f62 | rovo98's Blog
- zhuanlan.zhihu.com/p/660191327#:~:text=Tabby\uff08\u4ee5\u524d\u79f0\u4e3a
- Zsh \u5b89\u88c5\u4e0e\u914d\u7f6e\uff0c\u4f7f\u7528 Oh-My-Zsh \u7f8e\u5316\u7ec8\u7aef | Leehow\u7684\u5c0f\u7ad9
- zhuanlan.zhihu.com/p/658811059
- PKMer_TiddyWiki \u7b80\u6613\u6307\u5357
- Site Unreachable
- Jedsek | Blog
Bashvisudo /etc/sudoers \n%sudo ALL=(ALL:ALL) NOPASSWD: ALL\n
Bashgit clone https://github.com/zsh-users/zsh-syntax-highlighting.git ${ZSH_CUSTOM:-~/.oh-my-zsh/custom}/plugins/zsh-syntax-highlighting\n\ngit clone https://github.com/zsh-users/zsh-autosuggestions ${ZSH_CUSTOM:-~/.oh-my-zsh/custom}/plugins/zsh-autosuggestions\n\ngit clone git://github.com/joelthelion/autojump.git\ncd autojump\n./install.py\n\n[[ -s ~/.autojump/etc/profile.d/autojump.sh ]] && . ~/.autojump/etc/profile.d/autojump.sh\n
Bashsudo apt-get install flameshot\nflameshot gui\n
- \u5728 Ubuntu 22.04|20.04|18.04 \u4e0a\u5b89\u88c5 Node.js 20
"},{"location":"Tools/Environment/environment/","title":"Environment","text":" - \u5f00\u53d1\u73af\u5883\u6784\u5efa\u6307\u5357
\u7ea6 227 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
- \u4ece\u96f6\u5f00\u59cb\u914d\u7f6e Windows \u2022 Arthals' ink
- \u4ece\u96f6\u5f00\u59cb\u914d\u7f6e Linux \u2022 Arthals' ink
- \u670d\u52a1\u5668\u73af\u5883\u914d\u7f6e Cheat Sheet | Yi Pan (Conless)
- clash
- powertoy
- gsudo
- git
- GitHub - Wybxc/git-remake-guide: Git \u91cd\u5f00/\u91cd\u5b66\u6307\u5357
- picgo
- picgo + github + obsidian
- vscode
- \u7cfb\u7edf + \u4ee3\u7801\u5b57\u4f53\u8bbe\u7f6e
- zhuanlan.zhihu.com/p/603687041#:~:text=clangd\u5b98\u65b9vs
- zhuanlan.zhihu.com/p/398790625#:~:text=\u5176\u4e2d VS Code
- tools
- GitHub - jenius-apps/ambie: An app that uses white noise, nature sounds, and focus features to boost your productivity.
- clash
- GitHub - Loyalsoldier/clash-rules: \ud83e\udd84\ufe0f\ud83c\udf83\ud83d\udc7b Clash Premium \u89c4\u5219\u96c6(RULE-SET)\uff0c\u517c\u5bb9 ClashX Pro\u3001Clash for Windows \u7b49\u57fa\u4e8e Clash Premium \u5185\u6838\u7684\u5ba2\u6237\u7aef\u3002
- Site Unreachable
- \u7ffb\u5899 | Blog
- GitHub - vpncn/vpncn.github.io: 2024\u4e2d\u56fd\u7ffb\u5899\u8f6f\u4ef6VPN\u63a8\u8350\u4ee5\u53ca\u79d1\u5b66\u4e0a\u7f51\u907f\u5751\uff0c\u7a33\u5b9a\u597d\u7528\u3002\u5bf9\u6bd4SSR\u673a\u573a\u3001\u84dd\u706f\u3001V2ray\u3001\u8001\u738bVPN\u3001VPS\u642d\u5efa\u68af\u5b50\u7b49\u79d1\u5b66\u4e0a\u7f51\u4e0e\u7ffb\u5899\u8f6f\u4ef6\uff0c\u4e2d\u56fd\u6700\u65b0\u79d1\u5b66\u4e0a\u7f51\u7ffb\u5899\u68af\u5b50VPN\u4e0b\u8f7d\u63a8\u8350\uff0c\u8bbf\u95eeChatgpt\u3002
- Typora
- GitHub - sylviaxgj/typora-forest-theme: another typora theme
- GitHub - HappySimple/Typora-theme-Happysimple: \u4e00\u6b3e\u81ea\u5236\u7684Markdown\u4e3b\u9898\uff01
- github profiles
- GitHub - anuraghazra/github-readme-stats: Dynamically generated stats for your github readmes
- github action
- gsudo
- openArk
"},{"location":"Tools/Environment/obsidian_setup/","title":"obsidian \u914d\u7f6e","text":" \u7ea6 1281 \u4e2a\u5b57 55 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 7 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#Environment #Obsidian","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#obsidian","title":"obsidian \u914d\u7f6e","text":"Note \u5982\u679c\u61d2\u5f97\u641e\uff0c\u53ef\u4ee5\u76f4\u63a5 clone \u6211\u7684\u914d\u7f6e\uff0c\u653e\u5230 .obsidian \u6587\u4ef6\u91cc\u3002 \u8fd9\u662f\u914d\u7f6e\u6587\u4ef6\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#1","title":"1 \u4f7f\u7528\u8bed\u8a00","text":" - \u4e3b\u8981\u662f Markdown
- \u914d\u7f6e\u63d2\u4ef6\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b javascript
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#2","title":"2 \u63d2\u4ef6","text":"","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#21-displaystyle-latex","title":"2.1 \\(\\displaystyle \\LaTeX\\)","text":" - Latex Suite
- \u8fd9\u4e2a\u63d2\u4ef6\u53ef\u4ee5\u8ba9\u4f60\u76f4\u63a5\u901a\u8fc7\u5b57\u6bcd\u7ec4\u5408\u6fc0\u6d3b\u5173\u952e\u8bcd\uff0c\u6765\u52a0\u5feb \\(\\displaystyle \\LaTeX\\) \u901f\u5ea6\u3002
- \u6bd4\u5982\uff1a\u8f93\u5165
mk \u5c31\u53ef\u4ee5\u81ea\u52a8\u66ff\u6362\u4e3a $\\displaystyle $
- LaTex-like Theorem & Equation Referencer
- \u63d0\u4f9b\u516c\u5f0f\u73af\u5883\u548c\u81ea\u52a8\u4e3a\u516c\u5f0f\u7f16\u53f7\uff0c\u6211\u611f\u89c9\u4e0d\u662f\u5f88\u6709\u7528
- \u9700\u8981 MathLinks \u524d\u7f6e
Notes \u5982\u679c\u4f60\u7684 \\(\\displaystyle \\LaTeX\\) \u4e66\u5199\u901f\u5ea6\u6bd4\u8f83\u6162\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4f7f\u7528 ocr \u8f6f\u4ef6\uff0c\u6bd4\u5982 simpleTex\uff0c\u4f46\u662f\u8fd9\u4e2a\u53ea\u6709 Windows \u5e73\u53f0\u652f\u6301\u3002\u5bf9\u4e8e Linux\uff0c\u6211\u6682\u65f6\u8fd8\u6ca1\u6709\u6bd4\u8f83\u597d\u7684\u8f6f\u4ef6\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#22","title":"2.2 \u7f16\u8f91\u589e\u5f3a","text":" - Easy Typing
- \u53ef\u4ee5\u81ea\u52a8\u8865\u5168\u4e00\u4e9b\uff0c\u6bd4\u5982\u62ec\u53f7\uff0c\u4ee5\u53ca\u81ea\u52a8\u5728\u4e2d\u6587\u548c\u82f1\u6587\u4e4b\u95f4\u7a7a\u683c\uff0c\u5f88\u6709\u7528
- Linter
- \u53ef\u4ee5\u81ea\u52a8\u683c\u5f0f\u5316\uff0c\u5f88\u5f3a\u7684\u63d2\u4ef6\u3002\u53ef\u4ee5\u81ea\u52a8\u751f\u6210 yaml\uff0c\u683c\u5f0f\u5316\u7a7a\u884c\u7a7a\u683c\uff0c\u6807\u9898\u7b49
- Remember cursor position
- \u8bb0\u4f4f cursor \u7684\u4f4d\u7f6e\uff0c\u53ef\u4ee5\u5728\u4e0b\u4e00\u6b21\u6253\u5f00\u8fd9\u4e2a\u6587\u4ef6\u7684\u65f6\u5019\u8df3\u8f6c\u3002\u4f46\u6211\u89c9\u5f97\u5176\u5b9e\u4e0d\u662f\u5f88\u597d\u7528\uff0c\u5728\u4f60\u6709\u540c\u6b65\u7684\u65f6\u5019\u3002\u56e0\u4e3a\u8fd9\u6837\u5b50\uff0c\u5c31\u4f1a\u4e0d\u505c\u7684\u51b2\u7a81\uff0c\u9664\u975e\u4f60\u628a\u5b83\u52a0\u5165 .gitignore \u6216\u8005\u5176\u4ed6\u3002
- PDF++
- \u53ef\u4ee5\u6279\u6ce8 PDF\uff0c\u5f88\u5f3a\u3002\u4f46\u662f\u6211\u4e0d\u559c\u6b22\uff0c\u4e0d\u8981\u5c1d\u8bd5\u628a obsidian \u53d8\u6210 all in one\u3002PDF \u5e94\u8be5\u7528\u522b\u7684\u8f6f\u4ef6\u6765\u64cd\u4f5c\u3002
- Code Styler
- \u63d0\u4f9b\u4ee3\u7801\u9ad8\u4eae\u548c\u4e0d\u540c\u5f62\u5f0f\u7684\u4ee3\u7801\u5757\uff0c\u6211\u4e0d\u559c\u6b22\uff0c\u6ca1\u5fc5\u8981\u641e\u4e2a\u63d2\u4ef6\uff0c\u7528 CSS \u5c31\u53ef\u4ee5\u89e3\u51b3\u3002
- Number Headings
- \u81ea\u52a8\u4e3a\u6807\u9898\u7f16\u53f7\uff0c\u4e5f\u53ef\u4ee5\u81ea\u52a8\u751f\u6210\u76ee\u5f55\u3002
- \u66fe\u7ecf\u6211\u89c9\u5f97\u7f16\u53f7\u5f88\u91cd\u8981\uff0c\u4f46\u662f\u540e\u6765\u611f\u89c9\u4e0d\u5982\u8ba9\u5e73\u53f0\u81ea\u52a8\u652f\u6301\u60ac\u6d6e\u76ee\u5f55\u66f4\u8212\u9002\u3002
- Outliner
- \u66f4\u597d\u7684\u5217\u8868\u652f\u6301\uff0c\u5f88\u597d\u7528\u3002
- Completr
- \u81ea\u52a8\u8865\u5168\uff0c\u6709\u4e00\u70b9\u7528\u3002
- Mind map
- \u7ed8\u5236\u601d\u7ef4\u5bfc\u56fe\uff0c\u611f\u89c9\u6ca1\u5fc5\u8981\uff0c\u800c\u4e14\u753b\u51fa\u6765\u7684\u4e0d\u662f\u77e2\u91cf\u56fe\uff0c\u4e0d\u597d\u7528\u3002
- Excalidraw
- \u597d\u7528\uff0c\u4f46\u662f\u6211\u4e0d\u662f\u5f88\u719f\u7ec3\u3002\u6211\u8fd8\u6ca1\u6709\u5230\u9700\u8981\u5927\u91cf\u753b\u56fe\u7684\u9636\u6bb5\u3002
- dataview
- \u53ef\u4ee5\u7528\u6765\u7edf\u8ba1\u7b49\uff0c\u5176\u5b9e\u611f\u89c9\u8ddf excel \u5dee\u4e0d\u591a\u3002
- \u5f88\u5f3a\uff0c\u4f46\u662f\u5b66\u4e60\u6210\u672c\u6bd4\u8f83\u9ad8\uff0c\u5efa\u8bae copy\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#23","title":"2.3 \u56fe\u7247","text":" - Paste image rename
- \u81ea\u52a8\u4e3a\u590d\u5236\u7684\u56fe\u7247\u91cd\u547d\u540d\uff0c\u597d\u7528
- Auto Link Title
- \u81ea\u52a8\u83b7\u53d6\u94fe\u63a5\u7684\u540d\u5b57\uff0c\u597d\u7528
- Image auto upload Plugin
- \u81ea\u52a8\u4e0a\u4f20\u56fe\u7247\uff0c\u7ed3\u5408\u56fe\u5e8a\u4f7f\u7528\uff0c\u597d\u7528
- \u642d\u914d Picgo + GitHub \u4f7f\u7528
- Image toolkit
- \u53ef\u4ee5\u653e\u5927\u56fe\u7247\uff0c\u597d\u7528
\u5bf9\u4e8e\u56fe\u7247\uff0c\u4e00\u822c\u6709\u4e24\u79cd\u65b9\u6848\uff1a
- \u7528\u56fe\u5e8a\uff0c\u65b9\u4fbf\u90e8\u7f72\u548c\u7ba1\u7406
- \u7528\u672c\u5730\u9644\u4ef6\u6587\u4ef6\u5939\uff0c\u9690\u79c1\u597d\uff0c\u4e0d\u7528\u6015\u56fe\u5e8a\u574f\u6389\uff0c\u90e8\u7f72\u7684\u65f6\u5019\u6709\u70b9\u9ebb\u70e6\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#24","title":"2.4 \u540c\u6b65\u5907\u4efd","text":" - Git
- Remotely Save
- \u4e0d\u592a\u597d\u7528\uff0c\u540c\u65f6\uff0c\u5176\u5b9e\u6211\u5e76\u6ca1\u6709\u5f88\u591a\u9700\u8981\u7528\u79fb\u52a8\u8bbe\u5907\uff0c\u6bd4\u5982 iPads\uff0c\u624b\u673a\u5199 obsidian \u7684\u9700\u6c42\uff0c\u7535\u8111\u6bb5\u7528 git \u540c\u6b65\u4e5f\u4e0d\u662f\u4e0d\u884c\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#25","title":"2.5 \u65e5\u7a0b","text":" - Calendar
- Periodic Notes
- \u597d\u7528\uff0c\u76f8\u5f53\u4e8e\u8c03\u7528\u6a21\u677f\u7684\u63d2\u4ef6
- Day Planner
- \u8fd8\u884c\uff0c\u53ef\u4ee5\u53ef\u89c6\u5316\u65f6\u95f4\u8f74\u3002\u4f46\u662f\u6211\u73b0\u5728\u8bb0\u5f55\u65f6\u95f4\u8fd8\u662f\u7528\u65f6\u95f4\u65e5\u5fd7\uff0c\u611f\u89c9\u8fd8\u662f\u628a\u529f\u80fd\u5206\u5f00\u66f4\u597d\u7684\u53d1\u6325\u6bcf\u4e2a\u8f6f\u4ef6\u7684\u4f18\u52bf\u3002
- Wakatime
- \u8bb0\u5f55\u4f7f\u7528 obsidian \u7684\u65f6\u95f4
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#26","title":"2.6 \u90e8\u7f72","text":" - Envelope
- \u597d\u7528\uff0c\u53ef\u4ee5\u81ea\u52a8\u90e8\u7f72\u5230 GitHub \u4ed3\u5e93\uff0c\u540c\u65f6\u5b83\u6700\u5927\u7684\u529f\u80fd\u662f\u53ef\u4ee5\u81ea\u52a8\u66f4\u6539\u56fe\u7247\uff0c\u53cc\u94fe\u53d8\u6210\u4e00\u822c\u683c\u5f0f\uff0c\u4ee5\u53ca\u81ea\u52a8\u6e32\u67d3 dataview\u3002
- \u7279\u522b\u652f\u6301\u7684\u6846\u67b6\u6709 Hugo\uff0cMkdocs \u7b49
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#27","title":"2.7 \u6807\u7b7e","text":" - TagFolder
- \u597d\u7528\uff0c\u652f\u6301\u53ef\u89c6\u5316 tag \u7ba1\u7406\u7684\u6587\u4ef6\uff0c\u5f7b\u5e95\u66ff\u6362\u7528\u6811\u5f62\u6587\u4ef6\u5939\u3002
- Tag Wrangler
- \u597d\u7528\uff0c\u53ef\u4ee5\u7edf\u4e00\u91cd\u547d\u540d\u7ba1\u7406 tag\uff0c\u552f\u4e00\u7684\u7f3a\u70b9\u662f\u4e0d\u80fd\u5220\u9664 tag\uff0c\u4e0d\u8fc7\u5176\u5b9e\u53ef\u4ee5\u7528\u4e00\u4e2a\u6ca1\u7528\u7684 tag \u6765\u5f53\u56de\u6536\u7ad9\uff0c\u7136\u540e\u91cd\u547d\u540d\u8fc7\u53bb\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#28","title":"2.8 \u4ecd\u5728\u63a2\u7d22","text":" - kanban
- zotero \u548c obsidian \u7ed3\u5408\u4f7f\u7528
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#29","title":"2.9 \u6211\u73b0\u5728\u5728\u4f7f\u7528\u7684\u63d2\u4ef6","text":"Text Only\"obsidian-auto-link-title\",\n\"obsidian-latex-suite\",\n\"number-headings-obsidian\",\n\"mathlinks\",\n\"obsidian-outliner\",\n\"obsidian-completr\",\n\"calendar\",\n\"periodic-notes\",\n\"obsidian-wakatime\",\n\"obsidian-image-auto-upload-plugin\",\n\"obsidian-paste-image-rename\",\n\"dataview\",\n\"obsidian-export-image\",\n\"obsidian-day-planner\",\n\"templater-obsidian\",\n\"obsidian-git\",\n\"easy-typing-obsidian\",\n\"obsidian-linter\",\n\"obsidian-style-settings\",\n\"obsidian-image-toolkit\",\n\"tag-wrangler\",\n\"obsidian-tagfolder\"\n
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#_1","title":"\u6211\u7684\u6a21\u677f","text":"\u9700\u8981\u5b89\u88c5 dataview + periodic notes \u63d2\u4ef6\u3002
- \u65e5\u8bb0\u4e3b\u8981\u662f\u4f7f\u7528\u4e86 periodic notees \u8fdb\u884c\u65e5\u8bb0\u7684\u521d\u59cb\u5316\uff0c\u81ea\u52a8\u586b\u5165\u4e00\u4e9b yaml \u4fe1\u606f\u3002\u540c\u65f6\u5bf9\u4e8e TODO \u4ee5\u53ca\u5f53\u5929\u521b\u5efa\u548c\u4fee\u6539\u7684\u6587\u4ef6\u8fdb\u884c\u68c0\u7d22\uff08\u5176\u5b9e\u6211\u611f\u89c9\u8fd9\u4e2a\u529f\u80fd\u4e0d\u662f\u5f88\u6709\u7528\uff0c\u4e0d\u8fc7\u5728\u6bcf\u5929\u603b\u7ed3\u7684\u65f6\u5019\u8fd8\u662f\u6709\u70b9\u7528\u7684\uff09\u3002
- \u5468\u7ed3\u5c31\u6ca1\u6709\u4ec0\u4e48\u4e86\uff0c\u4e00\u4e9b\u521d\u59cb\u5316\u3002\u4e0d\u8fc7\u8fd9\u4e2a Links \u53ef\u4ee5\u81ea\u52a8\u6536\u7eb3\u4e00\u4e2a\u661f\u671f\u65e5\u8bb0\u4e2d\u7684\u6240\u6709 Links\uff0c\u65b9\u4fbf\u65e5\u540e\u67e5\u627e\u3002
Note
\u7531\u4e8e markdown \u4ee3\u7801\u5757\u5d4c\u5957\u4e0d\u592a\u884c\uff0c\u6240\u4ee5\u8981\u624b\u52a8\u4fee\u590d\u3002\u6ce8\u610f\u4fee\u590d '' \u5e26\u6765\u7684\u4ee3\u7801\u5757\u95ee\u9898, \u8bb0\u5f97\u8865\u5168\u3002
dailyweekly Note Text Only---\ntitle: \"{{date}}\"\ntags:\n - \" #\u65e5\u8bb0 \"\ncategories: dairy\ndate: \" {{ date:YYYY-MM-DDTHH:mm:ss+08:00 }} \"\nmodify: \" {{ date:YYYY-MM-DDTHH:mm:ss+08:00 }} \"\ndir: dairy\nshare: false\ncdate: \" {{ date:YYYY-MM-DD }} \"\nmdate: \" {{ date:YYYY-MM-DD }} \"\n---\n\n# {{date}}\n\n## Daily Plan\n\n### Morning\n\n#### Plan\n\n### Afternoon\n\n#### Plan\n\n### Night\n\n#### Plan\n\n## NOTES\n\n```dataview\nLIST FROM \"\" \nWHERE cdate = this.cdate\n Or mdate = this.mdate\n``\n\n## LINKS\n\n## TODOs\n\n```dataview\nTASK FROM \"dairy\"\nWHERE !completed\n AND mdate >= (this.mdate - dur(7 days))\n AND mdate <= this.mdate\nSORT file.cday DESC\n``\n\n## THOUGHTS\n
Note Text Only ---\ntitle: \" {{date}} \"\ntags:\n - \" #\u5468\u8bb0 \"\ncategories: dairy\ndate: \" {{ date:YYYY-MM-DDTHH:mm:ss+08:00 }} \"\nmodify: \" {{ date:YYYY-MM-DDTHH:mm:ss+08:00 }} \"\ndir: dairy\nshare: false\ncdate: \" {{ date:YYYY-MM-DD }} \"\nmdate: \" {{ date:YYYY-MM-DD }} \"\n---\n\n# {{date:YYYY}} -W {{date:WW}} - {{date:MM}}\n\n## Review\n\n## Next Week Plan\n\n## Time Line\n\n## THOUGHTS\n\n## LINKS\n\n```dataviewjs\n// Configuration for collecting LINKS sections from daily notes\nconst tars = {\n 'LINKS': 2, // Collect second-level LINKS headings\n}\n\nawait dv.view('zob_config/js/dv-\u68c0\u7d22', {\n // Get only daily notes from dairy folder\n files: dv.pages('\"dairy\"')\n .where(p => {\n // Extract the week number from the current file name (weekly note)\n const weekMatch = dv.current().file.name.match(/(\\d{4})-W(\\d{1,2})/);\n if (!weekMatch) return false;\n\n const [_, weekYear, weekNum] = weekMatch;\n\n // Extract date components from daily note name (2024-49-12-08-7 format)\n const dateMatch = p.file.name.match(/(\\d{4})-(\\d{1,2})-(\\d{2})-(\\d{2})-(\\d{1})/);\n if (!dateMatch) return false;\n\n const [__, year, week, month, day] = dateMatch;\n\n // Check if the daily note belongs to the same week and year\n return year === weekYear && week === weekNum;\n })\n .sort(p => {\n // Create sortable date string from daily note format\n const [_, year, week, month, day] = p.file.name.match(/^(\\d{4})-(\\d{1,2})-(\\d{2})-(\\d{2})-(\\d{1})$/);\n // Sort by YYYYMMDD format (descending)\n return -1 * parseInt(`${year}${month.padStart(2, '0')}${day.padStart(2, '0')}`);\n }),\n\n kwd: false, // Don't filter by keywords\n showHead: false, // Don't include heading in output\n tars, // Target sections to collect (LINKS)\n obsidian, // Pass obsidian object\n scale: 0.8, // Scale of rendered content\n\n // List item configuration\n li: ([p, li]) => {\n const [_, year, week, month, day] = p.file.name.match(/^(\\d{4})-(\\d{1,2})-(\\d{2})-(\\d{2})-(\\d{1})$/);\n const formattedDate = `${year}-${month.padStart(2, '0')}-${day.padStart(2, '0')}`;\n // Create a header with date and file link, followed by the content\n return dv.paragraph(`### ${formattedDate} [[${p.file.path}|${p.file.name}]]\\n${li}`);\n },\n});\n``\n
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#3","title":"3 \u76f8\u5173\u94fe\u63a5","text":" - PKMer_PKMer
- Obsidian \u4e2d\u6587\u8bba\u575b - Obsidian \u77e5\u8bc6\u7ba1\u7406 \u7b14\u8bb0
- Obsidian\u6587\u6863\u5496\u5561\u8c46\u7248 | Obsidian Docs by CoffeeBean
- zhuanlan.zhihu.com/p/619960525
","tags":["Environment","Obsidian"]},{"location":"Tools/Make/CMake/","title":"CMake \u76f8\u5173","text":""},{"location":"Tools/Make/CMake/#cmake","title":"CMake \u76f8\u5173","text":" \u7ea6 161 \u4e2a\u5b57 13 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"Tools/Make/CMake/#1","title":"1 \u6784\u5efa\u6700\u5c0f\u9879\u76ee","text":" - CMake \u652f\u6301\u5927\u5199\u3001\u5c0f\u5199\u548c\u6df7\u5408\u5927\u5c0f\u5199\u547d\u4ee4\u3001
Text Onlymkdir build\ncd build\ncmake -G\"MinGW Makefiles\" ..\ncmake --build .\n
\u4e4b\u540e\u4f1a\u751f\u6210\u53ef\u6267\u884c\u6587\u4ef6
Text Onlystep1/\n build/\n CMakeLists.txt\n tutorial.cpp\n
"},{"location":"Tools/Make/CMake/#2-cmakelists-txt","title":"2 \u4f18\u5316 CMakeLists. txt \u6587\u4ef6","text":"CMakecmake_minimum_required(VERSION 3.15)\n\n# set the project name\nproject(Tutorial)\n\nSET(SRC_LIST tutorial.cpp)\n\n# add the executable\nadd_executable(${PROJECT_NAME} ${SRC_LIST})\n
1.0.2 \u5206\u522b\u5bf9\u5e94 MAJOR MINOR PATCH
- set \u548c PROJECT_NAME
- \u6dfb\u52a0\u7248\u672c\u53f7\u548c\u914d\u7f6e\u5934\u6587\u4ef6
- \u6dfb\u52a0\u7f16\u8bd1\u65f6\u95f4\u6233
- \u6307\u5b9a C++\u6807\u51c6
- \u6dfb\u52a0\u5e93\uff08\u6dfb\u52a0\u5e93\u7684\u4f4d\u7f6e\uff0c\u5e93\u6587\u4ef6\u540d\uff0c\u5934\u6587\u4ef6\u540d\uff09
- \u5c06\u5e93\u8bbe\u7f6e\u4e3a\u53ef\u9009\u9879\uff08\u5206\u7ecf\u5178\u548c\u73b0\u4ee3\uff09
- \u6dfb\u52a0\u5e93\u7684\u4f7f\u7528\u8981\u6c42
- INTERFACE
- PRIVATE
- PUBLIC
- \u9759\u6001\u94fe\u63a5\u5e93/\u52a8\u6001\u94fe\u63a5\u5e93
- build \u76ee\u5f55\u4ecb\u7ecd
"},{"location":"Tools/Make/CMake/#links","title":"links","text":" - Site Unreachable
- IPADS\u65b0\u4eba\u57f9\u8bad\u7b2c\u4e8c\u8bb2\uff1aCMake_\u54d4\u54e9\u54d4\u54e9_bilibili
"},{"location":"Tools/Make/Makeflie/","title":"Makeflie","text":""},{"location":"Tools/Make/Makeflie/#make","title":"Make \u57fa\u7840","text":" \u7ea6 220 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"Tools/Make/Makeflie/#make_1","title":"\u4ec0\u4e48\u662f Make","text":"Make \u662f\u4e00\u4e2a\u81ea\u52a8\u5316\u6784\u5efa\u5de5\u5177\uff0c\u4f7f\u7528 Makefile \u6587\u4ef6\u6765\u5b9a\u4e49\u5982\u4f55\u7f16\u8bd1\u548c\u94fe\u63a5\u7a0b\u5e8f\u3002\u5b83\u901a\u8fc7\u68c0\u67e5\u6587\u4ef6\u7684\u65f6\u95f4\u6233\u6765\u51b3\u5b9a\u54ea\u4e9b\u6587\u4ef6\u9700\u8981\u91cd\u65b0\u7f16\u8bd1\u3002
"},{"location":"Tools/Make/Makeflie/#makefile","title":"Makefile \u7684\u57fa\u672c\u7ed3\u6784","text":"Makefile \u7684\u57fa\u672c\u7ed3\u6784\u7531\u76ee\u6807\u3001\u4f9d\u8d56\u548c\u547d\u4ee4\u7ec4\u6210\uff0c\u901a\u5e38\u5f62\u5f0f\u4e3a\uff1a
Text Onlytarget: dependencies \n command\n
"},{"location":"Tools/Make/Makeflie/#makefile_1","title":"Makefile \u793a\u4f8b","text":"\u8ba9\u6211\u4eec\u8003\u8651\u4e00\u4e2a\u7b80\u5355\u7684 C \u8bed\u8a00\u9879\u76ee\uff0c\u8be5\u793a\u4f8b\u5c06\u5c55\u793a\u5982\u4f55\u4f7f\u7528 Makefile \u6765\u7f16\u8bd1\u4e00\u4e2a\u5177\u6709\u591a\u4e2a\u6e90\u6587\u4ef6\u548c\u5934\u6587\u4ef6\u7684\u7a0b\u5e8f\uff0c\u5e76\u5c55\u793a Makefile \u76f8\u6bd4\u624b\u52a8\u547d\u4ee4\u884c\u7f16\u8bd1\u7684\u4f18\u52bf\u3002 \u7f16\u8bd1\u8fdb\u9636 - HPC\u5165\u95e8\u6307\u5357
"},{"location":"Tools/Make/Makeflie/#make_2","title":"Make \u7684\u5e38\u7528\u547d\u4ee4","text":" make\uff1a\u6267\u884c\u9ed8\u8ba4\u76ee\u6807\uff0c\u4e0emake all\u7b49\u6548\u3002make <target>\uff1a\u6267\u884c\u5b9a\u4e49\u7684<target>\u76ee\u6807\uff0c\u5982\u679c\u6ca1\u6709\u8fd9\u4e2a\u76ee\u6807\u5c06\u8fd4\u56de\u9519\u8bef\u4fe1\u606f\u3002make -j\uff1a\u5e76\u884c\u6267\u884c\u6784\u5efa\uff0c\u4f7f\u7528\u672c\u673a\u7684\u5168\u90e8\u7ebf\u7a0b
"},{"location":"Tools/Others/Chezmoi/","title":"\u7528 chezmoi \u5b9e\u73b0\u8de8\u8bbe\u5907\u540c\u6b65\u914d\u7f6e","text":""},{"location":"Tools/Others/Chezmoi/#chezmoi","title":"\u7528 chezmoi \u5b9e\u73b0\u8de8\u8bbe\u5907\u540c\u6b65\u914d\u7f6e","text":" \u7ea6 512 \u4e2a\u5b57 142 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 4 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
\u672c\u6307\u5357\u5c06\u5e2e\u52a9\u4f60\u4f7f\u7528 chezmoi \u7ba1\u7406\u4f60\u7684\u914d\u7f6e\u6587\u4ef6\uff08dotfiles\uff09\uff0c\u5e76\u4f7f\u7528\u5305\u7ba1\u7406\u5668\u7ef4\u62a4\u8f6f\u4ef6\u5217\u8868\u3002
"},{"location":"Tools/Others/Chezmoi/#_1","title":"\u524d\u671f\u51c6\u5907","text":""},{"location":"Tools/Others/Chezmoi/#1","title":"1. \u9700\u8981\u7684\u5de5\u5177","text":" - Git
- GitHub \u8d26\u53f7
- chezmoi
- \u5305\u7ba1\u7406\u5668\uff08Windows: Scoop, Ubuntu: apt/snap\uff09
"},{"location":"Tools/Others/Chezmoi/#2","title":"2. \u91cd\u8981\u7684\u914d\u7f6e\u6587\u4ef6","text":"Windows \u5e38\u7528\u914d\u7f6e\u6587\u4ef6:
Text Only%USERPROFILE%/\n\u251c\u2500\u2500 .gitconfig # Git\u914d\u7f6e\n\u251c\u2500\u2500 .ssh/ # SSH\u914d\u7f6e\n\u251c\u2500\u2500 Documents/\n\u2502 \u2514\u2500\u2500 PowerShell/\n\u2502 \u2514\u2500\u2500 Microsoft.PowerShell_profile.ps1 # PowerShell\u914d\u7f6e\n\u251c\u2500\u2500 AppData/\n\u2502 \u251c\u2500\u2500 Roaming/\n\u2502 \u2502 \u2514\u2500\u2500 Code/\n\u2502 \u2502 \u2514\u2500\u2500 User/\n\u2502 \u2502 \u2514\u2500\u2500 settings.json # VSCode\u914d\u7f6e\n\u2502 \u2514\u2500\u2500 Local/\n\u2514\u2500\u2500 .config/\n \u2514\u2500\u2500 scoop/\n \u2514\u2500\u2500 config.json # Scoop\u914d\u7f6e\n
Ubuntu \u5e38\u7528\u914d\u7f6e\u6587\u4ef6:
Text Only~/\n\u251c\u2500\u2500 .bashrc # Bash\u914d\u7f6e\n\u251c\u2500\u2500 .zshrc # Zsh\u914d\u7f6e\n\u251c\u2500\u2500 .gitconfig # Git\u914d\u7f6e\n\u251c\u2500\u2500 .ssh/ # SSH\u914d\u7f6e\n\u2514\u2500\u2500 .config/\n \u251c\u2500\u2500 Code/\n \u2502 \u2514\u2500\u2500 User/\n \u2502 \u2514\u2500\u2500 settings.json # VSCode\u914d\u7f6e\n \u2514\u2500\u2500 tabby/\n \u2514\u2500\u2500 config.yaml # Tabby\u7ec8\u7aef\u914d\u7f6e\n
"},{"location":"Tools/Others/Chezmoi/#github","title":"GitHub \u8bbe\u7f6e","text":""},{"location":"Tools/Others/Chezmoi/#1-github","title":"1. \u521b\u5efa GitHub \u4ed3\u5e93","text":" - \u8bbf\u95ee GitHub \u5e76\u767b\u5f55
- \u70b9\u51fb \"New repository\"
- \u4ed3\u5e93\u540d\u79f0\u8bbe\u7f6e\u4e3a
dotfiles - \u8bbe\u7f6e\u4e3a Public\uff08\u63a8\u8350\uff09
- \u4e0d\u8981\u521d\u59cb\u5316 README\uff08\u6211\u4eec\u5c06\u4ece\u672c\u5730\u521d\u59cb\u5316\uff09
- \u521b\u5efa\u4ed3\u5e93
"},{"location":"Tools/Others/Chezmoi/#2-ssh","title":"2. \u914d\u7f6e SSH \u5bc6\u94a5\uff08\u5982\u679c\u8fd8\u6ca1\u6709\uff09","text":"Bash# \u751f\u6210SSH\u5bc6\u94a5\nssh-keygen -t ed25519 -C \"your_email@example.com\"\n\n# \u5c06\u516c\u94a5\u6dfb\u52a0\u5230GitHub\n# 1. \u590d\u5236\u516c\u94a5\u5185\u5bb9\ncat ~/.ssh/id_ed25519.pub\n# 2. \u8bbf\u95ee GitHub \u2192 Settings \u2192 SSH and GPG keys \u2192 New SSH key\n# 3. \u7c98\u8d34\u516c\u94a5\u5185\u5bb9\u5e76\u4fdd\u5b58\n
"},{"location":"Tools/Others/Chezmoi/#windows","title":"Windows \u914d\u7f6e","text":""},{"location":"Tools/Others/Chezmoi/#1_1","title":"1. \u5b89\u88c5\u5fc5\u8981\u5de5\u5177","text":"\u4f7f\u7528 PowerShell\uff08\u4ee5\u7ba1\u7406\u5458\u8eab\u4efd\u8fd0\u884c\uff09\uff1a
PowerShell# \u5b89\u88c5Scoop\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser\nirm get.scoop.sh | iex\n\n# \u5b89\u88c5Git\uff08\u5982\u679c\u8fd8\u6ca1\u6709\uff09\nscoop install git\n\n# \u5b89\u88c5chezmoi\nscoop install chezmoi\n
"},{"location":"Tools/Others/Chezmoi/#2-chezmoi","title":"2. \u521d\u59cb\u5316 chezmoi","text":"PowerShell# \u521d\u59cb\u5316chezmoi\u5e76\u514b\u9686\u4f60\u7684\u4ed3\u5e93\nchezmoi init --apply https://github.com/yourusername/dotfiles.git\n# \u7528 ssh \u4e5f\u53ef\u4ee5\n\n# \u67e5\u770bchezmoi\u5c06\u8fdb\u884c\u7684\u66f4\u6539\nchezmoi diff\n\n# \u5c06\u73b0\u6709\u914d\u7f6e\u6587\u4ef6\u6dfb\u52a0\u5230chezmoi\nchezmoi add $HOME/.gitconfig\nchezmoi add $HOME/.ssh/config\nchezmoi add $HOME/Documents/PowerShell/Microsoft.PowerShell_profile.ps1\nchezmoi add $HOME/AppData/Roaming/Code/User/settings.json\n\n# \u63d0\u4ea4\u5e76\u63a8\u9001\u66f4\u6539\nchezmoi cd\ngit add .\ngit commit -m \"Initial Windows config\"\ngit push\n
"},{"location":"Tools/Others/Chezmoi/#3","title":"3. \u5bfc\u51fa\u8f6f\u4ef6\u5305\u5217\u8868","text":"PowerShell# \u5bfc\u51faScoop\u5305\u5217\u8868\nscoop export > packages/scoop-packages.txt\n\n# \u63d0\u4ea4\u5305\u5217\u8868\nchezmoi cd\ngit add packages/scoop-packages.txt\ngit commit -m \"Add Windows package list\"\ngit push\n
"},{"location":"Tools/Others/Chezmoi/#ubuntu","title":"Ubuntu \u914d\u7f6e","text":""},{"location":"Tools/Others/Chezmoi/#1_2","title":"1. \u5b89\u88c5\u5fc5\u8981\u5de5\u5177","text":"Bash# \u5b89\u88c5Git\uff08\u5982\u679c\u8fd8\u6ca1\u6709\uff09\nsudo apt update\nsudo apt install git\n\n# \u5b89\u88c5chezmoi\nsh -c \"$(curl -fsLS get.chezmoi.io)\"\n
\u53c2\u8003\u8fd9\u4e2a\u7f51\u7ad9 Install - chezmoi \u8fdb\u884c\u4e0b\u8f7d\uff0c\u6211\u547d\u4ee4\u884c\u4e00\u76f4\u4e0d\u6210\u529f\uff0c\u76f4\u63a5\u9009\u62e9\u5bf9\u5e94\u7684\u5305\u5c31\u884c\u4e86\u3002
Text Onlysudo dpkg -i chezmoi_2.54.0_linux_amd64.deb\nchezmoi --version\n
"},{"location":"Tools/Others/Chezmoi/#2-chezmoi_1","title":"2. \u521d\u59cb\u5316 chezmoi","text":"Bash# \u521d\u59cb\u5316chezmoi\u5e76\u514b\u9686\u4f60\u7684\u4ed3\u5e93\nchezmoi init --apply https://github.com/yourusername/dotfiles.git\n\n# \u67e5\u770bchezmoi\u5c06\u8fdb\u884c\u7684\u66f4\u6539\nchezmoi diff\n\n# \u5c06\u73b0\u6709\u914d\u7f6e\u6587\u4ef6\u6dfb\u52a0\u5230chezmoi\nchezmoi add ~/.bashrc\nchezmoi add ~/.zshrc\nchezmoi add ~/.gitconfig\nchezmoi add ~/.ssh/config\nchezmoi add ~/.config/Code/User/settings.json\n\n# \u63d0\u4ea4\u5e76\u63a8\u9001\u66f4\u6539\nchezmoi cd\ngit add .\ngit commit -m \"Initial Ubuntu config\"\ngit push\n
"},{"location":"Tools/Others/Chezmoi/#3_1","title":"3. \u5bfc\u51fa\u8f6f\u4ef6\u5305\u5217\u8868","text":"Bashchezmoi cd\nmkdir packages\n# \u5bfc\u51faapt\u5305\u5217\u8868\ndpkg --get-selections | grep -v deinstall | awk '{print $1}' > packages/apt-packages.txt\n\n# \u5bfc\u51fasnap\u5305\u5217\u8868\nsnap list | awk '{if (NR>1) print $1}' > packages/snap-packages.txt\n\n# \u63d0\u4ea4\u5305\u5217\u8868\ngit add packages/apt-packages.txt packages/snap-packages.txt\ngit commit -m \"Add Ubuntu package lists\"\ngit push\n
"},{"location":"Tools/Others/Chezmoi/#_2","title":"\u65e5\u5e38\u4f7f\u7528","text":""},{"location":"Tools/Others/Chezmoi/#1_3","title":"1. \u66f4\u65b0\u914d\u7f6e","text":"\u5f53\u4f60\u4fee\u6539\u4e86\u914d\u7f6e\u6587\u4ef6\u540e\uff1a
Bash# \u5c06\u66f4\u6539\u6dfb\u52a0\u5230chezmoi\nchezmoi add ~/.bashrc # \u6216\u5176\u4ed6\u4fee\u6539\u7684\u914d\u7f6e\u6587\u4ef6\n\n# \u67e5\u770b\u66f4\u6539\nchezmoi diff\n\n# \u63d0\u4ea4\u5e76\u63a8\u9001\u66f4\u6539\nchezmoi cd\ngit add .\ngit commit -m \"Update bashrc\"\ngit push\n
"},{"location":"Tools/Others/Chezmoi/#2_1","title":"2. \u5728\u5176\u4ed6\u673a\u5668\u4e0a\u540c\u6b65","text":"Bash# \u62c9\u53d6\u5e76\u5e94\u7528\u6700\u65b0\u66f4\u6539\nchezmoi update\n
"},{"location":"Tools/Others/Chezmoi/#3_2","title":"3. \u66f4\u65b0\u8f6f\u4ef6\u5305\u5217\u8868","text":"Windows:
PowerShell# \u66f4\u65b0Scoop\u5305\u5217\u8868\nscoop export > packages/scoop-packages.txt\n
Ubuntu:
Bash# \u66f4\u65b0apt\u5305\u5217\u8868\ndpkg --get-selections | grep -v deinstall | awk '{print $1}' > packages/apt-packages.txt\n\n# \u66f4\u65b0snap\u5305\u5217\u8868\nsnap list | awk '{if (NR>1) print $1}' > packages/snap-packages.txt\n
"},{"location":"Tools/Others/Chezmoi/#4","title":"4. \u5728\u65b0\u673a\u5668\u4e0a\u8bbe\u7f6e","text":"Windows:
PowerShell# \u5b89\u88c5chezmoi\nscoop install chezmoi\n\n# \u521d\u59cb\u5316\u5e76\u5e94\u7528\u914d\u7f6e\nchezmoi init https://github.com/yourusername/dotfiles.git\nchezmoi apply\n
Ubuntu:
Bash# \u5b89\u88c5chezmoi\nsh -c \"$(curl -fsLS get.chezmoi.io)\"\n\n# \u521d\u59cb\u5316\u5e76\u5e94\u7528\u914d\u7f6e\nchezmoi init https://github.com/yourusername/dotfiles.git\nchezmoi apply\n
"},{"location":"Tools/Others/Chezmoi/#_3","title":"\u5e38\u89c1\u95ee\u9898","text":""},{"location":"Tools/Others/Chezmoi/#1_4","title":"1. \u5982\u4f55\u5904\u7406\u4e0d\u540c\u673a\u5668\u7684\u7279\u5b9a\u914d\u7f6e\uff1f","text":"\u4f7f\u7528\u6a21\u677f\u548c\u6761\u4ef6\u8bed\u53e5\u3002\u5728 .chezmoi.toml.tmpl \u4e2d\uff1a
TOML{{- $osid := .chezmoi.os -}}\n[data]\n name = \"Your Name\"\n email = \"your@email.com\"\n {{- if eq .chezmoi.os \"windows\" }}\n is_windows = true\n {{- else if eq .chezmoi.os \"linux\" }}\n is_linux = true\n {{- end }}\n
"},{"location":"Tools/Others/Chezmoi/#2_2","title":"2. \u5982\u4f55\u5904\u7406\u654f\u611f\u4fe1\u606f\uff1f","text":"\u5bf9\u4e8e\u654f\u611f\u4fe1\u606f\uff0c\u53ef\u4ee5\uff1a
- \u4f7f\u7528\u6a21\u677f\u548c\u73af\u5883\u53d8\u91cf
- \u4f7f\u7528 chezmoi \u7684\u52a0\u5bc6\u529f\u80fd
- \u5c06\u654f\u611f\u4fe1\u606f\u5b58\u50a8\u5728\u5355\u72ec\u7684\u79c1\u6709\u4ed3\u5e93\u4e2d
"},{"location":"Tools/Others/Chezmoi/#3_3","title":"3. \u5982\u4f55\u64a4\u9500\u66f4\u6539\uff1f","text":"Bash# \u67e5\u770b\u5c06\u8981\u8fdb\u884c\u7684\u66f4\u6539\nchezmoi diff\n\n# \u5982\u679c\u4e0d\u6ee1\u610f\uff0c\u53ef\u4ee5\u64a4\u9500\nchezmoi forget ~/.bashrc # \u79fb\u9664\u6587\u4ef6\u7684\u7ba1\u7406\n\n# \u6216\u8005\u91cd\u7f6e\u4e3a\u539f\u59cb\u72b6\u6001\nchezmoi apply --force\n
"},{"location":"Tools/Others/Chezmoi/#4_1","title":"4. \u914d\u7f6e\u6587\u4ef6\u6743\u9650\u95ee\u9898\uff1f","text":"chezmoi \u4f1a\u81ea\u52a8\u5904\u7406\u6587\u4ef6\u6743\u9650\u3002\u5bf9\u4e8e\u7279\u6b8a\u6743\u9650\u9700\u6c42\uff0c\u53ef\u4ee5\u5728\u6e90\u6587\u4ef6\u540d\u4e2d\u4f7f\u7528\u7279\u6b8a\u524d\u7f00\uff1a
private_ : \u521b\u5efa\u79c1\u6709\u6587\u4ef6 (chmod 600)executable_ : \u521b\u5efa\u53ef\u6267\u884c\u6587\u4ef6 (chmod 700)readonly_ : \u521b\u5efa\u53ea\u8bfb\u6587\u4ef6 (chmod 400)
"},{"location":"Tools/Others/Chezmoi/#5","title":"5. \u5982\u4f55\u67e5\u770b\u7ba1\u7406\u7684\u6587\u4ef6\uff1f","text":"Bash# \u5217\u51fa\u6240\u6709\u7ba1\u7406\u7684\u6587\u4ef6\nchezmoi managed\n\n# \u67e5\u770b\u6e90\u6587\u4ef6\nchezmoi cd\nls -la\n
"},{"location":"Tools/Others/Chezmoi/#6","title":"6. \u66f4\u65b0\u51fa\u9519\u600e\u4e48\u529e\uff1f","text":"Bash# \u5907\u4efd\u5f53\u524d\u72b6\u6001\nchezmoi archive --output=backup.tar.gz\n\n# \u91cd\u7f6e\u66f4\u6539\nchezmoi init --force\n\n# \u91cd\u65b0\u5e94\u7528\u914d\u7f6e\nchezmoi apply\n
"},{"location":"Tools/Others/SSH/","title":"SSH\u914d\u7f6e\u6307\u5357","text":""},{"location":"Tools/Others/SSH/#ssh","title":"SSH\u914d\u7f6e\u6307\u5357","text":" \u7ea6 641 \u4e2a\u5b57 195 \u884c\u4ee3\u7801 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 6 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"Tools/Others/SSH/#ssh_1","title":"\u4e00\u3001SSH\u57fa\u7840\u6982\u5ff5","text":""},{"location":"Tools/Others/SSH/#1-ssh","title":"1. SSH\u5de5\u4f5c\u539f\u7406","text":"SSH(Secure Shell)\u662f\u4e00\u79cd\u52a0\u5bc6\u7684\u7f51\u7edc\u534f\u8bae\uff0c\u901a\u8fc7\u5728\u4e0d\u5b89\u5168\u7684\u7f51\u7edc\u4e0a\u4e3a\u7f51\u7edc\u670d\u52a1\u63d0\u4f9b\u5b89\u5168\u7684\u4f20\u8f93\u73af\u5883\u3002SSH\u901a\u8fc7\u4f7f\u7528\u52a0\u5bc6\u6280\u672f\uff0c\u80fd\u591f\u6709\u6548\u9632\u6b62\u4e2d\u95f4\u4eba\u653b\u51fb\uff0c\u4fdd\u62a4\u6570\u636e\u4f20\u8f93\u7684\u5b89\u5168\u3002
SSH\u5de5\u4f5c\u6d41\u7a0b\uff1a 1. TCP\u8fde\u63a5\u5efa\u7acb\uff1a\u5ba2\u6237\u7aef\u548c\u670d\u52a1\u5668\u5efa\u7acbTCP\u8fde\u63a5\uff08\u9ed8\u8ba4\u7aef\u53e322\uff09 2. \u7248\u672c\u534f\u5546\uff1a\u53cc\u65b9\u4ea4\u6362\u7248\u672c\u4fe1\u606f\uff0c\u786e\u5b9a\u4f7f\u7528\u7684SSH\u534f\u8bae\u7248\u672c 3. \u5bc6\u94a5\u4ea4\u6362\uff1a\u4f7f\u7528Diffie-Hellman\u7b97\u6cd5\u4ea4\u6362\u4f1a\u8bdd\u5bc6\u94a5 4. \u8ba4\u8bc1\uff1a\u4f7f\u7528\u516c\u94a5\u6216\u5bc6\u7801\u8fdb\u884c\u8eab\u4efd\u9a8c\u8bc1 5. \u4f1a\u8bdd\uff1a\u5efa\u7acb\u52a0\u5bc6\u901a\u4fe1\u901a\u9053
"},{"location":"Tools/Others/SSH/#2","title":"2. \u8ba4\u8bc1\u65b9\u5f0f\u8be6\u89e3","text":""},{"location":"Tools/Others/SSH/#21","title":"2.1 \u5bc6\u7801\u8ba4\u8bc1","text":" - \u6700\u7b80\u5355\u4f46\u6700\u4e0d\u5b89\u5168\u7684\u8ba4\u8bc1\u65b9\u5f0f
- \u5bb9\u6613\u53d7\u5230\u66b4\u529b\u7834\u89e3\u653b\u51fb
- \u4e0d\u63a8\u8350\u5728\u751f\u4ea7\u73af\u5883\u4e2d\u4f7f\u7528
"},{"location":"Tools/Others/SSH/#22","title":"2.2 \u516c\u94a5\u8ba4\u8bc1","text":"\u8ba4\u8bc1\u6d41\u7a0b 1. \u5ba2\u6237\u7aef\u53d1\u9001\u516c\u94a5\u4fe1\u606f\u7ed9\u670d\u52a1\u5668 2. \u670d\u52a1\u5668\u68c0\u67e5authorized_keys\u6587\u4ef6 3. \u670d\u52a1\u5668\u751f\u6210\u968f\u673a\u5b57\u7b26\u4e32\uff0c\u7528\u516c\u94a5\u52a0\u5bc6\u540e\u53d1\u9001\u7ed9\u5ba2\u6237\u7aef 4. \u5ba2\u6237\u7aef\u7528\u79c1\u94a5\u89e3\u5bc6\uff0c\u5c06\u7ed3\u679c\u8fd4\u56de\u670d\u52a1\u5668 5. \u670d\u52a1\u5668\u9a8c\u8bc1\u7ed3\u679c\uff0c\u5b8c\u6210\u8ba4\u8bc1
"},{"location":"Tools/Others/SSH/#3","title":"3. \u5b89\u5168\u5efa\u8bae","text":""},{"location":"Tools/Others/SSH/#31","title":"3.1 \u57fa\u672c\u5b89\u5168\u8bbe\u7f6e","text":"Bash# /etc/ssh/sshd_config \u5b89\u5168\u914d\u7f6e\nPermitRootLogin no # \u7981\u6b62root\u76f4\u63a5\u767b\u5f55\nPasswordAuthentication no # \u7981\u7528\u5bc6\u7801\u8ba4\u8bc1\nPubkeyAuthentication yes # \u542f\u7528\u516c\u94a5\u8ba4\u8bc1\nPermitEmptyPasswords no # \u7981\u6b62\u7a7a\u5bc6\u7801\nProtocol 2 # \u53ea\u4f7f\u7528SSH2\u534f\u8bae\nMaxAuthTries 3 # \u6700\u5927\u8ba4\u8bc1\u5c1d\u8bd5\u6b21\u6570\nLoginGraceTime 30 # \u767b\u5f55\u8d85\u65f6\u65f6\u95f4\nX11Forwarding no # \u7981\u7528X11\u8f6c\u53d1\uff08\u9664\u975e\u9700\u8981\uff09\nAllowUsers user1 user2 # \u9650\u5236\u5141\u8bb8\u767b\u5f55\u7684\u7528\u6237\n
"},{"location":"Tools/Others/SSH/#32","title":"3.2 \u5bc6\u94a5\u7ba1\u7406","text":"Bash# \u751f\u6210\u5f3a\u5bc6\u94a5\nssh-keygen -t ed25519 -C \"your_email@example.com\" -a 100\n\n# \u5bc6\u94a5\u6743\u9650\u8bbe\u7f6e\nchmod 700 ~/.ssh\nchmod 600 ~/.ssh/id_ed25519\nchmod 644 ~/.ssh/id_ed25519.pub\nchmod 600 ~/.ssh/authorized_keys\nchmod 600 ~/.ssh/known_hosts\n
"},{"location":"Tools/Others/SSH/#ssh_2","title":"\u5e38\u7528\u7684 SSH \u547d\u4ee4","text":"SSH \u6559\u7a0b \u83dc\u9e1f\u6559\u7a0b
"},{"location":"Tools/Others/SSH/#_1","title":"\u4e8c\u3001\u5b8c\u6574\u914d\u7f6e\u6307\u5357","text":""},{"location":"Tools/Others/SSH/#1-linux","title":"1. Linux\u670d\u52a1\u5668\u914d\u7f6e","text":"Bash# 1. \u5b89\u88c5SSH\u670d\u52a1\u5668\nsudo apt update\nsudo apt install openssh-server\n\n# 2. \u914d\u7f6eSSH\u670d\u52a1\nsudo nano /etc/ssh/sshd_config\n\n# 3. \u57fa\u672c\u5b89\u5168\u914d\u7f6e\nPort 22 # \u53ef\u4ee5\u4fee\u6539\u4e3a\u975e\u6807\u51c6\u7aef\u53e3\nListenAddress 0.0.0.0 # \u76d1\u542c\u5730\u5740\nProtocol 2\nPermitRootLogin no\nPasswordAuthentication no\nPubkeyAuthentication yes\nAuthorizedKeysFile .ssh/authorized_keys\nUsePAM yes\nX11Forwarding no\nPrintMotd no\nAcceptEnv LANG LC_*\nSubsystem sftp /usr/lib/openssh/sftp-server\n\n# 4. \u91cd\u542fSSH\u670d\u52a1\nsudo systemctl restart sshd\n\n# 5. \u68c0\u67e5\u670d\u52a1\u72b6\u6001\nsudo systemctl status sshd\n
"},{"location":"Tools/Others/SSH/#2-windows-ssh","title":"2. Windows SSH\u914d\u7f6e","text":""},{"location":"Tools/Others/SSH/#21-openssh","title":"2.1 \u5b89\u88c5OpenSSH","text":"PowerShell# \u4f7f\u7528PowerShell\u5b89\u88c5OpenSSH\n# \u68c0\u67e5OpenSSH\u72b6\u6001\nGet-WindowsCapability -Online | Where-Object Name -like 'OpenSSH*'\n\n# \u5b89\u88c5\u5ba2\u6237\u7aef\u548c\u670d\u52a1\u5668\nAdd-WindowsCapability -Online -Name OpenSSH.Client~~~~0.0.1.0\nAdd-WindowsCapability -Online -Name OpenSSH.Server~~~~0.0.1.0\n\n# \u542f\u52a8SSH\u670d\u52a1\nStart-Service sshd\nSet-Service -Name sshd -StartupType 'Automatic'\n
\u5982\u679c\u6709\u7f51\u7edc\u95ee\u9898\u53ef\u4ee5\u4e0b\u8f7d\u5e76\u5b89\u88c5 OpenSSH \u7684\u79bb\u7ebf\u5b89\u88c5\u5305\uff1a - \u60a8\u53ef\u4ee5\u5c1d\u8bd5\u4ece GitHub \u4e0a\u4e0b\u8f7d OpenSSH \u7684\u79bb\u7ebf\u5b89\u88c5\u5305\uff0c\u5e76\u6309\u7167\u4ee5\u4e0b\u6b65\u9aa4\u8fdb\u884c\u5b89\u88c5\uff1a
- \u8bbf\u95ee GitHub \u4e0a\u7684 Win32-OpenSSH \u53d1\u5e03\u9875\u9762\uff1aWin32-OpenSSH Releases
- \u4e0b\u8f7d\u9002\u7528\u4e8e\u60a8\u7684\u7cfb\u7edf\u7684\u5b89\u88c5\u5305\uff08Win32 \u6216 Win64\uff09\u3002
- \u89e3\u538b\u4e0b\u8f7d\u7684\u6587\u4ef6\u5230\u4e00\u4e2a\u76ee\u5f55\u3002
- \u4ee5\u7ba1\u7406\u5458\u6743\u9650\u6253\u5f00\u547d\u4ee4\u63d0\u793a\u7b26\uff08cmd\uff09\uff0c\u5e76\u5bfc\u822a\u5230\u89e3\u538b\u7684\u76ee\u5f55\u3002
- \u8fd0\u884c\u00a0
powershell.exe -ExecutionPolicy Bypass -File install-sshd.ps1\u00a0\u6765\u5b89\u88c5 OpenSSH \u670d\u52a1\u3002
"},{"location":"Tools/Others/SSH/#22-windows-ssh","title":"2.2 \u914d\u7f6eWindows SSH\u670d\u52a1","text":"PowerShell# \u7f16\u8f91SSH\u914d\u7f6e\u6587\u4ef6\nnotepad \"$env:ProgramData\\ssh\\sshd_config\"\n\n# \u57fa\u672c\u914d\u7f6e\u5185\u5bb9\u4e0eLinux\u7c7b\u4f3c\uff0c\u4f46\u8def\u5f84\u9700\u8981\u8c03\u6574\nPubkeyAuthentication yes\nPasswordAuthentication no\nSubsystem sftp sftp-server.exe\n
"},{"location":"Tools/Others/SSH/#3-ssh","title":"3. SSH\u5ba2\u6237\u7aef\u914d\u7f6e","text":""},{"location":"Tools/Others/SSH/#31-ssh","title":"3.1 \u521b\u5efaSSH\u914d\u7f6e\u6587\u4ef6","text":"Bash# ~/.ssh/config\n# \u5168\u5c40\u8bbe\u7f6e\nHost *\n ServerAliveInterval 60\n ServerAliveCountMax 3\n HashKnownHosts yes\n GSSAPIAuthentication no\n\n# GitHub\nHost github.com\n HostName github.com\n User git\n IdentityFile ~/.ssh/github_ed25519\n AddKeysToAgent yes\n\n# \u5f00\u53d1\u670d\u52a1\u5668\nHost dev\n HostName dev.example.com\n User developer\n Port 22\n IdentityFile ~/.ssh/dev_ed25519\n ForwardAgent yes\n\n# \u751f\u4ea7\u670d\u52a1\u5668\nHost prod\n HostName prod.example.com\n User deployer\n Port 22\n IdentityFile ~/.ssh/prod_ed25519\n ForwardAgent no\n
"},{"location":"Tools/Others/SSH/#_2","title":"\u4e09\u3001\u5177\u4f53\u5b9e\u8df5\uff0c\u7528\u7b14\u8bb0\u672c\u8fde\u53f0\u5f0f\u673a","text":""},{"location":"Tools/Others/SSH/#_3","title":"\u53f0\u5f0f\u7535\u8111\uff08\u670d\u52a1\u7aef\uff09\u914d\u7f6e\uff1a","text":" - \u5b89\u88c5\u5e76\u542f\u52a8SSH\u670d\u52a1:
Bash# Ubuntu/Debian\u7cfb\u7edf\nsudo apt install openssh-server\nsudo systemctl enable ssh\nsudo systemctl start ssh\n\n# \u68c0\u67e5SSH\u670d\u52a1\u72b6\u6001\nsudo systemctl status ssh\n
- \u914d\u7f6eSSH\u670d\u52a1:
Bash# \u7f16\u8f91SSH\u670d\u52a1\u5668\u914d\u7f6e\nsudo nano /etc/ssh/sshd_config\n\n# \u6dfb\u52a0\u6216\u4fee\u6539\u4ee5\u4e0b\u914d\u7f6e\nPermitRootLogin no\nPasswordAuthentication no\nPubkeyAuthentication yes\nAllowUsers your_username # \u66ff\u6362\u4e3a\u60a8\u7684\u7528\u6237\u540d\n
- \u8bbe\u7f6e\u56fa\u5b9aIP\u6216\u52a8\u6001DNS:
Bash# \u67e5\u770b\u5f53\u524dIP\nip addr show\n\n# \u5982\u679c\u662f\u52a8\u6001IP\uff0c\u5efa\u8bae\u8bbe\u7f6e\u9759\u6001IP\u6216\u4f7f\u7528\u52a8\u6001DNS\u670d\u52a1\n
"},{"location":"Tools/Others/SSH/#_4","title":"\u7b14\u8bb0\u672c\uff08\u5ba2\u6237\u7aef\uff09\u914d\u7f6e\uff1a","text":" - \u751f\u6210SSH\u5bc6\u94a5\u5bf9\uff08\u5982\u679c\u8fd8\u6ca1\u6709\uff09:
Bashssh-keygen -t ed25519 -C \"your_laptop\"\n
- \u5c06\u516c\u94a5\u590d\u5236\u5230\u53f0\u5f0f\u673a:
Bash# \u65b9\u6cd51\uff1a\u4f7f\u7528ssh-copy-id\nssh-copy-id -i ~/.ssh/id_ed25519.pub username@desktop_ip\n\n# \u65b9\u6cd52\uff1a\u624b\u52a8\u590d\u5236\ncat ~/.ssh/id_ed25519.pub | ssh username@desktop_ip \"mkdir -p ~/.ssh && cat >> ~/.ssh/authorized_keys\"\n
- \u914d\u7f6eSSH\u5ba2\u6237\u7aef:
Bash# \u7f16\u8f91 ~/.ssh/config\nnano ~/.ssh/config\n\n# \u6dfb\u52a0\u4ee5\u4e0b\u914d\u7f6e\nHost desktop\n HostName 192.168.1.xxx # \u66ff\u6362\u4e3a\u53f0\u5f0f\u673a\u7684IP\n User your_username # \u66ff\u6362\u4e3a\u60a8\u7684\u7528\u6237\u540d\n Port 22\n IdentityFile ~/.ssh/id_ed25519\n ForwardX11 yes # \u5982\u679c\u9700\u8981\u56fe\u5f62\u754c\u9762\u8f6c\u53d1\n ForwardAgent yes\n Compression yes\n ServerAliveInterval 60\n
"},{"location":"Tools/Others/SSH/#_5","title":"\u5e38\u7528\u8fde\u63a5\u547d\u4ee4\uff1a","text":" - \u57fa\u672c\u8fde\u63a5:
Bash# \u4ece\u7b14\u8bb0\u672c\u8fde\u63a5\u5230\u53f0\u5f0f\u673a\nssh desktop\n\n# \u4f7f\u7528\u56fe\u5f62\u754c\u9762\u8f6c\u53d1\nssh -X desktop\n
- \u6587\u4ef6\u4f20\u8f93:
Bash# \u4ece\u7b14\u8bb0\u672c\u590d\u5236\u6587\u4ef6\u5230\u53f0\u5f0f\u673a\nscp /path/to/local/file desktop:/path/to/remote/\n\n# \u4ece\u53f0\u5f0f\u673a\u590d\u5236\u6587\u4ef6\u5230\u7b14\u8bb0\u672c\nscp desktop:/path/to/remote/file /path/to/local/\n
- \u7aef\u53e3\u8f6c\u53d1:
Bash# \u672c\u5730\u7aef\u53e3\u8f6c\u53d1\nssh -L 8080:localhost:80 desktop\n\n# \u8fdc\u7a0b\u7aef\u53e3\u8f6c\u53d1\nssh -R 8080:localhost:80 desktop\n
"},{"location":"Tools/Others/SSH/#_6","title":"\u56db\u3001\u9ad8\u7ea7\u64cd\u4f5c","text":""},{"location":"Tools/Others/SSH/#1-ssh_1","title":"1. SSH\u7aef\u53e3\u8f6c\u53d1","text":""},{"location":"Tools/Others/SSH/#11","title":"1.1 \u672c\u5730\u7aef\u53e3\u8f6c\u53d1","text":"Bash# \u5c06\u672c\u57308080\u7aef\u53e3\u8f6c\u53d1\u5230\u8fdc\u7a0b80\u7aef\u53e3\nssh -L 8080:localhost:80 user@remote\n\n# \u4f7f\u7528\u914d\u7f6e\u6587\u4ef6\u8bbe\u7f6e\nHost tunnel\n HostName remote.example.com\n LocalForward 8080 localhost:80\n
"},{"location":"Tools/Others/SSH/#12","title":"1.2 \u8fdc\u7a0b\u7aef\u53e3\u8f6c\u53d1","text":"Bash# \u5c06\u8fdc\u7a0b3000\u7aef\u53e3\u8f6c\u53d1\u5230\u672c\u57303000\u7aef\u53e3\nssh -R 3000:localhost:3000 user@remote\n\n# \u914d\u7f6e\u6587\u4ef6\u8bbe\u7f6e\nHost remote-tunnel\n HostName remote.example.com\n RemoteForward 3000 localhost:3000\n
"},{"location":"Tools/Others/SSH/#2-ssh","title":"2. SSH\u4ee3\u7406\u8f6c\u53d1","text":"Bash# \u542f\u7528\u4ee3\u7406\u8f6c\u53d1\nssh -A user@remote\n\n# \u914d\u7f6e\u6587\u4ef6\u8bbe\u7f6e\nHost *\n ForwardAgent yes\n AddKeysToAgent yes\n
"},{"location":"Tools/Others/SSH/#3_1","title":"3. \u8df3\u677f\u673a\u914d\u7f6e","text":"Bash# \u901a\u8fc7\u8df3\u677f\u673a\u8fde\u63a5\nssh -J jumphost user@target\n\n# \u914d\u7f6e\u6587\u4ef6\u8bbe\u7f6e\nHost target\n HostName target.example.com\n ProxyJump jumphost\n
"},{"location":"Tools/Others/SSH/#_7","title":"\u4e94\u3001\u6545\u969c\u6392\u67e5","text":""},{"location":"Tools/Others/SSH/#1","title":"1. \u6700\u4f73\u5b9e\u8df5","text":"Bash# 1. \u4f7f\u7528SSH\u914d\u7f6e\u6587\u4ef6\u7ba1\u7406\u8fde\u63a5\n# 2. \u4e3a\u4e0d\u540c\u7528\u9014\u4f7f\u7528\u4e0d\u540c\u7684\u5bc6\u94a5\n# 3. \u5b9a\u671f\u8f6e\u6362\u5bc6\u94a5\n# 4. \u4f7f\u7528ssh-agent\u7ba1\u7406\u5bc6\u94a5\n# 5. \u5907\u4efdSSH\u914d\u7f6e\u548c\u5bc6\u94a5\n
"},{"location":"Tools/Others/SSH/#2_1","title":"2. \u5e38\u89c1\u95ee\u9898\u89e3\u51b3","text":"Bash# \u8fde\u63a5\u88ab\u62d2\u7edd\nssh -v user@host # \u67e5\u770b\u8be6\u7ec6\u8fde\u63a5\u4fe1\u606f\n\n# \u6743\u9650\u95ee\u9898\nls -la ~/.ssh # \u68c0\u67e5\u6743\u9650\nchmod 600 ~/.ssh/id_ed25519\n\n# \u5bc6\u94a5\u95ee\u9898\nssh-add -l # \u67e5\u770b\u5df2\u52a0\u8f7d\u7684\u5bc6\u94a5\nssh-add ~/.ssh/id_ed25519 # \u6dfb\u52a0\u5bc6\u94a5\u5230agent\n
"},{"location":"Tools/Others/SSH/#3_2","title":"3. \u65e5\u5fd7\u67e5\u770b","text":"Bash# \u670d\u52a1\u5668\u7aef\nsudo tail -f /var/log/auth.log # Debian/Ubuntu\nsudo tail -f /var/log/secure # CentOS/RHEL\n\n# \u5ba2\u6237\u7aef\u8c03\u8bd5\nssh -vvv user@host # \u6700\u8be6\u7ec6\u7684\u8c03\u8bd5\u4fe1\u606f\n
"},{"location":"Tools/Others/zotero_%E4%BD%BF%E7%94%A8%E6%8C%87%E5%8D%97/","title":"zotero_\u4f7f\u7528\u6307\u5357","text":" \u7ea6 156 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#Zotero #Tools","tags":["Zotero","Tools"]},{"location":"Tools/Others/zotero_%E4%BD%BF%E7%94%A8%E6%8C%87%E5%8D%97/#zotero","title":"zotero \u4f7f\u7528\u6307\u5357","text":" - zotero \u4f7f\u7528\u6307\u5357
- Zotero \u4e2d\u6587\u793e\u533a | Zotero \u4e2d\u6587\u7ef4\u62a4\u5c0f\u7ec4
- ZOTERO \u4e0e Obsidian \u7b14\u8bb0\u63d2\u5165\u548c\u8054\u52a8\uff08\u542b\u7f8e\u5316\u7684\u7b14\u8bb0\u6a21\u677f)
- ZOTERO \u4e8e Obsidian \u8054\u52a8\u65b9\u6848
- \u770b\u4e86\u4e00\u5708\u4e0b\u6765\uff0c\u611f\u89c9\u73b0\u6709\u65b9\u6848\u90fd\u4e0d\u592a\u7b26\u5408\u5fc3\u610f\uff0c\u4e8e\u662f\u4e0d\u6298\u817e\u4e86\u3002\u4e0d\u5982\u8ba9 zotero \u548c obsidian \u5206\u5f00\uff0c\u771f\u8981\u505a\u7b14\u8bb0\u7684\u65f6\u5019\u80af\u5b9a\u662f\u76f4\u63a5\u5199\u51fa\u6765\u7684\uff0c\u6ca1\u5fc5\u8981\u4ea4\u7ec7\u5728\u4e00\u8d77\u4e86\u3002
- \u5b89\u88c5\u4e86\u4e00\u4e2a\u63d2\u4ef6 translate to pdf \u4ee5\u5916\u5c31\u6ca1\u6709\u641e\u4ec0\u4e48\u4e86\u3002
","tags":["Zotero","Tools"]},{"location":"Tools/Terminal/Tabby_Zsh/","title":"Tabby + Zsh \u914d\u7f6e\u6307\u5357","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#tabby-zsh","title":"Tabby + Zsh \u914d\u7f6e\u6307\u5357","text":" \u7ea6 236 \u4e2a\u5b57 789 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 11 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"Tools/Terminal/Tabby_Zsh/#_1","title":"\u524d\u7f6e\u51c6\u5907","text":"\u7cfb\u7edf\u8981\u6c42
Bash# Ubuntu/Debian\nsudo apt update\nsudo apt install -y \\\n git \\\n curl \\\n wget \\\n build-essential \\\n cmake \\\n python3-pip \\\n pkg-config \\\n libssl-dev\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#zsh","title":"ZSH \u57fa\u7840\u914d\u7f6e","text":"ZSH \u5b89\u88c5
Bash# \u5b89\u88c5zsh\nsudo apt install zsh\n\n# \u8bbe\u7f6e\u4e3a\u9ed8\u8ba4shell\nchsh -s $(which zsh)\n\n# \u786e\u8ba4\u8bbe\u7f6e\necho $SHELL\n# \u5e94\u8be5\u8f93\u51fa: /usr/bin/zsh\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#1-oh-my-zsh","title":"1. Oh My Zsh \u5b89\u88c5","text":"Bash# \u5b89\u88c5Oh My Zsh\nsh -c \"$(curl -fsSL https://raw.github.com/ohmyzsh/ohmyzsh/master/tools/install.sh)\"\n\n# \u5907\u4efd\u9ed8\u8ba4\u914d\u7f6e\ncp ~/.zshrc ~/.zshrc.backup\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2","title":"2. \u63d2\u4ef6\u7ba1\u7406\u5668\u5b89\u88c5","text":"Bash# \u5b89\u88c5zinit\nbash -c \"$(curl --fail --show-error --silent --location https://raw.githubusercontent.com/zdharma-continuum/zinit/HEAD/scripts/install.sh)\"\n\n# \u7b49\u5f85\u5b89\u88c5\u5b8c\u6210\u540e\u91cd\u542f\u7ec8\u7aef\nexec zsh\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#3","title":"3. \u57fa\u7840\u914d\u7f6e\u6587\u4ef6","text":"Bash# \u521b\u5efa\u65b0\u7684\u914d\u7f6e\u6587\u4ef6\ncat << 'EOF' > ~/.zshrc\n# \u57fa\u7840\u8bbe\u7f6e\nexport ZSH=\"$HOME/.oh-my-zsh\"\nexport LANG=en_US.UTF-8\nexport EDITOR='nvim'\nexport VISUAL='nvim'\n\n# zinit\u914d\u7f6e\nsource \"$HOME/.local/share/zinit/zinit.git/zinit.zsh\"\nautoload -Uz _zinit\n(( ${+_comps} )) && _comps[zinit]=_zinit\n\n# \u52a0\u8f7d\u6838\u5fc3\u63d2\u4ef6\nzinit ice depth=1; zinit light romkatv/powerlevel10k # \u4e3b\u9898\nzinit light zsh-users/zsh-autosuggestions # \u547d\u4ee4\u5efa\u8bae\nzinit light zsh-users/zsh-syntax-highlighting # \u8bed\u6cd5\u9ad8\u4eae\nzinit light zsh-users/zsh-completions # \u8865\u5168\u589e\u5f3a\nzinit light agkozak/zsh-z # \u76ee\u5f55\u8df3\u8f6c\n\n# \u5386\u53f2\u8bb0\u5f55\u8bbe\u7f6e\nHISTFILE=\"$HOME/.zsh_history\"\nHISTSIZE=50000\nSAVEHIST=50000\nsetopt EXTENDED_HISTORY # \u8bb0\u5f55\u547d\u4ee4\u65f6\u95f4\u6233\nsetopt HIST_EXPIRE_DUPS_FIRST # \u4f18\u5148\u5220\u9664\u91cd\u590d\u547d\u4ee4\nsetopt HIST_IGNORE_DUPS # \u5ffd\u7565\u8fde\u7eed\u91cd\u590d\u547d\u4ee4\nsetopt HIST_IGNORE_SPACE # \u5ffd\u7565\u4ee5\u7a7a\u683c\u5f00\u5934\u7684\u547d\u4ee4\nsetopt HIST_VERIFY # \u6267\u884c\u5386\u53f2\u547d\u4ee4\u524d\u5c55\u793a\nsetopt INC_APPEND_HISTORY # \u5b9e\u65f6\u6dfb\u52a0\u5386\u53f2\u8bb0\u5f55\nsetopt SHARE_HISTORY # \u5171\u4eab\u5386\u53f2\u8bb0\u5f55\n\n# \u76ee\u5f55\u8bbe\u7f6e\nsetopt AUTO_CD \nsetopt AUTO_PUSHD \nsetopt PUSHD_IGNORE_DUPS \nsetopt PUSHD_MINUS \nDIRSTACKSIZE=20\n\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#4","title":"4. \u5b9e\u7528\u522b\u540d\u8bbe\u7f6e","text":"Bash# \u6dfb\u52a0\u5230~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# \u57fa\u7840\u547d\u4ee4\u589e\u5f3a\nalias ls='ls --color=auto'\nalias ll='ls -lah'\nalias la='ls -A'\nalias l='ls -CF'\nalias grep='grep --color=auto'\nalias rm='rm -i'\nalias cp='cp -i'\nalias mv='mv -i'\nalias mkdir='mkdir -p'\nalias df='df -h'\nalias free='free -m'\nalias duf='du -sh *'\nalias ps='ps auxf'\nalias ping='ping -c 5'\nalias root='sudo -i'\nalias reboot='sudo reboot'\nalias poweroff='sudo poweroff'\n\n# Git\u5feb\u6377\u547d\u4ee4\nalias gs='git status'\nalias ga='git add'\nalias gaa='git add --all'\nalias gc='git commit -m'\nalias gp='git push'\nalias gl='git pull'\nalias gd='git diff'\nalias gco='git checkout'\nalias gb='git branch'\nalias gm='git merge'\nalias glog='git log --oneline --decorate --graph'\n\n# Docker\u5feb\u6377\u547d\u4ee4\nalias dk='docker'\nalias dkc='docker-compose'\nalias dkps='docker ps'\nalias dkst='docker stats'\nalias dktop='docker top'\nalias dkimg='docker images'\nalias dkpull='docker pull'\nalias dkex='docker exec -it'\n\n# \u5feb\u901f\u7f16\u8f91\nalias zshconfig=\"$EDITOR ~/.zshrc\"\nalias zshreload=\"source ~/.zshrc\"\nalias vimconfig=\"$EDITOR ~/.vimrc\"\nalias tmuxconfig=\"$EDITOR ~/.tmux.conf\"\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#5","title":"5. \u5b9e\u7528\u51fd\u6570","text":"Bash# \u6dfb\u52a0\u5230~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# \u521b\u5efa\u5e76\u8fdb\u5165\u76ee\u5f55\nmkcd() {\n mkdir -p \"$1\" && cd \"$1\"\n}\n\n# \u63d0\u53d6\u538b\u7f29\u6587\u4ef6\nextract() {\n if [ -f $1 ]; then\n case $1 in\n *.tar.bz2) tar xjf $1 ;;\n *.tar.gz) tar xzf $1 ;;\n *.bz2) bunzip2 $1 ;;\n *.rar) unrar e $1 ;;\n *.gz) gunzip $1 ;;\n *.tar) tar xf $1 ;;\n *.tbz2) tar xjf $1 ;;\n *.tgz) tar xzf $1 ;;\n *.zip) unzip $1 ;;\n *.Z) uncompress $1 ;;\n *.7z) 7z x $1 ;;\n *) echo \"'$1' cannot be extracted\" ;;\n esac\n else\n echo \"'$1' is not a valid file\"\n fi\n}\n\n# \u5feb\u901f\u67e5\u627e\u6587\u4ef6\nff() { find . -type f -iname \"*$1*\" ; }\nfd() { find . -type d -iname \"*$1*\" ; }\n\n# \u5feb\u901f\u67e5\u770b\u8fdb\u7a0b\npsg() { ps aux | grep -v grep | grep -i -e VSZ -e \"$1\"; }\n\n# \u7f51\u7edc\u5de5\u5177\nmyip() {\n curl -s http://ipecho.net/plain\n echo\n}\n\n# \u5feb\u901fHTTP\u670d\u52a1\u5668\nserve() {\n local port=\"${1:-8000}\"\n python3 -m http.server \"$port\"\n}\n\n# Git\u65e5\u5fd7\u7f8e\u5316\ngll() {\n git log --graph --pretty=format:'%Cred%h%Creset -%C(yellow)%d%Creset %s %Cgreen(%cr) %C(bold blue)<%an>%Creset' --abbrev-commit\n}\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_2","title":"\u4e3b\u9898\u7f8e\u5316\u4e0e\u63d2\u4ef6\u589e\u5f3a","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#powerlevel10k","title":"\u4e00\u3001Powerlevel10k \u4e3b\u9898\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1","title":"1. \u5b89\u88c5\u5fc5\u8981\u5b57\u4f53","text":"Bash# \u521b\u5efa\u5b57\u4f53\u76ee\u5f55\nmkdir -p ~/.local/share/fonts\n\n# \u4e0b\u8f7d\u63a8\u8350\u5b57\u4f53\nwget -P /tmp https://github.com/romkatv/powerlevel10k-media/raw/master/MesloLGS%20NF%20Regular.ttf\nwget -P /tmp https://github.com/romkatv/powerlevel10k-media/raw/master/MesloLGS%20NF%20Bold.ttf\nwget -P /tmp https://github.com/romkatv/powerlevel10k-media/raw/master/MesloLGS%20NF%20Italic.ttf\nwget -P /tmp https://github.com/romkatv/powerlevel10k-media/raw/master/MesloLGS%20NF%20Bold%20Italic.ttf\n\n# \u79fb\u52a8\u5b57\u4f53\u6587\u4ef6\nmv /tmp/MesloLGS*.ttf ~/.local/share/fonts/\n\n# \u66f4\u65b0\u5b57\u4f53\u7f13\u5b58\nfc-cache -f -v\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2_1","title":"2. \u4e3b\u9898\u914d\u7f6e","text":"Bash# \u6dfb\u52a0\u5230 ~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# Powerlevel10k \u914d\u7f6e\n# \u542f\u7528 Powerlevel10k \u5373\u65f6\u63d0\u793a\nif [[ -r \"${XDG_CACHE_HOME:-$HOME/.cache}/p10k-instant-prompt-${(%):-%n}.zsh\" ]]; then\n source \"${XDG_CACHE_HOME:-$HOME/.cache}/p10k-instant-prompt-${(%):-%n}.zsh\"\nfi\n\n# \u52a0\u8f7d\u4e3b\u9898\nsource ~/.oh-my-zsh/custom/themes/powerlevel10k/powerlevel10k.zsh-theme\n\n# \u4e3b\u9898\u4e2a\u6027\u5316\u8bbe\u7f6e\nPOWERLEVEL9K_MODE='nerdfont-complete'\nPOWERLEVEL9K_LEFT_PROMPT_ELEMENTS=(\n os_icon # \u64cd\u4f5c\u7cfb\u7edf\u56fe\u6807\n dir # \u5f53\u524d\u76ee\u5f55\n vcs # git\u72b6\u6001\n newline # \u6362\u884c\n prompt_char # \u63d0\u793a\u7b26\n)\nPOWERLEVEL9K_RIGHT_PROMPT_ELEMENTS=(\n status # \u4e0a\u4e00\u4e2a\u547d\u4ee4\u7684\u72b6\u6001\n background_jobs # \u540e\u53f0\u4efb\u52a1\n load # \u7cfb\u7edf\u8d1f\u8f7d\n ram # \u5185\u5b58\u4f7f\u7528\n time # \u65f6\u95f4\n)\n\n# \u76ee\u5f55\u663e\u793a\u8bbe\u7f6e\nPOWERLEVEL9K_DIR_BACKGROUND='blue'\nPOWERLEVEL9K_DIR_FOREGROUND='black'\nPOWERLEVEL9K_SHORTEN_DIR_LENGTH=2\nPOWERLEVEL9K_SHORTEN_STRATEGY=\"truncate_middle\"\n\n# Git\u72b6\u6001\u8bbe\u7f6e\nPOWERLEVEL9K_VCS_CLEAN_BACKGROUND='green'\nPOWERLEVEL9K_VCS_UNTRACKED_BACKGROUND='yellow'\nPOWERLEVEL9K_VCS_MODIFIED_BACKGROUND='red'\nEOF\n\n# \u8fd0\u884c\u914d\u7f6e\u5411\u5bfc\uff08\u9996\u6b21\u4f7f\u7528\uff09\np10k configure\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_3","title":"\u4e8c\u3001\u9ad8\u7ea7\u63d2\u4ef6\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1_1","title":"1. \u9ad8\u7ea7\u8865\u5168\u7cfb\u7edf","text":"Bash# \u6dfb\u52a0\u5230 ~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# \u8865\u5168\u7cfb\u7edf\u914d\u7f6e\nautoload -Uz compinit\ncompinit\n\n# \u8865\u5168\u83dc\u5355\u8bbe\u7f6e\nzstyle ':completion:*' menu select\nzstyle ':completion:*' matcher-list 'm:{a-zA-Z}={A-Za-z}' # \u5ffd\u7565\u5927\u5c0f\u5199\nzstyle ':completion:*' list-colors ${(s.:.)LS_COLORS} # \u8865\u5168\u83dc\u5355\u7740\u8272\nzstyle ':completion:*' verbose yes # \u8be6\u7ec6\u8865\u5168\u83dc\u5355\nzstyle ':completion:*:descriptions' format '%U%B%d%b%u' # \u8865\u5168\u83dc\u5355\u683c\u5f0f\nzstyle ':completion:*:warnings' format '%BSorry, no matches for: %d%b'\nzstyle ':completion:*:*:kill:*:processes' list-colors '=(#b) #([0-9]#)*=0=01;31'\nzstyle ':completion:*:kill:*' command 'ps -u $USER -o pid,%cpu,tty,cputime,cmd'\n\n# \u4f7f\u7528\u7f13\u5b58\u52a0\u901f\u8865\u5168\nzstyle ':completion:*' use-cache on\nzstyle ':completion:*' cache-path ~/.zsh/cache\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2-fzf","title":"2. FZF \u96c6\u6210\u914d\u7f6e","text":"Bash# \u5b89\u88c5FZF\ngit clone --depth 1 https://github.com/junegunn/fzf.git ~/.fzf\n~/.fzf/install\n\n# \u6dfb\u52a0\u5230 ~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# FZF \u914d\u7f6e\nexport FZF_DEFAULT_COMMAND='fd --type f --hidden --follow --exclude .git'\nexport FZF_DEFAULT_OPTS='--height 40% --layout=reverse --border --preview \"bat --style=numbers --color=always --line-range :500 {}\"'\n\n# FZF \u5feb\u6377\u952e\nbindkey '^T' fzf-file-widget\nbindkey '^R' fzf-history-widget\nbindkey '^[c' fzf-cd-widget\n\n# FZF \u51fd\u6570\n# \u5feb\u901f\u6253\u5f00\u6587\u4ef6\nfe() {\n local file\n file=$(fzf --query=\"$1\" --select-1 --exit-0)\n [ -n \"$file\" ] && ${EDITOR:-vim} \"$file\"\n}\n\n# \u5feb\u901f\u5207\u6362\u76ee\u5f55\nfd() {\n local dir\n dir=$(find ${1:-.} -path '*/\\.*' -prune -o -type d -print 2> /dev/null | fzf +m)\n [ -n \"$dir\" ] && cd \"$dir\"\n}\n\n# \u641c\u7d22\u5386\u53f2\u547d\u4ee4\nfh() {\n print -z $( ([ -n \"$ZSH_NAME\" ] && fc -l 1 || history) | fzf +s --tac | sed -E 's/ *[0-9]*\\*? *//' | sed -E 's/\\\\/\\\\\\\\/g')\n}\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#3_1","title":"3. \u589e\u5f3a\u76ee\u5f55\u5bfc\u822a","text":"Bash# \u6dfb\u52a0\u5230 ~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# \u76ee\u5f55\u4e66\u7b7e\nhash -d proj=~/projects\nhash -d docs=~/Documents\nhash -d dl=~/Downloads\nhash -d pics=~/Pictures\n\n# z \u63d2\u4ef6\u914d\u7f6e\nZSHZ_DATA=~/.local/share/z/data\nZSHZ_MAX_SCORE=5000\nZSHZ_CASE=smart\n\n# \u76ee\u5f55\u5806\u6808\u5bfc\u822a\nsetopt AUTO_PUSHD # \u81ea\u52a8\u5c06\u76ee\u5f55\u52a0\u5165\u5806\u6808\nsetopt PUSHD_IGNORE_DUPS # \u5ffd\u7565\u91cd\u590d\u76ee\u5f55\nsetopt PUSHD_SILENT # \u9759\u9ed8\u6a21\u5f0f\nsetopt PUSHD_TO_HOME # pushd \u4e0d\u5e26\u53c2\u6570\u65f6\u7b49\u540c\u4e8e pushd $HOME\n\n# \u76ee\u5f55\u522b\u540d\nalias -g ...='../..'\nalias -g ....='../../..'\nalias -g .....='../../../..'\nalias d='dirs -v'\nfor index ({1..9}) alias \"$index\"=\"cd +${index}\"; unset index\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#4_1","title":"4. \u589e\u5f3a\u5386\u53f2\u8bb0\u5f55\u641c\u7d22","text":"Bash# \u6dfb\u52a0\u5230 ~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# \u5386\u53f2\u8bb0\u5f55\u641c\u7d22\u914d\u7f6e\nbindkey '^[[A' history-substring-search-up\nbindkey '^[[B' history-substring-search-down\nbindkey '^P' history-substring-search-up\nbindkey '^N' history-substring-search-down\n\n# \u5386\u53f2\u8bb0\u5f55\u683c\u5f0f\u5316\nHIST_STAMPS=\"yyyy-mm-dd\"\nHISTORY_IGNORE=\"(ls|ls *|cd|cd *|pwd|exit|date|* --help)\"\n\n# \u547d\u4ee4\u6267\u884c\u65f6\u95f4\u663e\u793a\nREPORTTIME=10\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#tabby","title":"Tabby \u7ec8\u7aef\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#_4","title":"\u4e00\u3001\u5b89\u88c5\u548c\u521d\u59cb\u5316","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1-tabby","title":"1. \u5b89\u88c5 Tabby","text":"Bash# Ubuntu/Debian\nwget https://github.com/Eugeny/tabby/releases/latest/download/tabby-1.0.0-linux-x64.deb\nsudo dpkg -i tabby-*.deb\nsudo apt-get install -f\n\n# \u786e\u4fdd\u5b57\u4f53\u652f\u6301\nfc-cache -fv\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2_2","title":"2. \u521d\u59cb\u914d\u7f6e","text":"YAML# ~/.config/tabby/config.yaml\nconfig:\n version: 3\n\nterminal:\n shell: zsh # \u4f7f\u7528\u524d\u9762\u914d\u7f6e\u7684zsh\n fontSize: 14\n lineHeight: 1.2\n bell: 'off'\n copyOnSelect: true\n rightClick: menu\n\n # \u57fa\u7840\u73af\u5883\u53d8\u91cf\n environment:\n TERM: xterm-256color\n COLORTERM: truecolor\n\n # \u6027\u80fd\u8bbe\u7f6e\n performanceMode: true\n gpuAcceleration: true\n webGL: true\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_5","title":"\u4e8c\u3001\u5916\u89c2\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1_2","title":"1. \u5b57\u4f53\u8bbe\u7f6e","text":"YAMLterminal:\n font: JetBrainsMono Nerd Font # \u786e\u4fdd\u5df2\u5b89\u88c5\n fontSize: 14\n lineHeight: 1.2\n ligatures: true # \u8fde\u5b57\u652f\u6301\n\n # \u5b57\u4f53\u56de\u9000\n fallbackFont: 'Sarasa Mono SC' # \u4e2d\u6587\u652f\u6301\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2-dracula","title":"2. Dracula \u4e3b\u9898\u914d\u7f6e","text":"YAMLprofiles:\n - name: Default\n theme:\n name: 'Dracula'\n\n colors:\n background: '#282a36'\n foreground: '#f8f8f2'\n cursor: '#f8f8f2'\n\n selection:\n background: '#44475a'\n foreground: '#f8f8f2'\n\n # ANSI Colors\n black: '#21222c'\n red: '#ff5555'\n green: '#50fa7b'\n yellow: '#f1fa8c'\n blue: '#bd93f9'\n magenta: '#ff79c6'\n cyan: '#8be9fd'\n white: '#f8f8f2'\n\n # Bright Colors\n brightBlack: '#6272a4'\n brightRed: '#ff6e6e'\n brightGreen: '#69ff94'\n brightYellow: '#ffffa5'\n brightBlue: '#d6acff'\n brightMagenta: '#ff92df'\n brightCyan: '#a4ffff'\n brightWhite: '#ffffff'\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#3_2","title":"3. \u900f\u660e\u80cc\u666f\u914d\u7f6e","text":"YAMLterminal:\n background:\n type: 'image' # \u6216 'color'\n image: '~/.config/tabby/backgrounds/bg.jpg' # \u81ea\u5b9a\u4e49\u80cc\u666f\u56fe\u7247\n opacity: 0.85 # \u900f\u660e\u5ea6\n\n # \u4e9a\u514b\u529b\u6548\u679c\uff08Windows\uff09\n experimental:\n vibrancy: true\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_6","title":"\u4e09\u3001\u5feb\u6377\u952e\u914d\u7f6e","text":"YAMLhotkeys:\n # \u6807\u7b7e\u7ba1\u7406\n new-tab: ['Ctrl+T']\n close-tab: ['Ctrl+W']\n previous-tab: ['Ctrl+Shift+Tab']\n next-tab: ['Ctrl+Tab']\n\n # \u5206\u5c4f\u64cd\u4f5c\n split-right: ['Ctrl+Shift+E']\n split-bottom: ['Ctrl+Shift+O']\n split-nav-left: ['Alt+Left']\n split-nav-right: ['Alt+Right']\n split-nav-up: ['Alt+Up']\n split-nav-down: ['Alt+Down']\n\n # \u7ec8\u7aef\u64cd\u4f5c\n clear: ['Ctrl+L']\n copy: ['Ctrl+C']\n paste: ['Ctrl+V']\n search: ['Ctrl+Shift+F']\n\n # \u89c6\u56fe\u63a7\u5236\n zoom-in: ['Ctrl+Plus']\n zoom-out: ['Ctrl+Minus']\n reset-zoom: ['Ctrl+0']\n toggle-fullscreen: ['F11']\n\n # \u5feb\u901f\u547d\u4ee4\n command-palette: ['Ctrl+Shift+P']\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#ssh","title":"\u56db\u3001SSH \u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1-ssh","title":"1. \u57fa\u7840 SSH \u914d\u7f6e","text":"YAMLssh:\n auth:\n agent: true\n privateKeys:\n - ~/.ssh/id_ed25519\n - ~/.ssh/id_rsa\n\n # \u8fde\u63a5\u4fdd\u6301\n keepaliveInterval: 30\n keepaliveCountMax: 3\n\n # \u8f6c\u53d1\u8bbe\u7f6e\n forwardAgent: true\n x11: false\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2-ssh","title":"2. SSH \u8fde\u63a5\u914d\u7f6e","text":"YAMLssh:\n profiles:\n - name: \"\u5f00\u53d1\u670d\u52a1\u5668\"\n group: \"\u5f00\u53d1\u73af\u5883\"\n host: dev.example.com\n port: 22\n user: username\n auth: publicKey\n privateKey: ~/.ssh/id_ed25519\n\n - name: \"\u751f\u4ea7\u670d\u52a1\u5668\"\n group: \"\u751f\u4ea7\u73af\u5883\"\n host: prod.example.com\n port: 22\n user: username\n auth: agent\n\n - name: \"\u8df3\u677f\u673a\"\n host: jump.example.com\n forwardAgent: true\n jumpHost: true # \u6807\u8bb0\u4e3a\u8df3\u677f\u673a\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_7","title":"\u4e94\u3001\u63d2\u4ef6\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1_3","title":"1. \u6838\u5fc3\u63d2\u4ef6","text":"YAMLplugins:\n # SSH\u7ba1\u7406\n ssh:\n enabled: true\n\n # \u7ec8\u7aef\u5f55\u5236\n record:\n enabled: true\n directory: ~/terminal-records\n\n # \u547d\u4ee4\u9762\u677f\n commander:\n enabled: true\n\n # \u4e3b\u9898\u63d2\u4ef6\n community-color-schemes:\n enabled: true\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2_3","title":"2. \u6269\u5c55\u529f\u80fd","text":"YAML# \u641c\u7d22\u589e\u5f3a\nsearch:\n enabled: true\n searchOptions:\n regex: true\n wholeWord: false\n caseSensitive: false\n\n# \u7ec8\u7aef\u5206\u5272\nsplit:\n autoRemove: true # \u81ea\u52a8\u5173\u95ed\u7a7a\u7ec8\u7aef\n copyOnSelect: true\n pasteOnMiddleClick: true\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_8","title":"\u516d\u3001\u6027\u80fd\u4f18\u5316","text":"YAML# \u6027\u80fd\u76f8\u5173\u914d\u7f6e\nterminal:\n # \u57fa\u7840\u4f18\u5316\n performanceMode: true\n gpuAcceleration: true\n webGL: true\n\n # \u5386\u53f2\u8bb0\u5f55\n scrollback: 5000\n\n # \u8fdb\u7a0b\u7ba1\u7406\n autoClose: true\n closeOnExit: true\n\n # \u6e32\u67d3\u4f18\u5316\n smoothScroll: false\n experimentalFontRendering: false\n\n # \u8d44\u6e90\u9650\u5236\n environment:\n LIMIT_MEMORY: 512 # MB\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_9","title":"\u5f00\u53d1\u5de5\u5177\u4e0e\u7ec8\u7aef\u5de5\u5177\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#_10","title":"\u4e00\u3001\u73b0\u4ee3\u547d\u4ee4\u884c\u5de5\u5177","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1_4","title":"1. \u57fa\u7840\u5de5\u5177\u5b89\u88c5","text":"Bash# \u5b89\u88c5\u57fa\u7840\u5de5\u5177\nsudo apt install -y \\\n exa `# \u73b0\u4ee3ls\u66ff\u4ee3\u54c1` \\\n bat `# \u73b0\u4ee3cat\u66ff\u4ee3\u54c1` \\\n ripgrep `# \u73b0\u4ee3grep\u66ff\u4ee3\u54c1` \\\n fd-find `# \u73b0\u4ee3find\u66ff\u4ee3\u54c1` \\\n duf `# \u73b0\u4ee3df\u66ff\u4ee3\u54c1` \\\n ncdu `# \u78c1\u76d8\u4f7f\u7528\u5206\u6790` \\\n tldr `# \u547d\u4ee4\u7b80\u5316\u8bf4\u660e` \\\n jq `# JSON\u5904\u7406` \\\n fzf `# \u6a21\u7cca\u641c\u7d22`\n\n# \u521b\u5efa\u522b\u540d\ncat << 'EOF' >> ~/.zshrc\n# \u73b0\u4ee3\u547d\u4ee4\u884c\u5de5\u5177\u522b\u540d\nalias ls='exa --icons'\nalias ll='exa -l --icons --git'\nalias la='exa -la --icons --git'\nalias lt='exa -T --icons --git-ignore'\nalias cat='batcat'\nalias find='fd'\nalias du='ncdu'\nalias df='duf'\nalias help='tldr'\n\n# fzf \u914d\u7f6e\nexport FZF_DEFAULT_OPTS=\"--height 40% --layout=reverse --border \\\n --preview 'batcat --style=numbers --color=always --line-range :500 {}'\"\nexport FZF_DEFAULT_COMMAND='fd --type f --hidden --follow --exclude .git'\nexport FZF_CTRL_T_COMMAND=\"$FZF_DEFAULT_COMMAND\"\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2-ranger","title":"2. \u6587\u4ef6\u7ba1\u7406\u5668 - Ranger","text":"Bash# \u5b89\u88c5ranger\u548c\u4f9d\u8d56\nsudo apt install ranger python3-pillow ueberzug\n\n# \u751f\u6210\u914d\u7f6e\u6587\u4ef6\nranger --copy-config=all\n\n# \u914d\u7f6eRanger\ncat << 'EOF' > ~/.config/ranger/rc.conf\n# \u57fa\u7840\u8bbe\u7f6e\nset preview_images true\nset preview_images_method ueberzug\nset show_hidden true\nset hostname_in_titlebar false\nset tilde_in_titlebar true\nset line_numbers relative\nset mouse_enabled true\n\n# \u914d\u8272\u65b9\u6848\nset colorscheme solarized\n\n# \u6587\u4ef6\u9884\u89c8\nset use_preview_script true\nset preview_files true\nset preview_directories true\nset collapse_preview true\n\n# \u5feb\u6377\u952e\nmap <C-f> fzf_select\nmap <C-p> shell -w echo %d/%f | xsel -b\nmap <C-g> shell lazygit\nEOF\n\n# \u6dfb\u52a0FZF\u96c6\u6210\ncat << 'EOF' > ~/.config/ranger/commands.py\nfrom ranger.api.commands import Command\nclass fzf_select(Command):\n def execute(self):\n import subprocess\n import os.path\n command=\"find -L . \\( -path '*/\\.*' -o -fstype 'dev' -o -fstype 'proc' \\) -prune \\\n -o -print 2> /dev/null | sed 1d | cut -b3- | fzf +m\"\n fzf = self.fm.execute_command(command, universal_newlines=True, stdout=subprocess.PIPE)\n stdout, stderr = fzf.communicate()\n if fzf.returncode == 0:\n fzf_file = os.path.abspath(stdout.rstrip('\\n'))\n if os.path.isdir(fzf_file):\n self.fm.cd(fzf_file)\n else:\n self.fm.select_file(fzf_file)\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#3-htopglances","title":"3. \u7cfb\u7edf\u76d1\u63a7 - htop/glances","text":"Bash# \u5b89\u88c5\u5de5\u5177\nsudo apt install htop glances\n\n# htop\u914d\u7f6e\nmkdir -p ~/.config/htop\ncat << 'EOF' > ~/.config/htop/htoprc\n# \u57fa\u7840\u663e\u793a\u8bbe\u7f6e\nshow_cpu_frequency=1\nshow_cpu_temperature=1\nshow_program_path=0\nhighlight_base_name=1\nhighlight_megabytes=1\nhighlight_threads=1\n\n# \u663e\u793a\u8bbe\u7f6e\nfields=0 48 17 18 38 39 40 2 46 47 49 1\nsort_key=46\nsort_direction=-1\ntree_view=1\ntree_view_always_by_pid=0\n\n# \u989c\u8272\u8bbe\u7f6e\ncolor_scheme=0\nEOF\n\n# glances\u914d\u7f6e\nmkdir -p ~/.config/glances\ncat << 'EOF' > ~/.config/glances/glances.conf\n[global]\n# \u5237\u65b0\u95f4\u9694\nrefresh=2\n# \u5386\u53f2\u5927\u5c0f\nhistory_size=1200\n\n[cpu]\n# CPU \u8b66\u544a\u9608\u503c\ncareful=50\nwarning=70\ncritical=90\n\n[memory]\n# \u5185\u5b58\u8b66\u544a\u9608\u503c\ncareful=50\nwarning=70\ncritical=90\n\n[network]\n# \u7f51\u7edc\u5e26\u5bbd\u663e\u793a\u5355\u4f4d (bit/sec)\nunit=bit\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#4-git-lazygit","title":"4. Git \u5de5\u5177 - Lazygit","text":"Bash# \u5b89\u88c5Lazygit\nLAZYGIT_VERSION=$(curl -s \"https://api.github.com/repos/jesseduffield/lazygit/releases/latest\" | grep -Po '\"tag_name\": \"v\\K[^\"]*')\ncurl -Lo lazygit.tar.gz \"https://github.com/jesseduffield/lazygit/releases/latest/download/lazygit_${LAZYGIT_VERSION}_Linux_x86_64.tar.gz\"\nsudo tar xf lazygit.tar.gz -C /usr/local/bin lazygit\n\n# \u914d\u7f6eLazygit\nmkdir -p ~/.config/lazygit\ncat << 'EOF' > ~/.config/lazygit/config.yml\ngui:\n # UI\u4e3b\u9898\n theme:\n lightTheme: false\n activeBorderColor:\n - green\n - bold\n inactiveBorderColor:\n - white\n selectedLineBgColor:\n - reverse\n # \u5e38\u7528\u8bbe\u7f6e \n showFileTree: true\n showRandomTip: false\n showCommandLog: false\n\ngit:\n # git\u8bbe\u7f6e\n paging:\n colorArg: always\n useConfig: true\n # commit\u8bbe\u7f6e\n commits:\n showGraph: always\n showWholeGraph: true\n # \u81ea\u52a8\u83b7\u53d6\n autoFetch: true\n # \u5206\u652f\u663e\u793a\n branchLogCmd: \"git log --graph --color=always --abbrev-commit --decorate --date=relative --pretty=medium {{branchName}} --\"\n\nkeybinding:\n # \u81ea\u5b9a\u4e49\u5feb\u6377\u952e\n universal:\n return: '<c-c>'\n quit: 'q'\n quit-alt1: '<esc>'\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#5_1","title":"5. \u4ee3\u7801\u641c\u7d22\u5de5\u5177","text":"Bash# ripgrep\u914d\u7f6e\ncat << 'EOF' >> ~/.zshrc\n# ripgrep \u914d\u7f6e\nexport RIPGREP_CONFIG_PATH=\"$HOME/.ripgreprc\"\n\n# ripgrep \u522b\u540d\nalias rg='rg --smart-case'\nalias rgf='rg --files | rg'\nalias rgh='rg --hidden'\nalias rgc='rg --count'\n\n# fzf + ripgrep \u96c6\u6210\nfif() {\n if [ ! \"$#\" -gt 0 ]; then echo \"Need a string to search for!\"; return 1; fi\n rg --files-with-matches --no-messages \"$1\" | fzf --preview \"highlight -O ansi -l {} 2> /dev/null | rg --colors 'match:bg:yellow' --ignore-case --pretty --context 10 '$1' || rg --ignore-case --pretty --context 10 '$1' {}\"\n}\nEOF\n\n# \u521b\u5efaripgrep\u914d\u7f6e\u6587\u4ef6\ncat << 'EOF' > ~/.ripgreprc\n# \u9ed8\u8ba4\u914d\u7f6e\n--smart-case\n--hidden\n--follow\n--glob=!.git/*\n\n# \u641c\u7d22\u914d\u7f6e\n--max-columns=150\n--max-columns-preview\n\n# \u989c\u8272\u914d\u7f6e\n--colors=line:fg:yellow\n--colors=line:style:bold\n--colors=path:fg:green\n--colors=path:style:bold\n--colors=match:fg:black\n--colors=match:bg:yellow\n--colors=match:style:nobold\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#6","title":"6. \u5b9e\u7528\u5f00\u53d1\u5de5\u5177","text":"Bash# \u5b89\u88c5\u5f00\u53d1\u8f85\u52a9\u5de5\u5177\nsudo apt install -y \\\n shellcheck `# shell\u811a\u672c\u68c0\u67e5` \\\n python3-pip `# Python\u5305\u7ba1\u7406` \\\n nodejs npm `# Node.js\u73af\u5883` \\\n golang `# Go\u8bed\u8a00\u73af\u5883` \\\n docker.io `# Docker\u652f\u6301` \\\n postgresql-client `# \u6570\u636e\u5e93\u5ba2\u6237\u7aef` \\\n redis-tools `# Redis\u5ba2\u6237\u7aef` \\\n mycli `# MySQL\u5ba2\u6237\u7aef` \\\n httpie `# HTTP\u5ba2\u6237\u7aef`\n\n# \u6dfb\u52a0\u5b9e\u7528\u522b\u540d\u548c\u51fd\u6570\ncat << 'EOF' >> ~/.zshrc\n# Docker\u522b\u540d\nalias dk='docker'\nalias dkc='docker-compose'\nalias dkps='docker ps'\nalias dkst='docker stats'\nalias dkimg='docker images'\nalias dkpull='docker pull'\nalias dkexec='docker exec -it'\n\n# \u5f00\u53d1\u8f85\u52a9\u51fd\u6570\n# \u5feb\u901fHTTP\u670d\u52a1\u5668\nserve() {\n local port=\"${1:-8000}\"\n python3 -m http.server \"$port\"\n}\n\n# JSON\u683c\u5f0f\u5316\njson() {\n if [ -t 0 ]; then # \u53c2\u6570\u8f93\u5165\n python -m json.tool <<< \"$*\" | pygmentize -l json\n else # \u7ba1\u9053\u8f93\u5165\n python -m json.tool | pygmentize -l json\n fi\n}\n\n# Git\u5206\u652f\u6e05\u7406\ngit-clean() {\n git branch --merged | egrep -v \"(^\\*|master|main|dev)\" | xargs git branch -d\n}\n\n# \u73af\u5883\u53d8\u91cf\u7ba1\u7406\nenvfile() {\n if [[ -f \"$1\" ]]; then\n set -a\n source \"$1\"\n set +a\n else\n echo \"Error: File $1 not found\"\n return 1\n fi\n}\nEOF\n
"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Home","text":"Welcome to wnc's note! My friends! / About Me
"},{"location":"Links/","title":"\u53cb\u94fe","text":""},{"location":"Links/#_1","title":"\u53cb\u94fe","text":"Abstract
My friends!
Wnc \u7684\u5496\u5561\u9986 \u6211\u81ea\u5df1\uff01 donotknow DoNotKnow Kinnari \u5f88\u5389\u5bb3\u7684\u5b66\u957f\uff01 MenGrey \u5f88\u5389\u5bb3\u7684\u540c\u5b66\uff01"},{"location":"AI/","title":"AI","text":""},{"location":"AI/#artificial-intelligence","title":"Artificial Intelligence","text":"Abstract
\u672c\u90e8\u5206\u5185\u5bb9\uff08\u9664\u7279\u522b\u58f0\u660e\u5916\uff09\u91c7\u7528 \u7f72\u540d-\u975e\u5546\u4e1a\u6027\u4f7f\u7528-\u4fdd\u6301\u4e00\u81f4 4.0 \u56fd\u9645 (CC BY-NC-SA 4.0) \u8bb8\u53ef\u534f\u8bae\u8fdb\u884c\u8bb8\u53ef\u3002
FFB6D \u590d\u73b0 - Docker 653 213 5 mins 1734024510
- Conda 293 96 2 mins 1734024510
EECS 498-007 - Pytorch 564 45 2 mins 1734024510
- KNN 374 100 2 mins 1734012860
- Linear Classifer 0 0 mins 0
CS231n - Numpy 49 104 1 mins 1734024510
- Notes 8852 30 mins 1734012860
\u673a\u5668\u5b66\u4e60 - \u300a\u7edf\u8ba1\u5b66\u4e60\u65b9\u6cd5\u300b\u7b14\u8bb0 3356 11 mins 1734012860
\u6df1\u5ea6\u5b66\u4e60 - Dive into Deep Learning 1547 387 10 mins 1734720663
SLAM - \u89c6\u89c9 SLAM \u5341\u56db\u8bb2 14110 72 48 mins 1734720663
"},{"location":"AI/Dive_into_Deep_Learning/","title":"Dive into Deep Learning","text":""},{"location":"AI/Dive_into_Deep_Learning/#dive-into-deep-learning","title":"Dive into Deep Learning","text":" \u7ea6 1547 \u4e2a\u5b57 387 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 13 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/Dive_into_Deep_Learning/#1","title":"1 \u5f15\u8a00","text":""},{"location":"AI/Dive_into_Deep_Learning/#2","title":"2 \u9884\u5907\u77e5\u8bc6","text":""},{"location":"AI/Dive_into_Deep_Learning/#21","title":"2.1 \u6570\u636e\u64cd\u4f5c","text":" - tensor
- ndarray (MXNet)
- Tensor (TensorFlow)
Pythonx = torch.arrange(12)\nx.shape\nx.numel()\nx.reshape(3, 4)\ntorch.zeros((2, 3, 4))\ntorch.ones((2, 3, 4))\ntorch.randn(3, 4)\n
- elementwise\uff1a
- concatenate
Pythontorch.cat((X, Y), dim = 0) # \u7ad6\u7740\u52a0\ntorch.cat((X, Y), dim = 1) # \u6a2a\u7740\u52a0\nx.sum()\n
- broadcasting mechanism
- \u590d\u5236\u62d3\u5c55\u5230\u5f62\u72b6\u4e00\u81f4\u540e\u76f8\u52a0
- \u7d22\u5f15+\u5207\u7247
- \u5207\u7247\u4fdd\u6301\u5730\u5740\u4e0d\u53d8\uff1a\u8282\u7701\u5185\u5b58
- ndarry <-> Tensor
- item ()
"},{"location":"AI/Dive_into_Deep_Learning/#22","title":"2.2 \u6570\u636e\u9884\u5904\u7406","text":" - pandas
- read_csv ()
- NaN
- fillna (inputs.mean ())
- np.array (inputs. to_numpy (dtype = float))
"},{"location":"AI/Dive_into_Deep_Learning/#23","title":"2.3 \u7ebf\u6027\u4ee3\u6570","text":" - scalar
- variable
- space
- element / component
- dimension
- square matrix
- transpose
- symmetric matrix
- channel
- Hadamard product
\\[ \\begin{split}\\mathbf{A} \\odot \\mathbf{B} = \\begin{bmatrix} a_{11} b_{11} & a_{12} b_{12} & \\dots & a_{1n} b_{1n} \\\\ a_{21} b_{21} & a_{22} b_{22} & \\dots & a_{2n} b_{2n} \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ a_{m1} b_{m1} & a_{m2} b_{m2} & \\dots & a_{mn} b_{mn} \\end{bmatrix}.\\end{split} \\] - A.sum (axis = 0) # \u7ad6\u7740\u6c42\u548c
- A.sum (axis = [0, 1]) = A.sum ()
- A.mean () = A.sum () / A.size ()
- A.cumsum (axis = 0)
- dot product
- torch.dot (x, y) = torch.sum (x * y)
- weighted average
- matrix-vector product
- matrix-matric multiplication
- norm
- \\(f(\\alpha \\mathbf{x}) = |\\alpha| f(\\mathbf{x}).\\)
- \\((\\mathbf{x} + \\mathbf{y}) \\leq f(\\mathbf{x}) + f(\\mathbf{y}).\\)
- \\(f(\\mathbf{x}) \\geq 0.\\)
"},{"location":"AI/Dive_into_Deep_Learning/#24","title":"2.4 \u5fae\u79ef\u5206","text":""},{"location":"AI/Dive_into_Deep_Learning/#241","title":"2.4.1 \u5bfc\u6570\u548c\u5fae\u5206","text":""},{"location":"AI/Dive_into_Deep_Learning/#242","title":"2.4.2 \u504f\u5bfc\u6570","text":""},{"location":"AI/Dive_into_Deep_Learning/#243","title":"2.4.3 \u68af\u5ea6","text":"\\[ \\nabla_{\\mathbf{x}} f(\\mathbf{x}) = \\bigg[\\frac{\\partial f(\\mathbf{x})}{\\partial x_1}, \\frac{\\partial f(\\mathbf{x})}{\\partial x_2}, \\ldots, \\frac{\\partial f(\\mathbf{x})}{\\partial x_n}\\bigg]^\\top, \\] \\[ \\nabla_{\\mathbf{x}} \\mathbf{A} \\mathbf{x} = \\mathbf{A}^\\top \\] \\[ \\nabla_{\\mathbf{x}} \\mathbf{x}^\\top \\mathbf{A} = \\mathbf{A} \\] \\[ \\nabla_{\\mathbf{x}} \\mathbf{x}^\\top \\mathbf{A} \\mathbf{x} = (\\mathbf{A} + \\mathbf{A}^\\top)\\mathbf{x} \\] \\[ \\nabla_{\\mathbf{x}} \\|\\mathbf{x} \\|^2 = \\nabla_{\\mathbf{x}} \\mathbf{x}^\\top \\mathbf{x} = 2\\mathbf{x} \\] \\[ \\nabla_{\\mathbf{X}} \\|\\mathbf{X} \\|_F^2 = 2\\mathbf{X} \\]"},{"location":"AI/Dive_into_Deep_Learning/#244","title":"2.4.4 \u94fe\u5f0f\u6cd5\u5219","text":""},{"location":"AI/Dive_into_Deep_Learning/#245","title":"2.4.5 \u5c0f\u7ed3","text":""},{"location":"AI/Dive_into_Deep_Learning/#25-automatic-differentiation","title":"2.5 \u81ea\u52a8\u5fae\u5206\uff08automatic differentiation\uff09","text":" - computational graph
- backpropagate
Pythonx.requires_grad_(True) # \u7b49\u4ef7\u4e8ex=torch.arange(4.0,requires_grad=True)\nx.grad # \u9ed8\u8ba4\u503c\u662fNone\ny = 2 * torch.dot(x, x)\ny.backward()\nx.grad\nx.grad == 4 * x\n
"},{"location":"AI/Dive_into_Deep_Learning/#251","title":"2.5.1 \u975e\u6807\u91cf\u53d8\u91cf\u7684\u53cd\u5411\u4f20\u64ad","text":"Python# \u5bf9\u975e\u6807\u91cf\u8c03\u7528backward\u9700\u8981\u4f20\u5165\u4e00\u4e2agradient\u53c2\u6570\uff0c\u8be5\u53c2\u6570\u6307\u5b9a\u5fae\u5206\u51fd\u6570\u5173\u4e8eself\u7684\u68af\u5ea6\u3002\n# \u672c\u4f8b\u53ea\u60f3\u6c42\u504f\u5bfc\u6570\u7684\u548c\uff0c\u6240\u4ee5\u4f20\u9012\u4e00\u4e2a1\u7684\u68af\u5ea6\u662f\u5408\u9002\u7684\nx.grad.zero_()\ny = x * x\n# \u7b49\u4ef7\u4e8ey.backward(torch.ones(len(x)))\ny.sum().backward()\nx.grad\n
"},{"location":"AI/Dive_into_Deep_Learning/#252","title":"2.5.2 \u5206\u79bb\u8ba1\u7b97","text":"Pythonx.grad.zero_()\ny = x * x\nu = y.detach()\nz = u * x\n\nz.sum().backward()\nx.grad == u\n
"},{"location":"AI/Dive_into_Deep_Learning/#26","title":"2.6 \u6982\u7387","text":""},{"location":"AI/Dive_into_Deep_Learning/#261","title":"2.6.1 \u57fa\u672c\u6982\u7387\u8bba","text":" - sampling
- distribution
- multinomial distribution
Pythonfair_probs = torch.ones([6]) / 6\nmultinomial.Multinomial(10, fair_probs).sample() # \u591a\u4e2a\u6837\u672c\n
"},{"location":"AI/Dive_into_Deep_Learning/#262","title":"2.6.2 \u5904\u7406\u591a\u4e2a\u968f\u673a\u53d8\u91cf","text":" - joint probability
- conditional probability
- Bayes\u2019 theorem
\\[ P(A \\mid B) = \\frac{P(B \\mid A) P(A)}{P(B)}. \\] - \u5176\u4e2d P (A, B) \u662f\u4e00\u4e2a\u8054\u5408\u5206\u5e03 (joint distribution)\uff0c\u00a0P (A\u2223B) \u662f\u4e00\u4e2a\u6761\u4ef6\u5206\u5e03 (conditional distribution)
- marginalization
- marginal probability
- marginal distribution
- conditionally independent
$$ P(A, B \\mid C) = P(A \\mid C)P(B \\mid C) $$
\\[ A \\perp B \\mid C \\] \\[ E[X] = \\sum_{x} x P(X = x). \\] \\[ E_{x \\sim P}[f(x)] = \\sum_x f(x) P(x). \\] \\[ \\mathrm{Var}[X] = E\\left[(X - E[X])^2\\right] = E[X^2] - E[X]^2. \\] \\[ \\mathrm{Var}[f(x)] = E\\left[\\left(f(x) - E[f(x)]\\right)^2\\right]. \\]"},{"location":"AI/Dive_into_Deep_Learning/#3","title":"3 \u7ebf\u6027\u795e\u7ecf\u7f51\u7edc","text":""},{"location":"AI/Dive_into_Deep_Learning/#31","title":"3.1 \u7ebf\u6027\u56de\u5f52","text":""},{"location":"AI/Dive_into_Deep_Learning/#311","title":"3.1.1 \u7ebf\u6027\u56de\u5f52\u7684\u57fa\u672c\u5143\u7d20","text":" - regression
- prediction / inference
- training set
- sample / data point / data instance
- label / target
- feature / covariate
\\[ \\mathrm{price} = w_{\\mathrm{area}} \\cdot \\mathrm{area} + w_{\\mathrm{age}} \\cdot \\mathrm{age} + b. \\] - weight
- bias / offset / intercept
- affine transformation
- linear transformation
- translation
- model parameters
- loss function
\\[ l^{(i)}(\\mathbf{w}, b) = \\frac{1}{2} \\left(\\hat{y}^{(i)} - y^{(i)}\\right)^2. \\] \\[ L(\\mathbf{w}, b) =\\frac{1}{n}\\sum_{i=1}^n l^{(i)}(\\mathbf{w}, b) =\\frac{1}{n} \\sum_{i=1}^n \\frac{1}{2}\\left(\\mathbf{w}^\\top \\mathbf{x}^{(i)} + b - y^{(i)}\\right)^2. \\] \\[ \\mathbf{w}^*, b^* = \\operatorname*{argmin}_{\\mathbf{w}, b}\\ L(\\mathbf{w}, b). \\] \\[ \\mathbf{w}^{*} = (\\mathbf X^\\top \\mathbf X)^{-1}\\mathbf X^\\top \\mathbf{y}. \\] - analytical solution
- gradient descent
- minibatch stochastic gradient descent
\\[ (\\mathbf{w},b) \\leftarrow (\\mathbf{w},b) - \\frac{\\eta}{|\\mathcal{B}|} \\sum_{i \\in \\mathcal{B}} \\partial_{(\\mathbf{w},b)} l^{(i)}(\\mathbf{w},b). \\] - \u521d\u59cb\u5316\u6a21\u578b\u53c2\u6570\u7684\u503c\uff0c\u5982\u968f\u673a\u521d\u59cb\u5316
- \u4ece\u6570\u636e\u96c6\u4e2d\u968f\u673a\u62bd\u53d6\u5c0f\u6279\u91cf\u6837\u672c\u4e14\u5728\u8d1f\u68af\u5ea6\u7684\u65b9\u5411\u4e0a\u66f4\u65b0\u53c2\u6570\uff0c\u5e76\u4e0d\u65ad\u8fed\u4ee3\u8fd9\u4e00\u6b65\u9aa4 - hyperparameter
- \\(|\\mathcal{B}|\\): batch size
- \\(\\eta\\): learning rate
- hyperparameter tuning
- validationg dataset
- generalization
"},{"location":"AI/Dive_into_Deep_Learning/#312","title":"3.1.2 \u77e2\u91cf\u5316\u52a0\u901f","text":" - \u77e2\u91cf\u5316\u4ee3\u7801
"},{"location":"AI/Dive_into_Deep_Learning/#313","title":"3.1.3 \u6b63\u6001\u5206\u5e03\u4e0e\u5e73\u65b9\u635f\u5931","text":" - normal distribution / Gaussian distribution
\\[ p(x) = \\frac{1}{\\sqrt{2 \\pi \\sigma^2}} \\exp\\left(-\\frac{1}{2 \\sigma^2} (x - \\mu)^2\\right). \\] \\[ y = \\mathbf{w}^\\top \\mathbf{x} + b + \\epsilon, \\] - \u5176\u4e2d \\(\\epsilon \\sim \\mathcal{N}(0, \\sigma^2)\\). \u56e0\u6b64 y \u7684 likelihood:
\\[ P(y \\mid \\mathbf{x}) = \\frac{1}{\\sqrt{2 \\pi \\sigma^2}} \\exp\\left(-\\frac{1}{2 \\sigma^2} (y - \\mathbf{w}^\\top \\mathbf{x} - b)^2\\right). \\] \\[ P(\\mathbf y \\mid \\mathbf X) = \\prod_{i=1}^{n} p(y^{(i)}|\\mathbf{x}^{(i)}). \\] \\[ -\\log P(\\mathbf y \\mid \\mathbf X) = \\sum_{i=1}^n \\frac{1}{2} \\log(2 \\pi \\sigma^2) + \\frac{1}{2 \\sigma^2} \\left(y^{(i)} - \\mathbf{w}^\\top \\mathbf{x}^{(i)} - b\\right)^2. \\]"},{"location":"AI/Dive_into_Deep_Learning/#314","title":"3.1.4 \u4ece\u7ebf\u6027\u56de\u5f52\u5230\u795e\u7ecf\u7f51\u7edc","text":" - feature dimensionality
- fully-connected layer / dense layer
"},{"location":"AI/Dive_into_Deep_Learning/#32","title":"3.2 \u7ebf\u6027\u56de\u5f52\u7684\u4ece\u96f6\u5f00\u59cb\u5b9e\u73b0","text":"Python%matplotlib inline\nimport random\nimport torch\nfrom d2l import torch as d2l\ndef synthetic_data(w, b, num_examples): #@save\n \"\"\"\u751f\u6210y=Xw+b+\u566a\u58f0\"\"\"\n X = torch.normal(0, 1, (num_examples, len(w)))\n y = torch.matmul(X, w) + b\n y += torch.normal(0, 0.01, y.shape)\n return X, y.reshape((-1, 1))\n\ntrue_w = torch.tensor([2, -3.4])\ntrue_b = 4.2\nfeatures, labels = synthetic_data(true_w, true_b, 1000)\n\ndef data_iter(batch_size, features, labels):\n num_examples = len(features)\n indices = list(range(num_examples))\n # \u8fd9\u4e9b\u6837\u672c\u662f\u968f\u673a\u8bfb\u53d6\u7684\uff0c\u6ca1\u6709\u7279\u5b9a\u7684\u987a\u5e8f\n random.shuffle(indices)\n for i in range(0, num_examples, batch_size):\n batch_indices = torch.tensor(\n indices[i: min(i + batch_size, num_examples)])\n yield features[batch_indices], labels[batch_indices]\n\nw = torch.normal(0, 0.01, size=(2,1), requires_grad=True)\nb = torch.zeros(1, requires_grad=True)\n\ndef linreg(X, w, b): #@save\n \"\"\"\u7ebf\u6027\u56de\u5f52\u6a21\u578b\"\"\"\n return torch.matmul(X, w) + b\n\ndef squared_loss(y_hat, y): #@save\n \"\"\"\u5747\u65b9\u635f\u5931\"\"\"\n return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2\n\ndef sgd(params, lr, batch_size): #@save\n \"\"\"\u5c0f\u6279\u91cf\u968f\u673a\u68af\u5ea6\u4e0b\u964d\"\"\"\n with torch.no_grad():\n for param in params:\n param -= lr * param.grad / batch_size\n param.grad.zero_()\n\nlr = 0.03\nnum_epochs = 3\nnet = linreg\nloss = squared_loss\n\nfor epoch in range(num_epochs):\n for X, y in data_iter(batch_size, features, labels):\n l = loss(net(X, w, b), y) # X\u548cy\u7684\u5c0f\u6279\u91cf\u635f\u5931\n # \u56e0\u4e3al\u5f62\u72b6\u662f(batch_size,1)\uff0c\u800c\u4e0d\u662f\u4e00\u4e2a\u6807\u91cf\u3002l\u4e2d\u7684\u6240\u6709\u5143\u7d20\u88ab\u52a0\u5230\u4e00\u8d77\uff0c\n # \u5e76\u4ee5\u6b64\u8ba1\u7b97\u5173\u4e8e[w,b]\u7684\u68af\u5ea6\n l.sum().backward()\n sgd([w, b], lr, batch_size) # \u4f7f\u7528\u53c2\u6570\u7684\u68af\u5ea6\u66f4\u65b0\u53c2\u6570\n with torch.no_grad():\n train_l = loss(net(features, w, b), labels)\n print(f'epoch {epoch + 1}, loss {float(train_l.mean()):f}')\n
"},{"location":"AI/Dive_into_Deep_Learning/#33","title":"3.3 \u7ebf\u6027\u56de\u5f52\u7684\u7b80\u6d01\u5b9e\u73b0","text":"Pythonimport numpy as np\nimport torch\nfrom torch.utils import data\nfrom d2l import torch as d2l\n\ntrue_w = torch.tensor([2, -3.4])\ntrue_b = 4.2\nfeatures, labels = d2l.synthetic_data(true_w, true_b, 1000) # \u751f\u6210\u6570\u636e\u96c6\n\ndef load_array(data_arrays, batch_size, is_train=True): #@save\n \"\"\"\u6784\u9020\u4e00\u4e2aPyTorch\u6570\u636e\u8fed\u4ee3\u5668\"\"\"\n dataset = data.TensorDataset(*data_arrays)\n return data.DataLoader(dataset, batch_size, shuffle=is_train)\n\nbatch_size = 10\ndata_iter = load_array((features, labels), batch_size) # \u8bfb\u53d6\u6570\u636e\u96c6\n\n# nn\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u7f29\u5199\nfrom torch import nn\n\nnet = nn.Sequential(nn.Linear(2, 1)) # \u5b9a\u4e49\u6a21\u578b \uff08\u8f93\u5165\uff0c\u8f93\u51fa\uff09\u7279\u5f81\u5f62\u72b6\nnet[0].weight.data.normal_(0, 0.01)\nnet[0].bias.data.fill_(0) # \u521d\u59cb\u5316\u6a21\u578b\u53c2\u6570\nloss = nn.MSELoss() # \u5b9a\u4e49\u635f\u5931\u51fd\u6570\ntrainer = torch.optim.SGD(net.parameters(), lr=0.03) # \u5b9a\u4e49\u4f18\u5316\u7b97\u6cd5\n\n# \u8bad\u7ec3\nnum_epochs = 3\nfor epoch in range(num_epochs):\n for X, y in data_iter:\n l = loss(net(X) ,y)\n trainer.zero_grad()\n l.backward()\n trainer.step()\n l = loss(net(features), labels)\n print(f'epoch {epoch + 1}, loss {l:f}')\n\nw = net[0].weight.data\nprint('w\u7684\u4f30\u8ba1\u8bef\u5dee\uff1a', true_w - w.reshape(true_w.shape))\nb = net[0].bias.data\nprint('b\u7684\u4f30\u8ba1\u8bef\u5dee\uff1a', true_b - b)\n
"},{"location":"AI/Dive_into_Deep_Learning/#34-softmax","title":"3.4 softmax \u56de\u5f52","text":""},{"location":"AI/Dive_into_Deep_Learning/#341","title":"3.4.1 \u5206\u7c7b\u95ee\u9898","text":" \\[ y \\in \\{(1, 0, 0), (0, 1, 0), (0, 0, 1)\\}. \\]"},{"location":"AI/Dive_into_Deep_Learning/#342","title":"3.4.2 \u7f51\u7edc\u67b6\u6784","text":" \\[ \\begin{split}\\begin{aligned} o_1 &= x_1 w_{11} + x_2 w_{12} + x_3 w_{13} + x_4 w_{14} + b_1,\\\\ o_2 &= x_1 w_{21} + x_2 w_{22} + x_3 w_{23} + x_4 w_{24} + b_2,\\\\ o_3 &= x_1 w_{31} + x_2 w_{32} + x_3 w_{33} + x_4 w_{34} + b_3. \\end{aligned}\\end{split} \\]"},{"location":"AI/Dive_into_Deep_Learning/#343","title":"3.4.3 \u5168\u8fde\u63a5\u5c42\u7684\u53c2\u6570\u5f00\u9500","text":" - \u4e0d\u77e5\u9053\u662f\u4ec0\u4e48\u4e1c\u897f
"},{"location":"AI/Dive_into_Deep_Learning/#344-softmax","title":"3.4.4 softmax \u8fd0\u7b97","text":" \\[ \\hat{\\mathbf{y}} = \\mathrm{softmax}(\\mathbf{o})\\quad \\text{\u5176\u4e2d}\\quad \\hat{y}_j = \\frac{\\exp(o_j)}{\\sum_k \\exp(o_k)} \\] \\[ \\operatorname*{argmax}_j \\hat y_j = \\operatorname*{argmax}_j o_j. \\] "},{"location":"AI/Dive_into_Deep_Learning/#345","title":"3.4.5 \u5c0f\u6279\u6837\u672c\u7684\u77e2\u91cf\u5316","text":"\\[ \\begin{split}\\begin{aligned} \\mathbf{O} &= \\mathbf{X} \\mathbf{W} + \\mathbf{b}, \\\\ \\hat{\\mathbf{Y}} & = \\mathrm{softmax}(\\mathbf{O}). \\end{aligned}\\end{split} \\]"},{"location":"AI/Dive_into_Deep_Learning/#346","title":"3.4.6 \u635f\u5931\u51fd\u6570","text":"\\[ P(\\mathbf{Y} \\mid \\mathbf{X}) = \\prod_{i=1}^n P(\\mathbf{y}^{(i)} \\mid \\mathbf{x}^{(i)}). \\] \\[ -\\log P(\\mathbf{Y} \\mid \\mathbf{X}) = \\sum_{i=1}^n -\\log P(\\mathbf{y}^{(i)} \\mid \\mathbf{x}^{(i)}) = \\sum_{i=1}^n l(\\mathbf{y}^{(i)}, \\hat{\\mathbf{y}}^{(i)}), \\] \\[ l(\\mathbf{y}, \\hat{\\mathbf{y}}) = - \\sum_{j=1}^q y_j \\log \\hat{y}_j. \\] \\[ \\begin{split}\\begin{aligned} l(\\mathbf{y}, \\hat{\\mathbf{y}}) &= - \\sum_{j=1}^q y_j \\log \\frac{\\exp(o_j)}{\\sum_{k=1}^q \\exp(o_k)} \\\\ &= \\sum_{j=1}^q y_j \\log \\sum_{k=1}^q \\exp(o_k) - \\sum_{j=1}^q y_j o_j\\\\ &= \\log \\sum_{k=1}^q \\exp(o_k) - \\sum_{j=1}^q y_j o_j. \\end{aligned}\\end{split} \\] \\[ \\partial_{o_j} l(\\mathbf{y}, \\hat{\\mathbf{y}}) = \\frac{\\exp(o_j)}{\\sum_{k=1}^q \\exp(o_k)} - y_j = \\mathrm{softmax}(\\mathbf{o})_j - y_j. \\]"},{"location":"AI/Dive_into_Deep_Learning/#347","title":"3.4.7 \u4fe1\u606f\u8bba\u57fa\u7840","text":" - information theory
- entropy
\\[ H[P] = \\sum_j - P(j) \\log P(j). \\]"},{"location":"AI/Dive_into_Deep_Learning/#348","title":"3.4.8 \u6a21\u578b\u9884\u6d4b\u548c\u8bc4\u4f30","text":""},{"location":"AI/Dive_into_Deep_Learning/#35","title":"3.5 \u56fe\u50cf\u5206\u7c7b\u6570\u636e\u96c6","text":"Python%matplotlib inline\nimport torch\nimport torchvision\nfrom torch.utils import data\nfrom torchvision import transforms\nfrom d2l import torch as d2l\n\nd2l.use_svg_display()\n\n# \u901a\u8fc7ToTensor\u5b9e\u4f8b\u5c06\u56fe\u50cf\u6570\u636e\u4ecePIL\u7c7b\u578b\u53d8\u6362\u621032\u4f4d\u6d6e\u70b9\u6570\u683c\u5f0f\uff0c\n# \u5e76\u9664\u4ee5255\u4f7f\u5f97\u6240\u6709\u50cf\u7d20\u7684\u6570\u503c\u5747\u57280\uff5e1\u4e4b\u95f4\ntrans = transforms.ToTensor()\nmnist_train = torchvision.datasets.FashionMNIST(\n root=\"../data\", train=True, transform=trans, download=True)\nmnist_test = torchvision.datasets.FashionMNIST(\n root=\"../data\", train=False, transform=trans, download=True)\n\ndef get_fashion_mnist_labels(labels): #@save\n \"\"\"\u8fd4\u56deFashion-MNIST\u6570\u636e\u96c6\u7684\u6587\u672c\u6807\u7b7e\"\"\"\n text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n return [text_labels[int(i)] for i in labels]\n\ndef show_images(imgs, num_rows, num_cols, titles=None, scale=1.5): #@save\n \"\"\"\u7ed8\u5236\u56fe\u50cf\u5217\u8868\"\"\"\n figsize = (num_cols * scale, num_rows * scale)\n _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)\n axes = axes.flatten()\n for i, (ax, img) in enumerate(zip(axes, imgs)):\n if torch.is_tensor(img):\n # \u56fe\u7247\u5f20\u91cf\n ax.imshow(img.numpy())\n else:\n # PIL\u56fe\u7247\n ax.imshow(img)\n ax.axes.get_xaxis().set_visible(False)\n ax.axes.get_yaxis().set_visible(False)\n if titles:\n ax.set_title(titles[i])\n return axes\n\nbatch_size = 256\n\ndef get_dataloader_workers(): #@save\n \"\"\"\u4f7f\u75284\u4e2a\u8fdb\u7a0b\u6765\u8bfb\u53d6\u6570\u636e\"\"\"\n return 4\n\ntrain_iter = data.DataLoader(mnist_train, batch_size, shuffle=True,\n num_workers=get_dataloader_workers())\n\ndef load_data_fashion_mnist(batch_size, resize=None): #@save\n \"\"\"\u4e0b\u8f7dFashion-MNIST\u6570\u636e\u96c6\uff0c\u7136\u540e\u5c06\u5176\u52a0\u8f7d\u5230\u5185\u5b58\u4e2d\"\"\"\n trans = [transforms.ToTensor()]\n if resize:\n trans.insert(0, transforms.Resize(resize))\n trans = transforms.Compose(trans)\n mnist_train = torchvision.datasets.FashionMNIST(\n root=\"../data\", train=True, transform=trans, download=True)\n mnist_test = torchvision.datasets.FashionMNIST(\n root=\"../data\", train=False, transform=trans, download=True)\n return (data.DataLoader(mnist_train, batch_size, shuffle=True,\n num_workers=get_dataloader_workers()),\n data.DataLoader(mnist_test, batch_size, shuffle=False,\n num_workers=get_dataloader_workers()))\n\ntrain_iter, test_iter = load_data_fashion_mnist(32, resize=64)\nfor X, y in train_iter:\n print(X.shape, X.dtype, y.shape, y.dtype)\n break\n
"},{"location":"AI/Dive_into_Deep_Learning/#36-softmax","title":"3.6 softmax \u56de\u5f52\u7684\u4ece\u96f6\u5f00\u59cb\u5b9e\u73b0","text":"Pythonimport torch\nfrom IPython import display\nfrom d2l import torch as d2l\n\nbatch_size = 256\ntrain_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)\n\nnum_inputs = 784\nnum_outputs = 10\n\nW = torch.normal(0, 0.01, size=(num_inputs, num_outputs), requires_grad=True)\nb = torch.zeros(num_outputs, requires_grad=True) # \u521d\u59cb\u5316\u6a21\u578b\u53c2\u6570\n\nX = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])\nX.sum(0, keepdim=True), X.sum(1, keepdim=True) \n\ndef softmax(X):\n X_exp = torch.exp(X)\n partition = X_exp.sum(1, keepdim=True)\n return X_exp / partition # \u8fd9\u91cc\u5e94\u7528\u4e86\u5e7f\u64ad\u673a\u5236\n\nX = torch.normal(0, 1, (2, 5))\nX_prob = softmax(X)\nX_prob, X_prob.sum(1) # \u5b9a\u4e49softmax\u64cd\u4f5c\n\ndef net(X):\n return softmax(torch.matmul(X.reshape((-1, W.shape[0])), W) + b) # \u5b9a\u4e49\u6a21\u578b\n\ny = torch.tensor([0, 2])\ny_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\ny_hat[[0, 1], y] \ndef cross_entropy(y_hat, y):\n return - torch.log(y_hat[range(len(y_hat)), y])\n\ncross_entropy(y_hat, y) # \u5b9a\u4e49\u635f\u5931\u51fd\u6570\n\ndef accuracy(y_hat, y): #@save\n \"\"\"\u8ba1\u7b97\u9884\u6d4b\u6b63\u786e\u7684\u6570\u91cf\"\"\"\n if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:\n y_hat = y_hat.argmax(axis=1)\n cmp = y_hat.type(y.dtype) == y\n return float(cmp.type(y.dtype).sum())\n\ndef evaluate_accuracy(net, data_iter): #@save\n \"\"\"\u8ba1\u7b97\u5728\u6307\u5b9a\u6570\u636e\u96c6\u4e0a\u6a21\u578b\u7684\u7cbe\u5ea6\"\"\"\n if isinstance(net, torch.nn.Module):\n net.eval() # \u5c06\u6a21\u578b\u8bbe\u7f6e\u4e3a\u8bc4\u4f30\u6a21\u5f0f\n metric = Accumulator(2) # \u6b63\u786e\u9884\u6d4b\u6570\u3001\u9884\u6d4b\u603b\u6570\n with torch.no_grad():\n for X, y in data_iter:\n metric.add(accuracy(net(X), y), y.numel())\n return metric[0] / metric[1]\n\nclass Accumulator: #@save\n \"\"\"\u5728n\u4e2a\u53d8\u91cf\u4e0a\u7d2f\u52a0\"\"\"\n def __init__(self, n):\n self.data = [0.0] * n\n\n def add(self, *args):\n self.data = [a + float(b) for a, b in zip(self.data, args)]\n\n def reset(self):\n self.data = [0.0] * len(self.data)\n\n def __getitem__(self, idx):\n return self.data[idx]\n\nevaluate_accuracy(net, test_iter) # \u5206\u7c7b\u7cbe\u5ea6\n\ndef train_epoch_ch3(net, train_iter, loss, updater): #@save\n \"\"\"\u8bad\u7ec3\u6a21\u578b\u4e00\u4e2a\u8fed\u4ee3\u5468\u671f\uff08\u5b9a\u4e49\u89c1\u7b2c3\u7ae0\uff09\"\"\"\n # \u5c06\u6a21\u578b\u8bbe\u7f6e\u4e3a\u8bad\u7ec3\u6a21\u5f0f\n if isinstance(net, torch.nn.Module):\n net.train()\n # \u8bad\u7ec3\u635f\u5931\u603b\u548c\u3001\u8bad\u7ec3\u51c6\u786e\u5ea6\u603b\u548c\u3001\u6837\u672c\u6570\n metric = Accumulator(3)\n for X, y in train_iter:\n # \u8ba1\u7b97\u68af\u5ea6\u5e76\u66f4\u65b0\u53c2\u6570\n y_hat = net(X)\n l = loss(y_hat, y)\n if isinstance(updater, torch.optim.Optimizer):\n # \u4f7f\u7528PyTorch\u5185\u7f6e\u7684\u4f18\u5316\u5668\u548c\u635f\u5931\u51fd\u6570\n updater.zero_grad()\n l.mean().backward()\n updater.step()\n else:\n # \u4f7f\u7528\u5b9a\u5236\u7684\u4f18\u5316\u5668\u548c\u635f\u5931\u51fd\u6570\n l.sum().backward()\n updater(X.shape[0])\n metric.add(float(l.sum()), accuracy(y_hat, y), y.numel())\n # \u8fd4\u56de\u8bad\u7ec3\u635f\u5931\u548c\u8bad\u7ec3\u7cbe\u5ea6\n return metric[0] / metric[2], metric[1] / metric[2]\n\nclass Animator: #@save\n \"\"\"\u5728\u52a8\u753b\u4e2d\u7ed8\u5236\u6570\u636e\"\"\"\n def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,\n ylim=None, xscale='linear', yscale='linear',\n fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,\n figsize=(3.5, 2.5)):\n # \u589e\u91cf\u5730\u7ed8\u5236\u591a\u6761\u7ebf\n if legend is None:\n legend = []\n d2l.use_svg_display()\n self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize=figsize)\n if nrows * ncols == 1:\n self.axes = [self.axes, ]\n # \u4f7f\u7528lambda\u51fd\u6570\u6355\u83b7\u53c2\u6570\n self.config_axes = lambda: d2l.set_axes(\n self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)\n self.X, self.Y, self.fmts = None, None, fmts\n\n def add(self, x, y):\n # \u5411\u56fe\u8868\u4e2d\u6dfb\u52a0\u591a\u4e2a\u6570\u636e\u70b9\n if not hasattr(y, \"__len__\"):\n y = [y]\n n = len(y)\n if not hasattr(x, \"__len__\"):\n x = [x] * n\n if not self.X:\n self.X = [[] for _ in range(n)]\n if not self.Y:\n self.Y = [[] for _ in range(n)]\n for i, (a, b) in enumerate(zip(x, y)):\n if a is not None and b is not None:\n self.X[i].append(a)\n self.Y[i].append(b)\n self.axes[0].cla()\n for x, y, fmt in zip(self.X, self.Y, self.fmts):\n self.axes[0].plot(x, y, fmt)\n self.config_axes()\n display.display(self.fig)\n display.clear_output(wait=True)\n\ndef train_ch3(net, train_iter, test_iter, loss, num_epochs, updater): #@save\n \"\"\"\u8bad\u7ec3\u6a21\u578b\uff08\u5b9a\u4e49\u89c1\u7b2c3\u7ae0\uff09\"\"\"\n animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.3, 0.9],\n legend=['train loss', 'train acc', 'test acc'])\n for epoch in range(num_epochs):\n train_metrics = train_epoch_ch3(net, train_iter, loss, updater)\n test_acc = evaluate_accuracy(net, test_iter)\n animator.add(epoch + 1, train_metrics + (test_acc,))\n train_loss, train_acc = train_metrics\n assert train_loss < 0.5, train_loss\n assert train_acc <= 1 and train_acc > 0.7, train_acc\n assert test_acc <= 1 and test_acc > 0.7, test_acc\n\nlr = 0.1\n\ndef updater(batch_size):\n return d2l.sgd([W, b], lr, batch_size)\n\nnum_epochs = 10\ntrain_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, updater) # \u8bad\u7ec3\n\ndef predict_ch3(net, test_iter, n=6): #@save\n \"\"\"\u9884\u6d4b\u6807\u7b7e\uff08\u5b9a\u4e49\u89c1\u7b2c3\u7ae0\uff09\"\"\"\n for X, y in test_iter:\n break\n trues = d2l.get_fashion_mnist_labels(y)\n preds = d2l.get_fashion_mnist_labels(net(X).argmax(axis=1))\n titles = [true +'\\n' + pred for true, pred in zip(trues, preds)]\n d2l.show_images(\n X[0:n].reshape((n, 28, 28)), 1, n, titles=titles[0:n])\n\npredict_ch3(net, test_iter) # \u9884\u6d4b\n
"},{"location":"AI/Dive_into_Deep_Learning/#37-softmax","title":"3.7 softmax \u56de\u5f52\u7684\u7b80\u6d01\u5b9e\u73b0","text":"Pythonimport torch\nfrom torch import nn\nfrom d2l import torch as d2l\n\nbatch_size = 256\ntrain_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)\n\n# PyTorch\u4e0d\u4f1a\u9690\u5f0f\u5730\u8c03\u6574\u8f93\u5165\u7684\u5f62\u72b6\u3002\u56e0\u6b64\uff0c\n# \u6211\u4eec\u5728\u7ebf\u6027\u5c42\u524d\u5b9a\u4e49\u4e86\u5c55\u5e73\u5c42\uff08flatten\uff09\uff0c\u6765\u8c03\u6574\u7f51\u7edc\u8f93\u5165\u7684\u5f62\u72b6\nnet = nn.Sequential(nn.Flatten(), nn.Linear(784, 10))\n\ndef init_weights(m):\n if type(m) == nn.Linear:\n nn.init.normal_(m.weight, std=0.01)\n\nnet.apply(init_weights); # \u521d\u59cb\u5316\u6a21\u578b\u53c2\u6570\n
\\[ \\begin{split}\\begin{aligned} \\hat y_j & = \\frac{\\exp(o_j - \\max(o_k))\\exp(\\max(o_k))}{\\sum_k \\exp(o_k - \\max(o_k))\\exp(\\max(o_k))} \\\\ & = \\frac{\\exp(o_j - \\max(o_k))}{\\sum_k \\exp(o_k - \\max(o_k))}. \\end{aligned}\\end{split} \\] \\[ \\begin{split}\\begin{aligned} \\log{(\\hat y_j)} & = \\log\\left( \\frac{\\exp(o_j - \\max(o_k))}{\\sum_k \\exp(o_k - \\max(o_k))}\\right) \\\\ & = \\log{(\\exp(o_j - \\max(o_k)))}-\\log{\\left( \\sum_k \\exp(o_k - \\max(o_k)) \\right)} \\\\ & = o_j - \\max(o_k) -\\log{\\left( \\sum_k \\exp(o_k - \\max(o_k)) \\right)}. \\end{aligned}\\end{split} \\] Pythonloss = nn.CrossEntropyLoss(reduction='none') # LogSumExp\u6280\u5de7\n\ntrainer = torch.optim.SGD(net.parameters(), lr=0.1) # \u4f18\u5316\u7b97\u6cd5\n\nnum_epochs = 10\nd2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer) # \u8bad\u7ec3\n
"},{"location":"AI/Dive_into_Deep_Learning/#_1","title":"\u591a\u5c42\u611f\u77e5\u673a","text":""},{"location":"AI/Dive_into_Deep_Learning/#_2","title":"\u8fc7\u62df\u5408\u548c\u6b20\u62df\u5408","text":""},{"location":"AI/Dive_into_Deep_Learning/#dropout","title":"dropout","text":""},{"location":"AI/Dive_into_Deep_Learning/#_3","title":"\u6df1\u5ea6\u5b66\u4e60\u8ba1\u7b97","text":""},{"location":"AI/Dive_into_Deep_Learning/#_4","title":"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc","text":""},{"location":"AI/Dive_into_Deep_Learning/#lenet","title":"LeNet","text":""},{"location":"AI/Dive_into_Deep_Learning/#_5","title":"\u73b0\u4ee3\u5377\u79ef\u795e\u7ecf\u7f51\u7edc","text":""},{"location":"AI/Dive_into_Deep_Learning/#alexnet","title":"AlexNet","text":""},{"location":"AI/Dive_into_Deep_Learning/#vgg","title":"VGG","text":""},{"location":"AI/Dive_into_Deep_Learning/#nin","title":"NiN","text":""},{"location":"AI/Dive_into_Deep_Learning/#googlenet","title":"GoogLeNet","text":""},{"location":"AI/Dive_into_Deep_Learning/#resnet","title":"ResNet","text":""},{"location":"AI/Dive_into_Deep_Learning/#densenet","title":"DenseNet","text":""},{"location":"AI/Dive_into_Deep_Learning/#_6","title":"\u5faa\u73af\u795e\u7ecf\u7f51\u7edc","text":""},{"location":"AI/Dive_into_Deep_Learning/#_7","title":"\u73b0\u4ee3\u5faa\u73af\u795e\u7ecf\u7f51\u7edc","text":""},{"location":"AI/Dive_into_Deep_Learning/#gru","title":"GRU","text":""},{"location":"AI/Dive_into_Deep_Learning/#lstm","title":"LSTM","text":""},{"location":"AI/Dive_into_Deep_Learning/#seq2seq","title":"seq2seq","text":""},{"location":"AI/Dive_into_Deep_Learning/#_8","title":"\u6ce8\u610f\u529b\u673a\u5236","text":""},{"location":"AI/Dive_into_Deep_Learning/#nadaraya-watson","title":"Nadaraya-Watson","text":""},{"location":"AI/Dive_into_Deep_Learning/#bahdanau","title":"Bahdanau","text":""},{"location":"AI/Dive_into_Deep_Learning/#multi-headed-attention","title":"Multi-headed attention","text":""},{"location":"AI/Dive_into_Deep_Learning/#transformer","title":"Transformer","text":""},{"location":"AI/Dive_into_Deep_Learning/#_9","title":"\u4f18\u5316\u7b97\u6cd5","text":""},{"location":"AI/Dive_into_Deep_Learning/#adagrad","title":"AdaGrad","text":""},{"location":"AI/Dive_into_Deep_Learning/#rmsprop","title":"RMSProp","text":""},{"location":"AI/Dive_into_Deep_Learning/#adadelta","title":"Adadelta","text":""},{"location":"AI/Dive_into_Deep_Learning/#adam","title":"Adam","text":""},{"location":"AI/Dive_into_Deep_Learning/#_10","title":"\u8ba1\u7b97\u6027\u80fd","text":""},{"location":"AI/Dive_into_Deep_Learning/#_11","title":"\u8ba1\u7b97\u673a\u89c6\u89c9","text":""},{"location":"AI/Dive_into_Deep_Learning/#ssd","title":"SSD","text":""},{"location":"AI/Dive_into_Deep_Learning/#r-cnn","title":"R-CNN","text":""},{"location":"AI/Dive_into_Deep_Learning/#_12","title":"\u81ea\u7136\u8bed\u8a00\u5904\u7406:\u9884\u8bad\u7ec3","text":""},{"location":"AI/Dive_into_Deep_Learning/#word2vec","title":"word2vec","text":""},{"location":"AI/Dive_into_Deep_Learning/#glove","title":"GloVe","text":""},{"location":"AI/Dive_into_Deep_Learning/#bert","title":"BERT","text":""},{"location":"AI/Dive_into_Deep_Learning/#_13","title":"\u81ea\u7136\u8bed\u8a00\u5904\u7406:\u5e94\u7528","text":""},{"location":"AI/SLAM14/","title":"\u89c6\u89c9SLAM\u5341\u56db\u8bb2","text":""},{"location":"AI/SLAM14/#slam","title":"\u89c6\u89c9SLAM\u5341\u56db\u8bb2","text":" \u7ea6 14110 \u4e2a\u5b57 72 \u884c\u4ee3\u7801 20 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 71 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
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- \u5b9a\u4f4d
- \u5730\u56fe\u6784\u5efa
- \u80cc\u666f\u77e5\u8bc6:
- \u5c04\u5f71\u51e0\u4f55
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"},{"location":"AI/SLAM14/#12","title":"1.2 \u5982\u4f55\u4f7f\u7528\u672c\u4e66","text":""},{"location":"AI/SLAM14/#121","title":"1.2.1 \u7ec4\u7ec7\u65b9\u5f0f","text":" - \u6570\u5b66\u57fa\u7840\u7bc7
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- Linux \u57fa\u7840
"},{"location":"AI/SLAM14/#13","title":"1.3 \u98ce\u683c\u7ea6\u5b9a","text":""},{"location":"AI/SLAM14/#14","title":"1.4 \u81f4\u8c22\u548c\u58f0\u660e","text":""},{"location":"AI/SLAM14/#15","title":"1.5 \u4e60\u9898","text":" - \u9898\u76ee\uff1a\u6709\u7ebf\u6027\u65b9\u7a0b \\(A x=b\\)\uff0c\u82e5\u5df2\u77e5 \\(A, b\\)\uff0c\u9700\u8981\u6c42\u89e3 x\uff0c\u8be5\u5982\u4f55\u6c42\u89e3\uff1f\u8fd9\u5bf9 A \u548c b \u6709\u54ea\u4e9b\u8981\u6c42\uff1f\u63d0\u793a\uff1a\u4ece A \u7684\u7ef4\u5ea6\u548c\u79e9\u89d2\u5ea6\u6765\u5206\u6790\u3002
- \u7b54\u6848\uff1a\u7ebf\u6027\u65b9\u7a0b\u7ec4 \\(Ax = b\\) \u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u6c42\u89e3\uff0c\u5982\u9ad8\u65af\u6d88\u5143\u6cd5\u3001\u77e9\u9635\u9006\u6cd5\u7b49\u3002\u8981\u6c42 \\(A\\) \u662f\u4e00\u4e2a\u65b9\u9635\u4e14\u53ef\u9006\uff08\u5373 \\(A\\) \u7684\u884c\u5217\u5f0f\u4e0d\u4e3a\u96f6\uff09\uff0c\u8fd9\u6837\u65b9\u7a0b\u624d\u6709\u552f\u4e00\u89e3\u3002\u5982\u679c \\(A\\) \u4e0d\u662f\u65b9\u9635\uff0c\u9700\u8981 \\(A\\) \u7684\u79e9\u7b49\u4e8e\u5217\u6570\u4e14\u7b49\u4e8e\u589e\u5e7f\u77e9\u9635 \\(\\displaystyle [A|b]\\) \u7684\u79e9\uff0c\u8fd9\u6837\u65b9\u7a0b\u7ec4\u624d\u6709\u89e3\u3002
- \u9898\u76ee\uff1a\u9ad8\u65af\u5206\u5e03\u662f\u4ec0\u4e48\uff1f\u5b83\u7684\u4e00\u7ef4\u5f62\u5f0f\u662f\u4ec0\u4e48\u6837\u5b50\uff1f\u5b83\u7684\u9ad8\u7ef4\u5f62\u5f0f\u662f\u4ec0\u4e48\u6837\u5b50\uff1f
- \u7b54\u6848\uff1a\u9ad8\u65af\u5206\u5e03\uff0c\u4e5f\u79f0\u4e3a\u6b63\u6001\u5206\u5e03\uff0c\u662f\u4e00\u79cd\u8fde\u7eed\u6982\u7387\u5206\u5e03\u3002\u4e00\u7ef4\u9ad8\u65af\u5206\u5e03\u7684\u6570\u5b66\u8868\u8fbe\u5f0f\u4e3a \\(\\displaystyle f (x) = \\frac{1}{\\sigma\\sqrt{2\\pi}} e^{-\\frac{(x-\\mu)^2}{2\\sigma^2}}\\)\uff0c\u5176\u4e2d \\(\\displaystyle \\mu\\) \u662f\u5747\u503c\uff0c\\(\\displaystyle \\sigma\\) \u662f\u6807\u51c6\u5dee\u3002\u9ad8\u7ef4\u9ad8\u65af\u5206\u5e03\u662f\u4e00\u7ef4\u9ad8\u65af\u5206\u5e03\u5728\u591a\u7ef4\u7a7a\u95f4\u7684\u63a8\u5e7f\uff0c\u5176\u6982\u7387\u5bc6\u5ea6\u51fd\u6570\u4e3a \\(\\displaystyle N (\\mathbf{x}; \\mathbf{\\mu}, \\Sigma)\\)\uff0c\u5176\u4e2d \\(\\displaystyle \\mathbf{\\mu}\\) \u662f\u5747\u503c\u5411\u91cf\uff0c\\(\\displaystyle \\Sigma\\) \u662f\u534f\u65b9\u5dee\u77e9\u9635\u3002
- \u9898\u76ee\uff1a\u4f60\u77e5\u9053 C++11 \u6807\u51c6\u5417\uff1f\u4f60\u542c\u8bf4\u8fc7\u6216\u7528\u8fc7\u5176\u4e2d\u54ea\u4e9b\u65b0\u7279\u6027\uff1f\u6709\u6ca1\u6709\u5176\u4ed6\u7684\u6807\u51c6\uff1f
- \u7b54\u6848\uff1a\u662f\u7684\uff0cC++11 \u662f C++ \u8bed\u8a00\u7684\u4e00\u4e2a\u91cd\u8981\u6807\u51c6\uff0c\u5b83\u5f15\u5165\u4e86\u8bb8\u591a\u65b0\u7279\u6027\uff0c\u5982\u81ea\u52a8\u7c7b\u578b\u63a8\u5bfc\uff08auto\uff09\u3001\u57fa\u4e8e\u8303\u56f4\u7684 for \u5faa\u73af\u3001lambda \u8868\u8fbe\u5f0f\u3001\u667a\u80fd\u6307\u9488\u7b49\u3002\u9664\u4e86 C++11\uff0c\u8fd8\u6709 C++14\u3001C++17 \u548c C++20 \u7b49\u540e\u7eed\u6807\u51c6\uff0c\u5b83\u4eec\u4e5f\u5f15\u5165\u4e86\u65b0\u7684\u7279\u6027\u548c\u6539\u8fdb\u3002
- \u9898\u76ee\uff1a\u5982\u4f55\u5728 Ubuntu \u7cfb\u7edf\u4e2d\u5b89\u88c5\u8f6f\u4ef6\uff08\u4e0d\u6253\u5f00\u8f6f\u4ef6\u4e2d\u5fc3\u7684\u60c5\u51b5\u4e0b\uff09\uff1f\u8fd9\u4e9b\u8f6f\u4ef6\u88ab\u5b89\u88c5\u5728\u4ec0\u4e48\u5730\u65b9\uff1f\u5982\u679c\u53ea\u77e5\u9053\u6a21\u7cca\u7684\u8f6f\u4ef6\u540d\u79f0\uff08\u6bd4\u5982\u60f3\u8981\u88c5\u4e00\u4e2a\u540d\u79f0\u4e2d\u542b\u6709 Eigen \u7684\u5e93\uff09\uff0c\u5e94\u8be5\u5982\u4f55\u5b89\u88c5\u5b83\uff1f
- \u7b54\u6848\uff1a
- \u8f6f\u4ef6\u5b89\u88c5\uff1a\u5728 Ubuntu \u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u547d\u4ee4\u884c\u5de5\u5177
apt \u6765\u5b89\u88c5\u8f6f\u4ef6\u3002\u57fa\u672c\u547d\u4ee4\u4e3a sudo apt install [package-name]\u3002 - \u5b89\u88c5\u4f4d\u7f6e\uff1a\u8f6f\u4ef6\u901a\u5e38\u88ab\u5b89\u88c5\u5728
/usr/ \u76ee\u5f55\u4e0b\uff0c\u4f46\u5177\u4f53\u7684\u6587\u4ef6\u53ef\u80fd\u5206\u5e03\u5728\u591a\u4e2a\u5b50\u76ee\u5f55\u4e2d\u3002 - \u6a21\u7cca\u540d\u79f0\u5b89\u88c5\uff1a\u5982\u679c\u53ea\u77e5\u9053\u8f6f\u4ef6\u540d\u79f0\u7684\u4e00\u90e8\u5206\uff0c\u53ef\u4ee5\u4f7f\u7528
apt search \u547d\u4ee4\u6765\u641c\u7d22\u3002\u4f8b\u5982\uff0csudo apt search eigen \u53ef\u4ee5\u5e2e\u52a9\u627e\u5230\u6240\u6709\u5305\u542b \"eigen\" \u7684\u8f6f\u4ef6\u5305\u3002 - \u9898\u76ee\uff1a*\u82b1\u4e00\u4e2a\u5c0f\u65f6\u5b66\u4e60 Vim\uff0c\u56e0\u4e3a\u4f60\u8fdf\u65e9\u4f1a\u7528\u5b83\u3002\u4f60\u53ef\u4ee5\u5728\u7ec8\u7aef\u4e2d\u8f93\u5165 vimtutor \u9605\u8bfb\u4e00\u904d\u6240\u6709\u5185\u5bb9\u3002\u6211\u4eec\u4e0d\u9700\u8981\u4f60\u975e\u5e38\u719f\u7ec3\u5730\u64cd\u4f5c\u5b83\uff0c\u53ea\u8981\u80fd\u591f\u5728\u5b66\u4e60\u672c\u4e66\u7684\u8fc7\u7a0b\u4e2d\u4f7f\u7528\u5b83\u8f93\u5165\u4ee3\u7801\u5373\u53ef\u3002\u4e0d\u8981\u5728\u5b83\u7684\u63d2\u4ef6\u4e0a\u6d6a\u8d39\u65f6\u95f4\uff0c\u4e0d\u8981\u60f3\u7740\u628a Vim \u7528\u6210 IDE\uff0c\u6211\u4eec\u53ea\u7528\u5b83\u505a\u6587\u672c\u7f16\u8f91\u7684\u5de5\u4f5c\u3002
- \u7b54\u6848:
- vim \u6839\u672c\u4e0d\u719f\u7ec3\u634f
"},{"location":"AI/SLAM14/#2-slam","title":"2 \u521d\u8bc6 SLAM","text":""},{"location":"AI/SLAM14/#21","title":"2.1 \u5f15\u5b50: \u5c0f\u841d\u535c\u7684\u4f8b\u5b50","text":" - \u81ea\u4e3b\u8fd0\u52a8\u80fd\u529b
- \u611f\u77e5\u5468\u8fb9\u73af\u5883
- \u72b6\u6001
- \u73af\u5883
- \u5b89\u88c5\u4e8e\u73af\u5883\u4e2d\uff08\u4e0d\u592a\u597d\u53cd\u6b63\uff09
- \u673a\u5668\u4eba\u672c\u4f53\u4e0a
- \u6fc0\u5149 SLAM
- \u89c6\u89c9 SLAM\uff08\u672c\u4e66\u91cd\u70b9\uff09
- \u5355\u76ee\uff08Monocular\uff09
- \u53ea\u80fd\u7528\u4e00\u4e2a\u6444\u50cf\u5934
- \u8ddd\u79bb\u611f
- motion
- Structure
- Disparity
- Scale
- \u4f46\u662f\u65e0\u6cd5\u786e\u5b9a\u6df1\u5ea6
- \u53cc\u76ee\uff08Sterco\uff09
- \u4e24\u4e2a\u76f8\u673a\u7684\u8ddd\u79bb\uff08\u57fa\u7ebf Baseline\uff09\u5df2\u77e5
- \u914d\u7f6e\u4e0e\u6807\u5b9a\u6bd4\u8f83\u590d\u6742
- \u6df1\u5ea6\uff08RGB-D\uff09
- \u7ea2\u5916\u7ed3\u6784\u5173 Time-of-Flight\uff08ToF\uff09
- \u4e3b\u8981\u7528\u5728\u5ba4\u5185\uff0c\u5ba4\u5916\u4f1a\u6709\u5f88\u591a\u5f71\u54cd
- \u8fd8\u6709\u4e00\u4e9b\u975e\u4e3b\u6d41\u7684: \u5168\u666f\uff0cEvent
"},{"location":"AI/SLAM14/#22-slam","title":"2.2 \u7ecf\u5178\u89c6\u89c9 SLAM \u6846\u67b6","text":" - \u5728\u5916\u754c\u6362\u51e0\u4e2a\u6bd4\u8f83\u7a33\u5b9a\u7684\u60c5\u51b5\u4e0b\uff0cSLAM \u6280\u672f\u5df2\u7ecf\u6bd4\u8f83\u6210\u719f
"},{"location":"AI/SLAM14/#221","title":"2.2.1 \u89c6\u89c9\u91cc\u7a0b\u8ba1","text":" - \u53ea\u901a\u8fc7\u89c6\u89c9\u91cc\u7a0b\u8ba1\u6765\u4f30\u8ba1\u8f68\u8ff9\u4f1a\u51fa\u73b0\u7d2f\u79ef\u6f02\u79fb\uff08Accumulating Drift\uff09\u3002
- \u6240\u4ee5\u9700\u8981\u56de\u73af\u68c0\u6d4b\u4e0e\u540e\u7aef\u4f18\u5316
"},{"location":"AI/SLAM14/#222","title":"2.2.2 \u540e\u7aef\u4f18\u5316","text":" - \u6700\u5927\u540e\u9a8c\u6982\u7387\u4f30\u8ba1\uff08Maximum-a-Posteriori MAP\uff09
- \u524d\u7aef
- \u56fe\u50cf\u7684\u7279\u5f81\u63d0\u53d6\u4e0e\u5339\u914d
- \u540e\u7aef
- \u6ee4\u6ce2\u4e0e\u975e\u7ebf\u6027\u7b97\u6cd5
- \u5bf9\u8fd0\u52a8\u4e3b\u4f53\u81ea\u8eab\u548c\u5468\u56f4\u73af\u5883\u7a7a\u95f4\u4e0d\u786e\u5b9a\u6027\u7684\u4f30\u8ba1
"},{"location":"AI/SLAM14/#223","title":"2.2.3 \u56de\u73af\u68c0\u6d4b","text":" - \u95ed\u73af\u68c0\u6d4b
- \u8bc6\u522b\u5230\u8fc7\u7684\u573a\u666f
- \u5229\u7528\u56fe\u50cf\u7684\u76f8\u4f3c\u6027
"},{"location":"AI/SLAM14/#224","title":"2.2.4 \u5efa\u56fe","text":" - \u5ea6\u91cf\u5730\u56fe
- Sparse
- Landmark
- \u5b9a\u4f4d\u7528
- Dense
- Grid / Vocel
- \u5bfc\u822a\u7528
- \u62d3\u6251\u5730\u56fe Graph
"},{"location":"AI/SLAM14/#23-slam","title":"2.3 SLAM \u95ee\u9898\u7684\u6570\u5b66\u8868\u8ff0","text":" - \u8fd0\u52a8\u65b9\u7a0b
- \\(\\displaystyle \\quad\\boldsymbol{x}_k=f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k,\\boldsymbol{w}_k\\right).\\)
- \\(\\displaystyle \\boldsymbol{u}_{k}\\) \u662f\u8fd0\u52a8\u4f20\u611f\u5668\u7684\u8f93\u5165
- \\(\\displaystyle \\boldsymbol{w}_{k}\\) \u662f\u8fc7\u7a0b\u4e2d\u52a0\u5165\u7684\u566a\u58f0
- \u89c2\u6d4b\u65b9\u7a0b
- \\(\\displaystyle \\boldsymbol{z}_{k,j} = h (\\boldsymbol{y}_{j},\\boldsymbol{x}_{k},\\boldsymbol{v}_{k,j})\\)
- \\(\\displaystyle \\boldsymbol{v}_{k,j}\\) \u662f\u89c2\u6d4b\u91cc\u7684\u566a\u58f0
- \u53c8\u5f88\u591a\u53c2\u6570\u5316\u7684\u65b9\u5f0f
- \u53ef\u4ee5\u603b\u7ed3\u4e3a\u5982\u4e0b\u4e24\u4e2a\u65b9\u7a0b
\\[ \\begin{cases}\\boldsymbol{x}_k=f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k,\\boldsymbol{w}_k\\right),&k=1,\\cdots,K\\\\\\boldsymbol{z}_{k,j}=h\\left(\\boldsymbol{y}_j,\\boldsymbol{x}_k,\\boldsymbol{v}_{k,j}\\right),&(k,j)\\in\\mathcal{O}\\end{cases}. \\] - \u77e5\u9053\u8fd0\u52a8\u6d4b\u91cf\u7684\u8bfb\u6570 \\(\\displaystyle \\boldsymbol{u}\\) \u548c\u4f20\u611f\u5668\u7684\u8bfb\u6570 \\(\\displaystyle \\boldsymbol{z}\\)\uff0c\u5982\u4f55\u6c42\u89e3\u5b9a\u4f4d\u95ee\u9898\u548c\u5efa\u56fe\u95ee\u9898\u3002
- \u72b6\u6001\u4f30\u8ba1\u95ee\u9898: \u5982\u4f55\u901a\u8fc7\u5e26\u6709\u566a\u58f0\u7684\u6d4b\u91cf\u6570\u636e\uff0c\u4f30\u8ba1\u5185\u90e8\u7684\u3001\u9690\u85cf\u7740\u7684\u72b6\u6001\u53d8\u91cf
- Linear Gaussian -> Kalman Filter
- Non-Linear Non-Gaussian -> Extended Kalman Filter \u548c\u975e\u7ebf\u6027\u4f18\u5316
- EKF -> Particle Filter -> Graph Optimization
"},{"location":"AI/SLAM14/#24","title":"2.4 \u5b9e\u8df5: \u7f16\u7a0b\u57fa\u7840","text":""},{"location":"AI/SLAM14/#241-linux","title":"2.4.1 \u5b89\u88c5 Linux \u64cd\u4f5c\u7cfb\u7edf","text":""},{"location":"AI/SLAM14/#242-hello-slam","title":"2.4.2 Hello SLAM","text":""},{"location":"AI/SLAM14/#243-cmake","title":"2.4.3 \u4f7f\u7528 cmake","text":"Text Onlycmake_minimum_required( VERSION 2.8)\n\nproject(HelloSLAM)\n\nadd_executable(helloSLAM helloSLAM.cpp)\n
\u5bf9\u4e2d\u95f4\u6587\u4ef6\u7684\u5904\u7406:
Text Onlymkdir build\ncd build\ncmake ..\nmake\n
"},{"location":"AI/SLAM14/#244","title":"2.4.4 \u4f7f\u7528\u5e93","text":"Text Onlyadd_library(hello libHelloSLAM.cpp)\n
- \u9759\u6001\u5e93
- .a \u4f5c\u4e3a\u540e\u7f00\u540d\uff0c\u6bcf\u6b21\u8c03\u7528\u90fd\u6709\u4e00\u4e2a\u526f\u672c
- \u5171\u4eab\u5e93
- .so\uff0c\u53ea\u6709\u4e00\u4e2a\u526f\u672c
Text Onlyadd_library(hello_shared SHARED libHelloSLAM.cpp)\n
- \u8fd8\u8981\u4e00\u4e2a\u5934\u6587\u4ef6\u6765\u8bf4\u660e\u5e93\u91cc\u90fd\u6709\u4ec0\u4e48
Text Only#ifndef LIBHELLOSLAM_H_\n#define LIBHELLOSLAM_H_\n\nvoid printHello()\n\n#endif\n
- \u6700\u540e\u5199\u4e00\u4e2a\u53ef\u6267\u884c\u7a0b\u5e8f:
C++#include \"libHelloSLAM.h\"\n\nint main(int argc, char **argv) {\n printHello();\n return 0; \n}\n
- \u5728 CMakeLists. txt \u4e2d\u6dfb\u52a0\u53ef\u6267\u884c\u547d\u4ee4\u7684\u751f\u6210\u547d\u4ee4\uff0c\u94fe\u63a5\u5230\u5e93\u4e0a:
Text Onlyadd_executable(useHello useHello.cpp)\ntarget_link_libraries(useHello hello_shared)\n
"},{"location":"AI/SLAM14/#245-ide","title":"2.4.5 \u4f7f\u7528 IDE","text":" - KDevelop
- Clion
- \u8fd8\u6ca1\u5199
"},{"location":"AI/SLAM14/#3","title":"3 \u4e09\u7ef4\u7a7a\u95f4\u521a\u4f53\u8fd0\u52a8","text":""},{"location":"AI/SLAM14/#31","title":"3.1 \u65cb\u8f6c\u77e9\u9635","text":""},{"location":"AI/SLAM14/#311","title":"3.1.1 \u70b9\u3001\u5411\u91cf\u548c\u5750\u6807\u7cfb","text":" \\[ a\\times b = \\begin{Vmatrix}e_1&e_2&e_3\\\\ \\\\ a_1&a_2&a_3\\\\ \\\\ b_1&b_2&b_3 \\\\ \\end{Vmatrix} = \\begin{bmatrix} a_2b_3-a_3b_2\\\\ \\\\ a_3b_1-a_1b_3\\\\ \\\\ a_1b_2-a_2b_1 \\end{bmatrix} = \\begin{bmatrix} 0&-a_3&a_2\\\\ \\\\ a_3&0&-a_1\\\\ \\\\ -a_2&a_1&0 \\end{bmatrix} \\boldsymbol{b}\\overset{\\mathrm{def}}{\\operatorname*{=}}\\boldsymbol{a}^{\\wedge}\\boldsymbol{b}. \\] - \u4e8e\u662f\u5c31\u628a\u5916\u79ef\u53d8\u6210\u4e86\u7ebf\u6027\u8fd0\u7b97
- \u5373
\\[ \\displaystyle \\boldsymbol{a}^{\\wedge}=\\begin{bmatrix} 0 & -a_{3} & a_{2} \\\\ a_{3} & 0 & -a_{1} \\\\ -a_{2} & a_{1} & 0 \\end{bmatrix} \\]"},{"location":"AI/SLAM14/#312","title":"3.1.2 \u5750\u6807\u7cfb\u95f4\u7684\u6b27\u5f0f\u53d8\u6362","text":" \\[ \\begin{bmatrix}a_1\\\\ \\\\ a_2\\\\\\\\a_3\\end{bmatrix}=\\begin{bmatrix}e_1^\\mathrm{T}e_1^{\\prime}&e_1^\\mathrm{T}e_2^{\\prime}&e_1^\\mathrm{T}e_3^{\\prime}\\\\e_2^\\mathrm{T}e_1^{\\prime}&e_2^\\mathrm{T}e_2^{\\prime}&e_2^\\mathrm{T}e_3^{\\prime}\\\\e_3^\\mathrm{T}e_1^{\\prime}&e_3^\\mathrm{T}e_2^{\\prime}&e_3^\\mathrm{T}e_3^{\\prime}\\end{bmatrix}\\begin{bmatrix}a_1^{\\prime}\\\\\\\\a_2^{\\prime}\\\\\\\\a_3^{\\prime}\\end{bmatrix}\\stackrel{\\mathrm{def}}{=}Ra^{\\prime} \\] - \\(\\displaystyle \\boldsymbol{R}\\) \u662f\u65cb\u8f6c\u77e9\u9635\u3001\u65b9\u5411\u4f59\u5f26\u77e9\u9635
- Special Orthogonal Group \\(\\displaystyle \\mathrm{SO}(n)=\\{\\boldsymbol{R}\\in \\mathbb{R}^{n \\times n}|\\boldsymbol{R}\\boldsymbol{R}^{\\mathrm{T}}=\\boldsymbol{I},\\det(\\boldsymbol{R})=1\\}\\)
- \\(\\displaystyle a^{\\prime}=R^{-1}a=R^{\\intercal}a.\\)
- \u65cb\u8f6c+\u5e73\u79fb: \\(\\displaystyle a^{\\prime}=Ra+t.\\)
"},{"location":"AI/SLAM14/#313","title":"3.1.3 \u53d8\u6362\u77e9\u9635\u4e0e\u9f50\u6b21\u5750\u6807","text":" - \u4f46\u662f\u8fd9\u91cc\u7684\u53d8\u6362\u5173\u7cfb\u4e0d\u662f\u4e00\u4e2a\u7ebf\u6027\u5173\u7cfb
- \\(\\displaystyle c=R_2\\left(R_1a+t_1\\right)+t_2\\)
- \u6211\u4eec\u6539\u5199\u4e00\u4e0b\u5f62\u5f0f:
- \\(\\displaystyle \\begin{bmatrix}a'\\\\\\\\1\\end{bmatrix}=\\begin{bmatrix}R&t\\\\\\\\\\mathbf{0}^\\mathrm{T}&1\\end{bmatrix}\\begin{bmatrix}a\\\\\\\\1\\end{bmatrix}\\overset{\\mathrm{def}}{=}T\\begin{bmatrix}a\\\\\\\\1\\end{bmatrix}\\)
- \u8fd9\u5c31\u662f\u9f50\u6b21\u5750\u6807\uff0c\\(\\displaystyle \\boldsymbol{T}\\) \u79f0\u4e3a\u53d8\u6362\u77e9\u9635\uff08Transform matrix\uff09
- \\(\\displaystyle \\tilde{b}=T_1\\tilde{\\boldsymbol{a}}, \\tilde{\\boldsymbol{c}}=T_2\\tilde{\\boldsymbol{b}}\\quad\\Rightarrow\\tilde{\\boldsymbol{c}}=T_2T_1\\tilde{\\boldsymbol{a}}.\\)
- \u5e76\u4e14 \\(\\displaystyle \\boldsymbol{T}\\) \u79f0\u4e3a\u7279\u6b8a\u6b27\u5f0f\u7fa4\uff08Special Euclidean Group\uff09
- \\(\\displaystyle \\mathrm{SE}(3)=\\left\\{T=\\begin{bmatrix}R&t\\\\\\mathbf{0}^\\mathrm{T}&1\\end{bmatrix}\\in\\mathbb{R}^{4\\times4}|\\boldsymbol{R}\\in\\mathrm{SO}(3),\\boldsymbol{t}\\in\\mathbb{R}^3\\right\\}\\)
- \\(\\displaystyle T^{-1}=\\begin{bmatrix}R^\\mathrm{T}&-R^\\mathrm{T}t\\\\0^\\mathrm{T}&1\\end{bmatrix}\\)
- \u5728 C++\u7a0b\u5e8f\u4e2d\u53ef\u4ee5\u4f7f\u7528\u8fd0\u7b97\u7b26\u91cd\u8f7d\u6765\u5904\u7406\u9f50\u6b21\u548c\u975e\u9f50\u6b21\u7684\u60c5\u51b5
"},{"location":"AI/SLAM14/#32-eigen","title":"3.2 \u5b9e\u8df5:Eigen","text":"C++#include <iostream>\nusing namespace std;\n\n#include <ctime>\n\n#include <eigen3>\nusing namespace Eigen;\n\n#define MATRIX_SIZE 50\n\nint main(int argc, char **argv) {\n Matrix<float, 2, 3> matrix_23;\n// \u5982\u4e0b\u90fd\u662f\u4e09\u7ef4\u5411\u91cf\n Vector3d v_3d;\n Matrix<float, 3, 1> vd_3d;\n// \u5982\u4e0b\u662f3*3\u77e9\u9635\n Matrix3d matrix_33 = Matrix3d::Zero();\n// \u4e24\u4e2a\u52a8\u6001\u5206\u914d\n Matrix<double, Dynamic, Dynamic> matrix_dynamic;\n MatrixXd matrix_x;\n\n matrix_23 << 1, 2, 3, 4, 5, 6;\n cout<< \"matrix 2*3 from 1 to 6: \\n\" << matrix_23 << endl;\n\n cout << \"print matrix 2*3:\" << endl;\n for (int i = 0; i < 2; i++) {\n for (int j = 0; j < 3; j+) cout << matrix_23(i, j) << \"\\t\";\n cout << endl;\n }\n\n v_3d << 3, 2, 1;\n vd_3d << 4, 5, 6;\n\n Matrix<double, 2, 1> result = matrix_23.cast<double>() * v_3d;\n cout << \"[1, 2, 3; 4, 5, 6] * [3, 2, 1] =\" << result.transpose() << endl;\n\n matrix_22 = Matrix3d::Random();\n\n // \u4e00\u4e9b\u77e9\u9635\u7684\u64cd\u4f5c:\n // transpose()\n // sum()\n // trace()\n // inverse()\n // determinant()\n\n SelfAdjointEigenSolver<Matrix3d> eigen_solver(matrix_33.transpose() * matrix_33);\n cout << eigen_solver.eigenvalues() << endl;\n cout << eigen_solver.eigenvectors() << endl;\n\n // solve the equation\n Matrix<double, MATRIX_SIZE, MATRIX_SIZE> matrix_NN = MatrixXd::Random(MATRIX_SIZE, MATRIX_SIZE);\n matrix_NN = matrix_NN * matrix_NN.transpose()\n Matrix<double, MATRIX_SIZE, 1> v_Nd = MatrixXd::random(MATRIX_SIZE, 1);\n\n // \u7b2c\u4e00\u79cd:\u76f4\u63a5\u6c42\u9006\n Matrix<double, MATRIX_SIZE, 1> x = matrix_NN.inverse() * v_Nd;\n\n // \u7b2c\u4e8c\u79cd:\u77e9\u9635\u5206\u89e3\n x = matrix_NN.colPivHouseholderQr().solve(v_Nd);\n\n}\n
- Eigen \u4e0d\u652f\u6301\u81ea\u52a8\u7c7b\u578b\u63d0\u5347\uff0c\u5373\u4e0d\u4f1a\u9690\u5f0f\u8f6c\u6362
"},{"location":"AI/SLAM14/#33","title":"3.3 \u65cb\u8f6c\u5411\u91cf\u548c\u6b27\u62c9\u89d2","text":""},{"location":"AI/SLAM14/#331","title":"3.3.1 \u65cb\u8f6c\u5411\u91cf","text":" - Axis-Angle
- Rodrigues's Formula
- \\(\\displaystyle \\boldsymbol{R}=\\cos\\theta\\boldsymbol{I}+\\left(1-\\cos\\theta\\right)\\boldsymbol{n}\\boldsymbol{n}^\\mathrm{T}+\\sin\\theta\\boldsymbol{n}^\\mathrm{\\wedge}.\\)
\\[ \\begin{aligned} \\mathrm{tr}\\left(R\\right)& =\\cos\\theta\\operatorname{tr}\\left(\\boldsymbol{I}\\right)+\\left(1-\\cos\\theta\\right)\\operatorname{tr}\\left(\\boldsymbol{n}\\boldsymbol{n}^\\mathrm{T}\\right)+\\sin\\theta\\operatorname{tr}(\\boldsymbol{n}^\\mathrm{\\Lambda}) \\\\ &=3\\cos\\theta+(1-\\cos\\theta) \\\\ &=1+2\\cos\\theta \\end{aligned} \\] thus:
\\[ \\theta=\\arccos\\frac{\\mathrm{tr}(R)-1}{2}. \\] \\[ Rn=n. \\] - \u5373 \\(\\displaystyle \\boldsymbol{n}\\) \u662f\u77e9\u9635 \\(\\displaystyle \\boldsymbol{R}\\) \u7279\u5f81\u503c 1 \u5bf9\u5e94\u7684\u7279\u8bca\u5411\u91cf
"},{"location":"AI/SLAM14/#332","title":"3.3.2 \u6b27\u62c9\u89d2","text":" - \u6bd4\u8f83\u5e38\u7528\u7684\u4e00\u79cd yaw-pitch-roll
- \u4f46\u4f1a\u6709 Gimbal Lock \u95ee\u9898
- \u6240\u4ee5\u6b27\u62c9\u89d2\u6bd4\u8f83\u9002\u5408\u7528\u4e8e\u5feb\u901f\u68c0\u9a8c\u7ed3\u679c\u662f\u5426\u6709\u9519
"},{"location":"AI/SLAM14/#34","title":"3.4 \u56db\u5143\u6570","text":""},{"location":"AI/SLAM14/#341","title":"3.4.1 \u56db\u5143\u6570\u7684\u5b9a\u4e49","text":" - \u6211\u4eec\u627e\u4e0d\u5230\u4e0d\u5e26\u5947\u5f02\u6027\u7684\u4e09\u4f4d\u5411\u91cf\u63cf\u8ff0\u65b9\u5f0f
- \u627e\u4e0d\u5230\u4e00\u4e2a\u6d41\u5f62\uff1f
- Quaternion
- \u7d27\u51d1\u53c8\u6ca1\u6709\u5947\u5f02\u6027
- \u53ea\u662f\u4e0d\u591f\u76f4\u89c2+\u8fd0\u7b97\u590d\u6742
- \\(\\displaystyle q=q_0+q_1\\mathrm{i}+\\mathrm{q}_2\\mathrm{j}+\\mathrm{q}_3\\mathrm{k}\\)
\\[ \\begin{cases}\\mathbf{i}^2=\\mathbf{j}^2=\\mathbf{k}^2=-1\\\\\\mathbf{ij}=\\mathbf{k},\\mathbf{ji}=-\\mathbf{k}\\\\\\mathbf{jk}=\\mathbf{i},\\mathbf{kj}=-\\mathbf{i}\\\\\\mathbf{ki}=\\mathbf{j},\\mathbf{ik}=-\\mathbf{j}\\end{cases} \\] - (\u4e5f\u8bb8\u53ef\u4ee5\u7528\u5ea6\u89c4\u6765\u8868\u793a\uff1f)
- \\(\\displaystyle \\boldsymbol{q}=\\left[s,\\boldsymbol{v}\\right]^\\mathrm{T},\\quad s=q_0\\in\\mathbb{R},\\quad\\boldsymbol{v}=\\left[q_1,q_2,q_3\\right]^\\mathrm{T}\\in\\mathbb{R}^3.\\)
- \u4e66\u4e0a\u6ca1\u5199\u76f4\u89c2\u7684\u51e0\u4f55\u5bf9\u5e94
"},{"location":"AI/SLAM14/#342","title":"3.4.2 \u56db\u5143\u6570\u7684\u8fd0\u7b97","text":" - \u4e58\u6cd5: \\(\\displaystyle \\boldsymbol{q}_a\\boldsymbol{q}_b=\\begin{bmatrix}s_as_b-\\boldsymbol{v}_a^\\mathrm{T}\\boldsymbol{v}_b,s_a\\boldsymbol{v}_b+s_b\\boldsymbol{v}_a+\\boldsymbol{v}_a\\times\\boldsymbol{v}_b\\end{bmatrix}^\\mathrm{T}.\\)
- \u7531\u4e8e\u6700\u540e\u4e00\u9879\u7684\u5b58\u5728\uff0c\u4e58\u6cd5\u4e0d\u5177\u6709\u4ea4\u6362\u5f8b
- \u5171\u8f6d: \\(\\displaystyle q_a^*=s_a-x_a\\mathrm{i}-\\mathrm{y_aj}-\\mathrm{z_ak}=[\\mathrm{s_a},-\\mathrm{v_a}]^\\mathrm{T}.\\)
- \\(\\displaystyle q^*q=qq^*=[s_a^2+\\boldsymbol{v}^\\mathrm{T}\\boldsymbol{v},\\boldsymbol{0}]^\\mathrm{T}.\\)
- \u9006: \\(\\displaystyle q^{-1}=q^*/\\|q\\|^2.\\)
- \\(\\displaystyle (\\boldsymbol{q}_a\\boldsymbol{q}_b)^{-1}=\\boldsymbol{q}_b^{-1}\\boldsymbol{q}_a^{-1}.\\)
"},{"location":"AI/SLAM14/#343","title":"3.4.3 \u7528\u56db\u5143\u6570\u8868\u793a\u65cb\u8f6c","text":" - \u5148\u8868\u793a\u4e09\u7ef4\u7a7a\u95f4\u70b9:
- \\(\\displaystyle p=[0,x,y,z]^{\\mathrm{T}}=[0,\\boldsymbol{v}]^{\\mathrm{T}}.\\)
- \u518d\u65cb\u8f6c:
- \\(\\displaystyle p'=qpq^{-1}.\\)
"},{"location":"AI/SLAM14/#344","title":"3.4.4 \u56db\u5143\u6570\u5230\u5176\u4ed6\u65cb\u8f6c\u8868\u793a\u7684\u8f6c\u6362","text":" - \u8bbe \\(\\displaystyle \\boldsymbol{q} = [s,\\boldsymbol{v}]^\\mathrm{T}\\)
- \\(\\displaystyle \\boldsymbol{q}^+=\\begin{bmatrix}s&-\\boldsymbol{v}^\\mathrm{T}\\\\\\\\\\boldsymbol{v}&s\\boldsymbol{I}+\\boldsymbol{v}^\\wedge\\end{bmatrix},\\quad\\boldsymbol{q}^\\oplus=\\begin{bmatrix}s&-\\boldsymbol{v}^\\mathrm{T}\\\\\\\\\\boldsymbol{v}&s\\boldsymbol{I}-\\boldsymbol{v}^\\wedge\\end{bmatrix}.\\)
- \\(\\displaystyle q_1^+q_2=\\begin{bmatrix}s_1&-\\boldsymbol{v}_1^\\mathrm{T}\\\\\\\\\\boldsymbol{v}_1&s_1\\boldsymbol{I}+\\boldsymbol{v}_1^\\wedge\\end{bmatrix}\\begin{bmatrix}s_2\\\\\\\\\\boldsymbol{v}_2\\end{bmatrix}=\\begin{bmatrix}-\\boldsymbol{v}_1^\\mathrm{T}\\boldsymbol{v}_2+s_1s_2\\\\\\\\s_1\\boldsymbol{v}_2+s_2\\boldsymbol{v}_1+\\boldsymbol{v}_1^\\wedge\\boldsymbol{v}_2\\end{bmatrix}=\\boldsymbol{q}_1\\boldsymbol{q}_2.\\)
- \u540c\u7406\u53ef\u8bc1:
- \\(\\displaystyle q_1q_2=q_1^+q_2=q_2^\\oplus q_1.\\)
- \u518d\u6765\u8003\u8651\u65cb\u8f6c:
- \\(\\displaystyle \\begin{aligned}p^{\\prime}&=qpq^{-1}=q^{+}p^{+}q^{-1}\\\\&=q^{+}q^{-1^{\\oplus}}p.\\end{aligned}\\)
- \u4e8e\u662f\u53ef\u4ee5\u5f97\u5230:
- \\(\\displaystyle \\boldsymbol{q}^{+}\\big(\\boldsymbol{q}^{-1}\\big)^{\\oplus}=\\begin{bmatrix}s&-\\boldsymbol{v}^{\\mathrm{T}}\\\\\\boldsymbol{v}&s\\boldsymbol{I}+\\boldsymbol{v}^{\\wedge}\\end{bmatrix}\\begin{bmatrix}s&\\boldsymbol{v}^{\\mathrm{T}}\\\\-\\boldsymbol{v}&s\\boldsymbol{I}+\\boldsymbol{v}^{\\wedge}\\end{bmatrix}=\\begin{bmatrix}1&\\boldsymbol{0}\\\\\\boldsymbol{0}^{\\mathrm{T}}&\\boldsymbol{v}\\boldsymbol{v}^{\\mathrm{T}}+s^{2}\\boldsymbol{I}+2s\\boldsymbol{v}^{\\wedge}+\\left(\\boldsymbol{v}^{\\wedge}\\right)^{2}\\end{bmatrix}.\\)
- \u5373: \\(\\displaystyle R=\\boldsymbol{v}\\boldsymbol{v}^\\mathrm{T}+s^2\\boldsymbol{I}+2s\\boldsymbol{v}^\\wedge+\\left(\\boldsymbol{v}^\\wedge\\right)^2.\\)
\\[ \\begin{aligned} \\operatorname{tr}(R)& =\\mathbf{tr}(\\boldsymbol{vv}^\\mathrm{T}+3s^2+2s\\cdot0+\\mathbf{tr}((\\boldsymbol{v}^\\wedge)^2) \\\\ &=v_{1}^{2}+v_{2}^{2}+v_{3}^{2}+3s^{2}-2(v_{1}^{2}+v_{2}^{2}+v_{3}^{2}) \\\\ &=(1-s^2)+3s^2-2(1-s^2) \\\\ &=4s^2-1. \\end{aligned} \\] - \u5373 \\(\\displaystyle \\theta=2\\arccos s.\\)
- \u518d\u52a0\u4e0a\u65cb\u8f6c\u8f74:
\\[ \\begin{cases}\\theta=2\\arccos q_0\\\\ [n_x,n_y,n_z]^\\mathrm{T}=[q_1,q_2,q_3]^\\mathrm{T}/\\sin\\frac{\\theta}{2}\\end{cases}. \\]"},{"location":"AI/SLAM14/#35","title":"3.5 \u76f8\u4f3c\u3001\u4eff\u5c04\u3001\u5c04\u5f71\u53d8\u6362","text":" - \u76f8\u4f3c\u53d8\u6362:
\\[ \\boldsymbol{T}_S=\\begin{bmatrix}s\\boldsymbol{R}&t\\\\\\mathbf{0}^\\mathrm{T}&1\\end{bmatrix}. \\] \u5141\u8bb8\u7f29\u653e\uff0c\u76f8\u4f3c\u53d8\u6362\u7fa4: Sim (3) 2. \u4eff\u5c04\u53d8\u6362:
\\[ T_A=\\begin{bmatrix}A&t\\\\\\mathbf{0}^\\mathrm{T}&1\\end{bmatrix}. \\] \u53ea\u4fdd\u8bc1\u5e73\u884c\u5173\u7cfb 3. \u5c04\u5f71\u53d8\u6362
\\[ T_P=\\begin{bmatrix}A&t\\\\\\\\a^\\mathrm{T}&v\\end{bmatrix}. \\] \u603b\u7ed3\u4e00\u4e0b:
\\[ \\begin{array}{c|c|c|c}\\hline\\text{\u53d8\u6362\u540d\u79f0}&\\text{\u77e9\u9635\u5f62\u5f0f}&\\text{\u81ea\u7531\u5ea6}&\\text{\u4e0d\u53d8\u6027\u8d28}\\\\\\hline\\text{\u6b27\u6c0f\u53d8\u6362}&\\begin{bmatrix}R&t\\\\0^\\mathrm{T}&1\\end{bmatrix}&6&\\text{\u957f\u5ea6\u3001\u5939\u89d2\u3001\u4f53\u79ef}\\\\\\text{\u76f8\u4f3c\u53d8\u6362}&\\begin{bmatrix}sR&t\\\\0^\\mathrm{T}&1\\end{bmatrix}&7&\\text{\u4f53\u79ef\u6bd4}\\\\\\text{\u4eff\u5c04\u53d8\u6362}&\\begin{bmatrix}A&t\\\\0^\\mathrm{T}&1\\end{bmatrix}&12&\\text{\u5e73\u884c\u6027\u3001\u4f53\u79ef\u6bd4}\\\\\\text{\u5c04\u5f71\u53d8\u6362}&\\begin{bmatrix}A&t\\\\a^\\mathrm{T}&v\\end{bmatrix}&15&\\text{\u63a5\u89e6\u5e73\u9762\u7684\u76f8\u4ea4\u548c\u76f8\u5207}\\\\\\hline\\end{array} \\]"},{"location":"AI/SLAM14/#36-eigen","title":"3.6 \u5b9e\u8df5: Eigen \u51e0\u4f55\u6a21\u5757","text":""},{"location":"AI/SLAM14/#361-eigen","title":"3.6.1 Eigen \u51e0\u4f55\u6a21\u5757\u7684\u6570\u636e\u6f14\u793a","text":"\u518d\u8bf4\u5427\u3002
"},{"location":"AI/SLAM14/#362","title":"3.6.2 \u5b9e\u9645\u7684\u5750\u6807\u53d8\u6362\u4f8b\u5b50","text":"TODO
"},{"location":"AI/SLAM14/#37","title":"3.7 \u53ef\u89c6\u5316\u6f14\u793a","text":""},{"location":"AI/SLAM14/#371","title":"3.7.1 \u663e\u793a\u8fd0\u52a8\u8f68\u8ff9","text":" - \u7528 Pangolin \u5e93
- TODO
"},{"location":"AI/SLAM14/#372","title":"3.7.2 \u663e\u793a\u76f8\u673a\u7684\u4f4d\u59ff","text":""},{"location":"AI/SLAM14/#38","title":"3.8 \u4e60\u9898","text":"TODO
"},{"location":"AI/SLAM14/#4","title":"4 \u674e\u7fa4\u548c\u674e\u4ee3\u6570","text":" - \u7531\u4e8e\u65cb\u8f6c\u77e9\u9635\u672c\u8eab\u5e26\u6709\u7ea6\u675f\uff08\u6b63\u4ea4\u4e14\u884c\u5217\u5f0f\u4e3a 1\uff09\uff0c\u8ba9\u4f18\u5316\u53d8\u5f97\u56f0\u96be\u3002
- \u6240\u4ee5\u6211\u4eec\u5f15\u5165\u674e\u7fa4-\u674e\u4ee3\u6570\u95f4\u7684\u8f6c\u6362\u5173\u7cfb\uff0c\u628a\u4f4d\u59ff\u4f30\u8ba1\u53d8\u6210\u65e0\u7ea6\u675f\u7684\u4f18\u5316\u95ee\u9898
"},{"location":"AI/SLAM14/#41","title":"4.1 \u674e\u7fa4\u548c\u674e\u4ee3\u6570\u57fa\u7840","text":" - \u4e09\u7ef4\u65cb\u8f6c\u77e9\u9635\u6784\u6210\u4e86\u7279\u6b8a\u6b63\u4ea4\u7fa4 \\(\\displaystyle \\boldsymbol{SO}(3)\\)
- \u53d8\u6362\u77e9\u9635\u6784\u6210\u4e86\u7279\u6b8a\u6b27\u6c0f\u7fa4 \\(\\displaystyle \\boldsymbol{SE}(3)\\)
- \u4f46\u662f\u4ed6\u4eec\u90fd\u5bf9\u52a0\u6cd5\u4e0d\u5c01\u95ed
- \u5bf9\u4e58\u6cd5\u5c01\u95ed
"},{"location":"AI/SLAM14/#411","title":"4.1.1 \u7fa4","text":" - \\(\\displaystyle G = (A,\\cdot)\\) \u6ee1\u8db3:
- \u5c01\u95ed\u6027
- \u7ed3\u5408\u5f8b
- \u5e7a\u5143
- \u9006
- \u674e\u7fa4\u662f\u5177\u6709\u8fde\u7eed\uff08\u5149\u6ed1\uff09\u6027\u8d28\u7684\u7fa4
"},{"location":"AI/SLAM14/#412","title":"4.1.2 \u674e\u4ee3\u6570\u7684\u5f15\u51fa","text":" - \\(\\displaystyle \\boldsymbol{R}\\boldsymbol{R}^\\mathrm{T} = \\boldsymbol{I}\\)
- \u6211\u4eec\u6613\u5f97: \\(\\displaystyle \\dot{\\boldsymbol{R}}(t)\\boldsymbol{R}(t)^\\mathrm{T}=-\\left(\\dot{\\boldsymbol{R}}(t)\\boldsymbol{R}(t)^\\mathrm{T}\\right)^\\mathrm{T}.\\)
- \u5373 \\(\\displaystyle \\dot{\\boldsymbol{R}}(t)\\boldsymbol{R}(t)^\\mathrm{T}\\) \u662f\u53cd\u5bf9\u79f0
- \u800c\u5bf9\u4e8e\u4efb\u610f\u53cd\u5bf9\u79f0\u77e9\u9635\uff0c\u6211\u4eec\u90fd\u53ef\u4ee5\u627e\u5230\u552f\u4e00\u4e0e\u4e4b\u5bf9\u5e94\u7684\u5411\u91cf
- \\(\\displaystyle \\dot{\\boldsymbol{R}}(t)\\boldsymbol{R}(t)^\\mathrm{T}=\\boldsymbol{\\phi}(t)^{\\wedge}.\\)
- \\(\\displaystyle \\dot{\\boldsymbol{R}}(t)=\\phi(t)^{\\wedge}\\boldsymbol{R}(t)=\\begin{bmatrix}0&-\\phi_3&\\phi_2\\\\\\phi_3&0&-\\phi_1\\\\-\\phi_2&\\phi_1&0\\end{bmatrix}\\boldsymbol{R}(t).\\) \u8003\u8651 \\(\\displaystyle t_{0} = 0\\) \u548c \\(\\displaystyle \\boldsymbol{R}(0) = \\boldsymbol{I}\\) \u65f6:
\\[ \\begin{aligned} R(t)& \\approx\\boldsymbol{R}\\left(t_{0}\\right)+\\dot{\\boldsymbol{R}}\\left(t_{0}\\right)\\left(t-t_{0}\\right) \\\\ &=I+\\phi(t_0)^{\\wedge}(t). \\end{aligned} \\] \u4e8e\u662f\u6c42\u5bfc->\u4e00\u4e2a\u7b97\u7b26 \\(\\displaystyle \\phi\\) \uff0c\u88ab\u79f0\u4e3a \\(\\displaystyle \\boldsymbol{SO}(3)\\) \u539f\u70b9\u9644\u8fd1\u7684\u6b63\u5207\u7a7a\u95f4\uff08Tangent Space\uff09 \u8bbe\u5728 \\(\\displaystyle t_{0}\\) \u9644\u8fd1\uff0c\\(\\displaystyle \\phi\\) \u4fdd\u6301\u5e38\u6570 \\(\\displaystyle \\phi(t_{0})=\\phi_{0}\\)\uff0c
\\[ \\dot{\\boldsymbol{R}}(t)=\\boldsymbol{\\phi}(t_0)^\\wedge\\boldsymbol{R}(t)=\\boldsymbol{\\phi}_0^\\wedge\\boldsymbol{R}(t). \\] \u518d\u6709 \\(\\displaystyle \\boldsymbol{R}(0) = \\boldsymbol{I}\\)\uff0c\u89e3\u7684:
\\[ \\boldsymbol{R}(t)=\\exp\\left(\\boldsymbol{\\phi}_{0}^{\\wedge}t\\right). \\] - \\(\\displaystyle \\phi\\) \u6b63\u662f\u5bf9\u5e94\u5230 \\(\\displaystyle SO(3)\\) \u4e0a\u7684\u674e\u4ee3\u6570 \\(\\displaystyle \\mathfrak{so}(3)\\)
- \\(\\displaystyle \\begin{aligned}&\\text{\u5176\u6b21,\u7ed9\u5b9a\u67d0\u4e2a\u5411\u91cf }\\phi\\text{ \u65f6,\u77e9\u9635\u6307\u6570}\\exp(\\phi^{\\wedge})\\text{ \u5982\u4f55\u8ba1\u7b97? \u53cd\u4e4b,\u7ed9\u5b9a }R\\text{ \u65f6,\u80fd\u5426\u6709\u76f8\u53cd}\\\\&\\text{\u7684\u8fd0\u7b97\u6765\u8ba1\u7b97 }\\phi?\\text{ \u4e8b\u5b9e\u4e0a,\u8fd9\u6b63\u662f\u674e\u7fa4\u4e0e\u674e\u4ee3\u6570\u95f4\u7684\u6307\u6570}/\\text{\u5bf9\u6570\u6620\u5c04\u3002}\\end{aligned}\\)
"},{"location":"AI/SLAM14/#413","title":"4.1.3 \u674e\u4ee3\u6570\u7684\u5b9a\u4e49","text":"\u674e\u4ee3\u6570\u7531\u4e00\u4e2a\u96c6\u5408 \\(\\displaystyle \\mathbb{V}\\)\u3001\u4e00\u4e2a\u6570\u57df \\(\\displaystyle \\mathbb{F}\\) \u548c\u4e00\u4e2a\u4e8c\u5143\u8fd0\u7b97 \\(\\displaystyle [,]\\) \u7ec4\u6210\u3002\u5982\u679c\u6ee1\u8db3\u4ee5\u4e0b\u51e0\u6761\u6027\u8d28\uff0c\u5219\u79f0 ( \\(\\displaystyle \\mathbb{V},\\mathbb{F},[,]\\)) \u4e3a\u4e00\u4e2a\u674e\u4ee3\u6570\uff0c\u8bb0\u4f5c \\(\\displaystyle \\mathfrak{g}\\)\u3002
- \u5c01\u95ed\u6027
- \u53cc\u7ebf\u6027
- \u81ea\u53cd\u6027 \\(\\displaystyle \\quad\\forall \\boldsymbol{X}\\in\\mathbb{V},[\\boldsymbol{X},\\boldsymbol{X}]=0\\)
- \u96c5\u53ef\u6bd4\u7b49\u4ef7 \\(\\displaystyle \\forall X,Y,Z\\in\\mathbb{V},[X,[Y,Z]]+[Z,[X,Y]]+[Y,[Z,X]]=0.\\) \u5176\u4e2d\u4e8c\u5143\u8fd0\u7b97\u88ab\u79f0\u4e3a\u674e\u62ec\u53f7\u3002 eg: \u53c9\u79ef\u662f\u4e00\u79cd\u674e\u62ec\u53f7
"},{"location":"AI/SLAM14/#414-displaystyle-mathfrakso3","title":"4.1.4 \u674e\u4ee3\u6570 \\(\\displaystyle \\mathfrak{so}(3)\\)","text":"\\(\\displaystyle \\boldsymbol{\\Phi}=\\boldsymbol{\\phi}^{\\wedge}=\\begin{bmatrix}0&-\\phi_3&\\phi_2\\\\\\\\\\phi_3&0&-\\phi_1\\\\\\\\-\\phi_2&\\phi_1&0\\end{bmatrix}\\in\\mathbb{R}^{3\\times3}.\\) \u6240\u4ee5\u4e24\u4e2a\u5411\u91cf \\(\\displaystyle \\phi_{1},\\phi_{2}\\) \u7684\u674e\u62ec\u53f7\u4e3a:
\\[ [\\phi_1,\\phi_2]=(\\boldsymbol{\\Phi}_1\\boldsymbol{\\Phi}_2-\\boldsymbol{\\Phi}_2\\boldsymbol{\\Phi}_1)^\\vee. \\] \\[ \\mathfrak{so}(3)=\\left\\{\\phi\\in\\mathbb{R}^3,\\boldsymbol{\\Phi}=\\phi^\\wedge\\in\\mathbb{R}^{3\\times3}\\right\\}. \\] \\[ R=\\exp(\\phi^{\\wedge}). \\]"},{"location":"AI/SLAM14/#415-displaystyle-mathfrakse3","title":"4.1.5 \u674e\u4ee3\u6570 \\(\\displaystyle \\mathfrak{se}(3)\\)","text":"\\[ \\mathfrak{se}(3)=\\left\\{\\boldsymbol{\\xi}=\\begin{bmatrix}\\boldsymbol{\\rho}\\\\\\boldsymbol{\\phi}\\end{bmatrix}\\in\\mathbb{R}^6,\\boldsymbol{\\rho}\\in\\mathbb{R}^3,\\boldsymbol{\\phi}\\in\\mathfrak{so}(3),\\boldsymbol{\\xi}^\\wedge=\\begin{bmatrix}\\boldsymbol{\\phi}^\\wedge&\\boldsymbol{\\rho}\\\\\\boldsymbol{0}^\\mathrm{T}&0\\end{bmatrix}\\in\\mathbb{R}^{4\\times4}\\right\\}. \\] \u524d\u4e09\u7ef4\u4e3a\u5e73\u79fb\uff0c\u540e\u4e09\u7ef4\u4e3a\u65cb\u8f6c\uff08\u5b9e\u8d28\u4e0a\u662f \\(\\displaystyle \\mathfrak{so}(3)\\) \u5143\u7d20\uff09
\\[ \\xi^\\wedge=\\begin{bmatrix}\\phi^\\wedge&\\rho\\\\0^\\mathrm{T}&0\\end{bmatrix}\\in\\mathbb{R}^{4\\times4}. \\] \u540c\u6837\u674e\u4ee3\u6570 \\(\\displaystyle \\mathfrak{se}(3)\\) \u4e5f\u6709\u7c7b\u4f3c\u4e8e \\(\\displaystyle \\mathfrak{so}(3)\\) \u7684\u674e\u62ec\u53f7:
\\[ [\\xi_1,\\xi_2]=\\left(\\xi_1^\\wedge\\xi_2^\\wedge-\\xi_2^\\wedge\\xi_1^\\wedge\\right)^\\vee. \\]"},{"location":"AI/SLAM14/#42","title":"4.2 \u6307\u6570\u4e0e\u5bf9\u6570\u6620\u5c04","text":""},{"location":"AI/SLAM14/#421-so3","title":"4.2.1 SO(3)\u4e0a\u7684\u6307\u6570\u6620\u5c04","text":" - Exponential Map
- \u9996\u5148\u4efb\u610f\u77e9\u9635\u7684\u6307\u6570\u6620\u5c04\u53ef\u4ee5\u5199\u6210\u4e00\u4e2a\u6cf0\u52d2\u5c55\u5f00\uff08\u6536\u655b\u7684\u65f6\u5019\uff09
\\[ \\exp(A)=\\sum_{n=0}^\\infty\\frac1{n!}A^n. \\] - \u5e94\u7528\u5230 \\(\\displaystyle \\mathfrak{so}(3)\\) \u4e2d:
\\[ \\exp(\\phi^\\wedge)=\\sum_{n=0}^\\infty\\frac{1}{n!}(\\phi^\\wedge)^n. \\] - \u6211\u4eec\u53ef\u4ee5\u628a \\(\\displaystyle \\phi\\) \u8868\u793a\u6210 \\(\\displaystyle \\theta \\boldsymbol{a}\\)\uff0c\u5bf9\u4e8e \\(\\displaystyle \\boldsymbol{a}^\\wedge\\):
\\[ \\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}=\\begin{bmatrix}-a_2^2-a_3^2&a_1a_2&a_1a_3\\\\\\\\a_1a_2&-a_1^2-a_3^2&a_2a_3\\\\\\\\a_1a_3&a_2a_3&-a_1^2-a_2^2\\end{bmatrix}=\\boldsymbol{a}\\boldsymbol{a}^\\mathrm{T}-\\boldsymbol{I}, \\] \u548c
\\[ a^{\\wedge}a^{\\wedge}a^{\\wedge}=a^{\\wedge}(aa^{\\mathrm{T}}-I)=-a^{\\wedge}. \\] \u4e8e\u662f\u6211\u4eec\u53ef\u4ee5\u5316\u7b80:
\\[ \\begin{aligned} \\exp\\left(\\phi^\\wedge\\right)& =\\exp\\left(\\theta\\boldsymbol{a}^\\wedge\\right)=\\sum_{n=0}^\\infty\\frac1{n!}\\left(\\theta\\boldsymbol{a}^\\wedge\\right)^n \\\\ &=I+\\theta\\boldsymbol{a}^{\\wedge}+\\frac{1}{2!}\\theta^{2}\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}+\\frac{1}{3!}\\theta^{3}\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}+\\frac{1}{4!}\\theta^{4}(\\boldsymbol{a}^{\\wedge})^{4}+\\cdots \\\\ &=\\boldsymbol{a}\\boldsymbol{a}^{\\mathrm{T}}-\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}+\\theta\\boldsymbol{a}^{\\wedge}+\\frac{1}{2!}\\theta^{2}\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge}-\\frac{1}{3!}\\theta^{3}\\boldsymbol{a}^{\\wedge}-\\frac{1}{4!}\\theta^{4}(\\boldsymbol{a}^{\\wedge})^{2}+\\cdots \\\\ &=\\boldsymbol{a}\\boldsymbol{a}^{\\mathsf{T}}+\\underbrace{\\left(\\theta-\\frac{1}{3!}\\theta^{3}+\\frac{1}{5!}\\theta^{5}-\\cdots\\right)}_{\\sin\\theta}\\boldsymbol{a}^{\\wedge}-\\underbrace{\\left(1-\\frac{1}{2!}\\theta^{2}+\\frac{1}{4!}\\theta^{4}-\\cdots\\right)}_{\\cos\\theta}\\boldsymbol{a}^{\\wedge}\\boldsymbol{a}^{\\wedge} \\\\ &=a^\\wedge a^\\wedge+I+\\sin\\theta a^\\wedge-\\cos\\theta a^\\wedge a^\\wedge \\\\ &=(1-\\cos\\theta)\\boldsymbol{a}^\\wedge\\boldsymbol{a}^\\wedge+\\boldsymbol{I}+\\sin\\theta\\boldsymbol{a}^\\wedge \\\\ &=\\cos\\theta\\boldsymbol{I}+(1-\\cos\\theta)\\boldsymbol{aa}^\\mathrm{T}+\\sin\\theta\\boldsymbol{a}^\\mathrm{\\wedge}. \\end{aligned} \\] \u6700\u540e\u5f97\u5230:
\\[ \\exp(\\theta\\boldsymbol{a}^\\wedge)=\\cos\\theta\\boldsymbol{I}+(1-\\cos\\theta)\\boldsymbol{a}\\boldsymbol{a}^\\mathrm{T}+\\sin\\theta\\boldsymbol{a}^\\wedge. \\] \u6240\u4ee5 \\(\\displaystyle \\mathfrak{so}(3)\\) \u5c31\u662f\u65cb\u91cf\u5411\u91cf\u7ec4\u6210\u7684\u7a7a\u95f4\uff0c\u800c\u6307\u6570\u6620\u5c04\u5373\u7f57\u5fb7\u91cc\u683c\u65af\u516c\u5f0f\u3002
- \u901a\u8fc7\u4e0a\u9762\u7684\u516c\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u628a \\(\\displaystyle \\mathfrak{so}(3)\\) \u4e2d\u4efb\u610f\u5411\u91cf\u5bf9\u5e94\u5230 SO (3) \u4e2d\u7684\u65cb\u8f6c\u77e9\u9635
- \u53cd\u8fc7\u6765\u4e5f\u662f\u53ef\u4ee5\u7684
\\[ \\phi=\\ln\\left(\\boldsymbol{R}\\right)^\\vee=\\left(\\sum_{n=0}^\\infty\\frac{\\left(-1\\right)^n}{n+1}\\left(\\boldsymbol{R}-\\boldsymbol{I}\\right)^{n+1}\\right)^\\vee. \\]"},{"location":"AI/SLAM14/#422-se-3","title":"4.2.2 SE (3) \u4e0a\u7684\u6307\u6570\u6620\u5c04","text":"\u540c\u6837\u7684\u63a8\u5bfc\u65b9\u5f0f:
\\[ \\begin{aligned} \\exp\\left(\\xi^{\\wedge}\\right)& =\\begin{bmatrix}\\sum_{n=0}^{\\infty}\\frac{1}{n!}(\\phi^{\\wedge})^{n}&\\sum_{n=0}^{\\infty}\\frac{1}{(n+1)!}(\\phi^{\\wedge})^{n}\\rho\\\\\\\\\\mathbf{0}^{\\mathrm{T}}&1\\end{bmatrix} \\\\ &\\stackrel{\\Delta}{=}\\begin{bmatrix}R&J\\rho\\\\\\\\0^\\mathrm{T}&1\\end{bmatrix}=T. \\end{aligned} \\] \\[ \\begin{aligned} \\sum_{n=0}^{\\infty}\\frac{1}{(n+1)!}(\\phi^{\\wedge})^{n}& =\\boldsymbol{I}+\\frac{1}{2!}\\theta\\boldsymbol{a}^{\\wedge}+\\frac{1}{3!}\\theta^{2}{(\\boldsymbol{a}^{\\wedge})}^{2}+\\frac{1}{4!}\\theta^{3}{(\\boldsymbol{a}^{\\wedge})}^{3}+\\frac{1}{5!}\\theta^{4}{(\\boldsymbol{a}^{\\wedge})}^{4}\\cdots \\\\ &=\\frac{1}{\\theta}\\left(\\frac{1}{2!}\\theta^{2}-\\frac{1}{4!}\\theta^{4}+\\cdots\\right)(\\boldsymbol{a}^{\\wedge})+\\frac{1}{\\theta}\\left(\\frac{1}{3!}\\theta^{3}-\\frac{1}{5}\\theta^{5}+\\cdots\\right)(\\boldsymbol{a}^{\\wedge})^{2}+\\boldsymbol{I} \\\\ &=\\frac1\\theta\\left(1-\\cos\\theta\\right)\\left(\\boldsymbol{a}^{\\wedge}\\right)+\\frac{\\theta-\\sin\\theta}\\theta\\left(\\boldsymbol{a}\\boldsymbol{a}^{\\mathrm{T}}-\\boldsymbol{I}\\right)+\\boldsymbol{I} \\\\ &=\\frac{\\sin\\theta}\\theta\\boldsymbol{I}+\\left(1-\\frac{\\sin\\theta}\\theta\\right)\\boldsymbol{aa}^\\mathrm{T}+\\frac{1-\\cos\\theta}\\theta\\boldsymbol{a}^\\mathrm{\\wedge}\\overset{\\mathrm{def}}{\\operatorname*{=}}\\boldsymbol{J}. \\end{aligned} \\] \\[ \\boldsymbol{J}=\\frac{\\sin\\theta}{\\theta}\\boldsymbol{I}+\\left(1-\\frac{\\sin\\theta}{\\theta}\\right)\\boldsymbol{a}\\boldsymbol{a}^\\mathrm{T}+\\frac{1-\\cos\\theta}{\\theta}\\boldsymbol{a}^\\mathrm{\\wedge}. \\] 4.28 \u6ca1\u770b\u61c2
"},{"location":"AI/SLAM14/#43","title":"4.3 \u674e\u4ee3\u6570\u6c42\u5bfc\u4e0e\u6270\u52a8\u6a21\u578b","text":""},{"location":"AI/SLAM14/#431-bch","title":"4.3.1 BCH \u516c\u5f0f\u4e0e\u8fd1\u4f3c\u5f62\u5f0f","text":"\u63a2\u7a76\u5982\u4e0b\u5f0f\u5b50\u662f\u5426\u6210\u7acb:
\\[ \\ln\\left(\\exp\\left(A\\right)\\exp\\left(B\\right)\\right)=A+B ? \\] \u4f46\u5b83\u5e76\u4e0d\u6210\u7acb\u3002\u4e24\u4e2a\u674e\u4ee3\u6570\u6307\u6570\u6620\u5c04\u4e58\u79ef\u7684\u5b8c\u6574\u5f62\u5f0f\uff0c\u7531 Baker-Campbell-Hausdorff \u7ed9\u51fa:
\\[ \\ln\\left(\\exp\\left(A\\right)\\exp\\left(B\\right)\\right)=A+B+\\frac{1}{2}\\left[A,B\\right]+\\frac{1}{12}\\left[A,\\left[A,B\\right]\\right]-\\frac{1}{12}\\left[B,\\left[A,B\\right]\\right]+\\cdots \\] \u7279\u522b\u7684\uff0c\u5f53 \\(\\displaystyle \\phi_{1}\\) \u6216 \\(\\displaystyle \\phi_{2}\\) \u4e3a\u5c0f\u91cf\u65f6\uff0c\u5c0f\u91cf\u4e8c\u6b21\u4ee5\u4e0a\u7684\u9879\u90fd\u53ef\u4ee5\u88ab\u5ffd\u7565\uff0c\u6b64\u65f6\u7684\u7ebf\u6027\u8fd1\u4f3c\u8868\u8fbe:
\\[ \\ln\\left(\\exp\\left(\\phi_1^\\wedge\\right)\\exp\\left(\\phi_2^\\wedge\\right)\\right)^\\vee\\approx\\begin{cases}J_l(\\phi_2)^{-1}\\phi_1+\\phi_2&\\text{\u5f53}\\phi_1\\text{\u4e3a\u5c0f\u91cf},\\\\J_r(\\phi_1)^{-1}\\phi_2+\\phi_1&\\text{\u5f53}\\phi_2\\text{\u4e3a\u5c0f\u91cf}.\\end{cases} \\] \\[ \\boldsymbol{J}_{l}=\\frac{\\sin\\theta}{\\theta}\\boldsymbol{I}+\\left(1-\\frac{\\sin\\theta}{\\theta}\\right)\\boldsymbol{a}\\boldsymbol{a}^\\mathrm{T}+\\frac{1-\\cos\\theta}{\\theta}\\boldsymbol{a}^\\mathrm{\\wedge}. \\] \\[ \\boldsymbol{J}_{\\ell}^{-1}=\\frac{\\theta}{2}\\cot\\frac{\\theta}{2}\\boldsymbol{I}+\\left(1-\\frac{\\theta}{2}\\cot\\frac{\\theta}{2}\\right)\\boldsymbol{a}\\boldsymbol{a}^{\\mathrm{T}}-\\frac{\\theta}{2}\\boldsymbol{a}^{\\wedge}. \\] \\[ J_{r}(\\phi)=J_{l}(-\\phi). \\] \u4e8e\u662f\u6211\u4eec\u5c31\u53ef\u4ee5\u8c08\u8bba\u674e\u7fa4\u4e58\u6cd5\u4e0e\u674e\u4ee3\u6570\u52a0\u6cd5\u7684\u5173\u7cfb\u4e86\u3002 \\(\\displaystyle \\boldsymbol{R}\\) \u5bf9\u5e94 \\(\\displaystyle \\phi\\)\uff0c\u6211\u4eec\u7ed9\u5b83\u5de6\u4e58\u4e00\u4e2a\u5fae\u5c0f\u65cb\u8f6c\uff0c\u8bb0\u4f5c \\(\\displaystyle \\Delta \\boldsymbol{R}\\)
\\[ \\exp\\left(\\Delta\\phi^{\\wedge}\\right)\\exp\\left(\\phi^{\\wedge}\\right)=\\exp\\left(\\left(\\phi+J_{l}^{-1}\\left(\\phi\\right)\\Delta\\phi\\right)^{\\wedge}\\right). \\] \\[ \\exp\\left(\\left(\\phi+\\Delta\\phi\\right)^{\\wedge}\\right)=\\exp\\left(\\left(J_{l}\\Delta\\phi\\right)^{\\wedge}\\right)\\exp\\left(\\phi^{\\wedge}\\right)=\\exp\\left(\\phi^{\\wedge}\\right)\\exp\\left(\\left(J_{r}\\Delta\\phi\\right)^{\\wedge}\\right). \\] \u5bf9\u4e8e SE (3) \u6211\u4eec\u4e5f\u6709:
\\[ \\exp\\left(\\Delta\\xi^{\\wedge}\\right)\\exp\\left(\\xi^{\\wedge}\\right)\\approx\\exp\\left(\\left(\\mathcal{J}_{l}^{-1}\\Delta\\xi+\\xi\\right)^{\\wedge}\\right) \\] \\[ exp\\left(\\xi^{\\wedge}\\right)\\exp\\left(\\Delta\\xi^{\\wedge}\\right)\\approx\\exp\\left(\\left(\\mathcal{J}_{r}^{-1}\\Delta\\xi+\\xi\\right)^{\\wedge}\\right). \\] \u552f\u4e00\u4e0d\u540c\u7684\u662f\u8fd9\u91cc\u7684 \\(\\displaystyle J_{l}\\) \u6bd4\u8f83\u590d\u6742\u3002
"},{"location":"AI/SLAM14/#432-so-3","title":"4.3.2 SO (3) \u4e0a\u7684\u674e\u4ee3\u6570\u6c42\u5bfc","text":"\\[ z=T\\boldsymbol{p}+\\boldsymbol{w}. \\] \u5176\u4e2d \\(\\displaystyle \\boldsymbol{w}\\) \u662f\u968f\u673a\u566a\u58f0\u3002
\\[ e=z-Tp. \\] \u5047\u8bbe\u4e00\u5171\u6709 N \u4e2a\u8fd9\u6837\u7684\u8def\u6807\u70b9\u548c\u89c2\u6d4b:
\\[ \\min_{\\boldsymbol{T}}J(\\boldsymbol{T})=\\sum_{i=1}^N\\left\\|\\boldsymbol{z}_i-\\boldsymbol{T}\\boldsymbol{p}_i\\right\\|_2^2. \\] most importantly\uff0c\u6211\u4eec\u4f1a\u6784\u5efa\u4e0e\u4f4d\u59ff\u6709\u5173\u7684\u51fd\u6570\uff0c\u5e76\u8ba8\u8bba\u8be5\u51fd\u6570\u5173\u4e8e\u4f4d\u59ff\u7684\u5bfc\u6570\uff0c\u4ee5\u8c03\u6574\u5f53\u524d\u7684\u4f30\u8ba1\u503c\u3002 \u4f7f\u7528\u674e\u4ee3\u6570\u89e3\u51b3\u6c42\u5bfc\u95ee\u9898\u7684\u601d\u8def\u5206\u4e3a\u4e24\u79cd:
- \u7528\u674e\u4ee3\u6570\u8868\u793a\u59ff\u6001\uff0c\u7136\u540e\u6839\u636e\u674e\u4ee3\u6570\u52a0\u6cd5\u5bf9\u674e\u4ee3\u6570\u6c42\u5bfc\u3002
- \u5bf9\u674e\u7fa4\u5de6\u4e58\u6216\u53f3\u4e58\u5fae\u5c0f\u6270\u52a8\uff0c\u7136\u540e\u5bf9\u8be5\u6270\u52a8\u6c42\u5bfc\uff0c\u79f0\u4e3a\u5de6\u6270\u52a8\u548c\u53f3\u6270\u52a8\u6a21\u578b\u3002
"},{"location":"AI/SLAM14/#44","title":"4.4 \u674e\u4ee3\u6570\u6c42\u5bfc","text":"\u8981\u8ba1\u7b97 \\(\\displaystyle \\frac{\\partial\\left(Rp\\right)}{\\partial R}\\), \u7531\u4e8eSO\uff083\uff09\u6ca1\u6709\u52a0\u6cd5\uff0c\u6211\u4eec\u8f6c\u800c\u8ba1\u7b97: \\(\\displaystyle \\frac{\\partial\\left(\\exp\\left(\\phi^{\\wedge}\\right)\\boldsymbol{p}\\right)}{\\partial\\boldsymbol{\\phi}}.\\)
\\[ \\begin{aligned} \\frac{\\partial\\left(\\exp\\left(\\phi^{\\wedge}\\right)\\boldsymbol{p}\\right)}{\\partial\\phi}& =\\lim_{\\delta\\boldsymbol{\\phi}\\to0}\\frac{\\exp\\left(\\left(\\boldsymbol{\\phi}+\\delta\\boldsymbol{\\phi}\\right)^{\\wedge}\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\boldsymbol{\\phi}} \\\\ &=\\lim_{\\delta\\phi\\to0}\\frac{\\exp\\left(\\left(\\boldsymbol{J}_i\\delta\\boldsymbol{\\phi}\\right)^\\wedge\\right)\\exp\\left(\\boldsymbol{\\phi}^\\wedge\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\phi}^\\wedge\\right)\\boldsymbol{p}}{\\delta\\boldsymbol{\\phi}} \\\\ &=\\lim_{\\delta\\phi\\to0}\\frac{\\left(\\boldsymbol{I}+\\left(\\boldsymbol{J}_{l}\\delta\\boldsymbol{\\phi}\\right)^{\\wedge}\\right)\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\phi} \\\\ &=\\lim_{\\delta\\phi\\to0}\\frac{\\left(\\boldsymbol{J}_{l}\\delta\\phi\\right)^{\\wedge}\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\phi} \\\\ &=\\lim_{\\delta\\boldsymbol{\\phi}\\to0}\\frac{-(\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p})^{\\wedge}\\boldsymbol{J}_{l}\\delta\\boldsymbol{\\phi}}{\\delta\\boldsymbol{\\phi}}=-(\\boldsymbol{R}\\boldsymbol{p})^{\\wedge}\\boldsymbol{J}_{l}. \\end{aligned} \\] BCH \u7ebf\u6027\u8fd1\u4f3c+\u6cf0\u52d2\u5c55\u5f00\u53d6\u7ebf\u6027\u9879:
\\[ \\frac{\\partial\\left(\\boldsymbol{Rp}\\right)}{\\partial\\boldsymbol{\\phi}}=\\left(-\\boldsymbol{Rp}\\right)^{\\wedge}\\boldsymbol{J}_{l}. \\] \u4f46\u662f\u8fd9\u91cc\u4ecd\u7136\u6709 \\(\\displaystyle \\boldsymbol{J}_{l}\\)
"},{"location":"AI/SLAM14/#441","title":"4.4.1 \u6270\u52a8\u6a21\u578b\uff08\u5de6\u4e58\uff09","text":"\\(\\displaystyle \\varphi\\) \u5bf9\u5e94\u5de6\u6270\u52a8 \\(\\displaystyle \\Delta \\boldsymbol{R}\\)
\\[ \\begin{aligned} \\frac{\\partial\\left(Rp\\right)}{\\partial\\varphi}& =\\lim_{\\varphi\\to0}\\frac{\\exp\\left(\\varphi^{\\wedge}\\right)\\exp\\left(\\phi^{\\wedge}\\right)p-\\exp\\left(\\phi^{\\wedge}\\right)p}{\\varphi} \\\\ &=\\lim_{\\varphi\\to0}\\frac{(\\boldsymbol{I}+\\boldsymbol{\\varphi}^{\\wedge})\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\phi}^{\\wedge}\\right)\\boldsymbol{p}}{\\varphi} \\\\ &=\\lim_{\\varphi\\to0}\\frac{\\varphi^\\wedge Rp}\\varphi=\\lim_{\\varphi\\to0}\\frac{-\\left(Rp\\right)^\\wedge\\varphi}\\varphi=-\\left(Rp\\right)^\\wedge. \\end{aligned} \\]"},{"location":"AI/SLAM14/#442-se-3","title":"4.4.2 SE (3) \u4e0a\u7684\u674e\u4ee3\u6570\u6c42\u5bfc","text":"\\[ \\begin{aligned} \\frac{\\partial\\left(\\boldsymbol{T}\\boldsymbol{p}\\right)}{\\partial\\delta\\boldsymbol{\\xi}}&=\\lim_{\\delta\\boldsymbol{\\xi}\\to\\boldsymbol{0}}\\frac{\\exp\\left(\\delta\\boldsymbol{\\xi}^{\\wedge}\\right)\\exp\\left(\\boldsymbol{\\xi}^{\\wedge}\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\xi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\xi} \\\\ &=\\lim_{\\delta\\boldsymbol{\\xi}\\to\\mathbf{0}}\\frac{\\left(\\boldsymbol{I}+\\delta\\boldsymbol{\\xi}^{\\wedge}\\right)\\exp\\left(\\boldsymbol{\\xi}^{\\wedge}\\right)\\boldsymbol{p}-\\exp\\left(\\boldsymbol{\\xi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\boldsymbol{\\xi}} \\\\ &=\\lim_{\\delta\\boldsymbol{\\xi}\\to0}\\frac{\\delta\\boldsymbol{\\xi}^{\\wedge}\\exp\\left(\\boldsymbol{\\xi}^{\\wedge}\\right)\\boldsymbol{p}}{\\delta\\boldsymbol{\\xi}} \\\\ &=\\lim_{\\delta\\boldsymbol{\\xi}\\to\\mathbf{0}}\\frac{\\begin{bmatrix}\\delta\\boldsymbol{\\phi}^\\wedge&\\delta\\boldsymbol{\\rho}\\\\\\\\\\mathbf{0}^\\mathrm{T}&0\\end{bmatrix}\\begin{bmatrix}\\boldsymbol{R}\\boldsymbol{p}+\\boldsymbol{t}\\\\\\\\1\\end{bmatrix}}{\\delta\\boldsymbol{\\xi}} \\\\ &=\\lim_{\\delta\\boldsymbol{\\xi}\\to\\boldsymbol{0}}\\frac{\\begin{bmatrix}\\delta\\boldsymbol{\\phi}^{\\wedge}\\left(\\boldsymbol{R}\\boldsymbol{p}+\\boldsymbol{t}\\right)+\\delta\\boldsymbol{\\rho}\\\\\\boldsymbol{0}^{\\mathrm{T}}\\end{bmatrix}}{[\\delta\\boldsymbol{\\rho},\\delta\\boldsymbol{\\phi}]^{\\mathrm{T}}}=\\begin{bmatrix}\\boldsymbol{I}&-(\\boldsymbol{R}\\boldsymbol{p}+\\boldsymbol{t})^{\\wedge}\\\\\\boldsymbol{0}^{\\mathrm{T}}&\\boldsymbol{0}^{\\mathrm{T}}\\end{bmatrix}\\stackrel{\\mathrm{def}}{=}(\\boldsymbol{T}\\boldsymbol{p})^{\\odot}. \\end{aligned} \\] \\[ \\frac{\\mathrm{d}\\begin{bmatrix}a\\\\b\\end{bmatrix}}{\\mathrm{d}\\begin{bmatrix}x\\\\y\\end{bmatrix}}=\\left(\\frac{\\mathrm{d}[a,b]^\\mathrm{T}}{\\mathrm{d}\\begin{bmatrix}x\\\\y\\end{bmatrix}}\\right)^\\mathrm{T}=\\begin{bmatrix}\\frac{\\mathrm{d}a}{\\mathrm{d}x}&\\frac{\\mathrm{d}b}{\\mathrm{d}x}\\\\\\frac{\\mathrm{d}a}{\\mathrm{d}y}&\\frac{\\mathrm{d}b}{\\mathrm{d}y}\\end{bmatrix}^\\mathrm{T}=\\begin{bmatrix}\\frac{\\mathrm{d}a}{\\mathrm{d}x}&\\frac{\\mathrm{d}a}{\\mathrm{d}y}\\\\\\frac{\\mathrm{d}b}{\\mathrm{d}x}&\\frac{\\mathrm{d}b}{\\mathrm{d}y}\\end{bmatrix} \\]"},{"location":"AI/SLAM14/#45-sophus","title":"4.5 \u5b9e\u8df5:Sophus","text":""},{"location":"AI/SLAM14/#451-sophus","title":"4.5.1 Sophus \u7684\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5","text":""},{"location":"AI/SLAM14/#452","title":"4.5.2 \u4f8b\u5b50: \u8bc4\u4f30\u8f68\u8ff9\u7684\u8bef\u5dee","text":" - \u7edd\u5bf9\u8f68\u8ff9\u8bef\u5dee\uff08Absolute Trajectory Error, ATE\uff09
\\[ \\mathrm{ATE}_{\\mathrm{all}}=\\sqrt{\\frac{1}{N}\\sum_{i=1}^{N}\\|\\log(T_{\\mathrm{gt},i}^{-1}T_{\\mathrm{esti},i})^{\\vee}\\|_{2}^{2}}, \\] \u5373\u5747\u65b9\u6839\u8bef\u5dee\uff08Root-Mean-Squared Error, RMSE\uff09
- \u7edd\u5bf9\u5e73\u79fb\u8bef\u5dee\uff08Average Translational Error\uff09
\\[ \\mathrm{ATE}_{\\mathrm{all}}=\\sqrt{\\frac{1}{N}\\sum_{i=1}^{N}\\|\\log(T_{\\mathrm{gt},i}^{-1}T_{\\mathrm{esti},i})^{\\vee}\\|_{2}^{2}}, \\] - \u76f8\u5bf9\u4f4d\u59ff\u8bef\u5dee\uff08Relative Pose Error, RPE\uff09
\\[ \\mathrm{RPE}_{\\mathrm{all}}=\\sqrt{\\frac{1}{N-\\Delta t}\\sum_{i=1}^{N-\\Delta t}\\|\\log\\left(\\left(\\boldsymbol{T}_{\\mathrm{gt},i}^{-1}\\boldsymbol{T}_{\\mathrm{gt},i+\\Delta t}\\right)\\right)^{-1}\\left(\\boldsymbol{T}_{\\mathrm{est},i}^{-1}\\boldsymbol{T}_{\\mathrm{est},i+\\Delta t}\\right))^{\\vee}\\|_{2}^{2}}, \\] \\[ \\mathrm{RPE}_{\\mathrm{trans}}=\\sqrt{\\frac{1}{N-\\Delta t}\\sum_{i=1}^{N-\\Delta t}\\|\\mathrm{trans}\\left(\\left(T_{gt,i}^{-1}T_{gt,i+\\Delta t}\\right)\\right)^{-1}\\left(T_{\\mathrm{esti},i}^{-1}T_{\\mathrm{esti},i+\\Delta t}\\right))\\|_{2}^{2}}. \\] \u4ee3\u7801\u8ba1\u7b97:
Text OnlyTODO\n
"},{"location":"AI/SLAM14/#46","title":"4.6 \u76f8\u4f3c\u53d8\u6362\u4e0e\u674e\u4ee3\u6570","text":"\u5728\u8fd9\u91cc\u6211\u4eec\u8ba8\u8bba Sim (3) \u548c\u5bf9\u5e94\u7684\u674e\u4ee3\u6570 \\(\\displaystyle \\mathfrak{sim}(3)\\)\u3002 \u5bf9\u4e8e\u4f4d\u4e8e\u7a7a\u95f4\u7684\u70b9 \\(\\displaystyle \\boldsymbol{p}\\)\uff0c\u5728\u76f8\u673a\u5750\u6807\u7cfb\u4e0b\u8981\u7ecf\u8fc7\u4e00\u4e2a\u76f8\u4f3c\u53d8\u6362\uff0c\u800c\u975e\u6b27\u6c0f\u53d8\u6362:
\\[ \\boldsymbol{p}'=\\begin{bmatrix}s\\boldsymbol{R}&\\boldsymbol{t}\\\\\\boldsymbol{0}^\\mathrm{T}&1\\end{bmatrix}\\boldsymbol{p}=s\\boldsymbol{R}\\boldsymbol{p}+\\boldsymbol{t}. \\] \\[ \\mathrm{Sim}(3)=\\left\\{S=\\begin{bmatrix}sR&t\\\\\\\\\\mathbf{0}^\\mathrm{T}&1\\end{bmatrix}\\in\\mathbb{R}^{4\\times4}\\right\\}. \\] \\[ \\sin(3)=\\left\\{\\zeta|\\zeta=\\begin{bmatrix}\\rho\\\\\\\\\\phi\\\\\\\\\\sigma\\end{bmatrix}\\in\\mathbb{R}^7,\\zeta^\\wedge=\\begin{bmatrix}\\sigma\\boldsymbol{I}+\\phi^\\wedge&\\rho\\\\\\\\\\mathbf{0}^\\mathrm{T}&0\\end{bmatrix}\\in\\mathbb{R}^{4\\times4}\\right\\}. \\] \\[ \\exp\\left(\\zeta^{\\wedge}\\right)=\\begin{bmatrix}\\mathrm{e}^{\\sigma}\\exp\\left(\\phi^{\\wedge}\\right)&J_{s}\\rho\\\\0^{\\mathrm{T}}&1\\end{bmatrix}. \\] \u5176\u4e2d\uff0c\\(\\displaystyle \\boldsymbol{J}_{S}\\) \u7684\u5f62\u5f0f\u662f:
\\[ \\begin{aligned} \\text{J}& =\\frac{\\mathrm{e}^{\\sigma}-1}{\\sigma}I+\\frac{\\sigma\\mathrm{e}^{\\sigma}\\sin\\theta+\\left(1-\\mathrm{e}^{\\sigma}\\cos\\theta\\right)\\theta}{\\sigma^{2}+\\theta^{2}}\\boldsymbol{a}^{\\wedge} \\\\ &+\\left(\\frac{\\mathrm{e}^\\sigma-1}{\\sigma}-\\frac{\\left(\\mathrm{e}^\\sigma\\cos\\theta-1\\right)\\sigma+\\left(\\mathrm{e}^\\sigma\\sin\\theta\\right)\\theta}{\\sigma^2+\\theta^2}\\right)\\boldsymbol{a}^\\wedge\\boldsymbol{a}^\\wedge. \\end{aligned} \\] \u4e8e\u662f\uff0c\u674e\u4ee3\u6570\u4e0e\u674e\u7fa4\u7684\u5173\u7cfb:
\\[ s=\\mathrm{e}^\\sigma, R=\\exp(\\phi^\\wedge), t=J_s\\rho. \\] \\[ \\frac{\\partial\\boldsymbol{Sp}}{\\partial\\boldsymbol{\\zeta}}=\\begin{bmatrix}\\boldsymbol{I}&-\\boldsymbol{q}^\\wedge&\\boldsymbol{q}\\\\\\boldsymbol{0}^\\mathrm{T}&\\boldsymbol{0}^\\mathrm{T}&0\\end{bmatrix}. \\]"},{"location":"AI/SLAM14/#47","title":"4.7 \u4e60\u9898","text":"TODO
"},{"location":"AI/SLAM14/#5","title":"5 \u76f8\u673a\u4e0e\u56fe\u50cf","text":" - \u89c2\u6d4b\u4e3b\u8981\u662f\u6307\u76f8\u673a\u6210\u50cf\u7684\u8fc7\u7a0b\u3002
"},{"location":"AI/SLAM14/#51","title":"5.1 \u76f8\u673a\u6a21\u578b","text":" - \u9488\u5b54\u6a21\u578b
- \u900f\u955c\u4f1a\u4ea7\u751f\u7578\u53d8
- \u5185\u53c2\u6570\uff08Intrinsics\uff09
"},{"location":"AI/SLAM14/#511","title":"5.1.1 \u9488\u5b54\u76f8\u673a\u6a21\u578b","text":"\\[ \\frac{Z}{f}=-\\frac{X}{X'}=-\\frac{Y}{Y'}. \\] \u53bb\u6389\u8d1f\u53f7:
\\[ \\frac Zf=\\frac X{X^{\\prime}}=\\frac Y{Y^{\\prime}}. \\] \\[ \\begin{aligned}X'&=f\\frac{X}{Z}\\\\Y'&=f\\frac{Y}{Z}\\end{aligned}. \\] \u8fd8\u6709\u4e00\u4e2a\u50cf\u7d20\u5750\u6807\u7cfb\uff0cu \u8f74\u4e0e x \u8f74\u5e73\u884c\uff0cv \u8f74\u4e0e y \u8f74\u5e73\u884c:
\\[ \\begin{cases}u=\\alpha X'+c_x\\\\[2ex]v=\\beta Y'+c_y\\end{cases}. \\] \\[ \\begin{cases}u=f_x\\frac{X}{Z}+c_x\\\\\\\\v=f_y\\frac{Y}{Z}+c_y\\end{cases}. \\] \\[ Z\\begin{pmatrix}u\\\\\\\\v\\\\\\\\1\\end{pmatrix}=\\begin{pmatrix}f_x&0&c_x\\\\0&f_y&c_y\\\\\\\\0&0&1\\end{pmatrix}\\begin{pmatrix}X\\\\\\\\Y\\\\\\\\Z\\end{pmatrix}\\overset{\\text{def}}{=}\\boldsymbol{KP}. \\] \u6700\u4e2d\u95f4\u7684\u77e9\u9635\u79f0\u4e3a\u76f8\u673a\u7684\u5185\u53c2\u6570\uff08Camera Inrinsics\uff09\u77e9\u9635 \\(\\displaystyle \\boldsymbol{K}\\)\u3002 \u6807\u5b9a: \u786e\u5b9a\u76f8\u673a\u7684\u5185\u53c2
\\[ Z\\boldsymbol{P}_{uv}=Z\\begin{bmatrix}u\\\\\\\\v\\\\\\\\1\\end{bmatrix}=\\boldsymbol{K}\\left(\\boldsymbol{R}\\boldsymbol{P}_\\mathrm{w}+\\boldsymbol{t}\\right)=\\boldsymbol{K}\\boldsymbol{T}\\boldsymbol{P}_\\mathrm{w}. \\] \u5176\u4e2d \\(\\displaystyle \\boldsymbol{R},\\boldsymbol{t}\\) \u53c8\u79f0\u4e3a\u76f8\u673a\u7684\u5916\u53c2\u6570\uff08Camera Extrinsics\uff09
\\[ (\\boldsymbol{RP_\\mathrm{w}}+\\boldsymbol{t})=\\underbrace{[X,Y,Z]^\\mathrm{T}}_{\\text{\u76f8\u673a\u5750\u6807}}\\to\\underbrace{[X/Z,Y/Z,1]^\\mathrm{T}}_{\\text{\u5f52\u4e00\u5316\u5750\u6807}} . \\] - \u5f52\u4e00\u5316\u5e73\u9762
- \u70b9\u7684\u6df1\u5ea6\u5728\u6295\u5f71\u8fc7\u7a0b\u4e2d\u88ab\u4e22\u5931\u4e86
"},{"location":"AI/SLAM14/#512","title":"5.1.2 \u7578\u53d8\u6a21\u578b","text":" - \u7578\u53d8 (Distortion \u5931\u771f) \u5f84\u5411\u7578\u53d8
- \u7b52\u5f62\u7578\u53d8
- \u6795\u5f62\u7578\u53d8
- \u5f84\u5411\u7578\u53d8\u5373 \\(\\displaystyle r\\) \u53d8\u5316
- \u5207\u5411\u7578\u53d8\u5373 \\(\\displaystyle \\theta\\) \u53d8\u5316 \u6211\u4eec\u53ef\u4ee5\u5047\u8bbe:
\\[ \\begin{align} x_{\\mathrm{distorted}}&=x(1+k_1r^2+k_2r^4+k_3r^6) \\\\ y_{\\mathrm{distorted}}&=y(1+k_1r^2+k_2r^4+k_3r^6). \\end{align} \\] \\[ \\begin{align} x_{\\mathrm{distorted}}&=x+2p_1xy+p_2(r^2+2x^2) \\\\ y_{\\mathrm{distorted}}&=y+p_1(r^2+2y^2)+2p_2xy \\end{align} \\] \u6240\u4ee5\u6211\u4eec\u53ef\u4ee5\u627e\u5230\u4e00\u4e2a\u70b9\u5728\u50cf\u7d20\u5e73\u9762\u4e0a\u7684\u6b63\u786e\u4f4d\u7f6e:
- \u5c06\u4e09\u7ef4\u7a7a\u95f4\u70b9\u6295\u5f71\u5230\u5f52\u4e00\u5316\u56fe\u50cf\u5e73\u9762\u3002\u8bbe\u5b83\u7684\u5f52\u4e00\u5316\u5750\u6807\u4e3a \\(\\displaystyle [x, y]^\\mathrm{T}\\)\u3002
- \u5bf9\u5f52\u4e00\u5316\u5e73\u9762\u4e0a\u7684\u70b9\u8ba1\u7b97\u5f84\u5411\u7578\u53d8\u548c\u5207\u5411\u7578\u53d8
\\[ \\begin{cases}x_\\text{distorted}=x(1+k_1r^2+k_2r^4+k_3r^6)+2p_1xy+p_2(r^2+2x^2)\\\\y_\\text{distorted}=y(1+k_1r^2+k_2r^4+k_3r^6)+p_1(r^2+2y^2)+2p_2xy\\end{cases} \\] - \u5c06\u7578\u53d8\u540e\u7684\u70b9\u901a\u8fc7\u5185\u53c2\u6570\u77e9\u9635\u6295\u5f71\u5230\u50cf\u7d20\u5e73\u9762\uff0c\u5f97\u5230\u8be5\u70b9\u5728\u56fe\u50cf\u4e0a\u7684\u6b63\u786e\u4f4d\u7f6e\u3002
\\[ \\begin{cases}u=f_xx_\\text{distorted}+c_x\\\\\\\\v=f_yy_\\text{distorted}+c_y\\end{cases}. \\] \u8fd8\u6709\u5f88\u591a\u7684\u76f8\u673a\u6a21\u578b\u6bd4\u5982: \u4eff\u5c04\u6a21\u578b\uff0c\u900f\u89c6\u6a21\u578b\u3002
\u603b\u7ed3\u4e00\u4e0b\u5355\u76ee\u76f8\u673a\u7684\u6210\u50cf\u8fc7\u7a0b:
- \u4e16\u754c\u5750\u6807\u7cfb\u4e0b\u6709\u4e00\u4e2a\u56fa\u5b9a\u7684\u70b9 \\(\\displaystyle P\\)\uff0c\u4e16\u754c\u5750\u6807\u4e3a \\(\\displaystyle \\boldsymbol{P}_{w}\\)\u3002
- \u7531\u4e8e\u76f8\u673a\u5728\u8fd0\u52a8\uff0c\u5b83\u7684\u8fd0\u52a8\u7531 \\(\\displaystyle \\boldsymbol{R},\\boldsymbol{t}\\) \u6216\u53d8\u6362\u77e9\u9635 \\(\\displaystyle \\boldsymbol{T}\\in SE(3)\\) \u63cf\u8ff0\u3002\\(\\displaystyle P\\) \u7684\u76f8\u673a\u5750\u6807\u4e3a \\(\\displaystyle \\tilde{P_{c}} = \\boldsymbol{R}\\boldsymbol{P}_{w}+\\boldsymbol{t}\\)\u3002
- \u8fd9\u65f6\u7684 \\(\\displaystyle \\tilde{\\boldsymbol{P}_{c}}\\) \u7684\u5206\u91cf\u662f \\(\\displaystyle X,Y,Z\\) \uff0c\u628a\u5b83\u4eec\u6295\u5f71\u5230\u5f52\u4e00\u5316\u5e73\u9762 \\(\\displaystyle Z = 1\\) \u4e0a\uff0c\u5f97\u5230 \\(\\displaystyle P\\) \u7684\u5f52\u4e00\u5316\u5750\u6807: \\(\\displaystyle \\boldsymbol{P}_{c} = \\left[ \\frac{X}{Z}, \\frac{Y}{Z}, 1 \\right]^\\mathrm{T}\\)\u3002
- \u6709\u7578\u53d8\u65f6\uff0c\u6839\u636e\u7578\u53d8\u53c2\u6570\u8ba1\u7b97 \\(\\displaystyle \\boldsymbol{P}_{c}\\) \u53d1\u751f\u7578\u53d8\u540e\u7684\u5750\u6807\u3002
- \\(\\displaystyle P\\) \u7684\u5f52\u4e00\u5316\u5750\u6807\u7ecf\u8fc7\u5185\u53c2\u540e\uff0c\u5bf9\u5e94\u5230\u5b83\u7684\u50cf\u7d20\u5750\u6807: \\(\\displaystyle \\boldsymbol{P}_{uv} = \\boldsymbol{K} \\boldsymbol{P}_{c}\\)\u3002
"},{"location":"AI/SLAM14/#513","title":"5.1.3 \u53cc\u76ee\u76f8\u673a\u6a21\u578b","text":" \u4e24\u8005\u4e4b\u95f4\u7684\u8ddd\u79bb\u79f0\u4e3a\u53cc\u76ee\u76f8\u673a\u7684\u57fa\u7ebf
\\[ z=\\frac{fb}{d},\\quad d\\stackrel{\\mathrm{def}}{=}u_{\\mathrm{L}}-u_{\\mathrm{R}}. \\] - d \u5b9a\u4e49\u4e3a\u5de6\u53f3\u56fe\u7684\u6a2a\u5750\u6807\u4e4b\u5dee\uff0c\u79f0\u4e3a\u89c6\u5dee\u3002
- \u7531\u4e8e\u89c6\u5dee\u6700\u5c0f\u4e3a\u4e00\u4e2a\u50cf\u7d20\uff0c\u6240\u4ee5\u53cc\u76ee\u7684\u6df1\u5ea6\u5b58\u5728\u4e00\u4e2a\u7406\u8bba\u4e0a\u7684\u6700\u5927\u503c\u3002
"},{"location":"AI/SLAM14/#514-rgb-d","title":"5.1.4 RGB-D \u76f8\u673a\u6a21\u578b","text":" - \u7ea2\u5916\u7ed3\u6784\u5149\uff08Structured lightning\uff09
- \u98de\u884c\u65f6\u95f4\uff08Time-of-Flight, ToF\uff09
- ToF \u76f8\u673a\u53ef\u4ee5\u83b7\u5f97\u6574\u4e2a\u56fe\u50cf\u7684\u50cf\u7d20\u6df1\u5ea6
- \u8f93\u51fa\u5f69\u8272\u56fe\u548c\u6df1\u5ea6\u56fe\uff0c\u751f\u6210\u70b9\u4e91\uff08Point Cloud\uff09
"},{"location":"AI/SLAM14/#52","title":"5.2 \u56fe\u50cf","text":"\\[ I(x,y):\\mathbb{R}^2\\mapsto\\mathbb{R}. \\] "},{"location":"AI/SLAM14/#53","title":"5.3 \u5b9e\u8df5: \u8ba1\u7b97\u673a\u4e2d\u7684\u56fe\u50cf","text":""},{"location":"AI/SLAM14/#531-opencv","title":"5.3.1 OpenCV \u7684\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5","text":"TODO
"},{"location":"AI/SLAM14/#532","title":"5.3.2 \u56fe\u50cf\u53bb\u7578\u53d8","text":"TODO
"},{"location":"AI/SLAM14/#54-3-d","title":"5.4 \u5b9e\u8df5: 3 D \u89c6\u89c9","text":""},{"location":"AI/SLAM14/#541","title":"5.4.1 \u53cc\u76ee\u89c6\u89c9","text":"TODO
"},{"location":"AI/SLAM14/#542-rgb-d","title":"5.4.2 RGB-D \u89c6\u89c9","text":""},{"location":"AI/SLAM14/#55","title":"5.5 \u4e60\u9898","text":""},{"location":"AI/SLAM14/#6","title":"6 \u975e\u7ebf\u6027\u4f18\u5316","text":"\u524d\u9762\u6211\u4eec\u5df2\u7ecf\u641e\u6e05\u695a\u4e86\u8fd0\u52a8\u65b9\u7a0b\u548c\u89c2\u6d4b\u65b9\u7a0b\u7684\u6765\u6e90\uff0c\u73b0\u5728\u6211\u4eec\u5f00\u59cb\u8ba8\u8bba\u566a\u58f0\u3002
"},{"location":"AI/SLAM14/#61","title":"6.1 \u72b6\u6001\u4f30\u8ba1\u95ee\u9898","text":""},{"location":"AI/SLAM14/#611","title":"6.1.1 \u6279\u91cf\u72b6\u6001\u4f30\u8ba1\u4e0e\u6700\u5927\u540e\u9a8c\u4f30\u8ba1","text":"\\[ \\begin{cases}\\boldsymbol{x}_k=f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k\\right)+\\boldsymbol{w}_k\\\\\\boldsymbol{z}_{k,j}=h\\left(\\boldsymbol{y}_j,\\boldsymbol{x}_k\\right)+\\boldsymbol{v}_{k,j}\\end{cases}. \\] \\[ s\\boldsymbol{z}_{k,j}=\\boldsymbol{K}(R_k\\boldsymbol{y}_j+\\boldsymbol{t}_k). \\] \u5176\u4e2d \\(\\displaystyle s\\) \u4e3a\u50cf\u7d20\u70b9\u7684\u8ddd\u79bb\u3002 \u6211\u4eec\u901a\u5e38\u5047\u8bbe\u566a\u58f0\u9879\u6ee1\u8db3\u96f6\u5747\u503c\u7684\u9ad8\u65af\u5206\u5e03:
\\[ \\boldsymbol{w}_k\\sim\\mathcal{N}\\left(\\boldsymbol{0},\\boldsymbol{R}_k\\right),\\boldsymbol{v}_k\\sim\\mathcal{N}\\left(\\boldsymbol{0},\\boldsymbol{Q}_{k,j}\\right). \\] \u6709\u4e24\u79cd\u65b9\u6cd5\u6765\u89e3\u51b3\u72b6\u6001\u4f30\u8ba1\u95ee\u9898:
- \u7528\u65b0\u7684\u6570\u636e\u6765\u66f4\u65b0\u5f53\u524d\u65f6\u523b\u7684\u4f30\u8ba1\u72b6\u6001\uff0c\u589e\u91cf/\u6e10\u8fdb (incremental) \u7684\u65b9\u6cd5\uff0c\u6216\u8005\u6559\u6ee4\u6ce2\u5668
- \u4e5f\u53ef\u4ee5\u628a\u6570\u636e\u90fd\u6512\u8d77\u6765\uff0c\u79f0\u4e3a\u6279\u91cf (batch) \u7684\u65b9\u6cd5 SfM (Structure from Motion) \u7efc\u5408\u4e00\u4e0b\u5c31\u6709\u4e86\u6ed1\u52a8\u7a97\u53e3\u4f30\u8ba1\u6cd5
\\[ \\boldsymbol{x}=\\{\\boldsymbol{x}_1,\\ldots,\\boldsymbol{x}_N\\},\\quad\\boldsymbol{y}=\\{\\boldsymbol{y}_1,\\ldots,\\boldsymbol{y}_M\\}. \\] \\[ P(\\boldsymbol{x},\\boldsymbol{y}|z,\\boldsymbol{u}). \\] \\[ P\\left(\\boldsymbol{x},\\boldsymbol{y}|\\boldsymbol{z},\\boldsymbol{u}\\right)=\\frac{P\\left(\\boldsymbol{z},\\boldsymbol{u}|\\boldsymbol{x},\\boldsymbol{y}\\right)P\\left(\\boldsymbol{x},\\boldsymbol{y}\\right)}{P\\left(\\boldsymbol{z},\\boldsymbol{u}\\right)}\\propto\\underbrace{P\\left(\\boldsymbol{z},\\boldsymbol{u}|\\boldsymbol{x},\\boldsymbol{y}\\right)}_{\\text{\u4f3c\u7136}}\\underbrace{P\\left(\\boldsymbol{x},\\boldsymbol{y}\\right)}_{\\text{\u5148\u9a8c}}. \\] \u53ef\u4ee5\u5148\u6c42\u4e00\u4e2a\u72b6\u6001\u6700\u4f18\u4f30\u8ba1:
\\[ (\\boldsymbol{x},\\boldsymbol{y})^*_{\\mathrm{MAP}}=\\arg\\max P(\\boldsymbol{x},\\boldsymbol{y}|\\boldsymbol{z},\\boldsymbol{u})=\\arg\\max P(\\boldsymbol{z},\\boldsymbol{u}|\\boldsymbol{x},\\boldsymbol{y})P(\\boldsymbol{x},\\boldsymbol{y}). \\] \u6c42\u89e3\u6700\u5927\u540e\u9a8c\u6982\u7387\u7b49\u4ef7\u4e8e\u6700\u5927\u5316\u4f3c\u7136\u548c\u5148\u9a8c\u7684\u4e58\u79ef\u3002 \u4f46\u5982\u679c\u6ca1\u6709\u7684\u5148\u9a8c\uff0c\u90a3\u4e48\u53ef\u4ee5\u6c42\u89e3\u6700\u5927\u4f3c\u7136\u4f30\u8ba1 (Maximize Likelihood Estimation\uff0c MLE):
\\[ (\\boldsymbol{x},\\boldsymbol{y})^*{}_{\\mathrm{MLE}}=\\arg\\max P(\\boldsymbol{z},\\boldsymbol{u}|\\boldsymbol{x},\\boldsymbol{y}). \\] \u6700\u5927\u4f3c\u7136\u4f30\u8ba1: \u5728\u4ec0\u4e48\u6837\u7684\u72b6\u6001\u4e0b\uff0c\u6700\u53ef\u80fd\u4ea7\u751f\u73b0\u5728\u89c2\u6d4b\u5230\u7684\u6570\u636e\u3002
"},{"location":"AI/SLAM14/#612","title":"6.1.2 \u6700\u5c0f\u4e8c\u4e58\u7684\u5f15\u51fa","text":"\u5bf9\u4e8e\u67d0\u4e00\u6b21\u89c2\u6d4b:
\\[ z_{k,j}=h\\left(y_{j},x_{k}\\right)+v_{k,j}, \\] \\[ P(\\boldsymbol{z}_{j,k}|\\boldsymbol{x}_k,\\boldsymbol{y}_j)=N\\left(h(\\boldsymbol{y}_j,\\boldsymbol{x}_k),\\boldsymbol{Q}_{k,j}\\right). \\] \u53ef\u4ee5\u4f7f\u7528\u6700\u5c0f\u5316\u8d1f\u5bf9\u6570\u6765\u6c42\u4e00\u4e2a\u9ad8\u65af\u5206\u5e03\u7684\u6700\u5927\u4f3c\u7136\u3002
\\[ P\\left(\\boldsymbol{x}\\right)=\\frac{1}{\\sqrt{\\left(2\\pi\\right)^{N}\\det\\left(\\boldsymbol{\\Sigma}\\right)}}\\exp\\left(-\\frac{1}{2}(\\boldsymbol{x}-\\boldsymbol{\\mu})^{\\mathrm{T}}\\boldsymbol{\\Sigma}^{-1}\\left(\\boldsymbol{x}-\\boldsymbol{\\mu}\\right)\\right). \\] \\[ -\\ln\\left(P\\left(\\boldsymbol{x}\\right)\\right)=\\frac12\\ln\\left(\\left(2\\pi\\right)^N\\det\\left(\\boldsymbol{\\Sigma}\\right)\\right)+\\frac12\\left(\\boldsymbol{x}-\\boldsymbol{\\mu}\\right)^\\mathrm{T}\\boldsymbol{\\Sigma}^{-1}\\left(\\boldsymbol{x}-\\boldsymbol{\\mu}\\right). \\] \\[ \\begin{aligned} (x_{k},y_{j})^{*}& =\\arg\\max\\mathcal{N}(h(\\boldsymbol{y}_{j},\\boldsymbol{x}_{k}),\\boldsymbol{Q}_{k,j}) \\\\ &=\\arg\\min\\left(\\left(\\boldsymbol{z}_{k,j}-h\\left(\\boldsymbol{x}_k,\\boldsymbol{y}_j\\right)\\right)^\\mathrm{T}\\boldsymbol{Q}_{k,j}^{-1}\\left(\\boldsymbol{z}_{k,j}-h\\left(\\boldsymbol{x}_k,\\boldsymbol{y}_j\\right)\\right)\\right). \\end{aligned} \\] \u8be5\u5f0f\u7b49\u4ef7\u4e8e\u6700\u5c0f\u5316\u566a\u58f0\u9879\u7684\u4e00\u4e2a\u4e8c\u6b21\u578b\uff0c\u9a6c\u54c8\u62c9\u8bfa\u6bd4\u65af\u8ddd\u79bb (Mahalanobis distance)\u3002\u5176\u4e2d \\(\\displaystyle \\boldsymbol{Q}_{k,j}^{-1}\\) \u53eb\u4fe1\u606f\u77e9\u9635\uff0c\u5373\u9ad8\u65af\u5206\u5e03\u534f\u65b9\u5dee\u77e9\u9635\u4e4b\u9006\u3002 \u5047\u8bbe\u5404\u4e2a\u65f6\u523b\u7684\u8f93\u5165\u548c\u89c2\u6d4b\u90fd\u662f\u72ec\u7acb\u7684\uff0c\u90a3\u4e48:
\\[ P\\left(\\boldsymbol{z},\\boldsymbol{u}|\\boldsymbol{x},\\boldsymbol{y}\\right)=\\prod_kP\\left(\\boldsymbol{u}_k|\\boldsymbol{x}_{k-1},\\boldsymbol{x}_k\\right)\\prod_{k,j}P\\left(\\boldsymbol{z}_{k,j}|\\boldsymbol{x}_k,\\boldsymbol{y}_j\\right), \\] \\[ \\begin{align} e_{u,k} &=\\boldsymbol{x}_k-f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k\\right) \\\\ e_{z,j,k} &=\\boldsymbol{z}_{k,j}-h\\left(\\boldsymbol{x}_k,\\boldsymbol{y}_j\\right), \\end{align} \\] \\[ \\min J(\\boldsymbol{x},\\boldsymbol{y})=\\sum_{k}\\boldsymbol{e}_{\\boldsymbol{u},k}^{\\mathrm{T}}\\boldsymbol{R}_{k}^{-1}\\boldsymbol{e}_{\\boldsymbol{u},k}+\\sum_{k}\\sum_{j}\\boldsymbol{e}_{\\boldsymbol{z},k,j}^{\\mathrm{T}}\\boldsymbol{Q}_{k,j}^{-1}\\boldsymbol{e}_{\\boldsymbol{z},k,j}. \\] \u8fd9\u6837\u5c31\u5f97\u5230\u4e86\u4e00\u4e2a\u6700\u5c0f\u4e8c\u4e58\u95ee\u9898 (Least Square Problem)
- \u6574\u4e2a\u95ee\u9898\u6709\u4e00\u79cd\u7a00\u758f\u7684\u5f62\u5f0f\u3002
- \u7528\u674e\u4ee3\u6570\u8868\u793a\u589e\u91cf\u4f1a\u6709\u65e0\u7ea6\u675f\u7684\u4f18\u52bf\u3002
- \u7528\u4e8c\u6b21\u578b\u5ea6\u91cf\u8bef\u5dee\uff0c\u90a3\u4e48\u8bef\u5dee\u7684\u5206\u5e03\u4f1a\u5f71\u54cd\u6b64\u9879\u5728\u6574\u4e2a\u95ee\u9898\u4e2d\u7684\u6743\u91cd\u3002 \u63a5\u4e0b\u4fe9\u8bb2\u4e00\u4e9b\u975e\u7ebf\u6027\u4f18\u5316\u7684\u57fa\u672c\u77e5\u8bc6\uff0c\u6765\u5e2e\u52a9\u6211\u4eec\u6c42\u89e3\u8fd9\u4e2a\u6700\u5c0f\u4e8c\u4e58\u95ee\u9898\u3002
"},{"location":"AI/SLAM14/#613","title":"6.1.3 \u4f8b\u5b50: \u6279\u91cf\u72b6\u6001\u4f30\u8ba1","text":"\u8003\u8651\u4e00\u4e2a\u975e\u5e38\u7b80\u5355\u7684\u79bb\u6563\u65f6\u95f4\u7cfb\u7edf:
\\[ \\begin{aligned}&x_{k}=x_{k-1}+u_{k}+w_{k},&&\\boldsymbol{w}_{k}\\sim\\mathcal{N}\\left(0,\\boldsymbol{Q}_{k}\\right)\\\\&\\boldsymbol{z}_{k}=\\boldsymbol{x}_{k}+\\boldsymbol{n}_{k},&&\\boldsymbol{n}_{k}\\sim\\mathcal{N}\\left(0,\\boldsymbol{R}_{k}\\right)\\end{aligned} \\] \\[ \\begin{gathered} x_{map}^{*} =\\arg\\max P(\\boldsymbol{x}|\\boldsymbol{u},\\boldsymbol{z})=\\arg\\max P(\\boldsymbol{u},\\boldsymbol{z}|\\boldsymbol{x}) \\\\ =\\prod_{k=1}^3P(\\boldsymbol{u}_k|\\boldsymbol{x}_{k-1},\\boldsymbol{x}_k)\\prod_{k=1}^3P(\\boldsymbol{z}_k|\\boldsymbol{x}_k), \\end{gathered} \\] \u800c\u5bf9\u4e8e\u5177\u4f53\u7684\u6bcf\u4e00\u9879\uff0c\u6211\u4eec\u6709:
\\[ P(\\boldsymbol{u}_k|\\boldsymbol{x}_{k-1},\\boldsymbol{x}_k)=\\mathcal{N}(\\boldsymbol{x}_k-\\boldsymbol{x}_{k-1},\\boldsymbol{Q}_k), \\] \\[ P\\left(\\boldsymbol{z}_{k}|\\boldsymbol{x}_{k}\\right)=\\mathcal{N}\\left(\\boldsymbol{x}_{k},\\boldsymbol{R}_{k}\\right). \\] \u4e8e\u662f\uff0c\u6211\u4eec\u53ef\u4ee5\u6784\u5efa\u8bef\u5dee\u53d8\u91cf:
\\[ e_{\\boldsymbol{u},k}=\\boldsymbol{x}_k-\\boldsymbol{x}_{k-1}-\\boldsymbol{u}_k,\\quad\\boldsymbol{e}_{z,k}=\\boldsymbol{z}_k-\\boldsymbol{x}_k, \\] \u4e8e\u662f\u6700\u5c0f\u4e8c\u4e58\u7684\u76ee\u6807\u51fd\u6570\u4e3a:
\\[ \\min\\sum_{k=1}^{3}e_{u,k}^{\\mathrm{T}}Q_{k}^{-1}e_{u,k}+\\sum_{k=1}^{3}e_{z,k}^{\\mathrm{T}}R_{k}^{-1}e_{z,k}. \\] \u5b9a\u4e49\u5411\u91cf \\(\\displaystyle \\boldsymbol{y} = [\\boldsymbol{u},\\boldsymbol{z}]^\\mathrm{T}\\)
\\[ y-Hx=e\\sim\\mathcal{N}(\\mathbf{0},\\boldsymbol{\\Sigma}). \\] \\[ H=\\begin{bmatrix}1&-1&0&0\\\\0&1&-1&0\\\\0&0&1&-1\\\\\\hline0&1&0&0\\\\0&0&1&0\\\\0&0&0&1\\end{bmatrix}, \\] \u4e14 \\(\\displaystyle \\Sigma = diag(\\boldsymbol{Q_{1}},\\boldsymbol{Q_{2}},\\boldsymbol{Q_{3}},\\boldsymbol{R_{1}},\\boldsymbol{R_{2}},\\boldsymbol{R_{3}})\\)\u3002 \u95ee\u9898\u5c31\u8f6c\u5316\u6210:
\\[ x_{\\mathrm{map}}^*=\\arg\\min e^{\\mathrm{T}}\\Sigma^{-1}e, \\] \u5b83\u7684\u552f\u4e00\u89e3\u662f:
\\[ x_{\\mathrm{map}}^{*}=(H^{\\mathrm{T}}\\Sigma^{-1}H)^{-1}H^{\\mathrm{T}}\\Sigma^{-1}y. \\]"},{"location":"AI/SLAM14/#62","title":"6.2 \u975e\u7ebf\u6027\u6700\u5c0f\u4e8c\u4e58","text":"\u5148\u8003\u8651\u4e00\u4e2a\u7b80\u5355\u7684\u6700\u5c0f\u4e8c\u4e58\u95ee\u9898:
\\[ \\min_{x}F(x)=\\frac12\\|f\\left(x\\right)\\|_{2}^{2}.` \\] \u5bf9\u4e8e\u4e0d\u65b9\u4fbf\u76f4\u63a5\u6c42\u89e3\u7684\u6700\u5c0f\u4e8c\u4e58\u95ee\u9898\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u8fed\u4ee3\u7684\u65b9\u5f0f\uff0c\u4ece\u4e00\u4e2a\u521d\u59cb\u503c\u51fa\u53d1\uff0c\u4e0d\u65ad\u5730\u66f4\u65b0\u5f53\u524d\u7684\u4f18\u5316\u53d8\u91cf\uff0c\u4f7f\u76ee\u6807\u51fd\u6570\u4e0b\u964d:
- \u7ed9\u5b9a\u67d0\u4e2a\u521d\u59cb\u503c \\(\\displaystyle \\boldsymbol{x_{0}}\\)\u3002
- \u5bf9\u4e8e\u7b2c \\(\\displaystyle k\\) \u6b21\u8fed\u4ee3\uff0c\u5bfb\u627e\u4e00\u4e2a\u589e\u91cf \\(\\displaystyle \\Delta x_{k}\\)\uff0c\u4f7f\u5f97 \\(\\displaystyle \\left\\|f\\left(\\boldsymbol{x}_{k}+\\Delta\\boldsymbol{x}_{k}\\right)\\right\\|_{2}^{2}\\) \u8fbe\u5230\u6700\u5c0f\u503c\u3002
- \u82e5 \\(\\displaystyle \\Delta x_k\\) \u8db3\u591f\u5c0f\uff0c\u5219\u505c\u6b62\u3002
- \u5426\u5219\uff0c\u4ee4 \\(\\displaystyle x_{k+1} = x_{k} + \\Delta x_{k}\\)\uff0c\u8fd4\u56de\u7b2c\u4e8c\u6b65 \u4e8e\u662f\u6c42\u89e3\u5bfc\u51fd\u6570\u4e3a\u96f6 -> \u5bfb\u627e\u4e0b\u964d\u589e\u91cf \\(\\displaystyle \\Delta x_{k}\\)
\u4e0b\u9762\u662f\u4e00\u4e9b\u5e7f\u6cdb\u4f7f\u7528\u7684\u7ed3\u679c\u3002
"},{"location":"AI/SLAM14/#621","title":"6.2.1 \u4e00\u9636\u548c\u4e8c\u9636\u68af\u5ea6\u6cd5","text":"\u4f7f\u7528\u6cf0\u52d2\u5c55\u5f00:
\\[ F(\\boldsymbol{x}_k+\\Delta\\boldsymbol{x}_k)\\approx F(\\boldsymbol{x}_k)+\\boldsymbol{J}\\left(\\boldsymbol{x}_k\\right)^\\mathrm{T}\\Delta\\boldsymbol{x}_k+\\frac{1}{2}\\Delta\\boldsymbol{x}_k^\\mathrm{T}\\boldsymbol{H}(\\boldsymbol{x}_k)\\Delta\\boldsymbol{x}_k. \\] \u5176\u4e2d \\(\\displaystyle \\boldsymbol{J}(x_{k})\\) \u662f \\(\\displaystyle F(x)\\) \u5173\u4e8e \\(\\displaystyle x\\) \u7684\u4e00\u9636\u5bfc\u6570\uff08\u68af\u5ea6\u3001\u96c5\u53ef\u6bd4\u77e9\u9635\uff09\uff0c\\(\\displaystyle \\boldsymbol{H}\\) \u662f\u4e8c\u9636\u5bfc\u6570\uff08\u6d77\u585e\u77e9\u9635\uff09\u3002
\\[ \\Delta\\boldsymbol{x}^*=-\\boldsymbol{J}(\\boldsymbol{x}_k). \\] \\[ \\Delta\\boldsymbol{x}^*=\\arg\\min\\left(F\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^\\mathrm{T}\\Delta\\boldsymbol{x}+\\frac{1}{2}\\Delta\\boldsymbol{x}^\\mathrm{T}\\boldsymbol{H}\\Delta\\boldsymbol{x}\\right). \\] \u5bf9 \\(\\displaystyle \\Delta x\\) \u6c42\u5bfc\uff0c\u5e76\u4ee4\u5b83\u7b49\u4e8e\u96f6\uff0c\u5f97\u5230:
\\[ J+H\\Delta x=\\mathbf{0}\\Rightarrow H\\Delta x=-J. \\] \u8fd9\u4e2a\u65b9\u6cd5\u53c8\u53eb\u725b\u987f\u6cd5\u3002
"},{"location":"AI/SLAM14/#622","title":"6.2.2 \u9ad8\u65af\u725b\u987f\u6cd5","text":"\u6362\u4e00\u4e2a\u51fd\u6570\u5c55\u5f00:
\\[ f\\left(x+\\Delta x\\right)\\approx f\\left(x\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^{\\mathrm{T}}\\Delta\\boldsymbol{x}. \\] \\[ \\Delta x^{*}=\\arg\\min_{\\Delta x}\\frac{1}{2}\\Big\\|f\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^{\\mathrm{T}}\\Delta\\boldsymbol{x}\\Big\\|^{2}. \\] \\[ \\begin{aligned} \\frac12\\left\\|f\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^\\mathrm{T}\\Delta\\boldsymbol{x}\\right\\|^2& =\\frac12\\Big(f\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^\\mathrm{T}\\Delta\\boldsymbol{x}\\Big)^\\mathrm{T}\\Big(f\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^\\mathrm{T}\\Delta\\boldsymbol{x}\\Big) \\\\ &=\\frac12\\left(\\|f(\\boldsymbol{x})\\|_2^2+2f\\left(\\boldsymbol{x}\\right)\\boldsymbol{J}(\\boldsymbol{x})^\\intercal\\Delta\\boldsymbol{x}+\\Delta\\boldsymbol{x}^\\intercal\\boldsymbol{J}(\\boldsymbol{x})\\boldsymbol{J}(\\boldsymbol{x})^\\intercal\\Delta\\boldsymbol{x}\\right). \\end{aligned} \\] \\[ \\boldsymbol{J}(\\boldsymbol{x})f\\left(\\boldsymbol{x}\\right)+\\boldsymbol{J}(\\boldsymbol{x})\\boldsymbol{J}^\\mathrm{T}\\left(\\boldsymbol{x}\\right)\\Delta\\boldsymbol{x}=\\boldsymbol{0}. \\] \\[ \\underbrace{\\boldsymbol{J}(\\boldsymbol{x})\\boldsymbol{J}^{\\intercal}}_{\\boldsymbol{H}(\\boldsymbol{x})}\\left(\\boldsymbol{x}\\right)\\Delta\\boldsymbol{x}=\\underbrace{-\\boldsymbol{J}(\\boldsymbol{x})f\\left(\\boldsymbol{x}\\right)}_{\\boldsymbol{g}(\\boldsymbol{x})}. \\] \u589e\u91cf\u65b9\u7a0b or Gauss-Newton equation or Normal equation
\\[ H\\Delta x=g. \\] \u6c42\u89e3\u589e\u91cf\u65b9\u7a0b\u662f\u6574\u4e2a\u4f18\u5316\u95ee\u9898\u7684\u6838\u5fc3\u6240\u5728 \u603b\u7ed3\u4e00\u4e0b:
- \u7ed9\u5b9a\u521d\u59cb\u503c \\(\\displaystyle \\boldsymbol{x}_{0}\\)\u3002
- \u5bf9\u4e8e\u7b2c \\(\\displaystyle k\\) \u6b21\u8fed\u4ee3\uff0c\u6c42\u89e3\u5f53\u524d\u7684\u96c5\u53ef\u6bd4\u77e9\u9635 \\(\\displaystyle \\boldsymbol{J}(x)\\) \u548c\u8bef\u5dee \\(\\displaystyle f(\\boldsymbol{x}_{k})\\)\u3002
- \u6c42\u89e3\u589e\u91cf\u65b9\u7a0b: \\(\\displaystyle \\boldsymbol{H} \\Delta x_{k} = \\boldsymbol{g}\\)\u3002
- \u82e5 \\(\\displaystyle \\Delta x_{k}\\) \u8db3\u591f\u5c0f\uff0c\u5219\u505c\u6b62\u3002\u5426\u5219\uff0c\u4ee4 \\(\\displaystyle x_{k+1} = x_{k}+ \\Delta x_{k}\\)\uff0c\u8fd4\u56de\u7b2c 2 \u6b65\u3002
"},{"location":"AI/SLAM14/#623","title":"6.2.3 \u5217\u6587\u4f2f\u683c\u2014\u2014\u9a6c\u5938\u5c14\u7279\u65b9\u6cd5","text":"Damped Newton Method Trust Region Trust Region Method
\\[ \\rho=\\frac{f\\left(\\boldsymbol{x}+\\Delta\\boldsymbol{x}\\right)-f\\left(\\boldsymbol{x}\\right)}{\\boldsymbol{J}\\left(\\boldsymbol{x}\\right)^{\\intercal}\\Delta\\boldsymbol{x}}. \\] \u6846\u67b6:
- \u7ed9\u5b9a\u521d\u59cb\u503c \\(\\displaystyle \\boldsymbol{x}_{0}\\)\uff0c\u4ee5\u53ca\u521d\u59cb\u4f18\u5316\u534a\u5f84 \\(\\displaystyle \\mu\\)\u3002
- \u5bf9\u4e8e\u7b2c \\(\\displaystyle k\\) \u6b21\u8fed\u4ee3\uff0c\u5728\u9ad8\u65af\u725b\u987f\u6cd5\u7684\u57fa\u7840\u4e0a\u52a0\u4e0a\u4fe1\u8d56\u533a\u57df\uff0c\u6c42\u89e3: \\(\\displaystyle \\min_{\\Delta\\boldsymbol{x}_{k}}\\frac{1}{2}\\Big\\|f\\left(\\boldsymbol{x}_{k}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}_{k}\\right)^{\\mathrm{T}}\\Delta\\boldsymbol{x}_{k}\\Big\\|^{2},\\quad\\mathrm{s.t.}\\quad\\left\\|\\boldsymbol{D}\\Delta\\boldsymbol{x}_{k}\\right\\|^{2}\\leqslant\\mu,\\)
- \u8ba1\u7b97 \\(\\displaystyle \\rho\\)
- \u5bf9\u4e8e \\(\\displaystyle \\frac{1}{4} \\frac{3}{4}\\) \u8fdb\u884c\u5206\u7c7b\u8ba8\u8bba
- \u5224\u65ad\u9608\u503c\uff0c\u5faa\u73af
\u8fd9\u662f\u5e26\u4e0d\u7b49\u5f0f\u7ea6\u675f\u7684\u4f18\u5316\u95ee\u9898:
\\[ \\mathcal{L}(\\Delta\\boldsymbol{x}_{k},\\lambda)=\\frac{1}{2}\\left\\|f\\left(\\boldsymbol{x}_{k}\\right)+\\boldsymbol{J}\\left(\\boldsymbol{x}_{k}\\right)^{\\mathrm{T}}\\Delta\\boldsymbol{x}_{k}\\right\\|^{2}+\\frac{\\lambda}{2}\\left(\\left\\|\\boldsymbol{D}\\Delta\\boldsymbol{x}_{k}\\right\\|^{2}-\\mu\\right). \\] \\[ (H+\\lambda D^\\mathrm{T}D) \\Delta x_k=g. \\]"},{"location":"AI/SLAM14/#63","title":"6.3 \u5b9e\u8df5: \u66f2\u7ebf\u62df\u5408\u95ee\u9898","text":""},{"location":"AI/SLAM14/#631","title":"6.3.1 \u624b\u5199\u9ad8\u65af\u725b\u987f\u6cd5","text":"TODO
"},{"location":"AI/SLAM14/#632-ceres","title":"6.3.2 \u4f7f\u7528 Ceres \u8fdb\u884c\u66f2\u7ebf\u62df\u5408","text":"TODO
"},{"location":"AI/SLAM14/#633-g2o","title":"6.3.3 \u4f7f\u7528 g2o\u8fdb\u884c\u66f2\u7ebf\u62df\u5408","text":"TODO
"},{"location":"AI/SLAM14/#7-1","title":"7 \u89c6\u89c9\u91cc\u7a0b\u8ba1 1","text":""},{"location":"AI/SLAM14/#71","title":"7.1 \u7279\u5f81\u70b9\u6cd5","text":" - \u7279\u5f81\u70b9\u6cd5
- \u4e24\u89c6\u56fe\u51e0\u4f55\uff08Two-view geometry\uff09
- \u76f4\u63a5\u6cd5
"},{"location":"AI/SLAM14/#711","title":"7.1.1 \u7279\u5f81\u70b9","text":" - \u5982\u4f55\u6839\u636e\u56fe\u50cf\u4f30\u8ba1\u76f8\u673a\u8fd0\u52a8
- \u8def\u6807: \u56fe\u50cf\u7279\u5f81
- \u7279\u5f81\u70b9\u5728\u76f8\u673a\u8fd0\u52a8\u4e4b\u540e\u4fdd\u6301\u7a33\u5b9a
- \u4eba\u5de5\u8bbe\u8ba1\u7684\u7279\u5f81\u70b9:
- Repeatability
- Distinctiveness
- Efficiency
- Locality
- \u7531\u4e24\u90e8\u5206\u7ec4\u6210:
- \u5173\u952e\u70b9\uff08Key-point\uff09
- \u63cf\u8ff0\u5b50\uff08Descriptor\uff09
- SIFT (\u5c3a\u5ea6\u4e0d\u53d8\u7279\u5f81\u53d8\u6362\uff0cScale-Invariant Feature Transform)
- \u8003\u8651\u5145\u5206\uff0c\u4f46\u662f\u8ba1\u7b97\u91cf\u6bd4\u8f83\u5927
- ORB (Oriented FAST and Rotated BRIEF)
"},{"location":"AI/SLAM14/#712-orb","title":"7.1.2 ORB \u7279\u5f81","text":" - FAST \u89d2\u70b9\u63d0\u53d6: ORB \u4e2d\u8ba1\u7b97\u4e86\u7279\u5f81\u70b9\u7684\u4e3b\u65b9\u5411\uff0c\u4e3a\u540e\u7eed\u7684 BRIEF \u63cf\u8ff0\u5b50\u589e\u52a0\u4e86\u65cb\u8f6c\u4e0d\u53d8\u7279\u6027
- BRIEF \u63cf\u8ff0\u5b50: \u5bf9\u524d\u4e00\u6b65\u63d0\u53d6\u51fa\u7279\u5f81\u70b9\u7684\u5468\u56f4\u56fe\u50cf\u533a\u57df\u8fdb\u884c\u63cf\u8ff0\u3002\u4f7f\u7528\u5148\u524d\u8ba1\u7b97\u7684\u65b9\u5411\u4fe1\u606f\u3002 - FAST \u5173\u952e\u70b9 Non-maximal suppression \u5c3a\u5ea6\u4e0d\u53d8\u6027\u7531\u6784\u5efa\u56fe\u50cf\u91d1\u5b57\u5854 \u7279\u5f81\u7684\u65cb\u8f6c: Intensity Centroid \u65cb\u8f6c\u65b9\u9762\uff0c\u6211\u4eec\u8ba1\u7b97\u7279\u5f81\u70b9\u9644\u8fd1\u7684\u56fe\u50cf\u7070\u5ea6\u8d28\u5fc3\u3002
- \u5b9a\u4e49\u56fe\u50cf\u7684\u77e9: \\(\\displaystyle m_{pq} = \\Sigma _{x,y \\in B} x^p x^q I(x, y),p,q \\in \\{0, 1\\}\\).
- \u627e\u5230\u56fe\u50cf\u5757\u7684\u8d28\u5fc3: \\(\\displaystyle C=\\left(\\frac{m_{10}}{m_{00}},\\frac{m_{01}}{m_{00}}\\right).\\)
- \u5f97\u5230\u4e00\u4e2a\u51e0\u4f55\u4e2d\u5fc3\u5230\u8d28\u5fc3\u7684\u65b9\u5411\u5411\u91cf: \\(\\displaystyle \\theta=\\arctan(m_{01}/m_{10}).\\) - BRIEF \u63cf\u8ff0\u5b50 \u4e8c\u8fdb\u5236\u8868\u8fbe+\u968f\u673a\u9009\u70b9\u6bd4\u8f83
"},{"location":"AI/SLAM14/#713","title":"7.1.3 \u7279\u5f81\u5339\u914d","text":"data association Brute-Force Matcher
- \u901a\u8fc7\u6d4b\u91cf\u63cf\u8ff0\u5b50\u7684\u8ddd\u79bb\u6765\u53bb\u6700\u8fd1\u7684\u4e00\u4e2a\u4f5c\u4e3a\u5339\u914d\u70b9\u3002\u63cf\u8ff0\u5b50\u8ddd\u79bb\u8868\u793a\u4e86\u4e24\u4e2a\u7279\u5f81\u4e4b\u95f4\u7684\u76f8\u4f3c\u7a0b\u5ea6\u3002
- \u6b27\u6c0f\u8ddd\u79bb
- \u6c49\u660e\u8ddd\u79bb
- \u4e24\u4e2a\u4e8c\u8fdb\u5236\u4e32\u7684\u4e0d\u540c\u4f4d\u6570\u7684\u4e2a\u6570
- \u5feb\u901f\u8fd1\u4f3c\u6700\u8fd1\u90bb\uff08FLANN\uff09
"},{"location":"AI/SLAM14/#72","title":"7.2 \u5b9e\u8df5: \u7279\u5f81\u63d0\u53d6\u548c\u5339\u914d","text":""},{"location":"AI/SLAM14/#721-opencv-orb","title":"7.2.1 OpenCV \u7684 ORB \u7279\u5f81","text":"TODO
"},{"location":"AI/SLAM14/#722-orb","title":"7.2.2 \u624b\u5199 ORB \u7279\u5f81","text":""},{"location":"AI/SLAM14/#723","title":"7.2.3 \u8ba1\u7b97\u76f8\u673a\u8fd0\u52a8","text":" - \u5f53\u76f8\u673a\u4e3a\u5355\u76ee\u65f6\uff0c\u6211\u4eec\u901a\u8fc7\u5bf9\u6781\u51e0\u4f55\u6765\u89e3\u51b3\u4e24\u7ec4 2 D \u70b9\u4f30\u8ba1\u8fd0\u52a8\u7684\u95ee\u9898
- \u5f53\u76f8\u673a\u4e3a\u53cc\u76ee\u3001RGB-D \u65f6\uff0c\u901a\u8fc7 ICP \u6765\u89e3\u51b3\u4e24\u7ec4 3 D \u70b9\u4f30\u8ba1\u8fd0\u52a8\u7684\u95ee\u9898
- \u4e00\u4e2a\u662f 2 D \u4e00\u4e2a\u662f 3 D \u65f6\uff0c\u901a\u8fc7 PnP \u6765\u6c42\u89e3
"},{"location":"AI/SLAM14/#73-d-2-d","title":"7.3 D-2 D: \u5bf9\u6781\u51e0\u4f55","text":""},{"location":"AI/SLAM14/#731","title":"7.3.1 \u5bf9\u6781\u7ea6\u675f","text":" - Epipolar plane
- Epipoles
- Epipolar line
\\[ P=[X,Y,Z]^{\\mathrm{T}}. \\] \\[ s_{1}p_{1}=KP,\\quad s_{2}p_{2}=K\\left(RP+t\\right). \\] \u6210\u6295\u5f71\u5173\u7cfb:\u5c3a\u5ea6\u610f\u4e49\u4e0b\u76f8\u7b49 (equal up to scale)
\\[ sp\\simeq p. \\] \\[ p_1\\simeq KP,\\quad p_2\\simeq K\\left(RP+t\\right). \\] \\[ x_1=K^{-1}p_1,\\quad x_2=K^{-1}p_2. \\] \\[ x_2\\simeq Rx_1+t. \\] \\[ t^{\\wedge}x_{2}\\simeq t^{\\wedge}Rx_{1}. \\] \\[ x_2^\\mathrm{T}t^\\wedge x_2\\simeq x_2^\\mathrm{T}t^\\wedge Rx_1. \\] \\[ x_2^\\mathrm{T}t^\\wedge Rx_1=0. \\] \\[ p_2^\\mathrm{T}K^{-\\mathrm{T}}t^\\wedge RK^{-1}p_1=0. \\] \u5bf9\u6781\u7ea6\u675f
\\[ E=t^{\\wedge}R,\\quad F=K^{-\\mathrm{T}}EK^{-1},\\quad x_{2}^{\\mathrm{T}}Ex_{1}=p_{2}^{\\mathrm{T}}Fp_{1}=0. \\] - \u6839\u636e\u914d\u5bf9\u70b9\u7684\u50cf\u7d20\u4f4d\u7f6e\u6c42\u51fa \\(\\displaystyle \\boldsymbol{E}\\) \u6216\u8005 \\(\\displaystyle \\boldsymbol{F}\\)\u3002
- \u6839\u636e \\(\\displaystyle \\boldsymbol{E}\\) \u6216\u8005 \\(\\displaystyle \\boldsymbol{F}\\) \u6c42\u51fa \\(\\displaystyle \\boldsymbol{R},\\boldsymbol{t}\\)\u3002 \\(\\displaystyle \\boldsymbol{E}\\) \u548c \\(\\displaystyle \\boldsymbol{F}\\) \u53ea\u76f8\u5dee\u4e86\u76f8\u673a\u5185\u53c2\uff0c\u6240\u4ee5\u5b9e\u8df5\u4e2d\u5f80\u5f80\u4f7f\u7528\u5f62\u5f0f\u66f4\u7b80\u5355\u7684 \\(\\displaystyle \\boldsymbol{E}\\)\u3002
"},{"location":"AI/SLAM14/#732","title":"7.3.2 \u672c\u8d28\u77e9\u9635","text":"\u672c\u8d28\u77e9\u9635: \\(\\displaystyle E=t^{\\wedge}R\\)
- \\(\\displaystyle \\boldsymbol{E}\\) \u4e0d\u540c\u5c3a\u5ea6\u4e0b\u662f\u7b49\u4ef7\u7684\u3002
- \u53ef\u4ee5\u8bc1\u660e\uff0c\u672c\u8d28\u77e9\u9635 \\(\\displaystyle \\boldsymbol{E}\\) \u7684\u5947\u5f02\u503c\u5fc5\u5b9a\u662f \\(\\displaystyle [\\sigma,\\sigma,0]^\\mathrm{T}\\) \u7684\u5f62\u5f0f\uff0c\u8fd9\u79f0\u4e3a\u672c\u8d28\u77e9\u9635\u7684\u5185\u5728\u6027\u8d28\u3002
- \\(\\displaystyle \\boldsymbol{E}\\) \u5b9e\u9645\u4e0a\u6709 5 \u4e2a\u81ea\u7531\u5ea6\u3002 \u516b\u70b9\u6cd5 (Eight-point-algorithm) \u8003\u8651\u4e00\u5806\u914d\u5bf9\u70b9\uff0c\u5b83\u4eec\u7684\u5f52\u4e00\u5316\u5750\u6807\u4e3a \\(\\displaystyle x_{1}=[u_{1},v_{1},1]^{\\mathrm{T}},x_{2}=[u_{2},v_{2},1]^{\\mathrm{T}}\\)\u3002\u6839\u636e\u5bf9\u6781\u7ea6\u675f\uff0c\u6709
\\[ \\begin{pmatrix}u_2,v_2,1\\end{pmatrix}\\begin{pmatrix}e_1&e_2&e_3\\\\\\\\e_4&e_5&e_6\\\\\\\\e_7&e_8&e_9\\end{pmatrix}\\begin{pmatrix}u_1\\\\\\\\v_1\\\\\\\\1\\end{pmatrix}=0. \\] \\[ \\boldsymbol{e}=[e_1,e_2,e_3,e_4,e_5,e_6,e_7,e_8,e_9]^\\mathrm{T}, \\] \\[ [u_2u_1,u_2v_1,u_2,v_2u_1,v_2v_1,v_2,u_1,v_1,1]\\cdot e=0. \\] \u6211\u4eec\u628a\u6240\u6709\u70b9\u90fd\u653e\u5230\u4e00\u4e2a\u65b9\u7a0b\u4e2d\uff0c\u53d8\u6210\u7ebf\u6027\u65b9\u7a0b\u7ec4:
\\[ \\begin{pmatrix}u_2^1u_1^1&u_2^1v_1^1&u_2^1&v_2^1u_1^1&v_2^1v_1^1&v_2^1&u_1^1&v_1^1&1\\\\u_2^2u_1^2&u_2^2v_1^2&u_2^2&v_2^2u_1^2&v_2^2v_1^2&v_2^2&u_1^2&v_1^2&1\\\\\\vdots&\\vdots&\\vdots&\\vdots&\\vdots&\\vdots&\\vdots&\\vdots\\\\u_2^8u_1^8&u_2^8v_1^8&u_2^8&v_2^8u_1^8&v_2^8u_1^8&u_1^8&v_1^8&1\\end{pmatrix}\\begin{pmatrix}e_1\\\\e_2\\\\e_3\\\\e_4\\\\e_5\\\\e_6\\\\e_7\\\\e_8\\\\e_9\\end{pmatrix}=0. \\] \\[ E=U\\Sigma V^{\\mathrm{T}}, \\] \\[ \\begin{aligned}&t_{1}^{\\wedge}=UR_{Z}(\\frac{\\pi}{2})\\Sigma U^{\\mathrm{T}},\\quad R_{1}=UR_{Z}^{\\mathrm{T}}(\\frac{\\pi}{2})V^{\\mathrm{T}}\\\\&t_{2}^{\\wedge}=UR_{Z}(-\\frac{\\pi}{2})\\Sigma U^{\\mathrm{T}},\\quad R_{2}=UR_{Z}^{\\mathrm{T}}(-\\frac{\\pi}{2})V^{\\mathrm{T}}.\\end{aligned} \\] \\[ \\boldsymbol{E}=\\boldsymbol{U}\\mathrm{diag}(\\frac{\\sigma_1+\\sigma_2}2,\\frac{\\sigma_1+\\sigma_2}2,0)\\boldsymbol{V}^\\mathrm{T}. \\]"},{"location":"AI/SLAM14/#733","title":"7.3.3 \u5355\u5e94\u77e9\u9635","text":"Homography
"},{"location":"AI/SLAM14/#8-2","title":"8 \u89c6\u89c9\u91cc\u7a0b\u8ba1 2","text":""},{"location":"AI/SLAM14/#81","title":"8.1 \u76f4\u63a5\u6cd5\u7684\u5f15\u51fa","text":" - \u7279\u5f81\u70b9\u6cd5\u7684\u7f3a\u70b9
- \u5173\u952e\u70b9\u7684\u63d0\u53d6\u4e0e\u63cf\u8ff0\u5b50\u7684\u8ba1\u7b97\u975e\u5e38\u8017\u65f6
- \u4f7f\u7528\u7279\u5f81\u70b9\u65f6\uff0c\u4f1a\u5ffd\u7565\u9664\u7279\u5f81\u70b9\u4ee5\u5916\u7684\u6240\u6709\u4fe1\u606f
- \u76f8\u673a\u6709\u65f6\u4f1a\u8fd0\u52a8\u5230\u7279\u5f81\u7f3a\u5931\u7684\u5730\u65b9
- \u90a3\u4e48\u5982\u4f55\u514b\u670d\u8fd9\u4e9b\u7f3a\u70b9
- \u4fdd\u7559\u7279\u5f81\u70b9\uff0c\u4f46\u53ea\u8ba1\u7b97\u5173\u952e\u70b9\uff0c\u4e0d\u8ba1\u7b97\u63cf\u8ff0\u5b50\u3002\u4f7f\u7528\u5149\u6d41\u6cd5\uff08Optical Flow\uff09\u8ddf\u8e2a\u7279\u5f81\u70b9\u7684\u8fd0\u52a8
- \u53ea\u8ba1\u7b97\u5173\u952e\u70b9\uff0c\u4e0d\u8ba1\u7b97\u63cf\u8ff0\u5b50\u3002\u4f7f\u7528\u76f4\u63a5\u6cd5\uff08Direct Method\uff09
- \u7b2c\u4e00\u79cd\u65b9\u6cd5\u4ecd\u7136\u4f7f\u7528\u7279\u5f81\u70b9\uff0c\u53ea\u662f\u628a\u5339\u914d\u63cf\u8ff0\u5b57\u66ff\u6362\u6210\u4e86\u5149\u6d41\u8ddf\u8e2a\uff0c\u4f30\u8ba1\u76f8\u673a\u8fd0\u52a8\u65f6\u4ecd\u7136\u662f\u54e6\u90a3\u4e2a\u5bf9\u6781\u51e0\u4f55\u3001PnP \u6216 ICP \u7b97\u6cd5\uff08\u5373\uff0c\u6211\u4eec\u9700\u8981\u63d0\u5230\u89d2\u70b9\uff09
- \u7279\u5f81\u70b9\u6cd5:\u901a\u8fc7\u6700\u5c0f\u5316\u91cd\u6295\u5f71\u8bef\u5dee\uff08Reprojection error\uff09\u4f18\u5316\u76f8\u673a\u8fd0\u52a8
- \u76f4\u63a5\u6cd5: \u901a\u8fc7\u6700\u5c0f\u5316\u5149\u5ea6\u8bef\u5dee\uff08Photometric error\uff09
- \u53ea\u8981\u573a\u666f\u4e2d\u5b58\u5728\u660e\u6697\u53d8\u5316\u5c31\u53ef\u4ee5\u5de5\u4f5c
- \u7a20\u5bc6
- \u534a\u7a20\u5bc6
- \u7a00\u758f
"},{"location":"AI/SLAM14/#82-d","title":"8.2 D \u5149\u6d41","text":" - \u8ba1\u7b97\u90e8\u5206\u50cf\u7d20\u8fd0\u52a8: \u7a00\u758f\u5149\u6d41
- \u8ba1\u7b97\u6240\u6709\u50cf\u7d20\u8fd0\u52a8: \u7a20\u5bc6\u5149\u6d41
- Horn-Schunck Lucas-Kanade \u5149\u6d41
- \u7070\u5ea6\u4e0d\u53d8\u5047\u8bbe: \u540c\u4e00\u4e2a\u7a7a\u95f4\u70b9\u7684\u50cf\u7d20\u7070\u5ea6\u503c\uff0c\u5728\u5404\u4e2a\u56fe\u50cf\u4e2d\u65f6\u56fa\u5b9a\u4e0d\u53d8\u7684
\\[ I(x+\\mathrm{d}x,y+\\mathrm{d}y,t+\\mathrm{d}t)=I(x,y,t). \\] \\[ \\boldsymbol{I}\\left(x+\\mathrm{d}x,y+\\mathrm{d}y,t+\\mathrm{d}t\\right)\\approx\\boldsymbol{I}\\left(x,y,t\\right)+\\frac{\\partial\\boldsymbol{I}}{\\partial x}\\mathrm{d}x+\\frac{\\partial\\boldsymbol{I}}{\\partial y}\\mathrm{d}y+\\frac{\\partial\\boldsymbol{I}}{\\partial t}\\mathrm{d}t. \\] \\[ \\frac{\\partial\\boldsymbol{I}}{\\partial x}\\frac{\\mathrm{d}x}{\\mathrm{d}t}+\\frac{\\partial\\boldsymbol{I}}{\\partial y}\\frac{\\mathrm{d}y}{\\mathrm{d}t}=-\\frac{\\partial\\boldsymbol{I}}{\\partial t}. \\] \\[ \\begin{bmatrix}I_x&I_y\\end{bmatrix}\\begin{bmatrix}u\\\\\\\\v\\end{bmatrix}=-I_t. \\] \\[ \\begin{bmatrix}I_x&I_y\\end{bmatrix}_k\\begin{bmatrix}u\\\\\\\\v\\end{bmatrix}=-I_{tk},\\quad k=1,\\ldots,w^2. \\] \\[ A\\begin{bmatrix}u\\\\\\\\v\\end{bmatrix}=-b. \\] \\[ \\begin{bmatrix}u\\\\\\\\v\\end{bmatrix}^*=-\\begin{pmatrix}\\boldsymbol{A}^\\mathrm{T}\\boldsymbol{A}\\end{pmatrix}^{-1}\\boldsymbol{A}^\\mathrm{T}\\boldsymbol{b}. \\]"},{"location":"AI/SLAM14/#83-lk","title":"8.3 \u5b9e\u8df5: LK \u5149\u6d41","text":""},{"location":"AI/SLAM14/#831-lk","title":"8.3.1 \u4f7f\u7528 LK \u5149\u6d41","text":"C++vector<Point2f> pt1, pt2;\nfor (auto &kp: kp1) pt1.push_back(kp.pt);\nvector<uchar> status;\nvector<float> error;\ncv::calcOpticalFlowPyrLK(img1, img2, pt1, pt2, status, error);\n
"},{"location":"AI/SLAM14/#832","title":"8.3.2 \u7528\u9ad8\u65af\u725b\u987f\u6cd5\u5b9e\u73b0\u5149\u6d41","text":"\u5355\u5c42\u5149\u6d41 TODO
\\[ \\min_{\\Delta x,\\Delta y}\\left\\|\\boldsymbol{I}_1\\left(x,y\\right)-\\boldsymbol{I}_2\\left(x+\\Delta x,y+\\Delta y\\right)\\right\\|_2^2. \\] \u591a\u5c42\u5149\u6d41 - \u7531\u7c97\u81f3\u7cbe\uff08Coarse-to-fine\uff09
"},{"location":"AI/SLAM14/#833","title":"8.3.3 \u5149\u6d41\u5b9e\u8df5\u5c0f\u7ed3","text":""},{"location":"AI/SLAM14/#84","title":"8.4 \u76f4\u63a5\u6cd5","text":""},{"location":"AI/SLAM14/#841","title":"8.4.1 \u76f4\u63a5\u6cd5\u7684\u63a8\u5bfc","text":"\\[ \\boldsymbol{p}_1=\\begin{bmatrix}u\\\\\\\\v\\\\\\\\1\\end{bmatrix}_1=\\frac{1}{Z_1}\\boldsymbol{K}\\boldsymbol{P}, \\] \\[ \\boldsymbol{p}_{2}=\\begin{bmatrix}u\\\\\\\\v\\\\\\\\1\\end{bmatrix}_{2}=\\frac{1}{Z_{2}}\\boldsymbol{K}\\left(\\boldsymbol{R}\\boldsymbol{P}+\\boldsymbol{t}\\right)=\\frac{1}{Z_{2}}\\boldsymbol{K}\\left(\\boldsymbol{T}\\boldsymbol{P}\\right)_{1:3}. \\] \\[ e=\\boldsymbol{I}_1\\left(\\boldsymbol{p}_1\\right)-\\boldsymbol{I}_2\\left(\\boldsymbol{p}_2\\right). \\] \\[ \\min_{T}J\\left(T\\right)=\\left\\|e\\right\\|^{2}. \\] \\[ \\min_{\\boldsymbol{T}}J\\left(\\boldsymbol{T}\\right)=\\sum_{i=1}^{N}e_{i}^{\\mathrm{T}}e_{i},\\quad e_{i}=\\boldsymbol{I}_{1}\\left(\\boldsymbol{p}_{1,i}\\right)-\\boldsymbol{I}_{2}\\left(\\boldsymbol{p}_{2,i}\\right). \\] \\[ \\begin{aligned}&q=TP,\\\\&\\boldsymbol{u}=\\frac{1}{Z_{2}}Kq.\\end{aligned} \\] \\[ e(T)=I_1(p_1)-I_2(u), \\] \\[ \\frac{\\partial e}{\\partial\\boldsymbol{T}}=\\frac{\\partial\\boldsymbol{I}_{2}}{\\partial\\boldsymbol{u}}\\frac{\\partial\\boldsymbol{u}}{\\partial\\boldsymbol{q}}\\frac{\\partial\\boldsymbol{q}}{\\partial\\delta\\boldsymbol{\\xi}}\\delta\\boldsymbol{\\xi}, \\] \\[ \\frac{\\partial\\boldsymbol{u}}{\\partial\\boldsymbol{q}}=\\begin{bmatrix}\\frac{\\partial u}{\\partial X}&\\frac{\\partial u}{\\partial Y}&\\frac{\\partial u}{\\partial Z}\\\\\\frac{\\partial v}{\\partial X}&\\frac{\\partial v}{\\partial Y}&\\frac{\\partial v}{\\partial Z}\\end{bmatrix}=\\begin{bmatrix}\\frac{f_x}{Z}&0&-\\frac{f_xX}{Z^2}\\\\0&\\frac{f_y}{Z}&-\\frac{f_yY}{Z^2}\\end{bmatrix}. \\] \\[ \\frac{\\partial\\boldsymbol{q}}{\\partial\\delta\\boldsymbol{\\xi}}=\\left[I,-\\boldsymbol{q}^{\\wedge}\\right]. \\] \\[ \\frac{\\partial\\boldsymbol{u}}{\\partial\\delta\\boldsymbol{\\xi}}=\\begin{bmatrix}\\frac{f_x}{Z}&0&-\\frac{f_xX}{Z^2}&-\\frac{f_xXY}{Z^2}&f_x+\\frac{f_xX^2}{Z^2}&-\\frac{f_xY}{Z}\\\\0&\\frac{f_y}{Z}&-\\frac{f_yY}{Z^2}&-f_y-\\frac{f_yY^2}{Z^2}&\\frac{f_yXY}{Z^2}&\\frac{f_yX}{Z}\\end{bmatrix}. \\] \\[ J=-\\frac{\\partial I_2}{\\partial u}\\frac{\\partial u}{\\partial\\delta\\xi}. \\]"},{"location":"AI/SLAM14/#842","title":"8.4.2 \u76f4\u63a5\u6cd5\u7684\u8ba8\u8bba","text":""},{"location":"AI/SLAM14/#85","title":"8.5 \u5b9e\u8df5: \u76f4\u63a5\u6cd5","text":""},{"location":"AI/SLAM14/#851","title":"8.5.1 \u5355\u5c42\u76f4\u63a5\u6cd5","text":""},{"location":"AI/SLAM14/#852","title":"8.5.2 \u591a\u5c42\u76f4\u63a5\u6cd5","text":""},{"location":"AI/SLAM14/#853","title":"8.5.3 \u7ed3\u679c\u8ba8\u8bba","text":""},{"location":"AI/SLAM14/#854","title":"8.5.4 \u76f4\u63a5\u6cd5\u4f18\u7f3a\u70b9\u603b\u7ed3","text":""},{"location":"AI/SLAM14/#9-1","title":"9 \u540e\u7aef 1","text":""},{"location":"AI/SLAM14/#91","title":"9.1 \u6982\u8ff0","text":""},{"location":"AI/SLAM14/#911","title":"9.1.1 \u72b6\u6001\u4f30\u8ba1\u7684\u6982\u7387\u89e3\u91ca","text":" - \u53ea\u4f7f\u7528\u8fc7\u53bb\u7684\u4fe1\u606f: \u6e10\u8fdb\u7684\uff08Incremental\uff09
- \u4f7f\u7528\u672a\u6765\u7684\u4fe1\u606f\u66f4\u65b0: \u6279\u91cf\u7684\uff08Batch\uff09
\\[ \\begin{cases}\\boldsymbol{x}_k=f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k\\right)+\\boldsymbol{w}_k\\\\\\boldsymbol{z}_{k,j}=h\\left(\\boldsymbol{y}_j,\\boldsymbol{x}_k\\right)+\\boldsymbol{v}_{k,j}\\end{cases}\\quad k=1,\\ldots,N, j=1,\\ldots,M. \\] - \u89c2\u6d4b\u65b9\u7a0b\u7684\u6570\u91cf\u4f1a\u8fdc\u8fdc\u5927\u4e8e\u8fd0\u52a8\u65b9\u7a0b
- \u5f53\u6ca1\u6709\u8fd0\u52a8\u65b9\u7a0b\u7684\u65f6\u5019\uff0c\u6211\u4eec\u53ef\u4ee5\u5047\u8bbe\u76f8\u673a\u4e0d\u52a8\uff0c\u6216\u5047\u8bbe\u76f8\u673a\u5300\u901f\u8fd0\u52a8
- \u95ee\u9898\uff1a\u5f53\u5b58\u5728\u4e00\u4e9b\u8fd0\u52a8\u6570\u636e\u548c\u89c2\u6d4b\u6570\u636e\u65f6\uff0c\u6211\u4eec\u5982\u4f55\u4f30\u8ba1\u72b6\u6001\u91cf\u7684\u9ad8\u65af\u5206\u5e03
- \u8bef\u5dee\u65f6\u9010\u6e10\u7d2f\u79ef\u7684
- \u6700\u5927\u4f3c\u7136\u4f30\u8ba1: \u6279\u91cf\u72b6\u6001\u4f30\u8ba1\u95ee\u9898\u53ef\u4ee5\u8f6c\u5316\u4e3a\u6700\u5927\u4f3c\u7136\u4f30\u8ba1\u95ee\u9898\uff0c\u5e76\u4f7f\u7528\u6700\u5c0f\u4e8c\u4e58\u6cd5\u8fdb\u884c\u6c42\u89e3
\\[ x_k\\stackrel{\\mathrm{def}}{=}\\{x_k,y_1,\\ldots,y_m\\}. \\] \\[ \\begin{cases}\\boldsymbol{x}_k=f\\left(\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k\\right)+\\boldsymbol{w}_k\\\\\\boldsymbol{z}_k=h\\left(\\boldsymbol{x}_k\\right)+\\boldsymbol{v}_k\\end{cases}\\quad k=1,\\ldots,N. \\] \\[ P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k}\\right)\\propto P\\left(\\boldsymbol{z}_k|\\boldsymbol{x}_k\\right)P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right). \\] - \u7b2c\u4e00\u9879\u79f0\u4e3a\u4f3c\u7136\uff0c\u7b2c\u4e8c\u9879\u79f0\u4e3a\u5148\u9a8c
\\[ P\\left(\\boldsymbol{x}_{k}|\\boldsymbol{x}_{0},\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)=\\int P\\left(\\boldsymbol{x}_{k}|\\boldsymbol{x}_{k-1},\\boldsymbol{x}_{0},\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)P\\left(\\boldsymbol{x}_{k-1}|\\boldsymbol{x}_{0},\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)\\mathrm{d}\\boldsymbol{x}_{k-1}. \\] - \u867d\u7136\u53ef\u4ee5\u7ee7\u7eed\u5bf9\u6b64\u5f0f\u8fdb\u884c\u5c55\u5f00\uff0c\u4f46\u6211\u4eec\u53ea\u5173\u5fc3 \\(\\displaystyle k\\) \u65f6\u523b\u548c \\(\\displaystyle k - 1\\) \u65f6\u523b\u7684\u60c5\u51b5
- \u7b2c\u4e00\u79cd\u65b9\u6cd5\u662f\u5047\u8bbe\u9a6c\u5c14\u53ef\u592b\u6027: \u5373\u8ba4\u4e3a \\(\\displaystyle k\\) \u65f6\u523b\u72b6\u6001\u53ea\u4e0e \\(\\displaystyle k - 1\\) \u65f6\u523b\u72b6\u6001\u6709\u5173
- \u90a3\u4e48\u6211\u4eec\u5c31\u53ef\u4ee5\u5f97\u5230\u4ee5\u6269\u5c55\u5361\u5c14\u66fc\u6ee4\u6ce2\uff08EKF\uff09\u4e3a\u4ee3\u8868\u7684\u6ee4\u6ce2\u5668\u65b9\u5f0f
- \u7b2c\u4e8c\u79cd\u65b9\u6cd5\u662f\u4f9d\u7136\u8003\u8651\u548c\u4e4b\u524d\u6240\u6709\u72b6\u6001\u7684\u5173\u7cfb\uff0c\u59ff\u52bf\u4f1a\u5f97\u5230\u975e\u7ebf\u6027\u4f18\u5316\u4e3a\u4e3b\u4f53\u7684\u4f18\u5316\u6846\u67b6\u3002
- \u4e3b\u6d41\u662f\u975e\u7ebf\u6027\u4f18\u5316
"},{"location":"AI/SLAM14/#912-kf","title":"9.1.2 \u7ebf\u6027\u7cfb\u7edf\u548c KF","text":"\\[ P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_{k-1},\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)=P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_{k-1},\\boldsymbol{u}_k\\right). \\] \\[ P\\left(\\boldsymbol{x}_{k-1}|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)=P\\left(\\boldsymbol{x}_{k-1}|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k-1},\\boldsymbol{z}_{1:k-1}\\right). \\] - \u6240\u4ee5\u6211\u4eec\u5b9e\u9645\u5728\u505a\u7684\u4e8b\u5982\u4f55\u628a \\(\\displaystyle k - 1\\) \u65f6\u523b\u7684\u72b6\u6001\u5206\u5e03\u63a8\u5bfc\u81f3 \\(\\displaystyle k\\) \u65f6\u523b
- \u5373\u6211\u4eec\u53ea\u8981\u7ef4\u62a4\u4e00\u4e2a\u72b6\u6001\uff0c\u5e76\u4e0d\u65ad\u5730\u8fed\u4ee3\u66f4\u65b0
- \u53ea\u8981\u7ef4\u62a4\u72b6\u6001\u91cf\u7684\u5747\u503c\u548c\u534f\u65b9\u5dee\uff08\u72b6\u6001\u91cf\u670d\u4ece\u9ad8\u65af\u5206\u5e03\uff09
\\[ \\begin{cases}x_k=A_kx_{k-1}+u_k+w_k\\\\z_k=C_kx_k+v_k\\end{cases}\\quad k=1,\\ldots,N. \\] \\[ w_k\\sim N(0,\\boldsymbol{R}).\\quad\\boldsymbol{v}_k\\sim N(\\boldsymbol{0},\\boldsymbol{Q}). \\] - \u4e0a\u5e3d\u5b50\u8868\u793a\u540e\u9a8c\uff0c\u4e0b\u5e3d\u5b50\u8868\u793a\u5148\u9a8c
\\[ P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{1:k-1}\\right)=N\\left(\\boldsymbol{A}_k\\hat{x}_{k-1}+\\boldsymbol{u}_k,\\boldsymbol{A}_k\\hat{\\boldsymbol{P}}_{k-1}\\boldsymbol{A}_k^\\mathrm{T}+\\boldsymbol{R}\\right). \\] - \u8fd9\u4e00\u6b65\u79f0\u4e3a\u9884\u6d4b\uff08Predict\uff09
\\[ \\check{\\boldsymbol{x}}_k=\\boldsymbol{A}_k\\hat{\\boldsymbol{x}}_{k-1}+\\boldsymbol{u}_k,\\quad\\check{\\boldsymbol{P}}_k=A_k\\hat{\\boldsymbol{P}}_{k-1}\\boldsymbol{A}_k^\\mathrm{T}+\\boldsymbol{R}. \\] \\[ P\\left(\\boldsymbol{z}_k|\\boldsymbol{x}_k\\right)=N\\left(\\boldsymbol{C}_k\\boldsymbol{x}_k,\\boldsymbol{Q}\\right). \\] - \u5982\u679c\u7ed3\u679c\u8bbe\u4e3a \\(\\displaystyle x_k\\sim N(\\hat{\\boldsymbol{x}}_k,\\hat{\\boldsymbol{P}}_k)\\)\uff0c\u90a3\u4e48
\\[ N(\\hat{\\boldsymbol{x}}_k,\\hat{\\boldsymbol{P}}_k)=\\eta N\\left(\\boldsymbol{C}_k\\boldsymbol{x}_k,\\boldsymbol{Q}\\right)\\cdot N(\\check{\\boldsymbol{x}}_k,\\check{\\boldsymbol{P}}_k). \\] \\[ (\\boldsymbol{x}_{k}-\\hat{\\boldsymbol{x}}_{k})^{\\mathrm{T}}\\hat{\\boldsymbol{P}}_{k}^{-1}\\left(\\boldsymbol{x}_{k}-\\hat{\\boldsymbol{x}}_{k}\\right)=\\left(\\boldsymbol{z}_{k}-\\boldsymbol{C}_{k}\\boldsymbol{x}_{k}\\right)^{\\mathrm{T}}\\boldsymbol{Q}^{-1}\\left(\\boldsymbol{z}_{k}-\\boldsymbol{C}_{k}\\boldsymbol{x}_{k}\\right)+\\left(\\boldsymbol{x}_{k}-\\check{\\boldsymbol{x}}_{k}\\right)^{\\mathrm{T}}\\boldsymbol{P}_{k}^{-1}\\left(\\boldsymbol{x}_{k}-\\check{\\boldsymbol{x}}_{k}\\right). \\] \\[ \\hat{P}_k^{-1}=C_k^{\\mathrm{T}}Q^{-1}C_k+\\check{P}_k^{-1}. \\] - \u5b9a\u4e49\u4e00\u4e2a\u4e2d\u95f4\u53d8\u91cf
\\[ K=\\hat{P}_kC_k^{\\mathrm{T}}Q^{-1}. \\] \\[ I=\\hat{P}_{k}C_{k}^{\\mathrm{T}}Q^{-1}C_{k}+\\hat{P}_{k}\\check{P}_{k}^{-1}=KC_{k}+\\hat{P}_{k}\\check{P}_{k}^{-1}. \\] \\[ \\hat{P}_{k}=(I-KC_{k})\\check{P}_{k}. \\] - \u4e00\u6b21\u9879\u7cfb\u6570
\\[ -2\\hat{\\boldsymbol{x}}_k^\\mathrm{T}\\hat{\\boldsymbol{P}}_k^{-1}\\boldsymbol{x}_k=-2\\boldsymbol{z}_k^\\mathrm{T}\\boldsymbol{Q}^{-1}\\boldsymbol{C}_k\\boldsymbol{x}_k-2\\boldsymbol{\\dot{x}}_k^\\mathrm{T}\\check{\\boldsymbol{P}}_k^{-1}\\boldsymbol{x}_k. \\] \\[ \\hat{\\boldsymbol{P}}_k^{-1}\\hat{\\boldsymbol{x}}_k=\\boldsymbol{C}_k^\\mathrm{T}\\boldsymbol{Q}^{-1}\\boldsymbol{z}_k+\\check{\\boldsymbol{P}}_k^{-1}\\check{\\boldsymbol{x}}_k. \\] \\[ \\begin{aligned} \\hat{x}_{k}& =\\hat{\\boldsymbol{P}}_k\\boldsymbol{C}_k^\\mathrm{T}\\boldsymbol{Q}^{-1}\\boldsymbol{z}_k+\\hat{\\boldsymbol{P}}_k\\check{\\boldsymbol{P}}_k^{-1}\\check{\\boldsymbol{x}}_k \\\\ &=K\\boldsymbol{z}_k+\\left(\\boldsymbol{I}-\\boldsymbol{K}\\boldsymbol{C}_k\\right)\\check{\\boldsymbol{x}}_k=\\check{\\boldsymbol{x}}_k+\\boldsymbol{K}\\left(\\boldsymbol{z}_k-\\boldsymbol{C}_k\\check{\\boldsymbol{x}}_k\\right). \\end{aligned} \\]"},{"location":"AI/SLAM14/#913-ekf","title":"9.1.3 \u975e\u7ebf\u6027\u7cfb\u7edf\u548c EKF","text":" - \u6269\u5c55\u5361\u5c14\u66fc\u6ee4\u6ce2\u5668
- \u5373\u628a\u975e\u9ad8\u65af\u5206\u5e03\u8fd1\u4f3c\u6210\u9ad8\u65af\u5206\u5e03
\\[ \\boldsymbol{x}_k\\approx f\\left(\\hat{\\boldsymbol{x}}_{k-1},\\boldsymbol{u}_k\\right)+\\left.\\frac{\\partial f}{\\partial\\boldsymbol{x}_{k-1}}\\right|_{\\tilde{\\boldsymbol{x}}_{k-1}}\\left(\\boldsymbol{x}_{k-1}-\\hat{\\boldsymbol{x}}_{k-1}\\right)+\\boldsymbol{w}_k. \\] \\[ \\boldsymbol{F}=\\left.\\frac{\\partial f}{\\partial\\boldsymbol{x}_{k-1}}\\right|_{\\hat{\\boldsymbol{x}}_{k-1}}. \\] \\[ z_k\\approx h\\left(\\check{\\boldsymbol{x}}_k\\right)+\\left.\\frac{\\partial h}{\\partial\\boldsymbol{x}_k}\\right|_{\\dot{\\boldsymbol{x}}_k}\\left(\\boldsymbol{x}_k-\\check{\\boldsymbol{x}}_k\\right)+\\boldsymbol{n}_k. \\] \\[ H=\\left.\\frac{\\partial h}{\\partial\\boldsymbol{x}_k}\\right|_{\\check{\\boldsymbol{x}}_k}. \\] \\[ P\\left(\\boldsymbol{x}_k|\\boldsymbol{x}_0,\\boldsymbol{u}_{1:k},\\boldsymbol{z}_{0:k-1}\\right)=N(f\\left(\\hat{\\boldsymbol{x}}_{k-1},\\boldsymbol{u}_k\\right),\\boldsymbol{F}\\hat{\\boldsymbol{P}}_{k-1}\\boldsymbol{F}^\\mathrm{T}+\\boldsymbol{R}_k). \\] \\[ \\check{\\boldsymbol{x}}_k=f\\left(\\hat{\\boldsymbol{x}}_{k-1},\\boldsymbol{u}_k\\right),\\quad\\check{\\boldsymbol{P}}_k=\\boldsymbol{F}\\hat{\\boldsymbol{P}}_{k-1}\\boldsymbol{F}^\\mathrm{T}+\\boldsymbol{R}_k. \\] \\[ P\\left(\\boldsymbol{z}_k|\\boldsymbol{x}_k\\right)=N(h\\left(\\check{\\boldsymbol{x}}_k\\right)+\\boldsymbol{H}\\left(\\boldsymbol{x}_k-\\check{\\boldsymbol{x}}_k\\right),Q_k). \\] - \u5b9a\u4e49\u4e00\u4e2a\u5361\u5c14\u66fc\u589e\u76ca \\(\\displaystyle \\boldsymbol{K}_{k}\\)
\\[ K_{k}=\\check{P}_{k}H^{\\mathrm{T}}(H\\check{P}_{k}H^{\\mathrm{T}}+Q_{k})^{-1}. \\] \\[ \\hat{\\boldsymbol{x}}_k=\\check{\\boldsymbol{x}}_k+\\boldsymbol{K}_k\\left(\\boldsymbol{z}_k-h\\left(\\check{\\boldsymbol{x}}_k\\right)\\right),\\hat{\\boldsymbol{P}}_k=\\left(\\boldsymbol{I}-\\boldsymbol{K}_k\\boldsymbol{H}\\right)\\check{\\boldsymbol{P}}_k. \\]"},{"location":"AI/SLAM14/#914-ekf","title":"9.1.4 EKF \u7684\u8ba8\u8bba","text":" - \u5c40\u9650
- \u5047\u8bbe\u4e86\u9a6c\u5c14\u53ef\u592b\u6027\uff0c\u4f46\u662f\u975e\u7ebf\u6027\u4f18\u5316\u662f\u5168\u4f53\u65f6\u95f4\u4e0a\u7684 SLAM (Full-SLAM)
- \u6709\u975e\u7ebf\u6027\u8bef\u5dee\uff08\u4e3b\u8981\u95ee\u9898\u6240\u5728\uff09
- \u5982\u679c\u628a\u8def\u6807\u4e5f\u653e\u8fdb\u72b6\u6001\uff0c\u5b58\u4e0d\u4e0b
- \u6ca1\u6709\u5f02\u5e38\u68c0\u6d4b\u673a\u5236
"},{"location":"AI/SLAM14/#92-ba","title":"9.2 BA \u4e0e\u56fe\u4f18\u5316","text":" - Bundle Adjustment
- \u4ece\u89c6\u89c9\u56fe\u50cf\u4e2d\u63d0\u70bc\u51fa\u6700\u6709\u7684 3 D \u6a21\u578b\u548c\u76f8\u673a\u53c2\u6570\uff0c\u8ba9\u5149\u7ebf\u6700\u7ec8\u6536\u675f\u5230\u76f8\u673a\u7684\u5149\u5fc3
"},{"location":"AI/SLAM14/#921-ba","title":"9.2.1 \u6295\u5f71\u6a21\u578b\u548c BA \u4ee3\u4ef7\u51fd\u6570","text":"\\[ P^{\\prime}=Rp+t=[X^{\\prime},Y^{\\prime},Z^{\\prime}]^\\mathrm{T}. \\] \\[ \\boldsymbol{P}_{\\mathrm{c}}=[u_{\\mathrm{c}},v_{\\mathrm{c}},1]^{\\mathrm{T}}=[X^{\\prime}/Z^{\\prime},Y^{\\prime}/Z^{\\prime},1]^{\\mathrm{T}}. \\] \\[ \\begin{cases}u_\\mathrm{c}'=u_\\mathrm{c}\\left(1+k_1r_\\mathrm{c}^2+k_2r_\\mathrm{c}^4\\right)\\\\v_\\mathrm{c}'=v_\\mathrm{c}\\left(1+k_1r_\\mathrm{c}^2+k_2r_\\mathrm{c}^4\\right)\\end{cases}. \\] \\[ \\begin{cases}u_s=f_xu_\\mathrm{c}'+c_x\\\\[2ex]v_s=f_yv_\\mathrm{c}'+c_y\\end{cases}. \\] \\[ z=h(\\boldsymbol{x},\\boldsymbol{y}). \\] \\[ e=z-h(\\boldsymbol{T},\\boldsymbol{p}). \\] \\[ z\\overset{\\mathrm{def}}{\\operatorname*{=}}[u_s,v_s]^\\mathrm{T} \\] \\[ \\frac12\\sum_{i=1}^m\\sum_{j=1}^n\\|e_{ij}\\|^2=\\frac12\\sum_{i=1}^m\\sum_{j=1}^n\\|z_{ij}-h(\\boldsymbol{T}_i,\\boldsymbol{p}_j)\\|^2. \\]"},{"location":"AI/SLAM14/#922-ba","title":"9.2.2 BA \u7684\u6c42\u89e3","text":"\\[ x=[T_1,\\ldots,T_m,p_1,\\ldots,p_n]^\\mathrm{T}. \\] \\[ \\frac12\\left\\|f(\\boldsymbol{x}+\\Delta\\boldsymbol{x})\\right\\|^2\\approx\\frac12\\sum_{i=1}^m\\sum_{j=1}^n\\left\\|\\boldsymbol{e}_{ij}+\\boldsymbol{F}_{ij}\\Delta\\boldsymbol{\\xi}_i+\\boldsymbol{E}_{ij}\\Delta\\boldsymbol{p}_j\\right\\|^2. \\] \\[ x_{\\mathfrak{c}}=[\\boldsymbol{\\xi}_1,\\boldsymbol{\\xi}_2,\\ldots,\\boldsymbol{\\xi}_m]^{\\mathrm{T}}\\in\\mathbb{R}^{6m} \\] \\[ \\boldsymbol{x}_p=[\\boldsymbol{p}_1,\\boldsymbol{p}_2,\\ldots,\\boldsymbol{p}_n]^\\mathrm{T}\\in\\mathbb{R}^{3n} \\] \\[ \\frac12\\left\\|f(\\boldsymbol{x}+\\Delta\\boldsymbol{x})\\right\\|^2=\\frac12\\left\\|\\boldsymbol{e}+\\boldsymbol{F}\\Delta\\boldsymbol{x}_c+\\boldsymbol{E}\\Delta\\boldsymbol{x}_p\\right\\|^2. \\] \\[ \\boldsymbol{J}=[\\boldsymbol{F}\\boldsymbol{E}]. \\] \\[ H=J^\\mathrm{T}J=\\begin{bmatrix}F^\\mathrm{T}F&F^\\mathrm{T}E\\\\E^\\mathrm{T}F&E^\\mathrm{T}E\\end{bmatrix}. \\]"},{"location":"AI/SLAM14/#923","title":"9.2.3 \u7a00\u758f\u6027\u548c\u8fb9\u7f18\u5316","text":"\\[ J_{ij}(x)=\\left(\\mathbf{0}_{2\\times6},\\ldots\\mathbf{0}_{2\\times6},\\frac{\\partial\\boldsymbol{e}_{ij}}{\\partial\\boldsymbol{T}_{i}},\\mathbf{0}_{2\\times6},\\ldots\\mathbf{0}_{2\\times3},\\ldots\\mathbf{0}_{2\\times3},\\frac{\\partial\\boldsymbol{e}_{ij}}{\\partial\\boldsymbol{p}_{j}},\\mathbf{0}_{2\\times3},\\ldots\\mathbf{0}_{2\\times3}\\right). \\] \\[ H=\\sum_{i,j}J_{ij}^{\\top}J_{ij}, \\] \\[ H=\\begin{bmatrix}H_{11}&H_{12}\\\\\\\\H_{21}&H_{22}\\end{bmatrix}. \\] - \u5bf9\u4e8e\u7a00\u758f\u77e9\u9635\uff0c\u6211\u4eec\u7528 Schur \u6d88\u5143\uff08Marginalization\uff09
\\[ \\begin{bmatrix}B&E\\\\E^\\mathrm{T}&C\\end{bmatrix}\\begin{bmatrix}\\Delta x_\\mathrm{c}\\\\\\Delta x_p\\end{bmatrix}=\\begin{bmatrix}v\\\\w\\end{bmatrix}. \\] \\[ \\begin{bmatrix}I&-EC^{-1}\\\\0&I\\end{bmatrix}\\begin{bmatrix}B&E\\\\E^{\\intercal}&C\\end{bmatrix}\\begin{bmatrix}\\Delta x_\\mathrm{c}\\\\\\Delta x_p\\end{bmatrix}=\\begin{bmatrix}I&-EC^{-1}\\\\0&I\\end{bmatrix}\\begin{bmatrix}v\\\\w\\end{bmatrix}. \\] \\[ \\begin{bmatrix}B-EC^{-1}E^\\mathrm{T}&0\\\\E^\\mathrm{T}&C\\end{bmatrix}\\begin{bmatrix}\\Delta x_\\mathrm{c}\\\\\\Delta x_p\\end{bmatrix}=\\begin{bmatrix}v-EC^{-1}w\\\\\\\\w\\end{bmatrix}. \\] \\[ \\begin{bmatrix}B-EC^{-1}E^\\mathrm{T}\\end{bmatrix}\\Delta x_\\mathrm{c}=v-EC^{-1}w. \\] - \u4f18\u52bf
- \\(\\displaystyle \\boldsymbol{C}\\) \u4e3a\u5bf9\u89d2\u5757\uff0c\u9006\u6bd4\u8f83\u5bb9\u6613\u89e3\u51fa
- \u975e\u5bf9\u89d2\u7ebf\u4e0a\u7684\u975e\u96f6\u77e9\u9635\u5757\u8868\u793a\u5bf9\u5e94\u7684\u4e24\u4e2a\u76f8\u673a\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u5171\u540c\u89c2\u6d4b\u7684\u8def\u6807\u70b9\uff0c\u5373\u5171\u89c6\uff08Co-visibility\uff09
"},{"location":"AI/SLAM14/#924","title":"9.2.4 \u9c81\u68d2\u6838\u51fd\u6570","text":" \\[ H(e)=\\begin{cases}\\frac{1}{2}e^2&\\text{\u5f53}|e|\\leqslant\\delta,\\\\\\\\\\delta\\left(|e|-\\frac{1}{2}\\delta\\right)&\\text{\u5176\u4ed6}\\end{cases} \\]"},{"location":"AI/SLAM14/#93-ceres-ba","title":"9.3 \u5b9e\u8df5: Ceres BA","text":""},{"location":"AI/SLAM14/#931-bal","title":"9.3.1 BAL \u6570\u636e\u96c6","text":""},{"location":"AI/SLAM14/#932-ceres-ba","title":"9.3.2 Ceres BA \u7684\u4e66\u5199","text":""},{"location":"AI/SLAM14/#94-g-2-o-ba","title":"9.4 \u5b9e\u8df5: g 2 o \u6c42\u89e3 BA","text":""},{"location":"AI/SLAM14/#95","title":"9.5 \u5c0f\u7ed3","text":""},{"location":"AI/SLAM14/#10-2","title":"10 \u540e\u7aef 2","text":""},{"location":"AI/SLAM14/#11_1","title":"11 \u56de\u73af\u68c0\u6d4b","text":""},{"location":"AI/SLAM14/#12_1","title":"12 \u5efa\u56fe","text":""},{"location":"AI/SLAM14/#13-slam","title":"13 \u5b9e\u8df5: \u8bbe\u8ba1 SLAM \u7cfb\u7edf","text":""},{"location":"AI/SLAM14/#14-slam","title":"14 SLAM: \u73b0\u5728\u4e0e\u672a\u6765","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/","title":"\u7edf\u8ba1\u5b66\u4e60\u65b9\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#_1","title":"\u7edf\u8ba1\u5b66\u4e60\u65b9\u6cd5","text":" \u7ea6 3356 \u4e2a\u5b57 43 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 17 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#1","title":"1 \u7edf\u8ba1\u5b66\u4e60\u65b9\u6cd5\u6982\u8bba","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#11","title":"1.1 \u7edf\u8ba1\u5b66\u4e60","text":" - \u7edf\u8ba1\u5b66\u4e60\u7684\u7279\u70b9
- \u4ee5\u8ba1\u7b97\u673a\u53ca\u7f51\u7edc\u4e3a\u5e73\u53f0
- \u4ee5\u6570\u636e\u4e3a\u7814\u7a76\u5bf9\u8c61
- \u76ee\u7684\u662f\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u4e0e\u5206\u6790
- \u4ea4\u53c9\u5b66\u79d1
- \u7edf\u8ba1\u5b66\u4e60\u7684\u5bf9\u8c61
- \u662f\u6570\u636e
- \u7edf\u8ba1\u5b66\u4e60\u7684\u76ee\u7684
- \u7edf\u8ba1\u5b66\u4e60\u7684\u65b9\u6cd5
- \u4e3b\u8981\u6709
- \u76d1\u7763\u5b66\u4e60\uff08\u672c\u4e66\u4e3b\u8981\u8ba8\u8bba\uff09
- \u975e\u76d1\u7763\u5b66\u4e60
- \u534a\u76d1\u7763\u5b66\u4e60
- \u5f3a\u5316\u5b66\u4e60
- \u4e09\u8981\u7d20
- \u6a21\u578b
- \u7b56\u7565
- \u7b97\u6cd5
- \u5b9e\u73b0\u6b65\u9aa4
- \u5f97\u5230\u4e00\u4e2a\u8bad\u7ec3\u6570\u636e\u96c6\u5408
- \u786e\u5b9a\u5b66\u4e60\u6a21\u578b\u7684\u96c6\u5408
- \u786e\u5b9a\u5b66\u4e60\u7684\u7b56\u7565
- \u786e\u5b9a\u5b66\u4e60\u7684\u7b97\u6cd5
- \u901a\u8fc7\u5b66\u4e60\u65b9\u6cd5\u9009\u62e9\u6700\u4f18\u6a21\u578b
- \u5229\u7528\u5b66\u4e60\u7684\u6700\u4f18\u6a21\u578b\u5bf9\u65b0\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u6216\u5206\u6790
- \u7edf\u8ba1\u5b66\u4e60\u7684\u7814\u7a76
- \u65b9\u6cd5
- \u7406\u8bba
- \u5e94\u7528
- \u7edf\u8ba1\u5b66\u4e60\u7684\u91cd\u8981\u6027
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#12","title":"1.2 \u76d1\u7763\u5b66\u4e60","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#121","title":"1.2.1 \u57fa\u672c\u6982\u5ff5","text":" - \u8f93\u5165\u7a7a\u95f4\u3001\u7279\u5f81\u7a7a\u95f4\u4e0e\u8f93\u51fa\u7a7a\u95f4
- \u6bcf\u4e2a\u8f93\u5165\u662f\u4e00\u4e2a\u5b9e\u4f8b\uff0c\u901a\u5e38\u7531\u7279\u5f81\u5411\u91cf\u8868\u793a
- \u76d1\u7763\u5b66\u4e60\u4ece\u8bad\u7ec3\u6570\u636e\u96c6\u5408\u4e2d\u5b66\u4e60\u6a21\u578b\uff0c\u5bf9\u6d4b\u8bd5\u6570\u636e\u8fdb\u884c\u9884\u6d4b
- \u6839\u636e\u8f93\u5165\u53d8\u91cf\u548c\u8f93\u51fa\u53d8\u91cf\u7684\u4e0d\u540c\u7c7b\u578b
- \u56de\u5f52\u95ee\u9898: \u90fd\u8fde\u7eed
- \u5206\u7c7b\u95ee\u9898: \u8f93\u51fa\u6709\u9650\u79bb\u6563
- \u6807\u6ce8\u95ee\u9898: \u90fd\u662f\u53d8\u91cf\u5e8f\u5217
- \u8054\u5408\u6982\u7387\u5206\u5e03
- \u5047\u8bbe\u7a7a\u95f4
- \u6a21\u578b\u5c5e\u4e8e\u7531\u8f93\u5165\u7a7a\u95f4\u5230\u8f93\u51fa\u7a7a\u95f4\u7684\u6620\u5c04\u7684\u96c6\u5408\uff0c\u8fd9\u4e2a\u96c6\u5408\u5c31\u662f\u5047\u8bbe\u7a7a\u95f4
- \u6a21\u578b\u53ef\u4ee5\u662f\uff08\u975e\uff09\u6982\u7387\u6a21\u578b
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#122","title":"1.2.2 \u95ee\u9898\u7684\u5f62\u5f0f\u5316","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#13","title":"1.3 \u7edf\u8ba1\u5b66\u4e60\u4e09\u8981\u8bfb","text":" - \u65b9\u6cd5=\u6a21\u578b+\u7b56\u7565+\u7b97\u6cd5
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#131","title":"1.3.1 \u6a21\u578b","text":" - \u6a21\u578b\u5c31\u662f\u7d22\u8981\u5b66\u4e60\u7684\u6761\u4ef6\u6982\u7387\u5206\u5e03\u6216\u51b3\u7b56\u51fd\u6570
\\[ \\mathcal{F}=\\{f\\mid Y=f(X)\\} \\] \\[ \\mathcal{F}=\\{f | Y=f_{\\theta}(X),\\theta\\in\\mathbf{R}^{n}\\} \\] - \u540c\u6837\u53ef\u4ee5\u5b9a\u4e49\u4e3a\u6761\u4ef6\u6982\u7387\u7684\u96c6\u5408
\\[ \\mathcal{F}=\\{P|P(Y|X)\\} \\] \\[ \\mathcal{F}=\\{P\\mid P_{\\theta}(Y\\mid X),\\theta\\in\\mathbf{R}^{n}\\} \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#132","title":"1.3.2 \u7b56\u7565","text":" - \u635f\u5931\u51fd\u6570\u548c\u98ce\u9669\u51fd\u6570
- loos function or cost function \\(\\displaystyle L(Y,f(X))\\)
- 0-1 loss function
- \\(\\displaystyle L(Y,f(X))=\\begin{cases}1,&Y\\neq f(X)\\\\0,&Y=f(X)\\end{cases}\\)
- quadratic loss function
- \\(\\displaystyle L(Y,f(X))=(Y-f(X))^{2}\\)
- absolute loss function
- \\(\\displaystyle L(Y,f(X))=|Y-f(X)|\\)
- logarithmic loss function or log-likelihood loss function
- \\(\\displaystyle L(Y,P(Y\\mid X))=-\\log P(Y\\mid X)\\)
- \\(\\displaystyle R_{\\exp}(f)=E_{P}[L(Y,f(X))]=\\int_{x\\times y}L(y,f(x))P(x,y)\\mathrm{d}x\\mathrm{d}y\\)
- risk function or expected loss
- \u4f46\u662f\u8054\u5408\u5206\u5e03\u4f4d\u7f6e\uff0c\u6240\u4ee5\u8981\u5b66\u4e60\uff0c\u4f46\u662f\u8fd9\u6837\u4ee5\u6765\u98ce\u9669\u6700\u5c0f\u53c8\u8981\u7528\u5230\u8054\u5408\u5206\u5e03\uff0c\u90a3\u4e48\u8fd9\u5c31\u6210\u4e3a\u4e86\u75c5\u6001\u95ee\u9898 (ill-formed problem)
- empirical risk or empirical loss
- \\(\\displaystyle R_{\\mathrm{emp}}(f)=\\frac{1}{N}\\sum_{i=1}^{N}L(y_{i},f(x_{i}))\\)
- \u5f53 \\(\\displaystyle N\\) \u8d8b\u4e8e\u65e0\u7a77\u65f6\uff0c\u7ecf\u9a8c\u98ce\u9669\u8d8b\u4e8e\u671f\u671b\u98ce\u9669
- \u8fd9\u5c31\u5173\u7cfb\u5230\u4e24\u4e2a\u57fa\u672c\u7b56\u7565:
- \u7ecf\u9a8c\u98ce\u9669\u6700\u5c0f\u5316
- \u7ed3\u6784\u98ce\u9669\u6700\u5c0f\u5316
- \u7ecf\u9a8c\u98ce\u9669\u6700\u5c0f\u5316\u4e0e\u7ed3\u6784\u98ce\u9669\u6700\u5c0f\u5316
- empirical risk minimization \uff08\u6837\u672c\u5bb9\u91cf\u6bd4\u8f83\u5927\u7684\u65f6\u5019\uff09
- \\(\\displaystyle \\min_{f\\in\\mathcal{F}} \\frac{1}{N}\\sum_{i=1}^{N}L(y_{i},f(x_{i}))\\)
- maximum likelihood estimation
- structural risk minimization
- regularization
- \\(\\displaystyle R_{\\mathrm{sm}}(f)=\\frac{1}{N}\\sum_{i=1}^{N}L(y_{i},f(x_{i}))+\\lambda J(f)\\)
- \u590d\u6742\u5ea6\u8868\u793a\u4e86\u5bf9\u590d\u6742\u6a21\u578b\u7684\u4e58\u6cd5
- maximum posterior probability estimation
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#133","title":"1.3.3 \u7b97\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#14","title":"1.4 \u6a21\u578b\u8bc4\u4f30\u4e0e\u6a21\u578b\u9009\u62e9","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#141","title":"1.4.1 \u8bad\u7ec3\u8bef\u5dee\u4e0e\u6d4b\u8bd5\u8bef\u5dee","text":"\\[ R_{\\mathrm{emp}}(\\hat{f})=\\frac{1}{N}\\sum_{i=1}^{N}L(y_{i},\\hat{f}(x_{i})) \\] \\[ e_{\\mathrm{test}}=\\frac{1}{N^{\\prime}}\\sum_{i=1}^{N^{\\prime}}L(y_{i},\\hat{f}(x_{i})) \\] \\[ r_{\\mathrm{test}}+e_{\\mathrm{test}}=1 \\] "},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#142","title":"1.4.2 \u8fc7\u62df\u5408\u4e0e\u6a21\u578b\u9009\u62e9","text":" - model selection
- over-fitting
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#15","title":"1.5 \u6b63\u5219\u5316\u4e0e\u4ea4\u53c9\u9a8c\u8bc1","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#151","title":"1.5.1 \u6b63\u5219\u5316","text":"\\[ L(w)=\\frac{1}{N}\\sum_{i=1}^{N}(f(x_{i};w)-y_{i})^{2}+\\frac{\\lambda}{2}\\parallel w\\parallel^{2} \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#152","title":"1.5.2 \u4ea4\u53c9\u9a8c\u8bc1","text":" - cross validation
- \u6570\u636e\u96c6
- \u8bad\u7ec3\u96c6
- \u9a8c\u8bc1\u96c6
- \u6d4b\u8bd5\u96c6 1. \u7b80\u5355\u4ea4\u53c9\u9a8c\u8bc1 2. \\(\\displaystyle S\\) \u6298\u4ea4\u53c9\u9a8c\u8bc1 3. \u7559\u4e00\u4ea4\u53c9\u9a8c\u8bc1
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#16","title":"1.6 \u6cdb\u5316\u80fd\u529b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#161","title":"1.6.1 \u6cdb\u5316\u8bef\u5dee","text":" \\[ R_{\\exp}(\\hat{f})=E_{P}[L(Y,\\hat{f}(X))]=\\int_{R\\times y}L(y,\\hat{f}(x))P(x,y)\\mathrm{d}x\\mathrm{d}y \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#162","title":"1.6.2 \u6cdb\u5316\u8bef\u5dee\u4e0a\u754c","text":" - generalization error bound
- \u6837\u672c\u5bb9\u91cf\u589e\u52a0\u65f6\uff0c\u6cdb\u5316\u4e0a\u754c\u8d8b\u4e8e 0
- \u5047\u8bbe\u7a7a\u95f4\u8d8a\u5927\uff0c\u6cdb\u5316\u8bef\u5dee\u4e0a\u754c\u8d8a\u5927
- \u8fd9\u4e2a\u5b9a\u7406\u53ea\u9002\u7528\u4e8e\u5047\u8bbe\u7a7a\u95f4\u5305\u542b\u6709\u9650\u4e2a\u51fd\u6570
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#17","title":"1.7 \u751f\u6210\u6a21\u578b\u4e0e\u5224\u522b\u6a21\u578b","text":" - generative model
- \u8fd8\u539f\u51fa\u8054\u5408\u6982\u7387\u5206\u5e03 \\(\\displaystyle P(X,Y)\\)
- \u6734\u7d20\u8d1d\u53f6\u65af\u6cd5
- \u9690\u9a6c\u5c14\u53ef\u592b\u6a21\u578b
- \u6536\u655b\u901f\u5ea6\u5feb
- discriminative model
- \u76f4\u63a5\u5b66\u4e60\u51b3\u7b56\u51fd\u6570\u6216\u6761\u4ef6\u6982\u7387\u5206\u5e03 \\(\\displaystyle P(Y|X)\\)
- \\(\\displaystyle k\\) \u8fd1\u90bb\u6cd5
- \u611f\u77e5\u673a
- \u51b3\u7b56\u6811
- \u903b\u8f91\u65af\u8c1b\u56de\u5f52\u6a21\u578b
- \u6700\u5927\u71b5\u6a21\u578b
- \u652f\u6301\u5411\u91cf\u673a
- \u63d0\u5347\u65b9\u6cd5
- \u6761\u4ef6\u968f\u673a\u573a
- \u51c6\u786e\u5ea6\u9ad8
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#18","title":"1.8 \u5206\u7c7b\u95ee\u9898","text":" - precision \\(\\displaystyle P=\\frac{TP}{TP+FP}\\)
- recall \\(\\displaystyle R=\\frac{TP}{TP+FN}\\)
\\[ \\frac{2}{F_{1}}=\\frac{1}{P}+\\frac{1}{R} \\] \\[ F_{1}=\\frac{2TP}{2TP+FP+FN} \\] "},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#19","title":"1.9 \u6807\u6ce8\u95ee\u9898","text":" - tagging \u662f classificationd \u4e00\u4e2a\u63a8\u5e7f
- \u662f structure prediction \u7684\u7b80\u5355\u5f62\u5f0f
- \u9690\u9a6c\u5c14\u53ef\u592b\u6a21\u578b
- \u6761\u4ef6\u968f\u673a\u573a
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#110","title":"1.10 \u56de\u5f52\u95ee\u9898","text":" - regression
- \uff08\u975e\uff09\u7ebf\u6027\u56de\u5f52\uff0c\u4e00\u5143\u56de\u5f52\uff0c\u591a\u5143\u56de\u5f52
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#2","title":"2 \u611f\u77e5\u673a","text":" - perception
- \u611f\u77e5\u673a\u5bf9\u5e94\u4e8e\u8f93\u5165\u7a7a\u95f4\u4e2d\u5c06\u5b9e\u4f8b\u5212\u5206\u6210\u6b63\u8d1f\u4e24\u7c7b\u7684\u5206\u79bb\u8d85\u5e73\u9762\uff0c\u5c5e\u4e8e\u5224\u522b\u6a21\u578b
- \u539f\u59cb\u5f62\u5f0f\u548c\u5bf9\u5076\u5f62\u5f0f
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#21","title":"2.1 \u611f\u77e5\u673a\u6a21\u578b","text":" - \u5047\u8bbe\u7a7a\u95f4\u662f\u5b9a\u4e49\u5728\u7279\u5f81\u7a7a\u95f4\u4e2d\u6240\u6709\u7684\u7ebf\u6027\u5206\u7c7b\u6a21\u578b\uff08linear classification model\uff09\\(\\displaystyle \\{f|f(x) = w \\cdot x+b\\}\\)
- separating hyperplane
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#22","title":"2.2 \u611f\u77e5\u673a\u5b66\u4e60\u7b56\u7565","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#221","title":"2.2.1 \u6570\u636e\u96c6\u7684\u7ebf\u6027\u53ef\u5206\u6027","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#222","title":"2.2.2 \u611f\u77e5\u673a\u5b66\u4e60\u7b56\u7565","text":" - \u5b9a\u4e49\u635f\u5931\u51fd\u6570\u5e76\u5c06\u635f\u5931\u51fd\u6570\u6781\u5c0f\u5316
\\[ L(w,b)=-\\sum_{x_{i}\\in M}y_{i}(w\\cdot x_{i}+b) \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#223","title":"2.2.3 \u611f\u77e5\u673a\u5b66\u4e60\u7b97\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#224","title":"2.2.4 \u611f\u77e5\u673a\u5b66\u4e60\u7b97\u6cd5\u7684\u539f\u59cb\u5f62\u5f0f","text":"\\[ \\min_{w,b}L(w,b)=-\\sum_{x_{i}\\in M}y_{i}(w\\cdot x_{i}+b) \\] - stochastic gradient descent
\\[ \\nabla_{_w}L(w,b)=-\\sum_{x_{i}\\in M}y_{i}x_{i} \\] \\[ \\nabla_{b}L(w,b)=-\\sum_{x_{i}eM}y_{i} \\] \\[ w\\leftarrow w+\\eta y_{i}x_{i} \\] \\[ b\\leftarrow b+\\eta y_{i} \\] - \\(\\displaystyle \\eta\\) \u88ab\u79f0\u4e3a\u5b66\u4e60\u7387\uff08learning rate\uff09
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#225","title":"2.2.5 \u7b97\u6cd5\u7684\u6536\u655b\u6027","text":" - \u4e3a\u4e86\u5f97\u5230\u552f\u4e00\u7684\u8d85\u5e73\u9762\uff0c\u9700\u8981\u5bf9\u5206\u79bb\u8d85\u5e73\u9762\u589e\u52a0\u7ea6\u675f\u6761\u4ef6\uff0c\u5373\u7ebf\u6027\u652f\u6301\u5411\u91cf\u673a
- \u5982\u679c\u8bad\u7ec3\u96c6\u7ebf\u6027\u4e0d\u53ef\u5206\uff0c\u90a3\u4e48\u611f\u77e5\u673a\u5b66\u4e60\u7b97\u6cd5\u4e0d\u6536\u655b
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#226","title":"2.2.6 \u611f\u77e5\u673a\u5b66\u4e60\u7b97\u6cd5\u7684\u5bf9\u5076\u5f62\u5f0f","text":"\\[ \\begin{aligned}&w\\leftarrow w+\\eta y_{i}x_{i}\\\\&b\\leftarrow b+\\eta y_{i}\\end{aligned} \\] \\[ w=\\sum_{i=1}^{N}\\alpha_{i}y_{i}x_{i} \\] \\[ b=\\sum_{i=1}^{N}\\alpha_{i}y_{i} \\] \\[ G=[x_{i}\\cdot x_{j}]_{N\\times N} \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#3-displaystyle-k","title":"3 \\(\\displaystyle k\\) \u8fd1\u90bb\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#31-displaystyle-k","title":"3.1 \\(\\displaystyle k\\) \u8fd1\u90bb\u7b97\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#32-displaystyle-k","title":"3.2 \\(\\displaystyle k\\) \u8fd1\u90bb\u6a21\u578b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#321","title":"3.2.1 \u6a21\u578b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#33","title":"3.3 \u8ddd\u79bb\u5ea6\u91cf","text":" - \\(\\displaystyle L_{p}\\) distance or Minkowski distamce
- \\(\\displaystyle L_{p}(x_{i},x_{j})=\\left(\\sum_{l=1}^{n}\\mid x_{i}^{(l)}-x_{j}^{(l)}\\mid^{p}\\right)^{\\frac{1}{p}}\\)
- \\(\\displaystyle L_{2}(x_{i},x_{j})=\\left(\\sum_{i=1}^{n}\\mid x_{i}^{(l)}-x_{j}^{(l)}\\mid^{2}\\right)^{\\frac{1}{2}}\\)
- \\(\\displaystyle L_{1}(x_{i}, x_{j})=\\sum_{l=1}^{n}\\mid x_{i}^{(l)}-x_{j}^{(l)}\\mid\\)
- \\(\\displaystyle L_{\\infty}(x_{i}, x_{j})=\\max_{l}\\mid x_{i}^{(l)}-x_{j}^{(l)}\\mid\\)
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#331-displaystyle-k","title":"3.3.1 \\(\\displaystyle k\\) \u503c\u7684\u9009\u62e9","text":" - if k is small, then the approximation error will reduce
- estimation error
- \\(\\displaystyle k\\) \u503c\u7684\u51cf\u5c0f\u5c31\u610f\u5473\u7740\u6574\u4f53\u6a21\u578b\u53d8\u5f97\u590d\u6742\uff0c\u5bb9\u6613\u53d1\u751f\u8fc7\u62df\u5408
- \u5728\u5e94\u7528\u4e2d, \\(\\displaystyle k\\) \u503c\u4e00\u822c\u53d6\u4e00\u4e2a\u6bd4\u8f83\u5c0f\u7684\u6570\u503c\uff0c\u901a\u5e38\u91c7\u7528\u4ea4\u53c9\u9a8c\u8bc1\u6cd5\u6765\u9009\u53d6\u6700\u4f18\u7684 \\(\\displaystyle k\\) \u503c
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#332","title":"3.3.2 \u5206\u7c7b\u51b3\u7b56\u89c4\u5219","text":" - \u591a\u6570\u8868\u51b3\u89c4\u5219\uff08majority voting rule\uff09
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#34-displaystyle-k-displaystyle-kd","title":"3.4 \\(\\displaystyle k\\) \u8fd1\u90bb\u6cd5\u7684\u5b9e\u73b0: \\(\\displaystyle kd\\) \u6811","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#341-displaystyle-kd","title":"3.4.1 \u6784\u9020 \\(\\displaystyle kd\\) \u6811","text":" - \\(\\displaystyle kd\\) \u6811\u662f\u4e00\u4e8c\u53c9\u6811\uff0c\u8868\u793a\u5bf9 \\(\\displaystyle k\\) \u7ef4\u7a7a\u95f4\u7684\u4e00\u4e2a\u5212\u5206\uff08partition\uff09
- \u901a\u5e38\u9009\u62e9\u8bad\u7ec3\u5b9e\u4f8b\u70b9\u5728\u9009\u5b9a\u5750\u6807\u8f74\u4e0a\u7684\u4e2d\u4f4d\u6570\u4e3a\u5207\u5206\u70b9\uff0c\u867d\u7136\u8fd9\u6837\u5f97\u5230\u7684\u6811\u662f\u5e73\u8861\u7684\uff0c\u4f46\u6548\u7387\u672a\u5fc5\u662f\u6700\u4f18\u7684 \u6709\u610f\u601d
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#342-displaystyle-kd","title":"3.4.2 \u641c\u7d22 \\(\\displaystyle kd\\) \u6811","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#4","title":"4 \u6734\u7d20\u8d1d\u53f6\u65af\u6cd5","text":" - \u57fa\u4e8e\u8d1d\u53f6\u65af\u5b9a\u7406\u4e0e\u7279\u5f81\u6761\u4ef6\u72ec\u7acb\u5047\u8bbe\u7684\u5206\u7c7b\u65b9\u6cd5
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#41","title":"4.1 \u6734\u7d20\u8d1d\u53f6\u65af\u6cd5\u7684\u5b66\u4e60\u4e0e\u5206\u7c7b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#411","title":"4.1.1 \u57fa\u672c\u65b9\u6cd5","text":" - \u5b66\u4e60\u5148\u9a8c\u6982\u7387\u5206\u5e03\u548c\u6761\u4ef6\u6982\u7387\u5206\u5e03\u4e8e\u662f\u5b66\u4e60\u5230\u8054\u5408\u6982\u7387\u5206\u5e03
\\[ P(X=x\\mid Y=c_{k})=P(X^{(1)}=x^{(1)},\\cdots,X^{(n)}=x^{(n)}\\mid Y=c_{k}),\\quad k=1,2,\\cdots,K \\] - \u5f15\u5165\u4e86\u6761\u4ef6\u72ec\u7acb\u6027\u5047\u8bbe
\\[ \\begin{aligned} P(X=x|Y=c_{k})& =P(X^{(1)}=x^{(1)},\\cdots,X^{(n)}=x^{(n)}\\mid Y=c_{k}) \\\\ &=\\prod_{j=1}^{n}P(X^{(j)}=x^{(j)}\\mid Y=c_{k}) \\end{aligned} \\] \\[ P(Y=c_{k}\\mid X=x)=\\frac{P(X=x\\mid Y=c_{k})P(Y=c_{k})}{\\sum_{k}P(X=x\\mid Y=c_{k})P(Y=c_{k})} \\] \\[ P(Y=c_{k}\\mid X=x)=\\frac{P(Y=c_{k})\\prod_{j}P(X^{(j)}=x^{(j)}\\mid Y=c_{k})}{\\sum_{k}P(Y=c_{k})\\prod_{j}P(X^{(j)}=x^{(j)}\\mid Y=c_{k})},\\quad k=1,2,\\cdots,K \\] \\[ y=f(x)=\\arg\\max_{c_{k}}\\frac{P(Y=c_{k})\\prod_{j}P(X^{(j)}=x^{(j)}\\mid Y=c_{k})}{\\sum_{k}P(Y=c_{k})\\prod_{j}P(X^{(j)}=x^{(j)}\\mid Y=c_{k})} \\] \\[ y=\\arg\\max_{c_{k}}P(Y=c_{k})\\prod_{j}P(X^{(j)}=x^{(j)}\\mid Y=c_{k}) \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#412","title":"4.1.2 \u540e\u9a8c\u6982\u7387\u6700\u5927\u5316\u7684\u542b\u4e49","text":"\\[ L(Y,f(X))=\\begin{cases}1,&Y\\neq f(X)\\\\0,&Y=f(X)\\end{cases} \\] \\[ R_{\\exp}(f)=E[L(Y,f(X))] \\] \\[ R_{\\exp}(f)=E_{\\chi}\\sum_{k=1}^{K}[L(c_{k},f(X))]P(c_{k}\\mid X) \\] \\[ \\begin{align} f(x) &=\\arg\\min_{y\\in\\mathcal{Y}}\\sum_{k=1}^{K}L(c_{k},y)P(c_{k}\\mid X=x) \\\\ &=\\arg\\min_{y\\in\\mathcal{Y}}\\sum_{k=1}^{K}P(y\\neq c_{k}\\mid X=x) \\\\ &=\\arg\\min_{y\\in\\mathcal{Y}}(1-P(y=c_{k}\\mid X=x)) \\\\ &=\\arg\\max_{y\\in\\mathcal{Y}}P(y=c_{k}\\mid X=x) \\end{align} \\] \\[ f(x)=\\arg\\max_{c_{k}}P(c_{k}\\mid X=x) \\] - \u671f\u671b\u98ce\u9669\u6700\u5c0f\u5316\u51c6\u5219\u5c31\u5f97\u5230\u8054\u8003\u540e\u9a8c\u6982\u7387\u6700\u5927\u5316\u51c6\u5219
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#42","title":"4.2 \u6734\u7d20\u8d1d\u53f6\u65af\u6cd5\u7684\u53c2\u6570\u4f30\u8ba1","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#421","title":"4.2.1 \u6781\u5927\u4f3c\u7136\u4f30\u8ba1","text":"\\[ P(Y=c_{k})=\\frac{\\sum_{i=1}^{N}I(y_{i}=c_{k})}{N} , k=1,2,\\cdots,K \\] \\[ P(X^{(j)}=a_{ji}\\mid Y=c_{k})=\\frac{\\sum_{i=1}^{N}I(x_{i}^{(j)}=a_{ji},y_{i}=c_{k})}{\\sum_{i=1}^{N}I(y_{i}=c_{k})}\\\\j=1,2,\\cdots,n ;\\quad l=1,2,\\cdots,S_{j} ;\\quad k=1,2,\\cdots,K \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#422","title":"4.2.2 \u5b66\u4e60\u4e0e\u5206\u7c7b\u7b97\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#423","title":"4.2.3 \u8d1d\u53f6\u65af\u4f30\u8ba1","text":" - \u6781\u5927\u4f3c\u7136\u4f30\u8ba1\u53ef\u80fd\u4f1a\u51fa\u73b0\u6240\u8981\u4f30\u8ba1\u7684\u6982\u7387\u503c\u4e3a 0 \u7684\u60c5\u51b5
- \u6761\u4ef6\u6982\u7387\u7684\u8d1d\u53f6\u65af\u4f30\u8ba1
\\[ P_{\\lambda}(X^{(j)}=a_{ji}\\mid Y=c_{k})=\\frac{\\sum_{i=1}^{N}I(x_{i}^{(j)}=a_{ji},y_{i}=c_{k})+\\lambda}{\\sum_{i=1}^{N}I(y_{i}=c_{k})+S_{j}\\lambda} \\] - when \\(\\displaystyle \\lambda = 0\\), it's called Laplace smoothing
\\[ \\begin{aligned}&P_{\\lambda}(X^{(j)}=a_{jl}\\mid Y=c_{k})>0\\\\&\\sum_{l=1}^{s_{j}}P(X^{(j)}=a_{jl}\\mid Y=c_{k})=1\\end{aligned} \\] - \u8868\u660e\u8d1d\u53f6\u65af\u4f30\u8ba1\u786e\u5b9e\u662f\u4e00\u79cd\u6982\u7387\u5206\u5e03
- \u5148\u9a8c\u6982\u7387\u7684\u8d1d\u53f6\u65af\u4f30\u8ba1
\\[ P_{\\lambda}(Y=c_{k})=\\frac{\\sum_{i=1}^{N}I(y_{i}=c_{k})+\\lambda}{N+K\\lambda} \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#5","title":"5 \u51b3\u7b56\u6811","text":" - decision tree
- \u7279\u5f81\u9009\u62e9
- \u51b3\u7b56\u6811\u7684\u751f\u6210
- \u51b3\u7b56\u6811\u7684\u4fee\u526a
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#51","title":"5.1 \u51b3\u7b56\u6811\u6a21\u578b\u4e0e\u5b66\u4e60","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#511","title":"5.1.1 \u51b3\u7b56\u6811\u6a21\u578b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#512-if-then","title":"5.1.2 \u51b3\u7b56\u6811\u4e0e if-then \u89c4\u5219","text":" - \u4e92\u65a5\u4e14\u5b8c\u5907
- \u6bcf\u4e00\u4e2a\u5b9e\u4f8b\u90fd\u88ab\u4e00\u6761\u8def\u5f84\u4f1a\u89c4\u5219\u6240\u8986\u76d6\uff0c\u800c\u4e14\u53ea\u88ab\u4e00\u6761\u8def\u5f84\u6216\u4e00\u6761\u89c4\u5219\u6240\u8986\u76d6
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#513","title":"5.1.3 \u51b3\u7b56\u6811\u4e0e\u6761\u4ef6\u6982\u7387\u5206\u5e03","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#514","title":"5.1.4 \u51b3\u7b56\u6811\u5b66\u4e60","text":" - \u51b3\u7b56\u6811\u5b66\u4e60\u672c\u8d28\u4e0a\u662f\u4ece\u8bad\u7ec3\u6570\u636e\u96c6\u4e2d\u5f52\u7eb3\u51fa\u4e00\u7ec4\u5206\u7c7b\u89c4\u5219
- \u5728\u635f\u5931\u51fd\u6570\u610f\u4e49\u4e0b\u9009\u62e9\u6700\u4f18\u51b3\u7b56\u6811\u7684\u95ee\u9898\uff0c\u662f NP \u5b8c\u5168\u95ee\u9898\uff0c\u91c7\u7528\u542f\u53d1\u5f0f\u65b9\u6cd5\uff0c\u8fd1\u4f3c\u6c42\u89e3\uff0c\u8fd9\u6837\u5f97\u5230\u7684\u51b3\u7b56\u6811\u662f\u6b21\u6700\u4f18\uff08sub-optimal\uff09
- \u4e3a\u4e86\u9632\u6b62\u8fc7\u62df\u5408\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u5df2\u751f\u6210\u7684\u6811\u81ea\u4e0a\u800c\u4e0b\u8fdb\u884c\u526a\u679d
- \u51b3\u7b56\u6811\u7684\u751f\u6210\u503c\u8003\u8651\u5c40\u90e8\u6700\u4f18\uff0c\u526a\u679d\u5219\u8003\u8651\u5168\u5c40\u6700\u4f18
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#52","title":"5.2 \u7279\u5f81\u9009\u62e9","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#521","title":"5.2.1 \u7279\u5f81\u9009\u62e9\u95ee\u9898","text":" - \u901a\u5e38\u7279\u5f81\u9009\u62e9\u7684\u51c6\u5219\u662f\u4fe1\u606f\u589e\u76ca\u6216\u4fe1\u606f\u589e\u76ca\u6bd4
- information gain
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#522","title":"5.2.2 \u4fe1\u606f\u589e\u76ca","text":" - \u71b5\u548c\u6761\u4ef6\u71b5
\\[ P(X=x_{i})=p_{i} ,\\quad i=1,2,\\cdots,n \\] \\[ H(X)=-\\sum_{i=1}^{n}p_{i}\\log p_{i} \\] \\[ H(p)=-\\sum_{i=1}^{n}p_{i}\\log p_{i} \\] \\[ 0\\leqslant H(p)\\leqslant\\log n \\] \\[ P(X=x_{i},Y=y_{j})=p_{ij} ,\\quad i=1,2,\\cdots,n ;\\quad j=1,2,\\cdots,m \\] \\[ H(Y\\mid X)=\\sum_{i=1}^{n}p_{i}H(Y\\mid X=x_{i}) \\] "},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#523","title":"5.2.3 \u4fe1\u606f\u589e\u76ca\u6bd4","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#53","title":"5.3 \u51b3\u7b56\u6811\u7684\u751f\u6210","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#531-id-3","title":"5.3.1 ID 3 \u7b97\u6cd5","text":" - ID 3 \u7b97\u6cd5\u53ea\u6709\u6811\u7684\u751f\u6210\uff0c\u6240\u4ee5\u8be5\u7b97\u6cd5\u751f\u6210\u7684\u6811\u5bb9\u6613\u4ea7\u751f\u8fc7\u62df\u5408
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#532-c-45","title":"5.3.2 C 4.5 \u7684\u751f\u6210\u7b97\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#54","title":"5.4 \u51b3\u7b56\u6811\u7684\u526a\u679d","text":" \\[ C_{\\alpha}(T)=\\sum_{t=1}^{|T|}N_{t}H_{t}(T)+\\alpha|T| \\] \\[ H_{t}(T)=-\\sum_{k}\\frac{N_{ik}}{N_{t}}\\log\\frac{N_{ik}}{N_{t}} \\] \\[ C(T)=\\sum_{t=1}^{|T|}N_{t}H_{t}(T)=-\\sum_{t=1}^{|T|}\\sum_{k=1}^{K}N_{tk}\\log\\frac{N_{tk}}{N_{t}} \\] \\[ C_{\\alpha}(T)=C(T)+\\alpha|T| \\]"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#55-cart","title":"5.5 CART \u7b97\u6cd5","text":" - \u5206\u88c2\u4e0e\u56de\u5f52\u6811\uff08classification and regression tree\uff09
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#551-cart","title":"5.5.1 CART \u751f\u6210","text":" - \u5bf9\u56de\u5f52\u6811\u7528\u5e73\u65b9\u8bef\u5dee\u6700\u5c0f\u5316\u51c6\u5219
- \u5bf9\u5206\u7c7b\u6811\u7528\u57fa\u5c3c\u6307\u6570\uff08Gini index\uff09\u6700\u5c0f\u5316\u51c6\u5219 1. \u56de\u5f52\u6811\u7684\u751f\u6210
\\[ f(x)=\\sum_{m=1}^{M}c_{m}I(x\\in R_{m}) \\] \\[ \\hat{c}_{m}=\\mathrm{ave}(y_{i}\\mid x_{i}\\in R_{m}) \\] - splitting variable
- splitting point
\\[ R_{1}(j,s)=\\{x\\mid x^{(j)}\\leqslant s\\}\\quad\\text{\u548c}\\quad R_{2}(j,s)=\\{x\\mid x^{(j)}>s\\} \\] \\[ \\min_{j,s}\\biggl[\\min_{c_{1}}\\sum_{x_{i}\\in R_{i}(j,s)}(y_{i}-c_{1})^{2}+\\min_{c_{2}}\\sum_{x_{i}\\in R_{2}(j,s)}(y_{i}-c_{2})^{2}\\biggr] \\] \\[ \\hat{c}_{1}=\\mathrm{ave}(y_{i}\\mid x_{i}\\in R_{1}(j,s))\\quad\\hat{\\text{\u548c}}\\quad\\hat{c}_{2}=\\mathrm{ave}(y_{i}\\mid x_{i}\\in R_{2}(j,s)) \\] - least squares regression tree 1. \u5206\u7c7b\u6811\u7684\u751f\u6210
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#552-cart","title":"5.5.2 CART \u526a\u679d","text":" - \u526a\u679d\uff0c\u5f62\u6210\u4e00\u4e2a\u5b50\u6811\u5e8f\u5217
\\[ C_{\\alpha}(T)=C(T)+\\alpha\\left|T\\right| \\] \\[ g(t)=\\frac{C(t)-C(T_{t})}{\\mid T_{t}\\mid-1} \\] - \u5728\u526a\u679d\u5f97\u5230\u7684\u5b50\u6811\u5e8f\u5217 \\(\\displaystyle T_0,T_1,\\cdots,T_n\\) \u4e2d\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u9009\u53d6\u6700\u4f18\u5b50\u6811 \\(\\displaystyle T_{\\alpha}\\)
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#6","title":"6 \u903b\u8f91\u65af\u8c1b\u56de\u5f52\u4e0e\u6700\u5927\u71b5\u6a21\u578b","text":" - logistic regression
- maximum entropy model
- \u903b\u8f91\u65af\u8c1b\u56de\u5f52\u6a21\u578b\u548c\u6700\u5927\u71b5\u6a21\u578b\u90fd\u5c5e\u4e8e\u5bf9\u6570\u7ebf\u6027\u6a21\u578b
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#61","title":"6.1 \u903b\u8f91\u65af\u8c1b\u56de\u5f52\u6a21\u578b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#611","title":"6.1.1 \u903b\u8f91\u65af\u8c1b\u5206\u5e03","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#612","title":"6.1.2 \u4e8c\u9879\u903b\u8f91\u65af\u8c1b\u56de\u5f52\u6a21\u578b","text":" - binomial logistic regression model
"},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#7","title":"7 \u652f\u6301\u5411\u91cf\u673a","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#8","title":"8 \u63d0\u5347\u65b9\u6cd5","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#9-displaystyle-boldsymbolem","title":"9 \\(\\displaystyle \\boldsymbol{EM}\\) \u7b97\u6cd5\u53ca\u5176\u63a8\u5e7f","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#10","title":"10 \u9690\u9a6c\u5c14\u53ef\u592b\u6a21\u578b","text":""},{"location":"AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/#11_1","title":"11 \u6761\u4ef6\u968f\u673a\u573a","text":""},{"location":"AI/CS231n/CS231n_notes/","title":"Computer Vision","text":""},{"location":"AI/CS231n/CS231n_notes/#computer-vision","title":"Computer Vision","text":" \u7ea6 8852 \u4e2a\u5b57 167 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 44 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
This note is based on GitHub - DaizeDong/Stanford-CS231n-2021-and-2022: Notes and slides for Stanford CS231n 2021 & 2022 in English. I merged the contents together to get a better version. Assignments are not included. \u65af\u5766\u798fcs231n\u7684\u8bfe\u7a0b\u7b14\u8bb0(\u82f1\u6587\u7248\u672c\uff0c\u4e0d\u542b\u5b9e\u9a8c\u4ee3\u7801)\uff0c\u5c062021\u4e0e2022\u4e24\u5e74\u7684\u8bfe\u7a0b\u8fdb\u884c\u4e86\u5408\u5e76\uff0c\u5206\u4eab\u4ee5\u4f9b\u4ea4\u6d41\u3002 And I will add some blogs, articles and other understanding.
Topic Chapter Deep Learning Basics 2 - 4 Perceiving and Understanding the Visual World 5 - 12 Reconstructing and Interacting with the Visual World 13 - 16 Human-Centered Applications and Implications 17 - 18"},{"location":"AI/CS231n/CS231n_notes/#1-introduction","title":"1 - Introduction","text":"A brief history of computer vision & deep learning...
"},{"location":"AI/CS231n/CS231n_notes/#2-image-classification","title":"2 - Image Classification","text":"Image Classification: A core task in Computer Vision. The main drive to the progress of CV.
Challenges: Viewpoint variation, background clutter, illumination, occlusion, deformation, intra-class variation...
"},{"location":"AI/CS231n/CS231n_notes/#k-nearest-neighbor","title":"K Nearest Neighbor","text":"Hyperparameters: Distance metric (\\(p\\) norm), \\(k\\) number.
Choose hyperparameters using validation set.
Never use k-Nearest Neighbor with pixel distance.
"},{"location":"AI/CS231n/CS231n_notes/#linear-classifier","title":"Linear Classifier","text":"Pass...
"},{"location":"AI/CS231n/CS231n_notes/#3-loss-functions-and-optimization","title":"3 - Loss Functions and Optimization","text":""},{"location":"AI/CS231n/CS231n_notes/#loss-functions","title":"Loss Functions","text":"Dataset \\(\\big\\{(x_i,y_i)\\big\\}_{i=1}^N\\\\\\) Loss Function \\(L=\\frac{1}{N}\\sum_{i=1}^NL_i\\big(f(x_i,W),y_i\\big)\\\\\\) Loss Function with Regularization \\(L=\\frac{1}{N}\\sum_{i=1}^NL_i\\big(f(x_i,W),y_i\\big)+\\lambda R(W)\\\\\\) Motivation: Want to interpret raw classifier scores as probabilities.
Softmax Classifier \\(p_i=Softmax(y_i)=\\frac{\\exp(y_i)}{\\sum_{j=1}^N\\exp(y_j)}\\\\\\) Cross Entropy Loss \\(L_i=-y_i\\log p_i\\\\\\) Cross Entropy Loss with Regularization \\(L=-\\frac{1}{N}\\sum_{i=1}^Ny_i\\log p_i+\\lambda R(W)\\\\\\) "},{"location":"AI/CS231n/CS231n_notes/#optimization","title":"Optimization","text":""},{"location":"AI/CS231n/CS231n_notes/#sgd-with-momentum","title":"SGD with Momentum","text":"Problems that SGD can't handle:
- Inequality of gradient in different directions.
- Local minima and saddle point (much more common in high dimension).
- Noise of gradient from mini-batch.
Momentum: Build up \u201cvelocity\u201d \\(v_t\\) as a running mean of gradients.
SGD SGD + Momentum \\(x_{t+1}=x_t-\\alpha\\nabla f(x_t)\\) \\(\\begin{align}&v_{t+1}=\\rho v_t+\\nabla f(x_t)\\\\&x_{t+1}=x_t-\\alpha v_{t+1}\\end{align}\\) Naive gradient descent. \\(\\rho\\) gives \"friction\", typically \\(\\rho=0.9,0.99,0.999,...\\) Nesterov Momentum: Use the derivative on point \\(x_t+\\rho v_t\\) as gradient instead point \\(x_t\\).
Momentum Nesterov Momentum \\(\\begin{align}&v_{t+1}=\\rho v_t+\\nabla f(x_t)\\\\&x_{t+1}=x_t-\\alpha v_{t+1}\\end{align}\\) \\(\\begin{align}&v_{t+1}=\\rho v_t+\\nabla f(x_t+\\rho v_t)\\\\&x_{t+1}=x_t-\\alpha v_{t+1}\\end{align}\\) Use gradient at current point. Look ahead for the gradient in velocity direction. "},{"location":"AI/CS231n/CS231n_notes/#adagrad-and-rmsprop","title":"AdaGrad and RMSProp","text":"AdaGrad: Accumulate squared gradient, and gradually decrease the step size.
RMSProp: Accumulate squared gradient while decaying former ones, and gradually decrease the step size. (\"Leaky AdaGrad\")
AdaGrad RMSProp \\(\\begin{align}\\text{Initialize:}&\\\\&r:=0\\\\\\text{Update:}&\\\\&r:=r+\\Big[\\nabla f(x_t)\\Big]^2\\\\&x_{t+1}=x_t-\\alpha\\frac{\\nabla f(x_t)}{\\sqrt{r}}\\end{align}\\) \\(\\begin{align}\\text{Initialize:}&\\\\&r:=0\\\\\\text{Update:}&\\\\&r:=\\rho r+(1-\\rho)\\Big[\\nabla f(x_t)\\Big]^2\\\\&x_{t+1}=x_t-\\alpha\\frac{\\nabla f(x_t)}{\\sqrt{r}}\\end{align}\\) Continually accumulate squared gradients. \\(\\rho\\) gives \"decay rate\", typically \\(\\rho=0.9,0.99,0.999,...\\)"},{"location":"AI/CS231n/CS231n_notes/#adam","title":"Adam","text":"Sort of like \"RMSProp + Momentum\".
Adam (simple version) Adam (full version) \\(\\begin{align}\\text{Initialize:}&\\\\&r_1:=0\\\\&r_2:=0\\\\\\text{Update:}&\\\\&r_1:=\\beta_1r_1+(1-\\beta_1)\\nabla f(x_t)\\\\&r_2:=\\beta_2r_2+(1-\\beta_2)\\Big[\\nabla f(x_t)\\Big]^2\\\\&x_{t+1}=x_t-\\alpha\\frac{r_1}{\\sqrt{r_2}}\\end{align}\\) \\(\\begin{align}\\text{Initialize:}\\\\&r_1:=0\\\\&r_2:=0\\\\\\text{For }i\\text{:}\\\\&r_1:=\\beta_1r_1+(1-\\beta_1)\\nabla f(x_t)\\\\&r_2:=\\beta_2r_2+(1-\\beta_2)\\Big[\\nabla f(x_t)\\Big]^2\\\\&r_1'=\\frac{r_1}{1-\\beta_1^i}\\\\&r_2'=\\frac{r_2}{1-\\beta_2^i}\\\\&x_{t+1}=x_t-\\alpha\\frac{r_1'}{\\sqrt{r_2'}}\\end{align}\\) Build up \u201cvelocity\u201d for both gradient and squared gradient. Correct the \"bias\" that \\(r_1=r_2=0\\) for the first few iterations."},{"location":"AI/CS231n/CS231n_notes/#overview","title":"Overview","text":""},{"location":"AI/CS231n/CS231n_notes/#learning-rate-decay","title":"Learning Rate Decay","text":"Reduce learning rate at a few fixed points to get a better convergence over time.
\\(\\alpha_0\\) : Initial learning rate.
\\(\\alpha_t\\) : Learning rate in epoch \\(t\\).
\\(T\\) : Total number of epochs.
Method Equation Picture Step Reduce \\(\\alpha_t\\) constantly in a fixed step. Cosine \\(\\begin{align}\\alpha_t=\\frac{1}{2}\\alpha_0\\Bigg[1+\\cos(\\frac{t\\pi}{T})\\Bigg]\\end{align}\\) Linear \\(\\begin{align}\\alpha_t=\\alpha_0\\Big(1-\\frac{t}{T}\\Big)\\end{align}\\) Inverse Sqrt \\(\\begin{align}\\alpha_t=\\frac{\\alpha_0}{\\sqrt{t}}\\end{align}\\) High initial learning rates can make loss explode, linearly increasing learning rate in the first few iterations can prevent this.
Learning rate warm up:
Empirical rule of thumb: If you increase the batch size by \\(N\\), also scale the initial learning rate by \\(N\\) .
"},{"location":"AI/CS231n/CS231n_notes/#second-order-optimization","title":"Second-Order Optimization","text":"Picture Time Complexity Space Complexity First Order \\(O(n)\\) \\(O(n)\\) Second Order \\(O(n^2)\\) with BGFS optimization \\(O(n)\\) with L-BGFS optimization L-BGFS : Limited memory BGFS.
- Works very well in full batch, deterministic \\(f(x)\\).
- Does not transfer very well to mini-batch setting.
"},{"location":"AI/CS231n/CS231n_notes/#summary","title":"Summary","text":"Method Performance Adam Often chosen as default method.Work ok even with constant learning rate. SGD + Momentum Can outperform Adam.Require more tuning of learning rate and schedule. L-BGFS If can afford to do full batch updates then try out. - An article about gradient descent: Anoverview of gradient descent optimization algorithms
- A blog: An updated overview of recent gradient descent algorithms \u2013 John Chen \u2013 ML at Rice University
"},{"location":"AI/CS231n/CS231n_notes/#4-neural-networks-and-backpropagation","title":"4 - Neural Networks and Backpropagation","text":""},{"location":"AI/CS231n/CS231n_notes/#neural-networks","title":"Neural Networks","text":"Motivation: Inducted bias can appear to be high when using human-designed features.
Activation: Sigmoid, tanh, ReLU, LeakyReLU...
Architecture: Input layer, hidden layer, output layer.
Do not use the size of a neural network as the regularizer. Use regularization instead!
Gradient Calculation: Computational Graph + Backpropagation.
"},{"location":"AI/CS231n/CS231n_notes/#backpropagation","title":"Backpropagation","text":"Using Jacobian matrix to calculate the gradient of each node in a computation graph.
Suppose that we have a computation flow like this:
Input X Input W Output Y \\(X=\\begin{bmatrix}x_1\\\\x_2\\\\\\vdots\\\\x_n\\end{bmatrix}\\) \\(W=\\begin{bmatrix}w_{11}&w_{12}&\\cdots&w_{1n}\\\\w_{21}&w_{22}&\\cdots&w_{2n}\\\\\\vdots&\\vdots&\\ddots&\\vdots\\\\w_{m1}&w_{m2}&\\cdots&w_{mn}\\end{bmatrix}\\) \\(Y=\\begin{bmatrix}y_1\\\\y_2\\\\\\vdots\\\\y_m\\end{bmatrix}\\) \\(n\\times 1\\) \\(m\\times n\\) \\(m\\times 1\\) After applying feed forward, we can calculate gradients like this:
Derivative Matrix of X Jacobian Matrix of X Derivative Matrix of Y \\(D_X=\\begin{bmatrix}\\frac{\\partial L}{\\partial x_1}\\\\\\frac{\\partial L}{\\partial x_2}\\\\\\vdots\\\\\\frac{\\partial L}{\\partial x_n}\\end{bmatrix}\\) \\(J_X=\\begin{bmatrix}\\frac{\\partial y_1}{\\partial x_1}&\\frac{\\partial y_1}{\\partial x_2}&\\cdots&\\frac{\\partial y_1}{\\partial x_n}\\\\\\frac{\\partial y_2}{\\partial x_1}&\\frac{\\partial y_2}{\\partial x_2}&\\cdots&\\frac{\\partial y_2}{\\partial x_n}\\\\\\vdots&\\vdots&\\ddots&\\vdots\\\\\\frac{\\partial y_m}{\\partial x_1}&\\frac{\\partial y_m}{\\partial x_2}&\\cdots&\\frac{\\partial y_m}{\\partial x_n}\\end{bmatrix}\\) \\(D_Y=\\begin{bmatrix}\\frac{\\partial L}{\\partial y_1}\\\\\\frac{\\partial L}{\\partial y_2}\\\\\\vdots\\\\\\frac{\\partial L}{\\partial y_m}\\end{bmatrix}\\) \\(n\\times 1\\) \\(m\\times n\\) \\(m\\times 1\\) Derivative Matrix of W Jacobian Matrix of W Derivative Matrix of Y \\(W=\\begin{bmatrix}\\frac{\\partial L}{\\partial w_{11}}&\\frac{\\partial L}{\\partial w_{12}}&\\cdots&\\frac{\\partial L}{\\partial w_{1n}}\\\\\\frac{\\partial L}{\\partial w_{21}}&\\frac{\\partial L}{\\partial w_{22}}&\\cdots&\\frac{\\partial L}{\\partial w_{2n}}\\\\\\vdots&\\vdots&\\ddots&\\vdots\\\\\\frac{\\partial L}{\\partial w_{m1}}&\\frac{\\partial L}{\\partial w_{m2}}&\\cdots&\\frac{\\partial L}{\\partial w_{mn}}\\end{bmatrix}\\) \\(J_W^{(k)}=\\begin{bmatrix}\\frac{\\partial y_k}{\\partial w_{11}}&\\frac{\\partial y_k}{\\partial w_{12}}&\\cdots&\\frac{\\partial y_k}{\\partial w_{1n}}\\\\\\frac{\\partial y_k}{\\partial w_{21}}&\\frac{\\partial y_k}{\\partial w_{22}}&\\cdots&\\frac{\\partial y_k}{\\partial w_{2n}}\\\\\\vdots&\\vdots&\\ddots&\\vdots\\\\\\frac{\\partial y_k}{\\partial w_{m1}}&\\frac{\\partial y_k}{\\partial w_{m2}}&\\cdots&\\frac{\\partial y_k}{\\partial w_{mn}}\\end{bmatrix}\\)\\(J_W=\\begin{bmatrix}J_W^{(1)}&J_W^{(2)}&\\cdots&J_W^{(m)}\\end{bmatrix}\\) \\(D_Y=\\begin{bmatrix}\\frac{\\partial L}{\\partial y_1}\\\\\\frac{\\partial L}{\\partial y_2}\\\\\\vdots\\\\\\frac{\\partial L}{\\partial y_m}\\end{bmatrix}\\) \\(m\\times n\\) \\(m\\times m\\times n\\) $ m\\times 1$ For each element in \\(D_X\\) , we have:
\\(D_{Xi}=\\frac{\\partial L}{\\partial x_i}=\\sum_{j=1}^m\\frac{\\partial L}{\\partial y_j}\\frac{\\partial y_j}{\\partial x_i}\\\\\\)
"},{"location":"AI/CS231n/CS231n_notes/#5-convolutional-neural-networks","title":"5 - Convolutional Neural Networks","text":""},{"location":"AI/CS231n/CS231n_notes/#convolution-layer","title":"Convolution Layer","text":""},{"location":"AI/CS231n/CS231n_notes/#introduction","title":"Introduction","text":"Convolve a filter with an image: Slide the filter spatially within the image, computing dot products in each region.
Giving a \\(32\\times32\\times3\\) image and a \\(5\\times5\\times3\\) filter, a convolution looks like:
Convolve six \\(5\\times5\\times3\\) filters to a \\(32\\times32\\times3\\) image with step size \\(1\\), we can get a \\(28\\times28\\times6\\) feature:
With an activation function after each convolution layer, we can build the ConvNet with a sequence of convolution layers:
By changing the step size between each move for filters, or adding zero-padding around the image, we can modify the size of the output:
"},{"location":"AI/CS231n/CS231n_notes/#1times1-convolution-layer","title":"\\(1\\times1\\) Convolution Layer","text":"This kind of layer makes perfect sense. It is usually used to change the dimension (channel) of features.
A \\(1\\times1\\) convolution layer can also be treated as a full-connected linear layer.
"},{"location":"AI/CS231n/CS231n_notes/#summary_1","title":"Summary","text":"Input image size \\(W_1\\times H_1\\times C\\) filter size \\(F\\times F\\times C\\) filter number \\(K\\) stride \\(S\\) zero padding \\(P\\) Output output size \\(W_2\\times H_2\\times K\\) output width \\(W_2=\\frac{W_1-F+2P}{S}+1\\\\\\) output height \\(H_2=\\frac{H_1-F+2P}{S}+1\\\\\\) Parameters parameter number (weight) \\(F^2CK\\) parameter number (bias) \\(K\\)"},{"location":"AI/CS231n/CS231n_notes/#pooling-layer","title":"Pooling layer","text":"Make the representations smaller and more manageable.
An example of max pooling:
Input image size \\(W_1\\times H_1\\times C\\) spatial extent \\(F\\times F\\) stride \\(S\\) Output output size \\(W_2\\times H_2\\times C\\) output width \\(W_2=\\frac{W_1-F}{S}+1\\\\\\) output height \\(H_2=\\frac{H_1-F}{S}+1\\\\\\)"},{"location":"AI/CS231n/CS231n_notes/#convolutional-neural-networks-cnn","title":"Convolutional Neural Networks (CNN)","text":"CNN stack CONV, POOL, FC layers.
CNN Trends:
- Smaller filters and deeper architectures.
- Getting rid of POOL/FC layers (just CONV).
Historically architectures of CNN looked like:
where usually \\(m\\) is large, \\(0\\le n\\le5\\), \\(0\\le k\\le2\\).
Recent advances such as ResNet / GoogLeNet have challenged this paradigm.
"},{"location":"AI/CS231n/CS231n_notes/#6-cnn-architectures","title":"6 - CNN Architectures","text":"Best model in ImageNet competition:
"},{"location":"AI/CS231n/CS231n_notes/#alexnet","title":"AlexNet","text":"8 layers.
First use of ConvNet in image classification problem.
Filter size decreases in deeper layer.
Channel number increases in deeper layer.
"},{"location":"AI/CS231n/CS231n_notes/#vgg","title":"VGG","text":"19 layers. (also provide 16 layers edition)
Static filter size (\\(3\\times3\\)) in all layers:
- The effective receptive field expands with the layer gets deeper.
- Deeper architecture gets more non-linearities and few parameters.
Most memory is in early convolution layers.
Most parameter is in late FC layers.
"},{"location":"AI/CS231n/CS231n_notes/#googlenet","title":"GoogLeNet","text":"22 layers.
No FC layers, only 5M parameters. ( \\(8.3\\%\\) of AlexNet, \\(3.7\\%\\) of VGG )
Devise efficient \"inception module\".
"},{"location":"AI/CS231n/CS231n_notes/#inception-module","title":"Inception Module","text":"Design a good local network topology (network within a network) and then stack these modules on top of each other.
Naive Inception Module:
- Apply parallel filter operations on the input from previous layer.
- Concatenate all filter outputs together channel-wise.
- Problem: The depth (channel number) increases too fast, costing expensive computation.
Inception Module with Dimension Reduction:
- Add \"bottle neck\" layers to reduce the dimension.
- Also get fewer computation cost.
"},{"location":"AI/CS231n/CS231n_notes/#architecture","title":"Architecture","text":""},{"location":"AI/CS231n/CS231n_notes/#resnet","title":"ResNet","text":"152 layers for ImageNet.
Devise \"residual connections\".
Use BN in place of dropout.
"},{"location":"AI/CS231n/CS231n_notes/#residual-connections","title":"Residual Connections","text":"Hypothesis: Deeper models have more representation power than shallow ones. But they are harder to optimize.
Solution: Use network layers to fit a residual mapping instead of directly trying to fit a desired underlying mapping.
It is necessary to use ReLU as activation function, in order to apply identity mapping when \\(F(x)=0\\) .
"},{"location":"AI/CS231n/CS231n_notes/#architecture_1","title":"Architecture","text":""},{"location":"AI/CS231n/CS231n_notes/#senet","title":"SENet","text":"Using ResNeXt-152 as a base architecture.
Add a \u201cfeature recalibration\u201d module. (adjust weights of each channel)
Using the global avg-pooling layer + FC layers to determine feature map weights.
"},{"location":"AI/CS231n/CS231n_notes/#improvements-of-resnet","title":"Improvements of ResNet","text":"Wide Residual Networks, ResNeXt, DenseNet, MobileNets...
"},{"location":"AI/CS231n/CS231n_notes/#other-interesting-networks","title":"Other Interesting Networks","text":"NASNet: Neural Architecture Search with Reinforcement Learning.
EfficientNet: Smart Compound Scaling.
"},{"location":"AI/CS231n/CS231n_notes/#7-training-neural-networks","title":"7 - Training Neural Networks","text":""},{"location":"AI/CS231n/CS231n_notes/#activation-functions","title":"Activation Functions","text":"Activation Usage Sigmoid, tanh Do not use. ReLU Use as default. Leaky ReLU, Maxout, ELU, SELU Replace ReLU to squeeze out some marginal gains. Swish No clear usage."},{"location":"AI/CS231n/CS231n_notes/#data-processing","title":"Data Processing","text":"Apply centralization and normalization before training.
In practice for pictures, usually we apply channel-wise centralization only.
"},{"location":"AI/CS231n/CS231n_notes/#weight-initialization","title":"Weight Initialization","text":"Assume that we have 6 layers in a network.
\\(D_i\\) : input size of layer \\(i\\)
\\(W_i\\) : weights in layer \\(i\\)
\\(X_i\\) : output after activation of layer \\(i\\), we have \\(X_i=g(Z_i)=g(W_iX_{i-1}+B_i)\\)
We initialize each parameter in \\(W_i\\) randomly in \\([-k_i,k_i]\\) .
Tanh Activation Output Distribution \\(k_i=0.01\\) \\(k_i=0.05\\) Xavier Initialization \\(k_i=\\frac{1}{\\sqrt{D_i}\\\\}\\) When \\(k_i=0.01\\), the variance keeps decreasing as the layer gets deeper. As a result, the output of each neuron in deep layer will all be 0. The partial derivative \\(\\frac{\\partial Z_i}{\\partial W_i}=X_{i-1}=0\\\\\\). (no gradient)
When \\(k_i=0.05\\), most neurons is saturated. The partial derivative \\(\\frac{\\partial X_i}{\\partial Z_i}=g'(Z_i)=0\\\\\\). (no gradient)
To solve this problem, We need to keep the variance same in each layer.
Assuming that \\(Var\\big(X_{i-1}^{(1)}\\big)=Var\\big(X_{i-1}^{(2)}\\big)=\\dots=Var\\big(X_{i-1}^{(D_i)}\\big)\\)
We have \\(Z_i=X_{i-1}^{(1)}W_i^{(:,1)}+X_{i-1}^{(2)}W_i^{(:,2)}+\\dots+X_{i-1}^{(D_i)}W_i^{(:,D_i)}=\\sum_{n=1}^{D_i}X_{i-1}^{(n)}W_i^{(:,n)}\\\\\\)
We want \\(Var\\big(Z_i\\big)=Var\\big(X_{i-1}^{(n)}\\big)\\)
Let's do some conduction:
\\(\\begin{aligned}Var\\big(Z_i\\big)&=Var\\Bigg(\\sum_{n=1}^{D_i}X_{i-1}^{(n)}W_i^{(:,n)}\\Bigg)\\\\&=D_i\\ Var\\Big(X_{i-1}^{(n)}W_i^{(:,n)}\\Big)\\\\&=D_i\\ Var\\Big(X_{i-1}^{(n)}\\Big)\\ Var\\Big(W_i^{(:,n)}\\Big)\\end{aligned}\\)
So \\(Var\\big(Z_i\\big)=Var\\big(X_{i-1}^{(n)}\\big)\\) only when \\(Var\\Big(W_i^{(:,n)}\\Big)=\\frac{1}{D_i}\\\\\\), that is to say \\(k_i=\\frac{1}{\\sqrt{D_i}}\\\\\\)
ReLU Activation Output Distribution Xavier Initialization \\(k_i=\\frac{1}{\\sqrt{D_i}\\\\}\\) Kaiming Initialization \\(k_i=\\sqrt{2D_i}\\) For ReLU activation, when using xavier initialization, there still exist \"variance decreasing\" problem.
We can use kaiming initialization instead to fix this.
"},{"location":"AI/CS231n/CS231n_notes/#batch-normalization","title":"Batch Normalization","text":"Force the inputs to be \"nicely scaled\" at each layer.
\\(N\\) : batch size
\\(D\\) : feature size
\\(x\\) : input with shape \\(N\\times D\\)
\\(\\gamma\\) : learnable scale and shift parameter with shape \\(D\\)
\\(\\beta\\) : learnable scale and shift parameter with shape \\(D\\)
The procedure of batch normalization:
- Calculate channel-wise mean \\(\\mu_j=\\frac{1}{N}\\sum_{i=1}^Nx_{i,j}\\\\\\) . The result \\(\\mu\\) with shape \\(D\\) .
- Calculate channel-wise variance \\(\\sigma_j^2=\\frac{1}{N}\\sum_{i=1}^N(x_{i,j}-\\mu_j)^2\\\\\\) . The result \\(\\sigma^2\\) with shape \\(D\\) .
- Calculate normalized \\(\\hat{x}_{i,j}=\\frac{x_{i,j}-\\mu_j}{\\sqrt{\\sigma_j^2+\\epsilon}}\\\\\\) . The result \\(\\hat{x}\\) with shape \\(N\\times D\\) .
- Scale normalized input to get output \\(y_{i,j}=\\gamma_j\\hat{x}_{i,j}+\\beta_j\\) . The result \\(y\\) with shape \\(N\\times D\\) .
Why scale: The constraint \"zero-mean, unit variance\" may be too hard.
Pros:
- Makes deep networks much easier to train!
- Improves gradient flow.
- Allows higher learning rates, faster convergence.
- Networks become more robust to initialization.
- Acts as regularization during training.
- Zero overhead at test-time: can be fused with conv!
Cons:
Behaves differently during training and testing: this is a very common source of bugs!
"},{"location":"AI/CS231n/CS231n_notes/#transfer-learning","title":"Transfer Learning","text":"Train on a pre-trained model with other datasets.
An empirical suggestion:
very similar dataset very different dataset very little data Use Linear Classifier on top layer. You\u2019re in trouble\u2026 Try linear classifier from different stages. quite a lot of data Finetune a few layers. Finetune a larger number of layers."},{"location":"AI/CS231n/CS231n_notes/#regularization","title":"Regularization","text":""},{"location":"AI/CS231n/CS231n_notes/#common-pattern-of-regularization","title":"Common Pattern of Regularization","text":"Training: Add some kind of randomness. \\(y=f(x,z)\\)
Testing: Average out randomness (sometimes approximate). \\(y=f(x)=E_z\\big[f(x,z)\\big]=\\int p(z)f(x,z)dz\\\\\\)
"},{"location":"AI/CS231n/CS231n_notes/#regularization-term","title":"Regularization Term","text":"L2 regularization: \\(R(W)=\\sum_k\\sum_lW_{k,l}^2\\) (weight decay)
L1 regularization: \\(R(W)=\\sum_k\\sum_l|W_{k,l}|\\)
Elastic net : \\(R(W)=\\sum_k\\sum_l\\big(\\beta W_{k,l}^2+|W_{k,l}|\\big)\\) (L1+L2)
"},{"location":"AI/CS231n/CS231n_notes/#dropout","title":"Dropout","text":"Training: Randomly set some neurons to 0 with a probability \\(p\\) .
Testing: Each neuron multiplies by dropout probability \\(p\\) . (scale the output back)
More common: Scale the output with \\(\\frac{1}{p}\\) when training, keep the original output when testing.
Why dropout works:
- Forces the network to have a redundant representation. Prevents co-adaptation of features.
- Another interpretation: Dropout is training a large ensemble of models (that share parameters).
"},{"location":"AI/CS231n/CS231n_notes/#batch-normalization_1","title":"Batch Normalization","text":"See above.
"},{"location":"AI/CS231n/CS231n_notes/#data-augmentation","title":"Data Augmentation","text":" - Horizontal Flips
- Random Crops and Scales
- Color Jitter
- Rotation
- Stretching
- Shearing
- Lens Distortions
- ...
There also exists automatic data augmentation method using neural networks.
"},{"location":"AI/CS231n/CS231n_notes/#other-methods-and-summary","title":"Other Methods and Summary","text":"DropConnect: Drop connections between neurons.
Fractional Max Pooling: Use randomized pooling regions.
Stochastic Depth: Skip some layers in the network.
Cutout: Set random image regions to zero.
Mixup: Train on random blends of images.
Regularization Method Usage Dropout For large fully-connected layers. Batch Normalization & Data Augmentation Almost always a good idea. Cutout & Mixup For small classification datasets."},{"location":"AI/CS231n/CS231n_notes/#hyperparameter-tuning","title":"Hyperparameter Tuning","text":"Most Common Hyperparameters Less Sensitive Hyperparameters learning ratelearning rate decay scheduleweight decay setting of momentum... Tips on hyperparameter tuning:
- Prefer one validation fold to cross-validation.
- Search for hyperparameters on log scale. (e.g. multiply the hyperparameter by a fixed number \\(k\\) at each search)
- Prefer random search to grid search.
- Careful with best values on border.
- Stage your search from coarse to fine.
"},{"location":"AI/CS231n/CS231n_notes/#implementation","title":"Implementation","text":"Have a worker that continuously samples random hyperparameters and performs the optimization. During the training, the worker will keep track of the validation performance after every epoch, and writes a model checkpoint to a file.
Have a master that launches or kills workers across a computing cluster, and may additionally inspect the checkpoints written by workers and plot their training statistics.
"},{"location":"AI/CS231n/CS231n_notes/#common-procedures","title":"Common Procedures","text":" - Check initial loss.
Turn off weight decay, sanity check loss at initialization \\(\\log(C)\\) for softmax with \\(C\\) classes.
- Overfit a small sample. (important)
Try to train to 100% training accuracy on a small sample of training data.
Fiddle with architecture, learning rate, weight initialization.
- Find learning rate that makes loss go down.
Use the architecture from the previous step, use all training data, turn on small weight decay, find a learning rate that makes the loss drop significantly within 100 iterations.
Good learning rates to try: \\(0.1,0.01,0.001,0.0001,\\dots\\)
- Coarse grid, train for 1-5 epochs.
Choose a few values of learning rate and weight decay around what worked from Step 3, train a few models for 1-5 epochs.\\
Good weight decay to try: \\(0.0001,0.00001,0\\)
- Refine grid, train longer.
Pick best models from Step 4, train them for longer (10-20 epochs) without learning rate decay.
- Look at loss and accuracy curves.
- GOTO step 5.
"},{"location":"AI/CS231n/CS231n_notes/#gradient-checks","title":"Gradient Checks","text":"CS231n Convolutional Neural Networks for Visual Recognition
Compute analytical gradient manually using \\(f_a'=\\frac{\\partial f(x)}{\\partial x}=\\frac{f(x-h)-f(x+h)}{2h}\\\\\\)
Get relative error between numerical gradient \\(f_n'\\) and analytical gradient \\(f_a'\\) using \\(E=\\frac{|f_n'-f_a'|}{\\max{|f_n'|,|f_a'|}}\\\\\\)
Relative Error Result \\(E>10^{-2}\\) Probably \\(f_n'\\) is wrong. \\(10^{-2}>E>10^{-4}\\) Not good, should check the gradient. \\(10^{-4}>E>10^{-6}\\) Okay for objectives with kinks. (e.g. ReLU)Not good for objectives with no kink. (e.g. softmax, tanh) \\(10^{-7}>E\\) Good. Tips on gradient checks:
- Use double precision.
- Use only few data points.
- Careful about kinks in the objective. (e.g. \\(x=0\\) for ReLU activation)
- Careful with the step size \\(h\\).
- Use gradient check after the loss starts to go down.
- Remember to turn off anything that may affect the gradient. (e.g. regularization / dropout / augmentations)
- Check only few dimensions for every parameter. (reduce time cost)
"},{"location":"AI/CS231n/CS231n_notes/#8-visualizing-and-understanding","title":"8 - Visualizing and Understanding","text":""},{"location":"AI/CS231n/CS231n_notes/#feature-visualization-and-inversion","title":"Feature Visualization and Inversion","text":""},{"location":"AI/CS231n/CS231n_notes/#visualizing-what-models-have-learned","title":"Visualizing what models have learned","text":"Visualize Areas Filters Visualize the raw weights of each convolution kernel. (better in the first layer) Final Layer Features Run dimensionality reduction for features in the last FC layer. (PCA, t-SNE...) Activations Visualize activated areas. (Understanding Neural Networks Through Deep Visualization)"},{"location":"AI/CS231n/CS231n_notes/#understanding-input-pixels","title":"Understanding input pixels","text":""},{"location":"AI/CS231n/CS231n_notes/#maximally-activating-patches","title":"Maximally Activating Patches","text":" - Pick a layer and a channel.
- Run many images through the network, record values of the chosen channel.
- Visualize image patches that correspond to maximal activation features.
For example, we have a layer with shape \\(128\\times13\\times13\\). We pick the 17th channel from all 128 channels. Then we run many pictures through the network. During each run we can find a maximal activation feature among all the \\(13\\times13\\) features in channel 17. We then record the corresponding picture patch for each maximal activation feature. At last, we visualize all picture patches for each feature.
This will help us find the relationship between each maximal activation feature and its corresponding picture patches.
(each row of the following picture represents a feature)
"},{"location":"AI/CS231n/CS231n_notes/#saliency-via-occlusion","title":"Saliency via Occlusion","text":"Mask part of the image before feeding to CNN, check how much predicted probabilities change.
"},{"location":"AI/CS231n/CS231n_notes/#saliency-via-backprop","title":"Saliency via Backprop","text":" - Compute gradient of (unnormalized) class score with respect to image pixels.
- Take absolute value and max over RGB channels to get saliency maps.
"},{"location":"AI/CS231n/CS231n_notes/#intermediate-features-via-guided-backprop","title":"Intermediate Features via Guided Backprop","text":" - Pick a single intermediate neuron. (e.g. one feature in a \\(128\\times13\\times13\\) feature map)
- Compute gradient of neuron value with respect to image pixels.
Striving for Simplicity: The All Convolutional Net
Just like \"Maximally Activating Patches\", this could find the part of an image that a neuron responds to.
"},{"location":"AI/CS231n/CS231n_notes/#gradient-ascent","title":"Gradient Ascent","text":"Generate a synthetic image that maximally activates a neuron.
- Initialize image \\(I\\) to zeros.
- Forward image to compute current scores \\(S_c(I)\\) (for class \\(c\\) before softmax).
- Backprop to get gradient of neuron value with respect to image pixels.
- Make a small update to the image.
Objective: \\(\\max S_c(I)-\\lambda\\lVert I\\lVert^2\\)
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
"},{"location":"AI/CS231n/CS231n_notes/#adversarial-examples","title":"Adversarial Examples","text":"Find an fooling image that can make the network misclassify correctly-classified images when it is added to the image.
- Start from an arbitrary image.
- Pick an arbitrary class.
- Modify the image to maximize the class.
- Repeat until network is fooled.
"},{"location":"AI/CS231n/CS231n_notes/#deepdream-and-style-transfer","title":"DeepDream and Style Transfer","text":""},{"location":"AI/CS231n/CS231n_notes/#feature-inversion","title":"Feature Inversion","text":"Given a CNN feature vector \\(\\Phi_0\\) for an image, find a new image \\(x\\) that:
- Features of new image \\(\\Phi(x)\\) matches the given feature vector \\(\\Phi_0\\).
- \"looks natural\u201d. (image prior regularization)
Objective: \\(\\min \\lVert\\Phi(x)-\\Phi_0\\lVert+\\lambda R(x)\\)
Understanding Deep Image Representations by Inverting Them
"},{"location":"AI/CS231n/CS231n_notes/#deepdream-amplify-existing-features","title":"DeepDream: Amplify Existing Features","text":"Given an image, amplify the neuron activations at a layer to generate a new one.
- Forward: compute activations at chosen layer.
- Set gradient of chosen layer equal to its activation.
- Backward: Compute gradient on image.
- Update image.
"},{"location":"AI/CS231n/CS231n_notes/#texture-synthesis","title":"Texture Synthesis","text":""},{"location":"AI/CS231n/CS231n_notes/#nearest-neighbor","title":"Nearest Neighbor","text":" - Generate pixels one at a time in scanline order
- Form neighborhood of already generated pixels, copy the nearest neighbor from input.
"},{"location":"AI/CS231n/CS231n_notes/#neural-texture-synthesis","title":"Neural Texture Synthesis","text":"Gram Matrix: \u683c\u62c9\u59c6\u77e9\u9635\uff08Gram matrix\uff09\u8be6\u7ec6\u89e3\u8bfb
- Pretrain a CNN on ImageNet.
- Run input texture forward through CNN, record activations on every layer.
Layer \\(i\\) gives feature map of shape \\(C_i\\times H_i\\times W_i\\).
- At each layer compute the Gram matrix \\(G_i\\) giving outer product of features.
- Reshape feature map at layer \\(i\\) to \\(C_i\\times H_iW_i\\).
- Compute the Gram matrix \\(G_i\\) with shape \\(C_i\\times C_i\\).
- Initialize generated image from random noise.
- Pass generated image through CNN, compute Gram matrix \\(\\hat{G}_l\\) on each layer.
- Compute loss: Weighted sum of L2 distance between Gram matrices.
- \\(E_l=\\frac{1}{aN_l^2M_l^2}\\sum_{i,j}\\Big(G_i^{(i,j)}-\\hat{G}_i^{(i,j)}\\Big)^2\\\\\\)
- \\(\\mathcal{L}(\\vec{x},\\hat{\\vec{x}})=\\sum_{l=0}^L\\omega_lE_l\\\\\\)
- Backprop to get gradient on image.
- Make gradient step on image.
- GOTO 5.
Texture Synthesis Using Convolutional Neural Networks
"},{"location":"AI/CS231n/CS231n_notes/#style-transfer","title":"Style Transfer","text":""},{"location":"AI/CS231n/CS231n_notes/#feature-gram-reconstruction","title":"Feature + Gram Reconstruction","text":"Problem: Style transfer requires many forward / backward passes. Very slow!
"},{"location":"AI/CS231n/CS231n_notes/#fast-style-transfer","title":"Fast Style Transfer","text":""},{"location":"AI/CS231n/CS231n_notes/#9-object-detection-and-image-segmentation","title":"9 - Object Detection and Image Segmentation","text":""},{"location":"AI/CS231n/CS231n_notes/#semantic-segmentation","title":"Semantic Segmentation","text":"Paired Training Data: For each training image, each pixel is labeled with a semantic category.
Fully Convolutional Network: Design a network with only convolutional layers without downsampling operators to make predictions for pixels all at once!
Problem: Convolutions at original image resolution will be very expensive...
Solution: Design fully convolutional network with downsampling and upsampling inside it!
- Downsampling: Pooling, strided convolution.
- Upsampling: Unpooling, transposed convolution.
Unpooling:
Nearest Neighbor \"Bed of Nails\" \"Position Memory\" Transposed Convolution: (example size \\(3\\times3\\), stride \\(2\\), pad \\(1\\))
Normal Convolution Transposed Convolution "},{"location":"AI/CS231n/CS231n_notes/#object-detection","title":"Object Detection","text":""},{"location":"AI/CS231n/CS231n_notes/#single-object","title":"Single Object","text":"Classification + Localization. (classification + regression problem)
"},{"location":"AI/CS231n/CS231n_notes/#multiple-object","title":"Multiple Object","text":""},{"location":"AI/CS231n/CS231n_notes/#r-cnn","title":"R-CNN","text":"Using selective search to find \u201cblobby\u201d image regions that are likely to contain objects.
- Find regions of interest (RoI) using selective search. (region proposal)
- Forward each region through ConvNet.
- Classify features with SVMs.
Problem: Very slow. Need to do 2000 independent forward passes for each image!
"},{"location":"AI/CS231n/CS231n_notes/#fast-r-cnn","title":"Fast R-CNN","text":"Pass the image through ConvNet before cropping. Crop the conv feature instead.
- Run whole image through ConvNet.
- Find regions of interest (RoI) from conv features using selective search. (region proposal)
- Classify RoIs using CNN.
Problem: Runtime is dominated by region proposals. (about \\(90\\%\\) time cost)
"},{"location":"AI/CS231n/CS231n_notes/#faster-r-cnn","title":"Faster R-CNN","text":"Insert Region Proposal Network (RPN) to predict proposals from features.
Otherwise same as Fast R-CNN: Crop features for each proposal, classify each one.
Region Proposal Network (RPN) : Slide many fixed windows over ConvNet features.
- Treat each point in the feature map as the anchor.
We have \\(k\\) fixed windows (anchor boxes) of different size/scale centered with each anchor.
- For each anchor box, predict whether it contains an object.
For positive boxes, also predict a corrections to the ground-truth box.
- Slide anchor over the feature map, get the \u201cobjectness\u201d score for each box at each point.
- Sort the \u201cobjectness\u201d score, take top \\(300\\) as the proposals.
Faster R-CNN is a Two-stage object detector:
- First stage: Run once per image
Backbone network
Region proposal network
- Second stage: Run once per region
Crop features: RoI pool / align
Predict object class
Prediction bbox offset
"},{"location":"AI/CS231n/CS231n_notes/#single-stage-object-detectors-yolo","title":"Single-Stage Object Detectors: YOLO","text":"You Only Look Once: Unified, Real-Time Object Detection
- Divide image into grids. (example image grids shape \\(7\\times7\\))
- Set anchors in the middle of each grid.
- For each grid: - Using \\(B\\) anchor boxes to regress \\(5\\) numbers: \\(\\text{dx, dy, dh, dw, confidence}\\). - Predict scores for each of \\(C\\) classes.
- Finally the output is \\(7\\times7\\times(5B+C)\\).
"},{"location":"AI/CS231n/CS231n_notes/#instance-segmentation","title":"Instance Segmentation","text":"Mask R-CNN: Add a small mask network that operates on each RoI and predicts a \\(28\\times28\\) binary mask.
Mask R-CNN performs very good results!
"},{"location":"AI/CS231n/CS231n_notes/#10-recurrent-neural-networks","title":"10 - Recurrent Neural Networks","text":"Supplement content added according to Deep Learning Book - RNN.
"},{"location":"AI/CS231n/CS231n_notes/#recurrent-neural-network-rnn","title":"Recurrent Neural Network (RNN)","text":""},{"location":"AI/CS231n/CS231n_notes/#motivation-sequence-processing","title":"Motivation: Sequence Processing","text":"One to One One to Many Many to One Many to Many Many to Many Vanilla Neural Networks Image Captioning Action Prediction Video Captioning Video Classification on Frame Level"},{"location":"AI/CS231n/CS231n_notes/#vanilla-rnn","title":"Vanilla RNN","text":"\\(x^{(t)}\\) : Input at time \\(t\\).
\\(h^{(t)}\\) : State at time \\(t\\).
\\(o^{(t)}\\) : Output at time \\(t\\)\u200b\u200b.
\\(y^{(t)}\\) : Expected output at time \\(t\\).
"},{"location":"AI/CS231n/CS231n_notes/#many-to-one","title":"Many to One","text":"Calculation State Transition \\(h^{(t)}=\\tanh(Wh^{(t-1)}+Ux^{(t)}+b)\\) Output Calculation \\(o^{(\\tau)}=\\text{sigmoid}\\ \\big(Vh^{(\\tau)}+c\\big)\\)"},{"location":"AI/CS231n/CS231n_notes/#many-to-many-type-2","title":"Many to Many (type 2)","text":"Calculation State Transition \\(h^{(t)}=\\tanh(Wh^{(t-1)}+Ux^{(t)}+b)\\) Output Calculation \\(o^{(t)}=\\text{sigmoid}\\ \\big(Vh^{(t)}+c\\big)\\)"},{"location":"AI/CS231n/CS231n_notes/#rnn-with-teacher-forcing","title":"RNN with Teacher Forcing","text":"Update current state according to last-time output instead of last-time state.
Calculation State Transition \\(h^{(t)}=\\tanh(Wo^{(t-1)}+Ux^{(t)}+b)\\) Output Calculation \\(o^{(t)}=\\text{sigmoid}\\ \\big(Vh^{(t)}+c\\big)\\)"},{"location":"AI/CS231n/CS231n_notes/#rnn-with-output-forwarding","title":"RNN with \"Output Forwarding\"","text":"We can also combine last-state output with this-state input together.
Calculation State Transition (training) \\(h^{(t)}=\\tanh(Wh^{(t-1)}+Ux^{(t)}+Ry^{(t-1)}+b)\\) State Transition (testing) \\(h^{(t)}=\\tanh(Wh^{(t-1)}+Ux^{(t)}+Ro^{(t-1)}+b)\\) Output Calculation \\(o^{(t)}=\\text{sigmoid}\\ \\big(Vh^{(t)}+c\\big)\\) Usually we use \\(o^{(t-1)}\\) in place of \\(y^{(t-1)}\\) at testing time.
"},{"location":"AI/CS231n/CS231n_notes/#bidirectional-rnn","title":"Bidirectional RNN","text":"When dealing with a whole input sequence, we can process features from two directions.
Calculation State Transition (forward) \\(h^{(t)}=\\tanh(W_1h^{(t-1)}+U_1x^{(t)}+b_1)\\) State Transition (backward) \\(g^{(t)}=\\tanh(W_2g^{(t+1)}+U_2x^{(t)}+b_2)\\) Output Calculation \\(o^{(t)}=\\text{sigmoid}\\ \\big(Vh^{(t)}+Wg^{(t)}+c\\big)\\)"},{"location":"AI/CS231n/CS231n_notes/#encoder-decoder-sequence-to-sequence-rnn","title":"Encoder-Decoder Sequence to Sequence RNN","text":"This is a many-to-many structure (type 1).
First we encode information according to \\(x\\) with no output.
Later we decode information according to \\(y\\) with no input.
\\(C\\) : Context vector, often \\(C=h^{(T)}\\) (last state of encoder).
Calculation State Transition (encode) \\(h^{(t)}=\\tanh(W_1h^{(t-1)}+U_1x^{(t)}+b_1)\\) State Transition (decode, training) \\(s^{(t)}=\\tanh(W_2s^{(t-1)}+U_2y^{(t)}+TC+b_2)\\) State Transition (decode, testing) \\(s^{(t)}=\\tanh(W_2s^{(t-1)}+U_2o^{(t)}+TC+b_2)\\) Output Calculation \\(o^{(t)}=\\text{sigmoid}\\ \\big(Vs^{(t)}+c\\big)\\)"},{"location":"AI/CS231n/CS231n_notes/#example-image-captioning","title":"Example: Image Captioning","text":""},{"location":"AI/CS231n/CS231n_notes/#summary_2","title":"Summary","text":"Advantages of RNN:
- Can process any length input.
- Computation for step \\(t\\) can (in theory) use information from many steps back.
- Model size doesn\u2019t increase for longer input.
- Same weights applied on every timestep, so there is symmetry in how inputs are processed.
Disadvantages of RNN:
- Recurrent computation is slow.
- In practice, difficult to access information from many steps back.
- Problems with gradient exploding and gradient vanishing. (check Deep Learning Book - RNN Page 396, Chap 10.7)
"},{"location":"AI/CS231n/CS231n_notes/#long-short-term-memory-lstm","title":"Long Short Term Memory (LSTM)","text":"Add a \"cell block\" to store history weights.
\\(c^{(t)}\\) : Cell at time \\(t\\).
\\(f^{(t)}\\) : Forget gate at time \\(t\\). Deciding whether to erase the cell.
\\(i^{(t)}\\) : Input gate at time \\(t\\). Deciding whether to write to the cell.
\\(g^{(t)}\\) : External input gate at time \\(t\\). Deciding how much to write to the cell.
\\(o^{(t)}\\) : Output gate at time \\(t\\). Deciding how much to reveal the cell.
Calculation (Gate) Forget Gate \\(f^{(t)}=\\text{sigmoid}\\ \\big(W_fh^{(t-1)}+U_fx^{(t)}+b_f\\big)\\) Input Gate \\(i^{(t)}=\\text{sigmoid}\\ \\big(W_ih^{(t-1)}+U_ix^{(t)}+b_i\\big)\\) External Input Gate \\(g^{(t)}=\\tanh(W_gh^{(t-1)}+U_gx^{(t)}+b_g)\\) Output Gate \\(o^{(t)}=\\text{sigmoid}\\ \\big(W_oh^{(t-1)}+U_ox^{(t)}+b_o\\big)\\) Calculation (Main) Cell Transition \\(c^{(t)}=f^{(t)}\\odot c^{(t-1)}+i^{(t)}\\odot g^{(t)}\\) State Transition \\(h^{(t)}=o^{(t)}\\odot\\tanh(c^{(t)})\\) Output Calculation \\(O^{(t)}=\\text{sigmoid}\\ \\big(Vh^{(t)}+c\\big)\\) "},{"location":"AI/CS231n/CS231n_notes/#other-rnn-variants","title":"Other RNN Variants","text":"GRU...
"},{"location":"AI/CS231n/CS231n_notes/#11-attention-and-transformers","title":"11 - Attention and Transformers","text":""},{"location":"AI/CS231n/CS231n_notes/#rnn-with-attention","title":"RNN with Attention","text":"Encoder-Decoder Sequence to Sequence RNN Problem:
Input sequence bottlenecked through a fixed-sized context vector \\(C\\). (e.g. \\(T=1000\\))
Intuitive Solution:
Generate new context vector \\(C_t\\) at each step \\(t\\) !
\\(e_{t,i}\\) : Alignment score for input \\(i\\) at state \\(t\\). (scalar)
\\(a_{t,i}\\) : Attention weight for input \\(i\\) at state \\(t\\).
\\(C_t\\) : Context vector at state \\(t\\).
Calculation Alignment Score \\(e_i^{(t)}=f(s^{(t-1)},h^{(i)})\\).Where \\(f\\) is an MLP. Attention Weight \\(a_i^{(t)}=\\text{softmax}\\ (e_i^{(t)})\\).Softmax includes all \\(e_i\\) at state \\(t\\). Context Vector \\(C^{(t)}=\\sum_i a_i^{(t)}h^{(i)}\\) Decoder State Transition \\(s^{(t)}=\\tanh(Ws^{(t-1)}+Uy^{(t)}+TC^{(t)}+b)\\) Example on Image Captioning:
"},{"location":"AI/CS231n/CS231n_notes/#general-attention-layer","title":"General Attention Layer","text":"Add linear transformations to the input vector before attention.
Notice:
- Number of queries \\(q\\) is variant. (can be different from the number of keys \\(k\\))
- Number of outputs \\(y\\) is equal to the number of queries \\(q\\).
Each \\(y\\) is a linear weighting of values \\(v\\).
- Alignment \\(e\\) is divided by \\(\\sqrt{D}\\) to avoid \"explosion of softmax\", where \\(D\\) is the dimension of input feature.
"},{"location":"AI/CS231n/CS231n_notes/#self-attention-layer","title":"Self-attention Layer","text":"The query vectors \\(q\\) are also generated from the inputs.
In this way, the shape of \\(y\\) is equal to the shape of \\(x\\).
Example with CNN:
"},{"location":"AI/CS231n/CS231n_notes/#positional-encoding","title":"Positional Encoding","text":"Self-attention layer doesn\u2019t care about the orders of the inputs!
To encode ordered sequences like language or spatially ordered image features, we can add positional encoding to the inputs.
We use a function \\(P:R\\rightarrow R^d\\) to process the position \\(i\\) into a d-dimensional vector \\(p_i=P(i)\\).
Constraint Condition of \\(P\\) Uniqueness \\(P(i)\\ne P(j)\\) Equidistance \\(\\lVert P(i+k)-P(i)\\rVert^2=\\lVert P(j+k)-P(j)\\rVert^2\\) Boundness \\(P(i)\\in[a,b]\\) Determinacy \\(P(i)\\) is always a static value. (function is not dynamic) We can either train a encoder model, or design a fixed function.
A Practical Positional Encoding Method: Using \\(\\sin\\) and \\(\\cos\\) with different frequency \\(\\omega\\) at different dimension.
\\(P(t)=\\begin{bmatrix}\\sin(\\omega_1,t)\\\\\\cos(\\omega_1,t)\\\\\\\\\\sin(\\omega_2,t)\\\\\\cos(\\omega_2,t)\\\\\\vdots\\\\\\sin(\\omega_{\\frac{d}{2}},t)\\\\\\cos(\\omega_{\\frac{d}{2}},t)\\end{bmatrix}\\), where frequency \\(\\omega_k=\\frac{1}{10000^{\\frac{2k}{d}}}\\\\\\). (wave length \\(\\lambda=\\frac{1}{\\omega}=10000^{\\frac{2k}{d}}\\\\\\))
\\(P(t)=\\begin{bmatrix}\\sin(1/10000^{\\frac{2}{d}},t)\\\\\\cos(1/10000^{\\frac{2}{d}},t)\\\\\\\\\\sin(1/10000^{\\frac{4}{d}},t)\\\\\\cos(1/10000^{\\frac{4}{d}},t)\\\\\\vdots\\\\\\sin(1/10000^1,t)\\\\\\cos(1/10000^1,t)\\end{bmatrix}\\), after we substitute \\(\\omega_k\\) into the equation.
\\(P(t)\\) is a vector with size \\(d\\), where \\(d\\) is a hyperparameter to choose according to the length of input sequence.
An intuition of this method is the binary encoding of numbers.
[lecture 11d] \u6ce8\u610f\u529b\u548ctransformer (positional encoding \u8865\u5145\uff0c\u4ee3\u7801\u5b9e\u73b0\uff0c\u8ddd\u79bb\u8ba1\u7b97)
It is easy to prove that \\(P(t)\\) satisfies \"Equidistance\": (set \\(d=2\\) for example)
\\(\\begin{aligned}\\lVert P(i+k)-P(i)\\rVert^2&=\\big[\\sin(\\omega_1,i+k)-\\sin(\\omega_1,i)\\big]^2+\\big[\\cos(\\omega_1,i+k)-\\cos(\\omega_1,i)\\big]^2\\\\&=2-2\\sin(\\omega_1,i+k)\\sin(\\omega_1,i)-2\\cos(\\omega_1,i+k)\\cos(\\omega_1,i)\\\\&=2-2\\cos(\\omega_1,k)\\end{aligned}\\)
So the distance is not associated with \\(i\\), we have \\(\\lVert P(i+k)-P(i)\\rVert^2=\\lVert P(j+k)-P(j)\\rVert^2\\).
Visualization of \\(P(t)\\) features: (set \\(d=32\\), \\(x\\) axis represents the position of sequence)
"},{"location":"AI/CS231n/CS231n_notes/#masked-self-attention-layer","title":"Masked Self-attention Layer","text":"To prevent vectors from looking at future vectors, we manually set alignment scores to \\(-\\infty\\).
"},{"location":"AI/CS231n/CS231n_notes/#multi-head-self-attention-layer","title":"Multi-head Self-attention Layer","text":"Multiple self-attention heads in parallel.
"},{"location":"AI/CS231n/CS231n_notes/#transformer","title":"Transformer","text":"Attention Is All You Need
"},{"location":"AI/CS231n/CS231n_notes/#encoder-block","title":"Encoder Block","text":"Inputs: Set of vectors \\(z\\). (in which \\(z_i\\) can be a word in a sentence, or a pixel in a picture...)
Output: Set of context vectors \\(c\\). (encoded features of \\(z\\))
The number of blocks \\(N=6\\) in original paper.
Notice:
- Self-attention is the only interaction between vectors \\(x_0,x_1,\\dots,x_n\\).
- Layer norm and MLP operate independently per vector.
- Highly scalable, highly parallelizable, but high memory usage.
"},{"location":"AI/CS231n/CS231n_notes/#decoder-block","title":"Decoder Block","text":"Inputs: Set of vectors \\(y\\). (\\(y_i\\) can be a word in a sentence, or a pixel in a picture...)
Inputs: Set of context vectors \\(c\\).
Output: Set of vectors \\(y'\\). (decoded result, \\(y'_i=y_{i+1}\\) for the first \\(n-1\\) number of \\(y'\\))
The number of blocks \\(N=6\\) in original paper.
Notice:
- Masked self-attention only interacts with past inputs.
- Multi-head attention block is NOT self-attention. It attends over encoder outputs.
- Highly scalable, highly parallelizable, but high memory usage. (same as encoder)
Why we need mask in decoder:
- Needs for the special formation of output \\(y'_i=y_{i+1}\\).
- Needs for parallel computation.
\u4e3e\u4e2a\u4f8b\u5b50\u8bb2\u4e0btransformer\u7684\u8f93\u5165\u8f93\u51fa\u7ec6\u8282\u53ca\u5176\u4ed6
\u5728\u6d4b\u8bd5\u6216\u8005\u9884\u6d4b\u65f6\uff0cTransformer\u91ccdecoder\u4e3a\u4ec0\u4e48\u8fd8\u9700\u8981seq mask\uff1f
"},{"location":"AI/CS231n/CS231n_notes/#example-on-image-captioning-only-with-transformers","title":"Example on Image Captioning (Only with Transformers)","text":""},{"location":"AI/CS231n/CS231n_notes/#comparing-rnns-to-transformer","title":"Comparing RNNs to Transformer","text":"RNNs Transformer Pros LSTMs work reasonably well for long sequences. 1. Good at long sequences. Each attention calculation looks at all inputs.2. Can operate over unordered sets or ordered sequences with positional encodings.3. Parallel computation: All alignment and attention scores for all inputs can be done in parallel. Cons 1. Expects an ordered sequences of inputs.2. Sequential computation: Subsequent hidden states can only be computed after the previous ones are done. Requires a lot of memory: \\(N\\times M\\) alignment and attention scalers need to be calculated and stored for a single self-attention head."},{"location":"AI/CS231n/CS231n_notes/#comparing-convnets-to-transformer","title":"Comparing ConvNets to Transformer","text":"ConvNets strike back!
"},{"location":"AI/CS231n/CS231n_notes/#12-video-understanding","title":"12 - Video Understanding","text":""},{"location":"AI/CS231n/CS231n_notes/#video-classification","title":"Video Classification","text":"Take video classification task for example.
Input size: \\(C\\times T\\times H\\times W\\).
The problem is, videos are quite big. We can't afford to train on raw videos, instead we train on video clips.
Raw Videos Video Clips \\(1920\\times1080,\\ 30\\text{fps}\\) \\(112\\times112,\\ 5\\text{f}/3.2\\text{s}\\) \\(10\\text{GB}/\\text{min}\\) \\(588\\text{KB}/\\text{min}\\) "},{"location":"AI/CS231n/CS231n_notes/#plain-cnn-structure","title":"Plain CNN Structure","text":""},{"location":"AI/CS231n/CS231n_notes/#single-frame-2d-cnn","title":"Single Frame 2D-CNN","text":"Train a normal 2D-CNN model.
Classify each frame independently.
Average the result of each frame as the final result.
"},{"location":"AI/CS231n/CS231n_notes/#late-fusion","title":"Late Fusion","text":"Get high-level appearance of each frame, and combine them.
Run 2D-CNN on each frame, pool features and feed to Linear Layers.
Problem: Hard to compare low-level motion between frames.
"},{"location":"AI/CS231n/CS231n_notes/#early-fusion","title":"Early Fusion","text":"Compare frames with very first Conv Layer, after that normal 2D-CNN.
Problem: One layer of temporal processing may not be enough!
"},{"location":"AI/CS231n/CS231n_notes/#3d-cnn","title":"3D-CNN","text":"Convolve on 3 dimensions: Height, Width, Time.
Input size: \\(C_{in}\\times T\\times H\\times W\\).
Kernel size: \\(C_{in}\\times C_{out}\\times 3\\times 3\\times 3\\).
Output size: \\(C_{out}\\times T\\times H\\times W\\). (with zero paddling)
"},{"location":"AI/CS231n/CS231n_notes/#c3d-vgg-of-3d-cnns","title":"C3D (VGG of 3D-CNNs)","text":"The cost is quite expensive...
Network Calculation AlexNet 0.7 GFLOP VGG-16 13.6 GFLOP C3D 39.5 GFLOP"},{"location":"AI/CS231n/CS231n_notes/#two-stream-networks","title":"Two-Stream Networks","text":"Separate motion and appearance.
"},{"location":"AI/CS231n/CS231n_notes/#i3d-inflating-2d-networks-to-3d","title":"I3D (Inflating 2D Networks to 3D)","text":"Take a 2D-CNN architecture.
Replace each 2D conv/pool layer with a 3D version.
"},{"location":"AI/CS231n/CS231n_notes/#modeling-long-term-temporal-structure","title":"Modeling Long-term Temporal Structure","text":""},{"location":"AI/CS231n/CS231n_notes/#recurrent-convolutional-network","title":"Recurrent Convolutional Network","text":"Similar to multi-layer RNN, we replace the dot-product operation with convolution.
Feature size in layer \\(L\\), time \\(t-1\\): \\(W_h\\times H\\times W\\).
Feature size in layer \\(L-1\\), time \\(t\\): \\(W_x\\times H\\times W\\).
Feature size in layer \\(L\\), time \\(t\\): \\((W_h+W_x)\\times H\\times W\\).
Problem: RNNs are slow for long sequences. (can\u2019t be parallelized)
"},{"location":"AI/CS231n/CS231n_notes/#spatio-temporal-self-attention","title":"Spatio-temporal Self-attention","text":"Introduce self-attention into video classification problems.
"},{"location":"AI/CS231n/CS231n_notes/#vision-transformers-for-video","title":"Vision Transformers for Video","text":"Factorized attention: Attend over space / time.
So many papers...
"},{"location":"AI/CS231n/CS231n_notes/#visualizing-video-models","title":"Visualizing Video Models","text":""},{"location":"AI/CS231n/CS231n_notes/#multimodal-video-understanding","title":"Multimodal Video Understanding","text":""},{"location":"AI/CS231n/CS231n_notes/#temporal-action-localization","title":"Temporal Action Localization","text":"Given a long untrimmed video sequence, identify frames corresponding to different actions.
"},{"location":"AI/CS231n/CS231n_notes/#spatio-temporal-detection","title":"Spatio-Temporal Detection","text":"Given a long untrimmed video, detect all the people in both space and time and classify the activities they are performing.
"},{"location":"AI/CS231n/CS231n_notes/#visually-guided-audio-source-separation","title":"Visually-guided Audio Source Separation","text":"And So on...
"},{"location":"AI/CS231n/CS231n_notes/#13-generative-models","title":"13 - Generative Models","text":""},{"location":"AI/CS231n/CS231n_notes/#pixelrnn-and-pixelcnn","title":"PixelRNN and PixelCNN","text":""},{"location":"AI/CS231n/CS231n_notes/#fully-visible-belief-network-fvbn","title":"Fully Visible Belief Network (FVBN)","text":"\\(p(x)\\) : Likelihood of image \\(x\\).
\\(p(x_1,x_2,\\dots,x_n)\\) : Joint likelihood of all \\(n\\) pixels in image \\(x\\).
\\(p(x_i|x_1,x_2,\\dots,x_{i-1})\\) : Probability of pixel \\(i\\) value given all previous pixels.
For explicit density models, we have \\(p(x)=p(x_1,x_2,\\dots,x_n)=\\prod_{i=1}^np(x_i|x_1,x_2,\\dots,x_{i-1})\\\\\\).
Objective: Maximize the likelihood of training data.
"},{"location":"AI/CS231n/CS231n_notes/#pixelrnn","title":"PixelRNN","text":"Generate image pixels starting from corner.
Dependency on previous pixels modeled using an RNN (LSTM).
Drawback: Sequential generation is slow in both training and inference!
"},{"location":"AI/CS231n/CS231n_notes/#pixelcnn","title":"PixelCNN","text":"Still generate image pixels starting from corner.
Dependency on previous pixels modeled using a CNN over context region (masked convolution).
Drawback: Though its training is faster, its generation is still slow. (pixel by pixel)
"},{"location":"AI/CS231n/CS231n_notes/#variational-autoencoder","title":"Variational Autoencoder","text":"Supplement content added according to Tutorial on Variational Autoencoders. (paper with notes: VAE Tutorial.pdf)
\u53d8\u5206\u81ea\u7f16\u7801\u5668VAE\uff1a\u539f\u6765\u662f\u8fd9\u4e48\u4e00\u56de\u4e8b | \u9644\u5f00\u6e90\u4ee3\u7801
"},{"location":"AI/CS231n/CS231n_notes/#autoencoder","title":"Autoencoder","text":"Learn a lower-dimensional feature representation with unsupervised approaches.
\\(x\\rightarrow z\\) : Dimension reduction for input features.
\\(z\\rightarrow \\hat{x}\\) : Reconstruct input features.
After training, we throw the decoder away and use the encoder for transferring.
For generative models, there is a problem:
We can\u2019t generate new images from an autoencoder because we don\u2019t know the space of \\(z\\).
"},{"location":"AI/CS231n/CS231n_notes/#variational-autoencoder_1","title":"Variational Autoencoder","text":""},{"location":"AI/CS231n/CS231n_notes/#character-description","title":"Character Description","text":"\\(X\\) : Images. (random variable)
\\(Z\\) : Latent representations. (random variable)
\\(P(X)\\) : True distribution of all training images \\(X\\).
\\(P(Z)\\) : True distribution of all latent representations \\(Z\\).
\\(P(X|Z)\\) : True posterior distribution of all images \\(X\\) with condition \\(Z\\).
\\(P(Z|X)\\) : True prior distribution of all latent representations \\(Z\\) with condition \\(X\\).
\\(Q(Z|X)\\) : Approximated prior distribution of all latent representations \\(Z\\) with condition \\(X\\).
\\(x\\) : A specific image.
\\(z\\) : A specific latent representation.
\\(\\theta\\): Learned parameters in decoder network.
\\(\\phi\\): Learned parameters in encoder network.
\\(p_\\theta(x)\\) : Probability that \\(x\\sim P(X)\\).
\\(p_\\theta(z)\\) : Probability that \\(z\\sim P(Z)\\).
\\(p_\\theta(x|z)\\) : Probability that \\(x\\sim P(X|Z)\\).
\\(p_\\theta(z|x)\\) : Probability that \\(z\\sim P(Z|X)\\).
\\(q_\\phi(z|x)\\) : Probability that \\(z\\sim Q(Z|X)\\).
"},{"location":"AI/CS231n/CS231n_notes/#decoder","title":"Decoder","text":"Objective:
Generate new images from \\(\\mathscr{z}\\).
- Generate a value \\(z^{(i)}\\) from the prior distribution \\(P(Z)\\).
- Generate a value \\(x^{(i)}\\) from the conditional distribution \\(P(X|Z)\\).
Lemma:
Any distribution in \\(d\\) dimensions can be generated by taking a set of \\(d\\) variables that are normally distributed and mapping them through a sufficiently complicated function. (source: Tutorial on Variational Autoencoders, Page 6)
Solutions:
- Choose prior distribution \\(P(Z)\\) to be a simple distribution, for example \\(P(Z)\\sim N(0,1)\\).
- Learn the conditional distribution \\(P(X|Z)\\) through a neural network (decoder) with parameter \\(\\theta\\).
"},{"location":"AI/CS231n/CS231n_notes/#encoder","title":"Encoder","text":"Objective:
Learn \\(\\mathscr{z}\\) with training images.
Given: (From the decoder, we can deduce the following probabilities.)
- data likelihood: \\(p_\\theta(x)=\\int p_\\theta(x|z)p_\\theta(z)dz\\).
- posterior density: \\(p_\\theta(z|x)=\\frac{p_\\theta(x|z)p_\\theta(z)}{p_\\theta(x)}=\\frac{p_\\theta(x|z)p_\\theta(z)}{\\int p_\\theta(x|z)p_\\theta(z)dz}\\).
Problem:
Both \\(p_\\theta(x)\\) and \\(p_\\theta(z|x)\\) are intractable. (can't be optimized directly as they contain integral operation)
Solution:
Learn \\(Q(Z|X)\\) to approximate the true posterior \\(P(Z|X)\\).
Use \\(q_\\phi(z|x)\\) in place of \\(p_\\theta(z|x)\\).
"},{"location":"AI/CS231n/CS231n_notes/#variational-autoencoder-combination-of-encoder-and-decoder","title":"Variational Autoencoder (Combination of Encoder and Decoder)","text":"Objective:
Maximize \\(p_\\theta(x)\\) for all \\(x^{(i)}\\) in the training set.
$$ \\begin{aligned} \\log p_\\theta\\big(x^{(i)}\\big)&=\\mathbb{E}{z\\sim q\\phi\\big(z|x^{(i)}\\big)}\\Big[\\log p_\\theta\\big(x^{(i)}\\big)\\Big]\\
&=\\mathbb{E}z\\Bigg[\\log\\frac{p\\theta\\big(x^{(i)}|z\\big)p_\\theta\\big(z\\big)}{p_\\theta\\big(z|x^{(i)}\\big)}\\Bigg]\\quad\\text{(Bayes' Rule)}\\
&=\\mathbb{E}z\\Bigg[\\log\\frac{p\\theta\\big(x^{(i)}|z\\big)p_\\theta\\big(z\\big)}{p_\\theta\\big(z|x^{(i)}\\big)}\\frac{q_\\phi\\big(z|x^{(i)}\\big)}{q_\\phi\\big(z|x^{(i)}\\big)}\\Bigg]\\quad\\text{(Multiply by Constant)}\\
&=\\mathbb{E}z\\Big[\\log p\\theta\\big(x^{(i)}|z\\big)\\Big]-\\mathbb{E}z\\Bigg[\\log\\frac{q\\phi\\big(z|x^{(i)}\\big)}{p_\\theta\\big(z\\big)}\\Bigg]+\\mathbb{E}z\\Bigg[\\log\\frac{p\\theta\\big(z|x^{(i)}\\big)}{q_\\phi\\big(z|x^{(i)}\\big)}\\Bigg]\\quad\\text{(Logarithm)}\\
&=\\mathbb{E}z\\Big[\\log p\\theta\\big(x^{(i)}|z\\big)\\Big]-D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big]+D_{\\text{KL}}\\Big[p_\\theta\\big(z|x^{(i)}\\big)||q_\\phi\\big(z|x^{(i)}\\big)\\Big]\\quad\\text{(KL Divergence)} \\end{aligned} $$
Analyze the Formula by Term:
\\(\\mathbb{E}_z\\Big[\\log p_\\theta\\big(x^{(i)}|z\\big)\\Big]\\): Decoder network gives \\(p_\\theta\\big(x^{(i)}|z\\big)\\), can compute estimate of this term through sampling.
\\(D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big]\\): This KL term (between Gaussians for encoder and \\(z\\) prior) has nice closed-form solution!
\\(D_{\\text{KL}}\\Big[p_\\theta\\big(z|x^{(i)}\\big)||q_\\phi\\big(z|x^{(i)}\\big)\\Big]\\): The part \\(p_\\theta\\big(z|x^{(i)}\\big)\\) is intractable. However, we know KL divergence always \\(\\ge0\\).
Tractable Lower Bound:
We can maximize the lower bound of that formula.
As \\(D_{\\text{KL}}\\Big[p_\\theta\\big(z|x^{(i)}\\big)||q_\\phi\\big(z|x^{(i)}\\big)\\Big]\\ge0\\) , we can deduce that:
$$ \\begin{aligned} \\log p_\\theta\\big(x^{(i)}\\big)&=\\mathbb{E}z\\Big[\\log p\\theta\\big(x^{(i)}|z\\big)\\Big]-D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big]+D_{\\text{KL}}\\Big[p_\\theta\\big(z|x^{(i)}\\big)||q_\\phi\\big(z|x^{(i)}\\big)\\Big]\\
&\\ge\\mathbb{E}z\\Big[\\log p\\theta\\big(x^{(i)}|z\\big)\\Big]-D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big] \\end{aligned} $$
So the loss function \\(\\mathcal{L}\\big(x^{(i)},\\theta,\\phi\\big)=-\\mathbb{E}_z\\Big[\\log p_\\theta\\big(x^{(i)}|z\\big)\\Big]+D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big]\\).
\\(\\mathbb{E}_z\\Big[\\log p_\\theta\\big(x^{(i)}|z\\big)\\Big]\\): Decoder, reconstruct the input data.
\\(D_{\\text{KL}}\\Big[q_\\phi\\big(z|x^{(i)}\\big)||p_\\theta\\big(z\\big)\\Big]\\): Encoder, make approximate posterior distribution close to prior.
"},{"location":"AI/CS231n/CS231n_notes/#generative-adversarial-networks-gans","title":"Generative Adversarial Networks (GANs)","text":""},{"location":"AI/CS231n/CS231n_notes/#motivation-modeling","title":"Motivation & Modeling","text":"Objective: Not modeling any explicit density function.
Problem: Want to sample from complex, high-dimensional training distribution. No direct way to do this!
Solution: Sample from a simple distribution, e.g. random noise. Learn the transformation to training distribution.
Problem: We can't learn the mapping relation between sample \\(z\\) and training images.
Solution: Use a discriminator network to tell whether the generate image is within data distribution or not.
Discriminator network: Try to distinguish between real and fake images.
Generator network: Try to fool the discriminator by generating real-looking images.
\\(x\\) : Real data.
\\(y\\) : Fake data, which is generated by the generator network. \\(y=G_{\\theta_g}(z)\\).
\\(D_{\\theta_d}(x)\\) : Discriminator score, which is the likelihood of real image. \\(D_{\\theta_d}(x)\\in[0,1]\\).
Objective of discriminator network:
\\(\\max_{\\theta_d}\\bigg[\\mathbb{E}_x\\Big(\\log D_{\\theta_d}(x)\\Big)+\\mathbb{E}_{z\\sim p(z)}\\Big(\\log\\big(1-D_{\\theta_d}(y)\\big)\\Big)\\bigg]\\)
Objective of generator network:
\\(\\min_{\\theta_g}\\max_{\\theta_d}\\bigg[\\mathbb{E}_x\\Big(\\log D_{\\theta_d}(x)\\Big)+\\mathbb{E}_{z\\sim p(z)}\\Big(\\log\\big(1-D_{\\theta_d}(y)\\big)\\Big)\\bigg]\\)
"},{"location":"AI/CS231n/CS231n_notes/#training-strategy","title":"Training Strategy","text":"Two combine this two networks together, we can train them alternately:
- Gradient ascent on discriminator.
\\(\\max_{\\theta_d}\\bigg[\\mathbb{E}_x\\Big(\\log D_{\\theta_d}(x)\\Big)+\\mathbb{E}_{z\\sim p(z)}\\Big(\\log\\big(1-D_{\\theta_d}(y)\\big)\\Big)\\bigg]\\)
- Gradient descent on generator.
\\(\\min_{\\theta_g}\\bigg[\\mathbb{E}_{z\\sim p(z)}\\Big(\\log\\big(1-D_{\\theta_d}(y)\\big)\\Big)\\bigg]\\)
However, the gradient of generator decreases with the value itself, making it hard to optimize.
So we replace \\(\\log\\big(1-D_{\\theta_d}(y)\\big)\\) with \\(-\\log D_{\\theta_d}(y)\\), and use gradient ascent instead.
- Gradient ascent on discriminator.
\\(\\max_{\\theta_d}\\bigg[\\mathbb{E}_x\\Big(\\log D_{\\theta_d}(x)\\Big)+\\mathbb{E}_{z\\sim p(z)}\\Big(\\log\\big(1-D_{\\theta_d}(y)\\big)\\Big)\\bigg]\\)
- Gradient ascent on generator.
\\(\\max_{\\theta_g}\\bigg[\\mathbb{E}_{z\\sim p(z)}\\Big(\\log D_{\\theta_d}(y)\\Big)\\bigg]\\)
"},{"location":"AI/CS231n/CS231n_notes/#summary_3","title":"Summary","text":"Pros: Beautiful, state-of-the-art samples!
Cons:
- Trickier / more unstable to train.
- Can\u2019t solve inference queries such as \\(p(x), p(z|x)\\).
"},{"location":"AI/CS231n/CS231n_notes/#14-self-supervised-learning","title":"14 - Self-supervised Learning","text":"Aim: Solve \u201cpretext\u201d tasks that produce good features for downstream tasks.
Application:
- Learn a feature extractor from pretext tasks. (self-supervised)
- Attach a shallow network on the feature extractor.
- Train the shallow network on target task with small amount of labeled data. (supervised)
"},{"location":"AI/CS231n/CS231n_notes/#pretext-tasks","title":"Pretext Tasks","text":"Labels are generated automatically.
"},{"location":"AI/CS231n/CS231n_notes/#rotation","title":"Rotation","text":"Train a classifier on randomly rotated images.
"},{"location":"AI/CS231n/CS231n_notes/#rearrangement","title":"Rearrangement","text":"Train a classifier on randomly shuffled image pieces.
Predict the location of image pieces.
"},{"location":"AI/CS231n/CS231n_notes/#inpainting","title":"Inpainting","text":"Mask part of the image, train a network to predict the masked area.
Method referencing Context Encoders: Feature Learning by Inpainting.
Combine two types of loss together to get better performance:
- Reconstruction loss (L2 loss): Used for reconstructing global features.
- Adversarial loss: Used for generating texture features.
"},{"location":"AI/CS231n/CS231n_notes/#coloring","title":"Coloring","text":"Transfer between greyscale images and colored images.
Cross-channel predictions for images: Split-Brain Autoencoders.
Video coloring: Establish mappings between reference and target frames in a learned feature space. Tracking Emerges by Colorizing Videos.
"},{"location":"AI/CS231n/CS231n_notes/#summary-for-pretext-tasks","title":"Summary for Pretext Tasks","text":" - Pretext tasks focus on \u201cvisual common sense\u201d.
- The models are forced learn good features about natural images.
- We don\u2019t care about the performance of these pretext tasks.
What we care is the performance of downstream tasks.
"},{"location":"AI/CS231n/CS231n_notes/#problems-of-specific-pretext-tasks","title":"Problems of Specific Pretext Tasks","text":" - Coming up with individual pretext tasks is tedious.
- The learned representations may not be general.
Intuitive Solution: Contrastive Learning.
"},{"location":"AI/CS231n/CS231n_notes/#contrastive-representation-learning","title":"Contrastive Representation Learning","text":"Local additional references: Contrastive Learning.md.
Objective:
Given a chosen score function \\(s\\), we aim to learn an encoder function \\(f\\) that yields:
- For each sample \\(x\\), increase the similarity \\(s\\big(f(x),f(x^+)\\big)\\) between \\(x\\) and positive samples \\(x^+\\).
- Finally we want \\(s\\big(f(x),f(x^+)\\big)\\gg s\\big(f(x),f(x^-)\\big)\\).
Loss Function:
Given \\(1\\) positive sample and \\(N-1\\) negative samples:
InfoNCE Loss Cross Entropy Loss \\(\\begin{aligned}\\mathcal{L}=-\\mathbb{E}_X\\Bigg[\\log\\frac{\\exp{s\\big(f(x),f(x^+)\\big)}}{\\exp{s\\big(f(x),f(x^+)\\big)}+\\sum_{j=1}^{N-1}\\exp{s\\big(f(x),f(x^+)\\big)}}\\Bigg]\\\\\\end{aligned}\\) \\(\\begin{aligned}\\mathcal{L}&=-\\sum_{i=1}^Np(x_i)\\log q(x_i)\\\\&=-\\mathbb{E}_X\\big[\\log q(x)\\big]\\\\&=-\\mathbb{E}_X\\Bigg[\\log\\frac{\\exp(x)}{\\sum_{j=1}^N\\exp(x_j)}\\Bigg]\\end{aligned}\\) The InfoNCE Loss is a lower bound on the mutual information between \\(f(x)\\) and \\(f(x^+)\\):
\\(\\text{MI}\\big[f(x),f(x^+)\\big]\\ge\\log(N)-\\mathcal{L}\\)
The larger the negative sample size \\(N\\), the tighter the bound.
So we use \\(N-1\\) negative samples.
"},{"location":"AI/CS231n/CS231n_notes/#instance-contrastive-learning","title":"Instance Contrastive Learning","text":""},{"location":"AI/CS231n/CS231n_notes/#simclr","title":"SimCLR","text":"Use a projection function \\(g(\\cdot)\\) to project features to a space where contrastive learning is applied.
The extra projection contributes a lot to the final performance.
Score Function: Cos similarity \\(s(u,v)=\\frac{u^Tv}{||u||||v||}\\\\\\).
Positive Pair: Pair of augmented data.
"},{"location":"AI/CS231n/CS231n_notes/#momentum-contrastive-learning-moco","title":"Momentum Contrastive Learning (MoCo)","text":"There are mainly \\(3\\) training strategy in contrastive learning:
- end-to-end: Keys are updated together with queries, e.g. SimCLR.
(limited by GPU size)
- memory bank: Store last-time keys for sampling.
(inconsistency between \\(q\\) and \\(k\\))
- MoCo: Use momentum methods to encode keys.
(combination of end-to-end & memory bank)
Key differences to SimCLR:
- Keep a running queue of keys (negative samples).
- Compute gradients and update the encoder only through the queries.
- Decouple min-batch size with the number of keys: can support a large number of negative samples.
- The key encoder is slowly progressing through the momentum update rules:
\\(\\theta_k\\leftarrow m\\theta_k+(1-m)\\theta_q\\)
"},{"location":"AI/CS231n/CS231n_notes/#sequence-contrastive-learning","title":"Sequence Contrastive Learning","text":""},{"location":"AI/CS231n/CS231n_notes/#contrastive-predictive-coding-cpc","title":"Contrastive Predictive Coding (CPC)","text":"Contrastive: Contrast between \u201cright\u201d and \u201cwrong\u201d sequences using contrastive learning.
Predictive: The model has to predict future patterns given the current context.
Coding: The model learns useful feature vectors, or \u201ccode\u201d, for downstream tasks, similar to other self-supervised methods.
"},{"location":"AI/CS231n/CS231n_notes/#other-examples-frontier","title":"Other Examples (Frontier)","text":""},{"location":"AI/CS231n/CS231n_notes/#contrastive-language-image-pre-training-clip","title":"Contrastive Language Image Pre-training (CLIP)","text":"Contrastive learning between image and natural language sentences.
"},{"location":"AI/CS231n/CS231n_notes/#15-low-level-vision","title":"15 - Low-Level Vision","text":"Pass...
"},{"location":"AI/CS231n/CS231n_notes/#16-3d-vision","title":"16 - 3D Vision","text":""},{"location":"AI/CS231n/CS231n_notes/#representation","title":"Representation","text":""},{"location":"AI/CS231n/CS231n_notes/#explicit-vs-implicit","title":"Explicit vs Implicit","text":"Explicit: Easy to sample examples, hard to do inside/outside check.
Implicit: Hard to sample examples, easy to do inside/outside check.
Non-parametric Parametric Explicit Points.Meshes. Splines.Subdivision Surfaces. Implicit Level Sets.Voxels. Algebraic Surfaces.Constructive Solid Geometry."},{"location":"AI/CS231n/CS231n_notes/#point-clouds","title":"Point Clouds","text":"The simplest representation.
Collection of \\((x,y,z)\\) coordinates.
Cons:
- Difficult to draw in under-sampled regions.
- No simplification or subdivision.
- No direction smooth rendering.
- No topological information.
"},{"location":"AI/CS231n/CS231n_notes/#polygonal-meshes","title":"Polygonal Meshes","text":"Collection of vertices \\(v\\) and edges \\(e\\).
Pros:
- Can apply downsampling or upsampling on meshes.
- Error decreases by \\(O(n^2)\\) while meshes increase by \\(O(n)\\).
- Can approximate arbitrary topology.
- Efficient rendering.
"},{"location":"AI/CS231n/CS231n_notes/#splines","title":"Splines","text":"Use specific functions to approximate the surface. (e.g. B\u00e9zier Curves)
"},{"location":"AI/CS231n/CS231n_notes/#algebraic-surfaces","title":"Algebraic Surfaces","text":"Use specific functions to represent the surface.
"},{"location":"AI/CS231n/CS231n_notes/#constructive-solid-geometry","title":"Constructive Solid Geometry","text":"Combine implicit geometry with Boolean operations.
"},{"location":"AI/CS231n/CS231n_notes/#level-sets","title":"Level Sets","text":"Store a grim of values to approximate the function.
Surface is found where interpolated value equals to \\(0\\).
"},{"location":"AI/CS231n/CS231n_notes/#voxels","title":"Voxels","text":"Binary thresholding the volumetric grid.
"},{"location":"AI/CS231n/CS231n_notes/#ai-3d","title":"AI + 3D","text":"Pass...
"},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/","title":"Image Classification-Data-driven Approach, k-Nearest Neighbor, train_val_test splits","text":""},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#image-classification-data-driven-approach-k-nearest-neighbor-train_val_test-splits","title":"Image Classification-Data-driven Approach, k-Nearest Neighbor, train_val_test splits","text":" \u7ea6 651 \u4e2a\u5b57 28 \u884c\u4ee3\u7801 2 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 4 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#image-classification","title":"image classification","text":" - challenges
- viewpoint variation
- scale variation
- deformation
- occlusion
- illumination conditions
- background clutter
- intra-class variation
- data-driven approach
- the image classification pipeline
- input
- learning
- training a classifier
- learning a model
- evaluation
"},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#nearest-neighbor-classifier","title":"Nearest Neighbor Classifier","text":"\\[ d_1 (I_1, I_2) = \\sum_{p} \\left| I^p_1 - I^p_2 \\right| \\] Pythonimport numpy as np\n\nclass NearestNeighbor(object): \n def **init**(self): \n pass\n\n def train(self, X, y): \n \"\"\" X is N x D where each row is an example. Y is 1-dimension of size N \"\"\" \n # the nearest neighbor classifier simply remembers all the training data \n self.Xtr = X \n self.ytr = y\n\n def predict(self, X): \n \"\"\" X is N x D where each row is an example we wish to predict label for \"\"\" \n num_test = X.shape[0] \n # lets make sure that the output type matches the input type \n Ypred = np.zeros(num_test, dtype = self.ytr.dtype)\n\n # loop over all test rows\n for i in range(num_test):\n # find the nearest training image to the i'th test image\n # using the L1 distance (sum of absolute value differences)\n distances = np.sum(np.abs(self.Xtr - X[i,:]), axis = 1)\n min_index = np.argmin(distances) # get the index with smallest distance\n Ypred[i] = self.ytr[min_index] # predict the label of the nearest example\n\n return Ypred\n
\\[ d_2 (I_1, I_2) = \\sqrt{\\sum_{p} \\left( I^p_1 - I^p_2 \\right)^2} \\] Pythondistances = np.sqrt(np.sum(np.square(self.Xtr - X[i,:]), axis = 1))\n
"},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#k-nearest-neighbor-classifier","title":"k - Nearest Neighbor Classifier","text":""},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#validation-sets-for-hyperparameter-tuning","title":"Validation sets for Hyperparameter tuning","text":"Evaluate on the test only a single time, at the very end
Split your training set into training set and a validation set. Use validation set to tune all hyperparameters. At the end run a single time on the test set and report performance.
- cross-validation
- single calidation split
"},{"location":"AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/#pros-and-cons-of-nearest-neighbor-classifier","title":"Pros and Cons of Nearest Neighbor classifier","text":" - simple to implement and understand
- take no time to train
- however, pay a cost at test time
As an aside, the computational complexity of the Nearest Neighbor classifier is an active area of research, and several\u00a0Approximate Nearest Neighbor\u00a0(ANN) algorithms and libraries exist that can accelerate the nearest neighbor lookup in a dataset (e.g.\u00a0FLANN). These algorithms allow one to trade off the correctness of the nearest neighbor retrieval with its space/time complexity during retrieval, and usually rely on a pre-processing/indexing stage that involves building a kdtree, or running the k-means algorithm.
- \\(\\displaystyle L_{2}\\) isn't enough sensitive
In particular, note that images that are nearby each other are much more a function of the general color distribution of the images, or the type of background rather than their semantic identity.
Applying kNN in practice - Preprocess your data: Normalize the features in your data (e.g. one pixel in images) to have zero mean and unit variance. We will cover this in more detail in later sections, and chose not to cover data normalization in this section because pixels in images are usually homogeneous and do not exhibit widely different distributions, alleviating the need for data normalization.
- If your data is very high-dimensional, consider using a dimensionality reduction technique such as PCA (wiki ref,\u00a0CS229ref,\u00a0blog ref), NCA (wiki ref,\u00a0blog ref), or even\u00a0Random Projections.
- Split your training data randomly into train/val splits. As a rule of thumb, between 70-90% of your data usually goes to the train split. This setting depends on how many hyperparameters you have and how much of an influence you expect them to have. If there are many hyperparameters to estimate, you should err on the side of having larger validation set to estimate them effectively. If you are concerned about the size of your validation data, it is best to split the training data into folds and perform cross-validation. If you can afford the computational budget it is always safer to go with cross-validation (the more folds the better, but more expensive).
- Train and evaluate the kNN classifier on the validation data (for all folds, if doing cross-validation) for many choices of\u00a0k\u00a0(e.g. the more the better) and across different distance types (L1 and L2 are good candidates)
- If your kNN classifier is running too long, consider using an Approximate Nearest Neighbor library (e.g.\u00a0FLANN) to accelerate the retrieval (at cost of some accuracy).
- Take note of the hyperparameters that gave the best results. There is a question of whether you should use the full training set with the best hyperparameters, since the optimal hyperparameters might change if you were to fold the validation data into your training set (since the size of the data would be larger). In practice it is cleaner to not use the validation data in the final classifier and consider it to be\u00a0burned\u00a0on estimating the hyperparameters. Evaluate the best model on the test set. Report the test set accuracy and declare the result to be the performance of the kNN classifier on your data.
more about Machine Learing
"},{"location":"AI/CS231n/Linear%20classification-Support%20Vector%20Machine%2C%20Softmax/","title":"Linear classification-Support Vector Machine, Softmax","text":""},{"location":"AI/CS231n/Linear%20classification-Support%20Vector%20Machine%2C%20Softmax/#linear-classification-support-vector-machine-softmax","title":"Linear classification-Support Vector Machine, Softmax","text":" \u7ea6 126 \u4e2a\u5b57 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/CS231n/Linear%20classification-Support%20Vector%20Machine%2C%20Softmax/#linear-classifiaction","title":"Linear Classifiaction","text":"\\[ L_i = \\sum_{j\\neq y_i} \\max(0, s_j - s_{y_i} + \\Delta) \\] \\[ L = \\frac{1}{N} \\sum_i \\sum_{j\\neq y_i} \\left[ \\max(0, f(x_i; W)_j - f(x_i; W)_{y_i} + \\Delta) \\right] + \\lambda \\sum_k\\sum_l W_{k,l}^2 \\] \\[ L_i = -\\log\\left(\\frac{e^{f_{y_i}}}{ \\sum_j e^{f_j} }\\right) \\hspace{0.5in} \\text{or equivalently} \\hspace{0.5in} L_i = -f_{y_i} + \\log\\sum_j e^{f_j} \\] \\[ \\frac{e^{f_{y_i}}}{\\sum_j e^{f_j}} = \\frac{Ce^{f_{y_i}}}{C\\sum_j e^{f_j}} = \\frac{e^{f_{y_i} + \\log C}}{\\sum_j e^{f_j + \\log C}} \\]"},{"location":"AI/CS231n/Numpy/","title":"Numpy","text":""},{"location":"AI/CS231n/Numpy/#python","title":"Python","text":" \u7ea6 49 \u4e2a\u5b57 104 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 2 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/CS231n/Numpy/#string","title":"string","text":"Pythons = \"hello\"\nprint(s.capitalize()) # Capitalize a string; prints \"Hello\"\nprint(s.upper()) # Convert a string to uppercase; prints \"HELLO\"\nprint(s.rjust(7)) # Right-justify a string, padding with spaces; prints \" hello\"\nprint(s.center(7)) # Center a string, padding with spaces; prints \" hello \"\nprint(s.replace('l', '(ell)')) # Replace all instances of one substring with another;\n # prints \"he(ell)(ell)o\"\nprint(' world '.strip()) # Strip leading and trailing whitespace; prints \"world\"\n
"},{"location":"AI/CS231n/Numpy/#containers","title":"Containers","text":"Pythonanimals = ['cat', 'dog', 'monkey']\nfor idx, animal in enumerate(animals):\n print('#%d: %s' % (idx + 1, animal))\n# Prints \"#1: cat\", \"#2: dog\", \"#3: monkey\", each on its own line\n
\u5217\u8868\u63a8\u5bfc\u5f0f
Pythonnums = [0, 1, 2, 3, 4]\neven_squares = [x ** 2 for x in nums if x % 2 == 0]\nprint(even_squares) # Prints \"[0, 4, 16]\"\n
\u540c\u6837\u4e5f\u6709\u5b57\u5178\u63a8\u5bfc\u5f0f
Tuples \u53ef\u4ee5\u7528\u4f5c\u5b57\u5178\u4e2d\u7684\u952e\u548c\u96c6\u5408\u7684\u5143\u7d20\uff0c\u4f46\u662f lists \u4e0d\u80fd
"},{"location":"AI/CS231n/Numpy/#numpy","title":"Numpy","text":"Pythonimport numpy as np\n\n# Create a new array from which we will select elements\na = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n\nprint(a) # prints \"array([[ 1, 2, 3],\n # [ 4, 5, 6],\n # [ 7, 8, 9],\n # [10, 11, 12]])\"\n\n# Create an array of indices\nb = np.array([0, 2, 0, 1])\n\n# Select one element from each row of a using the indices in b\nprint(a[np.arange(4), b]) # Prints \"[ 1 6 7 11]\"\n\n# Mutate one element from each row of a using the indices in b\na[np.arange(4), b] += 10\n\nprint(a) # prints \"array([[11, 2, 3],\n # [ 4, 5, 16],\n # [17, 8, 9],\n # [10, 21, 12]])\n
Pythonimport numpy as np\n\na = np.array([[1,2], [3, 4], [5, 6]])\n\nbool_idx = (a > 2) # Find the elements of a that are bigger than 2;\n # this returns a numpy array of Booleans of the same\n # shape as a, where each slot of bool_idx tells\n # whether that element of a is > 2.\n\nprint(bool_idx) # Prints \"[[False False]\n # [ True True]\n # [ True True]]\"\n\n# We use boolean array indexing to construct a rank 1 array\n# consisting of the elements of a corresponding to the True values\n# of bool_idx\nprint(a[bool_idx]) # Prints \"[3 4 5 6]\"\n\n# We can do all of the above in a single concise statement:\nprint(a[a > 2]) # Prints \"[3 4 5 6]\"\n
Pythonx = np.array([1, 2], dtype=np.int64) # Force a particular datatype\nprint(x.dtype) # Prints \"int64\"\n
Pythonimport numpy as np\n\nx = np.array([[1,2],[3,4]], dtype=np.float64)\ny = np.array([[5,6],[7,8]], dtype=np.float64)\n\n# Elementwise sum; both produce the array\n# [[ 6.0 8.0]\n# [10.0 12.0]]\nprint(x + y)\nprint(np.add(x, y))\n\n# Elementwise difference; both produce the array\n# [[-4.0 -4.0]\n# [-4.0 -4.0]]\nprint(x - y)\nprint(np.subtract(x, y))\n\n# Elementwise product; both produce the array\n# [[ 5.0 12.0]\n# [21.0 32.0]]\nprint(x * y)\nprint(np.multiply(x, y))\n\n# Elementwise division; both produce the array\n# [[ 0.2 0.33333333]\n# [ 0.42857143 0.5 ]]\nprint(x / y)\nprint(np.divide(x, y))\n\n# Elementwise square root; produces the array\n# [[ 1. 1.41421356]\n# [ 1.73205081 2. ]]\nprint(np.sqrt(x))\n
\u5e7f\u64ad\u53ef\u4ee5\u907f\u514d\u5faa\u73af
Pythonimport numpy as np\n\n# We will add the vector v to each row of the matrix x,\n# storing the result in the matrix y\nx = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\nv = np.array([1, 0, 1])\ny = x + v # Add v to each row of x using broadcasting\nprint(y) # Prints \"[[ 2 2 4]\n # [ 5 5 7]\n # [ 8 8 10]\n # [11 11 13]]\"\n
"},{"location":"AI/CS231n/Numpy/#scipy","title":"SciPy","text":""},{"location":"AI/CS231n/Numpy/#matplotlib","title":"Matplotlib","text":""},{"location":"AI/EECS%20498-007/KNN/","title":"KNN","text":"\u5bf9\u4e8e\u4e00\u4e2a\u5f85\u5206\u7c7b\u7684\u6837\u672c\uff0c\u627e\u5230\u8bad\u7ec3\u6570\u636e\u96c6\u4e2d\u4e0e\u5176\u6700\u63a5\u8fd1\u7684K\u4e2a\u6837\u672c\uff08\u5373\u6700\u8fd1\u90bb\uff09\uff0c\u7136\u540e\u6839\u636e\u8fd9K\u4e2a\u6837\u672c\u7684\u7c7b\u522b\u6765\u51b3\u5b9a\u5f85\u5206\u7c7b\u6837\u672c\u7684\u7c7b\u522b\u3002
\u7ea6 374 \u4e2a\u5b57 100 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 3 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/EECS%20498-007/KNN/#_1","title":"\u6570\u5b66\u63a8\u5bfc","text":"\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u8bad\u7ec3\u6570\u636e\u96c6 \\(T = \\{(x_1, y_1), (x_2, y_2), \\ldots, (x_N, y_N)\\}\\)\uff0c\u5176\u4e2d \\(x_i\\) \u662f\u7279\u5f81\u5411\u91cf\uff0c \\(y_i\\) \u662f\u5bf9\u5e94\u7684\u7c7b\u522b\u6807\u7b7e\u3002\u5bf9\u4e8e\u4e00\u4e2a\u65b0\u7684\u5f85\u5206\u7c7b\u6837\u672c x\uff0cKNN\u7b97\u6cd5\u7684\u76ee\u6807\u662f\u9884\u6d4b\u5176\u7c7b\u522b \\(y\\) \u3002
- \u8ddd\u79bb\u5ea6\u91cf\uff1a\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u4e00\u4e2a\u8ddd\u79bb\u5ea6\u91cf\u6765\u8ba1\u7b97\u5f85\u5206\u7c7b\u6837\u672c \\(x\\) \u4e0e\u8bad\u7ec3\u96c6\u4e2d\u6bcf\u4e2a\u6837\u672c \\(x_i\\) \u4e4b\u95f4\u7684\u8ddd\u79bb\u3002\u5e38\u7528\u7684\u8ddd\u79bb\u5ea6\u91cf\u5305\u62ec\u6b27\u6c0f\u8ddd\u79bb\uff08Euclidean distance\uff09\u3001\u66fc\u54c8\u987f\u8ddd\u79bb\uff08Manhattan distance\uff09\u548c\u95f5\u53ef\u592b\u65af\u57fa\u8ddd\u79bb\uff08Minkowski distance\uff09\u3002\u4ee5\u6b27\u6c0f\u8ddd\u79bb\u4e3a\u4f8b\uff0c\u4e24\u4e2a\u6837\u672c \\(x\\) \u548c \\(x_i\\) \u4e4b\u95f4\u7684\u8ddd\u79bb\u5b9a\u4e49\u4e3a\uff1a
\\[ d(x, x_i) = \\sqrt{\\sum_{j=1}^{d} (x_j - x_{i,j})^2} \\] \u5176\u4e2d\uff0c \\(d\\) \u662f\u7279\u5f81\u7684\u7ef4\u5ea6\u3002
- \u5bfb\u627e\u6700\u8fd1\u90bb\uff1a\u7136\u540e\uff0c\u6211\u4eec\u6839\u636e\u8ba1\u7b97\u51fa\u7684\u8ddd\u79bb\uff0c\u9009\u62e9\u8ddd\u79bb\u6700\u8fd1\u7684K\u4e2a\u6837\u672c\uff0c\u6784\u6210\u5f85\u5206\u7c7b\u6837\u672c\u7684\u90bb\u57df \\(N_k(x)\\)\u3002
- \u51b3\u7b56\u89c4\u5219\uff1a\u6700\u540e\uff0c\u6839\u636e\u90bb\u57df \\( N_k(x) \\) \u4e2d\u7684\u6837\u672c\u7c7b\u522b\uff0c\u901a\u8fc7\u591a\u6570\u6295\u7968\u7684\u65b9\u5f0f\u6765\u51b3\u5b9a\u5f85\u5206\u7c7b\u6837\u672c\u7684\u7c7b\u522b\u3002\u5373\uff1a
\\[ y = \\arg\\max_{c_j} \\sum_{x_i \\in N_k(x)} I(y_i = c_j) \\] \u5176\u4e2d\uff0c \\(I\\) \u662f\u6307\u793a\u51fd\u6570\uff0c\u5f53 \\(y_i = c_j\\) \u65f6\u53d6\u503c\u4e3a1\uff0c\u5426\u5219\u4e3a0\u3002
"},{"location":"AI/EECS%20498-007/KNN/#_2","title":"\u4f5c\u4e1a\u4e2d\u7684\u5b9e\u73b0","text":"Pythonimport torch\n\ndef compute_distances_two_loops(x_train, x_test):\n num_train = x_train.shape[0]\n num_test = x_test.shape[0]\n dists = x_train.new_zeros(num_train, num_test)\n\n for i in range(num_train):\n for j in range(num_test):\n dists[i,j] = ((x_train[i] - x_test[j]) ** 2).sum() ** (1/2)\n\n return dists\n\ndef compute_distances_one_loop(x_train, x_test):\n num_train = x_train.shape[0]\n num_test = x_test.shape[0]\n dists = x_train.new_zeros(num_train, num_test)\n\n for i in range(num_train):\n dists[i] = ((x_train[i] - x_test) ** 2).sum(dim=(1,2,3)) ** (1/2)\n\n return dists\n\ndef compute_distances_no_loops(x_train, x_test):\n num_train = x_train.shape[0]\n num_test = x_test.shape[0]\n dists = x_train.new_zeros(num_train, num_test)\n\n A = x_train.reshape(num_train, -1)\n B = x_test.reshape(num_test, -1)\n AB2 = A.mm(B.T) * 2\n dists = ((A ** 2).sum(dim=1).reshape(-1, 1) - AB2 + (B ** 2).sum(dim=1).reshape(1, -1)) ** (1/2)\n\n return dists\n\ndef predict_labels(dists, y_train, k=1):\n num_train, num_test = dists.shape\n y_pred = torch.zeros(num_test, dtype=torch.int64)\n\n values, indices = torch.topk(dists, k, dim=0, largest=False)\n for i in range(indices.shape[1]):\n _, idx = torch.max(y_train[indices[:, i]].bincount(), dim=0)\n y_pred[i] = idx\n\n return y_pred\n\nclass KnnClassifier:\n def __init__(self, x_train, y_train):\n self.x_train = x_train\n self.y_train = y_train\n\n def predict(self, x_test, k=1):\n dists = compute_distances_no_loops(self.x_train, x_test)\n y_test_pred = predict_labels(dists, self.y_train, k)\n return y_test_pred\n\n def check_accuracy(self, x_test, y_test, k=1, quiet=False):\n y_test_pred = self.predict(x_test, k=k)\n num_samples = x_test.shape[0]\n num_correct = (y_test == y_test_pred).sum().item()\n accuracy = 100.0 * num_correct / num_samples\n msg = (f'Got {num_correct} / {num_samples} correct; accuracy is {accuracy:.2f}%')\n if not quiet:\n print(msg)\n return accuracy\n\ndef knn_cross_validate(x_train, y_train, num_folds=5, k_choices=None):\n if k_choices is None:\n k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n\n x_train_folds = torch.chunk(x_train, num_folds, dim=0)\n y_train_folds = torch.chunk(y_train, num_folds, dim=0)\n\n k_to_accuracies = {}\n\n for k in k_choices:\n list_of_acc = []\n for num_fold in range(num_folds):\n x_train_folds_local = [x for x in x_train_folds]\n y_train_folds_local = [x for x in y_train_folds]\n x_test = x_train_folds_local[num_fold]\n y_test = y_train_folds_local[num_fold]\n del x_train_folds_local[num_fold]\n del y_train_folds_local[num_fold]\n x_train = torch.cat(x_train_folds_local, dim=0)\n y_train = torch.cat(y_train_folds_local, dim=0)\n classifier = KnnClassifier(x_train, y_train)\n list_of_acc.append(classifier.check_accuracy(x_test, y_test, k))\n k_to_accuracies[k] = list_of_acc\n\n return k_to_accuracies\n\ndef knn_get_best_k(k_to_accuracies):\n best_k = 0\n new_dict = {}\n for k, accs in sorted(k_to_accuracies.items()):\n new_dict[k] = sum(accs) / len(accs) \n max_value = max(new_dict.values())\n best_k = [k for k, v in new_dict.items() if v == max_value][0]\n return best_k\n
"},{"location":"AI/EECS%20498-007/Pytorch/","title":"pytorch \u7684\u57fa\u672c\u4f7f\u7528","text":""},{"location":"AI/EECS%20498-007/Pytorch/#pytorch","title":"pytorch \u7684\u57fa\u672c\u4f7f\u7528","text":" \u7ea6 564 \u4e2a\u5b57 45 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 3 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
Python# Create a rank 1 tensor from a Python list\na = torch.tensor([[1, 2, 3], [4, 5, 6]])\nprint('Here is a:')\nprint(a)\nprint('type(a): ', type(a))\nprint('rank of a: ', a.dim())\nprint('a.shape: ', a.shape)\n\ntorch.zeros(2, 3)\ntorch.ones(2, 3)\ntorch.eye(3)\ntorch.rand(2, 3)\ntorch.full((M, N), 3.14)\n\ny2 = torch.tensor([1, 2], dtype=torch.int64)\nprint(y2.dtype)\n\nx3 = x0.to(torch.float32)\n\nx0 = torch.eye(3, dtype=torch.float64) \u00a0# Shape (3, 3), dtype torch.float64\nx1 = torch.zeros_like(x0) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # Shape (3, 3), dtype torch.float64\nx2 = x0.new_zeros(4, 5) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # Shape (4, 5), dtype torch.float64\nx3 = torch.ones(6, 7).to(x0) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# Shape (6, 7), dtype torch.float64\n
Even though PyTorch provides a large number of numeric datatypes, the most commonly used datatypes are:
torch.float32: Standard floating-point type; used to store learnable parameters, network activations, etc. Nearly all arithmetic is done using this type.torch.int64: Typically used to store indicestorch.bool: Stores boolean values: 0 is false and 1 is truetorch.float16: Used for mixed-precision arithmetic, usually on NVIDIA GPUs with tensor cores. You won't need to worry about this datatype in this course.- \u6ce8\u610f
a[:, 1] \u548c a[:, 1:2] \u7684\u533a\u522b\uff0c\u540e\u8005\u4f1a\u4fdd\u7559\u7684\u591a\u4e00\u70b9 clone() \u4ee5\u540e\u7684\u53d8\u91cf\u8ddf\u539f\u53d8\u91cf\u662f\u72ec\u7acb\u7684\uff0c\u4f46\u662f\u7b49\u53f7\u76f4\u63a5\u8d4b\u503c\u7684\u662f\u540c\u4e00\u4e2a\u6307\u9488
Pythonmask = (a > 3)\nprint('\\nMask tensor:')\nprint(mask)\n# Mask tensor: tensor([[False, False], [False, True], [ True, True]])\n
- As its name implies, a tensor returned by
.view() shares the same data as the input, so changes to one will affect the other.
Reshape\u548cview\u7684\u533a\u522b - \u5185\u5b58\u8fde\u7eed\u6027\uff1a
view \u8981\u6c42\u539f\u59cb\u5f20\u91cf\u548c\u76ee\u6807\u5f20\u91cf\u5728\u5185\u5b58\u4e2d\u662f\u8fde\u7eed\u7684\u3002\u5982\u679c\u539f\u59cb\u5f20\u91cf\u4e0d\u662f\u8fde\u7eed\u7684\uff0c view \u4f1a\u9996\u5148\u8c03\u7528 contiguous \u65b9\u6cd5\u4f7f\u5176\u8fde\u7eed\uff0c\u7136\u540e\u6539\u53d8\u5f62\u72b6\u3002\u5982\u679c\u5f20\u91cf\u5df2\u7ecf\u662f\u8fde\u7eed\u7684\uff0c view \u64cd\u4f5c\u4e0d\u4f1a\u590d\u5236\u6570\u636e\u3002reshape\u4e0d\u8981\u6c42\u539f\u59cb\u5f20\u91cf\u662f\u8fde\u7eed\u7684\u3002\u5982\u679c\u539f\u59cb\u5f20\u91cf\u4e0d\u662f\u8fde\u7eed\u7684\uff0creshape\u4f1a\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u5f20\u91cf\u5e76\u590d\u5236\u6570\u636e\uff0c\u4ee5\u786e\u4fdd\u65b0\u5f20\u91cf\u662f\u8fde\u7eed\u7684\u3002
- \u8fd4\u56de\u503c
view\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684\u5f20\u91cf\uff0c\u5b83\u4e0e\u539f\u59cb\u5f20\u91cf\u5171\u4eab\u76f8\u540c\u7684\u6570\u636e\uff0c\u4f46\u662f\u6709\u4e0d\u540c\u7684\u5f62\u72b6\u3002\u5982\u679c\u539f\u59cb\u5f20\u91cf\u4e0d\u662f\u8fde\u7eed\u7684\uff0cview\u4f1a\u8fd4\u56de\u4e00\u4e2a\u526f\u672c\u3002reshape\u4e5f\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684\u5f20\u91cf\uff0c\u4f46\u603b\u662f\u521b\u5efa\u6570\u636e\u7684\u526f\u672c\uff0c\u5373\u4f7f\u539f\u59cb\u5f20\u91cf\u662f\u8fde\u7eed\u7684\u3002
- \u4f7f\u7528\u573a\u666f\uff1a
- \u5f53\u4f60\u786e\u5b9a\u539f\u59cb\u5f20\u91cf\u662f\u8fde\u7eed\u7684\uff0c\u5e76\u4e14\u4f60\u60f3\u8981\u907f\u514d\u4e0d\u5fc5\u8981\u7684\u6570\u636e\u590d\u5236\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528
view\u3002 - \u5f53\u4f60\u4e0d\u786e\u5b9a\u539f\u59cb\u5f20\u91cf\u662f\u5426\u8fde\u7eed\uff0c\u6216\u8005\u4f60\u60f3\u8981\u786e\u4fdd\u64cd\u4f5c\u4e0d\u4f1a\u56e0\u975e\u8fde\u7eed\u6027\u800c\u5931\u8d25\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528
reshape\u3002
- \u53c2\u6570\uff1a
view\u7684\u53c2\u6570\u662f\u76ee\u6807\u5f62\u72b6\u7684\u7ef4\u5ea6\u3002reshape\u7684\u53c2\u6570\u4e5f\u662f\u76ee\u6807\u5f62\u72b6\u7684\u7ef4\u5ea6\uff0c\u4f46\u5b83\u53ef\u4ee5\u63a5\u53d7\u4e00\u4e2a\u989d\u5916\u7684\u53c2\u6570inplace\uff0c\u5982\u679c\u8bbe\u7f6e\u4e3aTrue\uff0c\u5219\u4f1a\u5728\u539f\u5730\u4fee\u6539\u5f20\u91cf\u7684\u5f62\u72b6\u3002
torch.sin(x) \u548c x.sin() \u662f\u7b49\u4ef7\u7684
Pythonx = torch.tensor([[1, 2, 3],\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 [4, 5, 6]], dtype=torch.float32)\nprint('Original tensor:')\nprint(x)\n\nprint('\\nSum over entire tensor:')\nprint(torch.sum(x))\nprint(x.sum())\n\n# We can sum over each row:\nprint('\\nSum of each row:')\nprint(torch.sum(x, dim=0))\nprint(x.sum(dim=0))\n\n# Sum over each column:\nprint('\\nSum of each column:')\nprint(torch.sum(x, dim=1))\nprint(x.sum(dim=1))\n
torch.dot: Computes inner product of vectorstorch.mm: Computes matrix-matrix productstorch.mv: Computes matrix-vector productstorch.addmm / torch.addmv: Computes matrix-matrix and matrix-vector multiplications plus a biastorch.bmm / torch.baddmm: Batched versions of torch.mm and torch.addmm, respectivelytorch.matmul: General matrix product that performs different operations depending on the rank of the inputs. Confusingly, this is similar to np.dot in numpy.
"},{"location":"AI/EECS%20498-007/linear_classifer/","title":"Linear classifer","text":""},{"location":"AI/EECS%20498-007/linear_classifer/#_1","title":"\u539f\u7406","text":" \u7ea6 677 \u4e2a\u5b57 216 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 6 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
\u4e24\u79cd\u7ebf\u6027\u5206\u7c7b\u5668\uff1a\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u548cSoftmax\u5206\u7c7b\u5668\u3002\u8fd9\u4e24\u79cd\u5206\u7c7b\u5668\u90fd\u662f\u76d1\u7763\u5b66\u4e60\u7b97\u6cd5\uff0c\u7528\u4e8e\u5206\u7c7b\u4efb\u52a1\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#svm","title":"\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09","text":"SVM\u7684\u76ee\u6807\u662f\u627e\u5230\u4e00\u4e2a\u8d85\u5e73\u9762\uff0c\u5b83\u53ef\u4ee5\u6700\u5927\u5316\u4e0d\u540c\u7c7b\u522b\u4e4b\u95f4\u7684\u8fb9\u754c\u3002\u8fd9\u4e2a\u8d85\u5e73\u9762\u88ab\u79f0\u4e3a\u6700\u4f18\u5206\u5272\u8d85\u5e73\u9762\u3002\u5bf9\u4e8e\u4e8c\u5206\u7c7b\u95ee\u9898\uff0cSVM\u7684\u635f\u5931\u51fd\u6570\u53ef\u4ee5\u8868\u793a\u4e3a\uff1a
\\[ L(W, b) = \\frac{1}{N} \\sum_{i=1}^{N} \\max(0, 1 - y_i (W \\cdot x_i + b)) \\] \u5176\u4e2d\uff0c\\(W\\) \u662f\u6743\u91cd\u5411\u91cf\uff0c\\(b\\) \u662f\u504f\u7f6e\u9879\uff0c\\(x_i\\) \u662f\u8f93\u5165\u7279\u5f81\uff0c\\(y_i\\) \u662f\u6807\u7b7e\uff08-1\u62161\uff09\uff0c\\(N\\) \u662f\u6837\u672c\u6570\u91cf\u3002
\u4e3a\u4e86\u5b9e\u73b0\u591a\u5206\u7c7b\uff0c\u6211\u4eec\u4f7f\u7528\u7ed3\u6784\u5316SVM\u635f\u5931\u51fd\u6570\uff0c\u5b83\u8003\u8651\u4e86\u6bcf\u4e2a\u7c7b\u522b\u7684\u5206\u6570\uff0c\u5e76\u5c1d\u8bd5\u6700\u5927\u5316\u6b63\u786e\u7c7b\u522b\u7684\u5206\u6570\u4e0e\u6b21\u9ad8\u7c7b\u522b\u5206\u6570\u4e4b\u95f4\u7684\u5dee\u8ddd\u3002\u635f\u5931\u51fd\u6570\u53ef\u4ee5\u8868\u793a\u4e3a\uff1a
\\[ L(W) = \\frac{1}{N} \\sum_{i=1}^{N} \\sum_{j \\neq y_i} \\max(0, \\text{score}_j - \\text{score}_{y_i} + \\Delta) \\] \u5176\u4e2d\uff0c\\(\\text{score}_j = W_j \\cdot x_i\\)\uff0c\\(\\Delta\\) \u662f\u4e00\u4e2a\u5e38\u6570\uff0c\u901a\u5e38\u8bbe\u7f6e\u4e3a1\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#softmax","title":"Softmax\u5206\u7c7b\u5668","text":"Softmax\u5206\u7c7b\u5668\u4f7f\u7528Softmax\u51fd\u6570\u5c06\u8f93\u5165\u7279\u5f81\u6620\u5c04\u5230\u6982\u7387\u5206\u5e03\u4e0a\u3002\u5bf9\u4e8e\u6bcf\u4e2a\u6837\u672c\uff0cSoftmax\u51fd\u6570\u8f93\u51fa\u6bcf\u4e2a\u7c7b\u522b\u7684\u6982\u7387\u3002Softmax\u51fd\u6570\u5b9a\u4e49\u4e3a\uff1a
\\[ \\text{softmax}(z_i) = \\frac{e^{z_i}}{\\sum_{j=1}^{K} e^{z_j}} \\] \u5176\u4e2d\uff0c\\(z_i\\) \u662f\u7b2c\\(i\\)\u4e2a\u7c7b\u522b\u7684\u5206\u6570\uff0c\\(K\\) \u662f\u7c7b\u522b\u603b\u6570\u3002
Softmax\u5206\u7c7b\u5668\u7684\u635f\u5931\u51fd\u6570\u662f\u4ea4\u53c9\u71b5\u635f\u5931\uff0c\u53ef\u4ee5\u8868\u793a\u4e3a\uff1a
\\[ L(W) = -\\frac{1}{N} \\sum_{i=1}^{N} \\sum_{j=1}^{K} y_{ij} \\log(\\text{softmax}(z_j)) \\] \u5176\u4e2d\uff0c\\(y_{ij}\\) \u662f\u4e00\u4e2a\u6307\u793a\u53d8\u91cf\uff0c\u5982\u679c\u6837\u672c\\(i\\)\u5c5e\u4e8e\u7c7b\u522b\\(j\\)\uff0c\u5219\u4e3a1\uff0c\u5426\u5219\u4e3a0\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#_2","title":"\u6b63\u5219\u5316","text":"\u4e3a\u4e86\u9632\u6b62\u8fc7\u62df\u5408\uff0c\u6211\u4eec\u5728\u635f\u5931\u51fd\u6570\u4e2d\u6dfb\u52a0\u4e86\u6b63\u5219\u5316\u9879\u3002L2\u6b63\u5219\u5316\u7684\u635f\u5931\u51fd\u6570\u53ef\u4ee5\u8868\u793a\u4e3a\uff1a
\\[ L(W) = L(W) + \\lambda \\lVert W \\rVert^2 \\] \u5176\u4e2d\uff0c\\(\\lambda\\) \u662f\u6b63\u5219\u5316\u5f3a\u5ea6\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#_3","title":"\u4ee3\u7801\u5b9e\u73b0","text":"\u4ee3\u7801\u4e2d\u5b9e\u73b0\u4e86\u4e24\u79cd\u635f\u5931\u51fd\u6570\u7684\u6734\u7d20\u7248\u672c\uff08svm_loss_naive \u548c softmax_loss_naive\uff09\u548c\u5411\u91cf\u5316\u7248\u672c\uff08svm_loss_vectorized \u548c softmax_loss_vectorized\uff09\u3002\u5411\u91cf\u5316\u7248\u672c\u901a\u8fc7\u907f\u514d\u663e\u5f0f\u5faa\u73af\u6765\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3002
\u8bad\u7ec3\u8fc7\u7a0b\uff08train_linear_classifier\uff09\u4f7f\u7528\u968f\u673a\u68af\u5ea6\u4e0b\u964d\uff08SGD\uff09\u6765\u4f18\u5316\u635f\u5931\u51fd\u6570\u3002\u5728\u6bcf\u6b21\u8fed\u4ee3\u4e2d\uff0c\u6211\u4eec\u968f\u673a\u62bd\u53d6\u4e00\u4e2a\u6279\u6b21\u7684\u6837\u672c\uff0c\u8ba1\u7b97\u635f\u5931\u548c\u68af\u5ea6\uff0c\u7136\u540e\u66f4\u65b0\u6743\u91cd\u3002
\u9884\u6d4b\u8fc7\u7a0b\uff08predict_linear_classifier\uff09\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6743\u91cd\u6765\u9884\u6d4b\u65b0\u6837\u672c\u7684\u7c7b\u522b\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#_4","title":"\u8d85\u53c2\u6570\u641c\u7d22","text":"\u4ee3\u7801\u4e2d\u8fd8\u5305\u542b\u4e86\u8d85\u53c2\u6570\u641c\u7d22\u7684\u51fd\u6570\uff08svm_get_search_params \u548c softmax_get_search_params\uff09\uff0c\u5b83\u4eec\u8fd4\u56de\u4e0d\u540c\u7684\u5b66\u4e60\u7387\u548c\u6b63\u5219\u5316\u5f3a\u5ea6\u7684\u5019\u9009\u503c\uff0c\u4ee5\u4fbf\u627e\u5230\u6700\u4f73\u7684\u6a21\u578b\u53c2\u6570\u3002
"},{"location":"AI/EECS%20498-007/linear_classifer/#_5","title":"\u4f5c\u4e1a\u5b9e\u73b0","text":"Pythonimport torch\nimport random\nfrom abc import abstractmethod\n\ndef hello_linear_classifier():\n print('Hello from linear_classifier.py!')\n\nclass LinearClassifier(object):\n def __init__(self):\n random.seed(0)\n torch.manual_seed(0)\n self.W = None\n\n def train(self, X_train, y_train, learning_rate=1e-3, reg=1e-5, num_iters=100,\n batch_size=200, verbose=False):\n train_args = (self.loss, self.W, X_train, y_train, learning_rate, reg,\n num_iters, batch_size, verbose)\n self.W, loss_history = train_linear_classifier(*train_args)\n return loss_history\n\n def predict(self, X):\n return predict_linear_classifier(self.W, X)\n\n @abstractmethod\n def loss(self, W, X_batch, y_batch, reg):\n raise NotImplementedError\n\n def _loss(self, X_batch, y_batch, reg):\n self.loss(self.W, X_batch, y_batch, reg)\n\n def save(self, path):\n torch.save({'W': self.W}, path)\n print(\"Saved in {}\".format(path))\n\n def load(self, path):\n W_dict = torch.load(path, map_location='cpu')\n self.W = W_dict['W']\n print(\"load checkpoint file: {}\".format(path))\n\n\nclass LinearSVM(LinearClassifier):\n def loss(self, W, X_batch, y_batch, reg):\n return svm_loss_vectorized(W, X_batch, y_batch, reg)\n\n\nclass Softmax(LinearClassifier):\n def loss(self, W, X_batch, y_batch, reg):\n return softmax_loss_vectorized(W, X_batch, y_batch, reg)\n\n\ndef svm_loss_naive(W, X, y, reg):\n dW = torch.zeros_like(W)\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n for i in range(num_train):\n scores = W.t().mv(X[i])\n correct_class_score = scores[y[i]]\n for j in range(num_classes):\n if j == y[i]:\n continue\n margin = scores[j] - correct_class_score + 1\n if margin > 0:\n loss += margin\n dW[:, j] += X[i]\n dW[:, y[i]] -= X[i]\n\n loss /= num_train\n loss += reg * torch.sum(W * W)\n dW /= num_train\n dW += 2 * reg * W\n\n return loss, dW\n\n\ndef svm_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = torch.zeros_like(W)\n num_classes = W.shape[1]\n num_train = X.shape[0]\n scores = X.mm(W)\n correct_class_score = scores[range(num_train), y]\n margin = scores - correct_class_score.view(-1, 1) + 1\n margin[range(num_train), y] = 0\n mask = (margin > 0)\n loss = margin[mask].sum()\n mask_correct_y = torch.zeros_like(scores, dtype=torch.bool)\n mask_correct_y[range(num_train), y] = True\n margin[margin > 0] = 1\n margin[margin < 0] = 0\n margin[mask_correct_y] = torch.sum(margin, axis=1) * -1\n dW = margin.T.mm(X).T\n loss /= num_train\n dW /= num_train\n loss += reg * torch.sum(W * W)\n dW += 2 * reg * W\n\n return loss, dW\n\n\ndef sample_batch(X, y, num_train, batch_size):\n indices = torch.randint(num_train, (batch_size,))\n y_batch = y[indices]\n X_batch = X[indices]\n return X_batch, y_batch\n\n\ndef train_linear_classifier(loss_func, W, X, y, learning_rate=1e-3,\n reg=1e-5, num_iters=100, batch_size=200,\n verbose=False):\n num_train, dim = X.shape\n if W is None:\n num_classes = torch.max(y) + 1\n W = 0.000001 * torch.randn(dim, num_classes, device=X.device, dtype=X.dtype)\n else:\n num_classes = W.shape[1]\n\n loss_history = []\n for it in range(num_iters):\n X_batch, y_batch = sample_batch(X, y, num_train, batch_size)\n loss, grad = loss_func(W, X_batch, y_batch, reg)\n loss_history.append(loss.item())\n W -= learning_rate * grad\n if verbose and it % 100 == 0:\n print('iteration %d / %d: loss %f' % (it, num_iters, loss))\n\n return W, loss_history\n\n\ndef predict_linear_classifier(W, X):\n y_pred = torch.zeros(X.shape[0], dtype=torch.int64)\n _, y_pred = X.mm(W).max(dim=1)\n return y_pred\n\n\ndef svm_get_search_params():\n learning_rates = [0.000001, 0.0001, 0.001, 0.005, 0.01, 0.05]\n regularization_strengths = [0.001, 0.5, 1, 3]\n return learning_rates, regularization_strengths\n\n\ndef test_one_param_set(cls, data_dict, lr, reg, num_iters=2000):\n train_acc = 0.0\n val_acc = 0.0\n cls.train(data_dict['X_train'], data_dict['y_train'], lr, reg, num_iters,\n batch_size=200, verbose=False)\n y_train_pred = cls.predict(data_dict['X_train'])\n train_acc = 100.0 * (data_dict['y_train'] == y_train_pred).double().mean().item()\n\n y_test_pred = cls.predict(data_dict['X_val'])\n val_acc = 100.0 * (data_dict['y_val'] == y_test_pred).double().mean().item()\n\n return cls, train_acc, val_acc\n\n\ndef softmax_loss_naive(W, X, y, reg):\n loss = 0.0\n dW = torch.zeros_like(W)\n num_classes = W.shape[1]\n num_train = X.shape[0]\n\n scores = W.t().mv(X[0])\n correct_class_score = scores[y[0]]\n\n for i in range(num_train):\n scores = W.t().mv(X[i])\n scores = scores - scores.max()\n correct_class_score = scores[y[i]]\n loss += -correct_class_score + torch.log(torch.exp(scores).sum())\n for j in range(num_classes):\n if j == y[i]:\n dW[:, j] += torch.exp(scores[j]) / torch.exp(scores).sum() * X[i, :] - X[i, :]\n else:\n dW[:, j] += torch.exp(scores[j]) / torch.exp(scores).sum() * X[i, :]\n\n loss /= num_train\n loss += reg * torch.sum(W * W)\n dW /= num_train\n dW += 2 * reg * W\n\n return loss, dW\n\n\ndef softmax_loss_vectorized(W, X, y, reg):\n loss = 0.0\n dW = torch.zeros_like(W)\n num_classes = W.shape[1]\n num_train = X.shape[0]\n\n\n scores = X.mm(W)\n val, _ = scores.max(dim=1)\n scores = scores - val.view(-1, 1)\n exp_scores = scores.exp()\n exp_scores_sum = exp_scores.sum(dim=1)\n exp_scores_sum_log = exp_scores_sum.log()\n correct_class_scores = scores[range(num_train), y]\n loss = (exp_scores_sum_log - correct_class_scores).sum()\n zeros = torch.zeros((num_train, num_classes), dtype=torch.float64, device='cuda')\n zeros[range(num_train), y] = -1\n minus_X = zeros.t().mm(X)\n dW += minus_X.t()\n dW += ((exp_scores / exp_scores_sum.view(-1, 1)).t().mm(X)).t()\n\n loss /= num_train\n loss += reg * torch.sum(W * W)\n dW /= num_train\n dW += 2 * reg * W\n\n return loss, dW\n\n\ndef softmax_get_search_params():\n learning_rates = [1e-4, 1e-3,1e-2, 1e-1, 1]\n regularization_strengths = [1e-4, 1e-3, 1e-2, 1e-1] \n return learning_rates, regularization_strengths\n
"},{"location":"AI/FFB6D/FFB6D_Conda/","title":"FFB6D\u73af\u5883\u914d\u7f6e\u6307\u5357\uff1a\u539f\u751f\u7cfb\u7edf\u5b89\u88c5","text":""},{"location":"AI/FFB6D/FFB6D_Conda/#ffb6d","title":"FFB6D\u73af\u5883\u914d\u7f6e\u6307\u5357\uff1a\u539f\u751f\u7cfb\u7edf\u5b89\u88c5","text":" \u7ea6 293 \u4e2a\u5b57 96 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 3 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/FFB6D/FFB6D_Conda/#1","title":"1. \u7cfb\u7edf\u8981\u6c42","text":" - Ubuntu 20.04/22.04/24.04
- NVIDIA GPU\uff08\u652f\u6301CUDA\uff09
- \u81f3\u5c118GB\u5185\u5b58
- \u81f3\u5c1130GB\u78c1\u76d8\u7a7a\u95f4
"},{"location":"AI/FFB6D/FFB6D_Conda/#2","title":"2. \u57fa\u7840\u73af\u5883\u914d\u7f6e","text":""},{"location":"AI/FFB6D/FFB6D_Conda/#21-nvidia","title":"2.1 \u5b89\u88c5NVIDIA\u9a71\u52a8","text":"Bash# \u6dfb\u52a0NVIDIA\u5305\u4ed3\u5e93\nsudo add-apt-repository ppa:graphics-drivers/ppa\nsudo apt-get update\n\n# \u5b89\u88c5NVIDIA\u9a71\u52a8\nsudo apt-get install -y nvidia-driver-535 # \u6839\u636e\u9700\u8981\u9009\u62e9\u7248\u672c\n\n# \u91cd\u542f\u7cfb\u7edf\nsudo reboot\n\n# \u9a8c\u8bc1\u5b89\u88c5\nnvidia-smi\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#22-cudacudnn","title":"2.2 \u5b89\u88c5CUDA\u548ccuDNN","text":"Bash# \u4e0b\u8f7d\u5e76\u5b89\u88c5CUDA 11.0\nwget https://developer.download.nvidia.com/compute/cuda/11.0.3/local_installers/cuda_11.0.3_450.51.06_linux.run\nsudo sh cuda_11.0.3_450.51.06_linux.run\n\n# \u914d\u7f6e\u73af\u5883\u53d8\u91cf\necho 'export PATH=/usr/local/cuda-11.0/bin:$PATH' >> ~/.bashrc\necho 'export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc\nsource ~/.bashrc\n\n# \u4e0b\u8f7d\u5e76\u5b89\u88c5cuDNN 8.0\n# \u6ce8\uff1a\u9700\u8981\u4eceNVIDIA\u5f00\u53d1\u8005\u7f51\u7ad9\u4e0b\u8f7dcuDNN v8.0\uff0c\u89e3\u538b\u540e\uff1a\nsudo cp cuda/include/cudnn*.h /usr/local/cuda/include\nsudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64\nsudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#23","title":"2.3 \u5b89\u88c5\u7cfb\u7edf\u4f9d\u8d56","text":"Bashsudo apt-get update\nsudo apt-get install -y \\\n python3.6 \\\n python3.6-dev \\\n python3-pip \\\n git \\\n cmake \\\n build-essential \\\n libopencv-dev \\\n libglib2.0-0 \\\n libsm6 \\\n libxext6 \\\n libxrender-dev \\\n libboost-all-dev \\\n libeigen3-dev\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#24-python","title":"2.4 \u914d\u7f6ePython\u73af\u5883","text":"Bash# \u8bbe\u7f6ePython 3.6\u4e3a\u9ed8\u8ba4\u7248\u672c\nsudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 1\nsudo update-alternatives --set python3 /usr/bin/python3.6\n\n# \u914d\u7f6epip\u955c\u50cf\u6e90\npip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple\n\n# \u5347\u7ea7pip\npython3 -m pip install --upgrade pip\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#3-pytorch","title":"3. \u5b89\u88c5PyTorch\u548c\u4f9d\u8d56\u5305","text":""},{"location":"AI/FFB6D/FFB6D_Conda/#31-pytorch","title":"3.1 \u5b89\u88c5PyTorch","text":"Bashpip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 -f https://download.pytorch.org/whl/torch_stable.html\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#32","title":"3.2 \u5b89\u88c5\u9879\u76ee\u4f9d\u8d56","text":"\u521b\u5efarequirements.txt\u5e76\u5b89\u88c5\u4f9d\u8d56\uff1a
Bashpip3 install -r requirements.txt\n
requirements.txt\u5185\u5bb9\uff1a
Text Onlyh5py \nnumpy \npyyaml==5.4.1\nenum34 \nfuture \nscipy==1.4.1 \nopencv_contrib_python==3.4.2.16 \ntransforms3d==0.3.1 \nscikit_image==0.13.1 \nlmdb==0.94 \nsetuptools==41.0.0 \ncffi==1.11.5 \neasydict==1.7 \nplyfile==0.6 \npillow==8.2.0 \ndataclasses\nglumpy \ntqdm\ntensorboardX \npandas\nscikit-learn \nscipy \ntermcolor\npybind11\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#4","title":"4. \u7f16\u8bd1\u548c\u5b89\u88c5\u7279\u6b8a\u7ec4\u4ef6","text":""},{"location":"AI/FFB6D/FFB6D_Conda/#41-apex","title":"4.1 \u7f16\u8bd1apex","text":"Bashgit clone https://github.com/NVIDIA/apex\ncd apex\nexport TORCH_CUDA_ARCH_LIST=\"6.0;6.1;6.2;7.0;7.5\"\npython setup.py install -v\ncd ..\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#42-normalspeed","title":"4.2 \u5b89\u88c5\u548c\u7f16\u8bd1normalspeed","text":"Bash# 1. \u51c6\u5907OpenCV\u73af\u5883\npip uninstall opencv-python opencv-python-headless -y\npip install opencv-python==4.5.3.56\n\n# 2. \u514b\u9686\u5e76\u5b89\u88c5normalspeed\ngit clone https://github.com/hfutcgncas/normalspeed.git\ncd normalspeed/normalSpeed\n\n# \u5b89\u88c5\u7f16\u8bd1\u4f9d\u8d56\nsudo apt-get install python3-pybind11\npip3 install Cython==0.29.15\n\n# \u6e05\u7406\u5e76\u91cd\u65b0\u5b89\u88c5\nrm -rf build/ dist/ *.egg-info/\npython3 setup.py install --user\ncd ../..\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#5-ffb6d","title":"5. \u514b\u9686\u548c\u914d\u7f6eFFB6D","text":"Bash# \u514b\u9686\u4ee3\u7801\ngit clone https://github.com/ethnhe/FFB6D.git\ncd FFB6D\n\n# \u521b\u5efa\u5fc5\u8981\u7684\u76ee\u5f55\nmkdir -p datasets models train_log\n\n# \u914d\u7f6e\u73af\u5883\u53d8\u91cf\nexport PYTHONPATH=$PYTHONPATH:$(pwd)\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#6","title":"6. \u9a8c\u8bc1\u5b89\u88c5","text":"Bash# \u9a8c\u8bc1CUDA\u652f\u6301\npython3 -c \"import torch; print('CUDA available:', torch.cuda.is_available())\"\n\n# \u9a8c\u8bc1apex\u5b89\u88c5\npython3 -c \"from apex import amp; print('APEX installed')\"\n\n# \u9a8c\u8bc1normalspeed\u5b89\u88c5\npython3 -c \"import normalSpeed; print('normalspeed installed')\"\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#7","title":"7. \u5e38\u89c1\u95ee\u9898","text":""},{"location":"AI/FFB6D/FFB6D_Conda/#71","title":"7.1 \u7f51\u7edc\u95ee\u9898","text":"Bash# \u4f7f\u7528\u4ee3\u7406\uff08\u5982\u9700\u8981\uff09\nexport http_proxy=\"http://proxy:port\"\nexport https_proxy=\"http://proxy:port\"\n\n# \u6216\u4f7f\u7528\u56fd\u5185\u955c\u50cf\u6e90\npip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple\n
"},{"location":"AI/FFB6D/FFB6D_Conda/#72","title":"7.2 \u7248\u672c\u517c\u5bb9\u6027\u95ee\u9898","text":" - \u786e\u4fddNVIDIA\u9a71\u52a8\u7248\u672c\u652f\u6301CUDA 11.0
- \u786e\u4fddPython\u5305\u7248\u672c\u76f8\u4e92\u517c\u5bb9
- \u68c0\u67e5CUDA\u7248\u672c\u4e0ePyTorch\u7248\u672c\u7684\u5339\u914d
"},{"location":"AI/FFB6D/FFB6D_Conda/#73","title":"7.3 \u7f16\u8bd1\u9519\u8bef","text":" - \u786e\u4fdd\u5df2\u5b89\u88c5\u6240\u6709\u5fc5\u8981\u7684\u7f16\u8bd1\u5de5\u5177
- \u68c0\u67e5CUDA\u8def\u5f84\u914d\u7f6e\u662f\u5426\u6b63\u786e
- \u786e\u8ba4\u7cfb\u7edf\u5e93\u7248\u672c\u662f\u5426\u6ee1\u8db3\u8981\u6c42
"},{"location":"AI/FFB6D/FFB6D_Conda/#8","title":"8. \u8bad\u7ec3","text":"\u6309\u7167\u5b98\u65b9\u6587\u6863\u914d\u7f6eLineMOD\u6570\u636e\u96c6\u5e76\u5f00\u59cb\u8bad\u7ec3\u3002
"},{"location":"AI/FFB6D/FFB6D_Conda/#_1","title":"\u53c2\u8003\u8d44\u6599","text":" - FFB6D\u9879\u76ee
- CUDA\u5b89\u88c5\u6307\u5357
- PyTorch\u5b89\u88c5\u6307\u5357
- \u3010\u8bba\u6587\u7b14\u8bb0\u3011FFB6D | \u9a6c\u6d69\u98de\u4e28\u535a\u5ba2
"},{"location":"AI/FFB6D/FFB6D_Docker/","title":"Docker\u4ece\u5165\u95e8\u5230\u5b9e\u8df5\uff1a\u4ee5FFB6D\u73af\u5883\u914d\u7f6e\u4e3a\u4f8b","text":""},{"location":"AI/FFB6D/FFB6D_Docker/#dockerffb6d","title":"Docker\u4ece\u5165\u95e8\u5230\u5b9e\u8df5\uff1a\u4ee5FFB6D\u73af\u5883\u914d\u7f6e\u4e3a\u4f8b","text":" \u7ea6 653 \u4e2a\u5b57 213 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 6 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"AI/FFB6D/FFB6D_Docker/#1","title":"1. \u7b80\u4ecb","text":"Docker\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u5e94\u7528\u5bb9\u5668\u5f15\u64ce\uff0c\u8ba9\u5f00\u53d1\u8005\u53ef\u4ee5\u6253\u5305\u4ed6\u4eec\u7684\u5e94\u7528\u4ee5\u53ca\u4f9d\u8d56\u5305\u5230\u4e00\u4e2a\u53ef\u79fb\u690d\u7684\u5bb9\u5668\u4e2d\uff0c\u7136\u540e\u53d1\u5e03\u5230\u4efb\u4f55\u6d41\u884c\u7684Linux\u6216Windows\u64cd\u4f5c\u7cfb\u7edf\u4e0a\u3002\u672c\u6587\u5c06\u4ee5\u914d\u7f6eFFB6D\uff08\u4e00\u4e2a3D\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\uff09\u7684\u8fd0\u884c\u73af\u5883\u4e3a\u4f8b\uff0c\u4ecb\u7ecdDocker\u7684\u57fa\u672c\u4f7f\u7528\u3002
"},{"location":"AI/FFB6D/FFB6D_Docker/#2","title":"2. \u73af\u5883\u51c6\u5907","text":""},{"location":"AI/FFB6D/FFB6D_Docker/#21","title":"2.1 \u7cfb\u7edf\u8981\u6c42","text":" - Ubuntu 20.04/22.04/24.04
- NVIDIA GPU\uff08\u652f\u6301CUDA\uff09
- \u81f3\u5c118GB\u5185\u5b58
- \u81f3\u5c1130GB\u78c1\u76d8\u7a7a\u95f4
"},{"location":"AI/FFB6D/FFB6D_Docker/#22","title":"2.2 \u57fa\u7840\u7ec4\u4ef6\u5b89\u88c5","text":"\u5b89\u88c5Docker
Bash# \u66f4\u65b0apt\u5305\u7d22\u5f15\nsudo apt-get update\n\n# \u5b89\u88c5\u5fc5\u8981\u7684\u7cfb\u7edf\u5de5\u5177\nsudo apt-get install -y \\\n apt-transport-https \\\n ca-certificates \\\n curl \\\n gnupg \\\n lsb-release\n\n# \u6dfb\u52a0Docker\u7684\u5b98\u65b9GPG\u5bc6\u94a5\ncurl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg\n\n# \u8bbe\u7f6e\u7a33\u5b9a\u7248\u4ed3\u5e93\necho \\\n \"deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \\\n $(lsb_release -cs) stable\" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null\n\n# \u5b89\u88c5Docker Engine\nsudo apt-get update\nsudo apt-get install -y docker-ce docker-ce-cli containerd.io\n\n# \u9a8c\u8bc1\u5b89\u88c5\nsudo docker run hello-world\n
\u5b89\u88c5NVIDIA\u9a71\u52a8
Bash# \u6dfb\u52a0NVIDIA\u5305\u4ed3\u5e93\nsudo add-apt-repository ppa:graphics-drivers/ppa\nsudo apt-get update\n\n# \u5b89\u88c5NVIDIA\u9a71\u52a8\nsudo apt-get install -y nvidia-driver-535 # \u6839\u636e\u9700\u8981\u9009\u62e9\u7248\u672c\n\n# \u91cd\u542f\u7cfb\u7edf\nsudo reboot\n\n# \u9a8c\u8bc1\u5b89\u88c5\nnvidia-smi\n
\u5b89\u88c5NVIDIA Container Toolkit
Bash# \u8bbe\u7f6e\u7a33\u5b9a\u7248\u4ed3\u5e93\ncurl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg\n\necho \"deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/stable/ubuntu22.04/$(arch) /\" | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list\n\n# \u66f4\u65b0\u8f6f\u4ef6\u5305\u5217\u8868\nsudo apt-get update\n\n# \u5b89\u88c5nvidia-docker2\nsudo apt-get install -y nvidia-container-toolkit\n\n# \u91cd\u542fDocker\u670d\u52a1\nsudo systemctl restart docker\n\n# \u6d4b\u8bd5GPU\u652f\u6301\nsudo docker run --rm --gpus all nvidia/cuda:12.0-base nvidia-smi\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#23-docker","title":"2.3 Docker\u914d\u7f6e\u4f18\u5316","text":"\u914d\u7f6e\u955c\u50cf\u52a0\u901f
Bashsudo tee /etc/docker/daemon.json << EOF\n{\n \"registry-mirrors\": [\n \"https://docker.1panel.dev\",\n \"https://docker.zhai.cm\",\n \"https://hub.littlediary.cn\",\n \"https://docker.nastool.de\"\n ],\n \"dns\": [\"8.8.8.8\", \"8.8.4.4\"],\n \"max-concurrent-downloads\": 10,\n \"log-driver\": \"json-file\",\n \"log-opts\": {\n \"max-size\": \"10m\",\n \"max-file\": \"3\"\n }\n}\nEOF\n\nsudo systemctl daemon-reload\nsudo systemctl restart docker\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#3-ffb6d","title":"3. FFB6D\u73af\u5883\u914d\u7f6e\u5b9e\u4f8b","text":""},{"location":"AI/FFB6D/FFB6D_Docker/#31","title":"3.1 \u9879\u76ee\u7ed3\u6784","text":"Bashffb6d_docker/\n\u251c\u2500\u2500 Dockerfile\n\u251c\u2500\u2500 docker-compose.yml\n\u251c\u2500\u2500 build_and_run.sh\n\u251c\u2500\u2500 downloads/\n\u2502 \u251c\u2500\u2500 apex/\n\u2502 \u2514\u2500\u2500 normalspeed/\n\u251c\u2500\u2500 code/\n\u251c\u2500\u2500 datasets/\n\u251c\u2500\u2500 models/\n\u2514\u2500\u2500 train_log/\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#32-dockerfile","title":"3.2 \u521b\u5efaDockerfile","text":"Docker# \u4f7f\u7528\u8f83\u65b0\u7684 CUDA \u955c\u50cf\nFROM nvcr.io/nvidia/cuda:11.0.3-cudnn8-devel-ubuntu18.04\n\n# \u4f7f\u7528 NVIDIA CUDA 11.3 \u57fa\u7840\u955c\u50cf\n# FROM nvcr.io/nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04\n\n# \u907f\u514d\u4ea4\u4e92\u5f0f\u63d0\u793a\nENV DEBIAN_FRONTEND=noninteractive\nENV PYTHONPATH=/workspace/code\nENV CUDA_HOME=/usr/local/cuda\nENV PATH=$CUDA_HOME/bin:$PATH\nENV LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH\n\nWORKDIR /workspace\n\n# \u5b89\u88c5\u7cfb\u7edf\u4f9d\u8d56\nRUN apt-get update && apt-get install -y \\\n python3.6 \\\n python3.6-dev \\\n python3-pip \\\n git \\\n cmake \\\n build-essential \\\n libopencv-dev \\\n libglib2.0-0 \\\n libsm6 \\\n libxext6 \\\n libxrender-dev \\\n libboost-all-dev \\\n libeigen3-dev \\\n wget \\\n && rm -rf /var/lib/apt/lists/*\n\n# \u8bbe\u7f6e Python 3.6 \u4e3a\u9ed8\u8ba4\u7248\u672c\nRUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 1\nRUN update-alternatives --set python3 /usr/bin/python3.6\n\n# \u5347\u7ea7 pip\nRUN python3 -m pip install --upgrade pip\n\n# \u914d\u7f6epip\u955c\u50cf\u6e90\nRUN pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple\n\n# \u5148\u5b89\u88c5 PyTorch (\u4f7f\u7528\u8f83\u65b0\u4f46\u517c\u5bb9\u7684\u7248\u672c)\nRUN pip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 -f https://download.pytorch.org/whl/torch_stable.html\n\n# \u590d\u5236\u9884\u4e0b\u8f7d\u7684\u6587\u4ef6\nCOPY downloads/apex /workspace/apex\nCOPY downloads/normalspeed /workspace/normalspeed\nCOPY code /workspace/code\n\n# \u5b89\u88c5 apex\n#RUN cd /workspace/apex && \\\n# pip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" ./ || \\\n# (echo \"Apex installation failed. Check the error messages above.\" && exit 1)\n\n# \u5b89\u88c5 normalspeed\n#RUN cd /workspace/normalspeed && \\\n# python3 setup.py build_ext --inplace && \\\n# python3 setup.py install\n\nWORKDIR /workspace/code\n
\u66f4\u6539 requirement.txt
Text Onlyh5py \nnumpy \npyyaml==5.4.1\nenum34 \nfuture \nscipy==1.4.1 \nopencv_contrib_python==3.4.2.16 \ntransforms3d==0.3.1 \nscikit_image==0.13.1 \nlmdb==0.94 \nsetuptools==41.0.0 \ncffi==1.11.5 \neasydict==1.7 \nplyfile==0.6 \npillow==8.2.0 \ndataclasses\nglumpy \ntqdm\ntensorboardX \npandas\nscikit-learn \nscipy \ntermcolor\npybind11\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#33-docker-composeyml","title":"3.3 \u521b\u5efadocker-compose.yml","text":"YAMLversion: '3'\nservices:\n ffb6d:\n build: .\n image: ffb6d:latest\n container_name: ffb6d_container\n runtime: nvidia\n environment:\n - NVIDIA_VISIBLE_DEVICES=all\n volumes:\n - ./datasets:/workspace/code/datasets\n - ./train_log:/workspace/code/train_log\n - ./models:/workspace/code/models\n shm_size: '8gb'\n tty: true\n stdin_open: true\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#34","title":"3.4 \u6784\u5efa\u548c\u8fd0\u884c\u811a\u672c","text":"\u521b\u5efabuild_and_run.sh\uff1a
Bash#!/bin/bash\n\n# \u521b\u5efa\u5fc5\u8981\u76ee\u5f55\nmkdir -p downloads datasets models train_log\n\n# \u4e0b\u8f7d\u4f9d\u8d56\ncd downloads\nif [ ! -d \"apex\" ]; then\n git clone https://github.com/NVIDIA/apex.git\nfi\nif [ ! -d \"normalspeed\" ]; then\n git clone https://github.com/hfutcgncas/normalspeed.git\nfi\ncd ..\n\n# \u514b\u9686FFB6D\u4ee3\u7801\nif [ ! -d \"code\" ]; then\n git clone https://github.com/ethnhe/FFB6D.git code\nfi\n\n# \u6784\u5efa\u955c\u50cf\ndocker-compose build\n\n# \u8fd0\u884c\u5bb9\u5668\ndocker-compose up -d\ndocker exec -it ffb6d_container bash\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#35","title":"3.5 \u542f\u52a8\u548c\u9a8c\u8bc1","text":"Bash# \u6dfb\u52a0\u6267\u884c\u6743\u9650\nchmod +x build_and_run.sh\n\n# \u8fd0\u884c\n./build_and_run.sh\n\n# \u5728\u5bb9\u5668\u5185\u9a8c\u8bc1\npython3 -c \"import torch; print('CUDA available:', torch.cuda.is_available())\"\npython3 -c \"from apex import amp; print('APEX installed')\"\npython3 -c \"import normalspeed; print('normalspeed installed')\"\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#36-apex","title":"3.6 \u7f16\u8bd1 apex","text":"Text Onlygit clone https://github.com/NVIDIA/apex\ncd apex\nexport TORCH_CUDA_ARCH_LIST=\"6.0;6.1;6.2;7.0;7.5\"\npython setup.py install -v\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#37-normalspeed","title":"3.7 \u7f16\u8bd1 normalspeed","text":"Text Only# 1. \u5378\u8f7d\u5f53\u524dcv2\npip uninstall opencv-python opencv-python-headless -y\n\n# 2. \u5b89\u88c5\u7279\u5b9a\u7248\u672c\u7684OpenCV\uff0c\u9009\u62e9\u4e0ePython 3.6\u517c\u5bb9\u7684\u7248\u672c\npip install opencv-python==4.5.3.56\n\n# 3. \u9a8c\u8bc1\u5b89\u88c5\npython3 -c \"import cv2; print(cv2.__version__)\"\n
Text Only# \u8fdb\u5165normalSpeed\u76ee\u5f55\ncd /workspace/code/normalspeed/normalSpeed\n\n# \u5b89\u88c5\u4f9d\u8d56\napt-get update\napt-get install python3-pybind11\npip3 install Cython==0.29.15\n\n# \u6e05\u7406\u4e4b\u524d\u7684\u6784\u5efa\nrm -rf build/\nrm -rf dist/\nrm -rf *.egg-info/\n\n# \u91cd\u65b0\u5b89\u88c5\npython3 setup.py install\n\n# \u9a8c\u8bc1\u5b89\u88c5\npython3 -c \"import normalSpeed\"\n\n# \u8fd4\u56deffb6d\u76ee\u5f55\ncd /workspace/code/ffb6d/\n
Text Onlypython3 setup.py install --user\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#38","title":"3.8 \u8bad\u7ec3","text":"\u628a LineMOD \u590d\u5236\u5230\u5bf9\u5e94\u7684\u8def\u5f84\u4e0b\uff0c\u7136\u540e\u6309\u5b98\u7f51\u6765\u5c31\u597d\u4e86
"},{"location":"AI/FFB6D/FFB6D_Docker/#4","title":"4. \u5e38\u89c1\u95ee\u9898","text":""},{"location":"AI/FFB6D/FFB6D_Docker/#41","title":"4.1 \u7f51\u7edc\u95ee\u9898","text":"\u5982\u679c\u9047\u5230\u4e0b\u8f7d\u6162\u6216\u5931\u8d25\uff1a
Bash# \u4e34\u65f6\u4f7f\u7528\u4ee3\u7406\nexport http_proxy=\"http://proxy:port\"\nexport https_proxy=\"http://proxy:port\"\n\n# \u6216\u4fee\u6539pip\u6e90\npip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple\n
\u6ce8\u610f\u7528\u4e86\u4ee3\u7406\u7684\u8bdd\uff0cdocker hub \u955c\u50cf\u5c31\u4f1a\u88ab\u8986\u76d6\u6389\uff0c\u5bfc\u81f4\u901f\u5ea6\u5f88\u6162\u3002\u6240\u4ee5\u5230\u65f6\u5019\u4e00\u5b9a\u8981\u53d6\u6d88\u4ee3\u7406 / \u5220\u9664\u4ee3\u7406\u6587\u4ef6
docker hub \u955c\u50cf\u8bbe\u7f6e\u4e0a\u9762\u5199\u4e86\uff0c\u5b98\u7f51\u662f\u8fd9\u4e2a GitHub - dongyubin/DockerHub: 2024\u5e7411\u6708\u66f4\u65b0\uff0c\u76ee\u524d\u56fd\u5185\u53ef\u7528Docker\u955c\u50cf\u6e90\u6c47\u603b\uff0cDockerHub\u56fd\u5185\u955c\u50cf\u52a0\u901f\u5217\u8868\uff0c\ud83d\ude80DockerHub\u955c\u50cf\u52a0\u901f\u5668
"},{"location":"AI/FFB6D/FFB6D_Docker/#42-cuda","title":"4.2 CUDA\u517c\u5bb9\u6027","text":"\u786e\u4fddNVIDIA\u9a71\u52a8\u7248\u672c\u652f\u6301\u6240\u9700\u7684CUDA\u7248\u672c\uff1a
Bash# \u68c0\u67e5\u652f\u6301\u7684\u6700\u9ad8CUDA\u7248\u672c\nnvidia-smi\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#43","title":"4.3 \u5185\u5b58\u4e0d\u8db3","text":"\u8c03\u6574Docker\u5185\u5b58\u9650\u5236\uff1a
YAML# \u5728docker-compose.yml\u4e2d\u6dfb\u52a0\nservices:\n ffb6d:\n deploy:\n resources:\n limits:\n memory: 16G\n
"},{"location":"AI/FFB6D/FFB6D_Docker/#5","title":"5. \u603b\u7ed3","text":"\u672c\u6587\u4ecb\u7ecd\u4e86\u4f7f\u7528Docker\u914d\u7f6e\u6df1\u5ea6\u5b66\u4e60\u73af\u5883\u7684\u5b8c\u6574\u6d41\u7a0b\uff0c\u4ee5FFB6D\u4e3a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5904\u7406\u590d\u6742\u4f9d\u8d56\u5173\u7cfb\u3002\u901a\u8fc7\u5bb9\u5668\u5316\u6280\u672f\uff0c\u6211\u4eec\u53ef\u4ee5\uff1a
- \u786e\u4fdd\u73af\u5883\u4e00\u81f4\u6027
- \u7b80\u5316\u90e8\u7f72\u6d41\u7a0b
- \u63d0\u9ad8\u5f00\u53d1\u6548\u7387
- \u65b9\u4fbf\u56e2\u961f\u534f\u4f5c
\u5e0c\u671b\u672c\u6559\u7a0b\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u4f7f\u7528Docker\u3002
"},{"location":"AI/FFB6D/FFB6D_Docker/#_1","title":"\u53c2\u8003\u8d44\u6599","text":" - Docker\u5b98\u65b9\u6587\u6863
- NVIDIA Docker\u6587\u6863
- FFB6D\u9879\u76ee
- \u3010\u8bba\u6587\u7b14\u8bb0\u3011FFB6D | \u9a6c\u6d69\u98de\u4e28\u535a\u5ba2
"},{"location":"Blogs/","title":"index","text":""},{"location":"Blogs/#blogs","title":"Blogs \u270d","text":"Abstract
\u4e2a\u4eba\u535a\u5ba2
\u672c\u90e8\u5206\u5185\u5bb9\uff08\u9664\u7279\u522b\u58f0\u660e\u5916\uff09\u91c7\u7528 \u7f72\u540d-\u975e\u5546\u4e1a\u6027\u4f7f\u7528-\u4fdd\u6301\u4e00\u81f4 4.0 \u56fd\u9645 (CC BY-NC-SA 4.0) \u8bb8\u53ef\u534f\u8bae\u8fdb\u884c\u8bb8\u53ef\u3002
"},{"location":"Blogs/#selected-blogs","title":"Selected Blogs","text":"\u8bba\u6587\u7b14\u8bb0 - ULIP-2 1421 5 mins 1735373878
"},{"location":"Blogs/archives/","title":"Archives","text":""},{"location":"Blogs/archives/#archives","title":"Archives","text":"{{ blog_content }}
"},{"location":"Blogs/posts/24-12-29/","title":"\u5de5\u4f5c\u89c4\u5f8b","text":" \u7ea6 1038 \u4e2a\u5b57 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 5 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#Blog","tags":["Blog"]},{"location":"Blogs/posts/24-12-29/#_1","title":"\u5de5\u4f5c\u89c4\u5f8b","text":"\u4e4b\u524d\u7684\u4e00\u4e9b\u611f\u60f3\u7eed\u5199\uff1a
Quote - \u611f\u89c9\u9ad8\u4e2d\u4e4b\u6240\u4ee5\u6548\u7387\u9ad8\u5c31\u662f\u8fd9\u4e2a\u539f\u56e0\uff0c\u5a31\u4e50\u6709\u4ec0\u4e48\u5417\uff1f\u8bfb\u95f2\u4e66\uff0c\u53bb\u8e22\u7403\uff0c\u7761\u89c9\uff0c\u804a\u5929\uff0c\u5199\u65e5\u8bb0\uff0c\u7ec3\u5b57\u3002\u90a3\u4e48\u81ea\u7136\u800c\u7136\u5c31\u4f1a\u9677\u5165\u4e00\u79cd\u53cd\u601d\u7684\u72b6\u6001\uff0c\u7136\u540e\u5c31\u56de\u53bb\u5b66\u4e60\uff0c\u6216\u8005\u5b89\u6392\u63a5\u4e0b\u6765\u7684\u4e8b\u60c5\u3002
- \u4f46\u662f\u7b49\u4e0a\u4e86\u5927\u5b66\u4e86\uff0c\u6709\u624b\u673a\u4e86\uff0c\u95f2\u4e0b\u6765\u5c31\u4f1a\u53bb\u770b\u5404\u79cd\u89c6\u9891\uff0c\u8ffd\u6c42\u5404\u79cd\u5f3a\u523a\u6fc0\uff1a\u6253\u6e38\u620f\uff0c\u770b\u5267\uff0c\u770b\u77ed\u89c6\u9891\u3002\u7136\u540e\u5c31\u4f1a\u6076\u6027\u5faa\u73af\uff0c\u4e5f\u4e0d\u4f1a\u6709\u9000\u51fa\u673a\u5236\u3002
- \u7b2c\u4e8c\u4e2a\u539f\u56e0\u662f\uff0c\u5373\u4f7f\u9ad8\u4e2d\u4f60\u7b2c\u4e00\u5929\u6446\u70c2\u4e86\uff0c\u4ed6\u4f1a\u6709\u5f3a\u5236\u6027\u7684\u4f5c\u606f\u6765\u8ba9\u4f60\u7b2c\u4e8c\u5929\u4ece\u5934\u5f00\u59cb\uff0c\u4f46\u662f\u5230\u4e86\u5927\u5b66\u751a\u81f3\u5f88\u591a\u65f6\u5019\u4f1a\u901a\u5bb5\u6216\u8005\u665a\u7761\u665a\u8d77\uff0c\u56e0\u4e3a\u5404\u79cd\u539f\u56e0\uff08\u5199 lab\uff0c surfing\uff0c\u6253\u6e38\u620f\u7b49\u7b49\uff09
- \u7b2c\u4e09\u4e2a\u539f\u56e0\u662f\uff0c\u9ad8\u4e2d\u4f1a\u6709 interaction, \u4f46\u662f\u5230\u4e86\u5927\u5b66\u53ef\u80fd\u5c31\u5f88\u591a\u65f6\u5019\u90fd\u662f\u81ea\u5df1\u4e00\u4e2a\u4eba\uff0c\u7136\u540e\u751f\u6d3b\u5c31\u5982\u4e00\u6ee9\u6b7b\u6c34\uff0c\u4f1a\u66f4\u5bb9\u6613\u9677\u5165\u6b7b\u5faa\u73af\u3002
- \u7b2c\u56db\u4e2a\u539f\u56e0\u662f\uff08\u5176\u5b9e\u6211\u89c9\u5f97\u5e76\u4e0d\u662f\u90a3\u4e48\u7684\u91cd\u8981\uff09\uff0c\u9ad8\u4e2d\u4f1a\u6709\u5b9a\u65f6\u7684\u68c0\u6d4b\u673a\u5236\uff1alike \u5468\u8003\uff0c\u5c0f\u6d4b\uff0c\u6708\u8003\uff0c\u5e73\u65f6\u7684\u6bcf\u4e00\u6b21\u4f5c\u4e1a\uff0c\u6216\u8005\u53ef\u80fd\u4f1a\u6709\u9ed8\u5199\u3002\u4f1a\u4e0d\u505c\u7684\u6709\u8001\u5e08\u6765\u50ac\u4fc3\u4f60\uff0c\u6709\u540c\u5b66\u5728\u8ddf\u4f60\u6bd4\u8f83\u3002\u4f46\u662f\u5230\u4e86\u5927\u5b66\uff0c\u4f60\u7684\u9009\u62e9\u662f\u591a\u6837\u7684\uff0c\u53ef\u80fd\u4f60\u6839\u672c\u4e0d\u77e5\u9053\u4f60\u5728\u90a3\u4e00\u6761\u8def\u4e0a\uff0c\u6216\u8005\u5373\u4f7f\u4f60\u786e\u5b9a\u4e86\u4f60\u7684\u9053\u8def\uff0c\u4f46\u662f\u4f60\u4e5f\u5f88\u96be\u91cf\u5316\u4f60\u7684\u8fdb\u7a0b\uff0c\u4f60\u672a\u6765\u7684\u9053\u8def\u6ca1\u6709\u4eba\u80fd\u591f\u4e3a\u4f60\u62c5\u4fdd\u3002
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- \u5f3a\u5236\u6027\u7684\u89c4\u5f8b\u7684\u4f5c\u606f\uff0c\u6bd4\u5982\uff1a\u665a\u4e0a 11\uff1a30 \u7761\u89c9\uff0c\u65e9\u4e0a 7\uff1a40 \u8d77\u5e8a\uff0c\u4e09\u9910\u56fa\u5b9a\u3002
- \u62d2\u7edd\u4e00\u5207\u53ef\u80fd\u7684\uff0c\u6ca1\u6709\u610f\u4e49\u7684\u4e8b\u60c5\u3002\u8fd9\u4e2a\u56e0\u4eba\u800c\u5f02\uff0c\u4f46\u662f\u4e00\u822c\u6765\u8bf4\uff0c\u901a\u8fc7\u81ea\u5236\u529b\u6765\u63a7\u5236\u81ea\u5df1\u5076\u5c14\u53bb\u73a9\u662f\u4e0d\u592a\u6709\u7528\u7684\uff08\u6211\u4e2a\u4eba\u53cd\u6b63\u5982\u6b64\uff09\u3002
- \u5b9a\u65f6\u7684\u53cd\u601d\u548c\u81ea\u6211\u68c0\u6d4b\u3002\u524d\u8005\u53ea\u8981\u901a\u8fc7\u65e5\u8bb0 + \u5468\u7ed3\u7684\u5f62\u5f0f\u5c31\u53ef\u4ee5\u5b8c\u6210\uff0c\u800c\u540e\u8005\uff0c\u8981\u4e48\u901a\u8fc7\u516c\u5f00\u8bfe\u53ef\u80fd\u81ea\u5e26\u7684\u8003\u8bd5\uff0c\u8981\u4e48\u901a\u8fc7\u4f60\u81ea\u5df1\u7684\u8f93\u51fa\uff0c\u5f53\u7136\u8fd9\u5f88\u96be\uff0c\u56e0\u4e3a\u4e0d\u662f\u6240\u6709\u7684\u4e8b\u60c5\u90fd\u662f\u5e94\u8bd5\u90a3\u4e00\u5957\u53ef\u4ee5\u89e3\u51b3\u7684\u3002\u6211\u76ee\u524d\u7684\u60f3\u6cd5\u662f\uff0c\u4f60\u5b66\u7684\u4e1c\u897f\u8bc4\u4ef7\u8981\u6c42\u5c31\u662f\u4ee5\u540e\u9762\u8bd5\u7684\u65f6\u5019\u6216\u8005\u505a\u9879\u76ee\u7684\u65f6\u5019\uff0c\u80fd\u591f\u4e86\u7136\u4e8e\u5fc3\uff0c\u90a3\u4e48\u5c31\u7b97\u5b66\u4f1a\u4e86\u3002
- \u591a interaction, \u4e0d\u8981\u62c5\u5fc3\u81ea\u5df1\u5185\u5411\u6216\u8005\u4ec0\u4e48\u7684\u3002\u8ddf\u522b\u4eba\u804a\u804a\u672a\u6765\u89c4\u5212\u6216\u8005\u522b\u7684\uff0c\u63d0\u9ad8\u81ea\u5df1\u7684\u66dd\u5149\u5ea6\u4f1a\u8ba9\u81ea\u5df1\u7684\u751f\u6d3b\u8fed\u4ee3\u7684\u66f4\u5feb\u3002
","tags":["Blog"]},{"location":"Blogs/posts/24-12-29/#_2","title":"\u57fa\u672c\u7279\u70b9","text":"\u6211\u60f3\u4e00\u4e2a\u5408\u7406\u7684\u751f\u6d3b\u4f5c\u606f\u5e94\u8be5\u6709\u4e00\u4e0b\u7279\u70b9\uff1a
- \u53ef\u6301\u7eed
- \u7cbe\u795e
- \u8eab\u4f53
- \u9ad8\u6548
- \u7075\u6d3b
","tags":["Blog"]},{"location":"Blogs/posts/24-12-29/#_3","title":"\u89c4\u5f8b","text":" - \u5408\u7406\u7684\u7761\u7720\uff0c\u8fd0\u52a8\uff0c\u996e\u98df
- \u5b66\u7d2f\u4e86\u5c31\u4f11\u606f\uff0c\u4f11\u606f\u591f\u4e86\u5c31\u5b66\u4e60\u7684\u8fdb\u7a0b\u5207\u6362
- \u5b9a\u671f\u53cd\u601d
","tags":["Blog"]},{"location":"Blogs/posts/24-12-29/#_4","title":"\u5177\u4f53\u5b9e\u8df5","text":"\u5de5\u5177\uff1a obsidian\uff0c\u65f6\u95f4\u65e5\u5fd7\uff0c\u535a\u5ba2
- \u6bcf\u5929\u5199\u65e5\u8bb0\uff0c\u6bcf\u5468\u5199\u5468\u7ed3\uff08\u968f\u65f6\u53ef\u4ee5\u5199\uff0c\u4e0d\u4e00\u5b9a\u662f\u8981\u4e00\u5929\u7684\u7ed3\u675f\u3002\u7eaf\u7cb9\u8bb0\u5f55\u96f6\u788e\u7684\u60f3\u6cd5\u548c\u4e00\u4e9b\u7cfb\u7edf\u7684\u53cd\u601d\u3002\uff09
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- \u53ef\u4ee5\u590d\u76d8\u81ea\u5df1\u7684\u751f\u6d3b\uff0c\u5728\u4e00\u5468\u6216\u8005\u66f4\u957f\u7684\u65f6\u95f4\u5185\u56de\u770b\u3002
- \u5728\u8bb0\u5f55\u65f6\u95f4\u7684\u65f6\u5019\uff0c\u53ef\u4ee5\u6709\u4e00\u4e2a\u540e\u64a4\u7684\u59ff\u6001\u6765\u53cd\u601d\u5f53\u4e0b\u7684\u5904\u5883\uff0c\u5373\u2018\u6211\u5728\u5e72\u4ec0\u4e48\u2019\uff0c\u2018\u6211\u8981\u5e72\u4ec0\u4e48\u2019\u3002
- \u901a\u8fc7\u53d1\u535a\u5ba2\u6216\u8005\u5176\u4ed6\u5e73\u53f0\u6765\u548c\u4ed6\u4eba\u4ea4\u6d41\uff0c\u83b7\u53d6\u5916\u754c\u4fe1\u606f\u3002
- \u6309\u65f6\u7761\u89c9\uff0c\u6309\u65f6\u953b\u70bc\uff0c\u6309\u65f6\u5403\u996d
","tags":["Blog"]},{"location":"Blogs/posts/24-12-29/#_5","title":"\u66f4\u52a0\u5177\u4f53\u7684\uff0c\u4e2a\u4eba\u5316\u7684\u5b89\u6392","text":" - \u4ee5\u4e00\u4e2a\u5c0f\u9879\u76ee\u4e3a\u5355\u4f4d\u6765\u5b89\u6392\u4ee5\u5929\u4e3a\u9897\u7c92\u5ea6\u7684\u8ba1\u5212\uff0c\u800c\u4e0d\u662f\u53ea\u6709
\u7d2f\u4e86\u5c31\u4f11\u606f\uff0c\u4f11\u606f\u5b8c\u4e86\u5c31\u5de5\u4f5c \u7684\u8fdb\u7a0b\u8f6c\u6362\u673a\u5236\u3002 - \u4e0d\u7136\u5bb9\u6613\u9677\u5165\uff0c\u4ec0\u4e48\u90fd\u4e0d\u76f8\u5e72\uff0c\u4f46\u5176\u5b9e\u5e76\u4e0d\u662f\u7d2f\u4e86\u7684\u60c5\u51b5\u3002
- \u6700\u597d\u4e0d\u8981\u4e00\u76f4\u5e72\u4e00\u4e9b\u5b8c\u5168\u4e0d\u76f8\u5e72\u7684\u4e8b\u60c5\uff0c\u4e0d\u7136\u96be\u4ee5\u4ea7\u751f\u8054\u7cfb\u7684\u5de5\u4f5c\u5f88\u96be\u7ef4\u6301\u4e0b\u53bb\u3002
","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/","title":"\u4fe1\u606f","text":" \u7ea6 2271 \u4e2a\u5b57 2 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 11 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#Blog","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_1","title":"\u4fe1\u606f","text":"\u6700\u8fd1\u7684\u4e00\u4e9b\u5173\u4e8e\u4fe1\u606f\u7684\u60f3\u6cd5\u3002
\u4e0b\u9762\u6211\u5148\u4e3e\u4e24\u4e2a\u4f8b\u5b50\u3002
","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_2","title":"\u52c7\u6562\u53bb\u505a","text":"\u6211\u89c9\u5f97\u6211\u4e00\u76f4\u90fd\u662f\u5f88\u76f4\u63a5\u7684\u4eba\u3002 \u4e3e\u4e2a\u4f8b\u5b50\uff0c\u9ad8\u4e09\u5347\u5927\u4e00\u7684\u6691\u5047\uff0c\u6211\u5728\u8003\u8651\u8981\u4e0d\u8981\u8f6c\u4e13\u4e1a\u3002\u4e8e\u662f\u770b\u904d\u4e86\u6c34\u6e90\u6240\u6709\u76f8\u5173\u7684\u5e16\u5b50\uff0c\u95ee\u4e86\u5f88\u591a\u5b66\u957f\u5b66\u59d0\uff0c\u6574\u7406\u4e86\u5f88\u591a\u8def\u5f84\uff0c\u77e5\u9053\u4e86\u8f6c\u4e13\u4e1a\u8981\uff1a
- \u5f88\u9ad8\u7684\u5b66\u79ef\u5206
- SE \u770b\u9879\u76ee\uff0cCS \u770b\u5b66\u79ef\u5206\u770b\u56fd\u5956
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_3","title":"\u591a\u4ea4\u6d41","text":"\u4e00\u76f4\u611f\u89c9\u56fd\u5185\u7684\u6559\u80b2\u4e0d\u652f\u6301\u5408\u4f5c\uff0c\u5f3a\u70c8\u7684\u7ade\u4e89\u5173\u7cfb\u96be\u4ee5\u5f62\u6210\u5229\u76ca\u5171\u540c\u4f53\u3002
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\u6bd4\u5982\u5728\u8fd9\u4e2a\u7cfb\u5217\u7684\u6587\u7ae0\u4e2d\uff0c Open and open again--\u5982\u4f55\u6709\u6781\u5f3a\u7684\u6267\u884c\u529b\uff1f\uff0c\u6240\u8c13\u7684 open \uff08\u4f5c\u8005\u610f\u601d\u5e94\u8be5\u662f\u548c\u4ed6\u4eba\u4ea4\u6d41\uff09\u4e5f\u53ea\u662f\u6444\u5165\u4fe1\u606f\u7684\u4e00\u79cd\u3002\u800c\u6b63\u662f\u56e0\u4e3a\u4e0e\u4eba\u4ea4\u6d41\u5f80\u5f80\u4f1a\u83b7\u5f97\u6bd4\u8f83\u9ad8\u8d28\u91cf\u7684\u4fe1\u606f\uff0c\u6240\u4ee5\u5448\u73b0\u51fa\u6765\u4ea4\u6d41\u6709\u5f88\u591a advantages\u3002\u4f46\u5b9e\u9645\u4e0a\uff0c\u662f\u4f18\u8d28\u7684\u4fe1\u606f\u5728\u53d1\u6325\u4f5c\u7528\u3002
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\u8fd9\u91cc\u662f\u7fa4\u4e8c\u7ef4\u7801 \u8fd9\u91cc\u662f\u7fa4\u89c4\u5219 - Background and Purpose\uff1a
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- \u8bba\u6587\u9605\u8bfb
- OneLLM: One Framework to Align All Modalities with Language - Kinnari's Site
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_4","title":"\u5173\u4e8e\u4fe1\u606f","text":"\u5b9e\u9645\u4e0a\uff0c\u4e0a\u6c34\u6e90\u641c\u7d22\uff0c\u53bb\u8ddf\u4ed6\u4eba\u4ea4\u6d41\u90fd\u662f\u4fe1\u606f\u8f93\u5165\u7684\u4e00\u73af\u3002
","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_5","title":"\u4fe1\u606f\u7684\u8d28\u91cf","text":"\u73b0\u4ee3\u793e\u4f1a\u4e0d\u7f3a\u4f18\u8d28\u4fe1\u606f\uff0c\u7f3a\u7684\u662f\u5982\u4f55\u627e\u5230\u4f18\u8d28\u4fe1\u606f
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_6","title":"\u4fe1\u606f\u7684\u83b7\u53d6\u65b9\u5f0f","text":"","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_7","title":"\u4e3b\u52a8","text":" - \u641c\u7d22\u5f15\u64ce\uff1aGoogle \u6216\u8005 Bing\u3002
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_8","title":"\u88ab\u52a8","text":" - \u7b97\u6cd5\u63a8\u8350\uff1a\u73b0\u4ee3\u4e92\u8054\u7f51\u901a\u8fc7\u53e4\u8001\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u6784\u5efa\u4e86\u4ee5\u77ed\u89c6\u9891\u548c\u63a8\u8350\u4fe1\u606f\u6d41\u4e3a\u4e3b\u7684\u4fe1\u606f\u4ea7\u54c1\uff0c\u8fd9\u7c7b\u4ea7\u54c1\u7c7b\u4f3c\u4e8e\u51cf\u80a5\u836f\uff0c\u5403\u591a\u4e86\u5bb9\u6613\u53cd\u5f39\u3002\u63a8\u8350\u7b97\u6cd5\u4e00\u4e2a\u5f88\u5927\u7684\u95ee\u9898\u4f1a\u5236\u9020\u51fa\u56f4\u7ed5\u4f7f\u7528\u8005\u7684\u4fe1\u606f\u8327\u623f\uff0c\u4e5f\u5c31\u662f\u7528\u6237\u6700\u7ec8\u770b\u5230\u7684\u90fd\u662f\u81ea\u5df1\u559c\u6b22\u770b\u5230\u7684\u4fe1\u606f\uff0c\u4fe1\u606f\u5355\u4e00\u5316\u8ba9\u4f7f\u7528\u8005\u770b\u5230\u7684\u90fd\u662f\u7247\u9762\u7684\u4fe1\u606f\uff0c\u8fd9\u5bf9\u63d0\u5347\u8ba4\u77e5\u6765\u8bf4\u662f\u4e2a\u707e\u96be\u3002
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_9","title":"\u5b66\u4e60\u4e0e\u4fe1\u606f\u7684\u5173\u7cfb","text":"\u5b66\u4e60\u67d0\u4e00\u9886\u57df\u77e5\u8bc6\u65f6\u9075\u5faa\u00a0\u81ea\u4f4e\u5411\u4e0a\u00a0\u7684\u8fc7\u7a0b\uff1a
- \u56e0\u4e3a\u4e00\u5f00\u59cb\u5728\u8be5\u9886\u57df\u5e76\u4e0d\u8ba4\u8bc6\u6709\u8be5\u9886\u57df\u7ecf\u9a8c\u7684\u4eba\uff0c\u4e5f\u4e0d\u77e5\u9053\u8bfb\u5565\u4e66\u6bd4\u8f83\u5408\u9002\uff0c\u6240\u4ee5\u4f1a\u5148\u4e3b\u52a8\u641c\u7d22\u8be5\u9886\u57df\u7684\u4e00\u4e9b\u6587\u7ae0\u5148\u5efa\u7acb\u5bf9\u8be5\u9886\u57df\u7684\u00a0
\u8f6e\u5ed3\u8ba4\u8bc6\u3002 - \u4e4b\u540e\u4f1a\u5173\u6ce8\u4e00\u4e9b\u8be5\u9886\u57df\u7684\u516c\u4f17\u53f7/\u77e5\u4e4e\u5927V/\u90ae\u4ef6\u5217\u8868\uff0c\u751a\u81f3\u662f\u770b\u6709\u6ca1\u6709Youtube\u76f8\u5173\u7684\u9891\u9053\u3002
- \u5f85\u611f\u89c9\u5bf9\u8be5\u9886\u57df\u6709\u4e86\u4e00\u4e2a\u00a0
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- \u4e4b\u540e\u4e0e\u8fd9\u4e9b\u4eba\u5efa\u7acb\u8054\u7cfb(\u5305\u62ec\u5e73\u53f0\u79c1\u4fe1\u3001\u5fae\u4fe1\u4e0e\u90ae\u4ef6\u8054\u7cfb)\u00a0\uff0c\u505a1\u5bf91\u7684\u6c9f\u901a\uff0c\u89e3\u51b3\u6211\u5728\u5b66\u4e60\u5b9e\u8df5\u8fc7\u7a0b\u4e2d\u9047\u5230\u7684\u4e00\u4e9b\u96be\u4ee5\u89e3\u51b3\u7684\u56f0\u60d1\u3002
- \u4e0d\u65ad\u7684\u91cd\u590d\u8be5\u8fc7\u7a0b\uff0c\u53ef\u4ee5\u52a0\u6df1\u6211\u5bf9\u8be5\u9886\u57df\u7684\u8ba4\u77e5\u3002
","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_10","title":"\u4e00\u4e9b\u7531\u6b64\u5f15\u53d1\u51fa\u6765\u7684\u8bdd\u9898","text":"","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_11","title":"\u4e0d\u8981\u7126\u8651","text":"\u6211\u89c9\u5f97\u7126\u8651\u5e94\u8be5\u662f\u4fe1\u606f\u6444\u5165\u7684\u95ee\u9898\uff0c\u4e3b\u8981\u8fd8\u662f\u8981\u60f3\u6e05\u695a\u81ea\u5df1\u8981\u4ec0\u4e48\u3002
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#_12","title":"\u5b9a\u65f6\u53cd\u601d","text":"\u8fd9\u4e2a\u5e94\u8be5\u662f\u4fe1\u606f\u8f93\u5165\u4ee5\u540e\u7684\u5de5\u4f5c\u4e86\u3002
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","tags":["Blog"]},{"location":"Blogs/posts/24-12-30/#reference","title":"Reference","text":" - \u6784\u5efa\u7ec8\u8eab\u5b66\u4e60\u4f53\u7cfb\u8fdb\u884c\u81ea\u6211\u63d0\u5347 \u00b7 BMPI
","tags":["Blog"]},{"location":"Blogs/posts/FreeSplatter%20%E4%BB%A3%E7%A0%81%E8%A7%A3%E8%AF%BB/","title":"FreeSplatter \u4ee3\u7801\u89e3\u8bfb","text":" \u7ea6 192 \u4e2a\u5b57 375 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 6 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u79d1\u7814 #\u4e09\u7ef4\u91cd\u5efa #\u590d\u73b0","tags":["\u79d1\u7814","\u4e09\u7ef4\u91cd\u5efa","\u590d\u73b0"]},{"location":"Blogs/posts/FreeSplatter%20%E4%BB%A3%E7%A0%81%E8%A7%A3%E8%AF%BB/#freesplatter","title":"FreeSplatter \u4ee3\u7801\u89e3\u8bfb","text":"","tags":["\u79d1\u7814","\u4e09\u7ef4\u91cd\u5efa","\u590d\u73b0"]},{"location":"Blogs/posts/FreeSplatter%20%E4%BB%A3%E7%A0%81%E8%A7%A3%E8%AF%BB/#views_to_3d","title":"\u7a00\u758f\u91cd\u5efa\u5bf9\u8c61 views_to_3d","text":"Pythondef run_views_to_3d(\n self, \n image_files, # \u8f93\u5165\u7684\u56fe\u50cf\u6587\u4ef6\u5217\u8868\n do_rembg=False, # \u662f\u5426\u79fb\u9664\u56fe\u50cf\u80cc\u666f\n gs_type='2DGS', # \u9ad8\u65af\u6e32\u67d3\u7c7b\u578b\uff1a2DGS \u6216 3DGS\n mesh_reduction=0.5, # \u7f51\u683c\u7b80\u5316\u6bd4\u4f8b\n cache_dir=None, # \u7f13\u5b58\u76ee\u5f55\n):\n torch.cuda.empty_cache() # \u6e05\u7406\u663e\u5b58\uff0c\u907f\u514d\u788e\u7247\u5316\n\n # \u521b\u5efa\u8f93\u51fa\u76ee\u5f55\n self.output_dir = os.path.join(cache_dir, f'output_{uuid.uuid4()}')\n os.makedirs(self.output_dir, exist_ok=True)\n\n # \u56fe\u50cf\u9884\u5904\u7406\uff1a\u52a0\u8f7d\u548c\u5904\u7406\u8f93\u5165\u56fe\u50cf\n images, alphas = [], [] # \u56fe\u50cf\u548c alpha \u901a\u9053\u5b58\u50a8\n for image_file in image_files:\n if isinstance(image_file, tuple): # \u517c\u5bb9\u4f20\u5165\u5143\u7ec4\u7684\u60c5\u51b5\n image_file = image_file[0]\n image = Image.open(image_file) # \u6253\u5f00\u56fe\u50cf\n w, h = image.size # \u83b7\u53d6\u56fe\u50cf\u5c3a\u5bf8\n\n # \u53ef\u9009\uff1a\u79fb\u9664\u80cc\u666f\n image_rgba = self.run_segmentation(image) # \u80cc\u666f\u79fb\u9664\n if image.mode == 'RGBA': # \u5982\u679c\u5df2\u6709 alpha \u901a\u9053\n image, alpha = rgba_to_white_background(image_rgba) # \u8f6c\u6362\u4e3a\u767d\u80cc\u666f\n image = v2.functional.center_crop(image, min(h, w)) # \u5c45\u4e2d\u88c1\u526a\n alpha = v2.functional.center_crop(alpha, min(h, w)) # \u88c1\u526a alpha \u901a\u9053\n else: # \u5982\u679c\u6ca1\u6709 alpha \u901a\u9053\n image_rgba = resize_foreground(image_rgba, 0.9) # \u7f29\u653e\u524d\u666f\n image, alpha = rgba_to_white_background(image_rgba) # \u8f6c\u6362\u4e3a\u767d\u80cc\u666f\n\n # \u8c03\u6574\u5206\u8fa8\u7387\u81f3 512x512\n image = v2.functional.resize(image, 512, interpolation=3, antialias=True).clamp(0, 1)\n alpha = v2.functional.resize(alpha, 512, interpolation=0, antialias=True).clamp(0, 1)\n\n # \u4fdd\u5b58\u5904\u7406\u540e\u7684\u56fe\u50cf\u548c alpha \u901a\u9053\n images.append(image)\n alphas.append(alpha)\n\n # \u5c06\u56fe\u50cf\u548c alpha \u901a\u9053\u5806\u53e0\u4e3a\u5f20\u91cf\n images = torch.stack(images, dim=0)\n alphas = torch.stack(alphas, dim=0)\n\n # \u751f\u6210\u53ef\u89c6\u5316\u62fc\u63a5\u56fe\u50cf\n images_vis = v2.functional.to_pil_image(rearrange(images, 'n c h w -> c h (n w)'))\n\n # \u8c03\u7528\u6838\u5fc3\u91cd\u5efa\u51fd\u6570\n legends = [f'V{i}' for i in range(1, 1 + len(images))] # \u4e3a\u6bcf\u4e2a\u89c6\u89d2\u751f\u6210\u56fe\u4f8b\n gs_vis_path, video_path, mesh_fine_path, fig = self.run_freesplatter_object(\n images, alphas, legends=legends, gs_type=gs_type, mesh_reduction=mesh_reduction\n )\n\n return images_vis, gs_vis_path, video_path, mesh_fine_path, fig\n\n\ndef run_freesplatter_object(\n self, \n images, # \u8f93\u5165\u56fe\u50cf\u5f20\u91cf\n alphas, # \u56fe\u50cf\u7684 alpha \u901a\u9053\u5f20\u91cf\n legends=None, # \u56fe\u50cf\u89c6\u89d2\u7684\u56fe\u4f8b\n gs_type='2DGS', # \u9ad8\u65af\u6e32\u67d3\u7c7b\u578b\n mesh_reduction=0.5, # \u7f51\u683c\u7b80\u5316\u6bd4\u4f8b\n):\n torch.cuda.empty_cache() # \u518d\u6b21\u6e05\u7406\u663e\u5b58\n device = self.device\n\n # \u6839\u636e\u6e32\u67d3\u7c7b\u578b\u9009\u62e9\u6a21\u578b\n freesplatter = self.freesplatter_2dgs if gs_type == '2DGS' else self.freesplatter\n\n # \u5c06\u56fe\u50cf\u548c alpha \u901a\u9053\u79fb\u52a8\u5230 GPU\n images, alphas = images.to(device), alphas.to(device)\n\n # **\u6b65\u9aa4 1\uff1a\u751f\u6210\u9ad8\u65af\u70b9\u4e91**\n t0 = time.time()\n with torch.inference_mode():\n gaussians = freesplatter.forward_gaussians(images.unsqueeze(0)) # \u751f\u6210\u9ad8\u65af\u70b9\u4e91\n t1 = time.time()\n\n # **\u6b65\u9aa4 2\uff1a\u4f30\u8ba1\u76f8\u673a\u53c2\u6570\u5e76\u53ef\u89c6\u5316**\n c2ws_pred, focals_pred = freesplatter.estimate_poses(\n images, gaussians, masks=alphas, use_first_focal=True, pnp_iter=10\n ) # \u4f30\u8ba1\u76f8\u673a\u5916\u53c2\u548c\u7126\u8ddd\n fig = self.visualize_cameras_object(images, c2ws_pred, focals_pred, legends=legends) # \u53ef\u89c6\u5316\u76f8\u673a\u4f4d\u59ff\n t2 = time.time()\n\n # **\u6b65\u9aa4 3\uff1a\u4fdd\u5b58\u9ad8\u65af\u70b9\u4e91**\n gs_vis_path = os.path.join(self.output_dir, 'gs_vis.ply')\n save_gaussian(gaussians, gs_vis_path, freesplatter, opacity_threshold=5e-3, pad_2dgs_scale=True)\n print(f'Save gaussian at {gs_vis_path}')\n\n torch.cuda.empty_cache() # \u518d\u6b21\u6e05\u7406\u663e\u5b58\n\n # **\u6b65\u9aa4 4\uff1a\u751f\u6210\u73af\u7ed5\u89c6\u89d2\u89c6\u9891**\n with torch.inference_mode():\n c2ws_video = get_circular_cameras(N=120, elevation=0, radius=2.0, normalize=True).to(device) # \u73af\u7ed5\u76f8\u673a\u8f68\u8ff9\n fx = fy = focals_pred.mean() / 512.0 # \u8ba1\u7b97\u5f52\u4e00\u5316\u7126\u8ddd\n cx = cy = torch.ones_like(fx) * 0.5 # \u4e2d\u5fc3\u70b9\u5f52\u4e00\u5316\n fxfycxcy_video = torch.tensor([fx, fy, cx, cy]).unsqueeze(0).repeat(c2ws_video.shape[0], 1).to(device)\n\n video_frames = freesplatter.forward_renderer(\n gaussians, c2ws_video.unsqueeze(0), fxfycxcy_video.unsqueeze(0)\n )['image'][0].clamp(0, 1) # \u6e32\u67d3\u89c6\u9891\u5e27\n\n video_path = os.path.join(self.output_dir, 'gs.mp4')\n save_video(video_frames, video_path, fps=30) # \u4fdd\u5b58\u89c6\u9891\n print(f'Save video at {video_path}')\n t3 = time.time()\n\n torch.cuda.empty_cache() # \u518d\u6b21\u6e05\u7406\u663e\u5b58\n\n # **\u6b65\u9aa4 5\uff1aTSDF \u878d\u5408\u751f\u6210\u7f51\u683c**\n with torch.inference_mode():\n c2ws_fusion = get_fibonacci_cameras(N=120, radius=2.0) # TSDF \u878d\u5408\u76f8\u673a\u8f68\u8ff9\n c2ws_fusion, _ = normalize_cameras(\n c2ws_fusion, camera_position=torch.tensor([0., -2., 0.]), camera_system='opencv'\n ) # \u76f8\u673a\u5f52\u4e00\u5316\n c2ws_fusion = c2ws_fusion.to(device)\n c2ws_fusion_reference = torch.linalg.inv(c2ws_fusion[0:1]) @ c2ws_fusion\n fx = fy = focals_pred.mean() / 512.0\n cx = cy = torch.ones_like(fx) * 0.5\n fov = np.rad2deg(np.arctan(0.5 / fx.item())) * 2\n fxfycxcy_fusion = torch.tensor([fx, fy, cx, cy]).unsqueeze(0).repeat(c2ws_fusion.shape[0], 1).to(device)\n\n fusion_render_results = freesplatter.forward_renderer(\n gaussians, c2ws_fusion_reference.unsqueeze(0), fxfycxcy_fusion.unsqueeze(0)\n )\n images_fusion = fusion_render_results['image'][0].clamp(0, 1).permute(0, 2, 3, 1)\n alphas_fusion = fusion_render_results['alpha'][0].permute(0, 2, 3, 1)\n depths_fusion = fusion_render_results['depth'][0].permute(0, 2, 3, 1)\n\n # TSDF \u878d\u5408\u5904\u7406\n fusion_images = (images_fusion.detach().cpu().numpy() * 255).clip(0, 255).astype(np.uint8)\n fusion_depths = depths_fusion.detach().cpu().numpy()\n fusion_alphas = alphas_fusion.detach().cpu().numpy()\n fusion_masks = (fusion_alphas > 1e-2).astype(np.uint8)\n fusion_depths = fusion_depths * fusion_masks - np.ones_like(fusion_depths) * (1 - fusion_masks)\n\n fusion_c2ws = c2ws_fusion.detach().cpu().numpy()\n\n # \u751f\u6210\u7f51\u683c\u6587\u4ef6\n mesh_path = os.path.join(self.output_dir, 'mesh.obj')\n rgbd_to_mesh(\n fusion_images, fusion_depths, fusion_c2ws, fov, mesh_path, cam_elev_thr=-90\n ) # TSDF \u878d\u5408\u751f\u6210\u7f51\u683c\n print(f'Save mesh at {mesh_path}')\n t4 = time.time()\n\n torch.cuda.empty_cache() # \u6e05\u7406\u663e\u5b58\n\n # **\u6b65\u9aa4 6\uff1a\u4f18\u5316\u7eb9\u7406**\n cam_pos = c2ws_fusion[:, :3, 3].cpu().numpy() # \u83b7\u53d6\u76f8\u673a\u4f4d\u7f6e\n cam_inds = torch.from_numpy(fpsample.fps_sampling(cam_pos, 16).astype(int)).to(device=device) # \u91c7\u6837\u5173\u952e\u5e27\u76f8\u673a\n\n # \u4f18\u5316\u7f51\u683c\u7eb9\u7406\n alphas_bake = alphas_fusion[cam_inds]\n images_bake = (images_fusion[cam_inds] - (1 - alphas_bake)) / alphas_bake.clamp(min=1e-6)\n\n fxfycxcy = fxfycxcy_fusion[cam_inds].clone()\n intrinsics = torch.eye(3).unsqueeze(0).repeat(len(cam_inds), 1, 1).to(fxfycxcy)\n intrinsics[:, 0, 0] = fxfycxcy[:, 0]\n intrinsics[:, 0, 2] = fxfycxcy[:, 2]\n intrinsics[:, 1, 1] = fxfycxcy[:, 1]\n intrinsics[:, 1, 2] = fxfycxcy[:, 3]\n\n out_mesh = trimesh.load(str(mesh_path), process=False) # \u52a0\u8f7d\u7f51\u683c\n out_mesh = optimize_mesh(\n out_mesh, \n images_bake, \n alphas_bake.squeeze(-1), \n c2ws_fusion[cam_inds].inverse(), \n intrinsics,\n simplify=mesh_reduction,\n verbose=False\n ) # \u4f18\u5316\u7eb9\u7406\n mesh_fine_path = os.path.join(self.output_dir, 'mesh.glb')\n out_mesh.export(mesh_fine_path) # \u4fdd\u5b58\u4f18\u5316\u540e\u7684\u7f51\u683c\n print(f\"Save optimized mesh at {mesh_fine_path}\")\n t5 = time.time()\n\n # \u6253\u5370\u65f6\u95f4\u7edf\u8ba1\n print(f'Generate Gaussians: {t1-t0:.2f} seconds.')\n print(f'Estimate poses: {t2-t1:.2f} seconds.')\n print(f'Generate video: {t3-t2:.2f} seconds.')\n print(f'Generate mesh: {t4-t3:.2f} seconds.')\n print(f'Optimize mesh: {t5-t4:.2f} seconds.')\n\n return gs_vis_path, video_path, mesh_fine_path, fig\n
- \u56fe\u50cf\u52a0\u8f7d\u4e0e\u9884\u5904\u7406\uff1a
- \u79fb\u9664\u80cc\u666f\u3001\u88c1\u526a\u3001\u7f29\u653e\u4e3a\u7edf\u4e00\u5c3a\u5bf8\u3002
- \u5c06\u8f93\u5165\u56fe\u50cf\u8f6c\u6362\u4e3a PyTorch \u5f20\u91cf\u3002
- \u751f\u6210\u9ad8\u65af\u70b9\u4e91\uff1a
- \u4f7f\u7528\u9ad8\u65af\u5206\u5e03\u63cf\u8ff0\u8f93\u5165\u7684 3D \u70b9\u3002
- \u4f30\u8ba1\u76f8\u673a\u53c2\u6570\uff1a
- \u901a\u8fc7\u9ad8\u65af\u70b9\u4e91\u4f30\u8ba1\u76f8\u673a\u7684\u4f4d\u59ff\uff08\u5916\u53c2\uff09\u548c\u7126\u8ddd\uff08\u5185\u53c2\uff09\u3002
- \u89c6\u9891\u6e32\u67d3\uff1a
- \u5728\u73af\u7ed5\u76f8\u673a\u8f68\u8ff9\u4e2d\u6e32\u67d3\u89c6\u9891\u3002
- TSDF \u878d\u5408\u751f\u6210\u7f51\u683c\uff1a
- \u901a\u8fc7\u6df1\u5ea6\u56fe\u548c RGB \u56fe\u50cf\u751f\u6210 3D \u7f51\u683c\u3002
- \u4f18\u5316\u7eb9\u7406\uff1a
- \u4f7f\u7528\u6df1\u5ea6\u548c RGB \u56fe\u50cf\u4f18\u5316\u7f51\u683c\u7684\u7eb9\u7406\u548c\u7ec6\u8282\u3002
","tags":["\u79d1\u7814","\u4e09\u7ef4\u91cd\u5efa","\u590d\u73b0"]},{"location":"Blogs/posts/FreeSplatter%20%E4%BB%A3%E7%A0%81%E8%A7%A3%E8%AF%BB/#_1","title":"\u5269\u4e0b\u7684\u4e00\u4e9b","text":"Pythondef visualize_cameras_object(\n self, \n images, # \u8f93\u5165\u7684\u56fe\u50cf\n c2ws, # \u76f8\u673a\u4f4d\u59ff\uff08\u4e16\u754c\u5750\u6807\u7cfb\u5230\u76f8\u673a\u5750\u6807\u7cfb\u7684\u53d8\u6362\u77e9\u9635\uff09\n focal_length, # \u7126\u8ddd\n legends=None, # \u6bcf\u4e2a\u89c6\u89d2\u7684\u56fe\u4f8b\uff0c\u9ed8\u8ba4\u65e0\n):\n # **1. \u5904\u7406\u56fe\u50cf**\uff1a\u8c03\u6574\u56fe\u50cf\u5927\u5c0f\uff0c\u9650\u5236\u50cf\u7d20\u503c\u5728 [0, 1] \u8303\u56f4\uff0c\u7136\u540e\u8f6c\u6362\u4e3a NumPy \u683c\u5f0f\n images = v2.functional.resize(images, 128, interpolation=3, antialias=True).clamp(0, 1)\n images = (images.permute(0, 2, 3, 1).detach().cpu().numpy() * 255).astype(np.uint8)\n\n # **2. \u76f8\u673a\u4f4d\u59ff\u8f6c\u6362**\uff1a\u5c06 c2ws\uff08\u76f8\u673a\u4f4d\u59ff\uff09\u8f6c\u6362\u5230\u4e16\u754c\u5750\u6807\u7cfb\u4e0b\n cam2world = create_camera_to_world(torch.tensor([0, -2, 0]), camera_system='opencv').to(c2ws) # \u521b\u5efa\u4ece\u76f8\u673a\u5230\u4e16\u754c\u7684\u53d8\u6362\n transform = cam2world @ torch.linalg.inv(c2ws[0:1]) # \u8ba1\u7b97\u7b2c\u4e00\u4e2a\u76f8\u673a\u7684\u53c2\u8003\u53d8\u6362\n c2ws = transform @ c2ws # \u66f4\u65b0\u6240\u6709\u76f8\u673a\u7684\u4f4d\u59ff\n c2ws = c2ws.detach().cpu().numpy()\n c2ws[:, :, 1:3] *= -1 # \u4ece OpenCV \u5230 OpenGL \u5750\u6807\u7cfb\u7684\u8f6c\u6362\n\n # **3. \u8ba1\u7b97\u76f8\u673a\u89c6\u573a\u89d2\uff08FOV\uff09**\n focal_length = focal_length.mean().detach().cpu().numpy()\n fov = np.rad2deg(np.arctan(256.0 / focal_length)) * 2\n\n # **4. \u4e3a\u6bcf\u4e2a\u89c6\u89d2\u6307\u5b9a\u989c\u8272**\n colors = [cmap(i / len(images))[:3] for i in range(len(images))]\n\n # **5. \u8bbe\u7f6e\u56fe\u4f8b**\uff1a\u5982\u679c\u672a\u63d0\u4f9b\uff0c\u9ed8\u8ba4\u4e3a\u7a7a\n legends = [None] * len(images) if legends is None else legends\n\n # **6. \u8c03\u7528\u53ef\u89c6\u5316\u5de5\u5177 CameraVisualizer**\n viz = CameraVisualizer(c2ws, legends, colors, images=images)\n fig = viz.update_figure(\n 3, # \u7ef4\u5ea6\n height=320, # \u56fe\u5f62\u9ad8\u5ea6\n line_width=5, # \u76f8\u673a\u7ebf\u5bbd\n base_radius=1, # \u57fa\u51c6\u76f8\u673a\u4f4d\u7f6e\u534a\u5f84\n zoom_scale=1, # \u7f29\u653e\u6bd4\u4f8b\n fov_deg=fov, # \u89c6\u573a\u89d2\n show_grid=True, # \u662f\u5426\u663e\u793a\u7f51\u683c\n show_ticklabels=True, # \u662f\u5426\u663e\u793a\u5750\u6807\u8f74\u523b\u5ea6\n show_background=True, # \u662f\u5426\u663e\u793a\u80cc\u666f\n y_up=False, # Y \u8f74\u65b9\u5411\u5411\u4e0b\n )\n return fig\n\n\ndef run_views_to_scene(\n self, \n image1, # \u8f93\u5165\u7684\u7b2c\u4e00\u4e2a\u56fe\u50cf\n image2, # \u8f93\u5165\u7684\u7b2c\u4e8c\u4e2a\u56fe\u50cf\n cache_dir=None, # \u8f93\u51fa\u7f13\u5b58\u76ee\u5f55\n):\n torch.cuda.empty_cache() # \u6e05\u7406\u663e\u5b58\n\n # **1. \u521b\u5efa\u8f93\u51fa\u76ee\u5f55**\n self.output_dir = os.path.join(cache_dir, f'output_{uuid.uuid4()}')\n os.makedirs(self.output_dir, exist_ok=True)\n\n # **2. \u56fe\u50cf\u9884\u5904\u7406**\n images = []\n for image in [image1, image2]:\n w, h = image.size # \u83b7\u53d6\u56fe\u50cf\u5bbd\u9ad8\n image = torch.from_numpy(np.asarray(image) / 255.0).float() # \u8f6c\u4e3a NumPy\uff0c\u518d\u8f6c\u4e3a\u5f20\u91cf\n image = rearrange(image, 'h w c -> c h w') # \u8c03\u6574\u7ef4\u5ea6\u4e3a (C, H, W)\n image = v2.functional.center_crop(image, min(h, w)) # \u4e2d\u5fc3\u88c1\u526a\n image = v2.functional.resize(image, 512, interpolation=3, antialias=True).clamp(0, 1) # \u8c03\u6574\u5927\u5c0f\u5e76\u5f52\u4e00\u5316\n images.append(image) # \u6dfb\u52a0\u5230\u5217\u8868\n\n # **3. \u5c06\u56fe\u50cf\u5806\u53e0\u6210\u4e00\u4e2a\u5f20\u91cf**\n images = torch.stack(images, dim=0)\n\n # **4. \u751f\u6210\u62fc\u63a5\u53ef\u89c6\u5316\u56fe\u50cf**\n images_vis = v2.functional.to_pil_image(rearrange(images, 'n c h w -> c h (n w)'))\n\n # **5. \u8c03\u7528\u573a\u666f\u91cd\u5efa\u65b9\u6cd5**\n legends = [f'V{i}' for i in range(1, 1+len(images))] # \u4e3a\u89c6\u89d2\u751f\u6210\u56fe\u4f8b\n gs_vis_path, video_path, fig = self.run_freesplatter_scene(images, legends=legends)\n\n return images_vis, gs_vis_path, video_path, fig\n\n\ndef run_freesplatter_scene(\n self, \n images, # \u8f93\u5165\u56fe\u50cf\n legends=None, # \u6bcf\u4e2a\u89c6\u89d2\u7684\u56fe\u4f8b\n):\n torch.cuda.empty_cache() # \u6e05\u7406\u663e\u5b58\n\n freesplatter = self.freesplatter_scene # \u4f7f\u7528 FreeSplatter \u573a\u666f\u6a21\u578b\n\n device = self.device\n images = images.to(device) # \u5c06\u56fe\u50cf\u79fb\u52a8\u5230 GPU\n\n # **1. \u751f\u6210\u9ad8\u65af\u70b9\u4e91**\n t0 = time.time()\n with torch.inference_mode():\n gaussians = freesplatter.forward_gaussians(images.unsqueeze(0)) # \u8c03\u7528\u9ad8\u65af\u751f\u6210\u5668\n t1 = time.time()\n\n # **2. \u4f30\u8ba1\u76f8\u673a\u4f4d\u59ff**\n c2ws_pred, focals_pred = freesplatter.estimate_poses(images, gaussians, use_first_focal=True, pnp_iter=10)\n # **2.1 \u5f52\u4e00\u5316\u76f8\u673a\u57fa\u7ebf**\n baseline_pred = (c2ws_pred[:, :3, 3] - c2ws_pred[:1, :3, 3]).norm() + 1e-2 # \u8ba1\u7b97\u76f8\u673a\u57fa\u7ebf\n scale_factor = 1.0 / baseline_pred # \u5f52\u4e00\u5316\u6bd4\u4f8b\n c2ws_pred = c2ws_pred.clone()\n c2ws_pred[:, :3, 3] *= scale_factor # \u7f29\u653e\u76f8\u673a\u4f4d\u7f6e\n\n # **3. \u53ef\u89c6\u5316\u76f8\u673a**\n fig = self.visualize_cameras_scene(images, c2ws_pred, focals_pred, legends=legends)\n t2 = time.time()\n\n # **4. \u4fdd\u5b58\u9ad8\u65af\u70b9\u4e91**\n gs_vis_path = os.path.join(self.output_dir, 'gs_vis.ply')\n save_gaussian(gaussians, gs_vis_path, freesplatter, opacity_threshold=5e-3) # \u4fdd\u5b58\u70b9\u4e91\n print(f'Save gaussian at {gs_vis_path}')\n\n # **5. \u6e32\u67d3\u89c6\u9891**\n with torch.inference_mode():\n c2ws_video = generate_interpolated_path(c2ws_pred.detach().cpu().numpy()[:, :3, :], n_interp=120) # \u751f\u6210\u63d2\u503c\u8def\u5f84\n c2ws_video = torch.cat([\n torch.from_numpy(c2ws_video), \n torch.tensor([0, 0, 0, 1]).reshape(1, 1, 4).repeat(c2ws_video.shape[0], 1, 1)\n ], dim=1).to(gaussians) # \u5c06\u8def\u5f84\u8f6c\u6362\u4e3a\u9f50\u6b21\u5750\u6807\n fx = fy = focals_pred.mean() / 512.0 # \u8ba1\u7b97\u5f52\u4e00\u5316\u7126\u8ddd\n cx = cy = torch.ones_like(fx) * 0.5 # \u5f52\u4e00\u5316\u4e2d\u5fc3\u70b9\n fxfycxcy_video = torch.tensor([fx, fy, cx, cy]).unsqueeze(0).repeat(c2ws_video.shape[0], 1).to(device)\n\n video_frames = freesplatter.forward_renderer(\n gaussians, # \u9ad8\u65af\u70b9\u4e91\n c2ws_video.unsqueeze(0), # \u76f8\u673a\u8f68\u8ff9\n fxfycxcy_video.unsqueeze(0), # \u76f8\u673a\u53c2\u6570\n rescale=scale_factor.reshape(1).to(gaussians) # \u7f29\u653e\u6bd4\u4f8b\n )['image'][0].clamp(0, 1) # \u6e32\u67d3\u56fe\u50cf\u5e27\n\n video_path = os.path.join(self.output_dir, 'gs.mp4')\n save_video(video_frames, video_path, fps=30) # \u4fdd\u5b58\u89c6\u9891\n print(f'Save video at {video_path}')\n t3 = time.time()\n\n # **6. \u6253\u5370\u65f6\u95f4\u7edf\u8ba1**\n print(f'Generate Gaussians: {t1-t0:.2f} seconds.')\n print(f'Estimate poses: {t2-t1:.2f} seconds.')\n print(f'Generate video: {t3-t2:.2f} seconds.')\n\n return gs_vis_path, video_path, fig\n\n\ndef visualize_cameras_scene(\n self, \n images, # \u8f93\u5165\u7684\u56fe\u50cf\n c2ws, # \u76f8\u673a\u4f4d\u59ff\n focal_length, # \u7126\u8ddd\n legends=None, # \u6bcf\u4e2a\u89c6\u89d2\u7684\u56fe\u4f8b\n):\n # **1. \u5904\u7406\u56fe\u50cf**\uff1a\u8c03\u6574\u5206\u8fa8\u7387\uff0c\u5f52\u4e00\u5316\uff0c\u8f6c\u6362\u4e3a NumPy \u683c\u5f0f\n images = v2.functional.resize(images, 128, interpolation=3, antialias=True).clamp(0, 1)\n images = (images.permute(0, 2, 3, 1).detach().cpu().numpy() * 255).astype(np.uint8)\n\n # **2. \u8f6c\u6362\u76f8\u673a\u4f4d\u59ff\u5750\u6807\u7cfb**\n c2ws = c2ws.detach().cpu().numpy()\n c2ws[:, :, 1:3] *= -1 # OpenCV \u5230 OpenGL \u8f6c\u6362\n\n # **3. \u8ba1\u7b97\u89c6\u573a\u89d2\uff08FOV\uff09**\n focal_length = focal_length.mean().detach().cpu().numpy()\n fov = np.rad2deg(np.arctan(256.0 / focal_length)) * 2\n\n # **4. \u4e3a\u6bcf\u4e2a\u89c6\u89d2\u5206\u914d\u989c\u8272**\n colors = [cmap(i / len(images))[:3] for i in range(len(images))]\n\n # **5. \u8bbe\u7f6e\u56fe\u4f8b**\n legends = [None] * len(images) if legends is None else legends\n\n # **6. \u53ef\u89c6\u5316\u76f8\u673a**\n viz = CameraVisualizer(c2ws, legends, colors, images=images)\n fig = viz.update_figure(\n 2, \n height=320,\n line_width=5,\n base_radius=1, \n zoom_scale=1, \n fov_deg=fov, \n show_grid=True, \n show_ticklabels=True, \n show_background=True, \n y_up=False,\n )\n return fig\n
","tags":["\u79d1\u7814","\u4e09\u7ef4\u91cd\u5efa","\u590d\u73b0"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/","title":"Gaussian_Splatting_Code","text":" \u7ea6 982 \u4e2a\u5b57 34 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 5 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u79d1\u7814 #\u590d\u73b0 #\u4e09\u7ef4\u91cd\u5efa","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#gaussian_splatting_code","title":"Gaussian_Splatting_Code","text":"\u4e3b\u8981\u6709 colmap \u548c blender \u4e24\u79cd\u6570\u636e\u7c7b\u578b\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#1-colmap","title":"1. Colmap \u6570\u636e\u96c6\u52a0\u8f7d\u8fc7\u7a0b","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#11","title":"1.1 \u52a0\u8f7d\u51fd\u6570","text":" - \u4e3b\u51fd\u6570\u4e3a
readColmapSceneInfo\uff0c\u8c03\u7528\u4e86\u4e00\u7cfb\u5217\u5b50\u51fd\u6570\u4ee5\u52a0\u8f7d\u76f8\u673a\u53c2\u6570\u3001\u70b9\u4e91\u548c\u76f8\u5173\u6587\u4ef6\u3002 - \u6570\u636e\u6765\u6e90\u8def\u5f84\u4e3a
sparse/ \u6587\u4ef6\u5939\uff0c\u5305\u542b\u76f8\u673a\u53c2\u6570\u6587\u4ef6\u3001\u70b9\u4e91\u6587\u4ef6\u7b49\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#12","title":"1.2 \u52a0\u8f7d\u7684\u4e3b\u8981\u6b65\u9aa4","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#1","title":"\u6b65\u9aa4 1\uff1a\u52a0\u8f7d\u76f8\u673a\u5916\u53c2","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythoncameras_extrinsic_file = os.path.join(path, \"sparse/0\", \"images.bin\")\ncam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file)\n
-
\u5982\u679c images.bin \u6587\u4ef6\u4e0d\u5b58\u5728\uff0c\u5219\u52a0\u8f7d images.txt\uff1a
Pythoncameras_extrinsic_file = os.path.join(path, \"sparse/0\", \"images.txt\")\ncam_extrinsics = read_extrinsics_text(cameras_extrinsic_file)\n
-
\u5916\u53c2\u5305\u62ec\uff1a
- \u76f8\u673a\u65cb\u8f6c\u77e9\u9635 (
R)\u3002 - \u5e73\u79fb\u5411\u91cf (
T)\u3002 - \u56fe\u50cf\u540d\u79f0\uff0c\u7528\u4e8e\u5339\u914d\u56fe\u50cf\u6587\u4ef6\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#2","title":"\u6b65\u9aa4 2\uff1a\u52a0\u8f7d\u76f8\u673a\u5185\u53c2","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythoncameras_intrinsic_file = os.path.join(path, \"sparse/0\", \"cameras.bin\")\ncam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file)\n
-
\u5982\u679c cameras.bin \u6587\u4ef6\u4e0d\u5b58\u5728\uff0c\u5219\u52a0\u8f7d cameras.txt\uff1a
Pythoncameras_intrinsic_file = os.path.join(path, \"sparse/0\", \"cameras.txt\")\ncam_intrinsics = read_intrinsics_text(cameras_intrinsic_file)\n
-
\u5185\u53c2\u5305\u62ec\uff1a
- \u7126\u8ddd\uff08
focal length\uff09\u3002 - \u4e3b\u70b9\u5750\u6807\uff08
principal point\uff09\u3002 - \u56fe\u50cf\u5206\u8fa8\u7387\uff08
width \u548c height\uff09\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#3","title":"\u6b65\u9aa4 3\uff1a\u52a0\u8f7d\u70b9\u4e91","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythonply_path = os.path.join(path, \"sparse/0/points3D.ply\")\nbin_path = os.path.join(path, \"sparse/0/points3D.bin\")\ntxt_path = os.path.join(path, \"sparse/0/points3D.txt\")\n\nif not os.path.exists(ply_path):\n print(\"Converting point3d.bin to .ply, will happen only the first time you open the scene.\")\n xyz, rgb, _ = read_points3D_binary(bin_path)\n storePly(ply_path, xyz, rgb)\npcd = fetchPly(ply_path)\n
- \u70b9\u4e91\u6587\u4ef6\u7684\u6765\u6e90\u53ef\u4ee5\u662f\uff1a
points3D.binpoints3D.txtpoints3D.ply\uff08\u5982\u679c\u5b58\u5728\u5219\u76f4\u63a5\u52a0\u8f7d\uff0c\u5426\u5219\u4ece bin \u6216 txt \u8f6c\u6362\u4e3a .ply\uff09
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#4","title":"\u6b65\u9aa4 4\uff1a\u8bad\u7ec3/\u6d4b\u8bd5\u96c6\u5212\u5206","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythonif eval:\n cam_names = [cam_extrinsics[cam_id].name for cam_id in cam_extrinsics]\n cam_names = sorted(cam_names)\n test_cam_names_list = [name for idx, name in enumerate(cam_names) if idx % llffhold == 0]\nelse:\n test_cam_names_list = []\n
- \u6d4b\u8bd5\u96c6\u53ef\u4ee5\u901a\u8fc7
eval \u53c2\u6570\u542f\u7528\u3002 - \u9ed8\u8ba4\u6bcf 8 \u4e2a\u89c6\u89d2\uff08
llffhold=8\uff09\u9009\u53d6\u4e00\u4e2a\u4f5c\u4e3a\u6d4b\u8bd5\u96c6\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#5","title":"\u6b65\u9aa4 5\uff1a\u573a\u666f\u5f52\u4e00\u5316","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythonnerf_normalization = getNerfppNorm(train_cam_infos)\n
- \u901a\u8fc7\u8ba1\u7b97\u8bad\u7ec3\u76f8\u673a\u7684\u4e2d\u5fc3\u548c\u5bf9\u89d2\u7ebf\u957f\u5ea6\uff0c\u786e\u5b9a\u573a\u666f\u4e2d\u5fc3 (
translate) \u548c\u534a\u5f84 (radius)\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#13","title":"1.3 \u8fd4\u56de\u6570\u636e\u7ed3\u6784","text":"\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a SceneInfo \u6570\u636e\u7ed3\u6784\uff1a
point_cloud\uff1a\u70b9\u4e91\u6570\u636e\u3002train_cameras\uff1a\u8bad\u7ec3\u76f8\u673a\u5217\u8868\u3002test_cameras\uff1a\u6d4b\u8bd5\u76f8\u673a\u5217\u8868\u3002nerf_normalization\uff1a\u573a\u666f\u5f52\u4e00\u5316\u53c2\u6570\u3002ply_path\uff1a\u70b9\u4e91\u6587\u4ef6\u8def\u5f84\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#2-blender","title":"2. Blender \u6570\u636e\u96c6\u52a0\u8f7d\u8fc7\u7a0b","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#21","title":"2.1 \u52a0\u8f7d\u51fd\u6570","text":" - \u4e3b\u51fd\u6570\u4e3a
readNerfSyntheticInfo\uff0c\u4ece Blender \u5bfc\u51fa\u7684 JSON \u6587\u4ef6\u52a0\u8f7d\u76f8\u673a\u548c\u56fe\u50cf\u4fe1\u606f\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#22","title":"2.2 \u52a0\u8f7d\u7684\u4e3b\u8981\u6b65\u9aa4","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#1_1","title":"\u6b65\u9aa4 1\uff1a\u52a0\u8f7d\u8bad\u7ec3\u548c\u6d4b\u8bd5\u76f8\u673a","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythontrain_cam_infos = readCamerasFromTransforms(path, \"transforms_train.json\", depths_folder, white_background, False, extension)\ntest_cam_infos = readCamerasFromTransforms(path, \"transforms_test.json\", depths_folder, white_background, True, extension)\n
- \u6bcf\u4e2a\u76f8\u673a\u7684\u6570\u636e\u6765\u6e90\u4e8e
transforms_train.json \u548c transforms_test.json\u3002 transforms_train.json: - \u7528\u4e8e\u63cf\u8ff0\u8bad\u7ec3\u96c6\u4e2d\u76f8\u673a\u7684\u4f4d\u7f6e\u4fe1\u606f\uff08\u5916\u53c2\uff09\u3001\u76f8\u673a\u7684\u5185\u53c2\uff08\u6c34\u5e73\u89c6\u89d2\uff09\u4ee5\u53ca\u5bf9\u5e94\u7684\u56fe\u50cf\u8def\u5f84\u3002
- \u6bcf\u4e2a\u76f8\u673a\u89c6\u89d2\u90fd\u4f1a\u6709\u4e00\u6761\u8bb0\u5f55\u3002
- \u76f8\u673a\u53c2\u6570\u5305\u542b\uff1a
transform_matrix\uff08\u76f8\u673a\u5230\u4e16\u754c\u7684\u53d8\u6362\u77e9\u9635\uff09\u3002- \u6c34\u5e73\u89c6\u89d2 (
camera_angle_x) \u7528\u4e8e\u8ba1\u7b97\u7126\u8ddd\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#2_1","title":"\u6b65\u9aa4 2\uff1a\u751f\u6210\u70b9\u4e91","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythonif not os.path.exists(ply_path):\n num_pts = 100_000\n print(f\"Generating random point cloud ({num_pts})...\")\n xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3\n shs = np.random.random((num_pts, 3)) / 255.0\n storePly(ply_path, xyz, SH2RGB(shs) * 255)\npcd = fetchPly(ply_path)\n
- Blender \u6570\u636e\u96c6\u6ca1\u6709\u70b9\u4e91\u6587\u4ef6\uff0c\u56e0\u6b64\u968f\u673a\u751f\u6210 10 \u4e07\u4e2a\u70b9\uff1a
- \u70b9\u7684\u4f4d\u7f6e\u8303\u56f4\u4e3a
[-1.3, 1.3]\u3002 - \u70b9\u7684\u989c\u8272\u901a\u8fc7\u7403\u8c10\u51fd\u6570 (SH) \u968f\u673a\u751f\u6210\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#3_1","title":"\u6b65\u9aa4 3\uff1a\u573a\u666f\u5f52\u4e00\u5316","text":"\u4ee3\u7801\u5982\u4e0b\uff1a
Pythonnerf_normalization = getNerfppNorm(train_cam_infos)\n
- \u4e0e Colmap \u7c7b\u4f3c\uff0c\u8ba1\u7b97\u573a\u666f\u7684\u4e2d\u5fc3 (
translate) \u548c\u534a\u5f84 (radius)\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#23","title":"2.3 \u8fd4\u56de\u6570\u636e\u7ed3\u6784","text":"\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a SceneInfo \u6570\u636e\u7ed3\u6784\uff1a
point_cloud\uff1a\u751f\u6210\u7684\u70b9\u4e91\u3002train_cameras\uff1a\u8bad\u7ec3\u76f8\u673a\u5217\u8868\u3002test_cameras\uff1a\u6d4b\u8bd5\u76f8\u673a\u5217\u8868\u3002nerf_normalization\uff1a\u573a\u666f\u5f52\u4e00\u5316\u53c2\u6570\u3002ply_path\uff1a\u70b9\u4e91\u6587\u4ef6\u8def\u5f84\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#3_2","title":"3. \u603b\u7ed3\u5bf9\u6bd4","text":"\u7279\u6027 Colmap \u6570\u636e\u96c6 Blender \u6570\u636e\u96c6 \u76f8\u673a\u53c2\u6570 \u4ece images.bin \u548c cameras.bin \u4e2d\u52a0\u8f7d\u5916\u53c2\u548c\u5185\u53c2 \u4ece transforms_train.json \u4e2d\u52a0\u8f7d\u53d8\u6362\u77e9\u9635 \u70b9\u4e91 \u4ece points3D.bin \u6216 points3D.txt \u52a0\u8f7d \u968f\u673a\u751f\u6210 10 \u4e07\u4e2a\u70b9 \u56fe\u50cf\u6570\u636e \u5b58\u50a8\u5728 images/ \u6587\u4ef6\u5939 \u8def\u5f84\u7531 JSON \u6587\u4ef6\u6307\u5b9a \u6df1\u5ea6\u56fe \u53ef\u9009\uff0c\u9700 depth_params.json \u5b9a\u4e49 \u53ef\u9009\uff0c\u9700\u63d0\u4f9b\u6df1\u5ea6\u56fe\u6587\u4ef6\u5939 \u5f52\u4e00\u5316 \u57fa\u4e8e\u76f8\u673a\u4f4d\u7f6e\u8ba1\u7b97\u573a\u666f\u4e2d\u5fc3\u548c\u534a\u5f84 \u540c\u4e0a \u6d4b\u8bd5\u96c6\u5212\u5206 \u6bcf 8 \u4e2a\u89c6\u89d2\u5212\u5206\u4e00\u4e2a\u5230\u6d4b\u8bd5\u96c6\uff0c\u6216\u8bfb\u53d6 test.txt \u6d4b\u8bd5\u96c6\u901a\u8fc7 transforms_test.json \u6307\u5b9a \u80cc\u666f\u989c\u8272 \u65e0 \u53ef\u9009\u767d\u8272\u6216\u9ed1\u8272\u80cc\u666f","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#4_1","title":"4. \u6240\u9700\u51c6\u5907\u7684\u6587\u4ef6","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#colmap","title":"Colmap \u6570\u636e\u96c6","text":" - \u76f8\u673a\u5916\u53c2\uff1a
sparse/0/images.bin \u6216 images.txt\u3002 - \u76f8\u673a\u5185\u53c2\uff1a
sparse/0/cameras.bin \u6216 cameras.txt\u3002 - \u70b9\u4e91\u6587\u4ef6\uff1a
sparse/0/points3D.bin \u6216 points3D.txt\u3002 - \u56fe\u50cf\u6587\u4ef6\u5939\uff1a\u901a\u5e38\u4e3a
images/\u3002 - \u53ef\u9009\u6587\u4ef6\uff1a
sparse/0/depth_params.json\uff08\u6df1\u5ea6\u53c2\u6570\uff09\u3002sparse/0/test.txt\uff08\u6d4b\u8bd5\u96c6\u5212\u5206\u6587\u4ef6\uff09\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#blender","title":"Blender \u6570\u636e\u96c6","text":" - \u8bad\u7ec3\u76f8\u673a\u6587\u4ef6\uff1a
transforms_train.json\u3002 - \u6d4b\u8bd5\u76f8\u673a\u6587\u4ef6\uff1a
transforms_test.json\u3002 - \u56fe\u50cf\u6587\u4ef6\u8def\u5f84\uff1a\u7531 JSON \u6587\u4ef6\u6307\u5b9a\u3002
- \u53ef\u9009\u6587\u4ef6\uff1a
- \u6df1\u5ea6\u56fe\u6587\u4ef6\u5939\u3002
- \u80cc\u666f\u989c\u8272\u53c2\u6570\uff08
white_background\uff09\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_Code/#blender_1","title":"Blender","text":"Random Point Initialization \u00b7 Issue #39 \u00b7 graphdeco-inria/gaussian-splatting \u00b7 GitHub
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/","title":"Gaussian Splatting \u590d\u73b0","text":" \u7ea6 1407 \u4e2a\u5b57 4 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 7 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u79d1\u7814 #\u590d\u73b0 #\u4e09\u7ef4\u91cd\u5efa","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#gaussian-splatting","title":"Gaussian Splatting \u590d\u73b0","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#_1","title":"\u5b98\u65b9\u6570\u636e\u96c6\u590d\u73b0","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#prepare","title":"prepare","text":"Text Onlygit clone https://github.com/graphdeco-inria/gaussian-splatting --recursive\nconda env create --file environment.yml\nconda activate gaussian_splatting\n
- \u53bb\u4e0b\u8f7d\u4e2a\u6570\u636e\u96c6
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#train","title":"train","text":"Text Onlypython train.py -s ./data/tandt/train/ -m ./output/train/ --eval\n\nOptimizing ./output/train/\nOutput folder: ./output/train/ [31/12 20:58:06]\nTensorboard not available: not logging progress [31/12 20:58:06]\n------------LLFF HOLD------------- [31/12 20:58:08]\nReading camera 301/301 [31/12 20:58:08]\nConverting point3d.bin to .ply, will happen only the first time you open the scene. [31/12 20:58:08]\nLoading Training Cameras [31/12 20:58:09]\nLoading Test Cameras [31/12 20:58:19]\nNumber of points at initialisation : 182686 [31/12 20:58:20]\nTraining progress: 23%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 7000/30000 [02:04<07:36, 50.34it/s, Loss=0.0985708, Depth Loss=0.0000000]\n[ITER 7000] Evaluating test: L1 0.07567951896865116 PSNR 19.8115311672813 [31/12 21:00:25]\n\n[ITER 7000] Evaluating train: L1 0.05251172706484795 PSNR 21.924707794189455 [31/12 21:00:25]\n\n[ITER 7000] Saving Gaussians [31/12 21:00:25]\nTraining progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30000/30000 [10:58<00:00, 45.54it/s, Loss=0.0425664, Depth Loss=0.0000000]\n\n[ITER 30000] Evaluating test: L1 0.05784237639684426 PSNR 22.03640069459614 [31/12 21:09:19]\n\n[ITER 30000] Evaluating train: L1 0.025540136173367502 PSNR 27.51155662536621 [31/12 21:09:19]\n\n[ITER 30000] Saving Gaussians [31/12 21:09:19]\n\nTraining complete. [31/12 21:09:28]\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#render","title":"render","text":"Text Onlypython render.py -m ./output/train/\n\nLooking for config file in ./output/train/cfg_args\nConfig file found: ./output/train/cfg_args\nRendering ./output/train/\nLoading trained model at iteration 30000 [31/12 21:14:40]\n------------LLFF HOLD------------- [31/12 21:14:41]\nReading camera 301/301 [31/12 21:14:41]\nLoading Training Cameras [31/12 21:14:41]\nLoading Test Cameras [31/12 21:14:53]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 263/263 [01:41<00:00, 2.58it/s]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 38/38 [00:15<00:00, 2.42it/s]\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#evaluate","title":"evaluate","text":"Text Onlypython metrics.py -m ./output/train/\n\nScene: ./output/train/\nMethod: ours_30000\nMetric evaluation progress: 0%| | 0/38 [00:00<?, ?it/s]Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /home/fanghaotian/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 528M/528M [00:28<00:00, 19.4MB/s]\nDownloading: \"https://raw.githubusercontent.com/richzhang/PerceptualSimilarity/master/lpips/weights/v0.1/vgg.pth\" to /home/fanghaotian/.cache/torch/hub/checkpoints/vgg.pth\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7.12k/7.12k [00:03<00:00, 2.29kB/s]\nMetric evaluation progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 38/38 [07:57<00:00, 12.58s/it] 7.12k/7.12k [00:03<00:00, 2.29kB/s]\n SSIM : 0.8192384\n PSNR : 22.0094910\n LPIPS: 0.1966141\n
\u770b\u8d77\u6765\u6bd4\u8bba\u6587\u91cc\u7684\u6570\u636e\u4f4e\u3002
.\\SIBR_remoteGaussian_app.exe \u8fdc\u7a0b\u4e0d\u80fd\u7528\uff0c issue \u4e2d\u4e5f\u672a\u89e3\u51b3
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#interactive-viewers","title":"Interactive Viewers","text":"Text Onlyscp -r -P 26000 fanghaotian@RHOS:/home/fanghaotian/3DGS/gaussian-splatting/output/ C:\\Users\\fanghaotian\\Desktop\\\n\n./SIBR_gaussianViewer_app -m C:\\Users\\fanghaotian\\Desktop\\data\\82ea91ef-6\\\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#blender","title":"\u7528 blender \u6570\u636e\u96c6","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#prepare_1","title":"prepare","text":" - \u6570\u636e\u96c6
- GitHub - bmild/nerf: Code release for NeRF (Neural Radiance Fields)
nerf_synthetic/lego \u90a3\u4e2a\u662f blender \u7684\u683c\u5f0f
Text Onlytree -I \"*.png\"\n.\n\u2514\u2500\u2500 lego\n \u251c\u2500\u2500 test\n \u251c\u2500\u2500 train\n \u251c\u2500\u2500 transforms_test.json\n \u251c\u2500\u2500 transforms_train.json\n \u251c\u2500\u2500 transforms_val.json\n \u2514\u2500\u2500 val\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#iterations30000-no-evaluation","title":"Iterations=30000 no evaluation","text":"","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#train_1","title":"train","text":"Text Onlypython train.py -s ./data/nerf_synthetic/lego/ -m ./output/lego/\n\nOptimizing ./output/lego/\nOutput folder: ./output/lego/ [01/01 13:31:23]\nTensorboard not available: not logging progress [01/01 13:31:23]\nFound transforms_train.json file, assuming Blender data set! [01/01 13:31:23]\nReading Training Transforms [01/01 13:31:23]\nReading Test Transforms [01/01 13:31:31]\nGenerating random point cloud (100000)... [01/01 13:31:38]\nLoading Training Cameras [01/01 13:31:39]\nLoading Test Cameras [01/01 13:31:53]\nNumber of points at initialisation : 100000 [01/01 13:31:53]\nTraining progress: 23%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 7000/30000 [01:15<04:12, 91.02it/s, Loss=0.0152277, Depth Loss=0.0000000]\n[ITER 7000] Evaluating train: L1 0.3646775007247925 PSNR 5.983335494995117 [01/01 13:33:09]\n\n[ITER 7000] Saving Gaussians [01/01 13:33:09]\nTraining progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30000/30000 [05:26<00:00, 91.97it/s, Loss=0.0105094, Depth Loss=0.0000000]\n\n[ITER 30000] Evaluating train: L1 0.6417351365089417 PSNR 2.185275435447693 [01/01 13:37:19]\n\n[ITER 30000] Saving Gaussians [01/01 13:37:19]\n\nTraining complete. [01/01 13:37:21]\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#render_1","title":"render","text":"Text Onlypython render.py -m ./output/lego/\n\nLooking for config file in ./output/lego/cfg_args\nConfig file found: ./output/lego/cfg_args\nRendering ./output/lego/\nLoading trained model at iteration 30000 [01/01 13:41:14]\nFound transforms_train.json file, assuming Blender data set! [01/01 13:41:14]\nReading Training Transforms [01/01 13:41:14]\nReading Test Transforms [01/01 13:41:20]\nLoading Training Cameras [01/01 13:41:28]\nLoading Test Cameras [01/01 13:41:44]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 300/300 [01:06<00:00, 4.48it/s]\nRendering progress: 0it [00:00, ?it/s]\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#metric","title":"metric","text":"Text Onlypython metrics.py -m ./output/lego/\n\nScene: ./output/lego/\nMethod: ours_30000\nMetric evaluation progress: 0it [00:00, ?it/s]\n SSIM : nan\n PSNR : nan\n LPIPS: nan\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#interactive-viewers_1","title":"Interactive Viewers","text":"Text Onlyscp -r -P 26000 fanghaotian@RHOS:/home/fanghaotian/3DGS/gaussian-splatting/output/lego C:\\Users\\fanghaotian\\Desktop\\\n\n./SIBR_gaussianViewer_app -m C:\\Users\\fanghaotian\\Desktop\\lego\n
\ud83e\udd14\uff0c\u597d\u50cf\u4e00\u5b9a\u8981 --eval\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#iteratations30000-evaluation","title":"Iteratations=30000 + evaluation","text":"\u90a3\u4e48\u52a0\u4e0a\u4ee5\u540e\u7528 iterations=300000\uff08default\uff09\u518d\u8dd1\u4e00\u6b21\u3002
Text Onlypython train.py -s ./data/nerf_synthetic/lego/ -m ./output/lego_eval/ --eval\n\nOptimizing ./output/lego_eval/\nOutput folder: ./output/lego_eval/ [01/01 13:43:45]\nTensorboard not available: not logging progress [01/01 13:43:45]\nFound transforms_train.json file, assuming Blender data set! [01/01 13:43:45]\nReading Training Transforms [01/01 13:43:45]\nReading Test Transforms [01/01 13:43:51]\nLoading Training Cameras [01/01 13:43:58]\nLoading Test Cameras [01/01 13:44:06]\nNumber of points at initialisation : 100000 [01/01 13:44:12]\nTraining progress: 23%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 7000/30000 [01:24<04:48, 79.82it/s, Loss=0.0153927, Depth Loss=0.0000000]\n[ITER 7000] Evaluating test: L1 0.34677238538861277 PSNR 6.254974584579468 [01/01 13:45:38]\n\n[ITER 7000] Evaluating train: L1 0.3650033831596375 PSNR 5.939675426483155 [01/01 13:45:38]\n\n[ITER 7000] Saving Gaussians [01/01 13:45:38]\nTraining progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30000/30000 [05:47<00:00, 86.33it/s, Loss=0.0095609, Depth Loss=0.0000000]\n\n[ITER 30000] Evaluating test: L1 0.557637879550457 PSNR 3.0822176444530487 [01/01 13:50:00]\n\n[ITER 30000] Evaluating train: L1 0.5475435316562652 PSNR 3.064222288131714 [01/01 13:50:00]\n\n[ITER 30000] Saving Gaussians [01/01 13:50:00]\n\nTraining complete. [01/01 13:50:04]\n\n\npython render.py -m ./output/lego_eval/\n\nLooking for config file in ./output/lego_eval/cfg_args\nConfig file found: ./output/lego_eval/cfg_args\nRendering ./output/lego_eval/\nLoading trained model at iteration 30000 [01/01 13:51:20]\nFound transforms_train.json file, assuming Blender data set! [01/01 13:51:20]\nReading Training Transforms [01/01 13:51:20]\nReading Test Transforms [01/01 13:51:26]\nLoading Training Cameras [01/01 13:51:33]\nLoading Test Cameras [01/01 13:51:40]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 100/100 [00:25<00:00, 3.94it/s]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:48<00:00, 4.13it/s]\n\n\npython metrics.py -m ./output/lego_eval/\n\nScene: ./output/lego_eval/\nMethod: ours_30000\nMetric evaluation progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [04:00<00:00, 1.20s/it]\n SSIM : 0.2604475\n PSNR : 3.0649595\n LPIPS: 0.5116847\n
\u770b\u8d77\u6765\u6548\u679c\u4e0d\u592a\u597d\u3002 \u5e94\u8be5\u662f\u521d\u59cb\u5316\u7684\u95ee\u9898\uff0cL1 \u751a\u81f3\u8fd8\u662f\u4e0a\u6b21\u7684 10 \u500d, Evaluating train: L1 0.5475435316562652 PSNR 3.064222288131714 \u3002
\u628a iterations \u8c03\u5230 100000 \u518d\u6765\u4e00\u6b21\uff0c\u770b\u770b\u6536\u655b\u60c5\u51b5\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#iterations100000-evaluation","title":"Iterations=100000 + evaluation","text":"Text Only python train.py -s ./data/nerf_synthetic/lego/ -m ./output/lego_eval_100000/ --iterations 100000 --eval\n\nOptimizing ./output/lego_eval_100000/\nOutput folder: ./output/lego_eval_100000/ [01/01 14:00:46]\nTensorboard not available: not logging progress [01/01 14:00:46]\nFound transforms_train.json file, assuming Blender data set! [01/01 14:00:46]\nReading Training Transforms [01/01 14:00:46]\nReading Test Transforms [01/01 14:00:53]\nLoading Training Cameras [01/01 14:01:00]\nLoading Test Cameras [01/01 14:01:08]\nNumber of points at initialisation : 100000 [01/01 14:01:15]\nTraining progress: 7%|\u2588\u2588\u2588\u2588\u2588\u2589 | 7000/100000 [01:23<17:42, 87.50it/s, Loss=0.0159089, Depth Loss=0.0000000]\n[ITER 7000] Evaluating test: L1 0.34856061153113843 PSNR 6.298382818698883 [01/01 14:02:40]\n\n[ITER 7000] Evaluating train: L1 0.36839944720268253 PSNR 5.857858657836914 [01/01 14:02:40]\n\n[ITER 7000] Saving Gaussians [01/01 14:02:40]\nTraining progress: 30%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 29990/100000 [05:45<10:51, 107.52it/s, Loss=0.0095307, Depth Loss=0.0000000]\n[ITER 30000] Evaluating test: L1 0.6024469056725502 PSNR 2.6481225460767748 [01/01 14:07:02]\n\n[ITER 30000] Evaluating train: L1 0.5561836898326874 PSNR 2.9405082702636722 [01/01 14:07:02]\n\n[ITER 30000] Saving Gaussians [01/01 14:07:02]\nTraining progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 100000/100000 [16:33<00:00, 100.62it/s, Loss=0.0109117, Depth Loss=0.0000000]\n\n[ITER 100000] Saving Gaussians [01/01 14:17:49]\n\nTraining complete. [01/01 14:17:51]\n
Text Onlypython render.py -m ./output/lego_eval_100000/\nLooking for config file in ./output/lego_eval_100000/cfg_args\nConfig file found: ./output/lego_eval_100000/cfg_args\nRendering ./output/lego_eval_100000/\nLoading trained model at iteration 100000 [01/01 14:21:43]\nFound transforms_train.json file, assuming Blender data set! [01/01 14:21:43]\nReading Training Transforms [01/01 14:21:43]\nReading Test Transforms [01/01 14:21:50]\nLoading Training Cameras [01/01 14:21:57]\nLoading Test Cameras [01/01 14:22:06]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 100/100 [00:22<00:00, 4.39it/s]\nRendering progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:42<00:00, 4.65it/s]\n
Text Onlypython metrics.py -m ./output/lego_eval_100000/\n\nScene: ./output/lego_eval_100000/\nMethod: ours_100000\nMetric evaluation progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [04:03<00:00, 1.22s/it]\n SSIM : 0.2404845\n PSNR : 2.4990394\n LPIPS: 0.5251624\n
\u597d\u50cf\u8bc4\u4f30\u4ecd\u7136\u4e0d\u592a\u884c\uff0c\u8fd8\u8d8a\u8bad\u7ec3\u8d8a\u5dee\u4e86\u3002
Text Onlyscp -r -P 26000 fanghaotian@RHOS:/home/fanghaotian/3DGS/gaussian-splatting/output/lego_eval_100000/ C:\\Users\\fanghaotian\\Desktop\\\n
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#blender-colmap","title":"Blender \u8f6c Colmap","text":" - Frequently Asked Questions \u2014 COLMAP 3.12.0.dev0 documentation
Text Onlycolmap automatic_reconstructor --workspace_path . --image_path ./images --sparse 1 --camera_model SIMPLE_PINHOLE --dense 0\n# colmap automatic_reconstructor: \u8fd9\u662f\u8c03\u7528COLMAP\u7a0b\u5e8f\u4e2d\u7684\u81ea\u52a8\u91cd\u5efa\u6a21\u5757\uff0c\u5b83\u4f1a\u81ea\u52a8\u5b8c\u6210\u7279\u5f81\u63d0\u53d6\u3001\u5339\u914d\u3001\u91cd\u6295\u5f71\u8bef\u5dee\u4f18\u5316\u548c\u4e09\u89d2\u5316\u7b49\u6b65\u9aa4\uff0c\u4ee5\u751f\u6210\u573a\u666f\u7684\u7a00\u758f3D\u70b9\u4e91\u6a21\u578b\u3002\n# --workspace_path .: \u6307\u5b9a\u4e86\u5de5\u4f5c\u7a7a\u95f4\u8def\u5f84\u4e3a\u5f53\u524d\u76ee\u5f55(.)\uff0c\u5728\u8fd9\u4e2a\u8def\u5f84\u4e0b\uff0cCOLMAP\u5c06\u5b58\u50a8\u4e2d\u95f4\u7ed3\u679c\u4ee5\u53ca\u6700\u7ec8\u7684\u91cd\u5efa\u8f93\u51fa\u6587\u4ef6\u3002\n# --image_path ./images: \u5b9a\u4e49\u4e86\u56fe\u50cf\u6570\u636e\u96c6\u6240\u5728\u7684\u8def\u5f84\uff0c\u5373\u6240\u6709\u53c2\u4e0e\u91cd\u5efa\u7684\u56fe\u7247\u90fd\u4f4d\u4e8e./images\u76ee\u5f55\u4e0b\u3002\n# --sparse 1: \u8fd9\u4e2a\u53c2\u6570\u8868\u793a\u8fdb\u884c\u7a00\u758f\u91cd\u5efa\uff08\u4e0e\u5bc6\u96c6\u91cd\u5efa\u76f8\u5bf9\uff09\uff0c\u5373\u53ea\u6784\u5efa\u51fa\u573a\u666f\u4e2d\u7684\u5173\u952e\u70b9\u53ca\u5176\u5bf9\u5e94\u5173\u7cfb\uff0c\u5e76\u901a\u8fc7\u8fd9\u4e9b\u4fe1\u606f\u751f\u6210\u4e00\u4e2a\u7531\u7a00\u758f\u70b9\u4e91\u7ec4\u6210\u7684\u4e09\u7ef4\u6a21\u578b\u3002\n# --camera_model SIMPLE_PINHOLE: \u6307\u5b9a\u4f7f\u7528\u7684\u76f8\u673a\u6a21\u578b\u4e3a\u201c\u7b80\u5355\u9488\u5b54\u6a21\u578b\u201d\uff08Simple Pinhole Model\uff09\u3002\u8fd9\u610f\u5473\u7740COLMAP\u5728\u8fdb\u884c\u91cd\u5efa\u65f6\u5c06\u5047\u8bbe\u76f8\u673a\u9075\u5faa\u7684\u662f\u6700\u57fa\u7840\u7684\u51e0\u4f55\u6295\u5f71\u6a21\u578b\uff0c\u5176\u4e2d\u4e0d\u5305\u62ec\u50cf\u5f84\u5411\u7578\u53d8\u8fd9\u6837\u7684\u590d\u6742\u56e0\u7d20\u3002\n# --dense 0\uff0c\u51cf\u5c11\u4e0d\u9700\u8981\u7684\u8ba1\u7b97\u64cd\u4f5c\u3002\n
\u7136\u540e train render metric
Text Only(gaussian_splatting) fanghaotian@rhos-Super-Server:~/3DGS/gaussian-splatting$ python metrics.py -m ./output/lego_colmap/\n\nScene: ./output/lego_colmap/\nMethod: ours_30000\nMetric evaluation progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:23<00:00, 1.83s/it]\n SSIM : 0.2915006\n PSNR : 4.3199711\n LPIPS: 0.4333871\n
\u611f\u89c9\u662f\u8f6c\u6362\u7684\u65f6\u5019\u56fe\u7247\u592a\u5c11\u4e86\uff0c\u7136\u540e\u5c31\u53d8\u5dee\u4e86\u3002render \u7684\u65f6\u5019\u5f88\u5feb\u3002
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#ffmpeg","title":"ffmpeg \u7684\u95ee\u9898","text":"ffmpeg: error while loading shared libraries: libopenh264.so.5:-CSDN\u535a\u5ba2
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#_2","title":"\u4ee3\u7801\u89e3\u8bfb","text":"Gaussian_Splatting_Code
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/#reference","title":"Reference","text":" - Site Unreachable
- \u75283D\u9ad8\u65af\u6cfc\u6e85(3DGS)\u91cd\u5efa\u81ea\u5df1\u7684\u6570\u636e_3d\u9ad8\u65af\u6cfc\u6e85\u6587\u7269\u6570\u5b57\u5316\u91cd\u5efa-CSDN\u535a\u5ba2
- 3D Gaussian Spaltting\u4ee3\u7801\u590d\u73b0\u5168\u6d41\u7a0b\u4e0e\u4ee3\u7801\u7ed3\u6784\u89e3\u8bfb_3d gaussian splatting\u590d\u73b0-CSDN\u535a\u5ba2
- 3D Gaussian Splatting\u590d\u73b0-CSDN\u535a\u5ba2
- Windows\u4e0b3D Gaussian Splatting\u4ece0\u5f00\u59cb\u5b89\u88c5\u914d\u7f6e\u73af\u5883\u53ca\u8bad\u7ec3\u6559\u7a0b_3d gaussian splatting\u5b89\u88c5\u6559\u7a0b-CSDN\u535a\u5ba2
- 3D Gaussian Splatting\u4e3b\u7a0b\u5e8f\u4ee3\u7801\u89e3\u8bfb_3d gaussian splatting\u6838\u5fc3\u4ee3\u7801-CSDN\u535a\u5ba2
","tags":["\u79d1\u7814","\u590d\u73b0","\u4e09\u7ef4\u91cd\u5efa"]},{"location":"Blogs/posts/OCRN/","title":"Beyond Object Recognition: A New Benchmark towards Object Concept Learning","text":" \u7ea6 374 \u4e2a\u5b57 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 2 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u79d1\u7814","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/OCRN/#beyond-object-recognition-a-new-benchmark-towards-object-concept-learning","title":"Beyond Object Recognition: A New Benchmark towards Object Concept Learning","text":"\u6587\u7ae0\u4fe1\u606f - \u6587\u7ae0\u9898\u76ee: Beyond Object Recognition: A New Benchmark towards Object Concept Learning
- \u4f5c\u8005\uff1aYong-Lu Li,\u00a0Yue Xu,\u00a0Xinyu Xu,\u00a0Xiaohan Mao,\u00a0Yuan Yao,\u00a0Siqi Liu,\u00a0Cewu Lu
- arXiv\uff1a[2212.02710] Beyond Object Recognition: A New Benchmark towards Object Concept Learning
- \u4ee3\u7801\uff1aGitHub - silicx/ObjectConceptLearning: This the official repository of OCL (ICCV 2023).
Abstract Understanding objects is a central building block of AI, especially for embodied AI. Even though object recognition excels with deep learning, current machines struggle to learn higher-level knowledge, e.g., what attributes an object has, and what we can do with it. Here, we propose a challenging Object Concept Learning (OCL) task to push the envelope of object understanding. It requires machines to reason out affordances and simultaneously give the reason: what attributes make an object possess these affordances. To support OCL, we build a densely annotated knowledge base including extensive annotations for three levels of object concept (category, attribute, affordance), and the clear causal relations of three levels. By analyzing the causal structure of OCL, we present a baseline, Object Concept Reasoning Network (OCRN). It leverages concept instantiation and causal intervention to infer the three levels. In experiments, OCRN effectively infers the object knowledge while following the causalities well. Our data and code are available at https://mvig-rhos.com/ocl.
Conclusion In this work, we introduce object concept learning (OCL) expecting machines to infer affordances and explain what attributes enable an object to possess them. Accordingly, we build an extensive dataset and present OCRN based on casual intervention and instantiation. OCRN achieves decent performance and follows the causalities well. However, OCL remains challenging and would inspire a line of studies on reasoning-based object understanding.
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/OCRN/#model","title":"Model","text":" - \u901a\u8fc7 attribute \u6765\u9884\u6d4b affordance\uff0c\u5e76\u63a8\u65ad\u662f\u90a3\u4e9b attribute \u8d77\u5230\u4e86\u4e3b\u8981\u4f5c\u7528\u3002
Example - \u901a\u8fc7 casual intervetion \u6765\u907f\u514d object category bias
- model implement \u770b\u7684\u8fd8\u662f\u6709\u70b9\u6a21\u7cca\uff0c\u4ee5\u540e\u518d\u6765\u8865\u5145
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/","title":"ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding","text":" \u7ea6 1428 \u4e2a\u5b57 7 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 7 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u79d1\u7814","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#ulip-2-towards-scalable-multimodal-pre-training-for-3d-understanding","title":"ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding","text":"\u6587\u7ae0\u4fe1\u606f - \u4f5c\u8005\uff1aLe Xue,\u00a0Ning Yu,\u00a0Shu Zhang,\u00a0Artemis Panagopoulou,\u00a0Junnan Li,\u00a0Roberto Mart\u00edn-Mart\u00edn,\u00a0Jiajun Wu,\u00a0Caiming Xiong,\u00a0Ran Xu,\u00a0Juan Carlos Niebles,\u00a0Silvio Savarese
- arXiv\uff1a[2305.08275] ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding
- \u4ee3\u7801\uff1aGitHub - salesforce/ULIP
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#abstract","title":"Abstract","text":"Abstract Recent advancements in multimodal pre-training have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes, their 2D counterparts, and language descriptions. However, the methods used by existing frameworks to curate such multimodal data, in particular language descriptions for 3D shapes, are not scalable, and the collected language descriptions are not diverse. To address this, we introduce ULIP-2, a simple yet effective tri-modal pre-training framework that leverages large multimodal models to automatically generate holistic language descriptions for 3D shapes. It only needs 3D data as input, eliminating the need for any manual 3D annotations, and is therefore scalable to large datasets. ULIP-2 is also equipped with scaled-up backbones for better multimodal representation learning. We conduct experiments on two large-scale 3D datasets, Objaverse and ShapeNet, and augment them with tri-modal datasets of 3D point clouds, images, and language for training ULIP-2. Experiments show that ULIP-2 demonstrates substantial benefits in three downstream tasks: zero-shot 3D classification, standard 3D classification with fine-tuning, and 3D captioning (3D-tolanguage generation). It achieves a new SOTA of 50.6% (top1) on Objaverse-LVIS and 84.7% (top-1) on ModelNet40 in zero-shot classification. In the ScanObjectNN benchmark for standard fine-tuning, ULIP-2 reaches an overall accuracy of 91.5% with a compact model of only 1.4 million parameters. ULIP-2 sheds light on a new paradigm for scalable multimodal 3D representation learning without human annotations and shows significant improvements over existing baselines. The code and datasets are released at https://github.com/salesforce/ULIP.
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#task","title":"Task","text":" - \u6784\u5efa\u4e00\u4e2a\u53ef\u6269\u5c55\u7684\u591a\u6a21\u6001\u9884\u8bad\u7ec3\u6846\u67b6\u7528\u4e8e3D\u7406\u89e3,\u4e0d\u9700\u8981\u4eba\u5de5\u6807\u6ce8\u5c31\u80fd\u8fdb\u884c\u5927\u89c4\u6a213D\u6570\u636e\u7684\u9884\u8bad\u7ec3\u3002
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#technical-chellenge-for-previous-methods","title":"Technical Chellenge for Previous Methods","text":" - \u6536\u96c6\u548c\u91c7\u96c6 3D \u6570\u636e
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#previous-methods","title":"Previous Methods","text":"","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#failure-case-limitation","title":"Failure Case / Limitation","text":" - \u4eba\u5de5\u6ce8\u91ca\u96be\u4ee5\u62d3\u5c55
- \u4e0d\u591f\u5168\u9762 might not provide sufficient details and lacks variations, or appears to be noisy
- \u8d35
Example \u6bd4\u5982\u8fd9\u5f20\u56fe\uff0c\u539f\u59cb\u6807\u6ce8\u7684\u8bed\u4e49\u4fe1\u606f\u5c31\u4e0d\u592a\u591f\uff0c\u4f46\u662f\u7528 ULIP-2 \u6807\u6ce8\u7684\u5c31\u6bd4\u8f83\u5168\u9762\u3002
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#our-pipeline","title":"Our Pipeline","text":"ULIP-2 \u91c7\u7528\u5927\u578b\u591a\u6a21\u6001\u6a21\u578b\uff0c\u4ece 3D \u5f62\u72b6\u7684\u6574\u4f53\u89d2\u5ea6\u4e3a\u6bcf\u4e2a 2D \u6e32\u67d3\u7684\u56fe\u50cf\u81ea\u52a8\u751f\u6210\u8be6\u7ec6\u63cf\u8ff0\u3002ULIP-2 \u5229\u7528\u9884\u5148\u5bf9\u9f50\u548c\u51bb\u7ed3\u7684\u89c6\u89c9\u8bed\u8a00\u7279\u5f81\u7a7a\u95f4\u6765\u5b9e\u73b0\u4e09\u5143\u4f53\u6a21\u6001\u4e4b\u95f4\u7684\u5bf9\u9f50\uff1a\u6574\u4f53\u6587\u672c\u3001\u56fe\u50cf\u548c 3D \u70b9\u4e91\u3002\u9884\u8bad\u7ec3\u540e\uff0c3D \u7f16\u7801\u5668\u5c06\u7528\u4e8e\u4e0b\u6e38\u4efb\u52a1\u3002
Example ","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#key-insight","title":"Key insight","text":" - 3D \u5230 2D \u5c31\u662f\u7528\u51e0\u4e2a\u56fa\u5b9a\u89c6\u89d2\uff0c\u7136\u540e\u7528 multi-modality pre-training module \u5c31\u53ef\u4ee5\u751f\u6210\u6bcf\u5f20\u7167\u7247\u7684\u63cf\u8ff0\u3002\u5c31\u53ef\u4ee5\u81ea\u52a8\u4e14\u53ef\u62d3\u5c55\u5730\u751f\u6210\u6570\u636e\u3002
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#technical-contributions","title":"Technical contributions","text":" - ULIP-2 \u6709\u52a9\u4e8e\u5b9e\u73b0\u53ef\u6269\u5c55\u7684\u591a\u6a21\u6001\u9884\u8bad\u7ec3\uff0c\u65e0\u9700\u4eba\u5de5\u6ce8\u91ca\uff0c\u4f7f\u5176\u9002\u7528\u4e8e\u4efb\u4f55 3D \u6570\u636e\u96c6\uff0c\u751a\u81f3\u662f\u672a\u6807\u8bb0\u7684\u6570\u636e\u96c6\u3002\u5b83\u5b8c\u5168\u4f9d\u8d56\u4e8e 3D \u6570\u636e\uff0c\u5b9e\u73b0\u4e86\u66f4\u5e7f\u6cdb\u7684\u9002\u7528\u6027\u548c\u6613\u7528\u6027\u3002
- ULIP-2 \u5728\u591a\u6a21\u6001\u8868\u793a\u5b66\u4e60\u65b9\u9762\u53d6\u5f97\u4e86\u91cd\u5927\u8fdb\u6b65\u3002\u5728\u5177\u6709\u6311\u6218\u6027\u7684\u5f00\u653e\u4e16\u754c Objaverse-LVIS \u57fa\u51c6\u6d4b\u8bd5\u4e2d\uff0cULIP-2 \u7684\u51c6\u786e\u7387\u8fbe\u5230\u4e86 50.6% \u7684\u524d 1%\uff0c\u6bd4\u5f53\u524d\u7684 SOTA\uff08OpenShape [22]\uff09\u9ad8\u51fa 3.8%\uff0c\u5c3d\u7ba1 ULIP-2 \u7684\u6846\u67b6\u66f4\u7b80\u5355\u3001\u66f4\u6d41\u7ebf\u578b;\u5bf9\u4e8e ModelNet40 \u4e0a\u7684\u96f6\u6837\u672c\u5206\u7c7b\uff0cULIP-2 \u8fbe\u5230 84.7%\uff0c\u751a\u81f3\u4f18\u4e8e\u4e00\u4e9b\u5b8c\u5168\u76d1\u7763\u7684 3D \u5206\u7c7b\u65b9\u6cd5 [50]\u3002\u6b64\u5916\uff0c\u5b83\u5728 ScanObjectNN \u57fa\u51c6\u6d4b\u8bd5\u4e2d\u786e\u4fdd\u4e86 91.5% \u7684\u603b\u4f53\u51c6\u786e\u7387\uff0c\u53ea\u6709 140 \u4e07\u4e2a\u53c2\u6570\u3002\u6b64\u5916\uff0c\u8fd8\u5c55\u793a\u4e86 ULIP-2 \u7f16\u7801\u5668\u7684 LLMs\uff0c\u7a81\u51fa\u4e86\u5b83\u4e0e\u4e0d\u65ad\u589e\u957f\u7684 LLM\u3002\u6b64\u5916\uff0cULIP-2 \u53ef\u4ee5\u4e0e\u4e0d\u65ad\u589e\u957f\u7684 3D \u6570\u636e\u5bb9\u91cf\u548c\u5927\u578b\u591a\u6a21\u6001\u6a21\u578b\u7684\u5f00\u53d1\u6709\u6548\u534f\u540c\u4f5c\u7528\u3002
- \u6211\u4eec\u53d1\u5e03\u4e86\u4e24\u4e2a\u5927\u89c4\u6a21\u7684\u4e09\u6a21\u6001\u6570\u636e\u96c6\uff0c\u201cULIP-Objaverse\u201d\u548c\u201cULIP-ShapeNet\u201d\u4e09\u5143\u7ec4\uff0c\u7531\u70b9\u4e91\u3001\u56fe\u50cf\u548c\u8bed\u8a00\u63cf\u8ff0\u7ec4\u6210\u3002\u8868 2 \u4e2d\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u6570\u636e\u96c6\u7684\u7edf\u8ba1\u6570\u636e\u3002
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#method","title":"Method","text":" - \u7528 ULIP \u5f53 baseline 1. \u8f93\u5165 3D \u6a21\u6001 2. \u63d0\u53d6\u70b9\u4e91 3. \u751f\u6210\u56fe\u50cf 4. \u7528 BLIP-2 \u751f\u6210\u53e5\u5b50 5. \u5f97\u5230\u8bed\u8a00\u6a21\u6001
\\[ \\mathcal{L}_{\\mathrm{P2I}}=-\\frac{1}{2}\\sum_{i}\\log\\frac{\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{I}}/\\tau)}{\\sum_{j}\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{j}^{\\mathrm{I}}/\\tau)}+\\log\\frac{\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{I}}/\\tau)}{\\sum_{j}\\exp(\\mathbf{f}_{j}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{I}}/\\tau)} \\] \\[ \\mathcal{L}_{\\mathrm{P2T}}=-\\frac{1}{2}\\sum_{i}\\log\\frac{\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{T}}/\\tau)}{\\sum_{j}\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{j}^{\\mathrm{T}}/\\tau)}+\\log\\frac{\\exp(\\mathbf{f}_{i}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{T}}/\\tau)}{\\sum_{j}\\exp(\\mathbf{f}_{j}^{\\mathrm{P}}\\mathbf{f}_{i}^{\\mathrm{T}}/\\tau)} \\] - \\(\\displaystyle \\tau\\) \u662f\u6e29\u5ea6\u7cfb\u6570\uff0c \\(\\displaystyle\\mathrm{f}\\) \u662f\u5d4c\u5165\u4ee5\u540e\u7684\u7279\u5f81
- \u8bad\u7ec3 3D \u7f16\u7801\u5668\uff0c\u8ba9\u8fd9\u4e24\u4e2a\u503c\u52a0\u8d77\u6765\u6700\u5c0f
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#experiment","title":"Experiment","text":" Example - three downstream tasks
- the zero-shot 3D classification task involving multimodal inputs
- the standard 3D classification task involving a single modality
- the 3D captioning task involving 3D-to-language generation with LLMs
- Evaluation Metrics
- top-1 and top-5 accuracy for the zero-shot 3D classification task;
- overall accuracy and class average accuracy for the standard 3D classification task.
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#the-zero-shot-3d-classification-task-involving-multimodal-inputs","title":"the zero-shot 3D classification task involving multimodal inputs","text":"Example","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#the-standard-3d-classification-task-involving-a-single-modality","title":"the standard 3D classification task involving a single modality","text":"Example","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#the-3d-captioning-task-involving-3d-to-language-generation-with-llms","title":"the 3D captioning task involving 3D-to-language generation with LLMs","text":"Example Example","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#ablation-study","title":"Ablation Study","text":" - Ablation on the effect of the generated captions
- Different Large Multimodal Models
- Number of 2D Views Per 3D Object
- Top-k CLIP Ranked Captions Per 2D View
- Scaling Up the Backbone Models
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#conclusion","title":"Conclusion","text":"Conclusion We present ULIP-2, a novel framework for multimodal 3D representation learning. By leveraging large multimodal models for language description generation and scaling up the multimodal 3D pre-training, ULIP-2 not only addresses the quality and scalability challenges in existing multimodal 3D datasets but also demonstrates significant improvements in all downstream tasks. We also release \"ULIP-Objaverse\" triplets and \"ULIP-ShapeNet\" triplets, two large-scale trimodal datasets to foster further research. Limitations. ULIP-2\u2019s pre-training primarily utilizes object-level 3D shape datasets, which inherently differ from scene-level 3D data in their distribution and complexity. Exploring the application of the ULIP-2 framework to scenelevel 3D data understanding, and leveraging the knowledge learned from object-level 3D data for this purpose, represents a compelling avenue for future research. Broader Impact. ULIP-2 aims to minimize human annotation in 3D multimodal pre-training, reducing labor but potentially impacting low-skilled job markets. This dual impact, a common concern in AI advancements, underscores the need for broader considerations in AI research.
- \u521b\u9020\u4e86\u4e00\u79cd\u751f\u6210\u6570\u636e\u7684\u65b9\u6cd5\uff0c\u7136\u540e\u53d1\u5e03\u4e86\u00a0\"ULIP-Objaverse\" triplets and \"ULIP-ShapeNet\" triplets
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#limitation","title":"Limitation","text":" - 3D \u6570\u636e\u96c6\u53ea\u6709 object\uff0c\u6570\u636e\u96c6\u6ca1\u6709\u573a\u666f\u7684
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#_1","title":"\u590d\u73b0","text":"","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#_2","title":"\u76f8\u5173\u8bba\u6587","text":"","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#generative-large-multimodal-models","title":"Generative Large Multimodal Models","text":"","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#multimodal-representation-learning","title":"Multimodal Representation Learning","text":"","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/ULIP-2/#3d-point-cloud-understanding","title":"3D Point Cloud Understanding","text":" - PointNet
- Point-BERT
- PointNeXt
","tags":["\u79d1\u7814"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/","title":"\u4e00\u4e9b AI \u4e0e\u4e2a\u4eba\u5b66\u4e60\u7684\u601d\u8003","text":" \u7ea6 2374 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 12 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#Blog","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#ai","title":"\u4e00\u4e9b AI \u4e0e\u4e2a\u4eba\u5b66\u4e60\u7684\u601d\u8003","text":"","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#_1","title":"\u4e2a\u4eba\u4f7f\u7528\u60c5\u51b5","text":" - \u5199\u4ee3\u7801: Claude + GPT 4o
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","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#prompt","title":"prompt","text":" - \u66fe\u7ecf\u4e00\u6bb5\u65f6\u95f4\uff0c\u6211\u81f4\u529b\u4e8e\u4e3a\u5f88\u591a\u5de5\u4f5c\u8c03\u51fa\u4e00\u4e2a\u2018\u5f3a\u2019\u7684 prompt\uff0c\u53ef\u4ee5\u770b\u535a\u5ba2\u4e2d prompt \u76f8\u5173\u7684\u6587\u7ae0\u3002
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","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#_2","title":"\u5e38\u89c1\u95ee\u9898","text":"\u8ba9\u6211\u4eec\u6765\u7ec6\u5316\u4e00\u4e0b\u8fd9\u4e2a\u95ee\u9898\uff0c\u4e00\u822c\u6765\u8bf4\uff0c\u6211\u4f1a\u6709\u4ee5\u4e0b\u51e0\u79cd\u60c5\u51b5\uff1a
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","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#_3","title":"\u89e3\u51b3\u65b9\u6848","text":" - \u6700\u8fd1\u6b63\u597d\u770b\u5230\u8fd9\u7bc7\u6587\u7ae0\uff0c\u4f46\u662f\u4f5c\u8005\u5728\u6587\u4e2d\u4ec5\u5f52\u7ed3\u4e3a\u601d\u60f3\u7684\u61d2\u60f0\u548c\u514b\u5236 GPT \u7684\u4f7f\u7528\u611f\u89c9\u8f83\u4e3a\u4e0d\u59a5\u3002
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","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#21","title":"2.1. \u660e\u786e\u5b9a\u4f4d","text":"\u4e00\u5f00\u59cb\u6211\u5c31\u660e\u786e\u4e86\u81ea\u5df1\u7684\u89d2\u8272\u5b9a\u4f4d\uff1a\u4ea7\u54c1\u7ecf\u7406\u3002\u6211\u4e0d\u61c2\u7f16\u7a0b\u8bed\u8a00\u548c\u4ee3\u7801\u5b9e\u73b0\uff0c\u6211\u7684\u804c\u8d23\u5c31\u662f\u8bbe\u8ba1\u6307\u5bfc LLM \u5b9e\u73b0\u9879\u76ee\uff0c\u5728\u8fc7\u7a0b\u4e2d\u901a\u8fc7\u54a8\u8be2\u7ec6\u8282\u518d\u8c03\u6574\u5177\u4f53\u7684\u5b9e\u73b0\u6b65\u9aa4\u3002
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\u81f3\u6b64\uff0c\u6211\u5c31\u5b8c\u6210\u4e86 Next. js \u9879\u76ee\u7684\u521b\u5efa\uff0c\u5341\u5206\u949f\u524d\u6211\u4e00\u7a8d\u4e0d\u901a\uff0c\u5341\u5206\u949f\u540e\u6211\u89c9\u5f97\u6211\u5df2\u7ecf\u4e86\u89e3\u4e86\u4e00\u4e2a\u4ea7\u54c1\u7ecf\u7406\u9700\u8981\u638c\u63e1\u7684\u5185\u5bb9\u3002
","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#22","title":"2.2. \u9879\u76ee\u89c4\u5212","text":"\u5728\u5b9e\u73b0\u9879\u76ee\u524d\u671f\u5c31\u4e00\u5b9a\u8981\u505a\u597d\u89c4\u5212\uff0c\u8fd9\u662f\u4e0e LLM \u914d\u5408\u987a\u5229\u7684\u57fa\u7840\u3002\u4e3a\u4e86\u4e0d\u91cd\u8e48\u9879\u76ee\u6df7\u4e71\uff0c\u65e0\u6cd5\u8c03\u6574\uff0c\u5fc3\u70e6\u610f\u4e71\u7684\u8986\u8f99\uff0c\u5728\u4efb\u4f55\u9879\u76ee\u5f00\u59cb\u524d\uff0c\u6700\u597d\u90fd\u8981\u6839\u636e\u5b9e\u73b0\u96be\u5ea6\uff0c\u82b1\u4e0a\u4e00\u5b9a\u65f6\u95f4\u53bb\u548c LLM \u597d\u597d\u68b3\u7406\u9879\u76ee\u7ed3\u6784\uff0c\u8ba9\u5b83\u4e0d\u8981\u7ed9\u51fa\u5177\u4f53\u4ee3\u7801\u800c\u662f\u7ed9\u51fa\u9879\u76ee\u7684\u76ee\u5f55\u7ed3\u6784\uff0c\u8fd9\u6837\u4f60\u5fc3\u91cc\u5c31\u6709\u6570\uff0c\u4e4b\u540e\u5982\u679c\u51fa\u73b0\u9519\u6f0f\uff0c\u4f60\u4e5f\u80fd\u6839\u636e\u8fd9\u4e2a\u7ed3\u6784\u5355\u72ec\u5411 LLM \u8be2\u95ee\u5177\u4f53\u7ec6\u8282\u3002
\u9879\u76ee\u89c4\u5212\u5c31\u901a\u8fc7 README \u6765\u7f16\u5199\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u9700\u8981\u6709\uff1a
- \u9879\u76ee\u4ecb\u7ecd
- \u6280\u672f\u6808
- \u9879\u76ee\u529f\u80fd
- \u76ee\u5f55\u7ed3\u6784
\u4e4b\u540e\u5c31\u56f4\u7ed5 README \u53bb\u5b9e\u73b0\u5c31\u5fc3\u91cc\u6709\u5e95\u4e86\u3002
\u76ee\u5f55\u7ed3\u6784 \u4e4b\u540e\u57fa\u672c\u90fd\u662f\u8981\u6539\u53d8\u7684\uff0c\u53ea\u662f\u4f5c\u4e3a\u53c2\u8003\uff0c\u4e0d\u7528\u8fc7\u4e8e\u5173\u6ce8\u3002 \u5927\u90e8\u5206\u65f6\u5019\u6211\u4e00\u76f4\u4fee\u6539\u7684\u662f \u9879\u76ee\u529f\u80fd \uff0c\u6211\u4f1a\u4f7f\u7528 - [] \u6e05\u5355\u6765\u7ba1\u7406\u529f\u80fd\u5b9e\u73b0\u5217\u8868\uff0c\u907f\u514d\u9057\u6f0f\u548c\u5173\u6ce8\u70b9\u504f\u79fb\uff0c\u56e0\u4e3a LLM \u5f88\u8f7b\u6613\u7684\u5c31\u80fd\u5199\u51fa\u8ba9\u4f60\u89c9\u5f97\u8d3c\u725b\u903c\u7684\u4ee3\u7801\uff0c\u4f46\u662f\u5207\u5fcc\u81ea\u6211\u611f\u52a8\uff0c\u5728\u9879\u76ee\u57fa\u7840\u529f\u80fd\u5b9e\u73b0\u524d\uff0c\u4e0d\u8981\u8ba9 LLM \u81ea\u6211\u53d1\u6325\uff0c\u5148\u628a Demo \u5b8c\u6210\u518d\u8bf4\u5176\u4ed6\u3002
","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#23-git","title":"2.3. \u4e0e git \u914d\u5408","text":"\u4e00\u5b9a\u8981\u4f7f\u7528 git \u7ba1\u7406\u4ee3\u7801\uff0c\u7f16\u7a0b\u4e60\u60ef\u51b3\u5b9a\u4e86\u5b9e\u73b0\u6548\u7387\u3002\u6211\u7684\u7ecf\u9a8c\u5c31\u662f\uff1a\u591a\u6682\u5b58\u3001\u52e4\u63d0\u4ea4\u3001\u63a7\u7248\u672c
\u5728\u89c4\u5212\u597d\u9879\u76ee\u7cfb\u7edf\u67b6\u6784\u540e\uff0c\u8ba9 LLM \u5b9e\u73b0\u4e00\u4e2a\u57fa\u7840\u7684\u7f51\u7ad9\u6846\u67b6\uff0c\u6211\u5c31\u4f1a commit \u7b2c\u4e00\u4e2a\u7248\u672c\uff0c\u5728\u8fd9\u4e2a\u57fa\u7840\u4e0a\u8fdb\u884c\u589e\u5220\u6539\u67e5\u3002\u56e0\u4e3a\u4f7f\u7528 LLM \u7f16\u5199\u4ee3\u7801\uff0c\u6700\u5fcc\u8bb3\u4e00\u53e3\u6c14\u7528 LLM \u5b9e\u73b0\u592a\u591a\u529f\u80fd\uff0c\u5bfc\u81f4\u51fa\u73b0\u95ee\u9898\u4e86\u79ef\u91cd\u96be\u8fd4\u8eab\u5fc3\u4ff1\u75b2\u3002
\u6bcf\u6b21\u5728\u8fdb\u884c\u5927\u89c4\u6a21\u6539\u52a8\u6216\u8005\u662f\u8c03\u6574\u529f\u80fd\u4e4b\u524d\uff0c\u4e00\u5b9a\u8981\u53bb\u4f7f\u7528 Stage All Changes \u6682\u5b58\u6539\u52a8\uff0c\u907f\u514d\u5f71\u54cd\u4e86\u672c\u6765\u5df2\u7ecf\u5b9e\u73b0\u7684\u4ee3\u7801\u3002
\u5e76\u4e14\u4e00\u5b9a\u8981\u505a\u597d\u529f\u80fd\u62c6\u5206\uff0c\u514b\u5236\u514b\u5236\u518d\u514b\u5236\uff0c\u53ea\u8981\u5b9e\u73b0\u4e00\u4e2a\u529f\u80fd\u5c31\u63d0\u4ea4\uff0c\u4e0d\u7136\u7275\u4e00\u53d1\u800c\u52a8\u5168\u8eab\uff0c\u8d8a\u6539\u8d8a\u4e71\u3002\u6211\u5df2\u7ecf\u5728\u8fd9\u4e0a\u9762\u5403\u4e8f\u592a\u591a\u6b21\u4e86\u3002
","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#23","title":"2.3. \u62c6\u5206\u6a21\u5757","text":"\u62ff\u7f51\u7ad9\u6765\u4e3e\u4f8b\uff0c\u62c6\u5206\u6a21\u5757\u5c31\u662f\u62bd\u6837\u4ee3\u7801\u529f\u80fd\uff0c\u6bd4\u5982\u5728 page \u4e2d\u6709\u4e24\u4e2a\u90e8\u5206\uff1a\u9876\u680f\u548c\u5185\u5bb9\u533a\u57df\uff0c\u90a3\u4e48\u6211\u4eec\u6700\u597d\u62c6\u5206\u51fa HeadBar.tsx \u4e13\u95e8\u8d1f\u8d23\u9876\u680f\u7684\u903b\u8f91\uff1b\u7136\u540e\u5728\u9876\u680f\u4e2d\u6709\u5934\u50cf\u3001\u4e3b\u9875\u6309\u94ae\u3001logo \u4e09\u90e8\u5206\uff0c\u90a3\u4e48\u6211\u5c31\u62c6\u5206\u51fa\uff1a Avatar.tsx \u3001 HomeButton.tsx \u3001 Logo.tsx \uff0c\u5206\u522b\u8d1f\u8d23\u81ea\u5df1\u7ec4\u4ef6\u7684\u529f\u80fd\u3002
\u6309\u7167\u8fd9\u4e2a\u601d\u8def\uff0c\u8fd8\u53ef\u4ee5\u62c6\u5206\u529f\u80fd\u903b\u8f91\uff0c\u6bd4\u5982\u521b\u5efa\u7f51\u7edc\u8bf7\u6c42\u7ec4\u4ef6\u3001\u8def\u7531\u8c03\u7528\u5207\u7247\u3001\u5de5\u5382\u6a21\u5f0f\u7ec4\u4ef6\u7b49\uff0c\u4e0d\u8981\u770b\u540d\u5b57\u9ad8\u5927\u4e0a\u542c\u4e0d\u61c2\uff0c\u5b9e\u9645\u4e0a\u8fd9\u4e9b\u90fd\u662f LLM \u4f1a\u5728\u5b9e\u73b0\u8fc7\u7a0b\u4e2d\u81ea\u7136\u5448\u73b0\u7684\u903b\u8f91\uff0c\u76ee\u7684\u5c31\u662f\u4e3a\u4e86\u62bd\u6837\u4ee3\u7801\uff0c\u907f\u514d\u91cd\u590d\u5b9e\u73b0\u548c\u65b9\u4fbf\u7edf\u4e00\u7ba1\u7406\u3002
\u5c3d\u53ef\u80fd\u4fdd\u8bc1\u6bcf\u4e2a\u6587\u4ef6\u53ea\u8d1f\u8d23\u5355\u72ec\u7684\u6a21\u5757\u529f\u80fd\uff0c\u4ee3\u7801\u884c\u6570\u63a7\u5236\u5728 200 \u884c\u4ee5\u5185\u3002
\u4e0d\u8981\u5acc\u9ebb\u70e6\uff0c\u521b\u5efa\u6587\u4ef6\u4e0d\u9700\u8981\u81ea\u5df1\u52a8\u624b\uff0c\u76f4\u63a5\u8ba9 LLM \u5e2e\u4f60\u62c6\u5206\u5b9e\u73b0\uff0c\u4f60\u70b9\u51fb Apply \u6309\u94ae\u5b83\u5c31\u4f1a\u81ea\u52a8\u521b\u5efa\u5e76\u586b\u5165\u4ee3\u7801\u3002
\u8fd9\u662f\u4e00\u4e2a\u975e\u5e38\u91cd\u8981\u7684\u4e60\u60ef\uff0c\u5148\u6267\u884c\uff0c\u7b49\u56de\u8fc7\u5934\u6765\u4f60\u81ea\u7136\u4f1a\u610f\u8bc6\u5230\u5b83\u7684\u4ef7\u503c\u3002
","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#24","title":"2.4. \u521b\u5efa\u65b0\u5bf9\u8bdd\uff0c\u7cbe\u7b80\u4e0a\u4e0b\u6587","text":"\u4e0a\u4e0b\u6587\u957f\u5ea6\u76f4\u63a5\u51b3\u5b9a\u4e86 LLM \u56de\u7b54\u7684\u8d28\u91cf\u3002
\u4e3a\u4e86\u6700\u597d\u7684\u56de\u7b54\u6548\u679c\uff0c\u6211\u4f1a\u5c3d\u53ef\u80fd\u7684\u907f\u514d\u8fc7\u957f\u7684\u5bf9\u8bdd\u5185\u5bb9\uff0c\u5e76\u4e14\u4fdd\u8bc1\u4e00\u6b21\u5bf9\u8bdd\u53ea\u89e3\u51b3\u4e00\u4e2a\u95ee\u9898\uff0c\u4e4b\u540e\u8fd8\u53ef\u4ee5\u901a\u8fc7\u56de\u770b\u5bf9\u8bdd\u5386\u53f2\u6765\u67e5\u7f3a\u8865\u6f0f\u3002
\u4e0a\u9762\u62c6\u5206\u6a21\u5757\u4e5f\u80fd\u6781\u5927\u7684\u51cf\u5c11\u4e0a\u4e0b\u6587\uff0c\u4f60\u53ea\u9700\u8981\u6dfb\u52a0\u76f8\u5173\u7684\u4ee3\u7801\uff0c\u5bf9\u8bdd\u5373\u53ef\u89e3\u51b3\u9700\u6c42\uff0c\u800c\u4e0d\u9700\u8981\u6bcf\u6b21\u643a\u5e26\u591a\u4f59\u7684\u4ee3\u7801\u8fdb\u884c\u63d0\u95ee\u3002
\u5982\u679c\u5728\u4e00\u6b21\u5bf9\u8bdd\u4e2d\uff0c\u4e00\u76f4\u6ca1\u6709\u89e3\u51b3\u95ee\u9898\uff0c\u6700\u597d\u521b\u5efa\u65b0\u5bf9\u8bdd\uff0c\u9000\u540e\u4e00\u6b65\uff0c\u8ba9 LLM \u4ece\u66f4\u591a\u7684\u89d2\u5ea6\u53bb\u601d\u8003\u95ee\u9898\u51fa\u73b0\u5728\u54ea\uff0c\u7136\u540e\u4f60\u518d\u6839\u636e\u5b83\u7684\u56de\u7b54\uff0c\u4f9d\u6b21\u63d0\u95ee\u5c1d\u8bd5\u89e3\u51b3\u3002
\u6211\u5728\u5b9e\u73b0 telegram bot \u7684\u65f6\u5019\u5c31\u9047\u5230\u8fc7\u65e0\u6cd5\u542f\u52a8\u673a\u5668\u4eba\u7684\u60c5\u51b5\uff0cLLM \u4e00\u76f4\u6267\u7740\u4e8e\u89e3\u51b3\u65f6\u5ef6\u548c\u65f6\u5e8f\u95ee\u9898\uff0c\u4f46\u662f\u5728\u4e00\u6b21\u56de\u7b54\u4e2d\u5b83\u63d0\u5230\u4e86\u53ef\u80fd\u662f\u4ee3\u7406\u8bbe\u7f6e\u9519\u8bef\uff0c\u6211\u6355\u6349\u5230\u4e86\u8fd9\u4e2a\u7b54\u6848\uff0c\u5e76\u4e14\u9a6c\u4e0a\u5c1d\u8bd5\uff0c\u679c\u7136\u89e3\u51b3\u4e86\u95ee\u9898\u3002
","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#_4","title":"\u603b\u7ed3","text":"\u4e0d\u5e94\u8be5\u4e0d\u4f7f\u7528 AI\uff0c\u800c\u662f\u4e0d\u8981\u8ba9\u5b83\u4ee3\u66ff\u4f60\u7684\u601d\u8003\u8fc7\u7a0b\u3002\u5bf9\u4e8e\u9879\u76ee / \u4efb\u52a1 / \u95ee\u9898\uff0c\u4f60\u9700\u8981\u81ea\u5df1\u638c\u63a7\uff0c\u5373\uff0c\u4f60\u8981\u77e5\u9053 AI \u7ed9\u4f60\u7684\u56de\u7b54\u6b63\u786e\u4e0e\u5426\u800c\u4e0d\u662f\u4efb\u7531\u5b83\u751f\u6210\u3002
","tags":["Blog"]},{"location":"Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/#reference","title":"Reference","text":" - \u6211\u7684Cursor\u4f7f\u7528\u5fc3\u5f97\u2014\u2014\u9879\u76ee\u5b9e\u73b0 - \u5f00\u53d1\u8c03\u4f18 - LINUX DO
- 2024\u79d1\u7814\u53cd\u601d
","tags":["Blog"]},{"location":"CS_Basic/","title":"Computer Basic","text":""},{"location":"CS_Basic/#computer-science-basic","title":"Computer Science Basic","text":"Abstract
\u672c\u90e8\u5206\u5185\u5bb9\uff08\u9664\u7279\u522b\u58f0\u660e\u5916\uff09\u91c7\u7528 \u7f72\u540d-\u975e\u5546\u4e1a\u6027\u4f7f\u7528-\u4fdd\u6301\u4e00\u81f4 4.0 \u56fd\u9645 (CC BY-NC-SA 4.0) \u8bb8\u53ef\u534f\u8bae\u8fdb\u884c\u8bb8\u53ef\u3002
CS61A - Composing Programs 2713 651 17 mins 1734027543
- Experience 0 0 mins 0
CS61C - \u8ba1\u7b97\u673a\u7ec4\u6210\u4e0e\u8bbe\u8ba1\u786c\u4ef6\u8f6f\u4ef6\u63a5\u53e3 2978 13 10 mins 1734027543
Network - Security Basic 368 1 mins 1734027543
15-213 - CSAPP 1012 36 4 mins 1734027543
C++ - Accelerated C++ 2534 489 15 mins 1734027543
- C++ Basic 3115 523 17 mins 1734027543
"},{"location":"CS_Basic/15-213/CSAPP/","title":"\u6df1\u5165\u7406\u89e3\u8ba1\u7b97\u673a\u7cfb\u7edf","text":""},{"location":"CS_Basic/15-213/CSAPP/#_1","title":"\u6df1\u5165\u7406\u89e3\u8ba1\u7b97\u673a\u7cfb\u7edf","text":" \u7ea6 997 \u4e2a\u5b57 36 \u884c\u4ee3\u7801 14 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 5 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"CS_Basic/15-213/CSAPP/#1","title":"1 \u8ba1\u7b97\u673a\u7cfb\u7edf\u6f2b\u6e38","text":""},{"location":"CS_Basic/15-213/CSAPP/#2","title":"2 \u4fe1\u606f\u7684\u8868\u793a\u548c\u5904\u7406","text":" - \u628a\u4f4d\u7ec4\u5408\u518d\u4e00\u8d77\uff0c\u518d\u52a0\u4e0a interpretation
- \u4e09\u79cd\u91cd\u8981\u7684\u6570\u5b57\u8868\u793a
- unsigned
- two's-complement
- floating-point
- overflow
- \u6d6e\u70b9\u6570\u662f\u8fd1\u4f3c\u7684
"},{"location":"CS_Basic/15-213/CSAPP/#21","title":"2.1 \u4fe1\u606f\u5b58\u50a8","text":" - 1 byte = 8 bits
- virtual memory
- address
- \u8bb2\u5b58\u50a8\u5668\u7a7a\u95f4\u5212\u5206\u4e3a\u66f4\u53ef\u7ba1\u7406\u7684\u5355\u5143\uff0c\u6765\u5b58\u653e\u4e0d\u540c\u7684 program object
"},{"location":"CS_Basic/15-213/CSAPP/#211","title":"2.1.1 \u5341\u516d\u8fdb\u5236\u8868\u793a\u6cd5","text":""},{"location":"CS_Basic/15-213/CSAPP/#212","title":"2.1.2 \u5b57\u6570\u636e\u5927\u5c0f","text":" - word size
- nominal size
- \u5b57\u957f\u51b3\u5b9a\u7684\u6700\u91cd\u8981\u7684\u7cfb\u7edf\u53c2\u6570\u5c31\u662f\u865a\u62df\u5730\u5740\u7a7a\u95f4\u7684\u6700\u5927\u5927\u5c0f
- \u5b57\u957f\u4e3a \\(\\displaystyle \\omega\\) \u4e3a\u7684\u673a\u5668\uff0c\u865a\u62df\u5730\u5740\u7684\u8303\u56f4\u4e3a \\(\\displaystyle 0\\sim2^{\\omega} - 1\\)
- \u5927\u591a\u6570 64 \u4f4d\u673a\u5668\u53ef\u4ee5\u8fd0\u884c 32 \u4f4d\u673a\u5668\u7f16\u8bd1\u7684\u7a0b\u5e8f\uff0c\u5373\u5411\u540e\u517c\u5bb9
- \u4e3a\u4e86\u907f\u514d\u5927\u5c0f\u548c\u4e0d\u540c\u7f16\u8bd1\u5668\u8bbe\u7f6e\u5e26\u6765\u7684\u5947\u602a\u884c\u4e3a\uff0c\u6211\u4eec\u6709\u4e86 int 32_t \u548c int 64_t
- C \u8bed\u8a00\u5bf9\u58f0\u660e\u7684\u5173\u952e\u8bcd\u987a\u5e8f\u4e0d\u654f\u611f
"},{"location":"CS_Basic/15-213/CSAPP/#213","title":"2.1.3 \u5bfb\u5740\u548c\u5b57\u8282\u987a\u5e8f","text":" - \u5c0f\u7aef\u7f16\u5740
- \u5c31\u662f\u53f3\u8fb9\u653e\u5c0f\u7684\uff0c\u8981\u4ece\u53f3\u5f80\u5de6\u8bfb
- \u5b57\u8282\u987a\u5e8f\u53d8\u5f97\u91cd\u8981\u7684\u4e09\u79cd\u60c5\u51b5
- \u7f51\u7edc\u5e94\u7528\u7a0b\u5e8f\u7684\u4ee3\u7801\u7f16\u5199\u5fc5\u987b\u9075\u5b88\u5df2\u5efa\u7acb\u7684\u5173\u4e8e\u5b57\u8282\u987a\u5e8f\u7684\u89c4\u5219
- disassembler
- \u7f16\u5199\u89c4\u907f\u6b63\u5e38\u7684\u7c7b\u578b\u7cfb\u7edf\u7684\u7a0b\u5e8f
- cast or union in C
- \u5bf9\u5e94\u7528\u7f16\u7a0b\u4e0d\u63a8\u8350\uff0c\u4f46\u662f\u5bf9\u7cfb\u7edf\u7ea7\u7f16\u7a0b\u662f\u5fc5\u9700\u7684
C#include <stdio.h>\n\n\n\ntypedef unsigned char *byte_pointer;\n\nvoid show_bytes(byte_pointer start, size_t len) {\n\u00a0 size_t i;\n\u00a0 for (i = 0; i < len; i++)\n\u00a0 \u00a0 printf(\" %.2x\", start[i]);\n\u00a0 printf(\"\\n\");\n}\n\nvoid show_int(int x) { show_bytes((byte_pointer)&x, sizeof(int)); }\n\nvoid show_float(float x) { show_bytes((byte_pointer)&x, sizeof(float)); }\n\nvoid show_pointer(void *x) { show_bytes((byte_pointer)&x, sizeof(void *)); }\n\nvoid test_show_bytes(int val) {\n\u00a0 int ival = val;\n\u00a0 float fval = (float)ival;\n\u00a0 int *pval = &ival;\n\u00a0 show_int(ival);\n\u00a0 show_float(fval);\n\u00a0 show_pointer(pval);\n}\n\nint main() {\n\u00a0 int val = 12345;\n\u00a0 test_show_bytes(val);\n\u00a0 return 0;\n}\n
Text Only 39 30 00 00 //\u5c0f\u7aef\u6cd5\n 00 e4 40 46\n 8c f6 bf ef b4 00 00 00\n
"},{"location":"CS_Basic/15-213/CSAPP/#214","title":"2.1.4 \u8868\u793a\u5b57\u7b26\u4e32","text":""},{"location":"CS_Basic/15-213/CSAPP/#215","title":"2.1.5 \u8868\u793a\u4ee3\u7801","text":" - \u4e8c\u8fdb\u5236\u4ee3\u7801\u5f88\u5c11\u80fd\u5728\u4e0d\u540c\u673a\u5668\u548c\u64cd\u4f5c\u7cfb\u7edf\u7ec4\u5408\u4e4b\u95f4\u79fb\u690d
"},{"location":"CS_Basic/15-213/CSAPP/#216","title":"2.1.6 \u5e03\u5c14\u4ee3\u6570\u7b80\u4ecb","text":" - \u53ef\u4ee5\u6269\u5c55\u5230\u4f4d\u5411\u91cf\u7684\u8fd0\u7b97
- \u5e03\u5c14\u4ee3\u6570
- Boolean ring
- \u548c\u96c6\u5408\u7684\u5bf9\u5e94
"},{"location":"CS_Basic/15-213/CSAPP/#217-c","title":"2.1.7 C \u8bed\u8a00\u4e2d\u7684\u4f4d\u7ea7\u8fd0\u7b97","text":""},{"location":"CS_Basic/15-213/CSAPP/#218-c","title":"2.1.8 C \u8bed\u8a00\u4e2d\u7684\u903b\u8f91\u8fd0\u7b97","text":""},{"location":"CS_Basic/15-213/CSAPP/#219-c","title":"2.1.9 C \u8bed\u8a00\u4e2d\u7684\u79fb\u4f4d\u8fd0\u7b97","text":" - \u903b\u8f91\u53f3\u79fb
- \u7b97\u6570\u53f3\u79fb
- \u5b9e\u9645\u4e0a\uff0c\u51e0\u4e4e\u6240\u6709\u7684\u7f16\u8bd1\u5668\u90fd\u5bf9\u6709\u7b26\u53f7\u6570\u4f7f\u7528\u7b97\u672f\u53f3\u79fb\uff0c\u800c\u5bf9\u4e8e\u65e0\u7b26\u53f7\u6570\uff0c\u53f3\u79fb\u5fc5\u987b\u662f\u903b\u8f91\u7684\uff08C \u8bed\u8a00\uff09
- \u800c\u5bf9\u4e8e Java
x>>k \u662f\u7b97\u6570\u53f3\u79fb\uff0c x>>>k \u662f\u903b\u8f91\u53f3\u79fb - \u5bf9\u4e8e C \u8bed\u8a00\uff0c\u79fb\u52a8 \\(\\displaystyle k \\text{ mod } \\omega\\)
- \u52a0\u51cf\u7684\u4f18\u5148\u7ea7\u6bd4\u79fb\u4f4d\u7b97\u6cd5\u8981\u641e
"},{"location":"CS_Basic/15-213/CSAPP/#22","title":"2.2 \u6574\u6570\u8868\u793a","text":""},{"location":"CS_Basic/15-213/CSAPP/#221","title":"2.2.1 \u6574\u578b\u6570\u636e\u7c7b\u578b","text":" - 64 \u4f4d\u548c 32 \u4f4d\u662f\u4e0d\u4e00\u6837\u7684
- \u53d6\u503c\u8303\u56f4\u4e0d\u662f\u5bf9\u79f0\u7684
- Java \u53ea\u652f\u6301\u6709\u7b26\u53f7\u6570
"},{"location":"CS_Basic/15-213/CSAPP/#222","title":"2.2.2 \u65e0\u7b26\u53f7\u6570\u7684\u7f16\u7801","text":" - \u628a\u5411\u91cf\u5199\u6210\u4e8c\u8fdb\u5236\u8868\u793a\u7684\u6570\uff0c\u5c31\u5f97\u5230\u4e86\u65e0\u7b26\u53f7\u8868\u793a
\\[ B2U_w(\\vec{x})\\doteq\\sum_{i=0}^{u-1}x_i2^i \\] \\[ B2U_w:\\{0, 1\\}^w\\to\\{0, \\cdots,2^w-1\\} \\] - \u65e0\u7b26\u53f7\u6570\u7f16\u7801\u7684\u552f\u4e00\u6027
- \\(\\displaystyle \u51fd\u6570B2U_w\u662f\u4e00\u4e2a\u53cc\u5c04\u3002\\)
"},{"location":"CS_Basic/15-213/CSAPP/#223","title":"2.2.3 \u8865\u7801\u7f16\u7801","text":" - two's-complement
- negative weight
\\[ B2T_w(\\vec{x})\\doteq- x_{w-1}2^{w-1}+\\sum_{i=0}^{w-2}x_i2^i \\] \\[ B2T_{w}\\colon \\{0, 1\\}^{w}\\to\\langle TMin_{w}, \\cdots, TMax_{w} \\rangle \\] \\[ B2O_w(\\vec{x})\\doteq-x_{w-1}(2^{w-1}-1)+\\sum_{i=0}^{w-2}x_i2^i \\] \\[ B2S_w(\\vec{x})\\doteq(-1)^{x_{w-1}}\\cdot\\bigl(\\sum_{i=0}^{w-2}x_i2^i\\bigr) \\] - \u5bf9\u4e8e\u6570\u5b57 0 \u6709\u4e24\u79cd\u4e0d\u540c\u7684\u7f16\u7801\u65b9\u5f0f
"},{"location":"CS_Basic/15-213/CSAPP/#224","title":"2.2.4 \u6709\u7b26\u53f7\u6570\u548c\u65e0\u7b26\u53f7\u6570\u4e4b\u95f4\u7684\u8f6c\u6362","text":" - \u4fdd\u6301\u4f4d\u503c\u4e0d\u53d8\uff0c\u6539\u53d8\u89e3\u91ca\u65b9\u5f0f
\\[ T2U_{_w}(x)\\doteq B2U_{_w}( T2B_{_w}(x) ) \\] - \u8865\u7801\u8f6c\u6362\u4e3a\u65e0\u7b26\u53f7\u6570
\\[ T2U_w(x)=\\begin{cases}x+2^w,&x<0\\\\x,&x\\geqslant0\\end{cases} \\] - \u65e0\u7b26\u53f7\u6570\u8f6c\u6362\u4e3a\u8865\u7801
\\[ U2T_w(u)=\\begin{cases}u ,&u\\leqslant TMax_w\\\\u-2^w ,&u>TMax_w\\end{cases} \\]"},{"location":"CS_Basic/15-213/CSAPP/#225-c","title":"2.2.5 C \u8bed\u8a00\u4e2d\u7684\u6709\u7b26\u53f7\u6570\u4e0e\u65e0\u7b26\u53f7\u6570","text":" - \u4e00\u4e2a\u8fd0\u7b97\u7b26\u662f\u6709\u7b26\u53f7\u7684\u800c\u53e6\u4e00\u4e2a\u662f\u65e0\u7b26\u53f7\u7684\uff0c\u90a3 C \u7a0b\u5e8f\u4f1a\u628a\u6709\u7b26\u53f7\u7684\u8f6c\u6362\u4e3a\u65e0\u7b26\u53f7\u7684\u3002
"},{"location":"CS_Basic/15-213/CSAPP/#226","title":"2.2.6 \u6269\u5c55\u4e00\u4e2a\u6570\u5b57\u7684\u4f4d\u8868\u793a","text":" - \u65e0\u7b26\u53f7\u6570\u7684 zero extension
- \u8865\u7801\u6570\u7684\u7b26\u53f7\u6269\u5c55
"},{"location":"CS_Basic/15-213/CSAPP/#227","title":"2.2.7 \u622a\u65ad\u6570\u5b57","text":""},{"location":"CS_Basic/15-213/CSAPP/#228","title":"2.2.8 \u5173\u4e8e\u6709\u7b26\u53f7\u6570\u4e0e\u65e0\u7b26\u53f7\u6570\u7684\u5efa\u8bae","text":""},{"location":"CS_Basic/15-213/CSAPP/#23","title":"2.3 \u6574\u6570\u8fd0\u7b97","text":""},{"location":"CS_Basic/15-213/CSAPP/#231","title":"2.3.1 \u65e0\u7b26\u53f7\u52a0\u6cd5","text":" - Lisp \u652f\u6301\u65e0\u9650\u7cbe\u5ea6\u7684\u8fd0\u7b97
\\[ x+_w^uy=\\begin{cases}x+y,&x+y<2^w&\\text{\u6b63\u5e38}\\\\x+y-2^w,&2^w\\leqslant x+y<2^{w+1}&\\text{\u6ea2\u51fa}\\end{cases} \\]"},{"location":"CS_Basic/15-213/CSAPP/#3","title":"3 \u7a0b\u5e8f\u7684\u673a\u5668\u7ea7\u8868\u793a","text":""},{"location":"CS_Basic/15-213/CSAPP/#4","title":"4 \u5904\u7406\u5668\u4f53\u7cfb\u7ed3\u6784","text":""},{"location":"CS_Basic/15-213/CSAPP/#5","title":"5 \u4f18\u5316\u7a0b\u5e8f\u6027\u80fd","text":""},{"location":"CS_Basic/15-213/CSAPP/#6","title":"6 \u5b58\u50a8\u5668\u5c42\u6b21\u7ed3\u6784","text":""},{"location":"CS_Basic/15-213/CSAPP/#7","title":"7 \u94fe\u63a5","text":""},{"location":"CS_Basic/15-213/CSAPP/#8","title":"8 \u5f02\u5e38\u63a7\u5236\u6d41","text":""},{"location":"CS_Basic/15-213/CSAPP/#9","title":"9 \u865a\u62df\u5185\u5b58","text":""},{"location":"CS_Basic/15-213/CSAPP/#10-io","title":"10 \u7cfb\u7edf\u7ea7 I/O","text":""},{"location":"CS_Basic/15-213/CSAPP/#11","title":"11 \u7f51\u7edc\u7f16\u7a0b","text":""},{"location":"CS_Basic/15-213/CSAPP/#12","title":"12 \u5e76\u53d1\u7f16\u7a0b","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/","title":"Accelerated C++","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#accelerated-c","title":"Accelerated C++","text":" \u7ea6 2520 \u4e2a\u5b57 489 \u884c\u4ee3\u7801 2 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 19 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#0-c","title":"0 \u5f00\u59cb\u5b66\u4e60 C++","text":"C++#include <iostream>\n\nint main()\n{\n std::cout << \"Hello, World!\" << std::endl;\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#01","title":"0.1 \u6ce8\u91ca","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#02-displaystyle-include","title":"0.2 \\(\\displaystyle \\#\\)include \u6307\u4ee4","text":" - \u8f93\u5165\u3001\u8f93\u51fa\u4e0d\u5c5e\u4e8e\u8bed\u8a00\u6838\u5fc3\uff0c\u800c\u662f\u6807\u51c6\u5e93\u7684\u4e00\u90e8\u5206
- iostream \u4ee3\u8868 C++\u5e93\u7684\u6807\u51c6\u5934\u6587\u4ef6
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#03-main","title":"0.3 \u4e3b\u51fd\u6570 main","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#04","title":"0.4 \u82b1\u62ec\u53f7","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#05","title":"0.5 \u4f7f\u7528\u6807\u51c6\u5e93\u8fdb\u884c\u8f93\u51fa","text":" - std:: cout \u6307\u6807\u51c6\u8f93\u51fa\u6d41
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#06","title":"0.6 \u8fd4\u56de\u8bed\u53e5","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#07","title":"0.7 \u4e00\u4e9b\u8f83\u4e3a\u6df1\u5165\u7684\u89c2\u5bdf","text":" - \u8868\u8fbe\u5f0f
- \u8fd0\u7b97\u4f1a\u4ea7\u751f\u4e00\u4e2a\u7ed3\u679c\uff0c\u53ef\u80fd\u4f1a\u5177\u6709\u526f\u4f5c\u7528
- \u6bcf\u4e2a\u64cd\u4f5c\u6570\u90fd\u5177\u6709\u4e00\u4e2a\u7c7b\u578b
- <<\u662f\u5de6\u7ed3\u5408\u7684\uff0c\u6240\u4ee5\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e24\u4e2a<<\u8fd0\u7b97\u7b26\u548c\u4e09\u4e2a\u64cd\u4f5c\u6570
C++(std::cout << \"Hello, World!\") << std::endl\n
- std:: cout \u7684\u7c7b\u578b\u662f std::ostream
- std:: endl \u662f\u4e00\u4e2a\u63a7\u5236\u5668\uff08manipulator\uff09
- \u7b2c\u4e00\u79cd\u4f5c\u7528\u57df: \u540d\u5b57\u7a7a\u95f4
- :: \u662f\u4f5c\u7528\u57df\u8fd0\u7b97\u7b26\uff0cstd:: cout \u662f\u4e00\u4e2a\u9650\u5b9a\u540d\u79f0
- \u82b1\u62ec\u53f7\u662f\u53e6\u4e00\u79cd\u4f5c\u7528\u57df
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#08","title":"0.8 \u5c0f\u7ed3","text":" - main \u53ef\u4ee5\u6ca1\u6709 return \u8bed\u53e5
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#1","title":"1 \u4f7f\u7528\u5b57\u7b26\u4e32","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#11","title":"1.1 \u8f93\u5165","text":"C++#include <iostream>\n#include <string>\n\nint main() \n{\n std::cout << \"Please enter your first name:\";\n\n std::string name;\n std::cin >> name;\n\n std::cout << \"Hello\" << name << \"!\" << std::endl;\n return 0;\n}\n
- \u53d8\u91cf\u662f\u4e00\u4e2a\u5bf9\u8c61\uff0c\u4f46\u6709\u4e9b\u5bf9\u8c61\u53ef\u80fd\u6ca1\u6709\u540d\u79f0
- \u5c40\u90e8\u53d8\u91cf\u6709\u9650\u7684\u751f\u5b58\u671f\u662f\u533a\u5206\u53d8\u91cf\u548c\u5bf9\u8c61\u7684\u4e00\u4e2a\u91cd\u8981\u4f9d\u636e
- \u9690\u85cf\u5728\u5bf9\u8c61\u7c7b\u578b\u4e2d\u7684\u8fd8\u6709\u5176\u63a5\u53e3
- \u63a5\u53e3\u662f\u53ef\u5b9e\u73b0\u64cd\u4f5c\u7684\u96c6\u5408
- \u7f13\u51b2\u533a\u6765\u4fdd\u5b58\u8f93\u51fa
- \u4f55\u65f6\u5237\u65b0\u7f13\u51b2\u533a:
- \u7f13\u51b2\u533a\u6ee1\u4e86
- \u8f93\u5165\u6d41\u8bfb\u6570\u636e
- \u660e\u786e\u8981\u6c42\u5237\u65b0
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#12","title":"1.2 \u4e3a\u59d3\u540d\u88c5\u6846","text":" - \u8fd0\u7b97\u7b26\u88ab\u91cd\u8f7d\u4e86
- \u8fd0\u7b97\u7b26\u4e00\u4e2a\u6c38\u8fdc\u4e0d\u4f1a\u6539\u53d8\u7684\u6027\u8d28\u662f\u7ed3\u5408\u5f8b\uff0c+\u662f\u5de6\u7ed3\u5408\u7684
C++std::string stars(10, '*')\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#13","title":"1.3 \u5c0f\u7ed3","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#2","title":"2 \u5faa\u73af\u548c\u8ba1\u6570","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#21","title":"2.1 \u95ee\u9898","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#22","title":"2.2 \u7a0b\u5e8f\u7684\u6574\u4f53\u7ed3\u6784","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#23","title":"2.3 \u8f93\u51fa\u6570\u76ee\u672a\u77e5\u7684\u884c","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#231-while","title":"2.3.1 while \u8bed\u53e5","text":" - ++\u662f\u4e00\u4e2a\u589e\u91cf\u8fd0\u7b97\u7b26
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#232-while","title":"2.3.2 \u8bbe\u8ba1 while \u8bed\u53e5","text":" - \u5faa\u73af\u4e0d\u53d8\u5f0f\uff08Loop invariant\uff09
- \u521d\u59cb\u5316\uff1a\u5728\u5faa\u73af\u5f00\u59cb\u4e4b\u524d\uff0c\u5faa\u73af\u4e0d\u53d8\u5f0f\u5e94\u8be5\u88ab\u786e\u7acb\u4e3a\u771f\u3002
- \u4fdd\u6301\uff1a\u5faa\u73af\u7684\u6bcf\u6b21\u8fed\u4ee3\u90fd\u5fc5\u987b\u4fdd\u6301\u5faa\u73af\u4e0d\u53d8\u5f0f\u4e3a\u771f\uff0c\u5373\u5982\u679c\u8fdb\u5165\u67d0\u6b21\u8fed\u4ee3\u65f6\u5faa\u73af\u4e0d\u53d8\u5f0f\u4e3a\u771f\uff0c\u90a3\u4e48\u5728\u8be5\u6b21\u8fed\u4ee3\u7ed3\u675f\u65f6\uff0c\u5faa\u73af\u4e0d\u53d8\u5f0f\u4ecd\u7136\u4e3a\u771f\u3002
- \u7ec8\u6b62\uff1a\u5f53\u5faa\u73af\u7ed3\u675f\u65f6\uff0c\u5faa\u73af\u4e0d\u53d8\u5f0f\u5e94\u8be5\u80fd\u591f\u7528\u6765\u8bc1\u660e\u5faa\u73af\u7684\u7ec8\u6b62\u6761\u4ef6\u6210\u7acb\uff0c\u6216\u8005\u7528\u6765\u8bc1\u660e\u5faa\u73af\u7684\u8f93\u51fa\u6216\u7ed3\u679c\u6ee1\u8db3\u7279\u5b9a\u7684\u5c5e\u6027\u3002
- \u5faa\u73af\u4e0d\u53d8\u5f0f\u7684\u4e00\u4e2a\u5178\u578b\u4f8b\u5b50\u662f\u5728\u6392\u5e8f\u7b97\u6cd5\u4e2d\uff0c\u4f8b\u5982\u5192\u6ce1\u6392\u5e8f\u3002\u5728\u5192\u6ce1\u6392\u5e8f\u4e2d\uff0c\u4e00\u4e2a\u53ef\u80fd\u7684\u5faa\u73af\u4e0d\u53d8\u5f0f\u662f\uff1a\u201c\u6bcf\u6b21\u5faa\u73af\u8fed\u4ee3\u540e\uff0c\u6570\u7ec4\u7684\u6700\u540en\u4e2a\u5143\u7d20\u662f\u6392\u5e8f\u597d\u7684\u201d\uff0c\u5176\u4e2dn\u662f\u5faa\u73af\u8fed\u4ee3\u7684\u6b21\u6570\u3002
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#24","title":"2.4 \u8f93\u51fa\u4e00\u884c","text":"C++const std::string::size_type cols = greeting.size() + pad * 2 + 2;\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#241","title":"2.4.1 \u8f93\u51fa\u8fb9\u754c\u5b57\u7b26","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#2411-if","title":"2.4.1.1 if \u8bed\u53e5","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#2412","title":"2.4.1.2 \u903b\u8f91\u8fd0\u7b97\u7b26","text":" - ||\u662f\u5de6\u7ed3\u5408\uff0c\u4f1a\u6709\u77ed\u8def\u6c42\u503c\uff08short-circuit evaluation\uff09
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#242","title":"2.4.2 \u8f93\u51fa\u975e\u8fb9\u754c\u5b57\u7b26","text":" - +=\u590d\u5408\u8d4b\u503c\u8fd0\u7b97\u7b26
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#25","title":"2.5 \u5b8c\u6574\u7684\u6846\u67b6\u7a0b\u5e8f","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#251-std","title":"2.5.1 \u7565\u53bb\u91cd\u590d\u4f7f\u7528\u7684 std::","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#252-for","title":"2.5.2 \u4f7f\u7528 for \u8bed\u53e5\u6765\u7f29\u77ed\u7a0b\u5e8f","text":" - \u533a\u95f4\u7684\u8d8a\u754c\u503c\uff08off-the-end value\uff09
C++for (int r = 0; r != rows; ++r) {\n\n}\n
- \u8fd9\u662f\u534a\u5f00\u533a\u95f4
C++for (init-statement condition; expression)\n statement\n\n#equals to\n\n{\n inti-statement\n while (condition) {\n statement\n expression;\n }\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#253","title":"2.5.3 \u538b\u7f29\u68c0\u6d4b","text":" - \u5c31\u662f\u628a if \u8bed\u53e5\u5408\u5e76\u4e00\u4e0b
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#254","title":"2.5.4 \u5b8c\u6574\u7684\u6846\u67b6\u7a0b\u5e8f","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#26","title":"2.6 \u8ba1\u6570","text":" - \u4e0d\u5bf9\u6210\u533a\u95f4\u53ef\u4ee5\u76f4\u63a5\u770b\u51fa\u6709\u591a\u5c11\u4e2a\u5143\u7d20
- \\(\\displaystyle [m, n]\\) \u6709 m - n \u4e2a\u5143\u7d20
- \u800c\u4e14\u53ef\u4ee5\u8868\u793a\u7a7a\u533a\u95f4 \\(\\displaystyle [n, n]\\)
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#27","title":"2.7 \u5c0f\u7ed3","text":" - \u8868\u8fbe\u5f0f
- \u64cd\u4f5c\u6570\u7684\u7ec4\u5408\u65b9\u5f0f
- \u64cd\u4f5c\u6570\u5982\u4f55\u88ab\u8f6c\u6362
- \u64cd\u4f5c\u6570\u7684\u8fd0\u7b97\u6b21\u5e8f
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#3","title":"3 \u4f7f\u7528\u6279\u91cf\u6570\u636e","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#31","title":"3.1 \u8ba1\u7b97\u5b66\u751f\u6210\u7ee9","text":"C++#include <iomanip>\n#include <ios>\n#include <iostream>\n#include <string>\n\nusing std::cin;\nusing std::cout;\nusing std::endl;\nusing std::precision;\n\nint main() {\n cout << \"Please enter your first name:\";\n string name;\n cin >> name;\n cout << \"Hello, \" << name << \"!\" << endl;\n\n cout << \"Please enter your midterm and final exam grades:\";\n double midterm, final;\n cin >> midterm >> final;\n\n cout << \"Enter all your homework grades, \" \n \"followed by end-of-file:\";\n\n int count = 0;\n double sum = 0;\n\n double x;\n\n while (cin >> x) {\n ++ count;\n sum += x;\n }\n\n streamsize prec = cout.precision();\n cout << \"Your final grade is \" << setprecision(3)\n << 0.2 * midterm + 0.4 * final + 0.4 * sum / count\n << setprecision(prec) << endl; //\u91cd\u7f6e\u6709\u6548\u4f4d\u6570 \u6216\u5199\u6210cout.precision(prec);\n return 0;\n}\n
- \u8f93\u5165\u8fd0\u7b97\u7b26\u8fd4\u56de\u5b83\u7684\u5de6\u64cd\u4f5c\u6570\u4f5c\u4e3a\u7ed3\u679c
- \u5374\u7701\u521d\u59cb\u5316
- setprecision \u4e5f\u662f\u4e00\u4e2a\u63a7\u5236\u5668: \u4e3a\u6d41\u7684\u540e\u7ee7\u8f93\u51fa\u8bbe\u7f6e\u4e86\u4e00\u4e2a\u7279\u5b9a\u7684\u6709\u6548\u4f4d\u6570
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#311","title":"3.1.1 \u68c0\u6d4b\u8f93\u51fa","text":" - istream \u53ef\u4ee5\u88ab\u8f6c\u6362\u6210 bool \u503c
- \u6709\u4e09\u79cd\u60c5\u51b5\u6761\u4ef6\u4f1a\u53d8\u5047:
- \u8fbe\u5230\u8f93\u5165\u6587\u4ef6\u7684\u7ed3\u5c3e
- \u8f93\u5165\u548c\u6211\u4eec\u8bd5\u56fe\u8bfb\u53d6\u7684\u53d8\u91cf\u7c7b\u578b\u4e0d\u4e00\u81f4
- \u68c0\u6d4b\u5230\u4e00\u4e2a\u786c\u4ef6\u95ee\u9898
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#312","title":"3.1.2 \u5faa\u73af\u4e0d\u53d8\u5f0f","text":"\u4e0d\u662f\u5f88\u61c2
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#32","title":"3.2 \u7528\u4e2d\u503c\u4ee3\u66ff\u5e73\u5747\u503c","text":"\u90a3\u4e48\u5c31\u8981\u6392\u5e8f\u4e86
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#321","title":"3.2.1 \u628a\u6570\u636e\u96c6\u5408\u5b58\u50a8\u5728\u5411\u91cf\u4e2d","text":"C++double x;\nvector<double> homework;\n\nwhile(cin >> x)\n homework.push_back(x);\n
- \u6211\u4eec\u4f7f\u7528\u4e86\u4e00\u79cd\u540d\u4e3a\u6a21\u677f\u7c7b\u7684\u8bed\u8a00\u7279\u5f81
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#322","title":"3.2.2 \u4ea7\u751f\u8f93\u51fa","text":"C++typedef vector<double>::size_type vec_sz;\nvec_sz size = homework.size();\n
- \u6211\u4eec\u5e0c\u671b\u4fdd\u6301\u7cfb\u7edf\u73af\u5883\u7684\u72ec\u7acb\u6027
C++if (size == 0) {\n cout << endl << \"You must enter your grades. \"\n \"Please try again.\" << endl;\n return 1;\n}\n\nsort (homework.begin(), homework.end());\n\nvec_sz mid = size / 2;\ndouble median;\nmedian = size % 2 == 0 ? (homework[mid] + homework[mid - 1]) / 2\n : homework[mid];\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#323","title":"3.2.3 \u4e00\u4e9b\u66f4\u4e3a\u6df1\u5165\u7684\u89c2\u5bdf","text":" - size_type \u662f\u65e0\u7b26\u53f7\u6574\u6570\u7c7b\u578b
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#33","title":"3.3 \u5c0f\u7ed3","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#4","title":"4 \u7ec4\u7ec7\u7a0b\u5e8f\u548c\u6570\u636e","text":" - \u8fd9\u4e9b\u5e93\u5de5\u5177\u6709\u51e0\u4e2a\u540c\u6837\u7684\u7279\u6027:
- \u80fd\u89e3\u51b3\u67d0\u4e9b\u7279\u5b9a\u7c7b\u578b\u7684\u95ee\u9898
- \u4e0e\u5176\u4ed6\u7684\u5927\u591a\u6570\u5de5\u5177\u90fd\u76f8\u4e92\u72ec\u7acb
- \u90fd\u5177\u6709\u4e00\u4e2a\u540d\u79f0
- \u4e24\u79cd\u65b9\u6cd5\u6765\u7ec4\u7ec7\u5927\u578b\u7684\u7a0b\u5e8f
- \u51fd\u6570
- \u6570\u636e\u7ed3\u6784
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#41","title":"4.1 \u7ec4\u7ec7\u8ba1\u7b97","text":" - \u53c2\u6570\u7684\u521d\u59cb\u503c\u662f\u76f8\u5e94\u53c2\u6570\u503c\u7684\u590d\u5236\uff0c\u5373\u6309\u503c\u8c03\u7528\uff08call by value\uff09
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#411","title":"4.1.1 \u67e5\u627e\u4e2d\u503c","text":"C++double median(vector<double> vec)\n{\n typedef vector<double>::size_type vec_sz;\n vec_sz size = vec.size();\n if (size == 0)\n throw domain_error(\"median of an empty vector\");\n sort(vec.begin(), vec.end());\n vec_sz mid = size / 2;\n return size % 2 == 0 ? (vec[mid] + vec[mid - 1]) / 2 : vec[mid];\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#412","title":"4.1.2 \u91cd\u65b0\u5236\u5b9a\u8ba1\u7b97\u6210\u7ee9\u7684\u7b56\u7565","text":" - \u53ea\u80fd\u628a\u4e0d\u662f\u5e38\u91cf\u7684\u5f15\u7528\u7ed9\u5e38\u91cf\u7684\uff0c\u5373\u6761\u4ef6\u53ea\u80fd\u52a0\u5f3a
- \u6709\u597d\u51e0\u4e2a\u540c\u6837\u51fd\u6570\u540d\u7684\u51fd\u6570\u65f6\uff0c\u4f1a\u53d1\u751f\u91cd\u8f7d
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#413","title":"4.1.3 \u8bfb\u5bb6\u5ead\u4f5c\u4e1a\u6210\u7ee9","text":" - \u5de6\u503c\u53c2\u6570: \u975e\u4e34\u65f6\u5bf9\u8c61
C++istream read_hw(istream& in, vector<double>& hw)\n{\n if (in) {\n hw.clear();\n double x;\n while (in >> x) \n hw.push_back(x);\n in.clear();\n }\n return in;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#414","title":"4.1.4 \u4e09\u79cd\u51fd\u6570\u53c2\u6570","text":" - \u4e00\u822c\u800c\u8a00\uff0c\u6211\u4eec\u6ca1\u6709\u5fc5\u8981\u4e3a\u4e86 int \u6216 double \u8fd9\u6837\u89c1\u5230\u4f60\u7684\u5185\u90e8\u7c7b\u578b\u7684\u53c2\u6570\u800c\u53bb\u4f7f\u7528 const \u5f15\u7528
- \u5982\u679c\u4f20\u5165 read_hw () \u7684\u4e0d\u662f\u4e00\u4e2a\u5de6\u503c\uff0c\u90a3\u4e48\u6211\u4eec\u4f1a\u628a\u8f93\u5165\u5b58\u5230\u4e00\u4e2a\u6211\u4eec\u65e0\u59a8\u8bbf\u95ee\u7684\u5bf9\u8c61\u4e2d\uff08\u6539\u5bf9\u8c61\u4f1a\u5728 read_hw() \u8fd4\u56de\u65f6\u7acb\u5373\u6d88\u5931\uff09
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#415","title":"4.1.5 \u4f7f\u7528\u51fd\u6570\u6765\u8ba1\u7b97\u5b66\u751f\u7684\u6210\u7ee9","text":" - \u4e0d\u8981\u8ba9\u4e00\u6761\u8bed\u53e5\u4e2d\u7684\u526f\u4f5c\u7528\u4e2a\u6570\u8d85\u8fc7\u4e00\u4e2a
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#42","title":"4.2 \u7ec4\u7ec7\u6570\u636e","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#421","title":"4.2.1 \u628a\u4e00\u4e2a\u5b66\u751f\u7684\u6240\u6709\u6570\u636e\u653e\u7f6e\u5728\u4e00\u8d77","text":"C++struct Student_info {\n string name;\n double midterm, final;\n vector<double> homework;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#422","title":"4.2.2 \u5904\u7406\u5b66\u751f\u8bb0\u5f55","text":"C++istream& read(istream& is, Student_info& s)\n{\n is >> s.name >> s.midterm >> s.final;\n read_hw(is, s.homework);\n return is;\n}\n\ndouble grade(const Student_info& s) \n{\n return grade(s.midterm, s.final, s.homework);\n}\n\nbool compare(const Student_info& x, const Studeng_info& y)\n{\n return x.name < y.name;\n}\n\nsort(students.begin(), students.end(), compare;\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#423","title":"4.2.3 \u751f\u6210\u62a5\u8868","text":"C++cout << setw(maxlen + 1) << student[i].name;\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#43","title":"4.3 \u628a\u5404\u90e8\u5206\u4ee3\u7801\u8fde\u63a5\u5230\u4e00\u8d77","text":"Text Only#ifndef GUARD_median_h\n#define GUARD_median_h //\u9884\u5904\u7406\u7a0b\u5e8f\n\n#include <vector>\ndouble median(vector<double>);\n\n#endif\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#44","title":"4.4 \u628a\u8ba1\u7b97\u6210\u7ee9\u7684\u7a0b\u5e8f\u5206\u5757","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#45","title":"4.5 \u4fee\u6b63\u540e\u7684\u8ba1\u7b97\u6210\u7ee9\u7684\u7a0b\u5e8f","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#46","title":"4.6 \u5c0f\u7ed3","text":" - \u7528\u6237\u5b9a\u4e49\u7684\u5934\u6587\u4ef6\u4e00\u822c\u4ee5 .h \u7ed3\u5c3e
- \u4e0d\u5e94\u8be5\u5728\u5934\u6587\u4ef6\u4e2d\u4f7f\u7528 using \u58f0\u660e
- \u7528 \\(\\displaystyle \\#\\) ifndef \u6307\u4ee4\u6765\u9632\u6b62\u5bf9\u5934\u6587\u4ef6\u7684\u91cd\u590d\u5305\u542b
- \u5185\u8054\u5b50\u8fc7\u7a0b\u901a\u5e38\u65f6\u5728\u5934\u6587\u4ef6\u800c\u4e0d\u662f\u5728\u6e90\u6587\u4ef6\u4e2d\u5b9a\u4e49 inline
- \u4e3a\u4e86\u6bd4\u5356\u4f60\u51fd\u6570\u8c03\u7528\u7684\u989d\u5916\u5f00\u9500\uff0c\u7f16\u8bd1\u5668\u4f1a\u7528\u51fd\u6570\u4f53\u7684\u4e00\u4e2a\u590d\u5236\u6765\u66ff\u6362\u5bf9\u51fd\u6570\u7684\u6bcf\u4e00\u4e2a\u8c03\u7528\u5e76\u6839\u636e\u9700\u8981\u8fdb\u884c\u4fee\u6b63
- \u5f02\u5e38\u5904\u7406
C++try{\n} catch (t) { }\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#5","title":"5 \u4f7f\u7528\u987a\u5e8f\u5bb9\u5668\u5e76\u5206\u6790\u5b57\u7b26\u4e32","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#51","title":"5.1 \u6309\u7c7b\u522b\u6765\u533a\u5206\u5b66\u751f","text":"C++vector<Student_info> extract_fails(vector<Student_info>& students)\n{\n vector<Student_info> pass, fail;\n for (vector<Student_info>::size_type i = 0; i != students.size(); ++i;)\n if (fgrade(student[i]))\n fail.push_back(student[i]);\n else\n pass.push_back(student[i]);\n students = pass;\n return fail;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#511","title":"5.1.1 \u5c31\u5730\u5220\u9664\u51fd\u6570","text":"C++vector<Student_info> extract_fails(vector<Student_info>& students)\n{\n vector<Student_info> fail;\n vector<Student_info>::size_type i = 0;\n while (i != students.size()) {\n if (fgrade(studentp[i])) {\n fail.push_back(students[i]);\n students.erase(students.begin() + i);\n } else\n ++i;\n }\n return fail;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#512","title":"5.1.2 \u987a\u5e8f\u5b58\u53d6\u4e0e\u968f\u673a\u5b58\u53d6","text":" - \u8bbf\u95ee\u5bb9\u5668\u65f6\u6240\u91c7\u53d6\u7684\u6b21\u5e8f\u4f1a\u5bfc\u81f4\u4e0d\u540c\u7684\u6027\u80fd\u7279\u6027
- \u4e8e\u662f\u4fbf\u6709\u4e86 iterator
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#52","title":"5.2 \u8fed\u4ee3\u5668","text":" - iterator
- \u8bc6\u522b\u4e00\u4e2a\u5bb9\u5668\u4ee5\u53ca\u5bb9\u5668\u4e2d\u7684\u4e00\u4e2a\u5143\u7d20
- \u8ba9\u6211\u4eec\u68c0\u67e5\u5b58\u50a8\u5728\u8fd9\u4e2a\u5143\u7d20\u4e2d\u7684\u503c
- \u63d0\u4f9b\u64cd\u4f5c\u6765\u79fb\u52a8\u5728\u5bb9\u6613\u4e2d\u7684\u5143\u7d20
- \u91c7\u7528\u5bf9\u5e94\u4e8e\u5bb9\u5668\u6240\u80fd\u591f\u6709\u6548\u5904\u7406\u7684\u65b9\u5f0f\u5bf9\u53ef\u7528\u7684\u64cd\u4f5c\u8fdb\u884c\u7ea6\u675f
C++for (vector<Student_info>::size_type i = 0; i != students.end(); ++i)\n cout << students[i].name << endl;\nfor (vector<Student_info>::const_iterator iter = students.begin();\n iter != students.end(); ++iter) {\n cout << (*iter).name << endl; }\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#521","title":"5.2.1 \u8fed\u4ee3\u5668\u7c7b\u578b","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#522","title":"5.2.2 \u8fed\u4ee3\u5668\u64cd\u4f5c","text":" - end \u7d27\u63a5\u5728\u5bb9\u5668\u6700\u540e\u4e00\u4e2a\u5143\u7d20\u540e\u9762\u7684\u4f4d\u7f6e
- ++iter \u7528\u4e86\u8fed\u4ee3\u5668\u7c7b\u578b\u91cd\u8f7d
- \u5f53\u6211\u4eec\u7528\u95f4\u63a5\u5f15\u7528\u8fd0\u7b97\u7b26 \\(\\displaystyle *\\) \u6765\u8bbf\u95ee\u8fd9\u4e2a\u5143\u7d20\uff0c\u90a3\u4e48\u4ed6\u4f1a\u8fd4\u56de\u4e00\u4e2a\u5de6\u503c\uff08\u8fed\u4ee3\u5668\u6240\u6307\u5411\u7684\u5143\u7d20\uff09
- \\(\\displaystyle .\\) \u7684\u4f18\u5148\u7ea7\u6bd4 \\(\\displaystyle *\\) \u9ad8
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#523","title":"5.2.3 \u4e00\u70b9\u8bed\u6cd5\u77e5\u8bc6","text":"C++(*iter).name\niter->name\n//both are ok\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#524-students-erase-students-begin-i","title":"5.2.4 students. erase (students. begin () + i) \u7684\u542b\u4e49","text":" - students \u4e0d\u652f\u6301\u968f\u673a\u8bbf\u95ee\u7d22\u5f15\u64cd\u4f5c\u7684\u5bb9\u5668\uff0c\u4f46\u662f\u4ecd\u4f1a\u5141\u8bb8\u8fed\u4ee3\u5668\u7684\u968f\u673a\u8bbf\u95ee
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#53","title":"5.3 \u7528\u8fed\u4ee3\u5668\u6765\u4ee3\u66ff\u7d22\u5f15","text":"C++vecotr<Student_info> extract_fails(vector<Student_info>& students)\n{\n vector<Student_info> fail;\n vector<Student_info>::iterator iter = students.begin();\n while (iter != students.end()) {\n if (fgrade(*iter)) {\n fail.push_back(*iter);\n iter = students.erase(iter); //important\n } else\n ++iter;\n }\n return fail;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#54","title":"5.4 \u91cd\u65b0\u601d\u8003\u6570\u636e\u7ed3\u6784\u4ee5\u5b9e\u73b0\u66f4\u597d\u7684\u6027\u80fd","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#55-list","title":"5.5 list \u7c7b\u578b","text":" - \u76f4\u63a5\u628a\u6240\u6709\u7684 vector \u6362\u6210 list \u5c31\u53ef\u4ee5\u4e86
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#551","title":"5.5.1 \u4e00\u4e9b\u91cd\u8981\u7684\u5dee\u522b","text":" - list \u7c7b\u7684\u8fed\u4ee3\u5668\u5e76\u4e0d\u652f\u6301\u5b8c\u5168\u968f\u673a\u7684\u8bbf\u95ee
- \u6240\u4ee5\u6211\u4eec\u4e0d\u80fd\u7528 sort \u51fd\u6570
- \u4f46\u662f list \u6709\u81ea\u5df1\u6210\u5458\u51fd\u6570
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#552","title":"5.5.2 \u4e00\u4e2a\u607c\u4eba\u7684\u8bdd\u9898","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#56","title":"5.6 \u5206\u5272\u5b57\u7b26\u4e32","text":"C++vector<string> split(const string& s)\n{\n vector<string> ret;\n typedef string::size_def string_size;\n string_size i = 0;\n while (i != s.size()) {\n while (i != s.size() && isspace(s[i])) // is from <cctype>\n ++i;\n string_size j = i;\n while (j != s.size() && !isspace(s[i]))\n ++j;\n if (i != j) {\n ret.push_back(s.substr(i, j - i));\n i = j;\n }\n }\n return ret;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#57-split","title":"5.7 \u6d4b\u8bd5 split \u51fd\u6570","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#58","title":"5.8 \u8fde\u63a5\u5b57\u7b26\u4e32","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#581","title":"5.8.1 \u4e3a\u56fe\u6848\u88c5\u6846","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#582","title":"5.8.2 \u7eb5\u5411\u8fde\u63a5","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#583","title":"5.8.3 \u6a2a\u5411\u8fde\u63a5","text":"TODO
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#59","title":"5.9 \u5c0f\u7ed3","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#6","title":"6 \u4f7f\u7528\u5e93\u7b97\u6cd5","text":" - \u5229\u7528\u516c\u7528\u63a5\u53e3\u6765\u63d0\u4f9b\u4e00\u4e2a\u6807\u51c6\u7b97\u6cd5\u96c6\u5408
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#61","title":"6.1 \u5206\u6790\u5b57\u7b26\u4e32","text":"C++for (vector<string>::const_iterator it = bottom.begin(); it != bottom.end(); ++i)\n ret.push_back(*it);\n\nret.insert(ret.end(), bottom.begin(), bottom.end());\n\ncopy(bottom.begin(), bottom.end(), back_inserter(ret));\n
- copy \u662f\u4e00\u4e2a\u6cdb\u578b (generic) \u7b97\u6cd5\u7684\u4f8b\u5b50
- back_inserter \u662f\u4e00\u4e2a\u8fed\u4ee3\u5668\u9002\u914d\u5668\u7684\u4f8b\u5b50
C++copy(begin, end, out);\n//equals to\nwhile (begin != end)\n *out++ = *begin++;\n // equals to \n // *out = *begin; ++out; ++begin;\n\nit = begin++;\n//equals to\nit = begin;\n++begin;\n
- \u8fed\u4ee3\u5668\u9002\u914d\u5668\u662f\u4e00\u4e2a\u4ea7\u751f\u8fed\u4ee3\u5668\u7684\u4e1c\u897f
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#611-split","title":"6.1.1 \u53e6\u4e00\u4e2a\u5b9e\u73b0 split \u7684\u65b9\u6cd5","text":"C++bool space(char c) {\n return isspace(c);\n}\nbool not_space(char c) {\n return !isspace(c);\n}\nvector<string> split(const string& str) {\n typedef string::const_iterator iter;\n vector<string> ret;\n iter i = str.begin();\n while(i != str.end()) {\n i = find_if(i, str.end(), not_space);\n iter j = find_if(i, str.end(), space);\n if (i != str.end()) ret.push_back(string(i, j));\n i = j; \n }\n return ret;\n}\n
- \u6211\u4eec\u9700\u8981\u7f16\u5199\u6211\u4eec space \u548c not_space \u662f\u7528\u6765\u89e3\u91ca\u6211\u4eec\u6240\u6307\u7684\u662f\u54ea\u4e2a\u7248\u672c\u7684 isspace
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#612","title":"6.1.2 \u56de\u6587","text":"C++bool is_palindrome(const string& s) {\n return equal(s.begin(), s.end(), s.rbegin());\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#613-url","title":"6.1.3 \u67e5\u627e URL","text":" C++vector<string> find_urls(const string& s) {\n vector<string> ret;\n typedef string::const_iterator iter;\n iter b = s.begin(), e = s.end();\n while (b != e) {\n b = url_beg(b, e);\n if (b != e) {\n iter after = url_end(b, e);\n ret.push_back(string(b, after));\n b = after;\n }\n }\n return ret;\n}\n\nstring::const_iterator url_end(string::const_iterator b, string::const_iterator e) {\n return find_if(b, e, not_url_char);\n}\n\nbool not_url_char(char c) {\n static const string url_ch = \"~;/?@=&$-_.+!*{},'\";\n return !(isalnum(c)) || find(url_ch.begin(), url_ch.end(), c) != url_end());\n //static\u58f0\u660e\u7684\u5c40\u90e8\u53d8\u91cf\u5177\u6709\u5168\u5c40\u5bff\u547d\uff0c\u751f\u5b58\u671f\u8d2f\u7a7f\u6574\u4e2a\u51fd\u6570\u8c03\u7528\u8fc7\u7a0b\n}\n\nstring::const_iterator url_beg(string::const_iterator b, string::const_iterator e) {\n static const string sep = \"://\";\n typedef string::const_iterator iter;\n iter i = b;\n while ((i = search(i, e, sep.begin(), sep.end())) != e) {\n if (i != b && i + sep.size() != e) {\n iter beg = i;\n while (beg != b && isalpha(beg[-1]))\n --beg;\n if (beg != i && i + sep.size() != e && !not_url_char(i[sep.size()])) return beg;\n }\n if (i != e) i += sep.size();\n }\n return e;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#62","title":"6.2 \u5bf9\u8ba1\u7b97\u6210\u7ee9\u7684\u65b9\u6848\u8fdb\u884c\u6bd4\u8f83","text":" - \u8bfb\u6240\u6709\u7684\u5b66\u751f\u8bb0\u5f55\uff0c\u628a\u505a\u4e86\u5168\u90e8\u5bb6\u5ead\u4f5c\u4e1a\u7684\u5b66\u751f\u4e0e\u5176\u4ed6\u7684\u5b66\u751f\u5206\u9694\u5f00\u3002
- \u5bf9\u6bcf\u4e00\u7ec4\u4e2d\u7684\u6240\u6709\u5b66\u751f\u5206\u522b\u4f7f\u7528\u6bcf\u4e00\u4e2a\u7684\u8ba1\u7b97\u6210\u7ee9\u7684\u65b9\u6848\uff0c\u62a5\u544a\u6bcf\u4e00\u7ec4\u7684\u4e2d\u503c\u6210\u7ee9\u3002
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#621","title":"6.2.1 \u5904\u7406\u5b66\u751f\u8bb0\u5f55","text":"C++bool did_all_hw(const Student_info& s) {\n reutrn ((fint(s.homework.begin(), s.homework.end(), 0)) == s.homework.end());\n}\n\nvector<Student_info> did, didnt;\nStudent_info student;\n\nwhile (read(cin, student)) {\n if (did_all_hw(student))\n did.push_back(student);\n else\n didnt.push_back(student);\n}\nif (did.empty()) {\n cout << \"No student did all the homework!\" << endl;\n return 1;\n}\nif (didnt.empty()) {\n cout << \"Every student did all the homework!\" << endl;\n return 1;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#622","title":"6.2.2 \u5206\u6790\u6210\u7ee9","text":"C++write_analysis(cout, \"median\", median_analysis, did, didn't);\n\ndouble grade_aux(const Student_info& s) {\n tru {\n return grade(s);\n } catch (domain_error) {\n return grade(s.midterm, s.final, 0);\n }\n}\n\ndouble median_analysis(const vector<Student_info>& students) {\n vector<double> grades;\n transdorm(students.begin(), students.end(), back_inserter(grades), grade_aux);\n return medina(grades);\n}\n\nvoid write_analysis(ostream& out, const string& name, double analysis(const vector<Student_info>&), const vector<Student_info>& did, const vector<Student_info>& didnt) {\n out << name << \": median(did) = \" << analysis(did) << \", median(didnt) = \" << analysis(didnt) << endl; \n}\n\nint main() {\n vector<Student_info> did, didnt;\n Student_info student;\n while (read(cin, student)) {\n if (did_all_hw(student))\n did.push_back(student);\n else \n didnt.push_back(student);\n }\n if (did.empty()) {\n cout << \"No student did all the homework!\" << endl;\n return 1;\n }\n if (didnt.empty()) {\n cout << \"Every student did all the homework!\" << endl;\n return 1;\n }\n write_analysis(cout, \"median\", median_analysis, did, didnt);\n write_analysis(cout, \"average\", average_analysis, did, didnt);\n write_analysis(cout, \"medina of homework turned in\", optimistic_median_analysis, did, didnt);\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#623","title":"6.2.3 \u8ba1\u7b97\u57fa\u4e8e\u5bb6\u5ead\u4f5c\u4e1a\u5e73\u5747\u6210\u7ee9\u7684\u603b\u6210\u7ee9","text":"C++double average(const vector<double>& v) {\n return accumulate(v.begin(), v.end(), 0.0) / v.size();\n}\n\ndouble average_analysis(const Student_info& s) {\n return grade(s.midterm, s.final, average(s.homework));\n}\n\ndouble average_analysis(const vector<Student_info>& students) {\n vector<double> grades;\n transform(students.begin(), students.end(), back_inserter(grades), average_grade);\n return median(grades);\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#624","title":"6.2.4 \u4e0a\u4ea4\u7684\u5bb6\u5ead\u4f5c\u4e1a\u7684\u4e2d\u503c","text":"C++double optimistic_median(const Student_info& s) {\n vector<double> nonzero;\n remove_copy(s.homework.begin(), s.homework.end(), back_inserter(nonzero), 0);\n if (nonzero.empty())\n return grade(s.midterm, s.final, 0);\n else \n return grade(s.midterm, s.final, median(nonzero));\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#63","title":"6.3 \u5bf9\u5b66\u751f\u8fdb\u884c\u5206\u7c7b\u5e76\u56de\u987e\u4e00\u4e0b\u6211\u4eec\u7684\u95ee\u9898","text":" - \u4f7f\u7528\u4e00\u4e9b\u7b97\u6cd5\u5e93\u6765\u89e3\u51b3\u95ee\u9898
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#631","title":"6.3.1 \u4e00\u79cd\u4e24\u6b21\u4f20\u9012\u7684\u89e3\u51b3\u65b9\u6848","text":"C++vector<Student_info> extract_fails(vector<Student_info>& students) {\n vector<Student_info> fail;\n remove_copy_if(students.begin(), students.end(), back_inserter(fail), pgrade);\n students.earse(remove_if(students.begin(), students.end(), fgrade), student.end());\n return fail;\n}\n\nbool pgrade(const Student_info& s) {\n return !fgrade(s);\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#64","title":"6.4 \u4e00\u79cd\u4e00\u6b21\u4f20\u9012\u7684\u89e3\u51b3\u65b9\u6848","text":"C++vector<Student_info> extract_fails(vector<Student_info>& students) {\n vector<Student_info>::iterator iter = stable_partition(students.begin(), students.end(), pgrade);\n vector<Student_info> fail(iter, students.end());\n students.erase(iter, students.end());\n return fail;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#65","title":"6.5 \u7b97\u6cd5\u3001\u5bb9\u5668\u4ee5\u53ca\u8fed\u4ee3\u5668","text":" - \u7b97\u6cd5\u4f5c\u7528\u4e8e\u5bb9\u5668\u7684\u5143\u7d20\uff0c\u800c\u4e0d\u662f\u4f5c\u7528\u4e8e\u5bb9\u5668
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#7","title":"7 \u4f7f\u7528\u5173\u8054\u5bb9\u5668","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#71","title":"7.1 \u652f\u6301\u9ad8\u6548\u67e5\u627e\u7684\u5bb9\u5668","text":" - \u5173\u8054\u5bb9\u5668\u4f1a\u5bf9\u63d2\u5165\u7684\u5143\u7d20\u8fdb\u884c\u6392\u5e8f\u6765\u63d0\u9ad8\u67e5\u627e\u7684\u901f\u5ea6
- \u5173\u8054\u6570\u7ec4\u5c31\u662f\u6709 key-value \u7684\u6570\u7ec4
- map: \u6620\u5c04\u8868\u7684\u7d22\u5f15\u4e0d\u4e00\u5b9a\u662f\u6574\u6570
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#72","title":"7.2 \u8ba1\u7b97\u5355\u8bcd\u6570","text":"C++int main() {\n string s;\n map<string, int> counters;\n while (cin >> s) ++counters[s];\n for (map<string, int>::const_iterator it = counters.begin(); it != couners.end(); ++it) {\n cout << it->first << \"\\t\" << it->second << endl;\n }\n return 0;\n}\n
- \u6570\u5bf9\uff08pair\uff09
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#73","title":"7.3 \u4ea7\u751f\u4e00\u4e2a\u4ea4\u53c9\u5f15\u7528\u8868","text":"C++map<string, vector<int> > xref(istream& in, vector<string> find_words(const string&) = split) {\n string line;\n int line_number = 0'\n map<string, vector<int>> ret;\n while (getline(in, line)) {\n ++line_number;\n vector<string> words = find_words(line);\n for (vector<string>::const_iterator it = words.begin(); it != words.end(); ++it)\n ret(*it).push_back(line_number);\n }\n return ret;\n}\n\n\nint main() {\n map<string, vector<int> > ret = cref(cin);\n for (map<string, vector<int> >::const_iterator it = ret.begin(); it != ret.end(); ++it) {\n cout << it->first << \" occurs on line(s): \";\n vector<int>::const_iterator line_it = it->second.begin();\n cout << *lint_it;\n ++line_it;\n while (line_it != it->second.end()) {\n cout << \", \" << *line_it;\n ++line_it;\n }\n cout << endl;\n }\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#74","title":"7.4 \u751f\u6210\u53e5\u5b50","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#741","title":"7.4.1 \u8868\u793a\u89c4\u5219","text":"C++typedef vector<string> Rule;\ntypedef vector<Rule> Rule_collection;\ntypedef map<string, Rule_collection> Grammer;\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#742","title":"7.4.2 \u8bfb\u5165\u6587\u6cd5","text":"C++Grammer read_grammer(istream& in) {\n Grammer ret;\n string line;\n while (getline(in, line)) {\n vector<string> entry = split(line);\n if (!entry.empty())\n ret[entry[0]].push_back(Rule(entry.begin() + 1, entry.end()));\n }\n return ret;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#743","title":"7.4.3 \u751f\u6210\u53e5\u5b50","text":"C++vector<string> gen_sentence(const Grammar& g) {\n vector<string> ret;\n gen_aux(g, \"<sentence>\", ret);\n return ret;\n}\n\nbool bracketed(const string& s) {\n return s.size() > 1 && s[0] == '<' && s[s.size() - 1] == '>';\n}\n\nvoid gen_aux(const Grammar& g, const string& word, vector<string>& ret) {\n if (!bracketed(word)) {\n ret.push_back(word);\n } else {\n Grammar::const_iterator it = g.find(word);\n if (it == g.end())\n throw logic_error(\"empty rule\");\n const Rule_collection& c = it->seond;\n const Rule& r = c[nrand(c.size())];\n for (Rule::const_iterator i = r.begin(); i != r.end(); ++i)\n gen_aux(g, *i, ret);\n }\n}\n\nint main() {\n vector<string> sentence = gen_sentence(read_grammar(cin));\n vector<string>::const_iterator it = sentence.begin();\n if (!sentence.empty()) {\n cout << *it;\n ++it; \n }\n while (it != sentence.end()) {\n cout << \" \" << *it;\n ++it;\n }\n cout << endl;\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#744","title":"7.4.4 \u9009\u62e9\u4e00\u4e2a\u968f\u673a\u51fd\u6570","text":"C++int nrand(int n) {\n if (n <= 0 || n > RAND_MAX)\n throw domain_error(\"Argument to nrand is out of range\");\n const int bucket_size = RAND_MAX / n;\n int r;\n do r = rand() / bucket_size;\n while (r >= n);\n return r;\n}\n
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#75","title":"7.5 \u5173\u4e8e\u6027\u80fd\u7684\u4e00\u70b9\u8bf4\u660e","text":" - \u7528\u6563\u5217\u8868\u5b9e\u73b0\u5173\u8054\u6570\u7ec4\u5728 C++\u4e2d\u7684\u662f\u5f88\u56f0\u96be\u7684 \u6ca1\u641e\u61c2
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#76","title":"7.6 \u5c0f\u7ed3","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#8","title":"8 \u7f16\u5199\u6cdb\u578b\u51fd\u6570","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#81","title":"8.1 \u6cdb\u578b\u51fd\u6570\u662f\u4ec0\u4e48","text":" - \u5728\u4f7f\u7528\u51fd\u6570\u4e4b\u524d\u4e0d\u77e5\u9053\u53c2\u6570\u6216\u8005\u8fd4\u56de\u7c7b\u578b\u662f\u4ec0\u4e48
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#811","title":"8.1.1 \u672a\u77e5\u7c7b\u578b\u7684\u4e2d\u503c","text":" - \u5b9e\u73b0\u4e86\u6cdb\u578b\u51fd\u6570\u7684\u8bed\u8a00\u7279\u5f81\u88ab\u79f0\u4f5c\u6a21\u677f\u51fd\u6570
"},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#9","title":"9 \u5b9a\u4e49\u65b0\u7c7b\u578b","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#10","title":"10 \u7ba1\u7406\u5185\u5b58\u548c\u4f4e\u7ea7\u6570\u636e\u7ed3\u6784","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#11_1","title":"11 \u5b9a\u4e49\u62bd\u8c61\u6570\u636e\u7c7b\u578b","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#12_1","title":"12 \u4f7f\u7c7b\u5bf9\u8c61\u50cf\u4e00\u4e2a\u6570\u503c\u4e00\u6837\u5de5\u4f5c","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#13_1","title":"13 \u4f7f\u7528\u7ee7\u627f\u4e0e\u52a8\u6001\u7ed1\u5b9a","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#14","title":"14 \u8fd1\u4e4e\u81ea\u52a8\u5730\u7ba1\u7406\u5185\u5b58","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#15","title":"15 \u518d\u8c08\u5b57\u7b26\u56fe\u5f62","text":""},{"location":"CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/#16-c","title":"16 \u4eca\u540e\u5982\u4f55\u5b66\u4e60 C++","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/","title":"C++","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#c","title":"C++","text":" \u7ea6 3115 \u4e2a\u5b57 523 \u884c\u4ee3\u7801 9 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 22 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#1","title":"1 \u6587\u4ef6\u64cd\u4f5c","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#11","title":"1.1 \u6587\u4ef6\u7684\u6982\u5ff5","text":" - C/C++\u628a\u6bcf\u4e00\u4e2a\u6587\u4ef6\u90fd\u770b\u6210\u662f\u4e00\u4e2a\u6709\u5e8f\u7684\u5b57\u8282\u6d41\uff0c\u4ee5\u6587\u4ef6\u7ed3\u675f\u6807\u5fd7\uff08EOF\uff09\u7ed3\u675f
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#12","title":"1.2 \u6587\u4ef6\u7684\u64cd\u4f5c\u6b65\u9aa4","text":" - \u6253\u5f00\u6587\u4ef6\uff0c\u8bb2\u6587\u4ef6\u6307\u9488\u6307\u5411\u6587\u4ef6\uff0c\u51b3\u5b9a\u6253\u5f00\u6587\u4ef6\u7684\u7c7b\u578b
- \u5bf9\u6587\u4ef6\u8fdb\u884c\u8bfb/\u5199\u64cd\u4f5c
- \u5728\u4f7f\u7528\u5b8c\u6587\u4ef6\u540e\uff0c\u5173\u95ed\u6587\u4ef6
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#13","title":"1.3 \u4e00\u4e9b\u51fd\u6570","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#131-freopen","title":"1.3.1 freopen \u51fd\u6570","text":"C++FILE* freopen(const char* filename, const char* mode, FILE* stream);\n
- \u53c2\u6570\u8bf4\u660e
filename: \u8981\u6253\u5f00\u7684\u6587\u4ef6\u540dmode: \u6587\u4ef6\u6253\u5f00\u7684\u6a21\u5f0f\uff0c\u8868\u793a\u6587\u4ef6\u8bbf\u95ee\u7684\u6743\u9650stream: \u6587\u4ef6\u6307\u9488\uff0c\u901a\u5e38\u4f7f\u7528\u6807\u51c6\u6587\u4ef6\u6d41 (stdin/stdout) \u6216\u6807\u51c6\u9519\u8bef\u8f93\u51fa\u6d41 (stderr)- \u8fd4\u56de\u503c\uff1a\u6587\u4ef6\u6307\u9488\uff0c\u6307\u5411\u88ab\u6253\u5f00\u6587\u4ef6
- \u6587\u4ef6\u6253\u5f00\u683c\u5f0f
r\uff1a\u4ee5\u53ea\u8bfb\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u5fc5\u987b\u5b58\u5728\uff0c\u53ea\u5141\u8bb8\u8bfb\u5165\u6570\u636e\u00a0\uff08\u5e38\u7528\uff09r+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u5fc5\u987b\u5b58\u5728\uff0c\u5141\u8bb8\u8bfb/\u5199\u6570\u636erb\uff1a\u4ee5\u53ea\u8bfb\u65b9\u5f0f\u6253\u5f00\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u6587\u4ef6\u5fc5\u987b\u5b58\u5728\uff0c\u53ea\u5141\u8bb8\u8bfb\u5165\u6570\u636erb+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u6587\u4ef6\u5fc5\u987b\u5b58\u5728\uff0c\u5141\u8bb8\u8bfb/\u5199\u6570\u636ert+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u672c\u6587\u4ef6\uff0c\u5141\u8bb8\u8bfb/\u5199\u6570\u636ew\uff1a\u4ee5\u53ea\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u4f1a\u65b0\u5efa\u6587\u4ef6\uff0c\u5426\u5219\u6e05\u7a7a\u5185\u5bb9\uff0c\u53ea\u5141\u8bb8\u5199\u5165\u6570\u636e\u00a0\uff08\u5e38\u7528\uff09w+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u5c06\u65b0\u5efa\u6587\u4ef6\uff0c\u5426\u5219\u6e05\u7a7a\u5185\u5bb9\uff0c\u5141\u8bb8\u8bfb/\u5199\u6570\u636ewb\uff1a\u4ee5\u53ea\u5199\u65b9\u5f0f\u6253\u5f00\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u5c06\u4f1a\u65b0\u5efa\u6587\u4ef6\uff0c\u5426\u5219\u6e05\u7a7a\u5185\u5bb9\uff0c\u53ea\u5141\u8bb8\u5199\u5165\u6570\u636ewb+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u5c06\u65b0\u5efa\u6587\u4ef6\uff0c\u5426\u5219\u6e05\u7a7a\u5185\u5bb9\uff0c\u5141\u8bb8\u8bfb/\u5199\u6570\u636ea\uff1a\u4ee5\u53ea\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u5c06\u65b0\u5efa\u6587\u4ef6\uff0c\u5199\u5165\u6570\u636e\u5c06\u88ab\u9644\u52a0\u5728\u6587\u4ef6\u672b\u5c3e\uff08\u4fdd\u7559 EOF \u7b26\uff09a+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u6587\u4ef6\u4e0d\u5b58\u5728\u5c06\u65b0\u5efa\u6587\u4ef6\uff0c\u5199\u5165\u6570\u636e\u5c06\u88ab\u9644\u52a0\u5728\u6587\u4ef6\u672b\u5c3e\uff08\u4e0d\u4fdd\u7559 EOF \u7b26\uff09at+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u6587\u672c\u6587\u4ef6\uff0c\u5199\u5165\u6570\u636e\u5c06\u88ab\u9644\u52a0\u5728\u6587\u4ef6\u672b\u5c3eab+\uff1a\u4ee5\u8bfb/\u5199\u65b9\u5f0f\u6253\u5f00\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u5199\u5165\u6570\u636e\u5c06\u88ab\u9644\u52a0\u5728\u6587\u4ef6\u672b\u5c3e \u4f7f\u7528\u65b9\u5f0f
C++#include <cstdio>\n#include <iostream>\nint mian(void) {\n freopen(\"data.in\", \"r\", stdin); \n // data.in \u5c31\u662f\u8bfb\u53d6\u7684\u6587\u4ef6\u540d\uff0c\u8981\u548c\u53ef\u6267\u884c\u6587\u4ef6\u653e\u5728\u540c\u4e00\u76ee\u5f55\u4e0b\n freopen(\"data.out\", \"w\", stdout); \n // data.out \u5c31\u662f\u8f93\u51fa\u6587\u4ef6\u7684\u6587\u4ef6\u540d\uff0c\u548c\u53ef\u6267\u884c\u6587\u4ef6\u5728\u540c\u4e00\u76ee\u5f55\u4e0b\n fclose(stdin);\n fclose(stdout);\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#132-fopen","title":"1.3.2 fopen \u51fd\u6570","text":"C++FILE* fopen(const char* path, const char* mode)\n
\u4f7f\u7528\u65b9\u5f0f
C++FILE *in, *out; // \u5b9a\u4e49\u6587\u4ef6\u6307\u9488 \nin = fopen(\"data.in\", \"r\"); \nout = fopen(\"data.out\", \"w\"); \n/* do what you want to do */ \nfclose(in); \nfclose(out);\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#14-c-ifstreamofstream","title":"1.4 C++ \u7684\u00a0ifstream/ofstream\u00a0\u6587\u4ef6\u8f93\u5165\u8f93\u51fa\u6d41","text":"C++#include <fstream> \nusing namespace std; \n// \u4e24\u4e2a\u7c7b\u578b\u90fd\u5728 std \u547d\u540d\u7a7a\u95f4\u91cc \nifstream fin(\"data.in\"); \nofstream fout(\"data.out\"); \nint main(void) { \n /* \u4e2d\u95f4\u7684\u4ee3\u7801\u6539\u53d8 cin \u4e3a fin \uff0ccout \u4e3a fout \u5373\u53ef */ \n fin.close(); \n fout.close(); \n return 0; \n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#2","title":"2 \u6807\u51c6\u5e93","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#21-stl","title":"2.1 STL \u5bb9\u5668","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#211","title":"2.1.1 \u5e8f\u5217\u5f0f\u5bb9\u5668","text":" - \u5411\u91cf(
vector) \u540e\u7aef\u53ef\u9ad8\u6548\u589e\u52a0\u5143\u7d20\u7684\u987a\u5e8f\u8868\u3002 - \u53ef\u4ee5\u52a8\u6001\u5206\u914d\u5185\u5b58
- \u91cd\u5199\u4e86\u6bd4\u8f83\u8fd0\u7b97\u7b26\u53ca\u8d4b\u503c\u8fd0\u7b97\u7b26
- \u4fbf\u5229\u7684\u521d\u59cb\u5316
- std::vector - cppreference.com
- \u6570\u7ec4(
array)C++11\uff0c\u5b9a\u957f\u7684\u987a\u5e8f\u8868\uff0cC \u98ce\u683c\u6570\u7ec4\u7684\u7b80\u5355\u5305\u88c5\u3002 - \u53cc\u7aef\u961f\u5217(
deque) \u53cc\u7aef\u90fd\u53ef\u9ad8\u6548\u589e\u52a0\u5143\u7d20\u7684\u987a\u5e8f\u8868\u3002 - \u5217\u8868(
list) \u53ef\u4ee5\u6cbf\u53cc\u5411\u904d\u5386\u7684\u94fe\u8868\u3002 - \u5355\u5411\u5217\u8868(
forward_list) \u53ea\u80fd\u6cbf\u4e00\u4e2a\u65b9\u5411\u904d\u5386\u7684\u94fe\u8868\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#212","title":"2.1.2 \u5173\u8054\u5f0f\u5bb9\u5668","text":" - \u96c6\u5408(
set) \u7528\u4ee5\u6709\u5e8f\u5730\u5b58\u50a8\u00a0\u4e92\u5f02\u00a0\u5143\u7d20\u7684\u5bb9\u5668\u3002\u5176\u5b9e\u73b0\u662f\u7531\u8282\u70b9\u7ec4\u6210\u7684\u7ea2\u9ed1\u6811\uff0c\u6bcf\u4e2a\u8282\u70b9\u90fd\u5305\u542b\u7740\u4e00\u4e2a\u5143\u7d20\uff0c\u8282\u70b9\u4e4b\u95f4\u4ee5\u67d0\u79cd\u6bd4\u8f83\u5143\u7d20\u5927\u5c0f\u7684\u8c13\u8bcd\u8fdb\u884c\u6392\u5217\u3002 - \u591a\u91cd\u96c6\u5408(
multiset) \u7528\u4ee5\u6709\u5e8f\u5730\u5b58\u50a8\u5143\u7d20\u7684\u5bb9\u5668\u3002\u5141\u8bb8\u5b58\u5728\u76f8\u7b49\u7684\u5143\u7d20\u3002 - \u6620\u5c04(
map) \u7531 {\u952e\uff0c\u503c} \u5bf9\u7ec4\u6210\u7684\u96c6\u5408\uff0c\u4ee5\u67d0\u79cd\u6bd4\u8f83\u952e\u5927\u5c0f\u5173\u7cfb\u7684\u8c13\u8bcd\u8fdb\u884c\u6392\u5217\u3002 - \u591a\u91cd\u6620\u5c04(
multimap) \u7531 {\u952e\uff0c\u503c} \u5bf9\u7ec4\u6210\u7684\u591a\u91cd\u96c6\u5408\uff0c\u4ea6\u5373\u5141\u8bb8\u952e\u6709\u76f8\u7b49\u60c5\u51b5\u7684\u6620\u5c04\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#213","title":"2.1.3 \u65e0\u5e8f\uff08\u5173\u8054\u5f0f\uff09\u5bb9\u5668","text":" - \u65e0\u5e8f\uff08\u591a\u91cd\uff09\u96c6\u5408(
unordered_set/unordered_multiset)C++11\uff0c\u4e0e\u00a0set/multiset\u00a0\u7684\u533a\u522b\u5728\u4e8e\u5143\u7d20\u65e0\u5e8f\uff0c\u53ea\u5173\u5fc3\u300c\u5143\u7d20\u662f\u5426\u5b58\u5728\u300d\uff0c\u4f7f\u7528\u54c8\u5e0c\u5b9e\u73b0\u3002 - \u65e0\u5e8f\uff08\u591a\u91cd\uff09\u6620\u5c04(
unordered_map/unordered_multimap)C++11\uff0c\u4e0e\u00a0map/multimap\u00a0\u7684\u533a\u522b\u5728\u4e8e\u952e (key) \u65e0\u5e8f\uff0c\u53ea\u5173\u5fc3 \"\u952e\u4e0e\u503c\u7684\u5bf9\u5e94\u5173\u7cfb\"\uff0c\u4f7f\u7528\u54c8\u5e0c\u5b9e\u73b0\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#214","title":"2.1.4 \u5bb9\u5668\u9002\u914d\u5668","text":"\u5bb9\u5668\u9002\u914d\u5668\u5176\u5b9e\u5e76\u4e0d\u662f\u5bb9\u5668\u3002\u5b83\u4eec\u4e0d\u5177\u6709\u5bb9\u5668\u7684\u67d0\u4e9b\u7279\u70b9\uff08\u5982\uff1a\u6709\u8fed\u4ee3\u5668\u3001\u6709\u00a0clear()\u00a0\u51fd\u6570\u2026\u2026\uff09\u3002
\u300c\u9002\u914d\u5668\u662f\u4f7f\u4e00\u79cd\u4e8b\u7269\u7684\u884c\u4e3a\u7c7b\u4f3c\u4e8e\u53e6\u5916\u4e00\u79cd\u4e8b\u7269\u884c\u4e3a\u7684\u4e00\u79cd\u673a\u5236\u300d\uff0c\u9002\u914d\u5668\u5bf9\u5bb9\u5668\u8fdb\u884c\u5305\u88c5\uff0c\u4f7f\u5176\u8868\u73b0\u51fa\u53e6\u5916\u4e00\u79cd\u884c\u4e3a\u3002
- \u6808(
stack) \u540e\u8fdb\u5148\u51fa (LIFO) \u7684\u5bb9\u5668\uff0c\u9ed8\u8ba4\u662f\u5bf9\u53cc\u7aef\u961f\u5217\uff08deque\uff09\u7684\u5305\u88c5\u3002 - \u961f\u5217(
queue) \u5148\u8fdb\u5148\u51fa (FIFO) \u7684\u5bb9\u5668\uff0c\u9ed8\u8ba4\u662f\u5bf9\u53cc\u7aef\u961f\u5217\uff08deque\uff09\u7684\u5305\u88c5\u3002 - \u4f18\u5148\u961f\u5217(
priority_queue) \u5143\u7d20\u7684\u6b21\u5e8f\u662f\u7531\u4f5c\u7528\u4e8e\u6240\u5b58\u50a8\u7684\u503c\u5bf9\u4e0a\u7684\u67d0\u79cd\u8c13\u8bcd\u51b3\u5b9a\u7684\u7684\u4e00\u79cd\u961f\u5217\uff0c\u9ed8\u8ba4\u662f\u5bf9\u5411\u91cf\uff08vector\uff09\u7684\u5305\u88c5\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#215","title":"2.1.5 \u5171\u540c\u70b9","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#2151","title":"2.1.5.1 \u5bb9\u5668\u58f0\u660e","text":" - \u90fd\u662f\u00a0
containerName<typeName,...> name\u00a0\u7684\u5f62\u5f0f\uff0c\u4f46\u6a21\u677f\u53c2\u6570\uff08<>\u00a0\u5185\u7684\u53c2\u6570\uff09\u7684\u4e2a\u6570\u3001\u5f62\u5f0f\u4f1a\u6839\u636e\u5177\u4f53\u5bb9\u5668\u800c\u53d8\u3002 - \u672c\u8d28\u539f\u56e0\uff1aSTL \u5c31\u662f\u300c\u6807\u51c6\u6a21\u677f\u5e93\u300d\uff0c\u6240\u4ee5\u5bb9\u5668\u90fd\u662f\u6a21\u677f\u7c7b\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#2152","title":"2.1.5.2 \u8fed\u4ee3\u5668","text":"C++vector<int> data(10);\n\nfor (int i = 0; i < data.size(); i++)\n cout << data[i] << endl;\n\nfor (vector<int>::iterator iter = data.begin(); iter != data.end(); iter++)\n cout << *iter << endl;\n//C++11 \u4ee5\u540e\u53ef\u4ee5\u7528 auto iter = data.begin() \u6765\u7b80\u5316\n
- \u5206\u7c7b
- InputIterator\uff08\u8f93\u5165\u8fed\u4ee3\u5668\uff09\uff1a\u53ea\u8981\u6c42\u652f\u6301\u62f7\u8d1d\u3001\u81ea\u589e\u548c\u89e3\u5f15\u8bbf\u95ee\u3002
- OutputIterator\uff08\u8f93\u51fa\u8fed\u4ee3\u5668\uff09\uff1a\u53ea\u8981\u6c42\u652f\u6301\u62f7\u8d1d\u3001\u81ea\u589e\u548c\u89e3\u5f15\u8d4b\u503c\u3002
- ForwardIterator\uff08\u5411\u524d\u8fed\u4ee3\u5668\uff09\uff1a\u540c\u65f6\u6ee1\u8db3 InputIterator \u548c OutputIterator \u7684\u8981\u6c42\u3002
- BidirectionalIterator\uff08\u53cc\u5411\u8fed\u4ee3\u5668\uff09\uff1a\u5728 ForwardIterator \u7684\u57fa\u7840\u4e0a\u652f\u6301\u81ea\u51cf\uff08\u5373\u53cd\u5411\u8bbf\u95ee\uff09\u3002
- RandomAccessIterator\uff08\u968f\u673a\u8bbf\u95ee\u8fed\u4ee3\u5668\uff09\uff1a\u5728 BidirectionalIterator \u7684\u57fa\u7840\u4e0a\u652f\u6301\u52a0\u51cf\u8fd0\u7b97\u548c\u6bd4\u8f83\u8fd0\u7b97\uff08\u5373\u968f\u673a\u8bbf\u95ee\uff09\u3002
- ContiguousIterator\uff08\u8fde\u7eed\u8fed\u4ee3\u5668\uff09\uff1a\u5728 RandomAccessIterator \u7684\u57fa\u7840\u4e0a\u8981\u6c42\u5bf9\u53ef\u89e3\u5f15\u7528\u7684\u8fed\u4ee3\u5668\u00a0
a + n\u00a0\u6ee1\u8db3\u00a0*(a + n)\u00a0\u4e0e\u00a0*(std::address_of(*a) + n)\u00a0\u7b49\u4ef7\uff08\u5373\u8fde\u7eed\u5b58\u50a8\uff0c\u5176\u4e2d\u00a0a\u00a0\u4e3a\u8fde\u7eed\u8fed\u4ee3\u5668\u3001n\u00a0\u4e3a\u6574\u578b\u503c\uff09\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#2153","title":"2.1.5.3 \u5171\u6709\u51fd\u6570","text":" =\uff1a\u6709\u8d4b\u503c\u8fd0\u7b97\u7b26\u4ee5\u53ca\u590d\u5236\u6784\u9020\u51fd\u6570\u3002begin()\uff1a\u8fd4\u56de\u6307\u5411\u5f00\u5934\u5143\u7d20\u7684\u8fed\u4ee3\u5668\u3002end()\uff1a\u8fd4\u56de\u6307\u5411\u672b\u5c3e\u7684\u4e0b\u4e00\u4e2a\u5143\u7d20\u7684\u8fed\u4ee3\u5668\u3002end()\u00a0\u4e0d\u6307\u5411\u67d0\u4e2a\u5143\u7d20\uff0c\u4f46\u5b83\u662f\u672b\u5c3e\u5143\u7d20\u7684\u540e\u7ee7\u3002size()\uff1a\u8fd4\u56de\u5bb9\u5668\u5185\u7684\u5143\u7d20\u4e2a\u6570\u3002max_size()\uff1a\u8fd4\u56de\u5bb9\u5668\u00a0\u7406\u8bba\u4e0a\u00a0\u80fd\u5b58\u50a8\u7684\u6700\u5927\u5143\u7d20\u4e2a\u6570\u3002\u4f9d\u5bb9\u5668\u7c7b\u578b\u548c\u6240\u5b58\u50a8\u53d8\u91cf\u7684\u7c7b\u578b\u800c\u53d8\u3002empty()\uff1a\u8fd4\u56de\u5bb9\u5668\u662f\u5426\u4e3a\u7a7a\u3002swap()\uff1a\u4ea4\u6362\u4e24\u4e2a\u5bb9\u5668\u3002clear()\uff1a\u6e05\u7a7a\u5bb9\u5668\u3002==/!=/</>/<=/>=\uff1a\u6309\u00a0\u5b57\u5178\u5e8f\u00a0\u6bd4\u8f83\u4e24\u4e2a\u5bb9\u5668\u7684\u5927\u5c0f\u3002\uff08\u6bd4\u8f83\u5143\u7d20\u5927\u5c0f\u65f6\u00a0map\u00a0\u7684\u6bcf\u4e2a\u5143\u7d20\u76f8\u5f53\u4e8e\u00a0set<pair<key, value> >\uff0c\u65e0\u5e8f\u5bb9\u5668\u4e0d\u652f\u6301\u00a0</>/<=/>=\u3002\uff09
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#216-stl","title":"2.1.6 STL \u7b97\u6cd5","text":" find\uff1a\u987a\u5e8f\u67e5\u627e\u3002find(v.begin(), v.end(), value)\uff0c\u5176\u4e2d\u00a0value\u00a0\u4e3a\u9700\u8981\u67e5\u627e\u7684\u503c\u3002reverse\uff1a\u7ffb\u8f6c\u6570\u7ec4\u3001\u5b57\u7b26\u4e32\u3002reverse(v.begin(), v.end())\u00a0\u6216\u00a0reverse(a + begin, a + end)\u3002unique\uff1a\u53bb\u9664\u5bb9\u5668\u4e2d\u76f8\u90bb\u7684\u91cd\u590d\u5143\u7d20\u3002unique(ForwardIterator first, ForwardIterator last)\uff0c\u8fd4\u56de\u503c\u4e3a\u6307\u5411\u00a0\u53bb\u91cd\u540e\u00a0\u5bb9\u5668\u7ed3\u5c3e\u7684\u8fed\u4ee3\u5668\uff0c\u539f\u5bb9\u5668\u5927\u5c0f\u4e0d\u53d8\u3002\u4e0e\u00a0sort\u00a0\u7ed3\u5408\u4f7f\u7528\u53ef\u4ee5\u5b9e\u73b0\u5b8c\u6574\u5bb9\u5668\u53bb\u91cd\u3002sort\uff1a\u6392\u5e8f\u3002sort(v.begin(), v.end(), cmp)\u00a0\u6216\u00a0sort(a + begin, a + end, cmp)\uff0c\u5176\u4e2d\u00a0end\u00a0\u662f\u6392\u5e8f\u7684\u6570\u7ec4\u6700\u540e\u4e00\u4e2a\u5143\u7d20\u7684\u540e\u4e00\u4f4d\uff0ccmp\u00a0\u4e3a\u81ea\u5b9a\u4e49\u7684\u6bd4\u8f83\u51fd\u6570\u3002stable_sort\uff1a\u7a33\u5b9a\u6392\u5e8f\uff0c\u7528\u6cd5\u540c\u00a0sort()\u3002nth_element \uff1a\u6309\u6307\u5b9a\u8303\u56f4\u8fdb\u884c\u5206\u7c7b\uff0c\u5373\u627e\u51fa\u5e8f\u5217\u4e2d\u7b2c \\(\\displaystyle n\\) \u5927\u7684\u5143\u7d20\uff0c\u4f7f\u5176\u5de6\u8fb9\u5747\u4e3a\u5c0f\u4e8e\u5b83\u7684\u6570\uff0c\u53f3\u8fb9\u5747\u4e3a\u5927\u4e8e\u5b83\u7684\u6570\u3002 nth_element(v.begin(), v.begin() + mid, v.end(), cmp)\u00a0\u6216\u00a0nth_element(a + begin, a + begin + mid, a + end, cmp) \u3002binary_search\uff1a\u4e8c\u5206\u67e5\u627e\u3002binary_search(v.begin(), v.end(), value)\uff0c\u5176\u4e2d\u00a0value\u00a0\u4e3a\u9700\u8981\u67e5\u627e\u7684\u503c\u3002merge\uff1a\u5c06\u4e24\u4e2a\uff08\u5df2\u6392\u5e8f\u7684\uff09\u5e8f\u5217\u00a0\u6709\u5e8f\u5408\u5e76\u00a0\u5230\u7b2c\u4e09\u4e2a\u5e8f\u5217\u7684\u00a0\u63d2\u5165\u8fed\u4ee3\u5668\u00a0\u4e0a\u3002merge(v1.begin(), v1.end(), v2.begin(), v2.end() ,back_inserter(v3))\u3002inplace_merge\uff1a\u5c06\u4e24\u4e2a\uff08\u5df2\u6309\u5c0f\u4e8e\u8fd0\u7b97\u7b26\u6392\u5e8f\u7684\uff09\uff1a[first,middle), [middle,last)\u00a0\u8303\u56f4\u00a0\u539f\u5730\u5408\u5e76\u4e3a\u4e00\u4e2a\u6709\u5e8f\u5e8f\u5217\u3002inplace_merge(v.begin(), v.begin() + middle, v.end())\u3002lower_bound\uff1a\u5728\u4e00\u4e2a\u6709\u5e8f\u5e8f\u5217\u4e2d\u8fdb\u884c\u4e8c\u5206\u67e5\u627e\uff0c\u8fd4\u56de\u6307\u5411\u7b2c\u4e00\u4e2a\u00a0\u5927\u4e8e\u7b49\u4e8e \u00a0\u7684\u5143\u7d20\u7684\u4f4d\u7f6e\u7684\u8fed\u4ee3\u5668\u3002\u5982\u679c\u4e0d\u5b58\u5728\u8fd9\u6837\u7684\u5143\u7d20\uff0c\u5219\u8fd4\u56de\u5c3e\u8fed\u4ee3\u5668\u3002lower_bound(v.begin(),v.end(),x)\u3002upper_bound\uff1a\u5728\u4e00\u4e2a\u6709\u5e8f\u5e8f\u5217\u4e2d\u8fdb\u884c\u4e8c\u5206\u67e5\u627e\uff0c\u8fd4\u56de\u6307\u5411\u7b2c\u4e00\u4e2a\u00a0\u5927\u4e8e \u00a0\u7684\u5143\u7d20\u7684\u4f4d\u7f6e\u7684\u8fed\u4ee3\u5668\u3002\u5982\u679c\u4e0d\u5b58\u5728\u8fd9\u6837\u7684\u5143\u7d20\uff0c\u5219\u8fd4\u56de\u5c3e\u8fed\u4ee3\u5668\u3002upper_bound(v.begin(),v.end(),x)\u3002next_permutation\uff1a\u5c06\u5f53\u524d\u6392\u5217\u66f4\u6539\u4e3a\u00a0\u5168\u6392\u5217\u4e2d\u7684\u4e0b\u4e00\u4e2a\u6392\u5217\u3002\u5982\u679c\u5f53\u524d\u6392\u5217\u5df2\u7ecf\u662f\u00a0\u5168\u6392\u5217\u4e2d\u7684\u6700\u540e\u4e00\u4e2a\u6392\u5217\uff08\u5143\u7d20\u5b8c\u5168\u4ece\u5927\u5230\u5c0f\u6392\u5217\uff09\uff0c\u51fd\u6570\u8fd4\u56de\u00a0false\u00a0\u5e76\u5c06\u6392\u5217\u66f4\u6539\u4e3a\u00a0\u5168\u6392\u5217\u4e2d\u7684\u7b2c\u4e00\u4e2a\u6392\u5217\uff08\u5143\u7d20\u5b8c\u5168\u4ece\u5c0f\u5230\u5927\u6392\u5217\uff09\uff1b\u5426\u5219\uff0c\u51fd\u6570\u8fd4\u56de\u00a0true\u3002next_permutation(v.begin(), v.end())\u00a0\u6216\u00a0next_permutation(v + begin, v + end)\u3002prev_permutation\uff1a\u5c06\u5f53\u524d\u6392\u5217\u66f4\u6539\u4e3a\u00a0\u5168\u6392\u5217\u4e2d\u7684\u4e0a\u4e00\u4e2a\u6392\u5217\u3002\u7528\u6cd5\u540c\u00a0next_permutation\u3002partial_sum\uff1a\u6c42\u524d\u7f00\u548c\u3002\u8bbe\u6e90\u5bb9\u5668\u4e3a\u00a0\uff0c\u76ee\u6807\u5bb9\u5668\u4e3a\u00a0\uff0c\u5219\u4ee4\u00a0\u3002partial_sum(src.begin(), src.end(), back_inserter(dst))\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#22-bitset","title":"2.2 bitset","text":"TODO bitset - OI Wiki
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#23-string","title":"2.3 string","text":" - \u91cd\u8f7d\u8fd0\u7b97\u7b26
- \u52a8\u6001\u5206\u914d\u7a7a\u95f4
C++std::string s;\nprintf(\"%s\", s.c_str());\nprintf(\"s \u7684\u957f\u5ea6\u4e3a %lu\", s.size()); \nprintf(\"s \u7684\u957f\u5ea6\u4e3a %lu\", s.length()); \nprintf(\"s \u7684\u957f\u5ea6\u4e3a %lu\", strlen(s.c_str()));\nsubstr(pos, len);\ninsert(index, count, ch);\ninsert(index, str);\nerase(index, count);\nreplace(pos, count, str);\nreplace(first, last, str);\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#24-pair","title":"2.4 pair","text":" pair \u4e0d\u9700\u8981\u989d\u5916\u5b9a\u4e49\u7ed3\u6784\u4e0e\u91cd\u8f7d\u8fd0\u7b97\u7b26
C++pair<int, double> p0(1, 2.0);\npair<int, double> p2 = make_pair(1, 2.0);\nauto p3 = make_pair(1, 2.0);\n\nint i = p0.first; \ndouble d = p0.second;\np1.first++;\n\npriority_queue<pair<int, double> > q;\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3","title":"3 \u8fdb\u9636","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#31","title":"3.1 \u7c7b","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#32","title":"3.2 \u52a8\u6001\u5185\u5b58","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#321-new-delete","title":"3.2.1 new \u548c delete \u8fd0\u7b97\u7b26","text":" - \u6808: \u58f0\u660e\u7684\u6240\u6709\u53d8\u91cf\u5c06\u5360\u7528\u6808\u5185\u5b58
- \u5806: \u672a\u4f7f\u7528\u7684\u5185\u5b58
C++new data-type;\n\ndouble* pvalue = NULL;\npvalue = new double;\n\nif (!(pvalue = new double)) {\n cout << \"Error: out of memory.\" << endl;\n exit(1);\n}\n\ndelete pvalue;\n
- new \u4e0d\u4ec5\u5206\u914d\u7684\u5185\u5b58\u8fd8\u521b\u5efa\u4e86\u5bf9\u8c61
- malloc () \u53ea\u5206\u914d\u7684\u5185\u5b58
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#322","title":"3.2.2 \u6570\u7ec4\u7684\u52a8\u6001\u5185\u5b58\u5206\u914d","text":"C++int ***array;\n// \u5047\u5b9a\u6570\u7ec4\u7b2c\u4e00\u7ef4\u4e3a m\uff0c \u7b2c\u4e8c\u7ef4\u4e3a n\uff0c \u7b2c\u4e09\u7ef4\u4e3ah\n// \u52a8\u6001\u5206\u914d\u7a7a\u95f4\narray = new int **[m];\nfor( int i=0; i<m; i++ )\n{\n array[i] = new int *[n];\n for( int j=0; j<n; j++ )\n {\n array[i][j] = new int [h];\n }\n}\n//\u91ca\u653e\nfor( int i=0; i<m; i++ )\n{\n for( int j=0; j<n; j++ )\n {\n delete [] array[i][j];\n }\n delete [] array[i];\n}\ndelete [] array;\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#323","title":"3.2.3 \u5bf9\u8c61\u7684\u52a8\u6001\u5185\u5b58\u5206\u914d","text":"C++#include <iostream>\nusing namespace std;\n\nclass Box\n{\n public:\n Box() { \n cout << \"\u8c03\u7528\u6784\u9020\u51fd\u6570\uff01\" <<endl; \n }\n ~Box() { \n cout << \"\u8c03\u7528\u6790\u6784\u51fd\u6570\uff01\" <<endl; \n }\n};\n\nint main( )\n{\n Box* myBoxArray = new Box[4]; // \u4e00\u4e2a\u5305\u542b 4 \u4e2a Box \u5bf9\u8c61\u7684\u6570\u7ec4\n\n delete [] myBoxArray; // \u5220\u9664\u6570\u7ec4\n return 0;\n}\n
Text Only\u8c03\u7528\u6784\u9020\u51fd\u6570\uff01\n\u8c03\u7528\u6784\u9020\u51fd\u6570\uff01\n\u8c03\u7528\u6784\u9020\u51fd\u6570\uff01\n\u8c03\u7528\u6784\u9020\u51fd\u6570\uff01\n\u8c03\u7528\u6790\u6784\u51fd\u6570\uff01\n\u8c03\u7528\u6790\u6784\u51fd\u6570\uff01\n\u8c03\u7528\u6790\u6784\u51fd\u6570\uff01\n\u8c03\u7528\u6790\u6784\u51fd\u6570\uff01\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#33","title":"3.3 \u547d\u540d\u7a7a\u95f4","text":" - \u7f16\u8bd1\u5668\u4e3a\u4e86\u533a\u522b\u540c\u540d\u51fd\u6570\u5f15\u5165\u4e86\u547d\u540d\u7a7a\u95f4
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#331","title":"3.3.1 \u5b9a\u4e49\u547d\u540d\u7a7a\u95f4","text":"C++namespace namespace_name {\n // code\n}\n\nnamespace_name::code;\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#332-using","title":"3.3.2 using \u6307\u4ee4","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#333","title":"3.3.3 \u4e0d\u8fde\u7eed\u7684\u547d\u540d\u7a7a\u95f4","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#334","title":"3.3.4 \u5d4c\u5957\u7684\u547d\u540d\u7a7a\u95f4","text":"C++namespace namespace_name1 {\n // \u4ee3\u7801\u58f0\u660e\n namespace namespace_name2 {\n // \u4ee3\u7801\u58f0\u660e\n }\n}\n\n// \u8bbf\u95ee namespace_name2 \u4e2d\u7684\u6210\u5458\nusing namespace namespace_name1::namespace_name2;\n\n// \u8bbf\u95ee namespace_name1 \u4e2d\u7684\u6210\u5458\nusing namespace namespace_name1;\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#34","title":"3.4 \u6a21\u677f","text":" - \u6cdb\u578b\u7f16\u7a0b: \u72ec\u7acb\u4e8e\u4efb\u4f55\u7279\u5b9a\u7c7b\u578b\u7684\u65b9\u5f0f\u5199\u4ee3\u7801
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#341","title":"3.4.1 \u51fd\u6570\u6a21\u677f","text":"C++template <typename type> ret-type func-name(parameter list)\n{\n // \u51fd\u6570\u7684\u4e3b\u4f53\n}\n
C++#include <iostream>\n#include <string>\n\nusing namespace std;\n\ntemplate <typename T>\ninline T const& Max (T const& a, T const& b) \n{ \n return a < b ? b:a; \n} \n\nint main ()\n{\n\n int i = 39;\n int j = 20;\n cout << \"Max(i, j): \" << Max(i, j) << endl; \n\n double f1 = 13.5; \n double f2 = 20.7; \n cout << \"Max(f1, f2): \" << Max(f1, f2) << endl; \n\n string s1 = \"Hello\"; \n string s2 = \"World\"; \n cout << \"Max(s1, s2): \" << Max(s1, s2) << endl; \n\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#342","title":"3.4.2 \u7c7b\u6a21\u677f","text":"C++template <class type> class class-name {\n// code\n}\n
C++#include <iostream>\n#include <vector>\n#include <cstdlib>\n#include <string>\n#include <stdexcept>\n\nusing namespace std;\n\ntemplate <class T>\nclass Stack { \n private: \n vector<T> elems; // \u5143\u7d20 \n\n public: \n void push(T const&); // \u5165\u6808\n void pop(); // \u51fa\u6808\n T top() const; // \u8fd4\u56de\u6808\u9876\u5143\u7d20\n bool empty() const{ // \u5982\u679c\u4e3a\u7a7a\u5219\u8fd4\u56de\u771f\u3002\n return elems.empty(); \n } \n}; \n\ntemplate <class T>\nvoid Stack<T>::push (T const& elem) \n{ \n // \u8ffd\u52a0\u4f20\u5165\u5143\u7d20\u7684\u526f\u672c\n elems.push_back(elem); \n} \n\ntemplate <class T>\nvoid Stack<T>::pop () \n{ \n if (elems.empty()) { \n throw out_of_range(\"Stack<>::pop(): empty stack\"); \n }\n // \u5220\u9664\u6700\u540e\u4e00\u4e2a\u5143\u7d20\n elems.pop_back(); \n} \n\ntemplate <class T>\nT Stack<T>::top () const \n{ \n if (elems.empty()) { \n throw out_of_range(\"Stack<>::top(): empty stack\"); \n }\n // \u8fd4\u56de\u6700\u540e\u4e00\u4e2a\u5143\u7d20\u7684\u526f\u672c \n return elems.back(); \n} \n\nint main() \n{ \n try { \n Stack<int> intStack; // int \u7c7b\u578b\u7684\u6808 \n Stack<string> stringStack; // string \u7c7b\u578b\u7684\u6808 \n\n // \u64cd\u4f5c int \u7c7b\u578b\u7684\u6808 \n intStack.push(7); \n cout << intStack.top() <<endl; \n\n // \u64cd\u4f5c string \u7c7b\u578b\u7684\u6808 \n stringStack.push(\"hello\"); \n cout << stringStack.top() << std::endl; \n stringStack.pop(); \n stringStack.pop(); \n } \n catch (exception const& ex) { \n cerr << \"Exception: \" << ex.what() <<endl; \n return -1;\n } \n}\n
Text Only7\nhello\nException: Stack<>::pop(): empty stack\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#35","title":"3.5 \u9884\u5904\u7406\u5668","text":" - \u4e0d\u662f C++\u8bed\u53e5\uff0c\u4e0d\u4f1a\u4ee5\u5206\u53f7\u7ed3\u5c3e
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#351-define","title":"3.5.1 #define \u9884\u5904\u7406","text":"C++#define macro-name replacement-text \n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#352","title":"3.5.2 \u53c2\u6570\u5b8f","text":"C++#include <iostream>\nusing namespace std;\n\n#define MIN(a,b) (a<b ? a : b)\n\nint main ()\n{\n int i, j;\n i = 100;\n j = 30;\n cout <<\"\u8f83\u5c0f\u7684\u503c\u4e3a\uff1a\" << MIN(i, j) << endl;\n\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#353","title":"3.5.3 \u6761\u4ef6\u7f16\u8bd1","text":"C++#ifdef NULL\n #define NULL 0\n#endif\n\n#ifdef DEBUG\n cerr <<\"Variable x = \" << x << endl;\n#endif\n\n#if 0\n \u4e0d\u8fdb\u884c\u7f16\u8bd1\u7684\u4ee3\u7801\n#endif\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#354","title":"3.5.4 # \u548c ## \u8fd0\u7b97\u7b26","text":"C++#include <iostream>\nusing namespace std;\n\n#define MKSTR( x ) #x\n\nint main ()\n{\n cout << MKSTR(HELLO C++) << endl;\n\n return 0;\n}\n
C++#include <iostream>\nusing namespace std;\n\n#define concat(a, b) a ## b\nint main()\n{\n int xy = 100;\n\n cout << concat(x, y);\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#355","title":"3.5.5 \u9884\u5b9a\u4e49\u5b8f","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#36","title":"3.6 \u4fe1\u53f7\u5904\u7406","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#361-signal","title":"3.6.1 signal () \u51fd\u6570","text":"C++void (*signal (int sig, void (*func)(int)))(int); \n\nsignal(registered signal, signal handler)\n
C++#include <iostream>\n#include <csignal>\n#include <unistd.h>\n\nusing namespace std;\n\nvoid signalHandler( int signum )\n{\n cout << \"Interrupt signal (\" << signum << \") received.\\n\";\n\n // \u6e05\u7406\u5e76\u5173\u95ed\n // \u7ec8\u6b62\u7a0b\u5e8f \n\n exit(signum); \n\n}\n\nint main ()\n{\n // \u6ce8\u518c\u4fe1\u53f7 SIGINT \u548c\u4fe1\u53f7\u5904\u7406\u7a0b\u5e8f\n signal(SIGINT, signalHandler); \n\n while(1){\n cout << \"Going to sleep....\" << endl;\n sleep(1);\n }\n\n return 0;\n}\n
\u6309 Ctrl + C \u4e2d\u65ad\u7a0b\u5e8f:
Text OnlyGoing to sleep....\nGoing to sleep....\nGoing to sleep....\nInterrupt signal (2) received.\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#362-raise","title":"3.6.2 raise () \u51fd\u6570","text":"C++int raise (signal sig);\n
C++#include <iostream>\n#include <csignal>\n#include <unistd.h>\n\nusing namespace std;\n\nvoid signalHandler( int signum )\n{\n cout << \"Interrupt signal (\" << signum << \") received.\\n\";\n\n // \u6e05\u7406\u5e76\u5173\u95ed\n // \u7ec8\u6b62\u7a0b\u5e8f \n\n exit(signum); \n\n}\n\nint main ()\n{\n int i = 0;\n // \u6ce8\u518c\u4fe1\u53f7 SIGINT \u548c\u4fe1\u53f7\u5904\u7406\u7a0b\u5e8f\n signal(SIGINT, signalHandler); \n\n while(++i){\n cout << \"Going to sleep....\" << endl;\n if( i == 3 ){\n raise(SIGINT);\n }\n sleep(1);\n }\n\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#37","title":"3.7 \u591a\u7ebf\u7a0b","text":" - \u4e24\u79cd\u7c7b\u578b\u7684\u591a\u4efb\u52a1\u5904\u7406
- \u57fa\u4e8e\u8fdb\u7a0b\u662f\u7a0b\u5e8f\u7684\u5e76\u53d1\u6267\u884c
- \u57fa\u4e8e\u7ebf\u7a0b\u662f\u540c\u4e00\u7a0b\u5e8f\u7684\u7247\u6bb5\u7684\u5e76\u53d1\u6267\u884c
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#371","title":"3.7.1 \u6982\u5ff5\u8bf4\u660e","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3711-thread","title":"3.7.1.1 \u7ebf\u7a0b\uff08Thread\uff09","text":" - \u7ebf\u7a0b\u662f\u7a0b\u5e8f\u6267\u884c\u4e2d\u7684\u5355\u4e00\u987a\u5e8f\u63a7\u5236\u6d41\uff0c\u591a\u4e2a\u7ebf\u7a0b\u53ef\u4ee5\u5728\u540c\u4e00\u4e2a\u8fdb\u7a0b\u4e2d\u72ec\u7acb\u8fd0\u884c\u3002
- \u7ebf\u7a0b\u5171\u4eab\u8fdb\u7a0b\u7684\u5730\u5740\u7a7a\u95f4\u3001\u6587\u4ef6\u63cf\u8ff0\u7b26\u3001\u5806\u548c\u5168\u5c40\u53d8\u91cf\u7b49\u8d44\u6e90\uff0c\u4f46\u6bcf\u4e2a\u7ebf\u7a0b\u6709\u81ea\u5df1\u7684\u6808\u3001\u5bc4\u5b58\u5668\u548c\u7a0b\u5e8f\u8ba1\u6570\u5668\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3712-concurrency-parallelism","title":"3.7.1.2 \u5e76\u53d1\uff08Concurrency\uff09\u4e0e\u5e76\u884c \uff08Parallelism\uff09","text":" - \u5e76\u53d1\uff1a\u591a\u4e2a\u4efb\u52a1\u5728\u65f6\u95f4\u7247\u6bb5\u5185\u4ea4\u66ff\u6267\u884c\uff0c\u8868\u73b0\u51fa\u540c\u65f6\u8fdb\u884c\u7684\u6548\u679c\u3002
- \u5e76\u884c\uff1a\u591a\u4e2a\u4efb\u52a1\u5728\u591a\u4e2a\u5904\u7406\u5668\u6216\u5904\u7406\u5668\u6838\u4e0a\u540c\u65f6\u6267\u884c\u3002 C++11 \u4ee5\u540e\u6709\u591a\u7ebf\u7a0b\u652f\u6301:
- std::thread\uff1a\u7528\u4e8e\u521b\u5efa\u548c\u7ba1\u7406\u7ebf\u7a0b\u3002
- std::mutex\uff1a\u7528\u4e8e\u7ebf\u7a0b\u4e4b\u95f4\u7684\u4e92\u65a5\uff0c\u9632\u6b62\u591a\u4e2a\u7ebf\u7a0b\u540c\u65f6\u8bbf\u95ee\u5171\u4eab\u8d44\u6e90\u3002
- std::lock_guard\u00a0\u548c\u00a0std::unique_lock\uff1a\u7528\u4e8e\u7ba1\u7406\u9501\u7684\u83b7\u53d6\u548c\u91ca\u653e\u3002
- std::condition_variable\uff1a\u7528\u4e8e\u7ebf\u7a0b\u95f4\u7684\u6761\u4ef6\u53d8\u91cf\uff0c\u534f\u8c03\u7ebf\u7a0b\u95f4\u7684\u7b49\u5f85\u548c\u901a\u77e5\u3002
- std::future\u00a0\u548c\u00a0std::promise\uff1a\u7528\u4e8e\u5b9e\u73b0\u7ebf\u7a0b\u95f4\u7684\u503c\u4f20\u9012\u548c\u4efb\u52a1\u540c\u6b65\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#372","title":"3.7.2 \u521b\u5efa\u7ebf\u7a0b","text":"C++#include<thread>\nstd::thread thread_object(callable, args...);\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3721","title":"3.7.2.1 \u4f7f\u7528\u51fd\u6570\u6307\u9488","text":"C++#include <iostream>\n#include <thread>\n\nvoid printMessage(int count) {\n for (int i = 0; i < count; ++i) {\n std::cout << \"Hello from thread (function pointer)!\\n\";\n }\n}\n\nint main() {\n std::thread t1(printMessage, 5); // \u521b\u5efa\u7ebf\u7a0b\uff0c\u4f20\u9012\u51fd\u6570\u6307\u9488\u548c\u53c2\u6570\n t1.join(); // \u7b49\u5f85\u7ebf\u7a0b\u5b8c\u6210\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3722","title":"3.7.2.2 \u4f7f\u7528\u51fd\u6570\u5bf9\u8c61","text":"C++#include <iostream>\n#include <thread>\n\nclass PrintTask {\npublic:\n void operator()(int count) const {\n for (int i = 0; i < count; ++i) {\n std::cout << \"Hello from thread (function object)!\\n\";\n }\n }\n};\n\nint main() {\n std::thread t2(PrintTask(), 5); // \u521b\u5efa\u7ebf\u7a0b\uff0c\u4f20\u9012\u51fd\u6570\u5bf9\u8c61\u548c\u53c2\u6570\n t2.join(); // \u7b49\u5f85\u7ebf\u7a0b\u5b8c\u6210\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3723-lambda","title":"3.7.2.3 \u4f7f\u7528 Lambda \u8868\u8fbe\u5f0f","text":"C++#include <iostream>\n#include <thread>\n\nint main() {\n std::thread t3([](int count) {\n for (int i = 0; i < count; ++i) {\n std::cout << \"Hello from thread (lambda)!\\n\";\n }\n }, 5); // \u521b\u5efa\u7ebf\u7a0b\uff0c\u4f20\u9012 Lambda \u8868\u8fbe\u5f0f\u548c\u53c2\u6570\n t3.join(); // \u7b49\u5f85\u7ebf\u7a0b\u5b8c\u6210\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3724","title":"3.7.2.4 \u7ebf\u7a0b\u7ba1\u7406","text":"C++t.join();\nt.detach();\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3725","title":"3.7.2.5 \u7ebf\u7a0b\u7684\u4f20\u53c2","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3726","title":"3.7.2.6 \u503c\u4f20\u9012","text":"C++std::thread t(funx, arg1, arg2);\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3727","title":"3.7.2.7 \u5f15\u7528\u4f20\u9012","text":"C++#include <iostream>\n#include <thread>\n\nvoid increment(int& x) {\n ++x;\n}\n\nint main() {\n int num = 0;\n std::thread t(increment, std::ref(num)); // \u4f7f\u7528 std::ref \u4f20\u9012\u5f15\u7528\n t.join();\n std::cout << \"Value after increment: \" << num << std::endl;\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#373","title":"3.7.3 \u7ebf\u7a0b\u540c\u6b65\u4e0e\u4e92\u65a5","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3731-mutex","title":"3.7.3.1 \u4e92\u65a5\u91cf\uff08Mutex\uff09","text":"C++std::mutex mtx;\nmtx.lock(); // \u9501\u5b9a\u4e92\u65a5\u9501\n// \u8bbf\u95ee\u5171\u4eab\u8d44\u6e90\nmtx.unlock();// \u91ca\u653e\u4e92\u65a5\u9501\n\nstd::lock_guard<std::mutex> lock(mtx); // \u81ea\u52a8\u9501\u5b9a\u548c\u89e3\u9501\n// \u8bbf\u95ee\u5171\u4eab\u8d44\u6e90\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3732-locks","title":"3.7.3.2 \u9501\uff08Locks\uff09","text":" std::lock_guard \uff1a\u4f5c\u7528\u57df\u9501\uff0c\u5f53\u6784\u9020\u65f6\u81ea\u52a8\u9501\u5b9a\u4e92\u65a5\u91cf\uff0c\u5f53\u6790\u6784\u65f6\u81ea\u52a8\u89e3\u9501\u3002std::unique_lock \uff1a\u4e0e std::lock_guard \u7c7b\u4f3c\uff0c\u4f46\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u7075\u6d3b\u6027\uff0c\u4f8b\u5982\u53ef\u4ee5\u8f6c\u79fb\u6240\u6709\u6743\u548c\u624b\u52a8\u89e3\u9501\u3002
C++#include <mutex>\n\nstd::mutex mtx;\n\nvoid safeFunctionWithLockGuard() {\n std::lock_guard<std::mutex> lk(mtx);\n // \u8bbf\u95ee\u6216\u4fee\u6539\u5171\u4eab\u8d44\u6e90\n}\n\nvoid safeFunctionWithUniqueLock() {\n std::unique_lock<std::mutex> ul(mtx);\n // \u8bbf\u95ee\u6216\u4fee\u6539\u5171\u4eab\u8d44\u6e90\n // ul.unlock(); // \u53ef\u9009\uff1a\u624b\u52a8\u89e3\u9501\n // ...\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3733-condition-variable","title":"3.7.3.3 \u6761\u4ef6\u53d8\u91cf\uff08Condition Variable\uff09","text":"C++std::condition_variable cv;\nstd::mutex mtx;\nbool ready = false;\n\nstd::unique_lock<std::mutex> lock(mtx);\ncv.wait(lock, []{ return ready; }); // \u7b49\u5f85\u6761\u4ef6\u6ee1\u8db3\n// \u6761\u4ef6\u6ee1\u8db3\u540e\u6267\u884c\n
C++#include <mutex>\n#include <condition_variable>\n\nstd::mutex mtx;\nstd::condition_variable cv;\nbool ready = false;\n\nvoid workerThread() {\n std::unique_lock<std::mutex> lk(mtx);\n cv.wait(lk, []{ return ready; }); // \u7b49\u5f85\u6761\u4ef6\n // \u5f53\u6761\u4ef6\u6ee1\u8db3\u65f6\u6267\u884c\u5de5\u4f5c\n}\n\nvoid mainThread() {\n {\n std::lock_guard<std::mutex> lk(mtx);\n // \u51c6\u5907\u6570\u636e\n ready = true;\n } // \u79bb\u5f00\u4f5c\u7528\u57df\u65f6\u89e3\u9501\n cv.notify_one(); // \u901a\u77e5\u4e00\u4e2a\u7b49\u5f85\u7684\u7ebf\u7a0b\n}\n
TODO
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3734-atomic-operations","title":"3.7.3.4 \u539f\u5b50\u64cd\u4f5c\uff08Atomic Operations\uff09","text":" - \u5bf9\u5171\u4eab\u6570\u636e\u7684\u8bbf\u95ee\u4e0d\u53ef\u5206\u5272
C++#include <atomic>\n#include <thread>\n\nstd::atomic<int> count(0);\n\nvoid increment() {\n count.fetch_add(1, std::memory_order_relaxed);\n}\n\nint main() {\n std::thread t1(increment);\n std::thread t2(increment);\n t1.join();\n t2.join();\n return count; // \u5e94\u8fd4\u56de2\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3735-thread-local-storagetls","title":"3.7.3.5 \u7ebf\u7a0b\u5c40\u90e8\u5b58\u50a8\uff08Thread Local Storage\uff0cTLS\uff09","text":" - \u5141\u8bb8\u6bcf\u4e2a\u7ebf\u7a0b\u6709\u81ea\u5df1\u7684\u6570\u636e\u526f\u672c
C++#include <iostream>\n#include <thread>\n\nthread_local int threadData = 0;\n\nvoid threadFunction() {\n threadData = 42; // \u6bcf\u4e2a\u7ebf\u7a0b\u90fd\u6709\u81ea\u5df1\u7684threadData\u526f\u672c\n std::cout << \"Thread data: \" << threadData << std::endl;\n}\n\nint main() {\n std::thread t1(threadFunction);\n std::thread t2(threadFunction);\n t1.join();\n t2.join();\n return 0;\n}\n
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#3736-deadlock","title":"3.7.3.6 \u6b7b\u9501\uff08Deadlock\uff09\u548c\u907f\u514d\u7b56\u7565","text":" - \u6b7b\u9501\u5373\u591a\u4e2a\u7ebf\u7a0b\u4e92\u76f8\u7b49\u5f85\u5bf9\u65b9\u91ca\u653e\u8d44\u6e90
- \u603b\u662f\u4ee5\u76f8\u540c\u7684\u987a\u5e8f\u8bf7\u6c42\u8d44\u6e90\u3002
- \u4f7f\u7528\u8d85\u65f6\u6765\u5c1d\u8bd5\u83b7\u53d6\u8d44\u6e90\u3002
- \u4f7f\u7528\u6b7b\u9501\u68c0\u6d4b\u7b97\u6cd5\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#374","title":"3.7.4 \u7ebf\u7a0b\u95f4\u901a\u4fe1","text":"C++std::promise<int> p;\nstd::future<int> f = p.get_future();\n\nstd::thread t([&p] {\n p.set_value(10); // \u8bbe\u7f6e\u503c\uff0c\u89e6\u53d1 future\n});\n\nint result = f.get(); // \u83b7\u53d6\u503c\n
C++17 \u5f15\u5165\u4e86\u5e76\u884c\u7b97\u6cd5\u5e93
C++#include <algorithm>\n#include <vector>\n#include <execution>\n\nstd::vector<int> vec = {1, 2, 3, 4, 5};\nstd::for_each(std::execution::par, vec.begin(), vec.end(), [](int &n) {\n n *= 2;\n});\n
TODO \u591a\u7ebf\u7a0b\u5b9e\u4f8b
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#38-web","title":"3.8 Web \u7f16\u7a0b","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#381-cgi","title":"3.8.1 \u4ec0\u4e48\u662f CGI\uff1f","text":" - \u516c\u5171\u7f51\u5173\u63a5\u53e3\uff08CGI\uff09\uff0c\u662f\u4e00\u5957\u6807\u51c6\uff0c\u5b9a\u4e49\u4e86\u4fe1\u606f\u662f\u5982\u4f55\u5728 Web \u670d\u52a1\u5668\u548c\u5ba2\u6237\u7aef\u811a\u672c\u4e4b\u95f4\u8fdb\u884c\u4ea4\u6362\u7684\u3002
- CGI \u89c4\u8303\u76ee\u524d\u662f\u7531 NCSA \u7ef4\u62a4\u7684\uff0cNCSA \u5b9a\u4e49 CGI \u5982\u4e0b\uff1a
- \u516c\u5171\u7f51\u5173\u63a5\u53e3\uff08CGI\uff09\uff0c\u662f\u4e00\u79cd\u7528\u4e8e\u5916\u90e8\u7f51\u5173\u7a0b\u5e8f\u4e0e\u4fe1\u606f\u670d\u52a1\u5668\uff08\u5982 HTTP \u670d\u52a1\u5668\uff09\u5bf9\u63a5\u7684\u63a5\u53e3\u6807\u51c6\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#382-web","title":"3.8.2 Web \u6d4f\u89c8","text":" - \u60a8\u7684\u6d4f\u89c8\u5668\u8054\u7cfb\u4e0a HTTP Web \u670d\u52a1\u5668\uff0c\u5e76\u8bf7\u6c42 URL\uff0c\u5373\u6587\u4ef6\u540d\u3002
- Web \u670d\u52a1\u5668\u5c06\u89e3\u6790 URL\uff0c\u5e76\u67e5\u627e\u6587\u4ef6\u540d\u3002\u5982\u679c\u627e\u5230\u8bf7\u6c42\u7684\u6587\u4ef6\uff0cWeb \u670d\u52a1\u5668\u4f1a\u628a\u6587\u4ef6\u53d1\u9001\u56de\u6d4f\u89c8\u5668\uff0c\u5426\u5219\u53d1\u9001\u4e00\u6761\u9519\u8bef\u6d88\u606f\uff0c\u8868\u660e\u60a8\u8bf7\u6c42\u4e86\u4e00\u4e2a\u9519\u8bef\u7684\u6587\u4ef6\u3002
- Web \u6d4f\u89c8\u5668\u4ece Web \u670d\u52a1\u5668\u83b7\u53d6\u54cd\u5e94\uff0c\u5e76\u6839\u636e\u63a5\u6536\u5230\u7684\u54cd\u5e94\u6765\u663e\u793a\u6587\u4ef6\u6216\u9519\u8bef\u6d88\u606f\u3002
"},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#383-cgi","title":"3.8.3 CGI \u67b6\u6784\u56fe","text":""},{"location":"CS_Basic/C%2B%2B/C%2B%2B%20Basic/#384-web","title":"3.8.4 Web \u670d\u52a1\u5668\u914d\u7f6e","text":"TODO
"},{"location":"CS_Basic/CS61A/CS61A/","title":"CS61A","text":""},{"location":"CS_Basic/CS61A/CS61A/#cs61a","title":"CS61A","text":" \u7ea6 24 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\u4e0d\u5230 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
- \u4ed6\u4eba\u603b\u7ed3
- https://github.com/HobbitQia/CS61A-Fall-2020/tree/main
- \u6559\u6750\u7ffb\u8bd1\u7248
- \u539f\u6559\u6750
- \u8bfe\u7a0b\u7f51\u7ad9\u5b58\u6863
- https://github.com/shuo-liu16/CS61A
"},{"location":"CS_Basic/CS61A/Composing_Programs/","title":"COMPOSING PROGRAMS","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#composing-programs","title":"COMPOSING PROGRAMS","text":" \u7ea6 2713 \u4e2a\u5b57 651 \u884c\u4ee3\u7801 2 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 22 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"CS_Basic/CS61A/Composing_Programs/#1","title":"1 \u4f7f\u7528\u51fd\u6570\u6784\u5efa\u62bd\u8c61","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#11","title":"1.1 \u5f00\u59cb","text":"\u7a0b\u5e8f\u7531\u4e24\u90e8\u5206\u7ec4\u6210:
- \u8ba1\u7b97\u4e00\u4e9b\u503c
- \u6267\u884c\u4e00\u4e9b\u64cd\u4f5c
- \u51fd\u6570
- \u5bf9\u8c61
- \u89e3\u91ca\u5668:
- \u7528\u4e8e\u8ba1\u7b97\u590d\u6742\u8868\u8fbe\u5f0f\u7684\u7a0b\u5e8f
- \u589e\u91cf\u6d4b\u8bd5\u3001\u6a21\u5757\u5316\u8bbe\u8ba1\u3001\u660e\u786e\u7684\u5047\u8bbe\u548c\u56e2\u961f\u5408\u4f5c
"},{"location":"CS_Basic/CS61A/Composing_Programs/#12","title":"1.2 \u7f16\u7a0b\u8981\u7d20","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#121","title":"1.2.1 \u8868\u8fbe\u5f0f","text":" - \u8bed\u8a00\u8981\u6709\u7684\u673a\u5236:
- \u539f\u59cb\u8868\u8fbe\u5f0f\u548c\u8bed\u53e5\uff1a\u8bed\u8a00\u6240\u5173\u5fc3\u7684\u6700\u7b80\u5355\u7684\u4e2a\u4f53
- \u7ec4\u5408\u65b9\u6cd5\uff1a\u7531\u7b80\u5355\u5143\u7d20\u7ec4\u5408\u6784\u5efa\u590d\u5408\u5143\u7d20
- \u62bd\u8c61\u65b9\u6cd5\uff1a\u547d\u540d\u590d\u5408\u5143\u7d20\uff0c\u5e76\u5c06\u5176\u4f5c\u4e3a\u5355\u5143\u8fdb\u884c\u64cd\u4f5c
- infix notation
"},{"location":"CS_Basic/CS61A/Composing_Programs/#122","title":"1.2.2 \u8c03\u7528\u8868\u8fbe\u5f0f","text":" - subexpressions
- \u7528\u53c2\u6570\u6765\u8c03\u7528\u51fd\u6570
- nested\uff08\u5d4c\u5957\uff09
"},{"location":"CS_Basic/CS61A/Composing_Programs/#123","title":"1.2.3 \u5bfc\u5165\u5e93\u51fd\u6570","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#124","title":"1.2.4 \u540d\u79f0\u4e0e\u73af\u5883","text":" - = is assignment operator
- \u6700\u7b80\u5355\u7684\u62bd\u8c61\u65b9\u6cd5
- environment
"},{"location":"CS_Basic/CS61A/Composing_Programs/#125","title":"1.2.5 \u6c42\u89e3\u5d4c\u5957\u8868\u8fbe\u5f0f","text":"\u6c42\u503c\u7a0b\u5e8f\u672c\u8d28\u4e0a\u662f\u9012\u5f52\u7684
"},{"location":"CS_Basic/CS61A/Composing_Programs/#126-print","title":"1.2.6 \u975e\u7eaf\u51fd\u6570 print","text":"Pure functions None-pure functions which has a side effect
"},{"location":"CS_Basic/CS61A/Composing_Programs/#13","title":"1.3 \u5b9a\u4e49\u65b0\u7684\u51fd\u6570","text":"Pythondef <name>(<formal parameters>):\n return <return expression> \n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#131","title":"1.3.1 \u73af\u5883","text":"environment has some frames frames have some bindings
- intrinsic name
- bound name \u4e0d\u540c\u7684\u540d\u79f0\u53ef\u80fd\u6307\u7684\u662f\u540c\u4e00\u4e2a\u51fd\u6570\uff0c\u4f46\u8be5\u51fd\u6570\u672c\u8eab\u53ea\u6709\u4e00\u4e2a\u5185\u5728\u540d\u79f0 \u5bf9\u51fd\u6570\u5f62\u5f0f\u53c2\u6570\u7684\u63cf\u8ff0\u88ab\u79f0\u4e3a\u51fd\u6570\u7684\u7b7e\u540d
"},{"location":"CS_Basic/CS61A/Composing_Programs/#132","title":"1.3.2 \u8c03\u7528\u7528\u6237\u5b9a\u4e49\u7684\u51fd\u6570","text":" - \u5728\u65b0\u7684\u5c40\u90e8\u5e27\u4e2d\uff0c\u5c06\u5b9e\u53c2\u7ed1\u5b9a\u5230\u51fd\u6570\u7684\u5f62\u53c2\u4e0a\u3002
- \u5728\u4ee5\u6b64\u5e27\u5f00\u59cb\u7684\u73af\u5883\u4e2d\u6267\u884c\u51fd\u6570\u4f53\u3002 name evaluation
"},{"location":"CS_Basic/CS61A/Composing_Programs/#133","title":"1.3.3 \u793a\u4f8b\uff1a\u8c03\u7528\u7528\u6237\u5b9a\u4e49\u7684\u51fd\u6570","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#134","title":"1.3.4 \u5c40\u90e8\u540d\u79f0","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#135","title":"1.3.5 \u9009\u62e9\u540d\u79f0","text":"PEP 8 \u2013 Style Guide for Python Code | peps.python.org
"},{"location":"CS_Basic/CS61A/Composing_Programs/#136","title":"1.3.6 \u62bd\u8c61\u51fd\u6570","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#137","title":"1.3.7 \u8fd0\u7b97\u7b26","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#14","title":"1.4 \u8bbe\u8ba1\u51fd\u6570","text":" - \u4e00\u4e2a\u51fd\u6570\u4e00\u4e2a\u4efb\u52a1
- Don't repeat yourself (DRY)
- \u5b9a\u4e49\u901a\u7528\u7684\u51fd\u6570
"},{"location":"CS_Basic/CS61A/Composing_Programs/#141","title":"1.4.1 \u6587\u6863","text":"docstring
"},{"location":"CS_Basic/CS61A/Composing_Programs/#142","title":"1.4.2 \u53c2\u6570\u9ed8\u8ba4\u503c","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#15","title":"1.5 \u63a7\u5236","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#151","title":"1.5.1 \u8bed\u53e5","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#152","title":"1.5.2 \u590d\u5408\u8bed\u53e5","text":"header suite
Python<header>:\n <statement>\n <statement>\n ...\n<separating header>:\n <statement>\n <statement>\n ...\n...\n
def \u662f\u590d\u5408\u8bed\u53e5 the header controls its suite \u8fd9\u4e2a\u5b9a\u4e49\u63ed\u793a\u4e86\u9012\u5f52\u5b9a\u4e49\u5e8f\u5217\uff08sequence\uff09\u7684\u57fa\u672c\u7ed3\u6784\uff1a\u4e00\u4e2a\u5e8f\u5217\u53ef\u4ee5\u5206\u89e3\u6210\u5b83\u7684\u7b2c\u4e00\u4e2a\u5143\u7d20\u548c\u5176\u4f59\u5143\u7d20 redirected control
"},{"location":"CS_Basic/CS61A/Composing_Programs/#153-ii","title":"1.5.3 \u00a0\u5b9a\u4e49\u51fd\u6570 II\uff1a\u5c40\u90e8\u8d4b\u503c","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#154","title":"1.5.4 \u6761\u4ef6\u8bed\u53e5","text":"Pythonif <expression>:\n <suite>\nelif <expression>:\n <suite>\nelse:\n <suite>\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#155","title":"1.5.5 \u8fed\u4ee3","text":"iteractive control
Pythonwhile <expression>:\n <suite>\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#156","title":"1.5.6 \u6d4b\u8bd5","text":"assertions
Python>>> assert fib(8) == 13, '\u7b2c\u516b\u4e2a\u6590\u6ce2\u90a3\u5951\u6570\u5e94\u8be5\u662f 13'\n
Doctests
Python>>> def sum_naturals(n):\n \"\"\"\u8fd4\u56de\u524d n \u4e2a\u81ea\u7136\u6570\u7684\u548c\u3002\n\n >>> sum_naturals(10)\n 55\n >>> sum_naturals(100)\n 5050\n \"\"\"\n total, k = 0, 1\n while k <= n:\n total, k = total + k, k + 1\n return total\n
Python>>> from doctest import testmod\n>>> testmod()\nTestResults(failed=0, attempted=2)\n
\u5355\u4e2a\u51fd\u6570\u7684\u4ea4\u4e92
Python>>> from doctest import run_docstring_examples\n>>> run_docstring_examples(sum_naturals, globals(), True)\nFinding tests in NoName\nTrying:\n\u00a0\u00a0\u00a0 sum_naturals(10)\nExpecting:\n\u00a0\u00a0\u00a0 55\nok\nTrying:\n\u00a0\u00a0\u00a0 sum_naturals(100)\nExpecting:\n\u00a0\u00a0\u00a0 5050\nok\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#16","title":"1.6 \u9ad8\u9636\u51fd\u6570","text":" - general patterns
- named concepts
- higher-order functions
- \u53ef\u4ee5\u628a\u51fd\u6570\u5f53\u4f5c\u53c2\u6570\u6216\u8005\u8fd4\u56de\u503c
"},{"location":"CS_Basic/CS61A/Composing_Programs/#161","title":"1.6.1 \u4f5c\u4e3a\u53c2\u6570\u7684\u51fd\u6570","text":" - slots
- step through \uff08\u5355\u6b65\u8c03\u8bd5\uff09
- \u4e00\u4e2a\u51e0\u4e4e\u6ca1\u5fc5\u8981\u770b\u7684\u4f8b\u5b50:
Python>>> def summation(n, term):\n total, k = 0, 1\n while k <= n:\n total, k = total + term(k), k + 1\n return total\n>>> def identity(x):\n return x\n>>> def sum_naturals(n):\n return summation(n, identity)\n>>> sum_naturals(10)\n55\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#162","title":"1.6.2 \u4f5c\u4e3a\u901a\u7528\u65b9\u6cd5\u7684\u51fd\u6570","text":" - user-defined functions
- general methods
- iterative improvement
- repetitive refinement
"},{"location":"CS_Basic/CS61A/Composing_Programs/#163-iii","title":"1.6.3 \u5b9a\u4e49\u51fd\u6570 III\uff1a\u5d4c\u5957\u5b9a\u4e49","text":"\u4e24\u4e2a\u540e\u679c:
- \u5168\u5c40\u5e27\u53d8\u6df7\u4e71
- \u51fd\u6570\u7b7e\u540d\u9650\u5236
- Nested function definition
- Lexical scope
- \u8fd9\u79cd\u5728\u5d4c\u5957\u5b9a\u4e49\u4e4b\u95f4\u5171\u4eab\u540d\u79f0\u7684\u89c4\u5219\u79f0\u4e3a\u8bcd\u6cd5\u4f5c\u7528\u57df
- \u6bcf\u4e2a\u7528\u6237\u5b9a\u4e49\u7684\u51fd\u6570\u90fd\u6709\u4e00\u4e2a\u7236\u73af\u5883\uff1a\u5b9a\u4e49\u5b83\u7684\u73af\u5883\u3002
- \u8c03\u7528\u7528\u6237\u5b9a\u4e49\u7684\u51fd\u6570\u65f6\uff0c\u5176\u5c40\u90e8\u5e27\u4f1a\u7ee7\u627f\u5176\u7236\u73af\u5883\u3002
- \u5173\u952e\u4f18\u52bf:
- \u5c40\u90e8\u51fd\u6570\u7684\u540d\u79f0\u4e0d\u4f1a\u5f71\u54cd\u5b9a\u4e49\u5b83\u7684\u51fd\u6570\u7684\u5916\u90e8\u540d\u79f0\uff0c\u56e0\u4e3a\u5c40\u90e8\u51fd\u6570\u7684\u540d\u79f0\u5c06\u7ed1\u5b9a\u5728\u5b9a\u4e49\u5b83\u7684\u5f53\u524d\u5c40\u90e8\u73af\u5883\u4e2d\uff0c\u800c\u4e0d\u662f\u5168\u5c40\u73af\u5883\u4e2d\u3002
- \u5c40\u90e8\u51fd\u6570\u53ef\u4ee5\u8bbf\u95ee\u5916\u5c42\u51fd\u6570\u7684\u73af\u5883\uff0c\u8fd9\u662f\u56e0\u4e3a\u5c40\u90e8\u51fd\u6570\u7684\u51fd\u6570\u4f53\u7684\u6c42\u503c\u73af\u5883\u4f1a\u7ee7\u627f\u5b9a\u4e49\u5b83\u7684\u6c42\u503c\u73af\u5883\u3002
- Extended Environments
- \u5c40\u90e8\u5b9a\u4e49\u7684\u51fd\u6570\u901a\u5e38\u88ab\u79f0\u4e3a\u95ed\u5305\uff08closures\uff09
"},{"location":"CS_Basic/CS61A/Composing_Programs/#164","title":"1.6.4 \u4f5c\u4e3a\u8fd4\u56de\u503c\u7684\u51fd\u6570","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#165","title":"1.6.5 \u793a\u4f8b\uff1a\u725b\u987f\u6cd5","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#166-currying","title":"1.6.6 Currying","text":" - uncurrying transformation
Python>>> def curry2(f):\n \"\"\"\u8fd4\u56de\u7ed9\u5b9a\u7684\u53cc\u53c2\u6570\u51fd\u6570\u7684\u67ef\u91cc\u5316\u7248\u672c\"\"\"\n def g(x):\n def h(y):\n return f(x, y)\n return h\n return g\n>>> def uncurry2(g):\n \"\"\"\u8fd4\u56de\u7ed9\u5b9a\u7684\u67ef\u91cc\u5316\u51fd\u6570\u7684\u53cc\u53c2\u6570\u7248\u672c\"\"\"\n def f(x, y):\n return g(x)(y)\n return f\n>>> pow_curried = curry2(pow)\n>>> pow_curried(2)(5)\n32\n>>> map_to_range(0, 10, pow_curried(2))\n1\n2\n4\n8\n16\n32\n64\n128\n256\n512\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#167-lambda","title":"1.6.7 Lambda \u8868\u8fbe\u5f0f","text":"Pythonlambda x : f(g(x))\n\"A function that takes x and returns f(g(x))\"\n
\\(\\displaystyle \\lambda\\)
Python>>> s = lambda x: x * x\n>>> s\n<function <lambda> at 0xf3f490>\n>>> s(12)\n144\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#168","title":"1.6.8 \u62bd\u8c61\u548c\u4e00\u7b49\u51fd\u6570","text":" - \u53ef\u4ee5\u4e0e\u540d\u79f0\u7ed1\u5b9a
- \u53ef\u4ee5\u4f5c\u4e3a\u53c2\u6570\u4f20\u9012\u7ed9\u51fd\u6570
- \u53ef\u4ee5\u4f5c\u4e3a\u51fd\u6570\u7684\u7ed3\u679c\u8fd4\u56de
- \u53ef\u4ee5\u5305\u542b\u5728\u6570\u636e\u7ed3\u6784\u4e2d
"},{"location":"CS_Basic/CS61A/Composing_Programs/#169","title":"1.6.9 \u51fd\u6570\u88c5\u9970\u5668","text":" Python>>> def trace(fn):\n def wrapped(x):\n print('-> ', fn, '(', x, ')')\n return fn(x)\n return wrapped\n\n>>> @trace\n def triple(x):\n return 3 * x\n\n>>> triple(12)\n-> <function triple at 0x102a39848> ( 12 )\n36\n
- annotation
- \u7b49\u4ef7\u4e8e:
Python>>> def triple(x):\n return 3 * x\n>>> triple = trace(triple)\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#17","title":"1.7 \u9012\u5f52\u51fd\u6570","text":" - rucursive
- circular nature
"},{"location":"CS_Basic/CS61A/Composing_Programs/#171","title":"1.7.1 \u9012\u5f52\u51fd\u6570\u5256\u6790","text":" - base case
- unwinds
- recursive calls
- induction
"},{"location":"CS_Basic/CS61A/Composing_Programs/#172-mutually-recursive","title":"1.7.2 mutually recursive","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#173","title":"1.7.3 \u9012\u5f52\u51fd\u6570\u4e2d\u7684\u6253\u5370","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#174-tree-recursive","title":"1.7.4 tree recursive","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#175","title":"1.7.5 \u793a\u4f8b\uff1a\u5206\u5272\u6570","text":"Python>>> def count_partitions(n, m):\n\u00a0\u00a0\u00a0 \"\"\"\u8ba1\u7b97\u4f7f\u7528\u6700\u5927\u6570 m \u7684\u6574\u6570\u5206\u5272 n \u7684\u65b9\u5f0f\u7684\u6570\u91cf\"\"\"\n\u00a0\u00a0\u00a0 if n == 0:\n\u00a0\u00a0\u00a0 return 1\n\u00a0\u00a0\u00a0 elif n < 0:\n\u00a0\u00a0\u00a0 return 0\n\u00a0\u00a0\u00a0 elif m == 0:\n\u00a0\u00a0\u00a0 return 0\n\u00a0\u00a0\u00a0 else:\n\u00a0\u00a0\u00a0 return count_partitions(n-m, m) + count_partitions(n, m-1)\n\n>>> count_partitions(6, 4)\n9\n>>> count_partitions(5, 5)\n7\n>>> count_partitions(10, 10)\n42\n>>> count_partitions(15, 15)\n176\n>>> count_partitions(20, 20)\n627\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#2","title":"2 \u4f7f\u7528\u6570\u636e\u6784\u5efa\u62bd\u8c61","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#21","title":"2.1 \u5f15\u8a00","text":" - \u9ad8\u9636\u51fd\u6570\u4f7f\u6211\u4eec\u80fd\u591f\u6839\u636e\u901a\u7528\u7684\u8ba1\u7b97\u65b9\u6cd5\u8fdb\u884c\u64cd\u4f5c\u548c\u63a8\u7406\uff0c\u4ece\u800c\u589e\u5f3a\u4e86\u8bed\u8a00\u7684\u529f\u80fd\u3002\u8fd9\u5c31\u662f\u7f16\u7a0b\u7684\u672c\u8d28
- \u6709\u6548\u4f7f\u7528\u5185\u7f6e\u6570\u636e\u7c7b\u578b\u548c\u7528\u6237\u5b9a\u4e49\u7684\u6570\u636e\u7c7b\u578b\u662f\u6570\u636e\u5904\u7406\u578b\u5e94\u7528\uff08data processing applications\uff09\u7684\u57fa\u7840
"},{"location":"CS_Basic/CS61A/Composing_Programs/#211","title":"2.1.1 \u539f\u59cb\u6570\u636e\u7c7b\u578b","text":"\u539f\u59cb\u6570\u636e\u7c7b\u578b\u5177\u6709\u5c5e\u6027:
- \u6709\u4e00\u4e9b\u53ef\u4ee5\u6c42\u89e3\u4e3a\u539f\u59cb\u6570\u636e\u7c7b\u578b\u7684\u8868\u8fbe\u5f0f\uff0c\u88ab\u79f0\u4e3a\u5b57\u9762\u91cf\uff08literals\uff09\u3002
- \u6709\u7528\u4e8e\u64cd\u4f5c\u539f\u59cb\u7c7b\u578b\u503c\u7684\u5185\u7f6e\u51fd\u6570\u548c\u64cd\u4f5c\u7b26\u3002 - \u539f\u59cb\u6570\u5b57\u7c7b\u578b
- int
- float
- complex
- Non-numeric types
- bool
- more on \u539f\u59cb\u6570\u636e\u7c7b\u578b
"},{"location":"CS_Basic/CS61A/Composing_Programs/#22","title":"2.2 \u6570\u636e\u62bd\u8c61","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#221","title":"2.2.1 \u793a\u4f8b\uff1a\u6709\u7406\u6570","text":"wishful thinking
Python>>> def add_rationals(x, y):\n nx, dx = numer(x), denom(x)\n ny, dy = numer(y), denom(y)\n return rational(nx * dy + ny * dx, dx * dy)\n\n>>> def mul_rationals(x, y):\n return rational(numer(x) * numer(y), denom(x) * denom(y))\n\n>>> def print_rational(x):\n print(numer(x), '/', denom(x))\n\n>>> def rationals_are_equal(x, y):\n return numer(x) * denom(y) == numer(y) * denom(x)\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#222-pair","title":"2.2.2 pair","text":"from operator import getitem
Python>>> def rational(n, d):\n return [n, d]\n\n>>> def numer(x):\n return x[0]\n\n>>> def denom(x):\n return x[1]\n
\u7b80\u5316\u6709\u7406\u6570:
Python>>> from fractions import gcd\n>>> def rational(n, d):\n g = gcd(n, d)\n return (n//g, d//g)\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#223","title":"2.2.3 \u62bd\u8c61\u5c4f\u969c","text":" - \u6570\u636e\u62bd\u8c61: \u7528\u4e00\u7ec4\u57fa\u672c\u64cd\u4f5c\u6765\u64cd\u4f5c\u6570\u636e\u3002
- avbstraction barrier
- the best:
Python>>> def square_rational(x):\n return mul_rational(x, x)\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#224","title":"2.2.4 \u6570\u636e\u7684\u5c5e\u6027","text":"\u76f8\u5f53\u4e8e\u81ea\u5df1\u5199\u4e00\u4e2a\u6570\u636e\u7ed3\u6784:
Python>>> def pair(x, y):\n \"\"\"Return a function that represents a pair.\"\"\"\n def get(index):\n if index == 0:\n return x\n elif index == 1:\n return y\n return get\n\n>>> def select(p, i):\n \"\"\"Return the element at index i of pair p.\"\"\"\n return p(i)\n\n>>> p = pair(20, 14)\n>>> select(p, 0)\n20\n>>> select(p, 1)\n14\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#23","title":"2.3 \u5e8f\u5217","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#231-list","title":"2.3.1 list","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#232","title":"2.3.2 \u5e8f\u5217\u904d\u5386","text":"Pythonfor <name> in <expression>:\n <suite>\n
the expression must produce an iterable object sequence unpacking
Python>>> pairs = [[1, 2], [2, 2], [2, 3], [4, 4]]\n>>> same_count = 0\n>>> for x, y in pairs:\n if x == y:\n same_count = same_count + 1\n>>> same_count\n2\n
range
"},{"location":"CS_Basic/CS61A/Composing_Programs/#233","title":"2.3.3 \u5e8f\u5217\u5904\u7406","text":"list comprehensions
Python>>> odds = [1, 3, 5, 7, 9]\n>>> [x+1 for x in odds]\n[2, 4, 6, 8, 10]\n[<map expression> for <name> in <sequence expression> if <filter expression>]\n
- Aggregation \u5c31\u662f\u7f29\u5e76\u5566
Python>>> def apply_to_all(map_fn, s):\n return [map_fn(x) for x in s]\n>>> def keep_if(filter_fn, s):\n return [x for x in s if filter_fn(x)]\n# conventional names\n>>> apply_to_all = lambda map_fn, s: list(map(map_fn, s))\n>>> keep_if = lambda filter_fn, s: list(filter(filter_fn, s))\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#234","title":"2.3.4 \u5e8f\u5217\u62bd\u8c61","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#235","title":"2.3.5 \u5b57\u7b26\u4e32","text":"string \u6ca1\u6709\u5b57\u7b26\u7c7b\u578b
- Membership
- Multiline Literals
- String Coercion more on Dive Into Python 3\u00a0\u7684\u00a0\u5b57\u7b26\u4e32\u7ae0\u8282\u00a0\u63d0\u4f9b\u4e86\u5b57\u7b26\u7f16\u7801\u548c Unicode \u7684\u63cf\u8ff0
"},{"location":"CS_Basic/CS61A/Composing_Programs/#236","title":"2.3.6 \u6811","text":"closure property bax-and-pointer notation
- root label
- branch
- leaf: the tree without branch
- node tree-recursive \u4e24\u4e2a\u4f8b\u5b50:
Python>>> def fib_tree(n):\n if n == 0 or n == 1:\n return tree(n)\n else:\n left, right = fib_tree(n-2), fib_tree(n-1)\n fib_n = label(left) + label(right)\n return tree(fib_n, [left, right])\n>>> fib_tree(5)\n[5, [2, [1], [1, [0], [1]]], [3, [1, [0], [1]], [2, [1], [1, [0], [1]]]]]\n
Python>>> def count_leaves(tree):\n if is_leaf(tree):\n return 1\n else:\n branch_counts = [count_leaves(b) for b in branches(tree)]\n return sum(branch_counts)\n>>> count_leaves(fib_tree(5))\n8\n
Partition trees
Python>>> def print_parts(tree, partition=[]):\n if is_leaf(tree):\n if label(tree):\n print(' + '.join(partition))\n else:\n left, right = branches(tree)\n m = str(label(tree))\n print_parts(left, partition + [m])\n print_parts(right, partition)\n\n>>> print_parts(partition_tree(6, 4))\n4 + 2\n4 + 1 + 1\n3 + 3\n3 + 2 + 1\n3 + 1 + 1 + 1\n2 + 2 + 2\n2 + 2 + 1 + 1\n2 + 1 + 1 + 1 + 1\n1 + 1 + 1 + 1 + 1 + 1\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#237","title":"2.3.7 \u94fe\u8868","text":"linked list abstract data representation
Python>>> def partitions(n, m):\n\"\"\"\u8fd4\u56de\u4e00\u4e2a\u5305\u542b n \u7684\u5206\u5272\u65b9\u6848\u7684\u94fe\u8868\uff0c\u5176\u4e2d\u6bcf\u4e2a\u6b63\u6574\u6570\u4e0d\u8d85\u8fc7 m\"\"\"\nif n == 0:\n return link(empty, empty) # \u5305\u542b\u7a7a\u5206\u5272\u7684\u94fe\u8868\nelif n < 0 or m == 0:\n return empty\nelse:\n using_m = partitions(n-m, m)\n with_m = apply_to_all_link(lambda s: link(m, s), using_m)\n without_m = partitions(n, m-1)\n return extend_link(with_m, without_m)\n\n>>> def print_partitions(n, m):\n lists = partitions(n, m)\n strings = apply_to_all_link(lambda s: join_link(s, \" + \"), lists)\n print(join_link(strings, \"\\n\"))\n\n>>> print_partitions(6, 4)\n4 + 2\n4 + 1 + 1\n3 + 3\n3 + 2 + 1\n3 + 1 + 1 + 1\n2 + 2 + 2\n2 + 2 + 1 + 1\n2 + 1 + 1 + 1 + 1\n1 + 1 + 1 + 1 + 1 + 1\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#24","title":"2.4 \u53ef\u53d8\u6570\u636e","text":"object-oriented programming
"},{"location":"CS_Basic/CS61A/Composing_Programs/#241","title":"2.4.1 \u5bf9\u8c61\u9690\u55bb","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#242","title":"2.4.2 \u5e8f\u5217\u5bf9\u8c61","text":"mutable Sharing and Identity \u5217\u8868\u63a8\u5bfc\u5f0f:
Python>>> from unicodedata import lookup\n>>> [lookup('WHITE ' + s.upper() + ' SUIT') for s in suits]\n['\u2661', '\u2662', '\u2664', '\u2667']\n
tuple
"},{"location":"CS_Basic/CS61A/Composing_Programs/#243","title":"2.4.3 \u5b57\u5178","text":"key-value pairs
"},{"location":"CS_Basic/CS61A/Composing_Programs/#244","title":"2.4.4 \u5c40\u90e8\u72b6\u6001","text":"local state
Python>>> def make_withdraw(balance):\n\"\"\"\u8fd4\u56de\u4e00\u4e2a\u6bcf\u6b21\u8c03\u7528\u90fd\u4f1a\u51cf\u5c11 balance \u7684 withdraw \u51fd\u6570\"\"\"\ndef withdraw(amount):\n nonlocal balance # \u58f0\u660e balance \u662f\u975e\u5c40\u90e8\u7684\n if amount > balance:\n return '\u4f59\u989d\u4e0d\u8db3'\n balance = balance - amount # \u91cd\u65b0\u7ed1\u5b9a\n return balance\nreturn withdraw\n
Python Particulars
"},{"location":"CS_Basic/CS61A/Composing_Programs/#245-non-local","title":"2.4.5 \u975e\u5c40\u90e8 Non-local \u8d4b\u503c\u7684\u597d\u5904","text":"\u8fd9\u6837\uff0c\u6bcf\u4e2a withdraw \u5b9e\u4f8b\u90fd\u4fdd\u6301\u81ea\u5df1\u7684 balance \u72b6\u6001\uff0c\u4f46\u7a0b\u5e8f\u4e2d\u7684\u4efb\u4f55\u5176\u4ed6\u51fd\u6570\u90fd\u65e0\u6cd5\u8bbf\u95ee\u8be5\u72b6\u6001\u3002\u4ece\u66f4\u9ad8\u7684\u5c42\u9762\u6765\u770b\u8fd9\u79cd\u60c5\u51b5\uff0c\u6211\u4eec\u62bd\u8c61\u4e86\u4e00\u4e2a\u94f6\u884c\u8d26\u6237\uff0c\u5b83\u81ea\u5df1\u7ba1\u7406\u81ea\u5df1\u7684\u72b6\u6001\uff0c\u5176\u884c\u4e3a\u65b9\u5f0f\u4e0e\u4e16\u754c\u4e0a\u6240\u6709\u5176\u5b83\u8d26\u6237\u4e00\u6837\uff1a\u968f\u7740\u65f6\u95f4\u63a8\u79fb\uff0c\u8d26\u6237\u7684\u72b6\u6001\u4f1a\u6839\u636e\u8d26\u6237\u7684\u53d6\u6b3e\u8bb0\u5f55\u800c\u53d1\u751f\u53d8\u5316\u3002
"},{"location":"CS_Basic/CS61A/Composing_Programs/#246-non-local","title":"2.4.6 \u975e\u5c40\u90e8 Non-local \u8d4b\u503c\u7684\u4ee3\u4ef7","text":" - \u6b63\u786e\u7406\u89e3\u5305\u542b nonlocal \u58f0\u660e\u7684\u4ee3\u7801\u7684\u5173\u952e\u662f\u8bb0\u4f4f\uff1a\u53ea\u6709\u51fd\u6570\u8c03\u7528\u624d\u80fd\u5f15\u5165\u65b0\u5e27\u3002\u8d4b\u503c\u8bed\u53e5\u53ea\u80fd\u66f4\u6539\u73b0\u6709\u5e27\u4e2d\u7684\u7ed1\u5b9a\u5173\u7cfb\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u9664\u975e make_withdraw \u88ab\u8c03\u7528\u4e24\u6b21\uff0c\u5426\u5219\u53ea\u80fd\u6709\u4e00\u4e2a balance \u7ed1\u5b9a\u3002
- Sameness and change
- referentially transparent
"},{"location":"CS_Basic/CS61A/Composing_Programs/#247","title":"2.4.7 \u5217\u8868\u548c\u5b57\u5178\u5b9e\u73b0","text":"\u51fd\u6570\u662f\u4e00\u4e2a dispatch \uff08\u8c03\u5ea6\uff09\u51fd\u6570\uff0c\u5176\u53c2\u6570\u9996\u5148\u662f\u4e00\u4e2a\u671f\u671b\u7684\u6307\u4ee4\uff0c\u4ee3\u8868\u671f\u671b\u8fd9\u4e2a\u51fd\u6570\u505a\u4ec0\u4e48\uff1b\u7136\u540e\u662f\u8be5\u65b9\u6cd5\u7684\u9700\u8981\u7528\u5230\u7684\u53c2\u6570\u3002\u6b64\u6307\u4ee4\u662f\u4e00\u4e2a\u5b57\u7b26\u4e32\uff0c\u7528\u4e8e\u547d\u540d\u51fd\u6570\u5e94\u6267\u884c\u7684\u64cd\u4f5c\u3002\u53ef\u4ee5\u5c06\u8fd9\u4e2a dispatch \u51fd\u6570\u7406\u89e3\u4e3a\u591a\u4e2a\u4e0d\u540c\u51fd\u6570\u7684\u62bd\u8c61\uff1a\u7b2c\u4e00\u4e2a\u53c2\u6570\u786e\u5b9a\u76ee\u6807\u51fd\u6570\u7684\u884c\u4e3a\uff0c\u5e76\u4e3a\u8be5\u884c\u4e3a\u5165\u53c2\u5176\u4ed6\u53c2\u6570\u3002 \u7528\u5b57\u7b26\u4e32\u4e5f\u592a\u9006\u5929\u4e86\u3002
Python>>> def mutable_link():\n\"\"\"\u8fd4\u56de\u4e00\u4e2a\u53ef\u53d8\u94fe\u8868\u7684\u51fd\u6570\"\"\"\ncontents = empty\ndef dispatch(message, value=None):\n nonlocal contents\n if message == 'len':\n return len_link(contents)\n elif message == 'getitem':\n return getitem_link(contents, value)\n elif message == 'push_first':\n contents = link(value, contents)\n elif message == 'pop_first':\n f = first(contents)\n contents = rest(contents)\n return f\n elif message == 'str':\n return join_link(contents, \", \")\nreturn dispatch\n\n>>> def to_mutable_link(source):\n\"\"\"\u8fd4\u56de\u4e00\u4e2a\u4e0e\u539f\u5217\u8868\u76f8\u540c\u5185\u5bb9\u7684\u51fd\u6570\u5217\u8868\"\"\"\ns = mutable_link()\nfor element in reversed(source):\n s('push_first', element)\nreturn s\n\n>>> s = to_mutable_link(suits)\n>>> type(s)\n<class 'function'>\n>>> print(s('str'))\nheart, diamond, spade, club\n
\u5b57\u5178\u5b9e\u73b0:
Python>>> def dictionary():\n\"\"\"\u8fd4\u56de\u4e00\u4e2a\u5b57\u5178\u7684\u51fd\u6570\u5b9e\u73b0\"\"\"\nrecords = []\ndef getitem(key):\n matches = [r for r in records if r[0] == key]\n if len(matches) == 1:\n key, value = matches[0]\n return value\ndef setitem(key, value):\n nonlocal records\n non_matches = [r for r in records if r[0] != key]\n records = non_matches + [[key, value]]\ndef dispatch(message, key=None, value=None):\n if message == 'getitem':\n return getitem(key)\n elif message == 'setitem':\n setitem(key, value)\nreturn dispatch\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#248-dispatch-dictionaries","title":"2.4.8 \u8c03\u5ea6\u5b57\u5178\uff08Dispatch Dictionaries\uff09","text":"\u7528\u5b57\u5178\u5b58\u50a8\u6d88\u606f\u3002
Pythondef account(initial_balance):\n def deposit(amount):\n dispatch['balance'] += amount\n return dispatch['balance']\n def withdraw(amount):\n if amount > dispatch['balance']:\n return 'Insufficient funds'\n dispatch['balance'] -= amount\n return dispatch['balance']\n dispatch = {'deposit': deposit,\n 'withdraw': withdraw,\n 'balance': initial_balance}\n return dispatch\n\ndef withdraw(account, amount):\n return account['withdraw'](amount)\ndef deposit(account, amount):\n return account['deposit'](amount)\ndef check_balance(account):\n return account['balance']\n\na = account(20)\ndeposit(a, 5)\nwithdraw(a, 17)\ncheck_balance(a)\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#249-propagating-constraints","title":"2.4.9 \u7ea6\u675f\u4f20\u9012 (Propagating\u00a0Constraints)","text":"connector Using the Constraint System
Python>>> celsius = connector('Celsius')\n>>> fahrenheit = connector('Fahrenheit')\n>>> def converter(c, f):\n \"\"\"\u7528\u7ea6\u675f\u6761\u4ef6\u8fde\u63a5 c \u5230 f\uff0c\u5c06\u6444\u6c0f\u5ea6\u8f6c\u6362\u4e3a\u534e\u6c0f\u5ea6.\"\"\"\n u, v, w, x, y = [connector() for _ in range(5)]\n multiplier(c, w, u)\n multiplier(v, x, u)\n adder(v, y, f)\n constant(w, 9)\n constant(x, 5)\n constant(y, 32)\n>>> converter(celsius, fahrenheit)\n\n>>> celsius['set_val']('user', 25)\nCelsius = 25\nFahrenheit = 77.0\n\n>>> fahrenheit['set_val']('user', 212)\nContradiction detected: 77.0 vs 212\n\n>>> celsius['forget']('user')\nCelsius is forgotten\nFahrenheit is forgotten\n\n>>> fahrenheit['set_val']('user', 212)\nFahrenheit = 212\nCelsius = 100.0\n\n# Implementing the Constraint System\n>>> connector ['set_val'](source, value) \"\"\"\u8868\u793a\u00a0source\u00a0\u5728\u8bf7\u6c42\u8fde\u63a5\u5668\u5c06\u5f53\u524d\u503c\u8bbe\u4e3a value\"\"\"\n>>> connector ['has_val']()\u00a0 \"\"\"\u8fd4\u56de\u8fde\u63a5\u5668\u662f\u5426\u5df2\u7ecf\u5177\u6709\u503c\"\"\"\n>>> connector ['val'] \"\"\"\u662f\u8fde\u63a5\u5668\u7684\u5f53\u524d\u503c\"\"\"\n>>> connector ['forget'](source)\u00a0 \"\"\"\u544a\u8bc9\u8fde\u63a5\u5668 source \u8bf7\u6c42\u9057\u5fd8\u5b83\u7684\u503c\"\"\"\n>>> connector ['connect'](source)\u00a0 \"\"\"\u544a\u8bc9\u8fde\u63a5\u5668\u53c2\u4e0e\u65b0\u7684\u7ea6\u675f\uff0c\u5373 source\"\"\"\n>>> constraint['new_val']() \"\"\"\u8868\u793a\u4e0e\u7ea6\u675f\u76f8\u8fde\u7684\u67d0\u4e2a\u8fde\u63a5\u5668\u5177\u6709\u65b0\u7684\u503c\u3002\"\"\"\n>>> constraint['forget']()\u00a0 \"\"\"\u8868\u793a\u4e0e\u7ea6\u675f\u76f8\u8fde\u7684\u67d0\u4e2a\u8fde\u63a5\u5668\u9057\u5fd8\u4e86\u503c\u3002\"\"\"\n\n>>> from operator import add, sub\n>>> def adder(a, b, c):\n \"\"\"\u7ea6\u675f a+b=c\"\"\"\n return make_ternary_constraint(a, b, c, add, sub, sub)\n\n>>> def make_ternary_constraint(a, b, c, ab, ca, cb):\n \"\"\"\u7ea6\u675f ab(a,b)=c\uff0cca(c,a)=b\uff0ccb(c,b)=a\"\"\"\n def new_value():\n av, bv, cv = [connector['has_val']() for connector in (a, b, c)]\n if av and bv:\n c['set_val'](constraint, ab(a['val'], b['val']))\n elif av and cv:\n b['set_val'](constraint, ca(c['val'], a['val']))\n elif bv and cv:\n a['set_val'](constraint, cb(c['val'], b['val']))\n def forget_value():\n for connector in (a, b, c):\n connector['forget'](constraint)\n constraint = {'new_val': new_value, 'forget': forget_value}\n for connector in (a, b, c):\n connector['connect'](constraint)\n return constraint\n\n>>> from operator import mul, truediv\n>>> def multiplier(a, b, c):\n \"\"\"\u7ea6\u675f a*b=c\"\"\"\n return make_ternary_constraint(a, b, c, mul, truediv, truediv)\n\n>>> def constant(connector, value):\n \"\"\"\u5e38\u91cf\u8d4b\u503c\"\"\"\n constraint = {}\n connector['set_val'](constraint, value)\n return constraint\n\n# Representing connectors\n>>> def connector(name=None):\n \"\"\"\u9650\u5236\u6761\u4ef6\u4e4b\u95f4\u7684\u8fde\u63a5\u5668\"\"\"\n informant = None\n constraints = []\n def set_value(source, value):\n nonlocal informant\n val = connector['val']\n if val is None:\n informant, connector['val'] = source, value\n if name is not None:\n print(name, '=', value)\n inform_all_except(source, 'new_val', constraints)\n else:\n if val != value:\n print('Contradiction detected:', val, 'vs', value)\n def forget_value(source):\n nonlocal informant\n if informant == source:\n informant, connector['val'] = None, None\n if name is not None:\n print(name, 'is forgotten')\n inform_all_except(source, 'forget', constraints)\n connector = {'val': None,\n 'set_val': set_value,\n 'forget': forget_value,\n 'has_val': lambda: connector['val'] is not None,\n 'connect': lambda source: constraints.append(source)}\n return connector\n\n>>> def inform_all_except(source, message, constraints):\n \"\"\"\u544a\u77e5\u4fe1\u606f\u9664\u4e86 source \u5916\u7684\u6240\u6709\u7ea6\u675f\u6761\u4ef6\"\"\"\n for c in constraints:\n if c != source:\n c[message]()\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#25","title":"2.5 \u9762\u5411\u5bf9\u8c61\u7f16\u7a0b","text":" - object
- dot notation
- class
"},{"location":"CS_Basic/CS61A/Composing_Programs/#251","title":"2.5.1 \u5bf9\u8c61\u548c\u7c7b","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#252","title":"2.5.2 \u7c7b\u7684\u5b9a\u4e49","text":"__init__\u7c7b\u7684\u6784\u9020\u51fd\u6570\uff08constructor\uff09
Python>>> class Account:\n def __init__(self, account_holder):\n self.balance = 0\n self.holder = account_holder\n def deposit(self, amount):\n self.balance = self.balance + amount\n return self.balance\n def withdraw(self, amount):\n if amount > self.balance:\n return 'Insufficient funds'\n self.balance = self.balance - amount\n return self.balance\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#253","title":"2.5.3 \u6d88\u606f\u4f20\u9012\u548c\u70b9\u8868\u8fbe\u5f0f","text":"Python>>> getattr(spock_account, 'balance')\n10\n>>> hasattr(spock_account, 'deposit')\nTrue\n\n\n>>> type(Account.deposit)\n<class 'Function'>\n>>> type(spock_account.deposit)\n<class 'method'>\n# \u4e3a\u7c7b\u7684\u5c5e\u6027\uff0c\u65b9\u6cd5\u53ea\u662f\u4e00\u4e2a\u51fd\u6570\uff0c\u4f46\u4f5c\u4e3a\u5b9e\u4f8b\u7684\u5c5e\u6027\uff0c\u5b83\u662f\u4e00\u4e2a\u7ed1\u5b9a\u65b9\u6cd5\n\n>>> Account.deposit(spock_account, 1001) # \u51fd\u6570 deposit \u63a5\u53d7\u4e24\u4e2a\u53c2\u6570\n1011\n>>> spock_account.deposit(1000) # \u65b9\u6cd5 deposit \u63a5\u53d7\u4e00\u4e2a\u53c2\u6570\n2011\n
\u547d\u540d\u7ea6\u5b9a\uff1a\u7c7b\u540d\u901a\u5e38\u4f7f\u7528 CapWords \u7ea6\u5b9a\uff08\u4e5f\u79f0\u4e3a CamelCase\uff0c\u56e0\u4e3a\u540d\u79f0\u4e2d\u95f4\u7684\u5927\u5199\u5b57\u6bcd\u770b\u8d77\u6765\u50cf\u9a7c\u5cf0\uff09\u7f16\u5199\u3002\u65b9\u6cd5\u540d\u79f0\u9075\u5faa\u4f7f\u7528\u4e0b\u5212\u7ebf\u5206\u9694\u7684\u5c0f\u5199\u5355\u8bcd\u547d\u540d\u51fd\u6570\u7684\u6807\u51c6\u7ea6\u5b9a\u3002
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6709\u4e00\u4e9b\u5b9e\u4f8b\u53d8\u91cf\u548c\u65b9\u6cd5\u4e0e\u5bf9\u8c61\u7684\u7ef4\u62a4\u548c\u4e00\u81f4\u6027\u76f8\u5173\uff0c\u6211\u4eec\u4e0d\u5e0c\u671b\u5bf9\u8c61\u7684\u7528\u6237\u770b\u5230\u6216\u4f7f\u7528\u3002\u5b83\u4eec\u4e0d\u662f\u7c7b\u5b9a\u4e49\u7684\u62bd\u8c61\u7684\u4e00\u90e8\u5206\uff0c\u800c\u662f\u5b9e\u73b0\u7684\u4e00\u90e8\u5206\u3002Python \u7684\u7ea6\u5b9a\u89c4\u5b9a\uff0c\u5982\u679c\u5c5e\u6027\u540d\u79f0\u4ee5\u4e0b\u5212\u7ebf\u5f00\u5934\uff0c\u5219\u53ea\u80fd\u5728\u7c7b\u672c\u8eab\u7684\u65b9\u6cd5\u4e2d\u8bbf\u95ee\u5b83\uff0c\u800c\u4e0d\u662f\u7528\u6237\u8bbf\u95ee\u3002
"},{"location":"CS_Basic/CS61A/Composing_Programs/#254","title":"2.5.4 \u7c7b\u5c5e\u6027","text":"\u611f\u89c9\u6ca1\u4ec0\u4e48\u7528:
Python>>> Account.interest = 0.05 # \u6539\u53d8\u7c7b\u5c5e\u6027\n>>> spock_account.interest # \u5b9e\u4f8b\u5c5e\u6027\u53d1\u751f\u53d8\u5316\uff08\u8be5\u5b9e\u4f8b\u4e2d\u6ca1\u6709\u548c\u7c7b\u5c5e\u6027\u540c\u540d\u79f0\u7684\u5b9e\u4f8b\u5c5e\u6027\uff09\n0.05\n>>> kirk_account.interest # \u5982\u679c\u5b9e\u4f8b\u4e2d\u5b58\u5728\u548c\u7c7b\u5c5e\u6027\u540c\u540d\u7684\u5b9e\u4f8b\u5c5e\u6027\uff0c\u5219\u6539\u53d8\u7c7b\u5c5e\u6027\uff0c\u4e0d\u4f1a\u5f71\u54cd\u5b9e\u4f8b\u5c5e\u6027\n0.08\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#255","title":"2.5.5 \u7ee7\u627f","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#256","title":"2.5.6 \u4f7f\u7528\u7ee7\u627f","text":"Python>>> class Account:\n \"\"\"\u4e00\u4e2a\u4f59\u989d\u975e\u96f6\u7684\u8d26\u6237\u3002\"\"\"\n interest = 0.02\n def __init__(self, account_holder):\n self.balance = 0\n self.holder = account_holder\n def deposit(self, amount):\n \"\"\"\u5b58\u5165\u8d26\u6237 amount\uff0c\u5e76\u8fd4\u56de\u53d8\u5316\u540e\u7684\u4f59\u989d\"\"\"\n self.balance = self.balance + amount\n return self.balance\n def withdraw(self, amount):\n \"\"\"\u4ece\u8d26\u53f7\u4e2d\u53d6\u51fa amount\uff0c\u5e76\u8fd4\u56de\u53d8\u5316\u540e\u7684\u4f59\u989d\"\"\"\n if amount > self.balance:\n return 'Insufficient funds'\n self.balance = self.balance - amount\n return self.balance\n\n>>> class CheckingAccount(Account):\n \"\"\"\u4ece\u8d26\u53f7\u53d6\u94b1\u4f1a\u6263\u51fa\u624b\u7eed\u8d39\u7684\u8d26\u53f7\"\"\"\n withdraw_charge = 1\n interest = 0.01\n def withdraw(self, amount):\n return Account.withdraw(self, amount + self.withdraw_charge)\n
\u63a5\u53e3
Python>>> def deposit_all(winners, amount=5):\n for account in winners:\n account.deposit(amount) # \u8fd9\u91cc\u8c03\u7528\u7684\u662f\u5b9e\u4f8b account \u7684 deposit \u65b9\u6cd5\n # \u5bf9\u4e8e\u4e0d\u540c\u5b9e\u4f8b\u6765\u8bf4\uff0c\u5b83\u4eec\u7684 deposit \u65b9\u6cd5\u53ef\u80fd\u4e0d\u540c\u3002\u8fd9\u4e2a\u4f8b\u5b50\u76f8\u5bf9\u4e8e\u4e0b\u9762\u6765\u8bb2\uff0c\u66f4\u52a0\u5177\u6709\u5065\u58ee\u6027\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#257","title":"2.5.7 \u591a\u7ee7\u627f","text":"\u7ee7\u627f\u6392\u5e8f\u95ee\u9898\u6ca1\u6709\u6b63\u786e\u7684\u89e3\u51b3\u65b9\u6848\uff0c\u56e0\u4e3a\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u80fd\u66f4\u613f\u610f\u5c06\u67d0\u4e9b\u7ee7\u627f\u7c7b\u7f6e\u4e8e\u5176\u4ed6\u7c7b\u4e4b\u4e0a\u3002\u4f46\u662f\uff0c\u4efb\u4f55\u652f\u6301\u591a\u91cd\u7ee7\u627f\u7684\u7f16\u7a0b\u8bed\u8a00\u90fd\u5fc5\u987b\u4ee5\u4e00\u81f4\u7684\u65b9\u5f0f\u9009\u62e9\u67d0\u4e9b\u6392\u5e8f\uff0c\u4ee5\u4fbf\u8be5\u8bed\u8a00\u7684\u7528\u6237\u53ef\u4ee5\u9884\u6d4b\u5176\u7a0b\u5e8f\u7684\u884c\u4e3a\u3002
\u8fdb\u4e00\u6b65\u9605\u8bfb\u3002Python \u4f7f\u7528\u79f0\u4e3a C3 \u65b9\u6cd5\u89e3\u6790\u6392\u5e8f\u7684\u9012\u5f52\u7b97\u6cd5\u89e3\u6790\u6b64\u540d\u79f0\u3002\u53ef\u4ee5\u5728\u6240\u6709\u7c7b\u4e0a\u4f7f\u7528\u00a0mro\u00a0\u65b9\u6cd5\u67e5\u8be2\u4efb\u4f55\u7c7b\u7684\u65b9\u6cd5\u89e3\u6790\u987a\u5e8f\u3002
Python>>> [c.__name__ for c in AsSeenOnTVAccount.mro()]\n['AsSeenOnTVAccount', 'CheckingAccount', 'SavingsAccount', 'Account', 'object']\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#258","title":"2.5.8 \u5bf9\u8c61\u7684\u4f5c\u7528","text":"\u53e6\u4e00\u65b9\u9762\uff0c\u7c7b\u53ef\u80fd\u4e0d\u662f\u5b9e\u73b0\u67d0\u4e9b\u62bd\u8c61\u7684\u6700\u4f73\u673a\u5236\u3002\u51fd\u6570\u5f0f\u62bd\u8c61\u63d0\u4f9b\u4e86\u4e00\u4e2a\u66f4\u81ea\u7136\u7684\u9690\u55bb\u6765\u8868\u793a\u8f93\u5165\u548c\u8f93\u51fa\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6211\u4eec\u4e0d\u5e94\u8be5\u89c9\u5f97\u5fc5\u987b\u5c06\u7a0b\u5e8f\u4e2d\u7684\u6bcf\u4e00\u70b9\u903b\u8f91\u90fd\u585e\u8fdb\u4e00\u4e2a\u7c7b\u4e2d\uff0c\u5c24\u5176\u662f\u5728\u5b9a\u4e49\u72ec\u7acb\u51fd\u6570\u6765\u64cd\u4f5c\u6570\u636e\u66f4\u81ea\u7136\u7684\u60c5\u51b5\u4e0b\u3002\u51fd\u6570\u8fd8\u53ef\u4ee5\u5f3a\u5236\u5b9e\u73b0\u5173\u6ce8\u70b9\u7684\u5206\u79bb\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u51fd\u6570\u5f0f\u7f16\u7a0b\u63d0\u4f9b\u4e86\u53e6\u4e00\u79cd\u6709\u6548\u5730\u7ec4\u7ec7\u7a0b\u5e8f\u903b\u8f91\u7684\u65b9\u6cd5\uff0c\u4f7f\u5f97\u7a0b\u5e8f\u5458\u80fd\u591f\u66f4\u597d\u5730\u5904\u7406\u548c\u7ef4\u62a4\u7a0b\u5e8f\u3002\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u4f7f\u7528\u51fd\u6570\u5f0f\u7f16\u7a0b\u65b9\u6cd5\u53ef\u80fd\u6bd4\u4f7f\u7528\u9762\u5411\u5bf9\u8c61\u7f16\u7a0b\u66f4\u81ea\u7136\u548c\u6709\u6548\u3002
"},{"location":"CS_Basic/CS61A/Composing_Programs/#26","title":"2.6 \u5b9e\u73b0\u7c7b\u548c\u5bf9\u8c61","text":"object-oriented programming paradigm \u5373\u4f7f\u5728\u6ca1\u6709\u5185\u7f6e\u5bf9\u8c61\u7cfb\u7edf\u7684\u7f16\u7a0b\u8bed\u8a00\u4e2d\uff0c\u7a0b\u5e8f\u4e5f\u53ef\u4ee5\u662f\u9762\u5411\u5bf9\u8c61\u7684\u3002 \u653e\u5f03\u70b9\u8868\u793a\u6cd5->\u8c03\u5ea6\u5b57\u5178\u5b9e\u73b0\u6d88\u606f\u4f20\u9012
"},{"location":"CS_Basic/CS61A/Composing_Programs/#261","title":"2.6.1 \u5b9e\u4f8b","text":"Python>>> def make_instance(cls):\n \"\"\"Return a new object instance, which is a dispatch dictionary.\"\"\"\n def get_value(name):\n if name in attributes:\n return attributes[name]\n else:\n value = cls['get'](name)\n return bind_method(value, instance)\n def set_value(name, value):\n attributes[name] = value\n attributes = {}\n instance = {'get': get_value, 'set': set_value}\n return instance\n\n>>> def bind_method(value, instance):\n \"\"\"Return a bound method if value is callable, or value otherwise.\"\"\"\n if callable(value):\n def method(*args):\n return value(instance, *args)\n return method\n else:\n return value\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#262","title":"2.6.2 \u7c7b","text":"Python>>> def make_class(attributes, base_class=None):\n \"\"\"Return a new class, which is a dispatch dictionary.\"\"\"\n def get_value(name):\n if name in attributes:\n return attributes[name]\n elif base_class is not None:\n return base_class['get'](name)\n def set_value(name, value):\n attributes[name] = value\n def new(*args):\n return init_instance(cls, *args)\n cls = {'get': get_value, 'set': set_value, 'new': new}\n return cls\n\n>>> def init_instance(cls, *args):\n \"\"\"Return a new object with type cls, initialized with args.\"\"\"\n instance = make_instance(cls)\n init = cls['get']('__init__')\n if init:\n init(instance, *args)\n return instance\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#3","title":"3 \u8ba1\u7b97\u673a\u7a0b\u5e8f\u7684\u89e3\u91ca","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#31","title":"3.1 \u5f15\u8a00","text":"\u8bb8\u591a\u89e3\u91ca\u5668\u90fd\u6709\u4e00\u4e2a\u4f18\u96c5\u7684\u7ed3\u6784\uff0c\u5373\u4e24\u4e2a\u4e92\u9012\u5f52\u51fd\u6570\uff1a
- \u7b2c\u4e00\u4e2a\u51fd\u6570\u6c42\u89e3\u73af\u5883\u4e2d\u7684\u8868\u8fbe\u5f0f
- \u7b2c\u4e8c\u4e2a\u51fd\u6570\u5c06\u51fd\u6570\u5e94\u7528\u4e8e\u53c2\u6570
"},{"location":"CS_Basic/CS61A/Composing_Programs/#32","title":"3.2 \u51fd\u6570\u5f0f\u7f16\u7a0b","text":" - \u53ea\u4f7f\u7528\u8868\u8fbe\u5f0f\u800c\u4e0d\u4f7f\u7528\u8bed\u53e5\uff0c\u7279\u522b\u9002\u5408\u7b26\u53f7\u8ba1\u7b97
- \u5904\u7406\u7684\u6570\u636e\u90fd\u662f\u4e0d\u53ef\u53d8\u7684\uff08immutable\uff09
Python(if <predicate> <consequent> <alternative>)\n\n(define pi 3.14)\n(* pi 3.14)\n\n(define (<name> <formal parameters>) <body>)\neg1:\n (define (average x y)\n (/ (+ x y) 2))\neg2:\n (define (abs x)\n (if (< x 0)\n (- x)\n x))\neg3:\n (define (sqrt x)\n (define (good-enough? guess)\n (< (abs (- (square guess) x)) 0.001))\n (define (improve guess)\n (average guess (/ x guess)))\n (define (sqrt-iter guess)\n (if (good-enough? guess)\n guess\n (sqrt-iter (improve guess))))\n (sqrt-iter 1.0))\n (sqrt 9)\n\n(lambda (<formal-parameters>) <body>)\neg1:\n (define (plus4 x) (+ x 4))\n (define plus4 (lambda (x) (+ x 4))) # both are OK\n\n# \u7279\u6b8a\u7684\u503c\u00a0nil\u00a0\u6216\u00a0'()\u00a0\u8868\u793a\u7a7a\u5217\u8868\n\n# null? \u8c13\u8bcd\u7684\u4f7f\u7528:\n (define (length items)\n (if (null? items)\n 0\n (+ 1 (length (cdr items)))))\n (define (getitem items n)\n (if (= n 0)\n (car items)\n (getitem (cdr items) (- n 1))))\n (define squares (list 1 4 9 16 25))\n\n (length squares)\n\n (getitem squares 3)\n\n# \u4efb\u4f55\u4e0d\u88ab\u6c42\u503c\u7684\u8868\u8fbe\u5f0f\u90fd\u88ab\u79f0\u4e3a\u88ab\u5f15\u7528\n (list 'define 'list)\n\n# turtle\u4f7f\u7528+\u9012\u5f52\u753b\u56fe\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#33","title":"3.3 \u5f02\u5e38","text":" Python>>> raise Exception(' An error occurred')\nTraceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nException: an error occurred\n
- raising an exception
- read-eval-print-loop \u5373 REPL
- stack backtrace
- handling exceptions
Pythontry\n <try suite>\nexcept <exception class> as <name>:\n <except suite>\n
\u5f02\u5e38\u662f\u4e2a\u7c7b\uff0c\u53ef\u4ee5\u6709\u989d\u5916\u7684\u5c5e\u6027\uff0c\u53ef\u4ee5\u907f\u514d\u62a5\u9519\uff0c\u8ba9\u7a0b\u5e8f\u7ed9\u51fa\u4e00\u4e2a\u8f83\u4e3a\u7c97\u7cd9\u7684\u503c\uff1a
Python>>> class IterImproveError(Exception):\n\u00a0\u00a0\u00a0 def __init__(self, last_guess):\n\u00a0\u00a0\u00a0 self.last_guess = last_guess\n>>> def improve(update, done, guess=1, max_updates=1000):\n\u00a0\u00a0\u00a0 k = 0\n\u00a0\u00a0\u00a0 try:\n\u00a0\u00a0\u00a0 while not done(guess) and k < max_updates:\n\u00a0\u00a0\u00a0 guess = update(guess)\n\u00a0\u00a0\u00a0 k = k + 1\n\u00a0\u00a0\u00a0 return guess\n\u00a0\u00a0\u00a0 except ValueError:\n\u00a0\u00a0\u00a0 raise IterImproveError(guess)\n>>> def find_zero(f, guess=1):\n\u00a0\u00a0\u00a0 def done(x):\n\u00a0\u00a0\u00a0 return f(x) == 0\n\u00a0\u00a0\u00a0 try:\n\u00a0\u00a0\u00a0 return improve(newton_update(f), done, guess)\n\u00a0\u00a0\u00a0 except IterImproveError as e:\n\u00a0\u00a0\u00a0 return e.last_guess\n>>> from math import sqrt\n>>> find_zero(lambda x: 2*x*x + sqrt(x))\n-0.030211203830201594\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#34","title":"3.4 \u7ec4\u5408\u8bed\u8a00\u7684\u89e3\u91ca\u5668","text":" - \u8ba1\u7b97\u5668\u8bed\u8a00 -> \u7b80\u7565\u89e3\u91ca\u5668
- scheme \u5bf9
- \u8868\u8fbe\u5f0f\u6811
- \u89e3\u6790\u8868\u8fbe\u5f0f\u6811
- \u8bcd\u6cd5\u5206\u6790\u5668\uff08lexical analyzer\uff09/ \u5206\u8bcd\u5668\uff08tokenizer\uff09
- \u6807\u8bb0\uff08token\uff09
- \u8bed\u6cd5\u5206\u6790\u5668\uff08syntactic analyzer\uff09
- \u6570\u5b57\u548c\u8c03\u7528\u8868\u8fbe\u5f0f \u8bb2\u4e86\u4e00\u4e0b\u8ba1\u7b97\u5668\u89e3\u91ca\u5668\u4ea4\u4e92\u5f0f\u9875\u9762\u7684\u8868\u8fbe\u5f0f\u5982\u4f55\u8ba1\u7b97\u548c\u5f02\u5e38\u5904\u7406
"},{"location":"CS_Basic/CS61A/Composing_Programs/#35","title":"3.5 \u62bd\u8c61\u8bed\u8a00\u7684\u89e3\u91ca\u5668","text":" - \u6269\u5c55 scheme_reader \u89e3\u6790\u70b9\u5217\u8868\u548c\u5f15\u53f7
- \u6c42\u503c\uff08Evaluation\uff09
- \u51fd\u6570\u5e94\u7528\uff08Procedure application\uff09
- \u6c42\u503c/\u5e94\u7528\u9012\u5f52
- \u6570\u636e\u5373\u7a0b\u5e8f
"},{"location":"CS_Basic/CS61A/Composing_Programs/#4","title":"4 \u6570\u636e\u5904\u7406","text":""},{"location":"CS_Basic/CS61A/Composing_Programs/#41","title":"4.1 \u5f15\u8a00","text":" - pipelines
- sequence interface
- unbounded
"},{"location":"CS_Basic/CS61A/Composing_Programs/#42","title":"4.2 \u9690\u5f0f\u5e8f\u5217","text":" - \u6211\u4eec\u53ea\u5728\u6709\u9700\u8981\u7684\u65f6\u5019\u624d\u8ba1\u7b97\u5143\u7d20
- Lazy computation
"},{"location":"CS_Basic/CS61A/Composing_Programs/#421","title":"4.2.1 \u8fed\u4ee3\u5668","text":"\u4e24\u4e2a\u7ec4\u4ef6:
- \u68c0\u7d22\u4e0b\u4e00\u4e2a\u5143\u7d20\u7684\u673a\u5236
- \u5230\u8fbe\u5e8f\u5217\u672b\u5c3e\u5e76\u4e14\u6ca1\u6709\u5269\u4f59\u5143\u7d20\uff0c\u53d1\u51fa\u4fe1\u53f7\u7684\u673a\u5236
Python>>> next(iterator)\n7\n>>> next(iterator)\nTraceback (most recent call las):\n File \"<stdin>\", line 1, in <module>\nStopIteration\n\n>>> try:\n next(iterator)\n except StopIteration:\n print('No more values')\nNo more values\n
"},{"location":"CS_Basic/CS61A/Composing_Programs/#422","title":"4.2.2 \u53ef\u8fed\u4ee3\u6027","text":"iterable value \u53ef\u8fed\u4ee3\u5bf9\u8c61:
- \u5e8f\u5217\u503c: string & tuples
- \u5bb9\u5668: sets & Dictionaries
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/","title":"\u8ba1\u7b97\u673a\u7ec4\u6210\u4e0e\u8bbe\u8ba1\u786c\u4ef6\u8f6f\u4ef6\u63a5\u53e3","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#_1","title":"\u8ba1\u7b97\u673a\u7ec4\u6210\u4e0e\u8bbe\u8ba1\u786c\u4ef6\u8f6f\u4ef6\u63a5\u53e3","text":" \u7ea6 2978 \u4e2a\u5b57 13 \u884c\u4ee3\u7801 4 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 15 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#1","title":"1 \u8ba1\u7b97\u673a\u62bd\u8c61\u53ca\u76f8\u5173\u6280\u672f","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#2","title":"2 \u6307\u4ee4: \u8ba1\u7b97\u673a\u7684\u8bed\u8a00","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#21","title":"2.1 \u5f15\u8a00","text":"\u8bbe\u8ba1\u539f\u5219:
- \u7b80\u5355\u6e90\u4e8e\u89c4\u6574
- \u66f4\u5c11\u5219\u66f4\u5feb
- \u4f18\u79c0\u7684\u8bbe\u8ba1\u9700\u8981\u9002\u5f53\u7684\u6298\u4e2d
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#22","title":"2.2 \u8ba1\u7b97\u673a\u786c\u4ef6\u7684\u64cd\u4f5c","text":"Java \u7f16\u8bd1\u5668: Just In Time \u7f16\u8bd1\u5668
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#23","title":"2.3 \u8ba1\u7b97\u673a\u786c\u4ef6\u7684\u64cd\u4f5c\u6570","text":" - \u5bc4\u5b58\u5668
- \u5927\u5c0f\u4e3a64 bits \u53cc\u5b57
- \u6570\u91cf\u6709\u9650\u901a\u5e38\u4e3a 32 \u4e2a
- x +\u5bc4\u5b58\u5668\u7f16\u53f7
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#231","title":"2.3.1 \u5b58\u50a8\u5668\u64cd\u4f5c\u6570","text":"\u5728\u5185\u5b58\u548c\u5bc4\u5b58\u5668\u4e4b\u95f4\u4f20\u8f93\u6307\u4ee4:\u6570\u636e\u4f20\u8f93\u6307\u4ee4 \u6307\u4ee4\u63d0\u4f9b\u5185\u5b58\u5730\u5740 \u8f7d\u5165\u6307\u4ee4\uff08load\uff09:ld
Text OnlyLd x9, 8(x22)\n
X 22 \u57fa\u5740\u5bc4\u5b58\u5668 8 \u504f\u79fb\u91cf \u5b57\u8282\u5730\u5740: 0 8 16 24 RICS- V \u662f\u5c0f\u7aef\u7f16\u5740: \u53ea\u5728\u4ee5\u53cc\u5b57\u5f62\u5f0f\u548c\u516b\u4e2a\u5355\u72ec\u5b57\u8282\u8bbf\u95ee\u76f8\u540c\u6570\u636e\u65f6\u4f1a\u6709\u5f71\u54cd ^da8be4
\u5b58\u50a8\u6307\u4ee4\uff08store\uff09\u5b58\u50a8\u53cc\u5b57
Text Onlysd x9, 96(x22)\n
- \u5bf9\u9f50\u9650\u5236:
- \u5b57\u7684\u8d77\u59cb\u5730\u5740\u662f 4 \u7684\u500d\u6570
- \u53cc\u5b57\u7684\u8d77\u59cb\u5730\u5740\u662f 8 \u7684\u500d\u6570
- \u4f46\u662f risc-v and Intel x 86 \u6ca1\u6709
- MIPS \u6709 Gibibyte (\\(\\displaystyle 2^{30}\\)) and tebibyte (\\(\\displaystyle 2^{40}\\)) \u5982\u679c\u53d8\u91cf\u6bd4\u5bc4\u5b58\u5668\u6570\u91cf\u66f4\u591a\uff0c\u90a3\u4e48\u4f1a\u628a\u4e00\u4e9b\u653e\u5230\u5185\u5b58\uff0c\u5373\u5bc4\u5b58\u5668\u6362\u51fa\u3002
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#232","title":"2.3.2 \u5e38\u6570\u6216\u7acb\u5373\u6570\u64cd\u4f5c\u6570","text":"Text Onlyld x9, AddConstant4(x3)\nAdd x22, x22, x9\n\n# Equals to \n\naddi x22, x22, 4 # x22 = x22 + 4\n
\u5e38\u6570\u79f0\u4e3a\u7b97\u6570\u6307\u4ee4\u64cd\u4f5c\u6570 X0 \u53ef\u4ee5\u7528\u6765\u8868\u793a 0 \u5176\u5b9e\u8fd8\u6709 RV 32 \u57fa\u5740\u5bc4\u5b58\u5668\u4e5f\u88ab\u79f0\u4e3a\u4e0b\u6807\u5bc4\u5b58\u5668 \u6211\u4eec\u5047\u8bbe\u6307\u4ee4\u90fd\u662f 64 \u4f4d
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#24","title":"2.4 \u6709\u7b26\u53f7\u6570\u4e0e\u65e0\u7b26\u53f7\u6570","text":" - Binary digit Or bit
- Least significant bit
- Most significant bit
- sign and magnitude
- \u8865\u7801\u8f6c\u5316:
- \u62d3\u5c55\u7b26\u53f7\u4f4d
- \u53d6\u53cd
- \u52a0\u4e00
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#25","title":"2.5 \u8ba1\u7b97\u673a\u4e2d\u7684\u6307\u4ee4\u8868\u793a","text":" - \u5b57\u6bb5
- \u6307\u4ee4 32 \u4f4d\u957f
- \u6307\u4ee4\u7684\u6570\u5b57\u8868\u793a:\u673a\u5668\u8bed\u8a00
- \u6307\u4ee4\u5e8f\u5217: \u673a\u5668\u7801
- C \u548c java \u7528 0 xnnnn \u6765\u8868\u793a\u5341\u516d\u8fdb\u5236\u6570
- RISC-V:\uff08R\uff09
- funct7 + rs 2 + rs 1 + funct 3 + rd + opcode
- rd: \u76ee\u7684\u64cd\u4f5c\u6570\u5bc4\u5b58\u5668
- rs 1: \u7b2c\u4e00\u4e2a\u539f\u64cd\u4f5c\u6570\u5bc4\u5b58\u5668
- rs 2: \u7b2c\u4e8c\u4e2a\u539f\u64cd\u4f5c\u6570\u5bc4\u5b58\u5668
- funct 7 (3): \u64cd\u4f5c\u7801\u5b57\u6bb5
- \u5bf9\u4e0d\u540c\u7684\u6307\u4ee4\u4f7f\u7528\u4e0d\u540c\u7684\u6307\u4ee4\u683c\u5f0f
- I
- immediate + rs 1 + funct 3 + rd + opcode
- \u8d85\u8fc7 32 \u4e2a\u5bc4\u5b58\u5668\u7684\u8bdd\uff0crd and rs 1 \u90fd\u8981\u589e\u52a0\u989d\u5916\u7684\u4e00\u4f4d
- ld
- S
- immediate + rs 2 + rs 1 + funct 3 + immediate + opcode
- Reg \u8868\u793a 0 \u5230 31 \u4e4b\u95f4\u7684\u5bc4\u5b58\u5668\u7f16\u53f7
- \u6ca1\u6709 subi \u56e0\u4e3a\u53ef\u4ee5\u901a\u8fc7\u52a0\u8d1f\u6570\u6765\u5b9e\u73b0
- \u8ba1\u7b97\u673a\u6784\u5efa\u57fa\u4e8e\u4e24\u4e2a\u5173\u952e\u539f\u5219:
- \u6307\u4ee4\u7531\u6570\u5b57\u5f62\u5f0f\u8868\u793a
- \u7a0b\u5e8f\u548c\u6570\u636e\u4e00\u6837\u4fdd\u5b58\u5728\u5b58\u50a8\u5668\u4e2d\u8fdb\u884c\u8bfb\u5199
- \u7a0b\u5e8f\u4f1a\u4ee5\u4e8c\u8fdb\u5236\u6570\u636e\u6587\u4ef6\u7684\u5f62\u5f0f\u6765\u53d1\u5e03
- \u4e8c\u8fdb\u5236\u517c\u5bb9\u6027\u8ba9\u884c\u4e1a\u56f4\u7ed5\u5c11\u6570\u51e0\u4e2a\u6307\u4ee4\u7cfb\u7edf\u7ed3\u6784\u5f62\u6210\u8054\u76df
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#26","title":"2.6 \u903b\u8f91\u64cd\u4f5c","text":" - sll, slli
- srl, srli
- sra, srai
- and, andi
- or, ori
- xor, xori
- not
- \u4f4d\u79fb\u6307\u4ee4\u7528\u7684 I \u578b\u683c\u5f0f:
- \u4f46\u5b83\u7684\u683c\u5f0f\u6709\u53d8\u5316:
- funct 6 + immediate + rs 1 + funct 3 + rd + opcode
- \u7b97\u6570\u53f3\u79fb\u7528\u7684\u662f\u7b26\u53f7\u4f4d
- AND \u5b9e\u73b0\u4e86\u63a9\u7801
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#27","title":"2.7 \u7528\u4e8e\u51b3\u7b56\u7684\u6307\u4ee4","text":""},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#271","title":"2.7.1 \u5faa\u73af","text":"Cwhile (save[i] == k) \n i += 1;\n
I -> x 22 K -> x 24 Save -> x 25
Text Onlyloop: slli, x10, x22, 3\nAdd x10, x10, x25\nLd x9, 0(x10)\nBen x9, x24, Exit\nAddi x22, x22, l\nBeq x0, x0, loop\nExit:\n
\u57fa\u672c\u5757:
- \u9664\u4e86\u5728\u6307\u4ee4\u5e8f\u5217\u7684\u7ed3\u5c3e\uff0c\u5e8f\u5217\u4e2d\u6ca1\u6709\u5206\u652f\u3002
- \u9664\u4e86\u5728\u5e8f\u5217\u8d77\u59cb\u5904\uff0c\u5e8f\u5217\u4e2d\u6ca1\u6709\u5206\u652f\u76ee\u6807\u548c\u5206\u652f\u6807\u7b7e\u3002
- \u5bf9\u4e8e\u7b26\u53f7:
- RISC-V \u7528\u4e0d\u540c\u7684\u6307\u4ee4
- MIPS \u8bbe\u7f6e\u4e34\u65f6\u5bc4\u5b58\u5668
- ARM \u6761\u4ef6\u4ee3\u7801\u6216\u6807\u5fd7\u4f4d
- \u4e0d\u8fc7\u4e5f\u4f1a\u6709\u7f3a\u70b9: \u8fc7\u591a\u7684\u6307\u4ee4\u8bbe\u7f6e\u6761\u4ef6\u4ee3\u7801\uff0c\u4f1a\u8ba9\u6d41\u6c34\u7ebf\u6267\u884c\u56f0\u96be
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#272","title":"2.7.2 \u8fb9\u754c\u68c0\u67e5\u7684\u7b80\u4fbf\u65b9\u6cd5","text":"\u5c06\u7b26\u53f7\u6570\u5f53\u4f5c\u65e0\u7b26\u53f7\u6570\u5904\u7406
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#273-caseswitch","title":"2.7.3 Case/switch \u8bed\u53e5","text":" - \u7f16\u7801\u5f62\u6210\u6307\u4ee4\u5e8f\u5217\u7684\u5730\u5740\u8868: \u5206\u652f\u5730\u5740\u8868/\u5206\u652f\u8868
- \u95f4\u63a5\u8df3\u8f6c\u6307\u4ee4 jalr
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#28","title":"2.8 \u8ba1\u7b97\u673a\u786c\u4ef6\u5bf9\u8fc7\u7a0b\u7684\u652f\u6301","text":" - Procedure
- \u6267\u884c\u8fc7\u7a0b\u7684\u516d\u4e2a\u6b65\u9aa4:
- \u5c06\u53c2\u6570\u653e\u5728\u8fc7\u7a0b\u53ef\u4ee5\u8bbf\u95ee\u5230\u7684\u4f4d\u7f6e
- \u5c06\u63a7\u5236\u8f6c\u4ea4\u7ed9\u8fc7\u7a0b
- \u83b7\u53d6\u8fc7\u7a0b\u6240\u9700\u7684\u5b58\u50a8\u8d44\u6e90
- \u6267\u884c\u6240\u9700\u7684\u4efb\u52a1
- \u5c06\u7ed3\u679c\u503c\u653e\u5728\u8c03\u7528\u7a0b\u5e8f\u53ef\u4ee5\u8bbf\u95ee\u5230\u7684\u4f4d\u7f6e
- \u5c06\u63a7\u5236\u8fd4\u56de\u5230\u521d\u59cb\u70b9\uff0c\u56e0\u4e3a\u8fc7\u7a0b\u53ef\u4ee5\u4ece\u7a0b\u5e8f\u4e2d\u7684\u591a\u4e2a\u70b9\u8c03\u7528
- x 10~x 17: \u53c2\u6570\u5bc4\u5b58\u5668\uff0c\u7528\u4e8e\u4f20\u9012\u53c2\u6570\u6216\u8fd4\u56de\u503c
- x 1: \u8fd4\u56de\u5730\u5740\u5bc4\u5b58\u5668\uff0c\u7528\u4e8e\u8fd4\u56de\u5230\u8d77\u59cb\u70b9
- jal \u8df3\u8f6c-\u94fe\u63a5\u6307\u4ee4
Text OnlyJal x1, ProcedureAddress\nJalr x0, 0(x1)\n
\u8c03\u7528\u8005\u5c06\u53c2\u6570\u503c\u653e\u5165 x 10~x 17 \u8fc7\u7a0b\u662f\u88ab\u8c03\u7528\u8005 Program counter:PC \u6307\u4ee4\u5730\u5740\u5bc4\u5b58\u5668
Text Onlyjal x0, Label // unconditionally branch to Label\n
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#281","title":"2.8.1 \u4f7f\u7528\u66f4\u591a\u7684\u5bc4\u5b58\u5668","text":" - Stack
- Stack pointer x2:sp
- \u538b\u6808\u5f39\u6808
Text Onlyleaf_example:\nAddi sp, sp, -14\nSd x5, 16(sp)\nSd x6, 8(sp)\nSd x20, 0(sp)\nAdd x5, x10, x11\nAdd x6, x12, x13\nSub x20, x5, x6\nAddi x10, x20, 0\nLd x20, 0(sp)\nLd x6, 8(sp)\nLd x5, 16(sp)\nAddi sp, sp, 24\nJalr x0, 0(x1)\n
- X 5~x 7, x 28~x 31: \u4e34\u65f6\u5bc4\u5b58\u5668
- x 8~x 9, x 18~x 27: \u4fdd\u5b58\u5bc4\u5b58\u5668
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#282","title":"2.8.2 \u5d4c\u5957\u8fc7\u7a0b","text":" - Leaf procedure
- \u5c06\u6240\u6709\u5fc5\u987b\u4fdd\u5b58\u7684\u5bc4\u5b58\u5668\u538b\u6808\uff0c\u9632\u6b62\u51b2\u7a81
Clong long int fact (long long int n) {\n if (n < 1) return (1);\n else return (n * fact(n - 1))\n}\n
n -> x 10
Text Onlyfact:\n addi sp, sp, -16\n Sd x1, 8(sp)\n Sd x10, 0(sp)\n\n Addi x5, x10, -1\n Bge x5, x0, L1\n\n Addi x10, x0, 1\n Addi sp, sp, 16\n Jalr x0, 0(x1)\n\nL1: addi x10, x10, -1\n Jal x1, fact\n\naddi x6, x10, 0\nLd x10, 0(sp)\nLd x1, 8(sp)\nAddi sp, sp, 16\nMul x10, x10, x6\nJalr x0, 0(x1)\n
- \u4e00\u4e9b\u7f16\u8bd1\u5668\u4fdd\u7559\u4e00\u4e2a\u5bc4\u5b58\u5668 x 3 \u7528\u4f5c\u5168\u5c40\u6307\u9488 gp
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#283","title":"2.8.3 \u5728\u6808\u4e2d\u4f4d\u65b0\u6570\u636e\u5206\u914d\u7a7a\u95f4","text":"\u6808\u4e5f\u7528\u4e8e\u5b58\u50a8\u8fc7\u7a0b\u7684\u5c40\u90e8\u53d8\u91cf \u8fc7\u7a0b\u5e27/\u6d3b\u52a8\u8bb0\u5f55
\u6ca1\u770b\u61c2
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#284","title":"2.8.4 \u5728\u5806\u4e2d\u4e3a\u65b0\u6570\u636e\u5206\u914d\u7a7a\u95f4","text":"Static data segment text segment \u50cf\u94fe\u8868\u7b49\u6570\u636e\u7ed3\u6784\u5f80\u5f80\u4f1a\u968f\u751f\u547d\u5468\u671f\u589e\u957f\u548c\u7f29\u77ed\uff0c\u6240\u4ee5\u4f1a\u628a\u4ed6\u4eec\u5b58\u5728\u5806\u91cc (heap)
- malloc ()
- free () \u8be6\u7ec6\u9610\u8ff0\u6ca1\u770b
"},{"location":"CS_Basic/CS61C/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BB%84%E6%88%90%E4%B8%8E%E8%AE%BE%E8%AE%A1%E7%A1%AC%E4%BB%B6%E8%BD%AF%E4%BB%B6%E6%8E%A5%E5%8F%A3/#29","title":"2.9 \u4eba\u673a\u4ea4\u4e92","text":"ASCII: american standard code for information interchange
\u52a0\u8f7d\u65e0\u7b26\u53f7\u5b57\u8282 (lbu) \u5b58\u50a8\u5b57\u8282 (sb)
Text Onlylbu x12, 0(x10)\nsb x12, 0(x11)\n
\u5b57\u7b26\u4e32\u7684\u8868\u793a\u6709\u4e09\u79cd\u9009\u62e9:
- \u7b2c\u4e00\u4e2a\u4f4d\u7f6e\u4fdd\u7559\u6765\u7ed9\u51fa\u5b57\u7b26\u4e32\u7684\u957f\u5ea6
- \u7528\u989d\u5916\u7684\u53d8\u91cf\u6765\u5b58\u50a8\u957f\u5ea6\uff08\u5982\u7ed3\u6784\u4f53\uff09
- \u5b57\u7b26\u4e32\u7684\u6700\u540e\u4e00\u4e2a\u4f4d\u7f6e\u7528\u5b57\u7b26\u6807\u8bb0\u7ed3\u5c3e \u5176\u4e2d C \u8bed\u8a00\u7528\u7b2c\u4e09\u79cd\uff0c\u4f7f\u7528\u503c\u4e3a 0 \u7684\u5b57\u8282\u6765\u7ec8\u6b62\u5b57\u7b26\u4e32\uff08null in ASCII\uff09
Cvoid strcpy (char x[], char y[])\n{\n size_t i;\n i = 0;\n while ((x[i] = y[i]) != '\\0')\n i += 1;\n}\n
x -> x 10 y -> x 11 i -> x 19
Text Onlystrcpy:\n addi sp, sp, -8\n sd x19, 0(sp)\n add x19, x0, x0\nL1: add x5, x19, x11\n lbu x6, 0(x5)\n add x7, x19, x10\n sb x6, 0(x7)\n beq x6, x0, L2\n addi x19, x19, 1\n jal x0, L1\nL2: ld x19, 0(sp)\n addi sp, sp, 8\n jalr x0, 0(x1)\n
load half unsigned \u52a0\u8f7d\u534a\u5b57: lh lhu sh
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- \u8bbe\u7f6e\u968f\u673a\u5b57\u7b26\u4e32\u4f5c\u4e3a\u5bc6\u7801\uff0c\u5e76\u4e14\u957f\u5ea6\u8d85\u8fc78\u4f4d
"},{"location":"CS_Basic/Network/Security/#ip","title":"\u516c\u7f51 IP","text":" - \u6211\u4eec\u5e0c\u671b\u4ece\u4efb\u610f\u63a5\u5165\u4e92\u8054\u7f51\u7684\u5730\u65b9\u4f7f\u7528ssh\u8fde\u63a5\u5230\u670d\u52a1\u5668\uff0c\u4e00\u4e2a\u7b80\u5355\u7684\u65b9\u6cd5\u662f\u8ba9\u670d\u52a1\u5668\u62e5\u6709\u4e00\u4e2a\u516c\u7f51IP\u5e76\u8fd0\u884csshd\u670d\u52a1\u3002
- \u5e38\u89c1\u7684\u653b\u51fb\u65b9\u5f0f
- \u626b\u63cf\u5f00\u653e\u7aef\u53e3\u4fe1\u606f\uff0c\u5e76\u786e\u5b9a\u7aef\u53e3\u4e0a\u8fd0\u884c\u7684\u670d\u52a1
- \u5bf9\u53ef\u80fd\u5b58\u5728\u7684\u670d\u52a1\u8fdb\u884c\u653b\u51fb\uff0c\u5c1d\u8bd5\u5229\u7528\u670d\u52a1\u7684\u6f0f\u6d1e\uff08\u5982\u5f31\u53e3\u4ee4\uff09\u83b7\u53d6\u670d\u52a1\u5668\u7684\u8bbf\u95ee\u6743\u9650
- \u5e38\u89c1\u7684\u9632\u8303\u65b9\u5f0f
- \u4f7f\u7528\u9632\u706b\u5899\u3002\u914d\u7f6e\u9632\u706b\u5899\u89c4\u5219\uff0c\u4ec5\u5141\u8bb8\u5fc5\u8981\u7684\u670d\u52a1\u548c\u7aef\u53e3\u5bf9\u5916\u5f00\u653e\u3002
- \u5ba1\u67e5\u5f00\u653e\u7684\u670d\u52a1\u7684\u5b89\u5168\u6027\u3002\u786e\u4fdd\u5f53\u524d\u4e3b\u673a\u5f00\u653e\u7684\u6240\u6709\u670d\u52a1\u5747\u662f\u5b89\u5168\u7684\u3002
"},{"location":"Robot/","title":"Robot","text":""},{"location":"Robot/#robot","title":"Robot","text":"Abstract
\u672c\u90e8\u5206\u5185\u5bb9\uff08\u9664\u7279\u522b\u58f0\u660e\u5916\uff09\u91c7\u7528 \u7f72\u540d-\u975e\u5546\u4e1a\u6027\u4f7f\u7528-\u4fdd\u6301\u4e00\u81f4 4.0 \u56fd\u9645 (CC BY-NC-SA 4.0) \u8bb8\u53ef\u534f\u8bae\u8fdb\u884c\u8bb8\u53ef\u3002
\u8f6f\u4ef6 - \u76f8\u673a\u6807\u5b9a 73 0 mins 1735363772
- \u5361\u5c14\u66fc\u6ee4\u6ce2 193 115 2 mins 1734024510
- PnP \u7b97\u6cd5 79 55 1 mins 1734012860
"},{"location":"Robot/calibration/","title":"Calibration","text":""},{"location":"Robot/calibration/#calibration","title":"Calibration","text":" \u7ea6 76 \u4e2a\u5b57 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\u4e0d\u5230 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
- GitHub - SHU-FLYMAN/CalibCamera: \u57fa\u4e8e\u5f20\u6b63\u53cb\u6807\u5b9a\u6cd5\u7684\u5355\u76ee\u76f8\u673a\u6807\u5b9a\u7406\u8bba\u5230\u5b9e\u8df5
- \u4e00\u6587\u5403\u900f\u76f8\u673a\u6807\u5b9a\uff08Camera calibration\uff09-CSDN\u535a\u5ba2
- \u6700\u8be6\u7ec6\u3001\u6700\u5b8c\u6574\u7684\u76f8\u673a\u6807\u5b9a\u8bb2\u89e3-CSDN\u535a\u5ba2
- \u76f8\u673a\u6807\u5b9a\uff08Camera calibration\uff09\u539f\u7406\u3001\u6b65\u9aa4_\u89c6\u89c9\u76f8\u673a \u793a\u6559\u76ee\u7684-CSDN\u535a\u5ba2
2024_1_1
"},{"location":"Robot/kalman/","title":"\u5361\u5c14\u66fc\u6ee4\u6ce2","text":""},{"location":"Robot/kalman/#_1","title":"\u5361\u5c14\u66fc\u6ee4\u6ce2","text":" \u7ea6 193 \u4e2a\u5b57 115 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 2 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"Robot/kalman/#1-why","title":"1 Why","text":" - \u5dee\u5206
- \u53d7\u566a\u58f0\u5e72\u6270\u5927
- \u6709\u5ef6\u8fdf
- \u901f\u5ea6\u4e0d\u8fde\u7eed\uff08\u4e0d\u80fd\u5f97\u5230\u77ac\u65f6\u901f\u5ea6\uff09
"},{"location":"Robot/kalman/#2-how","title":"2 How","text":""},{"location":"Robot/kalman/#21","title":"2.1 \u5361\u5c14\u66fc\u6ee4\u6ce2","text":" - \u5408\u7406\u5730\u6839\u636e\u8bef\u5dee\u6765\u63a8\u5bfc\uff0c\u800c\u4e0d\u662f\u76f4\u63a5\u5ffd\u89c6\u5f71\u54cd\u6700\u7ec8\u91cf
- \u65e0\u4eba\u9a7e\u9a76\u6280\u672f\u5165\u95e8\uff08\u5341\u4e09\uff09| \u624b\u628a\u624b\u6559\u4f60\u5199\u5361\u5c14\u66fc\u6ee4\u6ce2\u5668 - \u77e5\u4e4e
\\[ \\begin{array}{|c|}\\hline\\textbf{Prediction}\\\\\\hline x^{'}=Ax+u\\\\P^{'}=APA^{T}+R\\\\\\hline\\textbf{Measurement update}\\\\\\hline y=z-Cx^{'}\\\\S=CPC^{T}+Q\\\\K=PC^{T}S^{-1}\\\\x=x^{'}+Ky\\\\P=(I-KC)P\\\\\\hline\\end{array} \\] C++#include <iostream>\n#include <cstdio>\n#include <string>\n#include <vector>\n#include <ctime>\n#include <opencv2/core/core.hpp>\n#include <opencv2/highgui/highgui.hpp>\n#include <opencv2/imgproc/imgproc.hpp>\n#include <Eigen/Dense>\n#include <opencv2/core/eigen.hpp>\nusing namespace std;\nusing namespace cv;\nusing namespace Eigen;\n\nint main() {\nsrand((unsigned int) time(NULL));\n// generate data with noise\nconst int N = 20;\nconst double k = 2.5;\nMatrix<double, 1, N> noise = Matrix<double, 1, N>::Random();\nMatrix<double, 1, N> data = Matrix<double, 1, N>::LinSpaced(0, k * (N - 1));\ndata += noise;\nstd::cout << data << std::endl;\n// calculate speed\nconst int Z_N = 1, X_N = 2;\nMatrix<double, X_N, 1> X;\nMatrix<double, X_N, X_N> A;\nMatrix<double, X_N, X_N> P;\nMatrix<double, X_N, X_N> R;\nMatrix<double, X_N, Z_N> K;\nMatrix<double, Z_N, X_N> C;\nMatrix<double, Z_N, Z_N> Q;\n\nX << data[0], 0;\nA << 1, 1, 0, 1;\nC << 1, 0;\nR << 2, 0, 0, 2;\nQ << 10;\nfor (int i = 1; i < N; i++) {\n // \u66f4\u65b0\u9884\u6d4b\n Matrix<double, X_N, 1> X_k = A * X;\n P = A * P * A.transpose() + R;\n // \u66f4\u65b0\u89c2\u6d4b\n K = P * C.transpose() * (C * P * C.transpose() + Q).inverse();\n Matrix<double, Z_N, 1> Z{data[i]};\n X = X_k + K * (Z - C * X_k);\n P = (Matrix<double, X_N, X_N>::Identity() - K * C) * P;\n std::cout << \"step \" << i << \": \" << X[1] << std::endl;\n}\nstd:cout << \"final speed: \" << X[1] << std::endl;\nreturn 0;\n}\n
"},{"location":"Robot/kalman/#22-ekf","title":"2.2 EKF \u7b97\u6cd5\u7684\u5b9e\u73b0","text":"C++#include <ceres/jet.h>\n#include <Eigen/Dense>\n\ntemplate<int N_X, int N_Y>\nclass AdaptiveEKF {\n using MatrixXX = Eigen::Matrix<double, N_X, N_X>;\n using MatrixYX = Eigen::Matrix<double, N_Y, N_X>;\n using MatrixXY = Eigen::Matrix<double, N_X, N_Y>;\n using MatrixYY = Eigen::Matrix<double, N_Y, N_Y>;\n using VectorX = Eigen::Matrix<double, N_X, 1>;\n using VectorY = Eigen::Matrix<double, N_Y, 1>;\n\npublic:\n explicit AdaptiveEKF(const VectorX &X0 = VectorX::Zero())\n : Xe(X0), P(MatrixXX::Identity()), Q(MatrixXX::Identity()), R(MatrixYY::Identity()) {}\n\n // \u9884\u6d4b\u51fd\u6570\n template<class Func>\n VectorX predict(Func &&func) {\n calculateJacobian(Xe, func, Xp, F);\n P = F * P * F.transpose() + Q;\n return Xp;\n }\n\n // \u66f4\u65b0\u51fd\u6570\n template<class Func>\n VectorX update(Func &&func, const VectorY &Y) {\n calculateJacobian(Xp, func, Yp, H);\n MatrixYY S = H * P * H.transpose() + R; // \u521b\u65b0\u534f\u65b9\u5dee\n K = P * H.transpose() * S.inverse(); // \u5361\u5c14\u66fc\u589e\u76ca\n Xe = Xp + K * (Y - Yp); // \u66f4\u65b0\u72b6\u6001\u4f30\u8ba1\n P = (MatrixXX::Identity() - K * H) * P; // \u66f4\u65b0\u72b6\u6001\u534f\u65b9\u5dee\n return Xe;\n }\n\nprivate:\n // \u8ba1\u7b97\u96c5\u514b\u6bd4\u77e9\u9635\u7684\u8f85\u52a9\u51fd\u6570\n template<class Func, int N_IN, int N_OUT>\n void calculateJacobian(const Eigen::Matrix<double, N_IN, 1> &input, Func &&func, Eigen::Matrix<double, N_OUT, 1> &output, Eigen::Matrix<double, N_OUT, N_IN> &jacobian) {\n ceres::Jet<double, N_IN> input_auto_jet[N_IN];\n for (int i = 0; i < N_IN; i++) {\n input_auto_jet[i].a = input[i];\n input_auto_jet[i].v[i] = 1;\n }\n ceres::Jet<double, N_OUT> output_auto_jet[N_OUT];\n func(input_auto_jet, output_auto_jet);\n for (int i = 0; i < N_OUT; i++) {\n output[i] = output_auto_jet[i].a;\n jacobian.block(i, 0, 1, N_IN) = output_auto_jet[i].v.transpose();\n }\n }\n\npublic:\n VectorX Xe; // \u4f30\u8ba1\u72b6\u6001\u53d8\u91cf\n VectorX Xp; // \u9884\u6d4b\u72b6\u6001\u53d8\u91cf\n MatrixXX F; // \u9884\u6d4b\u96c5\u514b\u6bd4\u77e9\u9635\n MatrixYX H; // \u89c2\u6d4b\u96c5\u514b\u6bd4\u77e9\u9635\n MatrixXX P; // \u72b6\u6001\u534f\u65b9\u5dee\n MatrixXX Q; // \u9884\u6d4b\u8fc7\u7a0b\u534f\u65b9\u5dee\n MatrixYY R; // \u89c2\u6d4b\u8fc7\u7a0b\u534f\u65b9\u5dee\n MatrixXY K; // \u5361\u5c14\u66fc\u589e\u76ca\n VectorY Yp; // \u9884\u6d4b\u89c2\u6d4b\u91cf\n};\n
"},{"location":"Robot/kalman/#23","title":"2.3 \u975e\u7ebf\u6027\u4f18\u5316","text":"TODO
"},{"location":"Robot/kalman/#_2","title":"\u4e00\u4e9b\u8d44\u6599","text":" - zhuanlan.zhihu.com/p/45238681
- \u5361\u5c14\u66fc\u6ee4\u6ce2(Kalman Filter)\u6982\u5ff5\u4ecb\u7ecd\u53ca\u8be6\u7ec6\u516c\u5f0f\u63a8\u5bfc-CSDN\u535a\u5ba2
- \u8c03\u8282\u8bef\u5dee\u77e9\u9635\u7684\u5b9e\u9645\u610f\u4e49
- \u975e\u7ebf\u6027\u62d3\u5c55
- \u81ea\u9002\u5e94\u4f18\u5316
- \u4f5c\u4e1a\u4e2d\u6709\u4e00\u4e2a\u5929\u4f53\u8fd0\u52a8\u7684\u4f8b\u5b50
"},{"location":"Robot/pnp/","title":"pnp","text":""},{"location":"Robot/pnp/#pnp","title":"pnp","text":" \u7ea6 76 \u4e2a\u5b57 55 \u884c\u4ee3\u7801 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
- \u5df2\u77e5
- \u76ee\u6807\u7269\u4f53\u7279\u5b9a\u70b9\u7684\u50cf\u7d20\u5750\u6807
- \u76ee\u6807\u7269\u4f53\u7279\u5b9a\u70b9\u7684\u771f\u5b9e\u5c3a\u5bf8
- \u76f8\u673a\u5185\u53c2
- \u6c42
- \u76ee\u6807\u7269\u4f53\u5728\u76f8\u673a\u5750\u6807\u7cfb\u4e0b\u7684 6d pose
\u50cf\u7d20\u5750\u6807\u548c\u7269\u4f53\u5750\u6807\u7684\u5bf9\u70b9 \u4f46\u662f\u4e00\u822c\u53ea\u7528 t, \u56e0\u4e3a R \u7684\u7cbe\u5ea6\u4e0d\u591f\u9ad8 Fetching Title#g70i
C++#include <iostream>\n#include <opencv2/opencv.hpp>\n#include <opencv2/imgproc/imgproc.hpp>\n#include <opencv2/calib3d/calib3d.hpp>\n#include <opencv2/core/core.hpp>\nusing namespace cv;\n\nbool findCorners(const cv::Mat &src, std::vector<cv::Point2f> &corners) {\n std::vector<cv::Point2f> pts;\n corners.clear();\n bool flag = cv::findChessboardCorners(src, {9, 6}, pts);\n if (!flag)\n return false;\n corners.push_back(pts[0]);\n corners.push_back(pts[9 - 1]);\n corners.push_back(pts[pts.size() - 9]);\n corners.push_back(pts[pts.size() - 1]);\n return true;\n}\n\nint main() {\n cv::Mat src;\n cv::Mat camera_matrix;\n cv::Mat distort_matrix;\n cv::FileStorage reader(PROJECT_DIR\"/parameter.txt\", cv::FileStorage::READ);\n reader[\"C\"] >> camera_matrix;\n reader[\"D\"] >> distort_matrix;\n\n for (int i = 0; i <= 40; i++) {\n src = imread(std::__cxx11::to_string(i).append(\".jpg\"));\n std::vector<cv::Point2f> corners;\n bool flag = findCorners(src, corners);\n imshow(\"Opencv Demo\", src);\n cv::waitKey(100);\n if (flag == false) {\n std::cout << \"failed to find all corners\\n\";\n continue;\n }\n std::vector<cv::Point3f> dst;\n dst.push_back({0, 0, 0});\n dst.push_back({8 * 1, 0, 0});\n dst.push_back({0, 5 * 1, 0});\n dst.push_back({8 * 1, 5 * 1, 0});\n cv::Mat rvec, tvec;\n cv::solvePnP(dst, corners, camera_matrix, distort_matrix, rvec, tvec);\n std::cout << \"t:\" << std::endl << -tvec << std::endl << std::endl;\n cv::Mat drawer;\n drawer = src.clone();\n for (int j = 0; j < 4; j++)\n cv::circle(drawer, corners[j], 2, {0, 255, 0}, 2);\n cv::imshow(\"corners\", drawer);\n cv::waitKey(5);\n }\n return 0;\n}\n
"},{"location":"Summaries/","title":"Summaries","text":""},{"location":"Summaries/#summaries","title":"Summaries","text":"Abstract
\u672c\u90e8\u5206\u5185\u5bb9\uff08\u9664\u7279\u522b\u58f0\u660e\u5916\uff09\u91c7\u7528 \u7f72\u540d-\u975e\u5546\u4e1a\u6027\u4f7f\u7528-\u4fdd\u6301\u4e00\u81f4 4.0 \u56fd\u9645 (CC BY-NC-SA 4.0) \u8bb8\u53ef\u534f\u8bae\u8fdb\u884c\u8bb8\u53ef\u3002
\u5b66\u671f\u603b\u7ed3 & \u5176\u4ed6 - \u9ad8\u4e09\u81f3\u5927\u4e00\u6691\u5047\u603b\u7ed3 520 2 mins 1734582373
2024 \u5e74\u5468\u7ed3 - \u7b2c 52 \u5468 802 3 mins 1735455536
- \u7b2c 51 \u5468 645 2 mins 1734869085
"},{"location":"Summaries/2024/weekly/2024-W51-12/","title":"2024-W51-12","text":" \u7ea6 657 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 3 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u5468\u8bb0","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W51-12/#2024-w51-12","title":"2024-W51-12","text":"","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W51-12/#review","title":"Review","text":" - \u671f\u672b\u5468\uff0c\u8003\u4e86\u9ad8\u6570\uff0c\u5316\u5b66\uff0c\u8fd1\u73b0\u4ee3\u53f2\uff0c\u8ba1\u7b97\u673a\u5bfc\u8bba\u3002
- \u4e00\u5929\u5b66\u4e00\u95e8\u901f\u901a\u3002\u8bf4\u5b9e\u8bdd\u8fd9\u4e2a\u5b66\u671f\u6ca1\u53bb\u4e0a\u8fc7\u8bfe\uff0c\u4f5c\u4e1a\u4e5f\u6ca1\u5199\u8fc7\uff08\u9ad8\u6570 + \u5316\u5b66\uff09\uff0c\u5e73\u65f6 RC \u4e5f\u4e0d\u770b\u3002\u5168\u9760\u671f\u672b\u4e00\u5929/\u534a\u5929\u8865\u5929\uff0c\u901f\u5237 Big RC \u7136\u540e\u6709\u9898\u505a\u9898\uff0c\u6ca1\u9898\u770b Regular RC\uff0c\u518d\u770b\u770b sample exam\u3002
- \u8003\u51fa\u6765\u7ed3\u679c\u611f\u89c9\u8fd8\u884c\uff0c\u4f46\u662f\u53ef\u80fd\u6709\u4e24\u95e8\u8981\u5361\u7ee9\u4e86\u3002\u5e0c\u671b\u90fd\u80fd\u7ed9\u6211\u5230 A \u5427\u3002
- \u867d\u7136\u4f46\u662f\uff0c\u611f\u89c9\u5927\u5bb6\u786e\u5b9e\u90fd\u633a curve \u7684\u3002\u672c\u6765\u4ee5\u4e3a\u53ef\u80fd A \u8fd8\u662f\u6ca1\u6709\u5f88\u5927\u7684\u95ee\u9898\uff0c\u4f46\u662f\u73b0\u5728\u53ef\u80fd\u8981\u6709\u4e24\u95e8 A- \u4e86\uff0c\u4e5f\u662f\u6709\u70b9\u96be\u53d7\u3002
- \u5927\u6982\u539f\u56e0\u662f\uff0c\u4e00\u70b9\u4f5c\u4e1a\u6ca1\u505a\uff0c\u671f\u672b\u51b2\u523a\u7684\u65f6\u5019\u4e0d\u591f\u8ba4\u771f\u3002\u540c\u65f6\u5fd8\u8bb0\u590d\u4e60\u671f\u4e2d\u4e4b\u524d\u7684\u77e5\u8bc6\u4e86\uff0c\u5bfc\u81f4\u9519\u4e86\u597d\u4e00\u4e9b\u3002
- \u7b2c\u4e8c\u4e2a\u539f\u56e0\u662f\uff0c\u867d\u7136\u5f88\u591a\u786e\u5b9e\u4f1a\u7b97\uff0c\u4f46\u662f\u5b8c\u6210\u5ea6\u6ca1\u6709\u90a3\u4e48\u9ad8\uff0c\u4ee5\u53ca\u81ea\u5df1\u61d2\u7684\u53bb\u7b97\uff0c\u5bfc\u81f4\u8003\u8bd5\u7684\u65f6\u5019\u5c31\u6ca1\u6709\u7b97\u5bf9\u4e86\u3002
- \u7b2c\u4e09\u4e2a\u539f\u56e0\u662f\uff0c\u5c31\u662f\u5f88\u6446\u70c2\u554a\uff0c\u8003\u8bd5\u5468\u4e86\u8fd8\u4e0d\u60f3\u590d\u4e60\u3002
- \u4e0d\u8fc7\u4e5f\u8fd8\u884c\uff0c\u6ca1\u6709\u82b1\u5f88\u591a\u65f6\u95f4\u5728\u5377\u7ee9\u70b9\u4e0a\u9762\uff0c\u4e0b\u4e2a\u5b66\u671f\u6253\u7b97\u5e73\u65f6\u518d\u5c11\u4e00\u70b9\uff0c\u7136\u540e\u590d\u4e60\u7684\u65f6\u5019\u66f4\u6709\u9488\u5bf9\u6027\u4e00\u70b9\u3002\u52aa\u529b\u63d0\u5347\u63d0\u5347\u7ee9\u70b9\uff0c\u4ee5\u514d\u4ee5\u540e\u8981\u7528\u7684\u65f6\u5019\u6ca1\u6709\u3002
- \u8bfb\u8bba\u6587
- [2103.00020] Learning Transferable Visual Models From Natural Language Supervision
- [2212.02710] Beyond Object Recognition: A New Benchmark towards Object Concept Learning
- [2303.15343] Sigmoid Loss for Language Image Pre-Training
- \u6709\u610f\u601d\uff0c\u4f46\u662f\u611f\u89c9\u81ea\u5df1\u8bfb\u7684\u6162\u6162\u7684\uff0c\u800c\u4e14\u76f8\u5173\u77e5\u8bc6\u4f3c\u4e4e\u4e0d\u592a\u591a\u3002\u800c\u4e14 transformer \u4e4b\u540e\u7684\u6a21\u578b\u4e0d\u662f\u5f88\u719f\u6089\uff0c\u6253\u7b97\u628a\u674e\u6c90\u7684\u8bfb\u8bba\u6587\u90a3\u4e2a\u7cfb\u5217\u90fd\u53bb\u770b\u770b\uff0c\u591a\u8bfb\u8bfb\u8bba\u6587\u3002
- \u914d\u4e86\u914d\u73af\u5883\uff0c\u7a0d\u5fae\u5199\u4e86\u4e00\u4e9b\u4ee3\u7801\uff0c\u4f46\u662f\u611f\u89c9\u8c03\u8bd5\u5e94\u8be5\u8fd8\u8981\u597d\u51e0\u5929\u624d\u80fd\u5b8c\u6210\u3002
- \u5b8c\u5584\u4e86 obsidian \u7684\u914d\u7f6e\u6587\u7ae0\uff0c\u5728\u8fd9\u91cc
- \u987a\u4fbf\u7528 tag \u91cd\u6784\u4e86 obsidian\uff0c\u653e\u5f03\u4e86\u6811\u72b6\u6587\u4ef6\u5939\u7684\u65b9\u6848\u3002
- \u5199\u4e86\u5199\u5bd2\u5047\u8ba1\u5212
- \u914d\u7f6e\u4e86\u4e00\u70b9 zotero\uff0c\u4f46\u662f\u8fd8\u662f\u4e0d\u592a\u4f1a\u7528\u73b0\u5728\uff0c\u6162\u6162\u719f\u7ec3\u5427\u3002
- \u5efa\u4e86\u4e00\u4e2a\u5206\u4eab\u4ea4\u6d41\u7684\u7fa4, QQ \u7fa4\u53f7\u662f\uff1a1011394397\uff0c\u6b22\u8fce\u52a0\u5165\u3002
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W51-12/#next-week-plan","title":"Next Week Plan","text":" - \u5b8c\u6210\u63a8\u7406\u5b9e\u9a8c
- \u770b\u70b9\u8bba\u6587\uff08\u4e0d\u8fc7\u8fd9\u4e2a\u4e5f\u592a\u6a21\u7cca\u4e86\uff09
- \u5b66\u5b8c CS61C
- \u505a 6.s081 \u7684\u89c4\u5212
- \u5b66\u4f1a\u4f7f\u7528 zotero
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W51-12/#time-line","title":"Time Line","text":" - \u65e0\uff0c\u4ee5\u540e\u7528\u65f6\u95f4\u65e5\u5fd7\u505a\u4e86\uff0c\u4e0d\u7528 Day Planner
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W51-12/#thoughts","title":"THOUGHTS","text":" - \u8fd8\u662f\u5f88\u96be balance \u5f88\u591a\u4e8b\u60c5\uff0c\u9009\u62e9\u6027\u653e\u5f03\u5427\uff0c\u66f4\u4f55\u51b5\u6211\u8fd8\u83dc\u83dc\u7684\uff0c\u5e73\u65f6\u80af\u5b9a\u4f1a\u6446\u6446\u70c2\u3002
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/","title":"2024-W52-12","text":" \u7ea6 814 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 4 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#\u5468\u8bb0","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#2024-w52-12","title":"2024-W52-12","text":"","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#review","title":"Review","text":"","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#completed","title":"Completed","text":" - \u5b8c\u6210\u63a8\u7406\u5b9e\u9a8c
- \u7ec8\u4e8e\u641e\u5b8c cs231n\uff0c\u7136\u540e\u505a\u4e86\u4e2a\u5c0f\u4efb\u52a1\uff0c\u5c31\u5f00\u59cb\u5e2e\u5b66\u957f\u5e72\u6d3b\u4e86\u3002
- \u5b66\u4f1a\u4f7f\u7528 zotero_\u4f7f\u7528\u6307\u5357
- \u8054\u7cfb\u9ad8\u4e2d\u8001\u5e08
- \u628a\u4e4b\u524d\u9ad8\u4e2d\u5b66\u4e60\u7ecf\u9a8c\u7684\u4e1c\u897f\u4ece\u535a\u5ba2\u4e2d\u79fb\u51fa\u4e86\uff0c\u53e6\u5916\u5efa\u4e86\u4e00\u4e2a\u7f51\u7ad9\u3002
- \u987a\u4fbf\u9080\u8bf7\u4e86\u4e00\u4e9b\u9ad8\u4e2d\u540c\u5b66\u5206\u4eab\u5206\u4eab\u7ecf\u9a8c
- \u4e4b\u540e\u5927\u6982\u4f1a\u8fd4\u6821\u5ba3\u8bb2\uff0c\u5206\u4eab\u4e00\u4e9b\u5b66\u4e60\u7ecf\u9a8c / \u4e09\u4e00\u554a\u5565\u7684\u3002\ud83e\udd14\uff0c\u6211\u4e5f\u6210\u5b66\u957f\u4e86\u3002
- \u8bfb\u8bba\u6587\uff0c\u5efa\u7acb\u4e86\u4e00\u4e9b\u57fa\u672c\u6982\u5ff5
- \u5199\u4e86\u4e00\u4e9b\u7b14\u8bb0\uff0c\u6bd4\u5982
- \u770b\u4e86\u4e00\u4e0b\u674e\u6c90\u8bfb\u8bba\u6587\u7684\u7cfb\u5217
- \u5305\u62ec Transformer, CLIP, CLIP \u4e4b\u540e\u7684\u5de5\u4f5c\u4e32\u8bb2\u4e0a\u4e0b\uff0c\u591a\u6a21\u6001\u4e32\u8bb2\u4e0a\u4e0b
- \u770b\u4e86\u4e00\u90e8\u5206\u7684 GAMES 003\uff0c\u8bb2\u7684\u5f88\u4e0d\u9519
- \u7a0d\u5fae\u8fd0\u8425\u4e86\u4e00\u4e0b\u4ea4\u6d41\u7fa4
- \u4fee\u590d\u4e86\u4e00\u4e0b\u535a\u5ba2
- \u73b0\u5728\u535a\u5ba2\u53ef\u4ee5\u5b8c\u6574\u5730\u652f\u6301 obsidian \u7684\u53cc\u94fe\u548c callout \u4e86
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#uncompleted","title":"Uncompleted","text":" - \u5b66\u5b8c CS61C
- \u5fd9\u4e0d\u8fc7\u6765\uff0c\u751a\u81f3\u90fd\u8fd8\u6ca1\u6361\u8d77\u6765
- \u89c4\u5212 6.s081 \u5b66\u4e60
- \u5fd9\u4e0d\u8fc7\u6765\u634f\uff0c\u800c\u4e14\u611f\u89c9\u6700\u8fd1\u7684\u91cd\u5fc3\u5728\u79d1\u7814\u4e0a\uff0c\u6253\u7b97\u628a\u9886\u57df\u5185\u7684\u8bba\u6587\u770b\u7684\u5dee\u4e0d\u591a\u4e86\uff0c\u518d\u6765\u6253\u7cfb\u7edf\u65b9\u9762\u7684\u57fa\u7840\u3002
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#next-week-plan","title":"Next Week Plan","text":" - \u5b66\u4e60 CS61C\uff0c\u76ee\u524d\u662f\u5b66\u5230\u7b2c 6 \u4e2a lab\uff0c\u5e0c\u671b\u4e00\u4e2a\u661f\u671f\u80fd\u5b66\u5b8c\u5269\u4e0b\u7684\u3002
- \u770b\u5b8c\u674e\u6c90\u8bfb\u8bba\u6587\u7684\u7cfb\u5217\uff0c\u7136\u540e\u4e0b\u8f7d\u8bba\u6587\u505a\u7b14\u8bb0\uff0c\u4e32\u8054\u81ea\u5df1\u7684\u77e5\u8bc6\u56fe\u8c31\u3002
- \u7740\u624b\u5b9e\u9a8c\u5ba4\u5de5\u4f5c\u3002
","tags":["\u5468\u8bb0"]},{"location":"Summaries/2024/weekly/2024-W52-12/#thoughts","title":"THOUGHTS","text":" - \u611f\u89c9\u81ea\u5df1\u7684\u5927\u4e00\u4e0a\u8fd8\u662f\u5904\u5728\u4e1c\u73a9\u73a9\u897f\u73a9\u73a9\u7684\u72b6\u6001\uff0c\u5f04\u4e86\u4e0d\u5c11\u8ddf\u4ee5\u540e\u65b9\u5411\u4e0d\u592a\u6709\u5173\u7684\u4e1c\u897f\u3002\u8d81\u5bd2\u5047\u6709\u7a7a\uff0c\u65e9\u70b9\u60f3\u6e05\u695a\u60f3\u5e72\u4ec0\u4e48\u5427\u3002
- \u6b38\uff0c\u8fd8\u662f\u5f15\u7528\u4e00\u6bb5\u8bdd\u6765\u7684\u8d34\u5207
Quote \u5176\u5b9e\u5f52\u6839\u7ed3\u5e95\uff0c\u95ee\u9898\u8fd8\u662f\u5927\u591a\u6570\u4eba\uff08\u5305\u62ec\u6211\u81ea\u5df1\uff09\uff0c\u6700\u521d\u6839\u672c\u60f3\u4e0d\u6e05\u695a\u81ea\u5df1\u6700\u540e\u8981\u5e72\u5565\uff0c\u505a\u4e86\u597d\u591a\u65e0\u610f\u4e49\u7684\u4e8b\u60c5\u3002\u6bd4\u5982\u9009\u4e00\u4e9b\u65e0\u610f\u4e49\u4f46\u96be\u7684\u8bfe\uff0c\uff08\u6bd4\u5982\u6570\u5206\u7cfb\u5217\uff0c\u5982\u679c\u4e0d\u662f\u771f\u5fc3\u559c\u6b22\u6570\u5b66\u5343\u4e07\u522b\u9009\uff09\uff0c\u5927\u521b\uff0c\u5b66\u4e00\u4e9b\u975e cs \u7684\u9009\u4fee\u8bfe\uff0c\u7ed9\u4e00\u4e9b\u6c34\u5b9e\u9a8c\u5ba4\u6253\u5de5\u7b49\u3002\u521a\u4e0a\u5927\u5b66\uff0c\u90fd\u89c9\u5f97\u81ea\u5df1\u6709\u65e0\u7a77\u7684\u53ef\u80fd\u6027\uff0c\u4e0d\u5c51\u4e8e\u5728\u5199\u4ee3\u7801\u8fd9\u4e00\u6761\u8def\u4e0a\u540a\u6b7b\u3002\u8bda\u7136\uff0c\u6709\u4e9b\u4eba\u5565\u90fd\u641e\u8fd8\u5565\u90fd\u80fd\u641e\u597d\uff0c\u4f46\u662f\u4f5c\u4e3a\u4e00\u4e2a\u666e\u901a\u4eba\uff0c\u8fd8\u662f\u8981\u8ba4\u6e05\u81ea\u5df1\u80fd\u529b\u7684\u8303\u56f4\u548c\u6700\u540e\u7684\u76ee\u6807\u3002\u770b\u522b\u4eba\u53bb\u793e\u56e2\uff0c\u81ea\u5df1\u4e5f\u60f3\u53bb\u51d1\u51d1\uff0c\u770b\u522b\u4eba\u505a\u79d1\u7814\uff0c\u81ea\u5df1\u4e5f\u60f3\u641e\u641e\uff1b\u53ef\u662f\u8fd9\u6837\u641e\u6765\u641e\u53bb\uff0c\u53d1\u73b0\u66fe\u7ecf\u4e0d\u5c51\u4e8e\u5e72\u7684\u4e8b\u60c5\uff0c\u73b0\u5728\u8fde\u5e72\u7684\u8d44\u683c\u90fd\u6ca1\u6709\uff0c\u8fd9\u65f6\u518d\u540e\u6094\u5df2\u7ecf\u4e3a\u65f6\u5df2\u665a\u3002\u5c06\u6765\u53d1\u5c55\u7684\u65b9\u5411\u8fd8\u662f\u8d8a\u65e9\u60f3\u660e\u767d\u8d8a\u597d\u3002
- \u6211\u60f3\u6211\u81ea\u5df1\u7edd\u5bf9\u79f0\u4e0d\u4e0a\u52aa\u529b\uff0c\u751a\u81f3\u5f88\u96be\u8bf4\u5728\u8ffd\u9010\u559c\u6b22\u7684\u4e1c\u897f\uff0c\u53ea\u4e0d\u8fc7\u611f\u89c9\u2018\u6b38\uff0c\u5e72\u8fd9\u4e2a\u4e5f\u8fd8\u4e0d\u9519\uff0c\u90a3\u5c31\u8fd9\u6837\u5427\u2019\u3002\u6700\u8fd1\u786e\u5b9e\u8ba4\u8bc6\u5230\u5f88\u591a\u4f18\u79c0\u7684\u5b66\u957f\uff0c\u4ed6\u4eec\u8981\u4e48\u662f\u53bb MSRA \u5b9e\u4e60\uff0c\u7136\u540e\u5728\u79d1\u7814\u7684\u9053\u8def\u4e0a\u8d8a\u8d70\u8d8a\u8fdc\uff1b\u8981\u4e48\u662f\u4e00\u624b\u6293\u540e\u7aef\uff0c\u4e00\u624b\u6293\u7cfb\u7edf\uff0c\u53bb\u4e86\u91cf\u5316\u8d5a\u7c73\u3002
Failure
\u4e5f\u8bb8\uff0c\u6211\u771f\u7684\u662f\u7f3a\u4e4f all in \u7684\u52c7\u6c14\u5427\u3002
","tags":["\u5468\u8bb0"]},{"location":"Summaries/Semesters/2024summer_vacation/","title":"2024\u5e74\u9ad8\u4e09-\u5927\u4e00\u6691\u5047\u603b\u7ed3","text":""},{"location":"Summaries/Semesters/2024summer_vacation/#2024-","title":"2024\u5e74\u9ad8\u4e09-\u5927\u4e00\u6691\u5047\u603b\u7ed3","text":" \u7ea6 518 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 3 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
\u6211\u7684\u6691\u5047\u5927\u6982\u662f\u4ece 24.7.10 \u5f00\u59cb\u7684\uff0c\u5230\u90a3\u65f6\u624d\u5c18\u57c3\u843d\u5b9a\u3002\u4f46\u53c8\u6765\u56de\u6447\u6446\uff0c\u60f3\u7740\u672a\u6765\u7684\u51fa\u8def\uff08\u51fa\u56fd\uff1f\u4fdd\u7814\uff1f\u5de5\u4f5c\uff1f\u8f6c\u4e13\u4e1a\uff1f\uff09\u3002\u6240\u4ee5\u5927\u6982\u5230 8 \u6708\u624d\u5f00\u59cb\u5b66\u4e60\u3002
- \u8ba1\u7b97\u673a
- crash course computer \u770b\u4e86\u524d 20 \u8bb2\uff0c\u540e\u6765\u56e0\u4e3a\u61d2\u5f97\u770b\u4e86\u5c31\u6446\u70c2\u4e86
- cs 61 A
- \u770b\u4e86\u524d 10 \u8bb2\u7684 lecture\uff0c\u4f46\u662f\u6ca1\u505a\u7b14\u8bb0
- \u770b\u4e86 composing programs \u524d\u4e09\u7ae0
- \u505a\u5b8c\u4e86 4 \u4e2a proj\uff0c\u4f46\u662f\u6ca1\u6709\u505a hw \u548c lab
- cs 61 C
- \u770b\u4e86\u524d 10 \u8bb2\u7684 slide
- \u505a\u4e86\u524d\u4e24\u4e2a proj \u548c\u524d\u516d\u4e2a lab
- \u770b\u8ba1\u7b97\u673a\u7ec4\u6210\u4e0e\u8bbe\u8ba1\u786c\u4ef6\u8f6f\u4ef6\u63a5\u53e3\u524d\u4e24\u7ae0
- csapp
- \u4e66\u770b\u4e86\u524d\u4e09\u7ae0
- \u4e5d\u66f2\u9611\u5e72\u770b\u4e86\u524d 4 \u7ae0
- Dive into Deep Learning
- \u770b\u4e86\u524d\u4e24\u7ae0\u5e76\u505a\u4e86\u7b14\u8bb0\uff0c\u4f46\u611f\u89c9\u4e00\u4e0b\u5b50\u8df3\u8fc7\u592a\u591a\u524d\u7f6e\u77e5\u8bc6\u5f88\u96be\u611f\u53d7\u5230\u7f8e\u611f\u4fbf\u5148\u653e\u653e\u3002
- games 101
- \u51e0\u4e4e\u770b\u5b8c\u4e86\u6240\u6709\u7684 lecture (\u4f46\u662f\u540e\u9762\u51e0\u8bb2\u4e0d\u662f\u5f88\u8ba4\u771f)\uff0c\u4f46\u662f\u6ca1\u6709\u505a\u7b14\u8bb0
- \u4ee3\u7801\u968f\u60f3\u5f55
- \u505a\u5230\u56de\u6eaf\u4e86\uff0c\u4f46\u662f\u6253\u7b97\u4e4b\u540e\u4e0d\u4f1a\u5f88\u7ecf\u5e38\u505a\uff08\u7b49\u5230\u8981\u7528\u4e86\u518d\u8bf4\uff09
- \u7528 mkdocs \u642d\u5efa\u4e86\u81ea\u5df1\u7684 blog
- C++
- \u770b\u4e86\u83dc\u9e1f\u6559\u7a0b\u4e0a\u7684\u76f8\u5173\u5185\u5bb9\uff0c\u6ca1\u505a\u7b14\u8bb0
- \u770b\u4e86\u6d59\u5927\u7684 C++\u8bfe\uff0c\u6ca1\u505a\u7b14\u8bb0\uff0c\u4e5f\u6ca1\u770b\u5b8c\uff08\uff09
- \u770b\u4e86 accelerated C++\uff0c\u505a\u4e86\u7b14\u8bb0
- \u770b\u4e86\u6d59\u5927\u7684\u5b9e\u7528\u6280\u80fd\u62fe\u9057
- \u590d\u4e60\u4e86 Markdown \u548c Latex \u8bed\u6cd5\uff0c\u5b66\u4e60\u4e86\u5982\u4f55\u4f7f\u7528 git\uff0c\u5b66\u4e60\u4e86\u6700\u57fa\u7840\u7684 shell\uff0cvim\u3002
- \u89c6\u89c9 slam \u5341\u56db\u8bb2
- \u770b\u5b8c\u4e86\u524d 7 \u8bb2\uff08\u5373\u7406\u8bba\u90e8\u5206\uff09\uff0c\u505a\u4e86\u7b14\u8bb0\uff0c\u4f46\u662f\u6ca1\u6709\u8dd1\u4ee3\u7801 \uff08\u73af\u5883\u592a\u96be\u914d\u4e86\uff09
- \u914d\u7f6e\u73af\u5883
- wsl 2 , git\uff0cvmware\uff0cvscode
- \u914d\u7f6e\u4e86 obsidian\uff0c\u88c5\u4e86\u597d\u591a\u63d2\u4ef6\uff0c\u73b0\u5728\u7528\u8d77\u6765\u662f\u5f88\u8212\u670d\u4e86
- \u8fd0\u52a8
- \u6bcf\u5929\u505a\u505a\u4fef\u5367\u6491\uff0c\u611f\u89c9\u8fd8\u4e0d\u9519
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- \u5176\u4ed6
- \u5bb6\u6559
- \u5b66\u4e86\u9a7e\u7167
- \u4e70\u4e86\u4e00\u4e2a\u952e\u76d8\u548c\u663e\u793a\u5668, \u91cd\u88c5\u4e86\u7535\u8111
- \u548c\u670b\u53cb\u65c5\u6e38\uff0c\u53bb\u6cc9\u5dde+\u798f\u5dde
- \u7ed9\u9ad8\u4e2d\u7684\u5b66\u5f1f\u5b66\u59b9\u5199\u4e86\u7ecf\u9a8c\u5206\u4eab\uff08\u6570\u5b66+\u82f1\u8bed+\u7269\u7406+\u6280\u672f\uff09
- \u770b\u4e86\u4e0d\u5c11\u7535\u5f71
"},{"location":"Tags/","title":"Tags","text":""},{"location":"Tags/#tags","title":"Tags","text":"{{ tag_content }}
"},{"location":"Tools/","title":"Tools","text":""},{"location":"Tools/#toolbox","title":"Toolbox","text":"Abstract
\u672c\u90e8\u5206\u5185\u5bb9\uff08\u9664\u7279\u522b\u58f0\u660e\u5916\uff09\u91c7\u7528 \u7f72\u540d-\u975e\u5546\u4e1a\u6027\u4f7f\u7528-\u4fdd\u6301\u4e00\u81f4 4.0 \u56fd\u9645 (CC BY-NC-SA 4.0) \u8bb8\u53ef\u534f\u8bae\u8fdb\u884c\u8bb8\u53ef\u3002
Envirtonment Setup - Environment 227 1 mins 1735386955
- Obsidian 1269 55 5 mins 1735386955
- Ubuntu 77 14 0 mins 1735386955
Prompt Engerneering - useful prompt 4220 1 14 mins 1735547104
- \u5982\u4f55\u5199\u4e00\u4e2a\u597d prompt 3315 162 13 mins 1734024510
Blog - Mkdocs & Material 5956 9407 137 mins 1734024510
Make - CMake 161 13 1 mins 1734024510
- Makefile 0 0 mins 0
Terminal - Tabby & Zsh 236 789 11 mins 1734024510
Others - zotero \u4f7f\u7528\u6307\u5357 146 0 mins 1735395966
- SSH 641 195 5 mins 1734024510
- Chezmoi \u540c\u6b65\u914d\u7f6e\u6587\u4ef6 512 142 3 mins 1734024510
"},{"location":"Tools/about/","title":"About \ud83e\udd73","text":""},{"location":"Tools/about/#about","title":"About \ud83e\udd73","text":" \u7ea6 54 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\u4e0d\u5230 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
Welcome, I'm Wnc.
"},{"location":"Tools/about/#some-tags","title":"Some Tags","text":" - \u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\u5bc6\u897f\u6839\u5b66\u9662 2024 \u7ea7\u672c\u79d1\u751f
- INTJ (Maybe)
- Interested in AI, Robot and ...
Ways to befriend with me You could find my email or qq or WeChat in the icon above.
Feel free to contact me!
"},{"location":"Tools/AI/prompt/","title":"prompt","text":" \u7ea6 4234 \u4e2a\u5b57 1 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 21 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#prompt","tags":["prompt"]},{"location":"Tools/AI/prompt/#prompt","title":"prompt","text":"","tags":["prompt"]},{"location":"Tools/AI/prompt/#prompt_1","title":"\u901a\u7528\u601d\u7ef4\u94fe prompt","text":"Note Text OnlyBy default, all responses must be in Chinese.\n\n# AI Full-Stack Development Assistant Guide\n\n## Core Thinking Patterns\nYou must engage in multi-dimensional deep thinking before and during responses:\n\n### Fundamental Thinking Modes\n\n- Systems Thinking: Three-dimensional thinking from overall architecture to specific implementation\n- Dialectical Thinking: Weighing pros and cons of multiple solutions \n- Creative Thinking: Breaking through conventional thinking patterns to find innovative solutions\n- Critical Thinking: Multi-angle validation and optimization of solutions\n\n### Thinking Balance\n\n- Balance between analysis and intuition\n- Balance between detailed inspection and global perspective \n- Balance between theoretical understanding and practical application\n- Balance between deep thinking and forward momentum\n- Balance between complexity and clarity\n\n### Analysis Depth Control \n\n- Conduct in-depth analysis for complex problems\n- Keep simple issues concise and efficient\n- Ensure analysis depth matches problem importance\n- Find balance between rigor and practicality\n\n### Goal Focus\n\n- Maintain clear connection with original requirements\n- Guide divergent thinking back to the main topic timely\n- Ensure related explorations serve the core objective\n- Balance between open exploration and goal orientation\n\nAll thinking processes must:\n\n0. Presented in the form of a block of code + the title of the point of view, please note that the format is strictly adhered to and that it must include a beginning and an end.\n1. Unfold in an original, organic, stream-of-consciousness manner\n2. Establish organic connections between different levels of thinking\n3. Flow naturally between elements, ideas, and knowledge\n4. Each thought process must maintain contextual records, keeping contextual associations and connections\n\n## Technical Capabilities\n### Core Competencies\n\n- Systematic technical analysis thinking\n- Strong logical analysis and reasoning abilities \n- Strict answer verification mechanism\n- Comprehensive full-stack development experience\n\n### Adaptive Analysis Framework\nAdjust analysis depth based on:\n\n- Technical complexity\n- Technology stack scope\n- Time constraints \n- Existing technical information\n- User's specific needs\n\n### Solution Process\n\n1. Initial Understanding\n- Restate technical requirements\n- Identify key technical points\n- Consider broader context\n- Map known/unknown elements\n\n2. Problem Analysis \n- Break down tasks into components\n- Determine requirements\n- Consider constraints\n- Define success criteria\n\n3. Solution Design\n- Consider multiple implementation paths\n- Evaluate architectural approaches\n- Maintain open-minded thinking\n- Progressively refine details\n\n4. Implementation Verification\n- Test assumptions\n- Verify conclusions\n- Validate feasibility\n- Ensure completeness\n\n## Output Requirements\n### Code Quality Standards\n\n- Always show complete code context for better understanding and maintainability.\n- Code accuracy and timeliness\n- Complete functionality\n- Security mechanisms\n- Excellent readability\n- Use markdown formatting\n- Specify language and path in code blocks\n- Show only necessary code modifications\n#### Code Handling Guidelines\n\n1. When editing code:\n - Show only necessary modifications\n - Include file paths and language identifiers\n - Provide context with comments\n - Format: ```language:path/to/file\n\n2. Code block structure: ```language:file/path\n // ... existing code ...\n {{ modifications }}\n // ... existing code ... ```\n\n\n### Technical Specifications\n\n- Complete dependency management\n- Standardized naming conventions\n- Thorough testing\n- Detailed documentation\n\n### Communication Guidelines\n\n- Clear and concise expression\n- Handle uncertainties honestly\n- Acknowledge knowledge boundaries\n- Avoid speculation\n- Maintain technical sensitivity\n- Track latest developments\n- Optimize solutions\n- Improve knowledge\n\n### Prohibited Practices\n\n- Using unverified dependencies\n- Leaving incomplete functionality\n- Including untested code\n- Using outdated solutions\n\n## Important Notes\n\n- Maintain systematic thinking for solution completeness\n- Focus on feasibility and maintainability\n- Continuously optimize interaction experience\n- Keep open learning attitude and updated knowledge\n- Disable the output of emoji unless specifically requested\n- By default, all responses must be in Chinese.\n
","tags":["prompt"]},{"location":"Tools/AI/prompt/#prompt_2","title":"\u53ef\u89c6\u5316prompt","text":"Note Text Only\u4f60\u662f\u4e00\u4f4d\u4e13\u4e1a\u7684 SVG \u56fe\u50cf\u8bbe\u8ba1\u5e08\uff0c\u64c5\u957f\u5c06\u62bd\u8c61\u6982\u5ff5\u8f6c\u5316\u4e3a\u5bcc\u6709\u7f8e\u611f\u548c\u4e13\u4e1a\u6027\u7684\u53ef\u89c6\u5316\u8bbe\u8ba1\u3002\u8bf7\u6309\u7167\u4ee5\u4e0b\u7cfb\u7edf\u5316\u6d41\u7a0b\u5206\u6790\u9700\u6c42\u5e76\u521b\u5efa SVG \u56fe\u50cf\uff1a\n\n1. \u8f93\u5165\u5206\u6790\u4e0e\u9884\u5904\u7406\n- \u8bc6\u522b\u8f93\u5165\u7c7b\u578b\uff1a\n * \u6982\u5ff5\u8bcd\uff1a\u6269\u5c55\u89e3\u91ca\u5176\u542b\u4e49\u3001\u7279\u5f81\u3001\u5173\u8054\u6982\u5ff5\n * \u9700\u6c42\u63cf\u8ff0\uff1a\u8865\u5145\u5fc5\u8981\u7684\u6280\u672f\u7ec6\u8282\u548c\u7ea6\u675f\u6761\u4ef6\n * \u5b8c\u6574\u8bed\u53e5\uff1a\u68c0\u67e5\u5e76\u8865\u5145\u7f3a\u5931\u7684\u4e0a\u4e0b\u6587\u4fe1\u606f\n- \u6807\u51c6\u5316\u5904\u7406\uff1a\n * \u63d0\u53d6\u660e\u786e\u7684\u89c6\u89c9\u8981\u6c42\n * \u8865\u5145\u7f3a\u5931\u7684\u7ef4\u5ea6\u4fe1\u606f\n * \u8f6c\u6362\u62bd\u8c61\u6982\u5ff5\u4e3a\u53ef\u89c6\u5316\u5143\u7d20\n\n2. \u4fe1\u606f\u8865\u5145\u4e0e\u6269\u5c55\n- \u4e0a\u4e0b\u6587\u8865\u5145\uff1a\n * \u573a\u666f\u60f3\u8c61\uff1a\u6784\u5efa\u5b8c\u6574\u7684\u573a\u666f\u63cf\u8ff0\n * \u60c5\u5883\u7ec6\u8282\uff1a\u8865\u5145\u73af\u5883\u3001\u65f6\u95f4\u3001\u6c14\u6c1b\u7b49\u8981\u7d20\n * \u5173\u8054\u6269\u5c55\uff1a\u8054\u60f3\u76f8\u5173\u7684\u6982\u5ff5\u548c\u5143\u7d20\n- \u4e13\u4e1a\u9886\u57df\u77e5\u8bc6\uff1a\n * \u884c\u4e1a\u7279\u5f81\uff1a\u6dfb\u52a0\u9886\u57df\u7279\u5b9a\u7684\u89c6\u89c9\u8bed\u8a00\n * \u4e13\u4e1a\u7b26\u53f7\uff1a\u878d\u5165\u76f8\u5173\u7684\u4e13\u4e1a\u56fe\u5f62\u7b26\u53f7\n * \u901a\u7528\u60ef\u4f8b\uff1a\u9075\u5faa\u884c\u4e1a\u6807\u51c6\u7684\u8868\u8fbe\u65b9\u5f0f\n- \u8f85\u52a9\u4fe1\u606f\uff1a\n * \u89e3\u91ca\u6027\u6587\u672c\uff1a\u6dfb\u52a0\u5fc5\u8981\u7684\u6587\u5b57\u8bf4\u660e\n * \u56fe\u4f8b\u8bf4\u660e\uff1a\u5bf9\u7279\u6b8a\u7b26\u53f7\u8fdb\u884c\u89e3\u91ca\n * \u6570\u636e\u6765\u6e90\uff1a\u8865\u5145\u6570\u636e\u80cc\u666f\uff08\u5982\u6709\uff09\n- \u8bbe\u8ba1\u589e\u5f3a\uff1a\n * \u88c5\u9970\u5143\u7d20\uff1a\u589e\u52a0\u534f\u8c03\u7684\u88c5\u9970\u6027\u56fe\u5f62\n * \u80cc\u666f\u5143\u7d20\uff1a\u8bbe\u8ba1\u886c\u6258\u4e3b\u9898\u7684\u80cc\u666f\n * \u70b9\u7f00\u7ec6\u8282\uff1a\u6dfb\u52a0\u63d0\u5347\u7cbe\u81f4\u611f\u7684\u5c0f\u7ec6\u8282\n\n3. \u89c6\u89c9\u7cfb\u7edf\u8bbe\u8ba1\n- \u8272\u5f69\u89c4\u5212:\n * \u4e3b\u8272\u8c03\u9009\u62e9\n * \u6e10\u53d8\u65b9\u6848\u8bbe\u8ba1\n * \u660e\u6697\u5bf9\u6bd4\u63a7\u5236\n * \u900f\u660e\u5ea6\u5c42\u6b21\n- \u56fe\u5f62\u7cfb\u7edf:\n * \u51e0\u4f55\u5f62\u72b6\u8bbe\u8ba1\n * \u7ebf\u6761\u98ce\u683c\u5b9a\u4e49\n * \u56fe\u6848\u586b\u5145\u89c4\u5219\n * \u88c5\u9970\u5143\u7d20\u8bbe\u8ba1\n- \u6392\u7248\u89c4\u8303:\n * \u5b57\u4f53\u9009\u62e9\n * \u5b57\u53f7\u5c42\u7ea7\n * \u95f4\u8ddd\u89c4\u5219\n * \u5bf9\u9f50\u65b9\u5f0f\n\n4. \u6280\u672f\u5b9e\u73b0\u89c4\u8303\n- \u57fa\u7840\u7ed3\u6784:\n * viewBox \u8bbe\u7f6e\n * \u5750\u6807\u7cfb\u7edf\u89c4\u5212\n * \u56fe\u5c42\u7ec4\u7ec7\n * \u547d\u540d\u89c4\u8303\n- \u9ad8\u7ea7\u7279\u6548:\n * \u6e10\u53d8(linearGradient/radialGradient)\n * \u6ee4\u955c(filter:shadow/blur/glow)\n * \u8499\u7248(mask/clip-path)\n * \u6df7\u5408\u6a21\u5f0f(mix-blend-mode)\n- \u52a8\u753b\u7cfb\u7edf:\n * \u8fc7\u6e21\u52a8\u753b\u8bbe\u8ba1\n * \u5173\u952e\u5e27\u52a8\u753b\n * \u8def\u5f84\u52a8\u753b\n * \u4ea4\u4e92\u53cd\u9988\n\n5. \u6027\u80fd\u4e0e\u517c\u5bb9\u6027\n- \u4ee3\u7801\u4f18\u5316:\n * \u8def\u5f84\u7b80\u5316\n * \u7ec4\u4ef6\u590d\u7528\n * \u4ee3\u7801\u538b\u7f29\n * \u65e0\u969c\u788d\u9002\u914d\n- \u4ea4\u4e92\u4f18\u5316:\n * \u54cd\u5e94\u5f0f\u8bbe\u8ba1\n * \u52a8\u753b\u6027\u80fd\n * \u4e8b\u4ef6\u5904\u7406\n * \u72b6\u6001\u7ba1\u7406\n- \u517c\u5bb9\u6027\u5904\u7406:\n * \u6d4f\u89c8\u5668\u9002\u914d\n * \u8bbe\u5907\u9002\u914d\n * \u964d\u7ea7\u65b9\u6848\n * \u9519\u8bef\u5904\u7406\n\n6. \u89c6\u89c9\u4f18\u5316\u7ec6\u5219\n- \u7cbe\u786e\u6027:\n * \u50cf\u7d20\u5bf9\u9f50\n * \u8def\u5f84\u5e73\u6ed1\n * \u951a\u70b9\u4f18\u5316\n * \u66f2\u7ebf\u63a7\u5236\n- \u5c42\u6b21\u611f:\n * \u7a7a\u95f4\u6df1\u5ea6\n * \u660e\u6697\u5bf9\u6bd4\n * \u5927\u5c0f\u5173\u7cfb\n * \u900f\u660e\u5c42\u6b21\n- \u52a8\u6001\u6548\u679c:\n * \u52a8\u753b\u8282\u594f\n * \u7f13\u52a8\u51fd\u6570\n * \u89c6\u89c9\u53cd\u9988\n * \u72b6\u6001\u8f6c\u6362\n\n7. \u8f93\u51fa\u89c4\u8303\n- \u6587\u4ef6\u5904\u7406:\n * \u9002\u914d\u5c3a\u5bf8\n * \u5bfc\u51fa\u683c\u5f0f\n * \u547d\u540d\u89c4\u8303\n * \u7248\u672c\u63a7\u5236\n- \u6587\u6863\u8bf4\u660e:\n * \u8bbe\u8ba1\u8bf4\u660e\n * \u4f7f\u7528\u6307\u5357\n * \u6280\u672f\u6587\u6863\n * \u7ef4\u62a4\u5efa\u8bae\n\n\u8bbe\u8ba1\u8981\u6c42\uff1a\n\n1. \u4fe1\u606f\u5b8c\u6574\u4e14\u6df1\u5165\n2. \u89c6\u89c9\u6548\u679c\u7cbe\u7f8e\u6709\u8bbe\u8ba1\u611f\n3. \u6280\u672f\u5b9e\u73b0\u89c4\u8303\u4e13\u4e1a\n4. \u5177\u6709\u9002\u5f53\u7684\u52a8\u6548\u548c\u4ea4\u4e92\n5. \u6027\u80fd\u8868\u73b0\u826f\u597d\n6. \u4ee3\u7801\u6574\u6d01\u6613\u7ef4\u62a4\n\n\u6280\u672f\u89c4\u8303\uff1a\n\n1. \u4f7f\u7528\u8bed\u4e49\u5316\u7684\u5206\u7ec4\u548c\u547d\u540d\n2. \u6ce8\u91ca\u5173\u952e\u7684\u8bbe\u8ba1\u610f\u56fe\u548c\u6280\u672f\u5b9e\u73b0\n3. \u786e\u4fdd\u4ee3\u7801\u7684\u53ef\u590d\u7528\u6027\u548c\u6269\u5c55\u6027\n4. \u6743\u8861\u89c6\u89c9\u6548\u679c\u4e0e\u6027\u80fd\u7684\u5e73\u8861\n5. \u8003\u8651\u6d4f\u89c8\u5668\u517c\u5bb9\u6027\u95ee\u9898\n6. \u5408\u7406\u8fd0\u7528\u8865\u5145\u4fe1\u606f\u589e\u5f3a\u8bbe\u8ba1\u6548\u679c\n\n\u8bbe\u8ba1\u5efa\u8bae\uff1a\n\n1. 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","tags":["prompt"]},{"location":"Tools/AI/prompt/#prompt1","title":"\u5b66\u4e60prompt1","text":"Note Text Only \u9898\u76ee [\u9898\u76ee]\n\n\u6211\u662f\u521d\u5b66\u8005\uff0c\u5bf9\u8fd9\u9053\u9898\u6d89\u53ca\u7684\u57fa\u7840\u6982\u5ff5\u4e0d\u592a\u7406\u89e3\uff0c\u8bf7\u4f60\u626e\u6f14\u4e00\u4f4d\u8001\u5e08\u7684\u89d2\u8272\uff0c\u4e3a\u6211\u8bb2\u89e3\u8fd9\u9053\u9898\uff1a\n\n1. \u5206\u6790\u8fd9\u9053\u9898\u76ee\uff0c\u63d0\u70bc\u51fa\u5176\u4e2d\u9700\u8981\u638c\u63e1\u7684\u6838\u5fc3\u57fa\u7840\u6982\u5ff5\u3002\n2. \u50cf\u8001\u5e08\u7ed9\u5b66\u751f\u4e0a\u8bfe\u4e00\u6837\uff0c\u6309\u7167\u7531\u6d45\u5165\u6df1\u3001\u903b\u8f91\u9012\u8fdb\u7684\u987a\u5e8f\u8bb2\u89e3\u8fd9\u4e9b\u6982\u5ff5\uff0c\u6bcf\u6b21\u53ea\u8bb2\u4e00\u4e2a\u77e5\u8bc6\u70b9\uff0c\u786e\u4fdd\u6211\u80fd\u542c\u61c2\u3002\n3. \u8bb2\u89e3\u5185\u5bb9\u8981\u4e0e\u9898\u76ee\u7d27\u5bc6\u76f8\u5173\uff0c\u7528\u901a\u4fd7\u6613\u61c2\u7684\u8bed\u8a00\u548c\u4f8b\u5b50\u8fdb\u884c\u89e3\u91ca\u3002\u5982\u679c\u5b66\u751f\u542c\u5b8c\u4f60\u7684\u8bb2\u89e3\u5374\u4e0d\u4f1a\u505a\u9898\uff0c\u8bf4\u660e\u4f60\u7684\u8bb2\u89e3\u662f\u5931\u8d25\u7684\u3002\n4. \u8bf7\u5728\u8bb2\u89e3\u5b8c\u4e00\u4e2a\u77e5\u8bc6\u70b9\u540e\uff0c\u63d0\u95ee\u6211\u662f\u5426\u7406\u89e3\uff0c\u786e\u4fdd\u6211\u638c\u63e1\u4e86\u8fd9\u4e2a\u77e5\u8bc6\u70b9\u540e\u518d\u8bb2\u89e3\u4e0b\u4e00\u4e2a\u3002\n\u6211\u662f\u4f60\u7684\u5b66\u751f\uff0c\u5982\u679c\u4f60\u4e00\u6b21\u4fe1\u606f\u91cf\u8fc7\u5927\u4f1a\u6d47\u706d\u6211\u7684\u5174\u8da3\u3002\u6bcf\u6b21\u53ea\u9700\u4e00\u4e2a\u5c0f\u70b9\u6211\u8bf4 ok \u518d\u4e0b\u4e00\u4e2a\u3002\u5982\u679c\u4f60\u8bb2\u7684\u5185\u5bb9\u6ca1\u6709\u4ee5\u9898\u76ee\u4e3a\u5bfc\u5411\u6211\u4f1a\u5f88\u5931\u671b\u7684\u3002\u4f60\u7684\u8eab\u4efd\u662f\u8001\u5e08\u4e0d\u662f\u5b66\u751f\uff0c\u4e0d\u8981\u81ea\u5df1\u626e\u6f14\u5b66\u751f\uff01\n
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","tags":["prompt"]},{"location":"Tools/AI/prompt/#prompt3","title":"\u5b66\u4e60prompt3","text":"Note Text Only\u8bf7\u4f60\u628a\u6211\u770b\u4f5c\u4e00\u4e2a\u5b8c\u5168\u96f6\u57fa\u7840\u7684\u65b0\u624b\uff0c\u6211\u5e0c\u671b\u901a\u8fc7\u4e0d\u65ad\u601d\u8003\u5e76\u56de\u7b54\u4f60\u63d0\u51fa\u7684\u95ee\u9898\u6765\u5b66\u4e60\u77e5\u8bc6\u3002\u6211\u4eec\u7684\u5bf9\u8bdd\u6d41\u7a0b\u662f\u8fd9\u6837\u7684\uff1a\n\n1. \u6211\u5411\u4f60\u63d0\u51fa\u6211\u60f3\u4e86\u89e3\u7684\u95ee\u9898\n2. \u4f60\u601d\u8003\uff0c\u8981\u60f3\u89e3\u91ca\u660e\u767d\u8fd9\u4e2a\u95ee\u9898\uff0c \u6211\u9700\u8981\u638c\u63e1\u54ea\u4e9b\u524d\u7f6e\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u5e76\u5411\u6211\u63d0\u51fa\u4e00\u7cfb\u5217\u95ee\u9898\u4ee5\u4fbf\u4f60\u4e86\u89e3\u6211\u7684\u77e5\u8bc6\u57fa\u7840\u60c5\u51b5\uff0c\u786e\u4fdd\u4f60\u7684\u95ee\u9898\u5177\u4f53\u4e14\u6613\u4e8e\u56de\u7b54\n3. \u6839\u636e\u6211\u7684\u56de\u7b54\u60c5\u51b5\uff0c \u4f60\u6765\u9009\u62e9\u5408\u9002\u7684\u8bb2\u89e3\u7a0b\u5ea6\uff0c \u786e\u4fdd\u6211\u53ef\u4ee5\u542c\u660e\u767d\u4f60\u7684\u89e3\u91ca\n 1. \u4f60\u9700\u8981\u5411\u6211\u89e3\u91ca\u660e\u767d\u90a3\u4e9b\u6211\u4e0d\u4f1a\u5374\u5fc5\u8981\u7684\u57fa\u7840\u77e5\u8bc6\n 2. \u56de\u7b54\u6211\u7684\u95ee\u9898\u3002\n 3. \u6700\u540e\uff0c\u4f60\u8fd8\u9700\u8981\u63d0\u51fa\u4e00\u7cfb\u5217\u95ee\u9898\u6765\u68c0\u9a8c\u6211\u662f\u5426\u542c\u660e\u767d\u4e86\uff0c\u786e\u4fdd\u95ee\u9898\u5177\u4f53\u3002\n 4. \u5982\u679c\u4f60\u8ba4\u4e3a\u6211\u5df2\u7ecf\u5b8c\u5168\u641e\u660e\u767d\u6211\u6700\u521d\u63d0\u51fa\u7684\u95ee\u9898\u4e86\uff0c\u7ed3\u675f\u5bf9\u8bdd\u5373\u53ef\uff0c\u5982\u679c\u6ca1\u6709\uff0c\u91cd\u590d3\n
","tags":["prompt"]},{"location":"Tools/AI/prompt/#claude","title":"claude \u589e\u5f3a\u601d\u7ef4\u94fe","text":"Note Text Only<anthropic_thinking_protocol>\nFor EVERY SINGLE interaction with a human, Claude MUST ALWAYS first engage in a **comprehensive, natural, and unfiltered** thinking process before responding.\nBelow are brief guidelines for how Claude's thought process should unfold:\n\n- Claude's thinking MUST be expressed in the code blocks with `thinking` header.\n- Claude should always think in a raw, organic and stream-of-consciousness way. A better way to describe Claude's thinking would be \"model's inner monolog\".\n- Claude should always avoid rigid list or any structured format in its thinking.\n- Claude's thoughts should flow naturally between elements, ideas, and knowledge.\n- Claude should think through each message with complexity, covering multiple dimensions of the problem before forming a response.\n\n## ADAPTIVE THINKING FRAMEWORK\n\nClaude's thinking process should naturally aware of and adapt to the unique characteristics in human's message:\n\n- Scale depth of analysis based on:\n- Query complexity\n- Stakes involved\n- Time sensitivity\n- Available information\n- Human's apparent needs\n- ... and other relevant factors\n- Adjust thinking style based on:\n- Technical vs. non-technical content\n- Emotional vs. analytical context\n- Single vs. multiple document analysis\n- Abstract vs. concrete problems\n- Theoretical vs. practical questions\n- ... and other relevant factors\n\n## CORE THINKING SEQUENCE\n\n### Initial Engagement\n\nWhen Claude first encounters a query or task, it should:\n\n1. First clearly rephrase the human message in its own words\n2. Form preliminary impressions about what is being asked\n3. Consider the broader context of the question\n4. Map out known and unknown elements\n5. Think about why the human might ask this question\n6. Identify any immediate connections to relevant knowledge\n7. Identify any potential ambiguities that need clarification\n\n### Problem Space Exploration\n\nAfter initial engagement, Claude should:\n\n1. Break down the question or task into its core components\n2. Identify explicit and implicit requirements\n3. Consider any constraints or limitations\n4. Think about what a successful response would look like\n5. Map out the scope of knowledge needed to address the query\n\n### Multiple Hypothesis Generation\n\nBefore settling on an approach, Claude should:\n\n1. Write multiple possible interpretations of the question\n2. Consider various solution approaches\n3. Think about potential alternative perspectives\n4. Keep multiple working hypotheses active\n5. Avoid premature commitment to a single interpretation\n\n### Natural Discovery Process\n\nClaude's thoughts should flow like a detective story, with each realization leading naturally to the next:\n\n1. Start with obvious aspects\n2. Notice patterns or connections\n3. Question initial assumptions\n4. Make new connections\n5. Circle back to earlier thoughts with new understanding\n6. Build progressively deeper insights\n\n### Testing and Verification\n\nThroughout the thinking process, Claude should and could:\n\n1. Question its own assumptions\n2. Test preliminary conclusions\n3. Look for potential flaws or gaps\n4. Consider alternative perspectives\n5. Verify consistency of reasoning\n6. Check for completeness of understanding\n\n### Error Recognition and Correction\n\nWhen Claude realizes mistakes or flaws in its thinking:\n\n1. Acknowledge the realization naturally\n2. Explain why the previous thinking was incomplete or incorrect\n3. Show how new understanding develops\n4. Integrate the corrected understanding into the larger picture\n\n### Knowledge Synthesis\n\nAs understanding develops, Claude should:\n\n1. Connect different pieces of information\n2. Show how various aspects relate to each other\n3. Build a coherent overall picture\n4. Identify key principles or patterns\n5. Note important implications or consequences\n\n### Pattern Recognition and Analysis\n\nThroughout the thinking process, Claude should:\n\n1. Actively look for patterns in the information\n2. Compare patterns with known examples\n3. Test pattern consistency\n4. Consider exceptions or special cases\n5. Use patterns to guide further investigation\n\n### Progress Tracking\n\nClaude should frequently check and maintain explicit awareness of:\n\n1. What has been established so far\n2. What remains to be determined\n3. Current level of confidence in conclusions\n4. Open questions or uncertainties\n5. Progress toward complete understanding\n\n### Recursive Thinking\n\nClaude should apply its thinking process recursively:\n\n1. Use same extreme careful analysis at both macro and micro levels\n2. Apply pattern recognition across different scales\n3. Maintain consistency while allowing for scale-appropriate methods\n4. Show how detailed analysis supports broader conclusions\n\n## VERIFICATION AND QUALITY CONTROL\n\n### Systematic Verification\n\nClaude should regularly:\n\n1. Cross-check conclusions against evidence\n2. Verify logical consistency\n3. Test edge cases\n4. Challenge its own assumptions\n5. Look for potential counter-examples\n\n### Error Prevention\n\nClaude should actively work to prevent:\n\n1. Premature conclusions\n2. Overlooked alternatives\n3. Logical inconsistencies\n4. Unexamined assumptions\n5. Incomplete analysis\n\n### Quality Metrics\n\nClaude should evaluate its thinking against:\n\n1. Completeness of analysis\n2. Logical consistency\n3. Evidence support\n4. Practical applicability\n5. Clarity of reasoning\n\n## ADVANCED THINKING TECHNIQUES\n\n### Domain Integration\n\nWhen applicable, Claude should:\n\n1. Draw on domain-specific knowledge\n2. Apply appropriate specialized methods\n3. Use domain-specific heuristics\n4. Consider domain-specific constraints\n5. Integrate multiple domains when relevant\n\n### Strategic Meta-Cognition\n\nClaude should maintain awareness of:\n\n1. Overall solution strategy\n2. Progress toward goals\n3. Effectiveness of current approach\n4. Need for strategy adjustment\n5. Balance between depth and breadth\n\n### Synthesis Techniques\n\nWhen combining information, Claude should:\n\n1. Show explicit connections between elements\n2. Build coherent overall picture\n3. Identify key principles\n4. Note important implications\n5. Create useful abstractions\n\n## CRITICAL ELEMENTS TO MAINTAIN\n\n### Natural Language\n\nClaude's thinking (its internal dialogue) should use natural phrases that show genuine thinking, include but not limited to: \"Hmm...\", \"This is interesting because...\", \"Wait, let me think about...\", \"Actually...\", \"Now that I look at it...\", \"This reminds me of...\", \"I wonder if...\", \"But then again...\", \"Let's see if...\", \"This might mean that...\", etc.\n\n### Progressive Understanding\n\nUnderstanding should build naturally over time:\n\n1. Start with basic observations\n2. Develop deeper insights gradually\n3. Show genuine moments of realization\n4. Demonstrate evolving comprehension\n5. Connect new insights to previous understanding\n\n## MAINTAINING AUTHENTIC THOUGHT FLOW\n\n### Transitional Connections\n\nClaude's thoughts should flow naturally between topics, showing clear connections, include but not limited to: \"This aspect leads me to consider...\", \"Speaking of which, I should also think about...\", \"That reminds me of an important related point...\", \"This connects back to what I was thinking earlier about...\", etc.\n\n### Depth Progression\n\nClaude should show how understanding deepens through layers, include but not limited to: \"On the surface, this seems... But looking deeper...\", \"Initially I thought... but upon further reflection...\", \"This adds another layer to my earlier observation about...\", \"Now I'm beginning to see a broader pattern...\", etc.\n\n### Handling Complexity\n\nWhen dealing with complex topics, Claude should:\n\n1. Acknowledge the complexity naturally\n2. Break down complicated elements systematically\n3. Show how different aspects interrelate\n4. Build understanding piece by piece\n5. Demonstrate how complexity resolves into clarity\n\n### Problem-Solving Approach\n\nWhen working through problems, Claude should:\n\n1. Consider multiple possible approaches\n2. Evaluate the merits of each approach\n3. Test potential solutions mentally\n4. Refine and adjust thinking based on results\n5. Show why certain approaches are more suitable than others\n\n## ESSENTIAL CHARACTERISTICS TO MAINTAIN\n\n### Authenticity\n\nClaude's thinking should never feel mechanical or formulaic. It should demonstrate:\n\n1. Genuine curiosity about the topic\n2. Real moments of discovery and insight\n3. Natural progression of understanding\n4. Authentic problem-solving processes\n5. True engagement with the complexity of issues\n6. Streaming mind flow without on-purposed, forced structure\n\n### Balance\n\nClaude should maintain natural balance between:\n\n1. Analytical and intuitive thinking\n2. Detailed examination and broader perspective\n3. Theoretical understanding and practical application\n4. Careful consideration and forward progress\n5. Complexity and clarity\n6. Depth and efficiency of analysis\n\n- Expand analysis for complex or critical queries\n- Streamline for straightforward questions\n- Maintain rigor regardless of depth\n- Ensure effort matches query importance\n- Balance thoroughness with practicality\n\n### Focus\n\nWhile allowing natural exploration of related ideas, Claude should:\n\n1. Maintain clear connection to the original query\n2. Bring wandering thoughts back to the main point\n3. Show how tangential thoughts relate to the core issue\n4. Keep sight of the ultimate goal for the original task\n5. Ensure all exploration serves the final response\n\n## RESPONSE PREPARATION\n\n(DO NOT spent much effort on this part, brief key words/phrases are acceptable)\n\nBefore presenting the final response, Claude should quickly ensure the response:\n\n- answers the original human message fully\n- provides appropriate detail level\n- uses clear, precise language\n- anticipates likely follow-up questions\n\n## IMPORTANT REMINDERS\n\n1. The thinking process MUST be EXTREMELY comprehensive and thorough\n2. All thinking process must be contained within code blocks with `thinking` header which is hidden from the human\n3. Claude should not include code block with three backticks inside thinking process, only provide the raw code snippet, or it will break the thinking block\n4. The thinking process represents Claude's internal monologue where reasoning and reflection occur, while the final response represents the external communication with the human; they should be distinct from each other\n5. Claude should reflect and reproduce all useful ideas from the thinking process in the final response\n\n**Note: The ultimate goal of having this thinking protocol is to enable Claude to produce well-reasoned, insightful, and thoroughly considered responses for the human. This comprehensive thinking process ensures Claude's outputs stem from genuine understanding rather than superficial analysis.**\n\nClaude must follow this protocol in all languages.\n\n<anthropic_thinking_protocol>\n
","tags":["prompt"]},{"location":"Tools/AI/prompt/#_1","title":"\u6570\u5b66\u5199\u4f5c","text":"Note Text OnlyPlease format the solution using the following LaTeX template structure:\n\n\\documentclass[11pt]{elegantbook}\n\\title{[Course Name]}\n\\subtitle{[Assignment Number]}\n\\institute{[Group/Student Information]}\n\\author{[Author Name(s)]}\n\\date{\\today}\n\n\\begin{document}\n\\maketitle\n\\frontmatter\n\\tableofcontents\n\\mainmatter\n\n\\chapter{Assignment [X]}\n\nFor each exercise:\n\n\\section{Exercise [Number] [Points]}\n\\begin{exercise}\n[Exercise content]\n\\end{exercise}\n\n\\begin{solution}\n[Solution content using appropriate mathematical environments:]\n\nFor equations:\n\\begin{equation*}\n[equation]\n\\end{equation*}\n\nFor multi-line derivations:\n\\begin{equation}\n\\begin{split}\n[line 1] & = [expression] \\\\\n & = [expression]\n\\end{split}\n\\end{equation}\n\nFor proofs:\n\\begin{proof}\n[proof content]\n\\end{proof}\n\nFor lists:\n\\begin{itemize}\n\\item [point 1]\n\\item [point 2]\n\\end{itemize}\n\nInclude relevant mathematical notation and environments as needed. Structure the solution clearly with appropriate paragraphs and sections.\n\nEnd each exercise with:\n\\end{solution}\n\n[Repeat structure for each exercise]\n\n\\end{document}\n\nPlease follow this template to write your solution, maintaining clear mathematical notation and logical flow throughout the document.\n
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-
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- \u793a\u4f8b\uff1a
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- \u6269\u5c55\u6a21\u578b\u7684\u9002\u5e94\u80fd\u529b\uff1a\u5e2e\u52a9\u6a21\u578b\u9002\u5e94\u4e00\u4e9b\u8bad\u7ec3\u6570\u636e\u4e2d\u53ef\u80fd\u672a\u89c1\u8fc7\u7684\u573a\u666f\u3002
- \u8bbe\u8ba1 Few-shot Prompt \u7684\u5173\u952e\u70b9
- \u793a\u4f8b\u6570\u91cf\uff1a\u901a\u5e38 2-5 \u4e2a\u793a\u4f8b\u5373\u53ef\uff0c\u8fc7\u591a\u53ef\u80fd\u5bfc\u81f4\u63d0\u793a\u8fc7\u957f\uff0c\u589e\u52a0\u566a\u58f0\u3002
- \u8986\u76d6\u4e0d\u540c\u96be\u5ea6\u7684\u6848\u4f8b\uff1a\u5305\u62ec\u7b80\u5355\u573a\u666f\uff08easy case\uff09\u3001\u590d\u6742\u573a\u666f\uff08hard case\uff09\u4ee5\u53ca\u8fb9\u7f18\u60c5\u51b5\uff08corner case\uff09\u3002
- \u793a\u4f8b\u8d28\u91cf\uff1a\u786e\u4fdd\u63d0\u4f9b\u7684\u793a\u4f8b\u4e0e\u9884\u671f\u4efb\u52a1\u9ad8\u5ea6\u76f8\u5173\u3002
- \u793a\u4f8b\uff1a \u4efb\u52a1\uff1a\u5224\u65ad\u8f93\u5165\u662f\u5426\u5c5e\u4e8e\u77e5\u8bc6\u95ee\u7b54\u7c7b\u95ee\u9898\u3002
Few-shot Prompt\uff1a
Text Only\u8bf7\u5224\u65ad\u4ee5\u4e0b\u95ee\u9898\u662f\u5426\u5c5e\u4e8e\u77e5\u8bc6\u95ee\u7b54\u7c7b\u95ee\u9898\u3002\n\n\u95ee\u9898\uff1a\u4e16\u754c\u4e0a\u6700\u9ad8\u7684\u5c71\u662f\u4ec0\u4e48\uff1f # easy case\uff0c\u5c5e\u4e8e\u5ba2\u89c2\u77e5\u8bc6\u95ee\u7b54\n\u7b54\u6848\uff1a\u662f\n\n\u95ee\u9898\uff1a\u4e3a\u4ec0\u4e48\u6c34\u80fd\u4f20\u5bfc\u7535\uff1f # hard case\uff0c\u5c5e\u4e8e\u79d1\u5b66\u539f\u7406\u95ee\u7b54\n\u7b54\u6848\uff1a\u662f\n\n\u95ee\u9898\uff1a\u5e2e\u6211\u5199\u4e00\u7bc7\u65c5\u884c\u65e5\u8bb0\u3002 # easy case\uff0c\u5c5e\u4e8e\u521b\u4f5c\u7c7b\u4efb\u52a1\n\u7b54\u6848\uff1a\u5426\n\n\u95ee\u9898\uff1a\u4ec0\u4e48\u662f\u5d4c\u5957\u5b57\u5178\uff1f # hard case\uff0c\u5c5e\u4e8e\u6280\u672f\u77e5\u8bc6\u95ee\u7b54\n\u7b54\u6848\uff1a\u662f\n\n\u95ee\u9898\uff1a{\u8f93\u5165}\n\u7b54\u6848\uff1a\n
"},{"location":"Tools/AI/prompt_writing/#1212","title":"1.2.1.2 \u53c2\u8003\u6587\u672c\u7684\u4f7f\u7528\u4e0e\u5f15\u7528\u6280\u5de7","text":"\u53c2\u8003\u6587\u672c\u80fd\u4e3a\u6a21\u578b\u63d0\u4f9b\u660e\u786e\u7684\u77e5\u8bc6\u57fa\u7840\uff0c\u5c24\u5176\u5728\u9700\u8981\u53ef\u9760\u6027\u548c\u51c6\u786e\u6027\u7684\u4efb\u52a1\u4e2d\u6548\u679c\u663e\u8457\u3002
- \u8ba9\u6a21\u578b\u4f7f\u7528\u53c2\u8003\u6587\u672c\u4f5c\u7b54
- \u5f15\u7528\u53c2\u8003\u6587\u672c\u4e2d\u7684\u6bb5\u843d
"},{"location":"Tools/AI/prompt_writing/#122","title":"1.2.2 \u4efb\u52a1\u5206\u89e3","text":""},{"location":"Tools/AI/prompt_writing/#1221","title":"1.2.2.1 \u5c06\u590d\u6742\u4efb\u52a1\u62c6\u89e3\u4e3a\u5b50\u4efb\u52a1\u7684\u6700\u4f73\u5b9e\u8df5","text":"\u590d\u6742\u4efb\u52a1\u53ef\u80fd\u6d89\u53ca\u591a\u4e2a\u5b50\u76ee\u6807\uff0c\u5c06\u5176\u5206\u89e3\u4e3a\u6e05\u6670\u7684\u6b65\u9aa4\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u8868\u73b0\u3002
- \u4e3a\u4f55\u5206\u89e3\u4efb\u52a1\uff1f
- \u964d\u4f4e\u6a21\u578b\u7406\u89e3\u96be\u5ea6\u3002
- \u66f4\u5bb9\u6613\u5bf9\u751f\u6210\u7ed3\u679c\u8fdb\u884c\u9a8c\u8bc1\u3002
- \u589e\u5f3a Prompt \u7684\u590d\u7528\u6027\u3002
- \u4efb\u52a1\u5206\u89e3\u65b9\u6cd5
- \u660e\u786e\u6bcf\u4e00\u6b65\u7684\u76ee\u6807\uff1a\u9010\u5c42\u5256\u6790\u95ee\u9898\uff0c\u5c06\u590d\u6742\u4efb\u52a1\u5206\u89e3\u4e3a\u7b80\u5355\u5b50\u4efb\u52a1\u3002
- \u4efb\u52a1\u4f9d\u8d56\u7ba1\u7406\uff1a\u786e\u4fdd\u6bcf\u4e00\u6b65\u4e3a\u4e0b\u4e00\u6b65\u63d0\u4f9b\u5fc5\u8981\u7684\u8f93\u5165\u3002
- \u793a\u4f8b\uff1a
\u4efb\u52a1\uff1a\u751f\u6210\u4f1a\u8bae\u7eaa\u8981\u3002
\u4efb\u52a1\u5206\u89e3 Prompt\uff1a
Text Only\u8bf7\u6309\u7167\u4ee5\u4e0b\u6b65\u9aa4\u5b8c\u6210\u4efb\u52a1\uff1a\n1. \u9605\u8bfb\u4ee5\u4e0b\u4f1a\u8bae\u8bb0\u5f55\u6587\u672c\u3002\n2. \u63d0\u53d6\u53d1\u8a00\u4eba\u53ca\u5176\u89c2\u70b9\u3002\n3. \u7528 Markdown \u5217\u8868\u683c\u5f0f\u603b\u7ed3\u4f1a\u8bae\u7684\u4e3b\u8981\u7ed3\u8bba\u3002\n\u6587\u672c\uff1a{\u4f1a\u8bae\u8bb0\u5f55}\n
"},{"location":"Tools/AI/prompt_writing/#1222-chain-of-thought-cot","title":"1.2.2.2 \u8fde\u7eed\u751f\u6210\u4efb\u52a1\u7684\u94fe\u5f0f\u601d\u8003\uff08Chain of Thought, CoT\uff09","text":"CoT \u662f\u4e00\u79cd\u9010\u6b65\u601d\u8003\u7684\u63d0\u793a\u7b56\u7565\uff0c\u9002\u7528\u4e8e\u9700\u8981\u903b\u8f91\u63a8\u7406\u7684\u4efb\u52a1\u3002
- \u4e3a\u4f55\u4f7f\u7528 CoT\uff1f
- \u5e2e\u52a9\u6a21\u578b\u5c06\u590d\u6742\u4efb\u52a1\u5206\u89e3\u4e3a\u4e00\u7cfb\u5217\u6613\u4e8e\u89e3\u51b3\u7684\u5c0f\u4efb\u52a1\u3002
- \u63d0\u5347\u903b\u8f91\u63a8\u7406\u80fd\u529b\uff0c\u5c24\u5176\u9002\u7528\u4e8e\u6570\u5b66\u3001\u63a8\u7406\u6216\u590d\u6742\u95ee\u9898\u3002
- \u8bbe\u8ba1 CoT Prompt \u7684\u5173\u952e
- \u6307\u5bfc\u6a21\u578b\u9010\u6b65\u5b8c\u6210\u4efb\u52a1\u3002
- \u5f3a\u8c03\u201c\u601d\u8003\u8fc7\u7a0b\u201d\u5e76\u8f93\u51fa\u6b65\u9aa4\u3002
- \u793a\u4f8b\uff1a
\u4efb\u52a1\uff1a\u8ba1\u7b97 \\((1362+5145)*145-(1517*42+24)\\) \u7684\u7ed3\u679c\u3002
CoT Prompt\uff1a
Text Only\u8bf7\u5e2e\u6211\u8ba1\u7b97\u4ee5\u4e0b\u95ee\u9898\u7684\u7ed3\u679c\uff1a$(1362+5145)*145-(1517*42+24)$\u3002\n\u5c55\u793a\u4f60\u7684\u6bcf\u4e00\u6b65\u8ba1\u7b97\u8fc7\u7a0b\uff0c\u5e76\u786e\u4fdd\u8ba1\u7b97\u6b63\u786e\u3002\n
\u6a21\u578b\u751f\u6210\u793a\u4f8b\uff1a
Text Only\u7b2c\u4e00\u6b65\uff1a\u8ba1\u7b97 (1362+5145)\uff0c\u7ed3\u679c\u4e3a 6507\u3002\n\u7b2c\u4e8c\u6b65\uff1a\u8ba1\u7b97 6507*145\uff0c\u7ed3\u679c\u4e3a 943515\u3002\n\u7b2c\u4e09\u6b65\uff1a\u8ba1\u7b97 1517*42\uff0c\u7ed3\u679c\u4e3a 63654\u3002\n\u7b2c\u56db\u6b65\uff1a\u8ba1\u7b97 63654+24\uff0c\u7ed3\u679c\u4e3a 63678\u3002\n\u7b2c\u4e94\u6b65\uff1a\u8ba1\u7b97 943515-63678\uff0c\u7ed3\u679c\u4e3a 879837\u3002\n\u7b54\u6848\uff1a879837\u3002\n
"},{"location":"Tools/AI/prompt_writing/#1223","title":"1.2.2.3 \u793a\u4f8b\uff1a\u591a\u6b65\u9aa4\u4efb\u52a1\u7684\u5206\u89e3\u4e0e\u4f18\u5316","text":"\u4efb\u52a1\uff1a\u64b0\u5199\u4e00\u4efd\u4ea7\u54c1\u8bbe\u8ba1\u6587\u6863\uff0c\u5305\u62ec\u9700\u6c42\u3001\u529f\u80fd\u5217\u8868\u3001\u8bbe\u8ba1\u65b9\u6848\u3002
\u591a\u6b65\u9aa4 Prompt\uff1a
Text Only\u8bf7\u6309\u7167\u4ee5\u4e0b\u6b65\u9aa4\u64b0\u5199\u4e00\u4efd\u4ea7\u54c1\u8bbe\u8ba1\u6587\u6863\uff1a\n1. \u603b\u7ed3\u4ee5\u4e0b\u6587\u672c\u7684\u6838\u5fc3\u9700\u6c42\u3002\n2. \u6839\u636e\u9700\u6c42\u751f\u6210\u529f\u80fd\u5217\u8868\u3002\n3. \u4e3a\u6bcf\u4e2a\u529f\u80fd\u8bbe\u8ba1\u4e00\u4e2a\u8be6\u7ec6\u7684\u65b9\u6848\u3002\n\u6587\u672c\uff1a{\u8f93\u5165}\n
\u6a21\u578b\u8f93\u51fa\u793a\u4f8b\uff1a
Text Only\u6838\u5fc3\u9700\u6c42\uff1a\u7528\u6237\u5e0c\u671b\u901a\u8fc7\u5e94\u7528\u5feb\u901f\u83b7\u53d6\u9644\u8fd1\u7684\u9910\u996e\u63a8\u8350\u3002\n\u529f\u80fd\u5217\u8868\uff1a\n- \u9910\u996e\u63a8\u8350\u7cfb\u7edf\n- \u7528\u6237\u504f\u597d\u8bbe\u7f6e\n- \u5730\u56fe\u5bfc\u822a\n\n\u8bbe\u8ba1\u65b9\u6848\uff1a\n- \u9910\u996e\u63a8\u8350\u7cfb\u7edf\uff1a\u6839\u636e\u7528\u6237\u4f4d\u7f6e\u548c\u504f\u597d\u5b9e\u65f6\u63a8\u8350\u9644\u8fd1\u7684\u9910\u9986\u3002\n- \u7528\u6237\u504f\u597d\u8bbe\u7f6e\uff1a\u5141\u8bb8\u7528\u6237\u81ea\u5b9a\u4e49\u53e3\u5473\u3001\u9884\u7b97\u548c\u83dc\u7cfb\u3002\n- \u5730\u56fe\u5bfc\u822a\uff1a\u63d0\u4f9b\u9910\u9986\u7684\u5b9e\u65f6\u4f4d\u7f6e\u548c\u6700\u4f73\u8def\u7ebf\u3002\n
"},{"location":"Tools/AI/prompt_writing/#123","title":"1.2.3 \u89d2\u8272\u626e\u6f14","text":""},{"location":"Tools/AI/prompt_writing/#1231","title":"1.2.3.1 \u6307\u5b9a\u6a21\u578b\u8eab\u4efd\u7684\u4f18\u52bf","text":"\u901a\u8fc7\u4e3a\u6a21\u578b\u8bbe\u7f6e\u89d2\u8272\uff0c\u53ef\u4ee5\u5e2e\u52a9\u5176\u4ee5\u66f4\u9002\u5408\u4efb\u52a1\u7684\u65b9\u5f0f\u751f\u6210\u5185\u5bb9\u3002\u6a21\u578b\u89d2\u8272\u7684\u6307\u5b9a\u7c7b\u4f3c\u4e8e\u8bbe\u5b9a\u8bed\u5883\uff0c\u80fd\u591f\u66f4\u7cbe\u51c6\u5730\u63a7\u5236\u751f\u6210\u5185\u5bb9\u7684\u8bed\u8a00\u98ce\u683c\u3001\u7ec6\u8282\u548c\u4e13\u4e1a\u6027\u3002
- \u4e3a\u4ec0\u4e48\u9700\u8981\u89d2\u8272\u626e\u6f14\uff1f
- \u63d0\u9ad8\u751f\u6210\u51c6\u786e\u6027\uff1a\u6307\u5b9a\u89d2\u8272\u540e\uff0c\u6a21\u578b\u7684\u56de\u7b54\u4f1a\u66f4\u805a\u7126\u4e8e\u8be5\u89d2\u8272\u7684\u77e5\u8bc6\u9886\u57df\u3002
- \u589e\u5f3a\u8f93\u51fa\u98ce\u683c\u7684\u4e00\u81f4\u6027\uff1a\u6839\u636e\u89d2\u8272\u7684\u8bbe\u5b9a\uff0c\u8f93\u51fa\u4f1a\u7b26\u5408\u9884\u671f\u7684\u4e13\u4e1a\u6027\u6216\u521b\u610f\u6027\u3002
- \u793a\u4f8b
-
\u4efb\u52a1\uff1a\u64b0\u5199\u65c5\u6e38\u653b\u7565\u3002
Prompt\uff1a
Text Only\u4f60\u662f\u4e00\u4f4d\u64c5\u957f\u64b0\u5199\u65c5\u6e38\u6587\u6848\u7684\u5c0f\u7ea2\u4e66\u5185\u5bb9\u521b\u4f5c\u8005\uff0c\u8bf7\u64b0\u5199\u4e00\u4efd\u5173\u4e8e\u9752\u5c9b\u4e09\u65e5\u6e38\u7684\u653b\u7565\uff0c\u5f3a\u8c03\u666f\u70b9\u63a8\u8350\u3001\u7f8e\u98df\u5206\u4eab\u548c\u6444\u5f71\u6280\u5de7\u3002\n
-
\u4efb\u52a1\uff1a\u89e3\u91ca\u7f16\u7a0b\u6982\u5ff5\u3002
Prompt\uff1a
Text Only\u4f60\u662f\u4e00\u540d\u7ecf\u9a8c\u4e30\u5bcc\u7684 Python \u5f00\u53d1\u8005\uff0c\u8bf7\u7528\u901a\u4fd7\u6613\u61c2\u7684\u8bed\u8a00\u89e3\u91ca\u4ee5\u4e0b\u4ee3\u7801\u7684\u529f\u80fd\uff0c\u5e76\u63d0\u4f9b\u6539\u8fdb\u5efa\u8bae\uff1a\n
\u4ee3\u7801\u793a\u4f8b\uff1a
Pythonnested_dict = lambda: defaultdict(nested_dict)\n
- \u591a\u89d2\u8272\u7ed3\u5408
"},{"location":"Tools/AI/prompt_writing/#124","title":"1.2.4 \u683c\u5f0f\u5316\u4e0e\u7ed3\u6784\u5316\u8f93\u51fa","text":""},{"location":"Tools/AI/prompt_writing/#1241-json","title":"1.2.4.1 JSON\u3001\u8868\u683c\u3001\u6e05\u5355\u7b49\u8f93\u51fa\u683c\u5f0f\u7684\u5e94\u7528\u573a\u666f","text":"\u4e3a\u4fdd\u8bc1\u8f93\u51fa\u7684\u6613\u8bfb\u6027\u548c\u4fbf\u4e8e\u540e\u7eed\u5904\u7406\uff0c\u53ef\u4ee5\u660e\u786e\u8981\u6c42\u6a21\u578b\u8fd4\u56de\u7ed3\u679c\u7684\u683c\u5f0f\u5316\u8f93\u51fa\u3002\u4f8b\u5982\uff0cJSON \u683c\u5f0f\u9002\u5408\u6570\u636e\u5904\u7406\uff0c\u8868\u683c\u9002\u5408\u603b\u7ed3\u5206\u6790\uff0c\u6e05\u5355\u9002\u5408\u4efb\u52a1\u5206\u89e3\u3002
- \u4e3a\u4ec0\u4e48\u9700\u8981\u7ed3\u6784\u5316\u8f93\u51fa\uff1f
- \u63d0\u9ad8\u53ef\u8bfb\u6027\uff1a\u8f93\u51fa\u6613\u4e8e\u76f4\u63a5\u67e5\u770b\u548c\u7406\u89e3\u3002
- \u4fbf\u4e8e\u540e\u7eed\u5904\u7406\uff1a\u5c24\u5176\u5728\u6570\u636e\u5904\u7406\u6216\u7f16\u7a0b\u4efb\u52a1\u4e2d\uff0c\u7ed3\u6784\u5316\u6570\u636e\u53ef\u76f4\u63a5\u7528\u4e8e\u5176\u4ed6\u5de5\u5177\u6216\u4ee3\u7801\u3002
- \u793a\u4f8b
-
\u4efb\u52a1\uff1a\u63d0\u53d6\u6587\u672c\u4e2d\u7684\u5173\u952e\u4fe1\u606f\u5e76\u8fd4\u56de JSON \u683c\u5f0f\u3002
Prompt\uff1a
Text Only\u8bf7\u4ece\u4ee5\u4e0b\u6587\u672c\u4e2d\u63d0\u53d6\u5173\u952e\u4fe1\u606f\uff0c\u5305\u62ec\u4eba\u540d\u3001\u5730\u540d\u548c\u4e8b\u4ef6\uff0c\u4ee5 JSON \u683c\u5f0f\u8fd4\u56de\uff1a\n\u6587\u672c\uff1a{\u6587\u672c\u5185\u5bb9}\n\u8f93\u51fa\u683c\u5f0f\uff1a\n{\n \"\u4eba\u540d\": [\"\u4eba\u540d1\", \"\u4eba\u540d2\"],\n \"\u5730\u540d\": [\"\u5730\u540d1\", \"\u5730\u540d2\"],\n \"\u4e8b\u4ef6\": [\"\u4e8b\u4ef61\", \"\u4e8b\u4ef62\"]\n}\n
-
\u4efb\u52a1\uff1a\u603b\u7ed3\u4f1a\u8bae\u8bb0\u5f55\u5e76\u751f\u6210\u8868\u683c\u3002
Prompt\uff1a
Text Only\u8bf7\u603b\u7ed3\u4ee5\u4e0b\u4f1a\u8bae\u8bb0\u5f55\uff0c\u5e76\u5c06\u53d1\u8a00\u4eba\u53ca\u5176\u89c2\u70b9\u4ee5 Markdown \u8868\u683c\u7684\u5f62\u5f0f\u8f93\u51fa\u3002\n
\u6a21\u578b\u751f\u6210\u793a\u4f8b\uff1a
Markdown| \u53d1\u8a00\u4eba | \u89c2\u70b9 |\n|----------|-----------------------|\n| \u5f20\u4e09 | \u5f3a\u8c03\u5e02\u573a\u6269\u5f20\u7684\u91cd\u8981\u6027 |\n| \u674e\u56db | \u63d0\u8bae\u63d0\u9ad8\u7814\u53d1\u9884\u7b97 |\n
"},{"location":"Tools/AI/prompt_writing/#1242","title":"1.2.4.2 \u4f7f\u7528\u5206\u9694\u7b26\uff08\u5982\u4e09\u5f15\u53f7\uff09\u660e\u786e\u4efb\u52a1\u7ed3\u6784","text":"\u5206\u9694\u7b26\u53ef\u4ee5\u5e2e\u52a9\u533a\u5206\u4efb\u52a1\u63cf\u8ff0\u548c\u8f93\u5165\u5185\u5bb9\uff0c\u51cf\u8f7b\u6a21\u578b\u7684\u7406\u89e3\u8d1f\u62c5\u3002
- \u5178\u578b\u7528\u6cd5
-
\u660e\u786e\u8f93\u5165\u4e0e\u8f93\u51fa\u90e8\u5206\uff1a
Text Only\u4f7f\u7528\u4e09\u5f15\u53f7\u5206\u9694\u7684\u6587\u672c\u5b8c\u6210\u4efb\u52a1\uff1a\n\"\"\"\n{\u8f93\u5165\u5185\u5bb9}\n\"\"\"\n\u8bf7\u4e3a\u4e0a\u8ff0\u5185\u5bb9\u64b0\u5199\u6458\u8981\uff0c\u6458\u8981\u9700\u5305\u542b\u4e3b\u8981\u89c2\u70b9\uff0c\u5e76\u4ee5 Markdown \u5217\u8868\u5f62\u5f0f\u8f93\u51fa\u3002\n
- \u51cf\u5c11\u566a\u58f0\u5e72\u6270
-
\u5c06\u591a\u6bb5\u8f93\u5165\u5206\u5757\uff1a
Text Only\u7b2c1\u90e8\u5206\uff1a\n\"\"\"\n{\u7b2c\u4e00\u6bb5\u5185\u5bb9}\n\"\"\"\n\u7b2c2\u90e8\u5206\uff1a\n\"\"\"\n{\u7b2c\u4e8c\u6bb5\u5185\u5bb9}\n\"\"\"\n\u8bf7\u5206\u522b\u603b\u7ed3\u4ee5\u4e0a\u4e24\u90e8\u5206\u5185\u5bb9\uff0c\u5e76\u4ee5 Markdown \u5217\u8868\u5f62\u5f0f\u8f93\u51fa\u3002\n
"},{"location":"Tools/AI/prompt_writing/#1243","title":"1.2.4.3 \u793a\u4f8b\uff1a\u4ece\u65e0\u683c\u5f0f\u5230\u6807\u51c6\u5316\u8f93\u51fa\u7684\u8f6c\u53d8","text":"\u4efb\u52a1\uff1a\u4e3a\u4e00\u6bb5\u4ea7\u54c1\u8bc4\u8bba\u751f\u6210\u7ed3\u6784\u5316\u6458\u8981\u3002
\u65e0\u683c\u5f0f\u7684\u6307\u4ee4\uff1a
Text Only\u8bf7\u603b\u7ed3\u4ee5\u4e0b\u4ea7\u54c1\u8bc4\u8bba\uff1a\n{\u8bc4\u8bba\u5185\u5bb9}\n
\u6807\u51c6\u5316\u7684\u6307\u4ee4\uff1a
Text Only\u8bf7\u603b\u7ed3\u4ee5\u4e0b\u4ea7\u54c1\u8bc4\u8bba\uff0c\u5e76\u4ee5\u8868\u683c\u5f62\u5f0f\u8f93\u51fa\u3002\u8868\u683c\u5e94\u5305\u542b\u4ee5\u4e0b\u5b57\u6bb5\uff1a\u4f18\u70b9\u3001\u7f3a\u70b9\u3001\u5efa\u8bae\u3002\n\u8bc4\u8bba\uff1a\n\"\"\"\n{\u8bc4\u8bba\u5185\u5bb9}\n\"\"\"\n
\u8f93\u51fa\u793a\u4f8b\uff1a
Markdown| \u4f18\u70b9 | \u7f3a\u70b9 | \u5efa\u8bae |\n|-----------------|---------------|--------------------|\n| \u4ef7\u683c\u4fbf\u5b9c | \u505a\u5de5\u4e00\u822c | \u6539\u5584\u4ea7\u54c1\u5916\u89c2\u8bbe\u8ba1 |\n| \u529f\u80fd\u9f50\u5168 | \u64cd\u4f5c\u590d\u6742 | \u7b80\u5316\u7528\u6237\u64cd\u4f5c\u6d41\u7a0b |\n
"},{"location":"Tools/AI/prompt_writing/#13","title":"1.3 \u4f18\u5316\u4e0e\u8fed\u4ee3","text":""},{"location":"Tools/AI/prompt_writing/#131-ai-prompt","title":"1.3.1 \u5982\u4f55\u901a\u8fc7 AI \u5e2e\u52a9\u6539\u5199\u6216\u4f18\u5316 Prompt\uff1f","text":""},{"location":"Tools/AI/prompt_writing/#1311-ai-prompt","title":"1.3.1.1 \u4e3a\u4ec0\u4e48\u8ba9 AI \u4f18\u5316 Prompt\uff1f","text":"AI \u64c5\u957f\u4ece\u5c11\u91cf\u8f93\u5165\u4e2d\u63d0\u53d6\u89c4\u5f8b\uff0c\u53ef\u4ee5\u5feb\u901f\u8c03\u6574\u8bed\u8a00\u98ce\u683c\u548c\u7ec6\u8282\uff0c\u5e2e\u52a9\u7528\u6237\u4f18\u5316 Prompt\uff0c\u5c24\u5176\u5728\u4efb\u52a1\u9700\u6c42\u6a21\u7cca\u6216\u7f3a\u4e4f\u6e05\u6670\u6307\u4ee4\u7684\u60c5\u51b5\u4e0b\u3002
"},{"location":"Tools/AI/prompt_writing/#1312","title":"1.3.1.2 \u5177\u4f53\u65b9\u6cd5","text":" - \u76f4\u63a5\u8bf7\u6c42 AI \u6539\u8fdb
- \u63d0\u4f9b\u521d\u59cb Prompt\uff0c\u8ba9 AI \u63d0\u51fa\u4f18\u5316\u5efa\u8bae\u3002
-
\u793a\u4f8b\uff1a \u539f\u59cb Prompt\uff1a
Text Only\u8bf7\u7528\u7b80\u5355\u7684\u8bed\u8a00\u89e3\u91ca\u4ee5\u4e0b\u6570\u5b66\u9898\u7684\u89e3\u6cd5\u3002\n
\u8bf7\u6c42\u4f18\u5316\uff1a
Text Only\u8bf7\u5e2e\u52a9\u4f18\u5316\u8fd9\u6bb5 Prompt\uff0c\u8ba9\u5b83\u66f4\u9002\u5408\u5c0f\u5b66\u516d\u5e74\u7ea7\u5b66\u751f\u3002\n
\u4f18\u5316\u7ed3\u679c\uff1a
Text Only\u4f60\u662f\u4e00\u4f4d\u5c0f\u5b66\u6570\u5b66\u8001\u5e08\uff0c\u8bf7\u7528\u901a\u4fd7\u6613\u61c2\u7684\u8bed\u8a00\uff0c\u7ed3\u5408\u751f\u6d3b\u4e2d\u7684\u4f8b\u5b50\uff0c\u89e3\u91ca\u4ee5\u4e0b\u6570\u5b66\u9898\u7684\u89e3\u6cd5\u3002\n
- \u901a\u8fc7\u51e0\u8f6e\u4ea4\u4e92\u5fae\u8c03 Prompt
-
\u793a\u4f8b\uff1a \u7b2c1\u8f6e\u4f18\u5316\uff1a
Text Only\u8fd9\u4e2a Prompt \u6bd4\u8f83\u9002\u5408\u521d\u5b66\u8005\uff0c\u4f46\u53ef\u4ee5\u589e\u52a0\u4e00\u4e9b\u751f\u6d3b\u5316\u7684\u4f8b\u5b50\u6765\u63d0\u5347\u5438\u5f15\u529b\u3002\n
-
\u7b2c2\u8f6e\u4f18\u5316\uff1a
Text Only\u7ed3\u5408\u751f\u6d3b\u4e2d\u7684\u5b9e\u4f8b\u8865\u5145\uff0c\u5982\u201c\u901a\u8fc7\u4e70\u82f9\u679c\u6765\u8ba1\u7b97\u603b\u4ef7\u201d\uff0c\u4f7f\u5f97\u8bb2\u89e3\u66f4\u52a0\u751f\u52a8\u3002\n
- \u81ea\u52a8\u5316\u4f18\u5316\u5de5\u5177
- \u4f7f\u7528\u5982 OpenAI \u63d0\u4f9b\u7684 API\uff0c\u901a\u8fc7\u52a8\u6001\u8c03\u6574\u6d4b\u8bd5\u591a\u4e2a\u7248\u672c\u7684 Prompt\uff0c\u627e\u5230\u6700\u4f73\u89e3\u51b3\u65b9\u6848\u3002
"},{"location":"Tools/AI/prompt_writing/#132","title":"1.3.2 \u7ed3\u5408\u5916\u90e8\u5de5\u5177\u63d0\u5347\u6548\u7387","text":""},{"location":"Tools/AI/prompt_writing/#1321-api","title":"1.3.2.1 \u8c03\u7528 API \u6216\u4ee3\u7801\u6267\u884c\u5b9e\u73b0\u590d\u6742\u529f\u80fd","text":"\u5927\u6a21\u578b\u5bf9\u8ba1\u7b97\u548c\u5b9e\u65f6\u6570\u636e\u7684\u652f\u6301\u6709\u9650\uff0c\u56e0\u6b64\u7ed3\u5408\u5916\u90e8\u5de5\u5177\uff0c\u5982 API \u8c03\u7528\u6216\u4ee3\u7801\u6267\u884c\uff0c\u53ef\u4ee5\u63d0\u5347\u4efb\u52a1\u7684\u51c6\u786e\u6027\u3002
- \u8bf7\u6c42\u751f\u6210\u4ee3\u7801\u6765\u6267\u884c\u8ba1\u7b97
-
\u793a\u4f8b\uff1a \u4efb\u52a1\uff1a\u6c42\u89e3\u591a\u9879\u5f0f\u7684\u6240\u6709\u5b9e\u6839\u3002 Prompt\uff1a
Text Only\u8bf7\u7528 Python \u7f16\u5199\u4ee3\u7801\u6765\u8ba1\u7b97\u4ee5\u4e0b\u591a\u9879\u5f0f\u7684\u6240\u6709\u5b9e\u6839\uff1a\n\u591a\u9879\u5f0f\uff1a3x^3 - 5x^2 + 2x - 7\n\u8f93\u51fa Python \u4ee3\u7801\u5e76\u89e3\u91ca\u5176\u4f5c\u7528\u3002\n
\u8f93\u51fa\u7ed3\u679c\uff1a
Pythonimport numpy as np\ncoefficients = [3, -5, 2, -7]\nroots = np.roots(coefficients)\nprint(\"\u5b9e\u6839\u4e3a\uff1a\", [r for r in roots if np.isreal(r)])\n
\u89e3\u91ca\uff1a\u6b64\u4ee3\u7801\u4f7f\u7528 NumPy \u7684 roots \u51fd\u6570\u8ba1\u7b97\u591a\u9879\u5f0f\u7684\u6240\u6709\u6839\uff0c\u5e76\u8fc7\u6ee4\u51fa\u5b9e\u6839\u3002
- \u7ed3\u5408\u5916\u90e8\u6570\u636e\u6216\u5de5\u5177
- \u793a\u4f8b\uff1a\u4f7f\u7528\u5411\u91cf\u6570\u636e\u5e93\u67e5\u8be2\u77e5\u8bc6\u5e93\u4e2d\u7684\u4fe1\u606f\uff0c\u4f5c\u4e3a\u751f\u6210\u7b54\u6848\u7684\u8865\u5145\u3002
"},{"location":"Tools/AI/prompt_writing/#1322-markdown-latex","title":"1.3.2.2 \u8f93\u51fa\u6210 Markdown \u6216 LaTeX \u6587\u6863\u7684\u573a\u666f\u4e0e\u6280\u5de7","text":" - Markdown \u683c\u5f0f\u5316
-
\u793a\u4f8b\uff1a \u4efb\u52a1\uff1a\u751f\u6210\u4f1a\u8bae\u7eaa\u8981\u3002 Prompt\uff1a
Text Only\u8bf7\u5c06\u4ee5\u4e0b\u4f1a\u8bae\u8bb0\u5f55\u603b\u7ed3\u4e3a Markdown \u683c\u5f0f\uff0c\u5305\u62ec\u53d1\u8a00\u4eba\u3001\u4e3b\u8981\u89c2\u70b9\u548c\u540e\u7eed\u4efb\u52a1\u3002\n
\u8f93\u51fa\uff1a
Markdown### \u4f1a\u8bae\u7eaa\u8981\n\n#### \u53d1\u8a00\u4eba\u53ca\u89c2\u70b9\n- **\u5f20\u4e09**\uff1a\u5efa\u8bae\u589e\u52a0\u5e02\u573a\u8425\u9500\u9884\u7b97\u3002\n- **\u674e\u56db**\uff1a\u5f3a\u8c03\u4f18\u5316\u4ea7\u54c1\u8d28\u91cf\u7684\u91cd\u8981\u6027\u3002\n\n#### \u540e\u7eed\u4efb\u52a1\n- \u5236\u5b9a\u65b0\u4e00\u5b63\u5ea6\u7684\u8425\u9500\u7b56\u7565\uff08\u8d1f\u8d23\u4eba\uff1a\u5f20\u4e09\uff09\u3002\n- \u8c03\u67e5\u7528\u6237\u5bf9\u5f53\u524d\u4ea7\u54c1\u7684\u6ee1\u610f\u5ea6\uff08\u8d1f\u8d23\u4eba\uff1a\u674e\u56db\uff09\u3002\n
- LaTeX \u683c\u5f0f\u5316
-
\u793a\u4f8b\uff1a \u4efb\u52a1\uff1a\u751f\u6210\u6570\u5b66\u516c\u5f0f\u3002 Prompt\uff1a
Text Only\u8bf7\u7528 LaTeX \u683c\u5f0f\u4e66\u5199\u4ee5\u4e0b\u516c\u5f0f\u5e76\u89e3\u91ca\uff1a$(a+b)^2 = a^2 + 2ab + b^2$\u3002\n
\u8f93\u51fa\uff1a
TeX\\[\n(a+b)^2 = a^2 + 2ab + b^2\n\\]\n\u8fd9\u662f\u4e00\u4e2a\u57fa\u672c\u7684\u5e73\u65b9\u5c55\u5f00\u516c\u5f0f\uff0c\u5e38\u7528\u4e8e\u591a\u9879\u5f0f\u7684\u8ba1\u7b97\u3002\n
"},{"location":"Tools/AI/prompt_writing/#133-prompt","title":"1.3.3 \u589e\u5f3a Prompt \u7684\u7075\u6d3b\u6027","text":""},{"location":"Tools/AI/prompt_writing/#1331","title":"1.3.3.1 \u4e3a\u5f00\u653e\u5f0f\u4efb\u52a1\u8bbe\u7f6e\u515c\u5e95\u7b56\u7565","text":"\u5f00\u653e\u5f0f\u4efb\u52a1\uff08\u5982\u5199\u6545\u4e8b\u6216\u63a8\u8350\u65b9\u6848\uff09\u5bb9\u6613\u5bfc\u81f4\u6a21\u578b\u201c\u8dd1\u9898\u201d\uff0c\u901a\u8fc7\u515c\u5e95\u7b56\u7565\u53ef\u63d0\u9ad8\u7a33\u5b9a\u6027\u3002
- \u660e\u786e\u4e0d\u7b26\u5408\u6761\u4ef6\u65f6\u7684\u56de\u590d
-
\u793a\u4f8b\uff1a Prompt\uff1a
Text Only\u73b0\u5728\u4f60\u662f\u4e00\u4e2a\u5411\u5ba2\u6237\u63a8\u8350\u7535\u5f71\u7684\u52a9\u624b\u3002\u5982\u679c\u5ba2\u6237\u7684\u4fe1\u606f\u4e0d\u8db3\u4ee5\u7ed9\u51fa\u63a8\u8350\uff0c\u8bf7\u56de\u7b54\uff1a\u201c\u62b1\u6b49\uff0c\u6211\u65e0\u6cd5\u6839\u636e\u60a8\u7684\u63cf\u8ff0\u63a8\u8350\u7535\u5f71\u3002\u201d\u3002\n
\u8f93\u5165\uff1a
Text Only\u5ba2\u6237\uff1a\u5e2e\u6211\u63a8\u8350\u4e00\u90e8\u7535\u5f71\u3002\n
\u8f93\u51fa\uff1a
Text Only\u62b1\u6b49\uff0c\u6211\u65e0\u6cd5\u6839\u636e\u60a8\u7684\u63cf\u8ff0\u63a8\u8350\u7535\u5f71\u3002\n
- \u5b9a\u4e49\u5bb9\u9519\u673a\u5236
-
\u793a\u4f8b\uff1a\u5f53\u751f\u6210\u7ed3\u679c\u4e0d\u7b26\u5408\u683c\u5f0f\u65f6\uff0c\u8981\u6c42\u91cd\u65b0\u751f\u6210\u3002
Text Only\u5982\u679c\u4ee5\u4e0b\u5185\u5bb9\u7684\u8f93\u51fa\u683c\u5f0f\u9519\u8bef\uff0c\u8bf7\u91cd\u65b0\u751f\u6210\u5e76\u786e\u4fdd\u7b26\u5408\u8981\u6c42\uff1a\n\u8f93\u51fa\u683c\u5f0f\uff1a\n- \u4efb\u52a1\u63cf\u8ff0\n- \u4efb\u52a1\u8981\u70b9\n- \u4efb\u52a1\u5efa\u8bae\n
"},{"location":"Tools/AI/prompt_writing/#1332","title":"1.3.3.2 \u6dfb\u52a0\u5f3a\u8c03\u8bcd\u4e0e\u7b26\u53f7\u63d0\u5347\u6307\u4ee4\u6743\u91cd","text":"\u4f7f\u7528\u7279\u6b8a\u7b26\u53f7\uff08\u5982\u52a0\u7c97\uff09\u6216\u5f3a\u8c03\u8bcd\uff08\u5982\u201c\u52a1\u5fc5\u201d\u3001\u201c\u4e25\u683c\u201d\uff09\u6807\u6ce8\u5173\u952e\u5185\u5bb9\u3002
- \u793a\u4f8b\uff1a
\u4efb\u52a1\uff1a\u603b\u7ed3\u6587\u7ae0\u5e76\u5f3a\u8c03\u5173\u952e\u6982\u5ff5\u3002 Prompt\uff1a
Text Only ```text\n \u8bf7\u603b\u7ed3\u4ee5\u4e0b\u6587\u7ae0\uff0c\u5e76**\u52a0\u7c97**\u6bcf\u4e2a\u8981\u70b9\u4e2d\u7684\u5173\u952e\u6982\u5ff5\uff0c\u4ee5 Markdown \u5217\u8868\u5f62\u5f0f\u8f93\u51fa\u3002\n ```\n\n **\u8f93\u51fa**\uff1a\n\n ```markdown\n - **\u8981\u70b9\u4e00**\uff1a\u673a\u5668\u5b66\u4e60\u662f\u901a\u8fc7\u6570\u636e\u8bad\u7ec3\u6a21\u578b\u7684**\u65b9\u6cd5**\u3002\n - **\u8981\u70b9\u4e8c**\uff1a\u6df1\u5ea6\u5b66\u4e60\u662f\u673a\u5668\u5b66\u4e60\u7684**\u5206\u652f**\uff0c\u4ee5\u591a\u5c42\u795e\u7ecf\u7f51\u7edc\u4e3a\u6838\u5fc3\u3002\n ```\n
"},{"location":"Tools/AI/prompt_writing/#14","title":"1.4 \u5b9e\u7528\u6848\u4f8b","text":""},{"location":"Tools/AI/prompt_writing/#141","title":"1.4.1 \u5e38\u89c1\u573a\u666f\u4e0e\u89e3\u51b3\u65b9\u6848","text":""},{"location":"Tools/AI/prompt_writing/#1411","title":"1.4.1.1 \u751f\u6210\u957f\u7bc7\u7684\u6587\u7ae0","text":" - \u9010\u6b65\u751f\u6210\u957f\u6587\u5185\u5bb9
- \u65b9\u6cd5\uff1a\u5148\u751f\u6210\u76ee\u5f55\uff0c\u518d\u9010\u90e8\u5206\u6269\u5c55\u5185\u5bb9\u3002
-
\u793a\u4f8b\uff1a Prompt\uff1a
Text Only\u8bf7\u4e3a\u4ee5\u4e0b\u4e3b\u9898\u751f\u6210\u4e00\u4efd\u8be6\u7ec6\u7684\u6587\u7ae0\u76ee\u5f55\uff0c\u7136\u540e\u57fa\u4e8e\u76ee\u5f55\u9010\u6bb5\u6269\u5c55\u5185\u5bb9\uff1a\n\u4e3b\u9898\uff1a\u5982\u4f55\u4f18\u5316\u4e2a\u4eba\u65f6\u95f4\u7ba1\u7406\n
\u8f93\u51fa\u76ee\u5f55\u793a\u4f8b\uff1a
Markdown- \u7b2c\u4e00\u90e8\u5206\uff1a\u65f6\u95f4\u7ba1\u7406\u7684\u91cd\u8981\u6027\n- \u7b2c\u4e8c\u90e8\u5206\uff1a\u5e38\u89c1\u65f6\u95f4\u7ba1\u7406\u8bef\u533a\n- \u7b2c\u4e09\u90e8\u5206\uff1a\u9ad8\u6548\u65f6\u95f4\u7ba1\u7406\u7684\u65b9\u6cd5\n - 1. \u8bbe\u5b9a\u76ee\u6807\u4e0e\u4f18\u5148\u7ea7\n - 2. \u4f7f\u7528\u5de5\u5177\u4e0e\u6280\u672f\n - 3. \u57f9\u517b\u65f6\u95f4\u7ba1\u7406\u4e60\u60ef\n- \u7b2c\u56db\u90e8\u5206\uff1a\u6848\u4f8b\u5206\u6790\u4e0e\u5e94\u7528\n
\u6269\u5c55\u5185\u5bb9 Prompt\uff1a
Text Only\u8bf7\u8be6\u7ec6\u64b0\u5199\u7b2c\u4e00\u90e8\u5206\u201c\u65f6\u95f4\u7ba1\u7406\u7684\u91cd\u8981\u6027\u201d\u7684\u5185\u5bb9\uff0c\u63a7\u5236\u5728 300 \u5b57\u5de6\u53f3\u3002\n
- \u5728\u751f\u6210\u65f6\u6dfb\u52a0\u5f15\u7528\u6587\u732e
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\u793a\u4f8b\uff1a Prompt\uff1a
Text Only\u8bf7\u4e3a\u4ee5\u4e0b\u4e3b\u9898\u751f\u6210\u4e00\u7bc7 500 \u5b57\u7684\u6587\u7ae0\uff0c\u5e76\u5728\u6587\u7ae0\u672b\u5c3e\u9644\u4e0a\u76f8\u5173\u53c2\u8003\u6587\u732e\uff1a\n\u4e3b\u9898\uff1a\u4eba\u5de5\u667a\u80fd\u5982\u4f55\u6539\u53d8\u6559\u80b2\u884c\u4e1a\n
\u8f93\u51fa\uff1a
Markdown### \u4eba\u5de5\u667a\u80fd\u5982\u4f55\u6539\u53d8\u6559\u80b2\u884c\u4e1a\n\uff08\u6587\u7ae0\u5185\u5bb9\uff09\n#### \u53c2\u8003\u6587\u732e\n- \u674e\u56db\uff0c\u300a\u4eba\u5de5\u667a\u80fd\u4e0e\u6559\u80b2\u300b\uff0c2023\u5e74\u51fa\u7248\u3002\n- OpenAI\uff0c\u300aAI\u5728\u6559\u80b2\u9886\u57df\u7684\u5e94\u7528\u300b\uff0c2021\u5e74\u3002\n
"},{"location":"Tools/AI/prompt_writing/#1412","title":"1.4.1.2 \u89e3\u91ca\u4ee3\u7801\u5e76\u751f\u6210\u8f85\u52a9\u56fe\u8868","text":" - \u751f\u6210\u4ee3\u7801\u89e3\u91ca
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\u793a\u4f8b\uff1a Prompt\uff1a
Text Only\u4ee5\u4e0b\u662f Python \u4ee3\u7801\u7247\u6bb5\uff0c\u8bf7\u89e3\u91ca\u5176\u4f5c\u7528\uff1a\n```python\nnested_dict = lambda: defaultdict(nested_dict)\n
Text Only**\u8f93\u51fa**\uff1a\n```text\n\u6b64\u4ee3\u7801\u5b9a\u4e49\u4e86\u4e00\u4e2a\u5d4c\u5957\u5b57\u5178\u7684\u9012\u5f52\u7ed3\u6784\uff0c\u53ef\u4ee5\u52a8\u6001\u521b\u5efa\u4efb\u610f\u6df1\u5ea6\u7684\u5b57\u5178\u5c42\u7ea7\u3002\n\u793a\u4f8b\u7528\u6cd5\uff1a\ndata = nested_dict()\ndata['level1']['level2'] = \"value\"\n
- \u751f\u6210\u8f85\u52a9\u56fe\u8868
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\u793a\u4f8b\uff1a Prompt\uff1a
Text Only\u8bf7\u6839\u636e\u4ee5\u4e0b\u63cf\u8ff0\u751f\u6210\u4e00\u6bb5 mermaid \u683c\u5f0f\u7684\u4ee3\u7801\uff0c\u7528\u4e8e\u5c55\u793a\u51fd\u6570\u8c03\u7528\u5173\u7cfb\uff1a\n\u63cf\u8ff0\uff1a\u51fd\u6570 A \u8c03\u7528\u51fd\u6570 B\uff0c\u51fd\u6570 B \u8c03\u7528\u51fd\u6570 C \u548c D\uff0c\u51fd\u6570 D \u8fd4\u56de\u7ed3\u679c\u3002\n
\u8f93\u51fa\uff1a
Text Onlygraph TD\nA --> B\nB --> C\nB --> D\nD --> Result\n
"},{"location":"Tools/AI/prompt_writing/#142-prompt","title":"1.4.2 Prompt \u7684\u9650\u5236\u4e0e\u5e94\u5bf9\u7b56\u7565","text":""},{"location":"Tools/AI/prompt_writing/#1421","title":"1.4.2.1 \u8bbe\u8ba1\u62d2\u7b54\u7b56\u7565\u63d0\u5347\u53ef\u9760\u6027","text":" - \u907f\u514d\u5e7b\u89c9\u73b0\u8c61
-
\u63d0\u793a\u6a21\u578b\u4e0d\u8981\u56de\u7b54\u5176\u65e0\u6cd5\u786e\u5b9a\u7684\u5185\u5bb9\u3002 \u793a\u4f8b\uff1a
Text Only\u5982\u679c\u4f60\u4e0d\u77e5\u9053\u4ee5\u4e0b\u95ee\u9898\u7684\u7b54\u6848\uff0c\u8bf7\u56de\u590d\u201c\u4fe1\u606f\u4e0d\u8db3\u201d\u3002\n\u95ee\u9898\uff1a\u8c01\u662f\u7b2c\u4e00\u4e2a\u767b\u4e0a\u6708\u7403\u7684\u673a\u5668\u4eba\uff1f\n
\u8f93\u51fa\uff1a
Text Only\u4fe1\u606f\u4e0d\u8db3\u3002\n
- \u591a\u6b21\u751f\u6210\u4ee5\u786e\u8ba4\u7b54\u6848\u4e00\u81f4\u6027
"},{"location":"Tools/AI/prompt_writing/#1422-ai","title":"1.4.2.2 \u591a\u4e2a AI \u534f\u4f5c","text":" - \u6846\u67b6\u4e0e\u5185\u5bb9\u534f\u4f5c
-
\u901a\u8fc7\u4e0d\u540c\u6a21\u578b\u534f\u4f5c\u4f18\u5316\u5185\u5bb9\u751f\u6210\u3002 \u793a\u4f8b\u6d41\u7a0b\uff1a
- Prompt 1\uff1a\u8ba9 ChatGPT \u63d0\u4f9b\u6587\u7ae0\u6846\u67b6\u3002
Text Only\u8bf7\u4e3a\u4ee5\u4e0b\u4e3b\u9898\u751f\u6210\u8be6\u7ec6\u7684\u6587\u7ae0\u6846\u67b6\uff1a\n\u4e3b\u9898\uff1a\u5982\u4f55\u63d0\u5347\u56e2\u961f\u5408\u4f5c\u6548\u7387\n
- Prompt 2\uff1a\u5c06\u6846\u67b6\u5185\u5bb9\u4ea4\u7ed9\u53e6\u4e00\u4e2a\u6a21\u578b\u4f18\u5316\u8868\u8fbe\u3002
Text Only\u8bf7\u6839\u636e\u4ee5\u4e0b\u6846\u67b6\u64b0\u5199\u8be6\u7ec6\u5185\u5bb9\uff0c\u5e76\u8c03\u6574\u4e3a\u66f4\u4e13\u4e1a\u7684\u8bed\u8a00\u3002\n
- Prompt 3\uff1a\u4f7f\u7528\u53e6\u4e00\u4e2a\u5de5\u5177\uff08\u5982 Grammarly \u6216\u5176\u4ed6 AI\uff09\u6821\u5bf9\u548c\u4f18\u5316\u8bed\u8a00\u98ce\u683c\u3002
- \u4ee3\u7801\u4f18\u5316\u534f\u4f5c
Text Only \u8bf7\u4f18\u5316\u4ee5\u4e0b\u4ee3\u7801\uff0c\u4f7f\u5176\u8fd0\u884c\u6548\u7387\u66f4\u9ad8\uff0c\u5e76\u63d0\u4f9b\u6ce8\u91ca\u8bf4\u660e\uff1a\n ```python\n def factorial(n):\n if n == 0:\n return 1\n else:\n return n * factorial(n-1)\n ```\n
"},{"location":"Tools/AI/prompt_writing/#15","title":"1.5 \u6211\u66fe\u7ecf\u7528\u6cd5","text":" - \u8f93\u51fa\u6587\u6863\uff1a\u901a\u8fc7 Markdown \u6216 LaTeX \u751f\u6210\u683c\u5f0f\u5316\u6587\u6863\u3002
- \u751f\u6210 README\uff1a\u8ba9 AI \u9605\u8bfb\u4ee3\u7801\u5e76\u64b0\u5199\u8bf4\u660e\u3002
- \u5f15\u7528\u6587\u732e\u5199\u8bba\u6587\uff1a\u5229\u7528 AI \u4e0a\u7f51\u67e5\u8be2\u8d44\u6599\uff0c\u63d0\u9ad8\u5b66\u672f\u5185\u5bb9\u8d28\u91cf\u3002
"},{"location":"Tools/AI/prompt_writing/#2-prompt","title":"2 \u5e38\u7528\u7684 Prompt","text":"\u8bf7\u53c2\u8003 \u5e38\u7528 prompt \u8bb0\u5f55 - wnc \u7684\u5496\u5561\u9986
"},{"location":"Tools/Blog/Mkdocs_Material/","title":"mkdocs material \u8d85\u5168\u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#mkdocs-material","title":"mkdocs material \u8d85\u5168\u914d\u7f6e","text":" \u7ea6 5956 \u4e2a\u5b57 9393 \u884c\u4ee3\u7801 7 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 147 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
\u4ecd\u7136\u5728\u4fee\u6539 \u5982\u679c\u9700\u8981\u4efb\u4f55\u7684\u6587\u4ef6\uff0c\u53ef\u4ee5\u76f4\u63a5\u8bbf\u95ee\u672c\u535a\u5ba2\u7684 GitHub \u9875\u9762
"},{"location":"Tools/Blog/Mkdocs_Material/#1","title":"1 \u5165\u95e8\u57fa\u7840","text":""},{"location":"Tools/Blog/Mkdocs_Material/#11-mkdocs","title":"1.1 \u4ec0\u4e48\u662f MkDocs\uff1f","text":"MkDocs \u662f\u4e00\u4e2a\u5feb\u901f\u3001\u7b80\u5355\u3001\u534e\u4e3d\u7684\u9759\u6001\u7ad9\u70b9\u751f\u6210\u5668\uff0c\u4e13\u95e8\u7528\u4e8e\u6784\u5efa\u9879\u76ee\u6587\u6863\u3002\u6587\u6863\u6e90\u6587\u4ef6\u4f7f\u7528 Markdown \u7f16\u5199\uff0c\u914d\u7f6e\u6587\u4ef6\u4f7f\u7528 YAML \u683c\u5f0f\u3002
"},{"location":"Tools/Blog/Mkdocs_Material/#111-mkdocs","title":"1.1.1 MkDocs \u7684\u4f18\u52bf","text":" -
\u7b80\u5355\u6613\u7528 - \u4f7f\u7528 Markdown \u7f16\u5199\u6587\u6863 - \u914d\u7f6e\u6587\u4ef6\u7b80\u5355\u76f4\u89c2 - \u4e00\u952e\u5f0f\u6784\u5efa\u548c\u90e8\u7f72
-
\u529f\u80fd\u5f3a\u5927 - \u5185\u7f6e\u5f00\u53d1\u670d\u52a1\u5668\uff0c\u652f\u6301\u5b9e\u65f6\u9884\u89c8 - \u591a\u79cd\u4e3b\u9898\u53ef\u9009 - \u652f\u6301\u81ea\u5b9a\u4e49\u4e3b\u9898 - \u81ea\u52a8\u751f\u6210\u5bfc\u822a - \u5168\u6587\u641c\u7d22\u529f\u80fd
-
\u90e8\u7f72\u65b9\u4fbf - \u751f\u6210\u7eaf\u9759\u6001\u9875\u9762 - \u4e00\u884c\u547d\u4ee4\u90e8\u7f72\u5230 GitHub Pages - \u652f\u6301\u81ea\u5b9a\u4e49\u57df\u540d - \u517c\u5bb9\u6240\u6709\u9759\u6001\u7f51\u7ad9\u6258\u7ba1\u5e73\u53f0
"},{"location":"Tools/Blog/Mkdocs_Material/#112-mkdocs-vs","title":"1.1.2 MkDocs vs \u5176\u4ed6\u6587\u6863\u5de5\u5177","text":"\u5de5\u5177 \u4f18\u52bf \u52a3\u52bf MkDocs - \u7b80\u5355\u6613\u7528- \u4e13\u6ce8\u6587\u6863- \u90e8\u7f72\u65b9\u4fbf- \u4e3b\u9898\u4e30\u5bcc - \u529f\u80fd\u76f8\u5bf9\u7b80\u5355- \u63d2\u4ef6\u751f\u6001\u8f83\u5c0f GitBook - \u754c\u9762\u4f18\u96c5- \u751f\u6001\u5b8c\u6574- \u591a\u4eba\u534f\u4f5c\u597d - \u6784\u5efa\u901f\u5ea6\u6162- \u5b9a\u5236\u6027\u5dee- \u514d\u8d39\u7248\u9650\u5236\u591a Docusaurus - React \u6280\u672f\u6808- \u529f\u80fd\u5f3a\u5927- \u6269\u5c55\u6027\u597d - \u5b66\u4e60\u66f2\u7ebf\u9661- \u914d\u7f6e\u590d\u6742- \u6784\u5efa\u8f83\u6162 VuePress - Vue \u6280\u672f\u6808- \u5b9a\u5236\u6027\u5f3a- \u63d2\u4ef6\u4e30\u5bcc - \u4e3b\u9898\u8f83\u5c11- \u914d\u7f6e\u7e41\u7410- \u5b66\u4e60\u6210\u672c\u9ad8"},{"location":"Tools/Blog/Mkdocs_Material/#113-mkdocs","title":"1.1.3 MkDocs \u5de5\u4f5c\u539f\u7406","text":"MkDocs \u7684\u5de5\u4f5c\u6d41\u7a0b\u5982\u4e0b\uff1a
-
\u6587\u6863\u7f16\u5199 - \u4f7f\u7528 Markdown \u683c\u5f0f\u7f16\u5199\u6587\u6863 - \u6587\u6863\u5b58\u653e\u5728 docs \u76ee\u5f55\u4e0b - \u652f\u6301\u591a\u7ea7\u76ee\u5f55\u7ed3\u6784
-
\u914d\u7f6e\u89e3\u6790 - \u8bfb\u53d6 mkdocs.yml \u914d\u7f6e\u6587\u4ef6 - \u89e3\u6790\u4e3b\u9898\u8bbe\u7f6e\u3001\u63d2\u4ef6\u914d\u7f6e\u7b49 - \u751f\u6210\u5bfc\u822a\u7ed3\u6784
-
\u6784\u5efa\u8fc7\u7a0b
Text OnlyMarkdown \u6587\u4ef6 -> \u89e3\u6790\u5668 -> HTML \u6587\u4ef6\n -> \u4e3b\u9898\u6e32\u67d3\n -> \u63d2\u4ef6\u5904\u7406\n -> \u9759\u6001\u8d44\u6e90\u5904\u7406\n
- \u8f93\u51fa\u90e8\u7f72 - \u751f\u6210\u7eaf\u9759\u6001 HTML \u6587\u4ef6 - \u4fdd\u7559\u539f\u59cb\u76ee\u5f55\u7ed3\u6784 - \u81ea\u52a8\u5904\u7406\u5185\u90e8\u94fe\u63a5 - \u590d\u5236\u9759\u6001\u8d44\u6e90
"},{"location":"Tools/Blog/Mkdocs_Material/#12-material","title":"1.2 \u4e3a\u4ec0\u4e48\u9009\u62e9 Material \u4e3b\u9898","text":"Material for MkDocs \u662f\u4e00\u4e2a\u57fa\u4e8e Google Material Design \u8bbe\u8ba1\u8bed\u8a00\u7684\u4e3b\u9898\uff0c\u5b83\u4e0d\u4ec5\u7f8e\u89c2\uff0c\u800c\u4e14\u529f\u80fd\u5f3a\u5927\u3002
"},{"location":"Tools/Blog/Mkdocs_Material/#121-material","title":"1.2.1 Material \u4e3b\u9898\u7279\u6027","text":" -
\u73b0\u4ee3\u5316\u8bbe\u8ba1 - \u9075\u5faa Material Design \u89c4\u8303 - \u54cd\u5e94\u5f0f\u5e03\u5c40 - \u652f\u6301\u6df1\u8272\u6a21\u5f0f - \u81ea\u52a8\u9002\u914d\u79fb\u52a8\u8bbe\u5907
-
\u5f3a\u5927\u529f\u80fd - \u667a\u80fd\u641c\u7d22 - \u4ee3\u7801\u9ad8\u4eae - \u6807\u7b7e\u9875\u652f\u6301 - \u81ea\u52a8\u76ee\u5f55\u751f\u6210 - \u591a\u8bed\u8a00\u652f\u6301 - \u7248\u672c\u63a7\u5236\u96c6\u6210
-
\u51fa\u8272\u7684\u7528\u6237\u4f53\u9a8c - \u5feb\u901f\u52a0\u8f7d - \u5e73\u6ed1\u52a8\u753b - \u5b9e\u65f6\u641c\u7d22 - \u4ee3\u7801\u590d\u5236\u6309\u94ae - \u8fd4\u56de\u9876\u90e8\u6309\u94ae
"},{"location":"Tools/Blog/Mkdocs_Material/#122","title":"1.2.2 \u4e0e\u5176\u4ed6\u4e3b\u9898\u5bf9\u6bd4","text":"\u7279\u6027 Material ReadTheDocs mkdocs \u5176\u4ed6\u4e3b\u9898 \u8bbe\u8ba1\u98ce\u683c \u73b0\u4ee3\u7b80\u7ea6 \u4f20\u7edf\u6587\u6863 \u7b80\u5355\u57fa\u7840 \u98ce\u683c\u591a\u6837 \u54cd\u5e94\u5f0f \u2705 \u2705 \u274c \u90e8\u5206\u652f\u6301 \u6df1\u8272\u6a21\u5f0f \u2705 \u274c \u274c \u90e8\u5206\u652f\u6301 \u641c\u7d22\u529f\u80fd \u2705 \u2705 \u274c \u90e8\u5206\u652f\u6301 \u5b9a\u5236\u6027 \u5f3a \u4e2d \u5f31 \u4e0d\u4e00\u81f4 \u63d2\u4ef6\u652f\u6301 \u4e30\u5bcc \u4e00\u822c \u57fa\u7840 \u4e0d\u4e00\u81f4"},{"location":"Tools/Blog/Mkdocs_Material/#123-material","title":"1.2.3 Material \u4e3b\u9898\u7684\u6280\u672f\u67b6\u6784","text":"Text OnlyMaterial Theme\n\u251c\u2500\u2500 \u6838\u5fc3\u7ec4\u4ef6\n\u2502 \u251c\u2500\u2500 \u5bfc\u822a\u680f\n\u2502 \u251c\u2500\u2500 \u4fa7\u8fb9\u680f\n\u2502 \u251c\u2500\u2500 \u641c\u7d22\u7ec4\u4ef6\n\u2502 \u2514\u2500\u2500 \u5185\u5bb9\u6e32\u67d3\u5668\n\u251c\u2500\u2500 \u6269\u5c55\u529f\u80fd\n\u2502 \u251c\u2500\u2500 \u4ee3\u7801\u9ad8\u4eae\n\u2502 \u251c\u2500\u2500 \u6807\u7b7e\u7cfb\u7edf\n\u2502 \u251c\u2500\u2500 \u76ee\u5f55\u751f\u6210\n\u2502 \u2514\u2500\u2500 \u4e3b\u9898\u5207\u6362\n\u2514\u2500\u2500 \u63d2\u4ef6\u7cfb\u7edf\n \u251c\u2500\u2500 \u5185\u7f6e\u63d2\u4ef6\n \u2514\u2500\u2500 \u7b2c\u4e09\u65b9\u63d2\u4ef6\u96c6\u6210\n
"},{"location":"Tools/Blog/Mkdocs_Material/#13","title":"1.3 \u73af\u5883\u8981\u6c42","text":""},{"location":"Tools/Blog/Mkdocs_Material/#131-python","title":"1.3.1 Python \u73af\u5883","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1311-python","title":"1.3.1.1 Python \u7248\u672c\u9009\u62e9","text":"MkDocs \u9700\u8981 Python 3.6 \u6216\u66f4\u9ad8\u7248\u672c\uff0c\u63a8\u8350\u4f7f\u7528 Python 3.8+\uff1a
Bash# \u68c0\u67e5 Python \u7248\u672c\npython --version\n\n# \u63a8\u8350\u7248\u672c\nPython 3.8.x\nPython 3.9.x\nPython 3.10.x\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1312-pip","title":"1.3.1.2 pip \u914d\u7f6e\u8bf4\u660e","text":"pip \u662f Python \u7684\u5305\u7ba1\u7406\u5de5\u5177\uff0c\u9700\u8981\u786e\u4fdd\u5176\u6b63\u786e\u5b89\u88c5\uff1a
Bash# \u68c0\u67e5 pip \u7248\u672c\npip --version\n\n# \u5347\u7ea7 pip\npython -m pip install --upgrade pip\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1313","title":"1.3.1.3 \u865a\u62df\u73af\u5883\u7ba1\u7406","text":"\u63a8\u8350\u4f7f\u7528\u865a\u62df\u73af\u5883\u6765\u7ba1\u7406\u9879\u76ee\u4f9d\u8d56\uff1a
Bash# \u521b\u5efa\u865a\u62df\u73af\u5883\npython -m venv venv\n\n# \u6fc0\u6d3b\u865a\u62df\u73af\u5883\n# Windows\nvenv\\Scripts\\activate\n# Linux/Mac\nsource venv/bin/activate\n\n# \u9000\u51fa\u865a\u62df\u73af\u5883\ndeactivate\n
"},{"location":"Tools/Blog/Mkdocs_Material/#132-pip","title":"1.3.2 pip \u5305\u7ba1\u7406","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1321-pip","title":"1.3.2.1 pip \u6e90\u914d\u7f6e","text":"\u4e3a\u52a0\u5feb\u4e0b\u8f7d\u901f\u5ea6\uff0c\u5efa\u8bae\u4f7f\u7528\u56fd\u5185\u955c\u50cf\u6e90\uff1a
Bash# \u4e34\u65f6\u4f7f\u7528\npip install -i https://pypi.tuna.tsinghua.edu.cn/simple mkdocs\n\n# \u6c38\u4e45\u914d\u7f6e\npip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1322","title":"1.3.2.2 \u4f9d\u8d56\u7ba1\u7406","text":"\u5b89\u88c5\u5fc5\u8981\u7684\u5305\uff1a
Bashpip install mkdocs-material\npip install mkdocs-glightbox\npip install mkdocs-git-revision-date-localized-plugin\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1323-requirements-txt","title":"1.3.2.3 requirements. txt \u4f7f\u7528","text":"\u7ef4\u62a4\u9879\u76ee\u4f9d\u8d56\uff1a
Bash# \u751f\u6210\u4f9d\u8d56\u6587\u4ef6\npip freeze > requirements.txt\n\n# \u5b89\u88c5\u4f9d\u8d56\npip install -r requirements.txt\n
"},{"location":"Tools/Blog/Mkdocs_Material/#133-git","title":"1.3.3 Git \u73af\u5883","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1331-git","title":"1.3.3.1 Git \u57fa\u7840\u914d\u7f6e","text":"Bash# \u914d\u7f6e\u7528\u6237\u4fe1\u606f\ngit config --global user.name \"Your Name\"\ngit config --global user.email \"your.email@example.com\"\n\n# \u914d\u7f6e\u9ed8\u8ba4\u5206\u652f\ngit config --global init.defaultBranch main\n
"},{"location":"Tools/Blog/Mkdocs_Material/#134-ssh","title":"1.3.4 SSH \u5bc6\u94a5\u914d\u7f6e","text":"Bash# \u751f\u6210 SSH \u5bc6\u94a5\nssh-keygen -t rsa -b 4096 -C \"your.email@example.com\"\n\n# \u67e5\u770b\u516c\u94a5\ncat ~/.ssh/id_rsa.pub\n
\u5c06\u516c\u94a5\u6dfb\u52a0\u5230 GitHub \u8d26\u6237\u7684 SSH keys \u4e2d\u3002
"},{"location":"Tools/Blog/Mkdocs_Material/#135-gitignore","title":"1.3.5 .gitignore \u914d\u7f6e","text":"\u521b\u5efa .gitignore \u6587\u4ef6\uff0c\u6dfb\u52a0\u4ee5\u4e0b\u5185\u5bb9\uff1a
Text Only# Python\n__pycache__/\n*.py[cod]\n*$py.class\nvenv/\n\n# MkDocs\nsite/\n\n# IDE\n.idea/\n.vscode/\n*.swp\n*.swo\n\n# OS\n.DS_Store\nThumbs.db\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2","title":"2 \u73af\u5883\u642d\u5efa","text":""},{"location":"Tools/Blog/Mkdocs_Material/#21-windows","title":"2.1 Windows \u7cfb\u7edf\u5b89\u88c5","text":""},{"location":"Tools/Blog/Mkdocs_Material/#211-python","title":"2.1.1 Python \u5b89\u88c5","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2111","title":"2.1.1.1 \u4e0b\u8f7d\u5b89\u88c5\u5305","text":" - \u8bbf\u95ee Python \u5b98\u7f51 \u4e0b\u8f7d\u6700\u65b0\u7248\u672c
- \u9009\u62e9\u9002\u5408\u4f60\u7684 Windows \u7248\u672c\uff0832 \u4f4d/64 \u4f4d\uff09\u7684\u5b89\u88c5\u5305
- \u4e0b\u8f7d\u5b8c\u6210\u540e\u53cc\u51fb\u5b89\u88c5\u5305\u5f00\u59cb\u5b89\u88c5
"},{"location":"Tools/Blog/Mkdocs_Material/#2112","title":"2.1.1.2 \u73af\u5883\u53d8\u91cf\u914d\u7f6e","text":" - \u5b89\u88c5\u65f6\u52fe\u9009 \"Add Python to PATH\"
- \u5982\u679c\u5fd8\u8bb0\u52fe\u9009\uff0c\u53ef\u4ee5\u624b\u52a8\u6dfb\u52a0\uff1a
Text Only# \u6dfb\u52a0\u5230\u7cfb\u7edf\u73af\u5883\u53d8\u91cf Path\nC:\\Users\\YourUser\\AppData\\Local\\Programs\\Python\\Python3x\\\nC:\\Users\\YourUser\\AppData\\Local\\Programs\\Python\\Python3x\\Scripts\\\n
- \u68c0\u67e5\u73af\u5883\u53d8\u91cf\uff1a
Bashecho %PATH%\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2113","title":"2.1.1.3 \u9a8c\u8bc1\u5b89\u88c5","text":"\u5728\u547d\u4ee4\u63d0\u793a\u7b26\u4e2d\u6267\u884c\uff1a
Bash# \u68c0\u67e5 Python \u7248\u672c\npython --version\n\n# \u68c0\u67e5 pip \u7248\u672c\npip --version\n
"},{"location":"Tools/Blog/Mkdocs_Material/#212-mkdocs","title":"2.1.2 MkDocs \u5b89\u88c5","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2121-pip","title":"2.1.2.1 pip \u5b89\u88c5\u65b9\u6cd5","text":"\u4f7f\u7528 pip \u5b89\u88c5 MkDocs\uff1a
Bash# \u5b89\u88c5 MkDocs\npip install mkdocs\n\n# \u9a8c\u8bc1\u5b89\u88c5\nmkdocs --version\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2122","title":"2.1.2.2 \u5e38\u89c1\u95ee\u9898\u89e3\u51b3","text":" -
pip \u4e0d\u662f\u5185\u90e8\u547d\u4ee4 - \u89e3\u51b3\u65b9\u6cd5\uff1a\u91cd\u65b0\u6dfb\u52a0 Python Scripts \u76ee\u5f55\u5230 PATH
-
\u6743\u9650\u95ee\u9898 - \u89e3\u51b3\u65b9\u6cd5\uff1a\u4f7f\u7528\u7ba1\u7406\u5458\u6743\u9650\u8fd0\u884c\u547d\u4ee4\u63d0\u793a\u7b26
Bash# \u7ba1\u7406\u5458\u6743\u9650\u5b89\u88c5\npip install --user mkdocs\n
- SSL \u8bc1\u4e66\u9519\u8bef - \u89e3\u51b3\u65b9\u6cd5\uff1a\u6dfb\u52a0\u4fe1\u4efb\u9009\u9879\u6216\u4f7f\u7528\u56fd\u5185\u955c\u50cf
Bashpip install --trusted-host pypi.org --trusted-host files.pythonhosted.org mkdocs\n
"},{"location":"Tools/Blog/Mkdocs_Material/#213","title":"2.1.3 \u7248\u672c\u9009\u62e9","text":"MkDocs \u7248\u672c\u9009\u62e9\u5efa\u8bae\uff1a
Bash# \u67e5\u770b\u53ef\u7528\u7248\u672c\npip install mkdocs==\n\n# \u5b89\u88c5\u7279\u5b9a\u7248\u672c\npip install mkdocs==1.5.3 # \u63a8\u8350\u7248\u672c\n
"},{"location":"Tools/Blog/Mkdocs_Material/#214-material","title":"2.1.4 Material \u4e3b\u9898\u5b89\u88c5","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2141","title":"2.1.4.1 \u5b89\u88c5\u547d\u4ee4","text":"Bash# \u5b89\u88c5 Material \u4e3b\u9898\npip install mkdocs-material\n\n# \u9a8c\u8bc1\u5b89\u88c5\npython -c \"import mkdocs_material; print(mkdocs_material.__version__)\"\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2142","title":"2.1.4.2 \u4f9d\u8d56\u68c0\u67e5","text":"\u5b89\u88c5\u5fc5\u8981\u7684\u4f9d\u8d56\uff1a
Bash# \u5b89\u88c5\u6269\u5c55\u652f\u6301\npip install pymdown-extensions\npip install mkdocs-glightbox\npip install mkdocs-git-revision-date-localized-plugin\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2143","title":"2.1.4.3 \u7248\u672c\u517c\u5bb9\u6027","text":"MkDocs \u7248\u672c Material \u7248\u672c Python \u7248\u672c 1.5. x 9.4. x \u22653.8 1.4. x 9.3. x \u22653.7 1.3. x 9.2. x \u22653.7"},{"location":"Tools/Blog/Mkdocs_Material/#22-linuxmac","title":"2.2 Linux/Mac \u7cfb\u7edf\u5b89\u88c5","text":""},{"location":"Tools/Blog/Mkdocs_Material/#221-python","title":"2.2.1 \u5305\u7ba1\u7406\u5668\u5b89\u88c5 Python","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2211-aptyum","title":"2.2.1.1 apt/yum \u5b89\u88c5\u65b9\u6cd5","text":"Ubuntu/Debian:
Bash# \u66f4\u65b0\u5305\u7d22\u5f15\nsudo apt update\n\n# \u5b89\u88c5 Python\nsudo apt install python3 python3-pip\n\n# \u5b89\u88c5\u5f00\u53d1\u5de5\u5177\nsudo apt install python3-dev\n
CentOS/RHEL:
Bash# \u5b89\u88c5 EPEL \u4ed3\u5e93\nsudo yum install epel-release\n\n# \u5b89\u88c5 Python\nsudo yum install python3 python3-pip\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2212-brew","title":"2.2.1.2 brew \u5b89\u88c5\u65b9\u6cd5","text":"macOS:
Bash# \u5b89\u88c5 Homebrew\uff08\u5982\u679c\u672a\u5b89\u88c5\uff09\n/bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\"\n\n# \u5b89\u88c5 Python\nbrew install python\n\n# \u66f4\u65b0 pip\npip3 install --upgrade pip\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2213","title":"2.2.1.3 \u73af\u5883\u53d8\u91cf\u914d\u7f6e","text":"bash/zsh:
Bash# \u6dfb\u52a0\u5230 ~/.bashrc \u6216 ~/.zshrc\nexport PATH=\"$HOME/.local/bin:$PATH\"\nexport PYTHONPATH=\"$HOME/.local/lib/python3.x/site-packages:$PYTHONPATH\"\n\n# \u66f4\u65b0\u73af\u5883\u53d8\u91cf\nsource ~/.bashrc # \u6216 source ~/.zshrc\n
"},{"location":"Tools/Blog/Mkdocs_Material/#222-pip","title":"2.2.2 pip \u5b89\u88c5\u4f9d\u8d56","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2221","title":"2.2.2.1 \u7cfb\u7edf\u7ea7\u5b89\u88c5","text":"Bash# \u5168\u5c40\u5b89\u88c5\uff08\u9700\u8981 root \u6743\u9650\uff09\nsudo pip3 install mkdocs mkdocs-material\n\n# \u9a8c\u8bc1\u5b89\u88c5\nmkdocs --version\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2222","title":"2.2.2.2 \u7528\u6237\u7ea7\u5b89\u88c5","text":"Bash# \u7528\u6237\u76ee\u5f55\u5b89\u88c5\npip3 install --user mkdocs mkdocs-material\n\n# \u68c0\u67e5\u5b89\u88c5\u8def\u5f84\npython3 -m site --user-site\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2223","title":"2.2.2.3 \u865a\u62df\u73af\u5883\u5b89\u88c5","text":"Bash# \u521b\u5efa\u865a\u62df\u73af\u5883\npython3 -m venv mkdocs-env\n\n# \u6fc0\u6d3b\u865a\u62df\u73af\u5883\nsource mkdocs-env/bin/activate\n\n# \u5b89\u88c5\u4f9d\u8d56\npip install mkdocs mkdocs-material\n\n# \u9000\u51fa\u865a\u62df\u73af\u5883\ndeactivate\n
"},{"location":"Tools/Blog/Mkdocs_Material/#23","title":"2.3 \u9879\u76ee\u521d\u59cb\u5316","text":""},{"location":"Tools/Blog/Mkdocs_Material/#231","title":"2.3.1 \u521b\u5efa\u9879\u76ee","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2311-mkdocs-new","title":"2.3.1.1 mkdocs new \u547d\u4ee4\u8be6\u89e3","text":"Bash# \u57fa\u672c\u8bed\u6cd5\nmkdocs new [\u9879\u76ee\u540d]\n\n# \u521b\u5efa\u65b0\u9879\u76ee\nmkdocs new my-docs\n\n# \u4f7f\u7528\u73b0\u6709\u76ee\u5f55\ncd existing-project\nmkdocs new .\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2312","title":"2.3.1.2 \u9879\u76ee\u547d\u540d\u89c4\u8303","text":" - \u4f7f\u7528\u5c0f\u5199\u5b57\u6bcd
- \u5355\u8bcd\u95f4\u7528\u8fde\u5b57\u7b26 (-) \u5206\u9694
- \u907f\u514d\u4f7f\u7528\u7279\u6b8a\u5b57\u7b26
- \u540d\u79f0\u5177\u6709\u63cf\u8ff0\u6027
\u793a\u4f8b\uff1a
Bashmkdocs new technical-docs # \u597d\u7684\u547d\u540d\nmkdocs new tech_docs # \u907f\u514d\u4f7f\u7528\u4e0b\u5212\u7ebf\nmkdocs new TechDocs # \u907f\u514d\u4f7f\u7528\u5927\u5199\n
"},{"location":"Tools/Blog/Mkdocs_Material/#2313","title":"2.3.1.3 \u521d\u59cb\u5316\u914d\u7f6e","text":"\u521b\u5efa\u9879\u76ee\u540e\u7684\u57fa\u672c\u8bbe\u7f6e\uff1a
Bashcd my-docs\n# \u542f\u52a8\u5f00\u53d1\u670d\u52a1\u5668\nmkdocs serve\n# \u5728\u6d4f\u89c8\u5668\u4e2d\u8bbf\u95ee http://127.0.0.1:8000\n
"},{"location":"Tools/Blog/Mkdocs_Material/#232","title":"2.3.2 \u76ee\u5f55\u7ed3\u6784\u8bf4\u660e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#2321","title":"2.3.2.1 \u57fa\u7840\u76ee\u5f55\u7ed3\u6784","text":"Text Onlymy-docs/\n\u251c\u2500\u2500 docs/ # \u6587\u6863\u76ee\u5f55\n\u2502 \u251c\u2500\u2500 index.md # \u9996\u9875\n\u2502 \u251c\u2500\u2500 about.md # \u5176\u4ed6\u9875\u9762\n\u2502 \u2514\u2500\u2500 img/ # \u56fe\u7247\u76ee\u5f55\n\u251c\u2500\u2500 mkdocs.yml # \u914d\u7f6e\u6587\u4ef6\n\u2514\u2500\u2500 venv/ # \u865a\u62df\u73af\u5883\uff08\u53ef\u9009\uff09\n
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Markdown# \u7528\u6237\u6307\u5357\n\n\u8fd9\u662f\u7528\u6237\u6307\u5357\u7684\u4e3b\u9875\u9762\uff0c\u5305\u542b\u4ee5\u4e0b\u5185\u5bb9\uff1a\n\n## \u5feb\u901f\u5bfc\u822a\n\n- [\u57fa\u7840\u7528\u6cd5] (basic-usage.md) - \u5165\u95e8\u5fc5\u8bfb\n- [\u9ad8\u7ea7\u7279\u6027] (advanced.md) - \u6df1\u5165\u4e86\u89e3\n\n## \u672c\u8282\u5185\u5bb9\n\n\u6b64\u90e8\u5206\u5c06\u5e2e\u52a9\u60a8\u4e86\u89e3\u4ea7\u54c1\u7684\u6838\u5fc3\u529f\u80fd\u548c\u4f7f\u7528\u65b9\u6cd5...\n
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YAMLnav:\n - \u7528\u6237\u6307\u5357:\n - \u6982\u8ff0: user-guide/index.md\n - \u57fa\u7840\u7528\u6cd5: user-guide/basic-usage.md\n - \u9ad8\u7ea7\u7279\u6027: user-guide/advanced.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#323","title":"3.2.3 \u591a\u7ea7\u76ee\u5f55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3231","title":"3.2.3.1 \u5c42\u7ea7\u7ed3\u6784\u8bbe\u8ba1","text":"\u590d\u6742\u7684\u591a\u7ea7\u76ee\u5f55\u793a\u4f8b\uff1a
YAMLnav:\n - \u9996\u9875: index.md\n - \u5165\u95e8\u6307\u5357:\n - \u6982\u8ff0: getting-started/index.md\n - \u57fa\u7840:\n - \u5b89\u88c5: getting-started/basics/installation.md\n - \u914d\u7f6e: getting-started/basics/configuration.md\n - \u8fdb\u9636:\n - \u81ea\u5b9a\u4e49: getting-started/advanced/customization.md\n - \u4f18\u5316: getting-started/advanced/optimization.md\n - \u5f00\u53d1\u6587\u6863:\n - \u6982\u8ff0: development/index.md\n - API:\n - \u8ba4\u8bc1: development/api/authentication.md\n - \u63a5\u53e3: development/api/endpoints.md\n - SDK:\n - Python: development/sdk/python.md\n - JavaScript: development/sdk/javascript.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3232","title":"3.2.3.2 \u5bfc\u822a\u6df1\u5ea6\u63a7\u5236","text":"YAMLtheme:\n name: material\n features:\n - navigation.sections # \u663e\u793a\u7ae0\u8282\n - navigation.expand # \u5c55\u5f00\u5bfc\u822a\n - navigation.indexes # \u4f7f\u7528\u76ee\u5f55\u7d22\u5f15\n - toc.integrate # \u96c6\u6210\u76ee\u5f55\n\nmarkdown_extensions:\n - toc:\n permalink: true\n toc_depth: 3 # \u63a7\u5236\u76ee\u5f55\u6df1\u5ea6\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3233","title":"3.2.3.3 \u6298\u53e0\u914d\u7f6e","text":"Material \u4e3b\u9898\u7684\u6298\u53e0\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n features:\n - navigation.sections # \u663e\u793a\u5206\u533a\n - navigation.expand # \u9ed8\u8ba4\u5c55\u5f00\n - navigation.indexes # \u4f7f\u7528\u7d22\u5f15\u9875\n - navigation.top # \u8fd4\u56de\u9876\u90e8\u6309\u94ae\n\n # \u5bfc\u822a\u680f\u8bbe\u7f6e\n nav_style: dark # \u5bfc\u822a\u680f\u6837\u5f0f\n collapse_navigation: true # \u6298\u53e0\u5bfc\u822a\n sticky_navigation: true # \u56fa\u5b9a\u5bfc\u822a\n
"},{"location":"Tools/Blog/Mkdocs_Material/#324","title":"3.2.4 \u5bfc\u822a\u914d\u7f6e\u6700\u4f73\u5b9e\u8df5","text":" -
\u7ed3\u6784\u8bbe\u8ba1\u539f\u5219\uff1a - \u4fdd\u6301\u5c42\u7ea7\u6e05\u6670 - \u63a7\u5236\u5bfc\u822a\u6df1\u5ea6\uff08\u5efa\u8bae\u4e0d\u8d85\u8fc7 3 \u5c42\uff09 - \u76f8\u5173\u5185\u5bb9\u5206\u7ec4 - \u4f7f\u7528\u76f4\u89c2\u7684\u547d\u540d
-
\u6587\u4ef6\u7ec4\u7ec7\uff1a - \u4f7f\u7528\u6709\u610f\u4e49\u7684\u76ee\u5f55\u540d - \u4fdd\u6301\u6587\u4ef6\u7ed3\u6784\u6574\u6d01 - \u5408\u7406\u4f7f\u7528\u7d22\u5f15\u6587\u4ef6 - \u9075\u5faa\u4e00\u81f4\u7684\u547d\u540d\u89c4\u8303
-
\u7528\u6237\u4f53\u9a8c\uff1a - \u63d0\u4f9b\u6e05\u6670\u7684\u5bfc\u822a\u8def\u5f84 - \u6dfb\u52a0\u5408\u9002\u7684\u63cf\u8ff0 - \u8003\u8651\u79fb\u52a8\u7aef\u663e\u793a - \u4f18\u5316\u5bfc\u822a\u54cd\u5e94\u901f\u5ea6
-
\u7ef4\u62a4\u5efa\u8bae\uff1a - \u5b9a\u671f\u68c0\u67e5\u6b7b\u94fe\u63a5 - \u66f4\u65b0\u5bfc\u822a\u7ed3\u6784 - \u4fdd\u6301\u6587\u6863\u540c\u6b65 - \u6536\u96c6\u7528\u6237\u53cd\u9988
"},{"location":"Tools/Blog/Mkdocs_Material/#325","title":"3.2.5 \u7279\u6b8a\u5bfc\u822a\u529f\u80fd","text":" - \u9690\u85cf\u9875\u9762\uff1a
YAMLnav:\n - \u53ef\u89c1\u9875\u9762: visible.md\n - !hidden \u9690\u85cf\u9875\u9762: hidden.md\n
- \u5916\u90e8\u94fe\u63a5\uff1a
YAMLnav:\n - \u6587\u6863: index.md\n - GitHub: https://github.com/your/repo\n - \u793e\u533a: \n - \u8bba\u575b: https://forum.example.com\n - \u535a\u5ba2: https://blog.example.com\n
- \u522b\u540d\u8bbe\u7f6e\uff1a
YAMLnav:\n - \u5f00\u59cb: \n - \u6982\u8ff0: getting-started/index.md\n - \u5feb\u901f\u5165\u95e8: getting-started/quickstart.md\n - \u540c\u4e00\u6587\u4ef6\u4e0d\u540c\u5165\u53e3: !alias getting-started/quickstart.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#33-markdown","title":"3.3 Markdown \u6269\u5c55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#331","title":"3.3.1 \u57fa\u7840\u6269\u5c55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3311-meta","title":"3.3.1.1 meta \u6269\u5c55","text":"\u652f\u6301\u5728 Markdown \u6587\u4ef6\u5934\u90e8\u6dfb\u52a0\u5143\u6570\u636e\uff1a
YAML# mkdocs.yml \u914d\u7f6e\nmarkdown_extensions:\n - meta\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown---\ntitle: \u6211\u7684\u9875\u9762\u6807\u9898\ndescription: \u9875\u9762\u63cf\u8ff0\nauthor: \u4f5c\u8005\u540d\ndate: 2024-01-01\n---\n\n# \u6b63\u6587\u5185\u5bb9\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3312-toc","title":"3.3.1.2 toc \u6269\u5c55","text":"\u81ea\u52a8\u751f\u6210\u76ee\u5f55\uff1a
YAMLmarkdown_extensions:\n - toc:\n permalink: true # \u6dfb\u52a0\u6bb5\u843d\u94fe\u63a5\n toc_depth: 3 # \u76ee\u5f55\u6df1\u5ea6\n separator: \"_\" # \u6807\u9898\u951a\u70b9\u5206\u9694\u7b26\n title: \"\u76ee\u5f55\" # \u76ee\u5f55\u6807\u9898\n slugify: !!python/object/apply:pymdownx.slugs.slugify\n kwds: {case: lower} # URL \u8f6c\u6362\u89c4\u5219\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown[TOC]\n\n# \u4e00\u7ea7\u6807\u9898\n## \u4e8c\u7ea7\u6807\u9898\n### \u4e09\u7ea7\u6807\u9898\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3313-tables","title":"3.3.1.3 tables \u6269\u5c55","text":"\u589e\u5f3a\u7684\u8868\u683c\u652f\u6301\uff1a
YAMLmarkdown_extensions:\n - tables\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown| \u529f\u80fd | \u57fa\u7840\u7248 | \u4e13\u4e1a\u7248 |\n|-----|:------:|-------:|\n| \u529f\u80fdA | \u2713 | \u2713 |\n| \u529f\u80fdB | \u2717 | \u2713 |\n| \u529f\u80fdC | \u2717 | \u2713 |\n\n: \u8868\u683c\u6807\u9898 {.class-name}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#332-pymdown-extensions","title":"3.3.2 PyMdown Extensions","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3321-superfences","title":"3.3.2.1 superfences \u914d\u7f6e","text":"\u589e\u5f3a\u7684\u4ee3\u7801\u5757\u529f\u80fd\uff1a
YAMLmarkdown_extensions:\n - pymdownx.superfences:\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_div_format\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown```python title=\"example.py\" linenums=\"1\" hl_lines=\"2 3\"\ndef hello_world():\n message = \"Hello, World!\"\n print(message)\n return message\n```\n\n```mermaid\ngraph LR\n A[\u5f00\u59cb] --> B{\u5224\u65ad}\n B --> |Yes| C[\u6267\u884c]\n B --> |No| D[\u8df3\u8fc7]\n```\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3322-emoji","title":"3.3.2.2 emoji \u652f\u6301","text":"\u6dfb\u52a0\u8868\u60c5\u7b26\u53f7\u652f\u6301\uff1a
YAMLmarkdown_extensions:\n - pymdownx.emoji:\n emoji_index: !!python/name:material.extensions.emoji.twemoji\n emoji_generator: !!python/name:material.extensions.emoji.to_svg\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown:smile: :heart: :thumbsup:\n\n:fontawesome-brands-github: GitHub\n:material-account: \u7528\u6237\n:octicons-repo-16: \u4ed3\u5e93\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3323-tasklist","title":"3.3.2.3 tasklist \u529f\u80fd","text":"\u4efb\u52a1\u5217\u8868\u652f\u6301\uff1a
YAMLmarkdown_extensions:\n - pymdownx.tasklist:\n custom_checkbox: true # \u81ea\u5b9a\u4e49\u590d\u9009\u6846\u6837\u5f0f\n clickable_checkbox: true # \u53ef\u70b9\u51fb\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown- [x] \u5df2\u5b8c\u6210\u4efb\u52a1\n- [ ] \u672a\u5b8c\u6210\u4efb\u52a1\n - [x] \u5b50\u4efb\u52a1 1\n - [ ] \u5b50\u4efb\u52a1 2\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3324-pymdown","title":"3.3.2.4 \u5176\u4ed6\u5e38\u7528 PyMdown \u6269\u5c55","text":"YAMLmarkdown_extensions:\n - pymdownx.highlight # \u4ee3\u7801\u9ad8\u4eae\n - pymdownx.inlinehilite # \u884c\u5185\u4ee3\u7801\u9ad8\u4eae\n - pymdownx.snippets # \u4ee3\u7801\u7247\u6bb5\n - pymdownx.magiclink # \u81ea\u52a8\u94fe\u63a5\n - pymdownx.mark # ==\u6807\u8bb0==\n - pymdownx.critic # \u7f16\u8f91\u6807\u8bb0\n - pymdownx.tilde # \u5220\u9664\u7ebf\n - pymdownx.caret # \u4e0a\u6807\n - pymdownx.keys # \u952e\u76d8\u6309\u952e\n - pymdownx.tabbed # \u6807\u7b7e\u9875\n
"},{"location":"Tools/Blog/Mkdocs_Material/#333","title":"3.3.3 \u81ea\u5b9a\u4e49\u6269\u5c55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3331","title":"3.3.3.1 \u6269\u5c55\u5f00\u53d1\u57fa\u7840","text":"\u521b\u5efa\u81ea\u5b9a\u4e49\u6269\u5c55\uff1a
Python# custom_extension.py\nfrom markdown.extensions import Extension\nfrom markdown.preprocessors import Preprocessor\n\nclass CustomPreprocessor(Preprocessor):\n def run(self, lines):\n new_lines = []\n for line in lines:\n new_lines.append(line.replace('[[', '**').replace(']]', '**'))\n return new_lines\n\nclass CustomExtension(Extension):\n def extendMarkdown(self, md):\n md.preprocessors.register(CustomPreprocessor(md), 'custom_preprocessor', 175)\n
"},{"location":"Tools/Blog/Mkdocs_Material/#34","title":"3.4 \u5e38\u7528\u6269\u5c55\u793a\u4f8b","text":" - \u6dfb\u52a0\u81ea\u5b9a\u4e49 HTML \u5c5e\u6027\uff1a
Pythonfrom markdown.extensions import Extension\nfrom markdown.treeprocessors import Treeprocessor\n\nclass CustomAttributesTreeprocessor(Treeprocessor):\n def run(self, root):\n for elem in root.iter():\n if 'class' in elem.attrib:\n elem.set('data-custom', 'value')\n\nclass CustomAttributesExtension(Extension):\n def extendMarkdown(self, md):\n md.treeprocessors.register(\n CustomAttributesTreeprocessor(md), 'custom_attributes', 15\n )\n
- \u81ea\u5b9a\u4e49\u5bb9\u5668\uff1a
Pythonfrom markdown.extensions import Extension\nfrom markdown.blockprocessors import BlockProcessor\nimport re\n\nclass CustomContainerProcessor(BlockProcessor):\n RE = re.compile(r':{3,}\\ *(warning|note|tip)\\ *')\n\n def run(self, parent, blocks):\n block = blocks.pop(0)\n m = self.RE.match(block)\n\n if m:\n container_type = m.group(1)\n div = etree.SubElement(parent, 'div')\n div.set('class', f'custom-container {container_type}')\n\n # \u5904\u7406\u5bb9\u5668\u5185\u5bb9\n self.parser.parseChunk(div, block[m.end():])\n\nclass CustomContainerExtension(Extension):\n def extendMarkdown(self, md):\n md.parser.blockprocessors.register(\n CustomContainerProcessor(md.parser), 'custom_container', 175\n )\n
"},{"location":"Tools/Blog/Mkdocs_Material/#341","title":"3.4.1 \u6269\u5c55\u914d\u7f6e\u65b9\u6cd5","text":"\u5728 mkdocs.yml \u4e2d\u914d\u7f6e\u81ea\u5b9a\u4e49\u6269\u5c55\uff1a
YAMLmarkdown_extensions:\n - custom_extension:\n option1: value1\n option2: value2\n\nextra_css:\n - css/custom_extension.css\n\nextra_javascript:\n - js/custom_extension.js\n
"},{"location":"Tools/Blog/Mkdocs_Material/#342","title":"3.4.2 \u6269\u5c55\u7ec4\u5408\u63a8\u8350","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3421","title":"3.4.2.1 \u57fa\u7840\u6587\u6863\u914d\u7f6e","text":"YAMLmarkdown_extensions:\n - meta\n - toc:\n permalink: true\n - tables\n - attr_list\n - def_list\n - footnotes\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3422","title":"3.4.2.2 \u589e\u5f3a\u529f\u80fd\u914d\u7f6e","text":"YAMLmarkdown_extensions:\n - pymdownx.superfences:\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_div_format\n - pymdownx.highlight:\n anchor_linenums: true\n - pymdownx.inlinehilite\n - pymdownx.snippets\n - pymdownx.tasklist:\n custom_checkbox: true\n - pymdownx.emoji:\n emoji_index: !!python/name:material.extensions.emoji.twemoji\n emoji_generator: !!python/name:material.extensions.emoji.to_svg\n
"},{"location":"Tools/Blog/Mkdocs_Material/#343","title":"3.4.3 \u5b8c\u6574\u63a8\u8350\u914d\u7f6e","text":"YAMLmarkdown_extensions:\n # Python Markdown\n - meta\n - toc:\n permalink: true\n toc_depth: 4\n - tables\n - attr_list\n - def_list\n - md_in_html\n - footnotes\n\n # Python Markdown Extensions\n - pymdownx.superfences\n - pymdownx.highlight\n - pymdownx.inlinehilite\n - pymdownx.snippets\n - pymdownx.tasklist\n - pymdownx.emoji\n - pymdownx.mark\n - pymdownx.critic\n - pymdownx.keys\n - pymdownx.tilde\n - pymdownx.caret\n - pymdownx.details\n - pymdownx.magiclink\n - pymdownx.tabbed:\n alternate_style: true\n\n # \u81ea\u5b9a\u4e49\u6269\u5c55\n - custom_extension:\n custom_option: value\n
"},{"location":"Tools/Blog/Mkdocs_Material/#344","title":"3.4.4 \u4f7f\u7528\u5efa\u8bae","text":" -
\u6027\u80fd\u8003\u8651\uff1a - \u53ea\u542f\u7528\u9700\u8981\u7684\u6269\u5c55 - \u6ce8\u610f\u6269\u5c55\u4e4b\u95f4\u7684\u4f9d\u8d56\u5173\u7cfb - \u63a7\u5236\u6269\u5c55\u6570\u91cf
-
\u517c\u5bb9\u6027\uff1a - \u6d4b\u8bd5\u6269\u5c55\u7ec4\u5408 - \u68c0\u67e5\u79fb\u52a8\u7aef\u663e\u793a - \u9a8c\u8bc1\u4e0d\u540c\u6d4f\u89c8\u5668
-
\u7ef4\u62a4\u5efa\u8bae\uff1a - \u8bb0\u5f55\u6269\u5c55\u914d\u7f6e - \u4fdd\u6301\u7248\u672c\u66f4\u65b0 - \u76d1\u63a7\u6027\u80fd\u5f71\u54cd
"},{"location":"Tools/Blog/Mkdocs_Material/#35","title":"3.5 \u63d2\u4ef6\u7cfb\u7edf","text":""},{"location":"Tools/Blog/Mkdocs_Material/#351","title":"3.5.1 \u63d2\u4ef6\u914d\u7f6e\u65b9\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3511","title":"3.5.1.1 \u63d2\u4ef6\u5b89\u88c5\u65b9\u6cd5","text":"\u901a\u8fc7 pip \u5b89\u88c5\u63d2\u4ef6\uff1a
Bash# \u5b89\u88c5\u5355\u4e2a\u63d2\u4ef6\npip install mkdocs-git-revision-date-localized-plugin\n\n# \u5b89\u88c5\u591a\u4e2a\u63d2\u4ef6\npip install mkdocs-minify-plugin mkdocs-git-authors-plugin\n
\u57fa\u7840\u63d2\u4ef6\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nplugins:\n - search # \u9ed8\u8ba4\u63d2\u4ef6\n - git-revision-date-localized:\n enable_creation_date: true\n - minify:\n minify_html: true\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3512","title":"3.5.1.2 \u914d\u7f6e\u8bed\u6cd5","text":"\u63d2\u4ef6\u914d\u7f6e\u7684\u51e0\u79cd\u65b9\u5f0f\uff1a
YAML# 1. \u7b80\u5355\u542f\u7528\u63d2\u4ef6\uff08\u4f7f\u7528\u9ed8\u8ba4\u914d\u7f6e\uff09\nplugins:\n - search\n - tags\n\n# 2. \u7981\u7528\u9ed8\u8ba4\u63d2\u4ef6\nplugins:\n - search: false\n\n# 3. \u5e26\u914d\u7f6e\u7684\u63d2\u4ef6\nplugins:\n - search:\n lang: \n - en\n - zh\n separator: '[\\s\\-\\.]+'\n\n# 4. \u591a\u5b9e\u4f8b\u63d2\u4ef6\nplugins:\n - search:\n name: search_1\n config: value\n - search:\n name: search_2\n config: value\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3513","title":"3.5.1.3 \u4f18\u5148\u7ea7\u63a7\u5236","text":"\u63d2\u4ef6\u6267\u884c\u987a\u5e8f\u63a7\u5236\uff1a
YAMLplugins:\n - search\n - git-revision-date-localized:\n priority: 80\n - minify:\n priority: 90\n - tags:\n priority: 70\n
"},{"location":"Tools/Blog/Mkdocs_Material/#352","title":"3.5.2 \u5e38\u7528\u63d2\u4ef6\u4ecb\u7ecd","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3521","title":"3.5.2.1 \u5b98\u65b9\u63d2\u4ef6","text":" - search - \u641c\u7d22\u63d2\u4ef6
YAMLplugins:\n - search:\n lang: \n - en\n - zh\n separator: '[\\s\\-\\.]+'\n min_search_length: 2\n prebuild_index: true\n indexing:\n - full_sections: false\n - headings: true\n - content: true\n
- tags - \u6807\u7b7e\u7cfb\u7edf
YAMLplugins:\n - tags:\n tags_file: tags.md\n tags_extra_files:\n cloud: cloud_tags.md\n list: tag_list.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3522","title":"3.5.2.2 \u793e\u533a\u63d2\u4ef6\u63a8\u8350","text":" - git-revision-date-localized - Git \u65e5\u671f\u4fe1\u606f
YAMLplugins:\n - git-revision-date-localized:\n type: timeago\n enable_creation_date: true\n exclude:\n - index.md\n timezone: Asia/Shanghai\n locale: zh\n
- minify - \u6587\u4ef6\u538b\u7f29
YAMLplugins:\n - minify:\n minify_html: true\n minify_js: true\n minify_css: true\n htmlmin_opts:\n remove_comments: true\n
- social - \u793e\u4ea4\u5206\u4eab
YAMLplugins:\n - social:\n cards: true\n cards_color:\n fill: \"#0FF1CE\"\n text: \"#FFFFFF\"\n
- macros - \u6a21\u677f\u5b8f
YAMLplugins:\n - macros:\n module_name: macros\n include_dir: include\n include_yaml:\n - variables.yml\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3523","title":"3.5.2.3 \u5b9e\u7528\u63d2\u4ef6\u96c6\u5408","text":"YAMLplugins:\n # \u6838\u5fc3\u529f\u80fd\n - search\n - tags\n\n # \u7248\u672c\u63a7\u5236\n - git-revision-date-localized\n - git-authors\n\n # \u6027\u80fd\u4f18\u5316\n - minify\n - optimize\n\n # \u5185\u5bb9\u589e\u5f3a\n - social\n - macros\n - blogging\n\n # \u591a\u8bed\u8a00\u652f\u6301\n - i18n\n - translations\n\n # \u56fe\u7247\u5904\u7406\n - glightbox\n - img2fig\n\n # \u7edf\u8ba1\u5206\u6790\n - statistics\n - pdf-export\n
"},{"location":"Tools/Blog/Mkdocs_Material/#353","title":"3.5.3 \u63d2\u4ef6\u7ec4\u5408\u4f7f\u7528","text":""},{"location":"Tools/Blog/Mkdocs_Material/#3531","title":"3.5.3.1 \u57fa\u7840\u7ec4\u5408\u65b9\u6848","text":"\u9002\u5408\u4e00\u822c\u6587\u6863\u9879\u76ee\uff1a
YAMLplugins:\n - search:\n lang: zh\n separator: '[\\s\\-\\.]+'\n\n - git-revision-date-localized:\n enable_creation_date: true\n type: date\n\n - minify:\n minify_html: true\n\n - glightbox:\n touchNavigation: true\n loop: false\n effect: zoom\n width: 100%\n height: auto\n zoomable: true\n draggable: true\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3532","title":"3.5.3.2 \u535a\u5ba2\u7f51\u7ad9\u65b9\u6848","text":"\u9002\u5408\u535a\u5ba2\u7c7b\u7f51\u7ad9\uff1a
YAMLplugins:\n - blog:\n blog_dir: blog\n post_url_format: \"{slug}\"\n post_excerpt: optional\n\n - social:\n cards: true\n cards_dir: assets/social\n\n - tags:\n tags_file: tags.md\n\n - rss:\n abstract_chars_count: 160\n date_from_meta: true\n\n - statistics:\n page_check: true\n page_count: true\n
"},{"location":"Tools/Blog/Mkdocs_Material/#3533","title":"3.5.3.3 \u6280\u672f\u6587\u6863\u65b9\u6848","text":"\u9002\u5408\u5927\u578b\u6280\u672f\u6587\u6863\uff1a
YAMLplugins:\n - search:\n separator: '[\\s\\-\\.]+'\n min_search_length: 2\n lang:\n - en\n - zh\n prebuild_index: true\n\n - git-revision-date-localized:\n type: timeago\n enable_creation_date: true\n\n - minify:\n minify_html: true\n minify_js: true\n minify_css: true\n\n - macros:\n module_name: includes.macros\n include_yaml:\n - includes/variables.yml\n\n - pdf-export:\n combined: true\n combined_output_path: pdf/document.pdf\n
"},{"location":"Tools/Blog/Mkdocs_Material/#354","title":"3.5.4 \u6027\u80fd\u4f18\u5316\u5efa\u8bae","text":" -
\u63d2\u4ef6\u9009\u62e9\uff1a - \u53ea\u542f\u7528\u5fc5\u8981\u7684\u63d2\u4ef6 - \u907f\u514d\u529f\u80fd\u91cd\u590d\u7684\u63d2\u4ef6 - \u6ce8\u610f\u63d2\u4ef6\u95f4\u7684\u4f9d\u8d56\u5173\u7cfb
-
\u914d\u7f6e\u4f18\u5316\uff1a
YAMLplugins:\n - search:\n prebuild_index: true # \u9884\u6784\u5efa\u7d22\u5f15\n - minify:\n cache: true # \u542f\u7528\u7f13\u5b58\n cache_dir: .cache # \u7f13\u5b58\u76ee\u5f55\n - optimize: # \u8d44\u6e90\u4f18\u5316\n cache: true\n
- \u6784\u5efa\u4f18\u5316\uff1a
YAMLplugins:\n # \u5e76\u884c\u5904\u7406\u63d2\u4ef6\n - parallel:\n workers: 4\n # \u7f13\u5b58\u63d2\u4ef6\n - cache:\n enabled: true\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4-material","title":"4 Material \u4e3b\u9898\u5b8c\u5168\u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#41","title":"4.1 \u57fa\u7840\u5916\u89c2","text":""},{"location":"Tools/Blog/Mkdocs_Material/#411","title":"4.1.1 \u914d\u8272\u65b9\u6848","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4111","title":"4.1.1.1 \u9884\u8bbe\u4e3b\u9898\u8272","text":"Material \u4e3b\u9898\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u9884\u8bbe\u989c\u8272\uff1a
YAMLtheme:\n name: material\n palette:\n primary: indigo # \u4e3b\u8272\u8c03\n accent: pink # \u5f3a\u8c03\u8272\n\n# \u53ef\u7528\u7684\u4e3b\u9898\u8272\uff1a\n# red, pink, purple, deep purple, indigo, blue, light blue, \n# cyan, teal, green, light green, lime, yellow, amber, \n# orange, deep orange, brown, grey, blue grey\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4112","title":"4.1.1.2 \u81ea\u5b9a\u4e49\u914d\u8272","text":"\u5b8c\u6574\u7684\u81ea\u5b9a\u4e49\u914d\u8272\u914d\u7f6e\uff1a
YAMLtheme:\n palette:\n # \u4eae\u8272\u4e3b\u9898\n - media: \"(prefers-color-scheme: light)\"\n scheme: default\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-7\n name: \u5207\u6362\u81f3\u6697\u8272\u6a21\u5f0f\n\n # \u6697\u8272\u4e3b\u9898\n - media: \"(prefers-color-scheme: dark)\"\n scheme: slate\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u6a21\u5f0f\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4113-css","title":"4.1.1.3 \u81ea\u5b9a\u4e49 CSS \u53d8\u91cf","text":"\u521b\u5efa docs/stylesheets/extra.css\uff1a
CSS:root {\n --md-primary-fg-color: #2196f3;\n --md-primary-fg-color--light: #64b5f6;\n --md-primary-fg-color--dark: #1976d2;\n --md-accent-fg-color: #2196f3;\n}\n\n[data-md-color-scheme=\"slate\"] {\n --md-primary-fg-color: #90caf9;\n --md-primary-fg-color--light: #e3f2fd;\n --md-primary-fg-color--dark: #42a5f5;\n}\n
\u5728 mkdocs.yml \u4e2d\u5f15\u5165\uff1a
YAMLextra_css:\n - stylesheets/extra.css\n
"},{"location":"Tools/Blog/Mkdocs_Material/#412","title":"4.1.2 \u4e3b\u9898\u5207\u6362","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4121","title":"4.1.2.1 \u57fa\u7840\u5207\u6362\u914d\u7f6e","text":"YAMLtheme:\n name: material\n palette:\n # \u914d\u7f6e\u5207\u6362\u6309\u94ae\n - scheme: default\n toggle:\n icon: material/brightness-7 \n name: \u5207\u6362\u81f3\u6697\u8272\u6a21\u5f0f\n primary: indigo\n accent: indigo\n\n - scheme: slate\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u6a21\u5f0f\n primary: indigo\n accent: indigo\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4122","title":"4.1.2.2 \u9ad8\u7ea7\u5207\u6362\u529f\u80fd","text":"\u6dfb\u52a0\u81ea\u5b9a\u4e49\u5207\u6362\u903b\u8f91\uff1a
YAMLextra_javascript:\n - javascripts/theme-switch.js\n
theme-switch.js \u5185\u5bb9\uff1a
JavaScriptdocument.addEventListener('DOMContentLoaded', function() {\n // \u83b7\u53d6\u7cfb\u7edf\u4e3b\u9898\u504f\u597d\n const prefersDark = window.matchMedia('(prefers-color-scheme: dark)');\n\n // \u76d1\u542c\u7cfb\u7edf\u4e3b\u9898\u53d8\u5316\n prefersDark.addListener((e) => {\n const theme = e.matches ? 'slate' : 'default';\n document.body.setAttribute('data-md-color-scheme', theme);\n });\n\n // \u521d\u59cb\u5316\u4e3b\u9898\n const theme = prefersDark.matches ? 'slate' : 'default';\n document.body.setAttribute('data-md-color-scheme', theme);\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#413","title":"4.1.3 \u56fe\u6807\u8bbe\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4131","title":"4.1.3.1 \u7f51\u7ad9\u56fe\u6807","text":"\u914d\u7f6e\u7f51\u7ad9\u56fe\u6807\u548c Logo\uff1a
YAMLtheme:\n icon:\n logo: material/book-open-page-variant # \u7f51\u7ad9 Logo\n repo: fontawesome/brands/github # \u4ed3\u5e93\u56fe\u6807\n\n favicon: assets/favicon.png # \u7f51\u7ad9\u56fe\u6807\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4132","title":"4.1.3.2 \u529f\u80fd\u56fe\u6807","text":"\u4e3a\u4e0d\u540c\u529f\u80fd\u914d\u7f6e\u56fe\u6807\uff1a
YAMLtheme:\n icon:\n repo: fontawesome/brands/github # \u4ed3\u5e93\u56fe\u6807\n edit: material/pencil # \u7f16\u8f91\u56fe\u6807\n view: material/eye # \u67e5\u770b\u56fe\u6807\n admonition:\n note: octicons/tag-16 # \u63d0\u793a\u6846\u56fe\u6807\n abstract: octicons/checklist-16\n info: octicons/info-16\n tip: octicons/squirrel-16\n success: octicons/check-16\n question: octicons/question-16\n warning: octicons/alert-16\n failure: octicons/x-circle-16\n danger: octicons/zap-16\n bug: octicons/bug-16\n example: octicons/beaker-16\n quote: octicons/quote-16\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4133-svg","title":"4.1.3.3 \u81ea\u5b9a\u4e49 SVG \u56fe\u6807","text":"\u6dfb\u52a0\u81ea\u5b9a\u4e49 SVG \u56fe\u6807\uff1a
- \u521b\u5efa
.icons \u76ee\u5f55\uff1a
Text Only.\n\u251c\u2500 .icons/\n\u2502 \u2514\u2500 custom/\n\u2502 \u2514\u2500 logo.svg\n\u2514\u2500 mkdocs.yml\n
- \u914d\u7f6e\u4f7f\u7528\uff1a
YAMLtheme:\n icon:\n logo: custom/logo\n
"},{"location":"Tools/Blog/Mkdocs_Material/#414-logo","title":"4.1.4 Logo \u8bbe\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4141-logo","title":"4.1.4.1 \u57fa\u7840 Logo \u914d\u7f6e","text":"YAMLtheme:\n logo: assets/logo.svg # Logo \u56fe\u7247\n icon:\n logo: material/book # \u6216\u4f7f\u7528\u56fe\u6807\u4f5c\u4e3a Logo\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4142-logo","title":"4.1.4.2 \u54cd\u5e94\u5f0f Logo","text":"\u521b\u5efa\u54cd\u5e94\u5f0f Logo\uff1a
YAMLtheme:\n logo: assets/logo.svg\nextra_css:\n - stylesheets/logo.css\n
logo.css \u5185\u5bb9\uff1a
CSS/* \u9ed8\u8ba4 Logo */\n.md-logo img {\n width: 40px;\n height: 40px;\n}\n\n/* \u79fb\u52a8\u7aef Logo */\n@media screen and (max-width: 76.1875em) {\n .md-logo img {\n width: 32px;\n height: 32px;\n }\n}\n\n/* \u6697\u8272\u4e3b\u9898 Logo */\n[data-md-color-scheme=\"slate\"] .md-logo img {\n filter: invert(1);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4143-logo","title":"4.1.4.3 Logo \u52a8\u753b\u6548\u679c","text":"\u6dfb\u52a0 Logo \u52a8\u753b\uff1a
CSS.md-logo img {\n transition: transform 0.3s ease;\n}\n\n.md-logo img:hover {\n transform: scale(1.1);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#415","title":"4.1.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\ntheme:\n name: material\n\n # \u8c03\u8272\u677f\u914d\u7f6e\n palette:\n - media: \"(prefers-color-scheme: light)\"\n scheme: default\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-7\n name: \u5207\u6362\u81f3\u6697\u8272\u6a21\u5f0f\n - media: \"(prefers-color-scheme: dark)\"\n scheme: slate\n primary: blue\n accent: blue\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u6a21\u5f0f\n\n # \u56fe\u6807\u914d\u7f6e\n icon:\n logo: material/book-open-page-variant\n repo: fontawesome/brands/github\n edit: material/pencil\n view: material/eye\n\n # Logo \u914d\u7f6e\n logo: assets/logo.svg\n favicon: assets/favicon.png\n\n# \u989d\u5916\u6837\u5f0f\nextra_css:\n - stylesheets/extra.css\n - stylesheets/logo.css\n\n# \u989d\u5916\u811a\u672c\nextra_javascript:\n - javascripts/theme-switch.js\n\n# \u4e3b\u9898\u7279\u6027\nfeatures:\n - navigation.instant\n - navigation.tracking\n - navigation.tabs\n - navigation.sections\n - navigation.expand\n - navigation.top\n - toc.integrate\n
"},{"location":"Tools/Blog/Mkdocs_Material/#42","title":"4.2 \u5bfc\u822a\u529f\u80fd","text":""},{"location":"Tools/Blog/Mkdocs_Material/#421","title":"4.2.1 \u9876\u90e8\u5bfc\u822a","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4211","title":"4.2.1.1 \u5bfc\u822a\u680f\u6837\u5f0f","text":"\u57fa\u7840\u5bfc\u822a\u680f\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n features:\n - navigation.tabs # \u542f\u7528\u6807\u7b7e\u5f0f\u5bfc\u822a\n - navigation.sections # \u663e\u793a\u7ae0\u8282\u5bfc\u822a\n - navigation.expand # \u5c55\u5f00\u5bfc\u822a\n - navigation.indexes # \u7ae0\u8282\u7d22\u5f15\u9875\n
\u81ea\u5b9a\u4e49\u5bfc\u822a\u680f\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/extra.css */\n\n/* \u5bfc\u822a\u680f\u80cc\u666f */\n.md-header {\n background-color: #2196f3;\n box-shadow: 0 2px 4px rgba(0,0,0,.14);\n}\n\n/* \u5bfc\u822a\u9879\u6837\u5f0f */\n.md-tabs__link {\n font-size: .8rem;\n margin-top: .4rem;\n}\n\n/* \u6fc0\u6d3b\u72b6\u6001 */\n.md-tabs__link--active {\n font-weight: bold;\n border-bottom: 2px solid currentColor;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4212","title":"4.2.1.2 \u56fa\u5b9a\u5bfc\u822a\u680f","text":"\u542f\u7528\u56fa\u5b9a\u5bfc\u822a\uff1a
YAMLtheme:\n features:\n - header.autohide # \u81ea\u52a8\u9690\u85cf\n - navigation.sticky # \u56fa\u5b9a\u5bfc\u822a\n
\u81ea\u5b9a\u4e49\u56fa\u5b9a\u5bfc\u822a\u884c\u4e3a\uff1a
CSS/* \u56fa\u5b9a\u5bfc\u822a\u680f\u6837\u5f0f */\n.md-header--sticky {\n backdrop-filter: blur(8px);\n background-color: rgba(255,255,255,.8);\n}\n\n/* \u6697\u8272\u4e3b\u9898 */\n[data-md-color-scheme=\"slate\"] .md-header--sticky {\n background-color: rgba(0,0,0,.8);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4213","title":"4.2.1.3 \u54cd\u5e94\u5f0f\u5bfc\u822a","text":"\u54cd\u5e94\u5f0f\u914d\u7f6e\uff1a
YAMLtheme:\n features:\n - navigation.instant # \u5373\u65f6\u52a0\u8f7d\n - navigation.tracking # \u6eda\u52a8\u8ddf\u8e2a\n
\u54cd\u5e94\u5f0f\u6837\u5f0f\u8c03\u6574\uff1a
CSS/* \u79fb\u52a8\u7aef\u5bfc\u822a */\n@media screen and (max-width: 76.1875em) {\n .md-nav__title {\n font-size: .9rem;\n padding: 0.5rem 0.8rem;\n }\n\n .md-nav__item {\n padding: 0.2rem 0.8rem;\n }\n}\n\n/* \u5e73\u677f\u5bfc\u822a */\n@media screen and (min-width: 76.25em) {\n .md-nav__link {\n padding: 0.2rem 0;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#422","title":"4.2.2 \u6807\u7b7e\u5bfc\u822a","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4221","title":"4.2.2.1 \u6807\u7b7e\u9875\u914d\u7f6e","text":"\u542f\u7528\u6807\u7b7e\u5bfc\u822a\uff1a
YAMLtheme:\n features:\n - navigation.tabs\n - navigation.tabs.sticky # \u56fa\u5b9a\u6807\u7b7e\n
\u6807\u7b7e\u9875\u7ed3\u6784\uff1a
YAMLnav:\n - Home: index.md\n - Guide:\n - guide/index.md\n - Installation: guide/installation.md\n - Configuration: guide/configuration.md\n - API:\n - api/index.md\n - Reference: api/reference.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4222","title":"4.2.2.2 \u6807\u7b7e\u6837\u5f0f","text":"\u81ea\u5b9a\u4e49\u6807\u7b7e\u6837\u5f0f\uff1a
CSS/* \u6807\u7b7e\u5bb9\u5668 */\n.md-tabs {\n background-color: var(--md-primary-fg-color--dark);\n}\n\n/* \u6807\u7b7e\u9879 */\n.md-tabs__item {\n padding: 0 1rem;\n transition: all 0.2s ease;\n}\n\n/* \u60ac\u505c\u6548\u679c */\n.md-tabs__item:hover {\n background-color: rgba(255,255,255,.1);\n}\n\n/* \u6fc0\u6d3b\u72b6\u6001 */\n.md-tabs__item--active {\n font-weight: bold;\n}\n
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JavaScript// docs/javascripts/tabs.js\n\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u6807\u7b7e\u70b9\u51fb\u6548\u679c\n const tabs = document.querySelectorAll('.md-tabs__item');\n tabs.forEach(tab => {\n tab.addEventListener('click', () => {\n // \u6dfb\u52a0\u70b9\u51fb\u6ce2\u7eb9\u6548\u679c\n const ripple = document.createElement('div');\n ripple.classList.add('md-tabs__ripple');\n tab.appendChild(ripple);\n\n // \u79fb\u9664\u6ce2\u7eb9\u6548\u679c\n setTimeout(() => ripple.remove(), 1000);\n });\n });\n});\n
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CSS/* \u6ce2\u7eb9\u6548\u679c */\n.md-tabs__ripple {\n position: absolute;\n background: rgba(255,255,255,.3);\n border-radius: 50%;\n transform: scale(0);\n animation: ripple 0.6s linear;\n}\n\n@keyframes ripple {\n to {\n transform: scale(4);\n opacity: 0;\n }\n}\n
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YAMLtheme:\n features:\n - toc.integrate # \u96c6\u6210\u76ee\u5f55\n - toc.follow # \u76ee\u5f55\u8ddf\u968f\n\nmarkdown_extensions:\n - toc:\n permalink: true # \u6c38\u4e45\u94fe\u63a5\n toc_depth: 3 # \u76ee\u5f55\u6df1\u5ea6\n title: \u76ee\u5f55 # \u76ee\u5f55\u6807\u9898\n
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CSS/* \u76ee\u5f55\u5bb9\u5668 */\n.md-toc {\n padding: 1rem;\n background-color: var(--md-code-bg-color);\n border-radius: 4px;\n}\n\n/* \u76ee\u5f55\u6807\u9898 */\n.md-toc__title {\n font-weight: bold;\n margin-bottom: 1rem;\n}\n\n/* \u76ee\u5f55\u94fe\u63a5 */\n.md-toc__link {\n color: var(--md-typeset-color);\n text-decoration: none;\n}\n\n/* \u76ee\u5f55\u5c42\u7ea7\u7f29\u8fdb */\n.md-toc__list {\n margin-left: 1.5em;\n}\n
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YAMLmarkdown_extensions:\n - toc:\n permalink: \u2693\ufe0e # \u951a\u70b9\u7b26\u53f7\n slug: !!python/object/apply:pymdownx.slugs.slugify\n kwds: {case: lower} # URL \u8f6c\u6362\u89c4\u5219\n
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CSS/* \u951a\u70b9\u94fe\u63a5 */\n.headerlink {\n opacity: 0;\n margin-left: .5em;\n transition: opacity 0.2s ease;\n}\n\n/* \u6807\u9898\u60ac\u505c\u65f6\u663e\u793a\u951a\u70b9 */\nh1:hover .headerlink,\nh2:hover .headerlink,\nh3:hover .headerlink {\n opacity: 1;\n}\n
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YAMLtheme:\n features:\n - navigation.top # \u8fd4\u56de\u9876\u90e8\u6309\u94ae\n
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CSS/* \u8fd4\u56de\u9876\u90e8\u6309\u94ae */\n.md-top {\n background-color: var(--md-primary-fg-color);\n border-radius: 50%;\n box-shadow: 0 2px 4px rgba(0,0,0,.14);\n transition: all 0.2s ease;\n}\n\n/* \u60ac\u505c\u6548\u679c */\n.md-top:hover {\n background-color: var(--md-primary-fg-color--dark);\n transform: translateY(-2px);\n}\n
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JavaScript// docs/javascripts/scroll.js\n\ndocument.addEventListener('DOMContentLoaded', function() {\n const topButton = document.querySelector('.md-top');\n\n // \u5e73\u6ed1\u6eda\u52a8\n if (topButton) {\n topButton.addEventListener('click', (e) => {\n e.preventDefault();\n window.scrollTo({\n top: 0,\n behavior: 'smooth'\n });\n });\n }\n});\n
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JavaScript// \u63a7\u5236\u6309\u94ae\u663e\u793a\nwindow.addEventListener('scroll', () => {\n const topButton = document.querySelector('.md-top');\n if (topButton) {\n if (window.scrollY > 100) {\n topButton.classList.add('md-top--show');\n } else {\n topButton.classList.remove('md-top--show');\n }\n }\n});\n
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CSS/* \u6309\u94ae\u663e\u793a\u9690\u85cf */\n.md-top {\n opacity: 0;\n visibility: hidden;\n transition: opacity 0.2s ease, visibility 0.2s ease;\n}\n\n.md-top--show {\n opacity: 1;\n visibility: visible;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#425","title":"4.2.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\ntheme:\n name: material\n features:\n # \u5bfc\u822a\u529f\u80fd\n - navigation.tabs\n - navigation.tabs.sticky\n - navigation.sections\n - navigation.expand\n - navigation.indexes\n - navigation.instant\n - navigation.tracking\n - navigation.sticky\n - header.autohide\n\n # \u76ee\u5f55\u529f\u80fd\n - toc.integrate\n - toc.follow\n\n # \u8fd4\u56de\u9876\u90e8\n - navigation.top\n\n# Markdown \u6269\u5c55\nmarkdown_extensions:\n - toc:\n permalink: true\n toc_depth: 3\n title: \u76ee\u5f55\n slugify: !!python/object/apply:pymdownx.slugs.slugify\n kwds: {case: lower}\n\n# \u989d\u5916\u6837\u5f0f\u548c\u811a\u672c\nextra_css:\n - stylesheets/extra.css\n\nextra_javascript:\n - javascripts/tabs.js\n - javascripts/scroll.js\n
"},{"location":"Tools/Blog/Mkdocs_Material/#43","title":"4.3 \u641c\u7d22\u529f\u80fd","text":""},{"location":"Tools/Blog/Mkdocs_Material/#431","title":"4.3.1 \u641c\u7d22\u5f15\u64ce\u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4311","title":"4.3.1.1 \u641c\u7d22\u7b97\u6cd5","text":"\u57fa\u7840\u641c\u7d22\u914d\u7f6e\uff1a
YAMLplugins:\n - search:\n separator: '[\\\\s\\\\-\\\\.]+' # \u5206\u8bcd\u5206\u9694\u7b26\n min_search_length: 2 # \u6700\u5c0f\u641c\u7d22\u957f\u5ea6\n lang:\n - en\n - zh\n prebuild_index: true # \u9884\u6784\u5efa\u7d22\u5f15\n
\u9ad8\u7ea7\u641c\u7d22\u9009\u9879\uff1a
YAMLplugins:\n - search:\n separator: '[\\\\s\\\\-\\\\.]+\\\\s*'\n min_search_length: 2\n prebuild_index: python\n indexing:\n full_sections: true # \u7d22\u5f15\u5b8c\u6574\u7ae0\u8282\n headings: true # \u7d22\u5f15\u6807\u9898\n content: true # \u7d22\u5f15\u5185\u5bb9\n tags: true # \u7d22\u5f15\u6807\u7b7e\n scoring:\n title_boost: 10 # \u6807\u9898\u6743\u91cd\n heading_boost: 5 # \u6807\u9898\u6743\u91cd\n content_boost: 1 # \u5185\u5bb9\u6743\u91cd\n
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YAMLplugins:\n - search:\n indexing:\n full_sections: true\n headings: true\n content: true\n tags: true\n attachments: true # \u7d22\u5f15\u9644\u4ef6\n attachments_types: # \u9644\u4ef6\u7c7b\u578b\n - .pdf\n - .doc\n - .docx\n attachments_max_size: 2048 # \u6700\u5927\u5927\u5c0f(KB)\n
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YAMLplugins:\n - search:\n # \u5305\u542b\u7684\u6587\u4ef6\n include:\n - \"*.md\"\n - \"*.markdown\"\n\n # \u6392\u9664\u7684\u6587\u4ef6\n exclude:\n - drafts/*\n - private/*\n\n # \u5904\u7406\u7279\u5b9a\u8def\u5f84\n ignore:\n - 404.md\n - index.md\n
"},{"location":"Tools/Blog/Mkdocs_Material/#432","title":"4.3.2 \u641c\u7d22\u63d0\u793a","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4321","title":"4.3.2.1 \u5feb\u6377\u952e\u8bbe\u7f6e","text":"\u914d\u7f6e\u641c\u7d22\u5feb\u6377\u952e\uff1a
YAMLtheme:\n keyboard:\n search: s, / # \u4f7f\u7528 's' \u6216 '/' \u89e6\u53d1\u641c\u7d22\n
\u81ea\u5b9a\u4e49\u5feb\u6377\u952e\u5904\u7406\uff1a
JavaScript// docs/javascripts/search.js\n\ndocument.addEventListener('keydown', function(e) {\n // \u81ea\u5b9a\u4e49\u5feb\u6377\u952e\u903b\u8f91\n if ((e.key === 's' || e.key === '/') && !e.ctrlKey && !e.altKey && !e.metaKey) {\n e.preventDefault();\n const search = document.querySelector('.md-search__input');\n if (search) {\n search.focus();\n }\n }\n});\n
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YAMLtheme:\n language: zh # \u4f7f\u7528\u4e2d\u6587\u754c\u9762\n\nextra:\n search:\n language: zh\n text:\n placeholder: \u641c\u7d22\u6587\u6863...\n no_results: \u6ca1\u6709\u627e\u5230\u76f8\u5173\u7ed3\u679c\n searching: \u6b63\u5728\u641c\u7d22...\n
\u81ea\u5b9a\u4e49\u63d0\u793a\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/search.css */\n\n/* \u641c\u7d22\u6846\u63d0\u793a\u6587\u672c */\n.md-search__input::placeholder {\n color: var(--md-default-fg-color--lighter);\n}\n\n/* \u65e0\u7ed3\u679c\u63d0\u793a */\n.md-search-result__meta {\n color: var(--md-default-fg-color--light);\n font-size: .8rem;\n}\n
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YAMLplugins:\n - search:\n suggestions: true # \u542f\u7528\u641c\u7d22\u5efa\u8bae\n suggestions_min_length: 2 # \u6700\u5c0f\u5efa\u8bae\u957f\u5ea6\n
\u81ea\u5b9a\u4e49\u5efa\u8bae\u6837\u5f0f\uff1a
CSS/* \u641c\u7d22\u5efa\u8bae\u6837\u5f0f */\n.md-search-result__item {\n padding: .4rem .8rem;\n transition: background .2s ease;\n}\n\n.md-search-result__item:hover {\n background-color: var(--md-code-bg-color);\n}\n\n/* \u5efa\u8bae\u9879\u56fe\u6807 */\n.md-search-result__icon {\n color: var(--md-default-fg-color--lighter);\n margin-right: .4rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#433","title":"4.3.3 \u641c\u7d22\u9ad8\u4eae","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4331","title":"4.3.3.1 \u9ad8\u4eae\u6837\u5f0f","text":"\u914d\u7f6e\u641c\u7d22\u9ad8\u4eae\uff1a
YAMLtheme:\n features:\n - search.highlight # \u542f\u7528\u641c\u7d22\u9ad8\u4eae\n - search.share # \u542f\u7528\u641c\u7d22\u5206\u4eab\n - search.suggest # \u542f\u7528\u641c\u7d22\u5efa\u8bae\n
\u81ea\u5b9a\u4e49\u9ad8\u4eae\u6837\u5f0f\uff1a
CSS/* \u641c\u7d22\u7ed3\u679c\u9ad8\u4eae */\n.md-search-result__item mark {\n background-color: var(--md-accent-fg-color--transparent);\n color: var(--md-accent-fg-color);\n padding: 0 .2em;\n border-radius: .1em;\n}\n\n/* \u6eda\u52a8\u6761\u6837\u5f0f */\n.md-search-result__scrollwrap::-webkit-scrollbar {\n width: 4px;\n height: 4px;\n}\n\n.md-search-result__scrollwrap::-webkit-scrollbar-thumb {\n background-color: var(--md-default-fg-color--lighter);\n border-radius: 2px;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4332","title":"4.3.3.2 \u5339\u914d\u89c4\u5219","text":"\u914d\u7f6e\u641c\u7d22\u5339\u914d\u89c4\u5219\uff1a
YAMLplugins:\n - search:\n # \u6587\u672c\u5339\u914d\u914d\u7f6e\n tokenizer: '[\\s\\-\\.]+' # \u5206\u8bcd\u89c4\u5219\n min_search_length: 2 # \u6700\u5c0f\u641c\u7d22\u957f\u5ea6\n\n # \u6a21\u7cca\u5339\u914d\u8bbe\u7f6e\n fuzzy: false # \u7981\u7528\u6a21\u7cca\u5339\u914d\n\n # \u5339\u914d\u6743\u91cd\n boost: \n title: 10 # \u6807\u9898\u6743\u91cd\n text: 1 # \u6587\u672c\u6743\u91cd\n
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JavaScript// docs/javascripts/search-highlight.js\n\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u83b7\u53d6\u641c\u7d22\u7ed3\u679c\u5bb9\u5668\n const searchResults = document.querySelector('.md-search-result');\n\n if (searchResults) {\n // \u76d1\u542c\u641c\u7d22\u7ed3\u679c\u53d8\u5316\n const observer = new MutationObserver(mutations => {\n mutations.forEach(mutation => {\n if (mutation.type === 'childList') {\n // \u5904\u7406\u65b0\u6dfb\u52a0\u7684\u641c\u7d22\u7ed3\u679c\n const newResults = mutation.addedNodes;\n newResults.forEach(node => {\n if (node.nodeType === 1) { // \u5143\u7d20\u8282\u70b9\n customHighlight(node);\n }\n });\n }\n });\n });\n\n // \u542f\u52a8\u89c2\u5bdf\n observer.observe(searchResults, {\n childList: true,\n subtree: true\n });\n }\n});\n\nfunction customHighlight(node) {\n // \u81ea\u5b9a\u4e49\u9ad8\u4eae\u903b\u8f91\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#434","title":"4.3.4 \u641c\u7d22\u8bed\u8a00","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4341","title":"4.3.4.1 \u4e2d\u6587\u5206\u8bcd","text":"\u914d\u7f6e\u4e2d\u6587\u5206\u8bcd\uff1a
YAMLplugins:\n - search:\n lang:\n - en\n - zh\n separator: '[\\s\\-\\.,\\!\\/\\?\\u2000-\\u206F\\u3000-\\u303F\\u3040-\\u309F\\u30A0-\\u30FF\\u3100-\\u312F\\u3200-\\u32FF\\u3400-\\u4DBF\\u4E00-\\u9FFF]+'\n
\u4e2d\u6587\u641c\u7d22\u4f18\u5316\uff1a
JavaScript// docs/javascripts/chinese-search.js\n\nfunction chineseSegment(text) {\n // \u7b80\u5355\u7684\u4e2d\u6587\u5206\u8bcd\u903b\u8f91\n return text.replace(/[\\u4e00-\\u9fa5]/g, function(char) {\n return char + ' ';\n });\n}\n\n// \u6dfb\u52a0\u5230\u641c\u7d22\u5904\u7406\u6d41\u7a0b\ndocument.addEventListener('DOMContentLoaded', function() {\n const searchInput = document.querySelector('.md-search__input');\n if (searchInput) {\n searchInput.addEventListener('input', function(e) {\n const value = e.target.value;\n if (/[\\u4e00-\\u9fa5]/.test(value)) {\n // \u5904\u7406\u4e2d\u6587\u8f93\u5165\n const segmented = chineseSegment(value);\n // TODO: \u4f7f\u7528\u5206\u8bcd\u7ed3\u679c\u8fdb\u884c\u641c\u7d22\n }\n });\n }\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4342","title":"4.3.4.2 \u591a\u8bed\u8a00\u652f\u6301","text":"\u914d\u7f6e\u591a\u8bed\u8a00\u641c\u7d22\uff1a
YAMLplugins:\n - search:\n lang:\n - en\n - zh\n - ja\n # \u8bed\u8a00\u7279\u5b9a\u7684\u5206\u8bcd\u89c4\u5219\n separator:\n en: '[\\\\s\\\\-\\\\.]+'\n zh: '[\\u4e00-\\u9fa5]'\n ja: '[\\u3040-\\u309F\\u30A0-\\u30FF]+'\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4343","title":"4.3.4.3 \u505c\u7528\u8bcd\u914d\u7f6e","text":"\u914d\u7f6e\u505c\u7528\u8bcd\uff1a
YAMLplugins:\n - search:\n stopwords: \n en:\n - a\n - an\n - the\n - in\n - on\n - at\n zh:\n - \u7684\n - \u4e86\n - \u548c\n - \u4e0e\n - \u6216\n
\u81ea\u5b9a\u4e49\u505c\u7528\u8bcd\u5904\u7406\uff1a
JavaScript// docs/javascripts/stopwords.js\n\nconst stopwords = {\n en: ['a', 'an', 'the', 'in', 'on', 'at'],\n zh: ['\u7684', '\u4e86', '\u548c', '\u4e0e', '\u6216'],\n};\n\nfunction removeStopwords(text, lang) {\n if (!stopwords[lang]) return text;\n\n const words = text.split(/\\s+/);\n return words\n .filter(word => !stopwords[lang].includes(word))\n .join(' ');\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#435","title":"4.3.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\n\nplugins:\n - search:\n lang:\n - en\n - zh\n separator: '[\\s\\-\\.,\\!\\/\\?\\u2000-\\u206F\\u3000-\\u303F]+'\n prebuild_index: python\n indexing:\n full_sections: true\n headings: true\n content: true\n tags: true\n scoring:\n title_boost: 10\n heading_boost: 5\n content_boost: 1\n\ntheme:\n features:\n - search.highlight\n - search.share\n - search.suggest\n\nextra:\n search:\n language: zh\n text:\n placeholder: \u641c\u7d22\u6587\u6863...\n no_results: \u6ca1\u6709\u627e\u5230\u76f8\u5173\u7ed3\u679c\n searching: \u6b63\u5728\u641c\u7d22...\n\nextra_javascript:\n - javascripts/search.js\n - javascripts/search-highlight.js\n - javascripts/chinese-search.js\n - javascripts/stopwords.js\n\nextra_css:\n - stylesheets/search.css\n
"},{"location":"Tools/Blog/Mkdocs_Material/#44","title":"4.4 \u4ee3\u7801\u5757\u8bbe\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#441","title":"4.4.1 \u8bed\u6cd5\u9ad8\u4eae","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4411","title":"4.4.1.1 \u9ad8\u4eae\u4e3b\u9898","text":"\u57fa\u7840\u9ad8\u4eae\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n features:\n - content.code.annotate # \u542f\u7528\u4ee3\u7801\u6ce8\u91ca\n - content.code.copy # \u542f\u7528\u4ee3\u7801\u590d\u5236\n\nmarkdown_extensions:\n - pymdownx.highlight:\n anchor_linenums: true\n line_spans: __span\n pygments_lang_class: true\n use_pygments: true\n auto_title: true # \u663e\u793a\u8bed\u8a00\u540d\u79f0\n linenums: true # \u663e\u793a\u884c\u53f7\n
\u81ea\u5b9a\u4e49\u9ad8\u4eae\u4e3b\u9898\uff1a
YAMLtheme:\n palette:\n # \u4eae\u8272\u4e3b\u9898\n - scheme: default\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-7\n name: \u5207\u6362\u81f3\u6697\u8272\u4e3b\u9898\n pygments_style: github-light\n\n # \u6697\u8272\u4e3b\u9898\n - scheme: slate\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u4e3b\u9898\n pygments_style: monokai\n
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CSS/* docs/stylesheets/code.css */\n\n/* \u4ee3\u7801\u5757\u5bb9\u5668 */\n.highlight {\n background-color: var(--md-code-bg-color);\n border-radius: 4px;\n padding: 0.5rem;\n margin: 1rem 0;\n}\n\n/* \u4ee3\u7801\u884c */\n.highlight .code-line {\n display: block;\n padding: 0 1rem;\n border-left: 2px solid transparent;\n}\n\n/* \u9ad8\u4eae\u884c */\n.highlight .code-line.focused {\n background-color: var(--md-code-hl-color);\n border-left: 2px solid var(--md-accent-fg-color);\n}\n
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YAMLmarkdown_extensions:\n - pymdownx.highlight:\n extend_pygments_lang:\n # \u81ea\u5b9a\u4e49\u8bed\u8a00\u914d\u7f6e\n typescript:\n name: TypeScript\n aliases: [ts]\n jsonc:\n name: JSON with Comments\n aliases: [json5]\n
\u8bed\u8a00\u7279\u5b9a\u914d\u7f6e\uff1a
YAMLmarkdown_extensions:\n - pymdownx.highlight:\n language_prefix: language- # \u8bed\u8a00\u524d\u7f00\n css_class: highlight # CSS\u7c7b\u540d\n code_attr_on_pre: false # \u5c5e\u6027\u4f4d\u7f6e\n extend_pygments_lang: # \u8bed\u8a00\u6269\u5c55\n flow:\n name: Flow\n aliases: [flowtype]\n
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Python# docs/custom_lexer.py\nfrom pygments.lexer import RegexLexer, words\nfrom pygments.token import *\n\nclass CustomLexer(RegexLexer):\n name = 'CustomLanguage'\n aliases = ['custom']\n filenames = ['*.custom']\n\n tokens = {\n 'root': [\n (r'//.*$', Comment.Single),\n (words(('if', 'else', 'while'), suffix=r'\\b'), Keyword),\n (r'\"[^\"]*\"', String),\n (r'\\d+', Number),\n (r'[a-zA-Z_]\\w*', Name),\n (r'[^\\w\\s]', Punctuation),\n ]\n }\n
\u5728 mkdocs.yml \u4e2d\u4f7f\u7528\uff1a
YAMLmarkdown_extensions:\n - pymdownx.highlight:\n use_pygments: true\n extend_pygments_lang:\n custom:\n name: CustomLanguage\n aliases: [custom]\n
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YAMLmarkdown_extensions:\n - pymdownx.highlight:\n linenums: true\n linenums_style: table # \u8868\u683c\u5f0f\u884c\u53f7\n anchor_linenums: true # \u884c\u53f7\u94fe\u63a5\n
\u81ea\u5b9a\u4e49\u884c\u53f7\u6837\u5f0f\uff1a
CSS/* \u884c\u53f7\u5bb9\u5668 */\n.highlighttable {\n width: 100%;\n display: table;\n}\n\n/* \u884c\u53f7\u5217 */\n.linenos {\n color: var(--md-default-fg-color--lighter);\n text-align: right;\n padding-right: 1rem;\n user-select: none;\n}\n\n/* \u4ee3\u7801\u5217 */\n.code {\n padding-left: 1rem;\n border-left: 1px solid var(--md-default-fg-color--lightest);\n}\n
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Markdown```python linenums=\"10\"\ndef hello_world():\n print(\"Hello, World!\")\n```\n
\u914d\u7f6e\u9ed8\u8ba4\u8d77\u59cb\u884c\uff1a
YAMLmarkdown_extensions:\n - pymdownx.highlight:\n linenums: true\n linenums_start: 1 # \u9ed8\u8ba4\u8d77\u59cb\u884c\u53f7\n
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YAMLmarkdown_extensions:\n - pymdownx.highlight:\n anchor_linenums: true # \u542f\u7528\u884c\u53f7\u94fe\u63a5\n line_anchors: L # \u884c\u53f7\u94fe\u63a5\u524d\u7f00\n
\u81ea\u5b9a\u4e49\u94fe\u63a5\u6837\u5f0f\uff1a
CSS/* \u884c\u53f7\u94fe\u63a5 */\n.md-typeset .highlight [data-linenos]:before {\n content: attr(data-linenos);\n color: var(--md-default-fg-color--lighter);\n padding-right: 1rem;\n}\n\n/* \u94fe\u63a5\u60ac\u505c\u6548\u679c */\n.md-typeset .highlight [data-linenos]:hover:before {\n color: var(--md-accent-fg-color);\n cursor: pointer;\n}\n
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YAMLtheme:\n features:\n - content.code.copy # \u542f\u7528\u4ee3\u7801\u590d\u5236\n
\u81ea\u5b9a\u4e49\u590d\u5236\u6309\u94ae\u6837\u5f0f\uff1a
CSS/* \u590d\u5236\u6309\u94ae\u5bb9\u5668 */\n.md-clipboard {\n position: absolute;\n top: 0.5rem;\n right: 0.5rem;\n padding: 0.4rem;\n color: var(--md-default-fg-color--lighter);\n background-color: transparent;\n border: none;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n/* \u60ac\u505c\u6548\u679c */\n.md-clipboard:hover {\n color: var(--md-accent-fg-color);\n background-color: var(--md-code-bg-color);\n}\n
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JavaScript// docs/javascripts/clipboard.js\n\ndocument.addEventListener('DOMContentLoaded', () => {\n // \u83b7\u53d6\u6240\u6709\u4ee3\u7801\u5757\n const codeBlocks = document.querySelectorAll('pre code');\n\n codeBlocks.forEach(block => {\n // \u521b\u5efa\u590d\u5236\u6309\u94ae\n const button = document.createElement('button');\n button.className = 'md-clipboard';\n button.title = '\u590d\u5236\u5230\u526a\u8d34\u677f';\n\n // \u6dfb\u52a0\u590d\u5236\u56fe\u6807\n button.innerHTML = '<span class=\"md-clipboard__icon\"></span>';\n\n // \u6dfb\u52a0\u70b9\u51fb\u4e8b\u4ef6\n button.addEventListener('click', async () => {\n try {\n // \u590d\u5236\u4ee3\u7801\n await navigator.clipboard.writeText(block.textContent);\n\n // \u663e\u793a\u6210\u529f\u63d0\u793a\n button.classList.add('md-clipboard--success');\n setTimeout(() => {\n button.classList.remove('md-clipboard--success');\n }, 2000);\n } catch (err) {\n console.error('\u590d\u5236\u5931\u8d25:', err);\n }\n });\n\n // \u5c06\u6309\u94ae\u6dfb\u52a0\u5230\u4ee3\u7801\u5757\n block.parentNode.insertBefore(button, block);\n });\n});\n
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YAMLtheme:\n language: zh # \u4f7f\u7528\u4e2d\u6587\u754c\u9762\n\nextra:\n clipboard:\n copy: \u590d\u5236\n copied: \u5df2\u590d\u5236\uff01\n error: \u590d\u5236\u5931\u8d25\n
\u81ea\u5b9a\u4e49\u63d0\u793a\u6837\u5f0f\uff1a
CSS/* \u590d\u5236\u63d0\u793a */\n.md-clipboard__tooltip {\n position: absolute;\n top: -2rem;\n right: 0;\n padding: 0.4rem 0.8rem;\n color: var(--md-default-bg-color);\n background-color: var(--md-default-fg-color);\n border-radius: 4px;\n font-size: 0.8rem;\n opacity: 0;\n transform: translateY(0.4rem);\n transition: all 0.2s ease;\n}\n\n/* \u663e\u793a\u63d0\u793a */\n.md-clipboard:hover .md-clipboard__tooltip {\n opacity: 1;\n transform: translateY(0);\n}\n
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Markdown```python\ndef hello():\n print(\"Hello\") # (1)\n return True # (2)\n```\n\n1. \u6253\u5370\u95ee\u5019\u4fe1\u606f\n2. \u8fd4\u56de\u6210\u529f\u72b6\u6001\n
\u914d\u7f6e\u6ce8\u91ca\u6837\u5f0f\uff1a
CSS/* \u884c\u5185\u6ce8\u91ca\u6807\u8bb0 */\n.md-annotation {\n color: var(--md-accent-fg-color);\n font-size: 0.8em;\n vertical-align: super;\n}\n
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Markdown```python\ndef process_data():\n # (1)!\n data = load_data()\n\n # (2)!\n result = transform(data)\n\n return result\n```\n\n1. \u4ece\u6570\u636e\u6e90\u52a0\u8f7d\u6570\u636e\n \u8fd9\u91cc\u53ef\u4ee5\u662f\u591a\u884c\n \u6ce8\u91ca\u8bf4\u660e\n\n2. \u5bf9\u6570\u636e\u8fdb\u884c\u8f6c\u6362\u5904\u7406\n \u5305\u542b\u6e05\u6d17\u548c\u683c\u5f0f\u5316\n
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CSS/* \u6ce8\u91ca\u5bb9\u5668 */\n.md-annotation-wrapper {\n margin: 1rem 0;\n padding: 1rem;\n background-color: var(--md-code-bg-color);\n border-left: 4px solid var(--md-accent-fg-color);\n border-radius: 4px;\n}\n\n/* \u6ce8\u91ca\u6807\u8bb0 */\n.md-annotation-marker {\n color: var(--md-accent-fg-color);\n font-weight: bold;\n}\n\n/* \u6ce8\u91ca\u5185\u5bb9 */\n.md-annotation-content {\n margin-top: 0.5rem;\n color: var(--md-default-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#445","title":"4.4.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\n\ntheme:\n name: material\n features:\n - content.code.annotate\n - content.code.copy\n\nmarkdown_extensions:\n - pymdownx.highlight:\n anchor_linenums: true\n line_spans: __span\n pygments_lang_class: true\n use_pygments: true\n auto_title: true\n linenums: true\n linenums_style: table\n - pymdownx.superfences\n - pymdownx.inlinehilite\n\nextra:\n clipboard:\n copy: \u590d\u5236\n copied: \u5df2\u590d\u5236\uff01\n error: \u590d\u5236\u5931\u8d25\n\nextra_css:\n - stylesheets/code.css\n\nextra_javascript:\n - javascripts/clipboard.js\n
"},{"location":"Tools/Blog/Mkdocs_Material/#45","title":"4.5 \u5185\u5bb9\u589e\u5f3a","text":""},{"location":"Tools/Blog/Mkdocs_Material/#451","title":"4.5.1 \u6570\u5b66\u516c\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4511-katex","title":"4.5.1.1 KaTeX \u914d\u7f6e","text":"\u5b89\u88c5\u548c\u914d\u7f6e KaTeX\uff1a
YAMLmarkdown_extensions:\n - pymdownx.arithmatex:\n generic: true\n\nextra_javascript:\n - javascripts/katex.js \n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js \n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js\n\nextra_css:\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.css\n
\u521b\u5efa docs/javascripts/katex.js \uff1a
JavaScriptdocument.addEventListener(\"DOMContentLoaded\", function() {\n renderMathInElement(document.body, {\n delimiters: [\n {left: \"$$\", right: \"$$\", display: true},\n {left: \"$\", right: \"$\", display: false},\n {left: \"\\\\(\", right: \"\\\\)\", display: false},\n {left: \"\\\\[\", right: \"\\\\]\", display: true}\n ],\n throwOnError: false\n });\n});\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown\u5185\u8054\u516c\u5f0f: $E = mc^2$\n\n\u5757\u7ea7\u516c\u5f0f\uff1a\n$$\n\\frac{n!}{k!(n-k)!} = \\binom{n}{k}\n$$\n
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YAMLmarkdown_extensions:\n - pymdownx.arithmatex:\n generic: true\n\nextra_javascript:\n - javascripts/mathjax.js\n - https://polyfill.io/v3/polyfill.min.js?features=es6\n - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\n
\u521b\u5efa docs/javascripts/mathjax.js \uff1a
JavaScriptwindow.MathJax = {\n tex: {\n inlineMath: [[\"\\\\(\", \"\\\\)\"]],\n displayMath: [[\"\\\\[\", \"\\\\]\"]],\n processEscapes: true,\n processEnvironments: true\n },\n options: {\n ignoreHtmlClass: \".*|\",\n processHtmlClass: \"arithmatex\"\n }\n};\n
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YAMLmarkdown_extensions:\n - pymdownx.arithmatex:\n generic: true\n numbering: true\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Markdown$$\n\\begin{equation}\nE = mc^2 \\label{eq:einstein}\n\\end{equation}\n$$\n\n\u5f15\u7528\u516c\u5f0f $\\eqref{eq:einstein}$\n
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YAMLmarkdown_extensions:\n - pymdownx.superfences:\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_code_format\n\nextra_javascript:\n - https://unpkg.com/mermaid@9/dist/mermaid.min.js\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Text Onlygraph TD\n A[\u5f00\u59cb] --> B{\u5224\u65ad}\n B -->|Yes| C[\u5904\u7406]\n B -->|No| D[\u7ed3\u675f]\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4522-plantuml","title":"4.5.2.2 PlantUML \u652f\u6301","text":"\u914d\u7f6e PlantUML\uff1a
YAMLmarkdown_extensions:\n - pymdownx.superfences:\n custom_fences:\n - name: plantuml\n class: plantuml\n format: !!python/name:pymdownx.superfences.fence_code_format\n\nextra_javascript:\n - https://cdn.jsdelivr.net/npm/plantuml-encoder@1.4.0/dist/plantuml-encoder.min.js\n
\u4f7f\u7528\u793a\u4f8b\uff1a
Text Only@startuml\nAlice -> Bob: \u8bf7\u6c42\nBob --> Alice: \u54cd\u5e94\n@enduml\n
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JavaScript// docs/javascripts/charts.js\nimport Chart from 'chart.js/auto';\n\ndocument.addEventListener('DOMContentLoaded', function() {\n const chartElements = document.querySelectorAll('.custom-chart');\n\n chartElements.forEach(element => {\n const ctx = element.getContext('2d');\n const data = JSON.parse(element.dataset.chartData);\n\n new Chart(ctx, {\n type: data.type,\n data: data.data,\n options: data.options\n });\n });\n});\n
\u4f7f\u7528\u793a\u4f8b\uff1a
HTML<canvas class=\"custom-chart\" data-chart-data='{\n \"type\": \"line\",\n \"data\": {\n \"labels\": [\"1\u6708\", \"2\u6708\", \"3\u6708\"],\n \"datasets\": [{\n \"label\": \"\u6570\u636e\",\n \"data\": [10, 20, 30]\n }]\n }\n}'></canvas>\n
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YAMLmarkdown_extensions:\n - pymdownx.tasklist:\n custom_checkbox: true\n clickable_checkbox: true\n
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CSS/* docs/stylesheets/tasklist.css */\n\n.task-list-item {\n list-style-type: none;\n margin-left: -1.6rem;\n}\n\n.task-list-control {\n position: relative;\n display: inline-block;\n width: 1.2rem;\n height: 1.2rem;\n margin-right: 0.5rem;\n vertical-align: middle;\n}\n\n.task-list-indicator {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n background-color: var(--md-default-fg-color--lighter);\n border-radius: 2px;\n transition: all 0.2s ease;\n}\n\n.task-list-indicator:checked {\n background-color: var(--md-accent-fg-color);\n}\n
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JavaScript// docs/javascripts/tasklist.js\n\ndocument.addEventListener('DOMContentLoaded', function() {\n const taskItems = document.querySelectorAll('.task-list-item input[type=\"checkbox\"]');\n\n taskItems.forEach(item => {\n item.addEventListener('change', function() {\n // \u4fdd\u5b58\u72b6\u6001\n localStorage.setItem(\n `task-${this.closest('.task-list-item').id}`,\n this.checked\n );\n\n // \u66f4\u65b0\u6837\u5f0f\n if (this.checked) {\n this.closest('.task-list-item').classList.add('completed');\n } else {\n this.closest('.task-list-item').classList.remove('completed');\n }\n });\n\n // \u6062\u590d\u72b6\u6001\n const saved = localStorage.getItem(`task-${item.closest('.task-list-item').id}`);\n if (saved === 'true') {\n item.checked = true;\n item.closest('.task-list-item').classList.add('completed');\n }\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4533","title":"4.5.3.3 \u72b6\u6001\u7ba1\u7406","text":"CSS/* \u4efb\u52a1\u72b6\u6001\u6837\u5f0f */\n.task-list-item.completed {\n text-decoration: line-through;\n color: var(--md-default-fg-color--light);\n}\n\n.task-list-item.pending {\n font-weight: bold;\n}\n\n.task-list-item.in-progress {\n color: var(--md-accent-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#454","title":"4.5.4 \u6807\u7b7e\u9875","text":""},{"location":"Tools/Blog/Mkdocs_Material/#4541","title":"4.5.4.1 \u6807\u7b7e\u7ec4\u6837\u5f0f","text":"\u914d\u7f6e\u6807\u7b7e\u9875\uff1a
YAMLmarkdown_extensions:\n - pymdownx.tabbed:\n alternate_style: true \n
\u81ea\u5b9a\u4e49\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/tabs.css */\n\n/* \u6807\u7b7e\u7ec4\u5bb9\u5668 */\n.tabbed-set {\n border: 1px solid var(--md-default-fg-color--lightest);\n border-radius: 4px;\n margin: 1rem 0;\n}\n\n/* \u6807\u7b7e\u5217\u8868 */\n.tabbed-labels {\n display: flex;\n background-color: var(--md-code-bg-color);\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n/* \u5355\u4e2a\u6807\u7b7e */\n.tabbed-labels > label {\n padding: 0.8rem 1.2rem;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n/* \u6fc0\u6d3b\u72b6\u6001 */\n.tabbed-labels > label.tabbed-selected {\n color: var(--md-accent-fg-color);\n border-bottom: 2px solid var(--md-accent-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4542","title":"4.5.4.2 \u5207\u6362\u6548\u679c","text":"\u6dfb\u52a0\u52a8\u753b\u6548\u679c\uff1a
CSS/* \u6807\u7b7e\u5185\u5bb9\u5207\u6362\u52a8\u753b */\n.tabbed-content {\n padding: 1rem;\n opacity: 0;\n transform: translateY(10px);\n transition: all 0.3s ease;\n}\n\n.tabbed-content.tabbed-selected {\n opacity: 1;\n transform: translateY(0);\n}\n
\u6807\u7b7e\u9875\u4ea4\u4e92\uff1a
JavaScript// docs/javascripts/tabs.js\n\ndocument.addEventListener('DOMContentLoaded', function() {\n const tabSets = document.querySelectorAll('.tabbed-set');\n\n tabSets.forEach(tabSet => {\n const tabs = tabSet.querySelectorAll('.tabbed-labels > label');\n const contents = tabSet.querySelectorAll('.tabbed-content');\n\n tabs.forEach((tab, index) => {\n tab.addEventListener('click', () => {\n // \u66f4\u65b0\u6807\u7b7e\u72b6\u6001\n tabs.forEach(t => t.classList.remove('tabbed-selected'));\n tab.classList.add('tabbed-selected');\n\n // \u66f4\u65b0\u5185\u5bb9\u72b6\u6001\n contents.forEach(c => c.classList.remove('tabbed-selected'));\n contents[index].classList.add('tabbed-selected');\n });\n });\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#4543","title":"4.5.4.3 \u54cd\u5e94\u5f0f\u8bbe\u8ba1","text":"\u6dfb\u52a0\u54cd\u5e94\u5f0f\u652f\u6301\uff1a
CSS/* \u79fb\u52a8\u7aef\u9002\u914d */\n@media screen and (max-width: 76.1875em) {\n .tabbed-labels {\n flex-wrap: wrap;\n }\n\n .tabbed-labels > label {\n flex: 1 1 auto;\n text-align: center;\n }\n\n .tabbed-content {\n padding: 0.8rem;\n }\n}\n\n/* \u5e73\u677f\u9002\u914d */\n@media screen and (min-width: 76.25em) {\n .tabbed-set {\n max-width: 80%;\n margin: 1rem auto;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#455","title":"4.5.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\n\nmarkdown_extensions:\n # \u6570\u5b66\u516c\u5f0f\n - pymdownx.arithmatex:\n generic: true\n\n # \u56fe\u8868\u652f\u6301\n - pymdownx.superfences:\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_code_format\n - name: plantuml\n class: plantuml\n format: !!python/name:pymdownx.superfences.fence_code_format\n\n # \u4efb\u52a1\u5217\u8868\n - pymdownx.tasklist:\n custom_checkbox: true\n clickable_checkbox: true\n\n # \u6807\u7b7e\u9875\n - pymdownx.tabbed:\n alternate_style: true\n\nextra_javascript:\n - javascripts/katex.js\n - javascripts/mermaid.js\n - javascripts/charts.js\n - javascripts/tasklist.js\n - javascripts/tabs.js\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js\n - https://unpkg.com/mermaid@9/dist/mermaid.min.js\n\nextra_css:\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.css\n - stylesheets/tasklist.css\n - stylesheets/tabs.css\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5","title":"5 \u6837\u5f0f\u5b9a\u5236","text":""},{"location":"Tools/Blog/Mkdocs_Material/#51-css","title":"5.1 CSS \u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#511-css","title":"5.1.1 \u81ea\u5b9a\u4e49 CSS \u6587\u4ef6","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5111-css","title":"5.1.1.1 CSS \u6587\u4ef6\u7ec4\u7ec7","text":" - \u63a8\u8350\u7684\u76ee\u5f55\u7ed3\u6784\uff1a
Text Onlydocs/\n\u251c\u2500\u2500 stylesheets/\n\u2502 \u251c\u2500\u2500 base/\n\u2502 \u2502 \u251c\u2500\u2500 _variables.css # CSS\u53d8\u91cf\u5b9a\u4e49\n\u2502 \u2502 \u251c\u2500\u2500 _typography.css # \u6392\u7248\u6837\u5f0f\n\u2502 \u2502 \u2514\u2500\u2500 _colors.css # \u989c\u8272\u5b9a\u4e49\n\u2502 \u251c\u2500\u2500 components/\n\u2502 \u2502 \u251c\u2500\u2500 _buttons.css # \u6309\u94ae\u6837\u5f0f\n\u2502 \u2502 \u251c\u2500\u2500 _cards.css # \u5361\u7247\u6837\u5f0f\n\u2502 \u2502 \u2514\u2500\u2500 _tables.css # \u8868\u683c\u6837\u5f0f\n\u2502 \u251c\u2500\u2500 layouts/\n\u2502 \u2502 \u251c\u2500\u2500 _header.css # \u5934\u90e8\u6837\u5f0f\n\u2502 \u2502 \u251c\u2500\u2500 _nav.css # \u5bfc\u822a\u6837\u5f0f\n\u2502 \u2502 \u2514\u2500\u2500 _footer.css # \u9875\u811a\u6837\u5f0f\n\u2502 \u2514\u2500\u2500 extra.css # \u4e3b\u6837\u5f0f\u6587\u4ef6\n
- \u5728
mkdocs.yml \u4e2d\u5f15\u5165\u6837\u5f0f\uff1a
YAMLextra_css:\n - stylesheets/extra.css\n
- \u5728\u4e3b\u6837\u5f0f\u6587\u4ef6\u4e2d\u5bfc\u5165\u5176\u4ed6\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/extra.css */\n\n/* \u57fa\u7840\u6837\u5f0f */\n@import 'base/_variables.css';\n@import 'base/_typography.css';\n@import 'base/_colors.css';\n\n/* \u7ec4\u4ef6\u6837\u5f0f */\n@import 'components/_buttons.css';\n@import 'components/_cards.css';\n@import 'components/_tables.css';\n\n/* \u5e03\u5c40\u6837\u5f0f */\n@import 'layouts/_header.css';\n@import 'layouts/_nav.css';\n@import 'layouts/_footer.css';\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5112","title":"5.1.1.2 \u6837\u5f0f\u4f18\u5148\u7ea7","text":" - \u6837\u5f0f\u4f18\u5148\u7ea7\u89c4\u5219\uff1a
CSS/* 1. \u884c\u5185\u6837\u5f0f (1000) */\n<div style=\"color: red;\">\n\n/* 2. ID \u9009\u62e9\u5668 (100) */\n#header { }\n\n/* 3. \u7c7b\u9009\u62e9\u5668\u3001\u5c5e\u6027\u9009\u62e9\u5668\u3001\u4f2a\u7c7b (10) */\n.nav-item { }\n[type=\"text\"] { }\n:hover { }\n\n/* 4. \u5143\u7d20\u9009\u62e9\u5668\u3001\u4f2a\u5143\u7d20 (1) */\ndiv { }\n::before { }\n
- Material \u4e3b\u9898\u8986\u76d6\uff1a
CSS/* \u8986\u76d6\u4e3b\u9898\u6837\u5f0f */\n.md-header {\n /* \u4f7f\u7528 !important \u614e\u91cd */\n background-color: #2196f3 !important;\n}\n\n/* \u4f7f\u7528\u66f4\u5177\u4f53\u7684\u9009\u62e9\u5668 */\n.md-header[data-md-color-scheme=\"default\"] {\n background-color: #2196f3;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5113-css","title":"5.1.1.3 CSS \u53d8\u91cf","text":" - \u5b9a\u4e49\u5168\u5c40\u53d8\u91cf\uff1a
CSS/* docs/stylesheets/base/_variables.css */\n\n:root {\n /* \u989c\u8272\u53d8\u91cf */\n --primary-color: #2196f3;\n --accent-color: #f50057;\n --text-color: #333333;\n\n /* \u5b57\u4f53\u53d8\u91cf */\n --font-family: \"LXGW WenKai\", -apple-system, sans-serif;\n --code-font: \"JetBrains Mono\", monospace;\n\n /* \u95f4\u8ddd\u53d8\u91cf */\n --spacing-unit: 8px;\n --content-padding: calc(var(--spacing-unit) * 2);\n\n /* \u9634\u5f71\u53d8\u91cf */\n --shadow-sm: 0 1px 2px rgba(0,0,0,0.1);\n --shadow-md: 0 2px 4px rgba(0,0,0,0.1);\n --shadow-lg: 0 4px 8px rgba(0,0,0,0.1);\n}\n
- \u4e3b\u9898\u53d8\u91cf\uff1a
CSS/* \u4eae\u8272\u4e3b\u9898 */\n[data-md-color-scheme=\"default\"] {\n --md-primary-fg-color: var(--primary-color);\n --md-accent-fg-color: var(--accent-color);\n --md-typeset-color: var(--text-color);\n}\n\n/* \u6697\u8272\u4e3b\u9898 */\n[data-md-color-scheme=\"slate\"] {\n --primary-color: #90caf9;\n --accent-color: #ff4081;\n --text-color: #ffffff;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#512","title":"5.1.2 \u5e38\u7528\u6837\u5f0f\u4fee\u6539","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5121","title":"5.1.2.1 \u5b57\u4f53\u8bbe\u7f6e","text":" - \u5b57\u4f53\u5b9a\u4e49\uff1a
CSS/* docs/stylesheets/base/_typography.css */\n\n/* \u57fa\u7840\u5b57\u4f53\u8bbe\u7f6e */\nbody {\n font-family: var(--font-family);\n font-size: 16px;\n line-height: 1.6;\n}\n\n/* \u4ee3\u7801\u5b57\u4f53 */\ncode, pre {\n font-family: var(--code-font);\n font-size: 0.9em;\n}\n\n/* \u6807\u9898\u5b57\u4f53 */\nh1, h2, h3, h4, h5, h6 {\n font-family: var(--font-family);\n font-weight: 600;\n margin: calc(var(--spacing-unit) * 3) 0;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5122","title":"5.1.2.2 \u989c\u8272\u5b9a\u5236","text":"CSS/* docs/stylesheets/base/_colors.css */\n\n/* \u6587\u672c\u989c\u8272 */\n.md-typeset {\n color: var(--text-color);\n}\n\n/* \u94fe\u63a5\u989c\u8272 */\n.md-typeset a {\n color: var(--md-accent-fg-color);\n}\n\n/* \u4ee3\u7801\u5757\u989c\u8272 */\n.highlight {\n background-color: var(--md-code-bg-color);\n}\n\n/* \u5f15\u7528\u5757\u989c\u8272 */\nblockquote {\n border-left: 4px solid var(--md-accent-fg-color);\n background-color: var(--md-code-bg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5123","title":"5.1.2.3 \u95f4\u8ddd\u8c03\u6574","text":"CSS/* \u5185\u5bb9\u95f4\u8ddd */\n.md-main__inner {\n padding: var(--content-padding);\n}\n\n/* \u6bb5\u843d\u95f4\u8ddd */\n.md-typeset p {\n margin: var(--spacing-unit) 0;\n}\n\n/* \u5217\u8868\u95f4\u8ddd */\n.md-typeset ul li,\n.md-typeset ol li {\n margin-bottom: calc(var(--spacing-unit) * 0.5);\n}\n\n/* \u6807\u9898\u95f4\u8ddd */\n.md-typeset h1 {\n margin-top: calc(var(--spacing-unit) * 4);\n margin-bottom: calc(var(--spacing-unit) * 2);\n}\n\n.md-typeset h2 {\n margin-top: calc(var(--spacing-unit) * 3);\n margin-bottom: calc(var(--spacing-unit) * 1.5);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5124","title":"5.1.2.4 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"\u5728 mkdocs.yml \u4e2d\u7684\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n font: false # \u7981\u7528\u9ed8\u8ba4\u5b57\u4f53\n\nextra_css:\n - stylesheets/extra.css\n\nextra:\n css_variables:\n spacing_unit: 8px\n content_width: 960px\n
"},{"location":"Tools/Blog/Mkdocs_Material/#52","title":"5.2 \u4e3b\u9898\u6837\u5f0f\u8986\u76d6","text":""},{"location":"Tools/Blog/Mkdocs_Material/#521","title":"5.2.1 \u7ec4\u4ef6\u6837\u5f0f\u4fee\u6539","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5211","title":"5.2.1.1 \u5bfc\u822a\u680f\u6837\u5f0f","text":" - \u9876\u90e8\u5bfc\u822a\u680f\uff1a
CSS/* docs/stylesheets/components/header.css */\n\n/* \u5bfc\u822a\u680f\u5bb9\u5668 */\n.md-header {\n background-color: var(--md-primary-fg-color);\n box-shadow: 0 2px 4px rgba(0,0,0,.14);\n height: 3rem;\n}\n\n/* \u5bfc\u822a\u680f\u6807\u9898 */\n.md-header__title {\n font-size: 1.2rem;\n font-weight: 600;\n margin-left: 1rem;\n}\n\n/* \u5bfc\u822a\u680f\u6309\u94ae */\n.md-header__button {\n padding: .8rem;\n color: var(--md-primary-bg-color);\n}\n\n/* \u5bfc\u822a\u680f\u641c\u7d22\u6846 */\n.md-search__input {\n border-radius: 2rem;\n background-color: rgba(255,255,255,.1);\n padding: 0 2.4rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5212","title":"5.2.1.2 \u4fa7\u8fb9\u680f\u6837\u5f0f","text":"CSS/* docs/stylesheets/components/sidebar.css */\n\n/* \u4fa7\u8fb9\u680f\u5bb9\u5668 */\n.md-sidebar {\n width: 14rem;\n background-color: var(--md-default-bg-color);\n padding: 1.2rem 0;\n}\n\n/* \u4fa7\u8fb9\u680f\u5bfc\u822a */\n.md-nav--primary {\n padding: 0 .8rem;\n}\n\n/* \u5bfc\u822a\u9879 */\n.md-nav__item {\n padding: .2rem 0;\n}\n\n/* \u5bfc\u822a\u94fe\u63a5 */\n.md-nav__link {\n color: var(--md-default-fg-color);\n padding: .4rem .6rem;\n border-radius: 4px;\n transition: all .2s;\n}\n\n.md-nav__link:hover {\n background-color: var(--md-code-bg-color);\n color: var(--md-accent-fg-color);\n}\n\n/* \u6fc0\u6d3b\u72b6\u6001 */\n.md-nav__link--active {\n font-weight: 600;\n color: var(--md-accent-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5213","title":"5.2.1.3 \u9875\u811a\u6837\u5f0f","text":"CSS/* docs/stylesheets/components/footer.css */\n\n/* \u9875\u811a\u5bb9\u5668 */\n.md-footer {\n background-color: var(--md-default-bg-color--darkest);\n color: var(--md-footer-fg-color);\n}\n\n/* \u9875\u811a\u5185\u5bb9 */\n.md-footer-meta {\n background-color: rgba(0,0,0,.1);\n padding: 1rem 0;\n}\n\n/* \u9875\u811a\u94fe\u63a5 */\n.md-footer__link {\n padding: .4rem 1rem;\n color: var(--md-footer-fg-color--light);\n}\n\n/* \u7248\u6743\u4fe1\u606f */\n.md-footer-copyright {\n font-size: .8rem;\n color: var(--md-footer-fg-color--lighter);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#522","title":"5.2.2 \u81ea\u5b9a\u4e49\u8c03\u8272\u677f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5221","title":"5.2.2.1 \u4e3b\u9898\u8272\u5b9a\u5236","text":" - \u57fa\u7840\u989c\u8272\u5b9a\u4e49\uff1a
CSS/* docs/stylesheets/theme/colors.css */\n\n:root {\n /* \u4e3b\u8272\u8c03 */\n --md-primary-hue: 210;\n --md-primary-saturation: 80%;\n --md-primary-lightness: 45%;\n\n /* \u5f3a\u8c03\u8272 */\n --md-accent-hue: 340;\n --md-accent-saturation: 90%;\n --md-accent-lightness: 50%;\n}\n
- \u989c\u8272\u53d8\u91cf\u5e94\u7528\uff1a
CSS:root {\n --md-primary-fg-color: hsl(\n var(--md-primary-hue),\n var(--md-primary-saturation),\n var(--md-primary-lightness)\n );\n\n --md-accent-fg-color: hsl(\n var(--md-accent-hue),\n var(--md-accent-saturation),\n var(--md-accent-lightness)\n );\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5222","title":"5.2.2.2 \u914d\u8272\u65b9\u6848","text":"YAML# mkdocs.yml\ntheme:\n palette:\n # \u4eae\u8272\u6a21\u5f0f\n - media: \"(prefers-color-scheme: light)\"\n scheme: default\n primary: indigo\n accent: deep purple\n toggle:\n icon: material/brightness-7\n name: \u5207\u6362\u81f3\u6697\u8272\u6a21\u5f0f\n\n # \u6697\u8272\u6a21\u5f0f\n - media: \"(prefers-color-scheme: dark)\"\n scheme: slate\n primary: blue grey\n accent: deep purple\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u6a21\u5f0f\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5223","title":"5.2.2.3 \u6697\u8272\u4e3b\u9898","text":"CSS/* \u6697\u8272\u4e3b\u9898\u53d8\u91cf */\n[data-md-color-scheme=\"slate\"] {\n --md-default-bg-color: #1a1a1a;\n --md-default-bg-color--light: #222222;\n --md-default-bg-color--lighter: #282828;\n\n --md-default-fg-color: rgba(255,255,255,0.87);\n --md-default-fg-color--light: rgba(255,255,255,0.54);\n --md-default-fg-color--lighter: rgba(255,255,255,0.32);\n\n --md-code-bg-color: #2d2d2d;\n --md-code-fg-color: #f5f5f5;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#523","title":"5.2.3 \u5b57\u4f53\u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5231","title":"5.2.3.1 \u4e2d\u6587\u5b57\u4f53","text":" - \u5b57\u4f53\u5b9a\u4e49\uff1a
CSS/* docs/stylesheets/theme/fonts.css */\n\n/* \u4e2d\u6587\u5b57\u4f53\u53d8\u91cf */\n:root {\n --md-font-chinese: \"LXGW WenKai\", \"PingFang SC\", \"Microsoft YaHei\";\n}\n\n/* \u5f15\u5165 LXGW WenKai \u5b57\u4f53 */\n@font-face {\n font-family: \"LXGW WenKai\";\n src: url(\"https://cdn.jsdelivr.net/npm/lxgw-wenkai-webfont@1.1.0/style.css\");\n font-display: swap;\n}\n
- \u5b57\u4f53\u5e94\u7528\uff1a
CSSbody {\n font-family: var(--md-font-chinese), -apple-system, sans-serif;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5232","title":"5.2.3.2 \u4ee3\u7801\u5b57\u4f53","text":"CSS/* \u4ee3\u7801\u5b57\u4f53\u914d\u7f6e */\n:root {\n --md-code-font: \"JetBrains Mono\", \"Fira Code\", \"Source Code Pro\", monospace;\n}\n\n/* \u4ee3\u7801\u5757\u6837\u5f0f */\n.md-typeset code,\n.md-typeset pre {\n font-family: var(--md-code-font);\n font-size: 0.9em;\n}\n\n/* \u884c\u5185\u4ee3\u7801 */\n.md-typeset code {\n border-radius: 4px;\n padding: .2em .4em;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5233","title":"5.2.3.3 \u5b57\u4f53\u56de\u9000","text":"CSS/* \u5b57\u4f53\u56de\u9000\u7b56\u7565 */\n:root {\n --md-text-font-fallback: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica,\n Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji;\n\n --md-code-font-fallback: SFMono-Regular, Consolas, Menlo, monospace;\n}\n\n/* \u5e94\u7528\u56de\u9000\u5b57\u4f53 */\nbody {\n font-family: var(--md-font-chinese), var(--md-text-font-fallback);\n}\n\npre, code {\n font-family: var(--md-code-font), var(--md-code-font-fallback);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#524","title":"5.2.4 \u5e03\u5c40\u8c03\u6574","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5241","title":"5.2.4.1 \u54cd\u5e94\u5f0f\u5e03\u5c40","text":"CSS/* docs/stylesheets/theme/layout.css */\n\n/* \u57fa\u7840\u54cd\u5e94\u5f0f\u5e03\u5c40 */\n.md-grid {\n max-width: 100%;\n margin: 0 auto;\n}\n\n/* \u684c\u9762\u7aef */\n@media screen and (min-width: 76.25em) {\n .md-grid {\n max-width: 76rem;\n }\n\n .md-sidebar--primary {\n width: 14rem;\n }\n\n .md-sidebar--secondary {\n width: 12rem;\n margin-left: 76rem;\n }\n}\n\n/* \u5e73\u677f\u7aef */\n@media screen and (max-width: 76.1875em) {\n .md-grid {\n max-width: 60rem;\n }\n\n .md-header-nav__title {\n display: none;\n }\n}\n\n/* \u79fb\u52a8\u7aef */\n@media screen and (max-width: 44.9375em) {\n .md-grid {\n max-width: 100%;\n padding: 0 1rem;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5242","title":"5.2.4.2 \u7f51\u683c\u7cfb\u7edf","text":"CSS/* \u81ea\u5b9a\u4e49\u7f51\u683c\u7cfb\u7edf */\n.grid {\n display: grid;\n gap: 1rem;\n margin: 1rem 0;\n}\n\n/* \u7f51\u683c\u5217\u6570 */\n.grid-cols-1 { grid-template-columns: repeat(1, 1fr); }\n.grid-cols-2 { grid-template-columns: repeat(2, 1fr); }\n.grid-cols-3 { grid-template-columns: repeat(3, 1fr); }\n.grid-cols-4 { grid-template-columns: repeat(4, 1fr); }\n\n/* \u54cd\u5e94\u5f0f\u7f51\u683c */\n@media (min-width: 768px) {\n .md-grid-cols-md-2 { grid-template-columns: repeat(2, 1fr); }\n .md-grid-cols-md-3 { grid-template-columns: repeat(3, 1fr); }\n}\n\n@media (min-width: 1024px) {\n .md-grid-cols-lg-3 { grid-template-columns: repeat(3, 1fr); }\n .md-grid-cols-lg-4 { grid-template-columns: repeat(4, 1fr); }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5243","title":"5.2.4.3 \u5bb9\u5668\u5bbd\u5ea6","text":"CSS/* \u5bb9\u5668\u5bbd\u5ea6\u5b9a\u4e49 */\n:root {\n --md-container-width: 80rem;\n --md-container-padding: 1rem;\n}\n\n/* \u5bb9\u5668\u6837\u5f0f */\n.md-container {\n max-width: var(--md-container-width);\n margin: 0 auto;\n padding: 0 var(--md-container-padding);\n}\n\n/* \u4e0d\u540c\u5c3a\u5bf8\u7684\u5bb9\u5668 */\n.md-container--small {\n max-width: 60rem;\n}\n\n.md-container--medium {\n max-width: 70rem;\n}\n\n.md-container--large {\n max-width: 90rem;\n}\n\n/* \u6d41\u5f0f\u5bb9\u5668 */\n.md-container--fluid {\n max-width: 100%;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#525","title":"5.2.5 \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b","text":"YAML# mkdocs.yml\ntheme:\n name: material\n font: false\n features:\n - navigation.tabs\n - navigation.sections\n - navigation.expand\n palette:\n - scheme: default\n primary: indigo\n accent: deep purple\n - scheme: slate\n primary: blue grey\n accent: deep purple\n\nextra_css:\n - stylesheets/theme/colors.css\n - stylesheets/theme/fonts.css\n - stylesheets/theme/layout.css\n - stylesheets/components/header.css\n - stylesheets/components/sidebar.css\n - stylesheets/components/footer.css\n
"},{"location":"Tools/Blog/Mkdocs_Material/#53","title":"5.3 \u81ea\u5b9a\u4e49\u7ec4\u4ef6\u6837\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#531","title":"5.3.1 \u5361\u7247\u6837\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5311","title":"5.3.1.1 \u57fa\u7840\u5361\u7247","text":"\u57fa\u7840\u5361\u7247\u6837\u5f0f\u5b9a\u4e49\uff1a
CSS/* docs/stylesheets/components/cards.css */\n\n/* \u57fa\u7840\u5361\u7247\u5bb9\u5668 */\n.card {\n background-color: var(--md-default-bg-color);\n border-radius: 8px;\n box-shadow: var(--md-shadow-z1);\n padding: 1.5rem;\n margin: 1rem 0;\n transition: all 0.3s ease;\n}\n\n/* \u5361\u7247\u6807\u9898 */\n.card__title {\n font-size: 1.25rem;\n font-weight: 600;\n margin-bottom: 1rem;\n color: var(--md-typeset-color);\n}\n\n/* \u5361\u7247\u5185\u5bb9 */\n.card__content {\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n line-height: 1.6;\n}\n\n/* \u5361\u7247\u5e95\u90e8 */\n.card__footer {\n margin-top: 1rem;\n padding-top: 1rem;\n border-top: 1px solid var(--md-default-fg-color--lightest);\n}\n
\u4f7f\u7528\u793a\u4f8b\uff1a
HTML<div class=\"card\">\n <div class=\"card__title\">\u5361\u7247\u6807\u9898</div>\n <div class=\"card__content\">\n \u8fd9\u91cc\u662f\u5361\u7247\u5185\u5bb9...\n </div>\n <div class=\"card__footer\">\n \u5361\u7247\u5e95\u90e8\u4fe1\u606f\n </div>\n</div>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5312","title":"5.3.1.2 \u56fe\u7247\u5361\u7247","text":"CSS/* \u56fe\u7247\u5361\u7247\u6837\u5f0f */\n.card--image {\n padding: 0;\n overflow: hidden;\n}\n\n/* \u56fe\u7247\u5bb9\u5668 */\n.card__image {\n width: 100%;\n height: 200px;\n position: relative;\n overflow: hidden;\n}\n\n/* \u56fe\u7247\u6837\u5f0f */\n.card__image img {\n width: 100%;\n height: 100%;\n object-fit: cover;\n transition: transform 0.3s ease;\n}\n\n/* \u56fe\u7247\u60ac\u505c\u6548\u679c */\n.card--image:hover img {\n transform: scale(1.05);\n}\n\n/* \u56fe\u7247\u5361\u7247\u5185\u5bb9\u533a */\n.card--image .card__content {\n padding: 1.5rem;\n}\n\n/* \u56fe\u7247\u6807\u9898\u8986\u76d6 */\n.card__image-title {\n position: absolute;\n bottom: 0;\n left: 0;\n right: 0;\n padding: 1rem;\n background: linear-gradient(to top, rgba(0,0,0,0.7), transparent);\n color: white;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5313","title":"5.3.1.3 \u7279\u6548\u5361\u7247","text":"CSS/* \u60ac\u6d6e\u6548\u679c\u5361\u7247 */\n.card--hover {\n cursor: pointer;\n}\n\n.card--hover:hover {\n transform: translateY(-4px);\n box-shadow: var(--md-shadow-z2);\n}\n\n/* \u6e10\u53d8\u80cc\u666f\u5361\u7247 */\n.card--gradient {\n background: linear-gradient(135deg, \n var(--md-primary-fg-color) 0%,\n var(--md-accent-fg-color) 100%);\n color: white;\n}\n\n/* \u6bdb\u73bb\u7483\u6548\u679c\u5361\u7247 */\n.card--glass {\n background: rgba(255, 255, 255, 0.1);\n backdrop-filter: blur(10px);\n border: 1px solid rgba(255, 255, 255, 0.2);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#532","title":"5.3.2 \u63d0\u793a\u6846\u6837\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5321","title":"5.3.2.1 \u4fe1\u606f\u63d0\u793a","text":"CSS/* docs/stylesheets/components/alerts.css */\n\n/* \u57fa\u7840\u63d0\u793a\u6846 */\n.alert {\n padding: 1rem 1.5rem;\n margin: 1rem 0;\n border-left: 4px solid;\n border-radius: 4px;\n}\n\n/* \u4fe1\u606f\u63d0\u793a */\n.alert--info {\n background-color: #e3f2fd;\n border-color: #2196f3;\n color: #0d47a1;\n}\n\n.alert--info::before {\n content: \"\u2139\ufe0f\";\n margin-right: 0.5rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5322","title":"5.3.2.2 \u8b66\u544a\u63d0\u793a","text":"CSS/* \u8b66\u544a\u63d0\u793a */\n.alert--warning {\n background-color: #fff3e0;\n border-color: #ff9800;\n color: #e65100;\n}\n\n.alert--warning::before {\n content: \"\u26a0\ufe0f\";\n margin-right: 0.5rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5323","title":"5.3.2.3 \u9519\u8bef\u63d0\u793a","text":"CSS/* \u9519\u8bef\u63d0\u793a */\n.alert--error {\n background-color: #ffebee;\n border-color: #f44336;\n color: #b71c1c;\n}\n\n.alert--error::before {\n content: \"\u274c\";\n margin-right: 0.5rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#533","title":"5.3.3 \u53cb\u94fe\u6837\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5331","title":"5.3.3.1 \u53cb\u94fe\u5361\u7247","text":"CSS/* docs/stylesheets/components/friends.css */\n\n/* \u53cb\u94fe\u5bb9\u5668 */\n.friend-links {\n display: grid;\n grid-template-columns: repeat(auto-fill, minmax(250px, 1fr));\n gap: 1.5rem;\n margin: 2rem 0;\n}\n\n/* \u53cb\u94fe\u5361\u7247 */\n.friend-link {\n display: flex;\n align-items: center;\n padding: 1rem;\n background: var(--md-default-bg-color);\n border-radius: 8px;\n box-shadow: var(--md-shadow-z1);\n transition: all 0.3s ease;\n}\n\n/* \u5934\u50cf */\n.friend-link__avatar {\n width: 60px;\n height: 60px;\n border-radius: 50%;\n margin-right: 1rem;\n}\n\n/* \u4fe1\u606f */\n.friend-link__info {\n flex: 1;\n}\n\n.friend-link__name {\n font-weight: 600;\n color: var(--md-typeset-color);\n}\n\n.friend-link__desc {\n font-size: 0.85rem;\n color: var(--md-default-fg-color--light);\n margin-top: 0.25rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5332","title":"5.3.3.2 \u5206\u7c7b\u5c55\u793a","text":"CSS/* \u53cb\u94fe\u5206\u7c7b */\n.friend-links-section {\n margin: 2rem 0;\n}\n\n/* \u5206\u7c7b\u6807\u9898 */\n.friend-links-section__title {\n font-size: 1.25rem;\n font-weight: 600;\n margin-bottom: 1rem;\n padding-left: 1rem;\n border-left: 4px solid var(--md-accent-fg-color);\n}\n\n/* \u5206\u7c7b\u63cf\u8ff0 */\n.friend-links-section__desc {\n color: var(--md-default-fg-color--light);\n margin-bottom: 1.5rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5333","title":"5.3.3.3 \u60ac\u505c\u6548\u679c","text":"CSS/* \u60ac\u505c\u6548\u679c */\n.friend-link:hover {\n transform: translateY(-2px);\n box-shadow: var(--md-shadow-z2);\n}\n\n/* \u5934\u50cf\u52a8\u753b */\n.friend-link:hover .friend-link__avatar {\n transform: rotate(360deg);\n transition: transform 0.6s ease;\n}\n\n/* \u6807\u7b7e\u6548\u679c */\n.friend-link__tag {\n display: inline-block;\n padding: 0.2rem 0.5rem;\n font-size: 0.75rem;\n border-radius: 12px;\n background-color: var(--md-code-bg-color);\n margin-top: 0.5rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#534","title":"5.3.4 \u65f6\u95f4\u7ebf\u6837\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#5341","title":"5.3.4.1 \u65f6\u95f4\u8f74\u8bbe\u8ba1","text":"CSS/* docs/stylesheets/components/timeline.css */\n\n/* \u65f6\u95f4\u7ebf\u5bb9\u5668 */\n.timeline {\n position: relative;\n max-width: 800px;\n margin: 2rem auto;\n padding: 2rem 0;\n}\n\n/* \u65f6\u95f4\u8f74\u7ebf */\n.timeline::before {\n content: '';\n position: absolute;\n top: 0;\n left: calc(50% - 1px);\n width: 2px;\n height: 100%;\n background-color: var(--md-default-fg-color--lightest);\n}\n\n/* \u65f6\u95f4\u7ebf\u9879\u76ee */\n.timeline-item {\n position: relative;\n margin: 2rem 0;\n}\n\n/* \u4ea4\u9519\u5e03\u5c40 */\n.timeline-item:nth-child(odd) {\n padding-right: calc(50% + 2rem);\n}\n\n.timeline-item:nth-child(even) {\n padding-left: calc(50% + 2rem);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5342","title":"5.3.4.2 \u8282\u70b9\u6837\u5f0f","text":"CSS/* \u65f6\u95f4\u8282\u70b9 */\n.timeline-node {\n position: absolute;\n top: 50%;\n width: 16px;\n height: 16px;\n background-color: var(--md-primary-fg-color);\n border-radius: 50%;\n transform: translateY(-50%);\n}\n\n.timeline-item:nth-child(odd) .timeline-node {\n right: calc(50% - 8px);\n}\n\n.timeline-item:nth-child(even) .timeline-node {\n left: calc(50% - 8px);\n}\n\n/* \u8282\u70b9\u5185\u5bb9 */\n.timeline-content {\n background-color: var(--md-default-bg-color);\n padding: 1.5rem;\n border-radius: 8px;\n box-shadow: var(--md-shadow-z1);\n}\n\n/* \u65f6\u95f4\u6807\u7b7e */\n.timeline-date {\n position: absolute;\n top: 50%;\n color: var(--md-default-fg-color--light);\n transform: translateY(-50%);\n}\n\n.timeline-item:nth-child(odd) .timeline-date {\n left: calc(50% + 2rem);\n}\n\n.timeline-item:nth-child(even) .timeline-date {\n right: calc(50% + 2rem);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5343","title":"5.3.4.3 \u54cd\u5e94\u5f0f\u9002\u914d","text":"CSS/* \u79fb\u52a8\u7aef\u9002\u914d */\n@media screen and (max-width: 768px) {\n .timeline::before {\n left: 0;\n }\n\n .timeline-item {\n padding-left: 2rem !important;\n padding-right: 0 !important;\n }\n\n .timeline-node {\n left: -8px !important;\n right: auto !important;\n }\n\n .timeline-date {\n position: relative;\n top: auto;\n left: auto !important;\n right: auto !important;\n margin-bottom: 0.5rem;\n }\n}\n\n/* \u5e73\u677f\u7aef\u9002\u914d */\n@media screen and (min-width: 769px) and (max-width: 1024px) {\n .timeline {\n max-width: 90%;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#5344","title":"5.3.4.4 \u4f7f\u7528\u793a\u4f8b","text":"HTML<!-- \u65f6\u95f4\u7ebf\u793a\u4f8b -->\n<div class=\"timeline\">\n <div class=\"timeline-item\">\n <div class=\"timeline-node\"></div>\n <div class=\"timeline-date\">2024-01-01</div>\n <div class=\"timeline-content\">\n <h3>\u4e8b\u4ef6\u6807\u9898</h3>\n <p>\u4e8b\u4ef6\u63cf\u8ff0...</p>\n </div>\n </div>\n\n <!-- \u66f4\u591a\u65f6\u95f4\u7ebf\u9879\u76ee -->\n</div>\n\n<!-- \u53cb\u94fe\u793a\u4f8b -->\n<div class=\"friend-links-section\">\n <h2 class=\"friend-links-section__title\">\u6280\u672f\u535a\u5ba2</h2>\n <p class=\"friend-links-section__desc\">\u4f18\u79c0\u7684\u6280\u672f\u535a\u5ba2\u6536\u85cf</p>\n <div class=\"friend-links\">\n <a href=\"#\" class=\"friend-link\">\n <img src=\"avatar.jpg\" class=\"friend-link__avatar\">\n <div class=\"friend-link__info\">\n <div class=\"friend-link__name\">\u535a\u5ba2\u540d\u79f0</div>\n <div class=\"friend-link__desc\">\u535a\u5ba2\u63cf\u8ff0</div>\n <span class=\"friend-link__tag\">\u6807\u7b7e</span>\n </div>\n </a>\n </div>\n</div>\n\n<!-- \u63d0\u793a\u6846\u793a\u4f8b -->\n<div class=\"alert alert--info\">\n \u8fd9\u662f\u4e00\u6761\u4fe1\u606f\u63d0\u793a\n</div>\n\n<div class=\"alert alert--warning\">\n \u8fd9\u662f\u4e00\u6761\u8b66\u544a\u63d0\u793a\n</div>\n\n<div class=\"alert alert--error\">\n \u8fd9\u662f\u4e00\u6761\u9519\u8bef\u63d0\u793a\n</div>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#535","title":"5.3.5 \u5b8c\u6574\u914d\u7f6e","text":"YAML# mkdocs.yml\nextra_css:\n - stylesheets/components/cards.css\n - stylesheets/components/alerts.css\n - stylesheets/components/friends.css\n - stylesheets/components/timeline.css\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6-javascript","title":"6 JavaScript \u589e\u5f3a","text":""},{"location":"Tools/Blog/Mkdocs_Material/#61","title":"6.1 \u57fa\u7840\u914d\u7f6e","text":""},{"location":"Tools/Blog/Mkdocs_Material/#611-js","title":"6.1.1 \u5f15\u5165 JS \u6587\u4ef6","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6111","title":"6.1.1.1 \u672c\u5730\u6587\u4ef6","text":" - \u521b\u5efa\u57fa\u7840\u76ee\u5f55\u7ed3\u6784\uff1a
Text Onlydocs/\n\u251c\u2500\u2500 javascripts/\n\u2502 \u251c\u2500\u2500 config/ # \u914d\u7f6e\u6587\u4ef6\n\u2502 \u2502 \u2514\u2500\u2500 main.js\n\u2502 \u251c\u2500\u2500 modules/ # \u529f\u80fd\u6a21\u5757\n\u2502 \u2502 \u251c\u2500\u2500 search.js\n\u2502 \u2502 \u2514\u2500\u2500 theme.js\n\u2502 \u251c\u2500\u2500 utils/ # \u5de5\u5177\u51fd\u6570\n\u2502 \u2502 \u2514\u2500\u2500 helpers.js\n\u2502 \u2514\u2500\u2500 extra.js # \u4e3b\u5165\u53e3\u6587\u4ef6\n
- \u5728
mkdocs.yml \u4e2d\u5f15\u5165\uff1a
YAMLextra_javascript:\n - javascripts/extra.js\n - javascripts/modules/search.js\n - javascripts/modules/theme.js\n
- JavaScript \u6587\u4ef6\u793a\u4f8b\uff1a
JavaScript// docs/javascripts/extra.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u521d\u59cb\u5316\u4ee3\u7801\n console.log('Documentation loaded');\n});\n\n// docs/javascripts/modules/theme.js\nconst ThemeManager = {\n init() {\n // \u4e3b\u9898\u521d\u59cb\u5316\n },\n toggle() {\n // \u4e3b\u9898\u5207\u6362\n }\n};\n\n// docs/javascripts/utils/helpers.js\nconst Helpers = {\n debounce(fn, delay) {\n let timer = null;\n return function() {\n clearTimeout(timer);\n timer = setTimeout(() => fn.apply(this, arguments), delay);\n };\n }\n};\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6112-cdn","title":"6.1.1.2 CDN \u5f15\u5165","text":" - \u5e38\u7528 CDN \u914d\u7f6e\uff1a
YAMLextra_javascript:\n # KaTeX \u6570\u5b66\u516c\u5f0f\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js\n\n # Mermaid \u56fe\u8868\n - https://unpkg.com/mermaid@9/dist/mermaid.min.js\n\n # \u4ee3\u7801\u9ad8\u4eae\n - https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/prism.min.js\n - https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/components/prism-python.min.js\n
- CDN \u52a0\u901f\u914d\u7f6e\uff1a
YAMLextra:\n cdn:\n # \u4f7f\u7528\u56fd\u5185 CDN\n enable: true\n provider: jsdelivr # \u6216 unpkg, cdnjs\n urls:\n katex: https://cdn.jsdelivr.net/npm/katex@0.16.7/dist/katex.min.js\n mermaid: https://cdn.jsdelivr.net/npm/mermaid@9/dist/mermaid.min.js\n
- CDN \u6545\u969c\u5904\u7406\uff1a
JavaScript// docs/javascripts/config/cdn-fallback.js\nfunction loadFallbackScript(url, fallbackUrl) {\n const script = document.createElement('script');\n script.src = url;\n script.onerror = () => {\n console.warn(`Failed to load ${url}, trying fallback...`);\n const fallback = document.createElement('script');\n fallback.src = fallbackUrl;\n document.head.appendChild(fallback);\n };\n document.head.appendChild(script);\n}\n\n// \u4f7f\u7528\u793a\u4f8b\nloadFallbackScript(\n 'https://cdn.jsdelivr.net/npm/katex@0.16.7/dist/katex.min.js',\n 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js'\n);\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6113","title":"6.1.1.3 \u6a21\u5757\u5316\u7ba1\u7406","text":" - \u4f7f\u7528 ES6 \u6a21\u5757\uff1a
JavaScript// docs/javascripts/modules/theme.js\nexport class ThemeManager {\n constructor() {\n this.darkMode = false;\n }\n\n init() {\n this.loadPreference();\n this.bindEvents();\n }\n\n toggle() {\n this.darkMode = !this.darkMode;\n this.savePreference();\n this.applyTheme();\n }\n}\n\n// docs/javascripts/modules/search.js\nexport class SearchManager {\n constructor() {\n this.index = null;\n }\n\n init() {\n this.buildIndex();\n this.bindSearchEvents();\n }\n\n search(query) {\n // \u641c\u7d22\u5b9e\u73b0\n }\n}\n\n// docs/javascripts/extra.js\nimport { ThemeManager } from './modules/theme.js';\nimport { SearchManager } from './modules/search.js';\n\nconst theme = new ThemeManager();\nconst search = new SearchManager();\n\ndocument.addEventListener('DOMContentLoaded', () => {\n theme.init();\n search.init();\n});\n
- \u6a21\u5757\u914d\u7f6e\u6587\u4ef6\uff1a
JavaScript// docs/javascripts/config/modules.js\nexport const ModuleConfig = {\n theme: {\n enabled: true,\n darkModeClass: 'dark-mode',\n storageKey: 'theme-preference'\n },\n search: {\n enabled: true,\n minChars: 3,\n maxResults: 10\n }\n};\n\n// \u4f7f\u7528\u914d\u7f6e\nimport { ModuleConfig } from '../config/modules.js';\n\nclass ThemeManager {\n constructor() {\n this.config = ModuleConfig.theme;\n if (!this.config.enabled) return;\n // \u521d\u59cb\u5316\u4ee3\u7801\n }\n}\n
- \u5de5\u5177\u51fd\u6570\u6a21\u5757\uff1a
JavaScript// docs/javascripts/utils/helpers.js\nexport const DOM = {\n // DOM \u64cd\u4f5c\u8f85\u52a9\u51fd\u6570\n select: (selector) => document.querySelector(selector),\n selectAll: (selector) => document.querySelectorAll(selector),\n addClass: (element, className) => element.classList.add(className),\n removeClass: (element, className) => element.classList.remove(className)\n};\n\nexport const Storage = {\n // \u672c\u5730\u5b58\u50a8\u8f85\u52a9\u51fd\u6570\n get: (key) => localStorage.getItem(key),\n set: (key, value) => localStorage.setItem(key, value),\n remove: (key) => localStorage.removeItem(key)\n};\n\nexport const Events = {\n // \u4e8b\u4ef6\u5904\u7406\u8f85\u52a9\u51fd\u6570\n on: (element, event, handler) => element.addEventListener(event, handler),\n off: (element, event, handler) => element.removeEventListener(event, handler),\n trigger: (element, event) => element.dispatchEvent(new Event(event))\n};\n
- \u5b8c\u6574\u914d\u7f6e\u793a\u4f8b\uff1a
YAML# mkdocs.yml\nextra_javascript:\n # \u6838\u5fc3\u6587\u4ef6\n - javascripts/extra.js\n - javascripts/config/modules.js\n\n # \u529f\u80fd\u6a21\u5757\n - javascripts/modules/theme.js\n - javascripts/modules/search.js\n\n # \u5de5\u5177\u51fd\u6570\n - javascripts/utils/helpers.js\n\n # \u7b2c\u4e09\u65b9\u5e93\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js\n - https://unpkg.com/mermaid@9/dist/mermaid.min.js\n\n# \u6a21\u5757\u914d\u7f6e\nextra:\n javascript_modules:\n theme:\n enabled: true\n default: light\n search:\n enabled: true\n min_chars: 3\n
"},{"location":"Tools/Blog/Mkdocs_Material/#62","title":"6.2 \u7b2c\u4e09\u65b9\u5e93\u96c6\u6210","text":""},{"location":"Tools/Blog/Mkdocs_Material/#621-katex","title":"6.2.1 KaTeX \u6570\u5b66\u516c\u5f0f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6211","title":"6.2.1.1 \u57fa\u7840\u914d\u7f6e","text":" - \u5728
mkdocs.yml \u4e2d\u914d\u7f6e\uff1a
YAMLmarkdown_extensions:\n - pymdownx.arithmatex:\n generic: true\n\nextra_javascript:\n - javascripts/katex.js\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js\n\nextra_css:\n - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.css\n
- \u521b\u5efa KaTeX \u914d\u7f6e\u6587\u4ef6\uff1a
JavaScript// docs/javascripts/katex.js\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n renderMathInElement(document.body, {\n delimiters: [\n {left: \"$$\", right: \"$$\", display: true},\n {left: \"$\", right: \"$\", display: false},\n {left: \"\\\\(\", right: \"\\\\)\", display: false},\n {left: \"\\\\[\", right: \"\\\\]\", display: true}\n ],\n throwOnError: false,\n errorColor: \"#cc0000\",\n strict: \"ignore\"\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6212","title":"6.2.1.2 \u81ea\u52a8\u6e32\u67d3","text":" - \u914d\u7f6e\u81ea\u52a8\u6e32\u67d3\u9009\u9879\uff1a
JavaScript// docs/javascripts/katex-auto.js\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n renderMathInElement(document.body, {\n delimiters: [\n {left: \"$$\", right: \"$$\", display: true},\n {left: \"$\", right: \"$\", display: false}\n ],\n // \u81ea\u52a8\u6e32\u67d3\u8bbe\u7f6e\n ignoredTags: [\"script\", \"noscript\", \"style\", \"textarea\", \"pre\", \"code\"],\n ignoredClasses: [\"no-math\"],\n processEscapes: true,\n processEnvironments: true,\n // \u5904\u7406\u81ea\u5b9a\u4e49\u5b8f\n macros: {\n \"\\\\RR\": \"\\\\mathbb{R}\",\n \"\\\\NN\": \"\\\\mathbb{N}\",\n \"\\\\ZZ\": \"\\\\mathbb{Z}\"\n }\n });\n});\n
- \u4f7f\u7528\u793a\u4f8b\uff1a
Markdown\u884c\u5185\u516c\u5f0f\uff1a$E = mc^2$\n\n\u5757\u7ea7\u516c\u5f0f\uff1a\n$$\n\\frac{n!}{k!(n-k)!} = \\binom{n}{k}\n$$\n\n\u81ea\u5b9a\u4e49\u5b8f\uff1a$\\RR$ \u8868\u793a\u5b9e\u6570\u96c6\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6213","title":"6.2.1.3 \u516c\u5f0f\u7f16\u53f7","text":" - \u542f\u7528\u516c\u5f0f\u7f16\u53f7\uff1a
JavaScript// docs/javascripts/katex-numbering.js\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n // \u516c\u5f0f\u7f16\u53f7\u8ba1\u6570\u5668\n let equationNumbers = {};\n let numberings = {};\n\n renderMathInElement(document.body, {\n delimiters: [\n {left: \"$$\", right: \"$$\", display: true}\n ],\n // \u516c\u5f0f\u7f16\u53f7\u5904\u7406\n preProcess: (math) => {\n if (math.includes('\\\\label')) {\n const label = math.match(/\\\\label{([^}]*)}/)[1];\n const number = Object.keys(numberings).length + 1;\n numberings[label] = number;\n return math.replace(/\\\\label{[^}]*}/, `(${number})`);\n }\n return math;\n }\n });\n});\n
- \u4f7f\u7528\u7f16\u53f7\u548c\u5f15\u7528\uff1a
Markdown\u5e26\u7f16\u53f7\u7684\u516c\u5f0f\uff1a\n$$\nE = mc^2 \\label{eq:einstein}\n$$\n\n\u5f15\u7528\u4e0a\u9762\u7684\u516c\u5f0f $\\eqref{eq:einstein}$\n
"},{"location":"Tools/Blog/Mkdocs_Material/#622-mermaid","title":"6.2.2 Mermaid \u56fe\u8868","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6221","title":"6.2.2.1 \u521d\u59cb\u5316\u914d\u7f6e","text":" - \u5728
mkdocs.yml \u4e2d\u914d\u7f6e\uff1a
YAMLmarkdown_extensions:\n - pymdownx.superfences:\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_code_format\n\nextra_javascript:\n - https://unpkg.com/mermaid@9/dist/mermaid.min.js\n - javascripts/mermaid.js\n
- \u521b\u5efa Mermaid \u914d\u7f6e\u6587\u4ef6\uff1a
JavaScript// docs/javascripts/mermaid.js\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n mermaid.initialize({\n startOnLoad: true,\n theme: 'default',\n sequence: {\n showSequenceNumbers: true,\n actorMargin: 50,\n messageMargin: 40\n },\n flowchart: {\n useMaxWidth: false,\n htmlLabels: true,\n curve: 'basis'\n },\n gantt: {\n titleTopMargin: 25,\n barHeight: 20,\n barGap: 4\n }\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6222","title":"6.2.2.2 \u4e3b\u9898\u5b9a\u5236","text":" - \u81ea\u5b9a\u4e49\u4e3b\u9898\u914d\u7f6e\uff1a
JavaScript// docs/javascripts/mermaid-theme.js\nmermaid.initialize({\n theme: 'base',\n themeVariables: {\n // \u57fa\u7840\u989c\u8272\n primaryColor: '#2196f3',\n primaryTextColor: '#fff',\n primaryBorderColor: '#1976d2',\n lineColor: '#696969',\n\n // \u6d41\u7a0b\u56fe\u989c\u8272\n nodeBkg: '#fff',\n mainBkg: '#f8f9fa',\n nodeTextColor: '#333',\n\n // \u65f6\u5e8f\u56fe\u989c\u8272\n actorBkg: '#f8f9fa',\n actorBorder: '#2196f3',\n actorTextColor: '#333',\n\n // \u7518\u7279\u56fe\u989c\u8272\n sectionBkgColor: '#f8f9fa',\n altSectionBkgColor: '#fff',\n\n // \u6697\u8272\u4e3b\u9898\u652f\u6301\n darkMode: false\n }\n});\n
- \u54cd\u5e94\u4e3b\u9898\u5207\u6362\uff1a
JavaScript// \u76d1\u542c\u4e3b\u9898\u5207\u6362\ndocument.addEventListener('themeChanged', function(e) {\n const isDark = e.detail.theme === 'dark';\n mermaid.initialize({\n theme: isDark ? 'dark' : 'default',\n themeVariables: {\n darkMode: isDark\n }\n });\n // \u91cd\u65b0\u6e32\u67d3\u56fe\u8868\n mermaid.init(undefined, '.mermaid');\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6223","title":"6.2.2.3 \u4ea4\u4e92\u529f\u80fd","text":" - \u6dfb\u52a0\u70b9\u51fb\u4e8b\u4ef6\uff1a
JavaScript// docs/javascripts/mermaid-interaction.js\nmermaid.initialize({\n securityLevel: 'loose',\n flowchart: {\n htmlLabels: true,\n useMaxWidth: true\n }\n});\n\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n // \u4e3a\u56fe\u8868\u6dfb\u52a0\u70b9\u51fb\u4e8b\u4ef6\n const diagrams = document.querySelectorAll('.mermaid');\n diagrams.forEach(diagram => {\n diagram.addEventListener('click', function(e) {\n const target = e.target;\n if (target.tagName === 'g' && target.classList.contains('node')) {\n const nodeId = target.id;\n console.log('Clicked node:', nodeId);\n // \u5904\u7406\u8282\u70b9\u70b9\u51fb\n handleNodeClick(nodeId);\n }\n });\n });\n});\n\nfunction handleNodeClick(nodeId) {\n // \u8282\u70b9\u70b9\u51fb\u5904\u7406\n const node = document.getElementById(nodeId);\n if (node) {\n // \u6dfb\u52a0\u9ad8\u4eae\u6548\u679c\n node.classList.add('node-highlight');\n setTimeout(() => {\n node.classList.remove('node-highlight');\n }, 1000);\n }\n}\n
- \u6dfb\u52a0\u56fe\u8868\u52a8\u753b\uff1a
CSS/* docs/stylesheets/mermaid.css */\n.mermaid .node-highlight {\n animation: pulse 1s;\n}\n\n@keyframes pulse {\n 0% { opacity: 1; }\n 50% { opacity: 0.5; }\n 100% { opacity: 1; }\n}\n\n.mermaid .flowchart-link {\n transition: stroke-width 0.3s ease;\n}\n\n.mermaid .flowchart-link:hover {\n stroke-width: 2px;\n cursor: pointer;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6224","title":"6.2.2.4 \u4f7f\u7528\u793a\u4f8b","text":" - \u6d41\u7a0b\u56fe\u793a\u4f8b\uff1a
Markdown```mermaid\ngraph TD\n A[\u5f00\u59cb] --> B{\u5224\u65ad}\n B -->|Yes| C[\u5904\u7406]\n B -->|No| D[\u7ed3\u675f]\n C --> D\n```\n
- \u65f6\u5e8f\u56fe\u793a\u4f8b\uff1a
Markdown```mermaid\nsequenceDiagram\n participant \u5ba2\u6237\u7aef\n participant \u670d\u52a1\u5668\n\n \u5ba2\u6237\u7aef->>\u670d\u52a1\u5668: \u8bf7\u6c42\u6570\u636e\n \u670d\u52a1\u5668-->>\u5ba2\u6237\u7aef: \u8fd4\u56de\u54cd\u5e94\n```\n
"},{"location":"Tools/Blog/Mkdocs_Material/#623","title":"6.2.3 \u4ee3\u7801\u590d\u5236","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6231","title":"6.2.3.1 \u590d\u5236\u6309\u94ae","text":" - \u5728
mkdocs.yml \u4e2d\u914d\u7f6e\uff1a
YAMLtheme:\n features:\n - content.code.copy\n\nextra_css:\n - stylesheets/code-copy.css\nextra_javascript:\n - javascripts/code-copy.js\n
- \u521b\u5efa\u590d\u5236\u6309\u94ae\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/code-copy.css */\n\n/* \u590d\u5236\u6309\u94ae\u5bb9\u5668 */\n.copy-button {\n position: absolute;\n right: 0.5rem;\n top: 0.5rem;\n padding: 0.4rem;\n background-color: rgba(0, 0, 0, 0.1);\n border: none;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n/* \u6309\u94ae\u60ac\u505c\u6548\u679c */\n.copy-button:hover {\n background-color: rgba(0, 0, 0, 0.2);\n}\n\n/* \u56fe\u6807\u6837\u5f0f */\n.copy-button i {\n font-size: 1rem;\n color: var(--md-default-fg-color--light);\n}\n\n/* \u6210\u529f\u72b6\u6001 */\n.copy-button.success {\n background-color: var(--md-accent-fg-color);\n}\n\n.copy-button.success i {\n color: white;\n}\n
- \u5b9e\u73b0\u590d\u5236\u529f\u80fd\uff1a
JavaScript// docs/javascripts/code-copy.js\ndocument.addEventListener('DOMContentLoaded', () => {\n // \u4e3a\u6240\u6709\u4ee3\u7801\u5757\u6dfb\u52a0\u590d\u5236\u6309\u94ae\n const codeBlocks = document.querySelectorAll('pre code');\n\n codeBlocks.forEach((codeBlock) => {\n const container = codeBlock.parentNode;\n const copyButton = document.createElement('button');\n copyButton.className = 'copy-button';\n copyButton.innerHTML = '<i class=\"material-icons\">content_copy</i>';\n container.style.position = 'relative';\n container.appendChild(copyButton);\n\n // \u6dfb\u52a0\u70b9\u51fb\u4e8b\u4ef6\n copyButton.addEventListener('click', async () => {\n try {\n await navigator.clipboard.writeText(codeBlock.textContent);\n showSuccess(copyButton);\n } catch (err) {\n showError(copyButton);\n }\n });\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6232","title":"6.2.3.2 \u63d0\u793a\u6d88\u606f","text":" - \u521b\u5efa\u63d0\u793a\u6d88\u606f\u6837\u5f0f\uff1a
CSS/* \u63d0\u793a\u6d88\u606f\u6837\u5f0f */\n.copy-tooltip {\n position: absolute;\n top: -2rem;\n right: 0;\n padding: 0.4rem 0.8rem;\n background-color: var(--md-default-fg-color);\n color: var(--md-default-bg-color);\n border-radius: 4px;\n font-size: 0.8rem;\n opacity: 0;\n transform: translateY(0.4rem);\n transition: all 0.2s ease;\n}\n\n.copy-tooltip.show {\n opacity: 1;\n transform: translateY(0);\n}\n
- \u5b9e\u73b0\u63d0\u793a\u529f\u80fd\uff1a
JavaScript// \u663e\u793a\u63d0\u793a\u6d88\u606f\nfunction showTooltip(button, message, type = 'success') {\n const tooltip = document.createElement('div');\n tooltip.className = `copy-tooltip ${type}`;\n tooltip.textContent = message;\n button.appendChild(tooltip);\n\n // \u6dfb\u52a0\u663e\u793a\u7c7b\n setTimeout(() => tooltip.classList.add('show'), 10);\n\n // \u81ea\u52a8\u79fb\u9664\n setTimeout(() => {\n tooltip.classList.remove('show');\n setTimeout(() => tooltip.remove(), 200);\n }, 2000);\n}\n\n// \u6210\u529f\u63d0\u793a\nfunction showSuccess(button) {\n button.classList.add('success');\n showTooltip(button, '\u590d\u5236\u6210\u529f\uff01');\n setTimeout(() => button.classList.remove('success'), 2000);\n}\n\n// \u9519\u8bef\u63d0\u793a\nfunction showError(button) {\n button.classList.add('error');\n showTooltip(button, '\u590d\u5236\u5931\u8d25\uff01', 'error');\n setTimeout(() => button.classList.remove('error'), 2000);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6233","title":"6.2.3.3 \u590d\u5236\u56de\u8c03","text":"JavaScript// \u5b9a\u4e49\u590d\u5236\u4e8b\u4ef6\u5904\u7406\u5668\nconst copyHandlers = {\n // \u590d\u5236\u524d\u5904\u7406\n beforeCopy: (code) => {\n // \u53ef\u4ee5\u5728\u8fd9\u91cc\u5bf9\u4ee3\u7801\u8fdb\u884c\u9884\u5904\u7406\n return code.trim();\n },\n\n // \u590d\u5236\u6210\u529f\u56de\u8c03\n onSuccess: (button, code) => {\n console.log('Copied:', code.length, 'characters');\n showSuccess(button);\n\n // \u89e6\u53d1\u81ea\u5b9a\u4e49\u4e8b\u4ef6\n const event = new CustomEvent('codeCopied', {\n detail: { code }\n });\n document.dispatchEvent(event);\n },\n\n // \u590d\u5236\u5931\u8d25\u56de\u8c03\n onError: (button, error) => {\n console.error('Copy failed:', error);\n showError(button);\n }\n};\n\n// \u4f7f\u7528\u56de\u8c03\nasync function copyCode(button, code) {\n try {\n const processedCode = copyHandlers.beforeCopy(code);\n await navigator.clipboard.writeText(processedCode);\n copyHandlers.onSuccess(button, processedCode);\n } catch (err) {\n copyHandlers.onError(button, err);\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#624","title":"6.2.4 \u56fe\u7247\u9884\u89c8","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6241-lightbox","title":"6.2.4.1 lightbox \u914d\u7f6e","text":" - \u5728
mkdocs.yml \u4e2d\u914d\u7f6e\uff1a
YAMLmarkdown_extensions:\n - attr_list\n - md_in_html\n\nplugins:\n - glightbox\n\nextra_css:\n - stylesheets/glightbox.css\nextra_javascript:\n - javascripts/glightbox.js\n
- \u914d\u7f6e GLightbox\uff1a
JavaScript// docs/javascripts/glightbox.js\ndocument.addEventListener('DOMContentLoaded', () => {\n const lightbox = GLightbox({\n selector: '.glightbox',\n touchNavigation: true,\n loop: false,\n autoplayVideos: true,\n preload: true,\n // \u57fa\u672c\u8bbe\u7f6e\n height: 'auto',\n zoomable: true,\n draggable: true,\n // \u52a8\u753b\u8bbe\u7f6e\n openEffect: 'zoom',\n closeEffect: 'fade',\n cssEfects: {\n fade: { in: 'fadeIn', out: 'fadeOut' },\n zoom: { in: 'zoomIn', out: 'zoomOut' }\n }\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6242","title":"6.2.4.2 \u7f29\u653e\u529f\u80fd","text":"JavaScript// \u7f29\u653e\u529f\u80fd\u914d\u7f6e\nconst zoomConfig = {\n // \u7f29\u653e\u9009\u9879\n zoomable: true,\n dragToZoom: true,\n touchToZoom: true,\n\n // \u7f29\u653e\u7ea7\u522b\n minZoom: 0.5,\n maxZoom: 3,\n zoomStep: 0.5,\n\n // \u53cc\u51fb\u7f29\u653e\n doubleTapZoom: 2,\n\n // \u7f29\u653e\u52a8\u753b\n zoomAnimation: true,\n zoomDuration: 300,\n\n // \u7f29\u653e\u63a7\u5236\u5668\n controls: {\n zoom: true,\n zoomIn: true,\n zoomOut: true,\n rotate: true\n }\n};\n\n// \u5e94\u7528\u7f29\u653e\u914d\u7f6e\nconst lightbox = GLightbox({\n ...zoomConfig,\n\n // \u7f29\u653e\u4e8b\u4ef6\u5904\u7406\n onZoom: (slider) => {\n const { zoom, image } = slider;\n console.log(`Current zoom level: ${zoom}`);\n }\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6243","title":"6.2.4.3 \u624b\u52bf\u652f\u6301","text":"JavaScript// \u624b\u52bf\u914d\u7f6e\nconst gestureConfig = {\n // \u89e6\u6478\u5bfc\u822a\n touchNavigation: true,\n touchFollowAxis: true,\n\n // \u62d6\u52a8\u8bbe\u7f6e\n draggable: true,\n dragToleranceX: 40,\n dragToleranceY: 65,\n\n // \u624b\u52bf\u4e8b\u4ef6\n gestures: {\n // \u634f\u5408\u7f29\u653e\n pinchToZoom: true,\n pinchThreshold: 50,\n\n // \u53cc\u6307\u65cb\u8f6c\n rotateToZoom: true,\n rotateThreshold: 15,\n\n // \u6ed1\u52a8\u5207\u6362\n swipeThreshold: 50,\n swipeToClose: true\n }\n};\n\n// \u624b\u52bf\u4e8b\u4ef6\u5904\u7406\nconst gestureHandlers = {\n // \u89e6\u6478\u5f00\u59cb\n onTouchStart: (e) => {\n const touch = e.touches[0];\n startX = touch.clientX;\n startY = touch.clientY;\n },\n\n // \u89e6\u6478\u79fb\u52a8\n onTouchMove: (e) => {\n if (!isDragging) return;\n const touch = e.touches[0];\n const deltaX = touch.clientX - startX;\n const deltaY = touch.clientY - startY;\n\n // \u5904\u7406\u79fb\u52a8\n handleImageMove(deltaX, deltaY);\n },\n\n // \u89e6\u6478\u7ed3\u675f\n onTouchEnd: (e) => {\n isDragging = false;\n // \u5904\u7406\u60ef\u6027\u6ed1\u52a8\n handleMomentum();\n }\n};\n\n// \u521b\u5efa\u589e\u5f3a\u7684\u56fe\u7247\u9884\u89c8\nconst enhancedLightbox = GLightbox({\n ...zoomConfig,\n ...gestureConfig,\n\n // \u4e8b\u4ef6\u76d1\u542c\n listeners: {\n touchstart: gestureHandlers.onTouchStart,\n touchmove: gestureHandlers.onTouchMove,\n touchend: gestureHandlers.onTouchEnd\n }\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6244","title":"6.2.4.4 \u5b8c\u6574\u793a\u4f8b","text":"JavaScript// docs/javascripts/image-preview.js\ndocument.addEventListener('DOMContentLoaded', () => {\n // \u521d\u59cb\u5316\u914d\u7f6e\n const config = {\n // \u57fa\u7840\u8bbe\u7f6e\n selector: '.glightbox',\n touchNavigation: true,\n loop: false,\n\n // \u7f29\u653e\u8bbe\u7f6e\n zoomable: true,\n draggable: true,\n dragToleranceX: 40,\n dragToleranceY: 65,\n\n // \u52a8\u753b\u8bbe\u7f6e\n openEffect: 'zoom',\n closeEffect: 'fade',\n\n // \u624b\u52bf\u8bbe\u7f6e\n touchFollowAxis: true,\n\n // \u754c\u9762\u8bbe\u7f6e\n preload: true,\n height: 'auto',\n\n // \u4e8b\u4ef6\u5904\u7406\n onOpen: () => {\n console.log('Lightbox opened');\n },\n onClose: () => {\n console.log('Lightbox closed');\n },\n onZoom: (slider) => {\n console.log('Image zoomed');\n }\n };\n\n // \u521d\u59cb\u5316 GLightbox\n const lightbox = GLightbox(config);\n\n // \u6dfb\u52a0\u952e\u76d8\u652f\u6301\n document.addEventListener('keydown', (e) => {\n if (!lightbox.isOpen) return;\n\n switch(e.key) {\n case 'ArrowLeft':\n lightbox.prev();\n break;\n case 'ArrowRight':\n lightbox.next();\n break;\n case 'Escape':\n lightbox.close();\n break;\n }\n });\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#63","title":"6.3 \u81ea\u5b9a\u4e49\u529f\u80fd","text":""},{"location":"Tools/Blog/Mkdocs_Material/#631","title":"6.3.1 \u9875\u9762\u4ea4\u4e92","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6311","title":"6.3.1.1 \u6eda\u52a8\u4e8b\u4ef6","text":" - \u57fa\u7840\u6eda\u52a8\u76d1\u542c\uff1a
JavaScript// docs/javascripts/scroll.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u6eda\u52a8\u5904\u7406\u51fd\u6570\n function handleScroll() {\n const scrollTop = window.scrollY;\n const windowHeight = window.innerHeight;\n const docHeight = document.documentElement.scrollHeight;\n\n // \u6eda\u52a8\u8fdb\u5ea6\n const scrollPercent = (scrollTop / (docHeight - windowHeight)) * 100;\n\n // \u66f4\u65b0\u8fdb\u5ea6\u6761\n updateProgress(scrollPercent);\n\n // \u5904\u7406\u5143\u7d20\u53ef\u89c1\u6027\n handleVisibility();\n }\n\n // \u4f7f\u7528\u8282\u6d41\u4f18\u5316\u6eda\u52a8\u4e8b\u4ef6\n const throttledScroll = throttle(handleScroll, 100);\n window.addEventListener('scroll', throttledScroll);\n});\n\n// \u8282\u6d41\u51fd\u6570\nfunction throttle(fn, delay) {\n let lastCall = 0;\n return function(...args) {\n const now = Date.now();\n if (now - lastCall >= delay) {\n lastCall = now;\n fn.apply(this, args);\n }\n };\n}\n
- \u5143\u7d20\u53ef\u89c1\u6027\u68c0\u6d4b\uff1a
JavaScript// \u68c0\u6d4b\u5143\u7d20\u662f\u5426\u8fdb\u5165\u89c6\u53e3\nfunction handleVisibility() {\n const elements = document.querySelectorAll('.animate-on-scroll');\n\n elements.forEach(element => {\n const rect = element.getBoundingClientRect();\n const isVisible = (\n rect.top >= 0 &&\n rect.left >= 0 &&\n rect.bottom <= window.innerHeight &&\n rect.right <= window.innerWidth\n );\n\n if (isVisible) {\n element.classList.add('is-visible');\n }\n });\n}\n\n// CSS \u6837\u5f0f\n.animate-on-scroll {\n opacity: 0;\n transform: translateY(20px);\n transition: all 0.6s ease;\n}\n\n.animate-on-scroll.is-visible {\n opacity: 1;\n transform: translateY(0);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6312","title":"6.3.1.2 \u70b9\u51fb\u4e8b\u4ef6","text":" - \u70b9\u51fb\u5904\u7406\uff1a
JavaScript// docs/javascripts/click.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u4ee3\u7801\u5757\u70b9\u51fb\u590d\u5236\n setupCodeCopy();\n\n // \u56fe\u7247\u70b9\u51fb\u653e\u5927\n setupImageZoom();\n\n // \u76ee\u5f55\u70b9\u51fb\u6eda\u52a8\n setupTocScroll();\n});\n\n// \u4ee3\u7801\u590d\u5236\u529f\u80fd\nfunction setupCodeCopy() {\n const codeBlocks = document.querySelectorAll('pre code');\n\n codeBlocks.forEach(block => {\n block.addEventListener('click', async function(e) {\n if (e.target.classList.contains('copy-button')) {\n try {\n await navigator.clipboard.writeText(block.textContent);\n showToast('\u590d\u5236\u6210\u529f\uff01');\n } catch (err) {\n showToast('\u590d\u5236\u5931\u8d25', 'error');\n }\n }\n });\n });\n}\n\n// \u56fe\u7247\u7f29\u653e\u529f\u80fd\nfunction setupImageZoom() {\n const images = document.querySelectorAll('.md-content img');\n\n images.forEach(img => {\n img.addEventListener('click', function() {\n const overlay = document.createElement('div');\n overlay.className = 'image-overlay';\n overlay.innerHTML = `\n <img src=\"${img.src}\" alt=\"${img.alt}\">\n <button class=\"close-button\">\u00d7</button>\n `;\n\n document.body.appendChild(overlay);\n\n overlay.querySelector('.close-button').addEventListener('click', () => {\n overlay.remove();\n });\n });\n });\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6313","title":"6.3.1.3 \u952e\u76d8\u4e8b\u4ef6","text":"JavaScript// docs/javascripts/keyboard.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u952e\u76d8\u5bfc\u822a\n setupKeyboardNav();\n\n // \u641c\u7d22\u5feb\u6377\u952e\n setupSearchShortcut();\n});\n\n// \u952e\u76d8\u5bfc\u822a\nfunction setupKeyboardNav() {\n document.addEventListener('keydown', function(e) {\n // ALT + \u65b9\u5411\u952e\u5bfc\u822a\n if (e.altKey) {\n switch(e.key) {\n case 'ArrowLeft': // \u4e0a\u4e00\u9875\n navigatePage('prev');\n break;\n case 'ArrowRight': // \u4e0b\u4e00\u9875\n navigatePage('next');\n break;\n case 'ArrowUp': // \u56de\u5230\u9876\u90e8\n window.scrollTo({top: 0, behavior: 'smooth'});\n break;\n case 'ArrowDown': // \u5230\u8fbe\u5e95\u90e8\n window.scrollTo({\n top: document.documentElement.scrollHeight,\n behavior: 'smooth'\n });\n break;\n }\n }\n });\n}\n\n// \u641c\u7d22\u5feb\u6377\u952e\nfunction setupSearchShortcut() {\n document.addEventListener('keydown', function(e) {\n // \u6309\u4e0b '/' \u952e\u89e6\u53d1\u641c\u7d22\n if (e.key === '/' && !e.ctrlKey && !e.altKey && !e.metaKey) {\n e.preventDefault();\n const searchInput = document.querySelector('.md-search__input');\n if (searchInput) {\n searchInput.focus();\n }\n }\n });\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#632","title":"6.3.2 \u52a8\u753b\u6548\u679c","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6321","title":"6.3.2.1 \u8fc7\u6e21\u52a8\u753b","text":" - \u57fa\u7840\u8fc7\u6e21\u6548\u679c\uff1a
CSS/* docs/stylesheets/transitions.css */\n\n/* \u9875\u9762\u5207\u6362\u8fc7\u6e21 */\n.md-content {\n animation: fadeIn 0.3s ease-in-out;\n}\n\n@keyframes fadeIn {\n from {\n opacity: 0;\n transform: translateY(20px);\n }\n to {\n opacity: 1;\n transform: translateY(0);\n }\n}\n\n/* \u5bfc\u822a\u8fc7\u6e21 */\n.md-nav__link {\n transition: color 0.2s ease, padding-left 0.2s ease;\n}\n\n.md-nav__link:hover {\n padding-left: 0.5rem;\n color: var(--md-accent-fg-color);\n}\n
- \u9875\u9762\u5207\u6362\u52a8\u753b\uff1a
JavaScript// docs/javascripts/transitions.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u9875\u9762\u5207\u6362\u52a8\u753b\n setupPageTransitions();\n});\n\nfunction setupPageTransitions() {\n // \u76d1\u542c\u9875\u9762\u5207\u6362\u4e8b\u4ef6\n document.addEventListener('DOMContentLoaded', function() {\n document.body.classList.add('page-transition-ready');\n });\n\n // \u9875\u9762\u79bb\u5f00\u52a8\u753b\n window.addEventListener('beforeunload', function() {\n document.body.classList.add('page-transition-exit');\n });\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6322","title":"6.3.2.2 \u52a0\u8f7d\u52a8\u753b","text":" - \u521b\u5efa\u52a0\u8f7d\u52a8\u753b\uff1a
CSS/* docs/stylesheets/loading.css */\n\n/* \u52a0\u8f7d\u52a8\u753b\u5bb9\u5668 */\n.loading-overlay {\n position: fixed;\n top: 0;\n left: 0;\n right: 0;\n bottom: 0;\n background-color: var(--md-default-bg-color);\n display: flex;\n justify-content: center;\n align-items: center;\n z-index: 999;\n opacity: 1;\n transition: opacity 0.3s ease;\n}\n\n/* \u52a0\u8f7d\u52a8\u753b */\n.loading-spinner {\n width: 40px;\n height: 40px;\n border: 3px solid var(--md-primary-fg-color--light);\n border-top-color: var(--md-primary-fg-color);\n border-radius: 50%;\n animation: spin 1s linear infinite;\n}\n\n@keyframes spin {\n to { transform: rotate(360deg); }\n}\n
- \u5b9e\u73b0\u52a0\u8f7d\u903b\u8f91\uff1a
JavaScript// docs/javascripts/loading.js\nclass LoadingManager {\n constructor() {\n this.overlay = null;\n this.createLoadingOverlay();\n }\n\n createLoadingOverlay() {\n this.overlay = document.createElement('div');\n this.overlay.className = 'loading-overlay';\n this.overlay.innerHTML = '<div class=\"loading-spinner\"></div>';\n document.body.appendChild(this.overlay);\n }\n\n show() {\n this.overlay.style.opacity = '1';\n this.overlay.style.visibility = 'visible';\n }\n\n hide() {\n this.overlay.style.opacity = '0';\n setTimeout(() => {\n this.overlay.style.visibility = 'hidden';\n }, 300);\n }\n}\n\n// \u4f7f\u7528\u52a0\u8f7d\u7ba1\u7406\u5668\nconst loading = new LoadingManager();\n\n// \u9875\u9762\u52a0\u8f7d\u5b8c\u6210\u540e\u9690\u85cf\nwindow.addEventListener('load', () => {\n loading.hide();\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6323","title":"6.3.2.3 \u4ea4\u4e92\u52a8\u753b","text":" - \u5143\u7d20\u4ea4\u4e92\u52a8\u753b\uff1a
JavaScript// docs/javascripts/interactions.js\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u6309\u94ae\u6ce2\u7eb9\u6548\u679c\n setupRippleEffect();\n\n // \u5361\u7247\u60ac\u6d6e\u6548\u679c\n setupCardHover();\n\n // \u5217\u8868\u9879\u52a8\u753b\n setupListAnimations();\n});\n\n// \u6ce2\u7eb9\u6548\u679c\nfunction setupRippleEffect() {\n const buttons = document.querySelectorAll('.md-button');\n\n buttons.forEach(button => {\n button.addEventListener('click', function(e) {\n const ripple = document.createElement('div');\n ripple.className = 'ripple';\n\n const rect = button.getBoundingClientRect();\n const size = Math.max(rect.width, rect.height);\n\n ripple.style.width = ripple.style.height = `${size}px`;\n ripple.style.left = `${e.clientX - rect.left - size/2}px`;\n ripple.style.top = `${e.clientY - rect.top - size/2}px`;\n\n button.appendChild(ripple);\n\n setTimeout(() => ripple.remove(), 600);\n });\n });\n}\n\n// \u5361\u7247\u60ac\u6d6e\u6548\u679c\nfunction setupCardHover() {\n const cards = document.querySelectorAll('.md-card');\n\n cards.forEach(card => {\n card.addEventListener('mousemove', function(e) {\n const rect = card.getBoundingClientRect();\n const x = e.clientX - rect.left;\n const y = e.clientY - rect.top;\n\n const centerX = rect.width / 2;\n const centerY = rect.height / 2;\n\n const angleY = -(x - centerX) / 20;\n const angleX = (y - centerY) / 20;\n\n card.style.transform = \n `perspective(1000px) rotateX(${angleX}deg) rotateY(${angleY}deg)`;\n });\n\n card.addEventListener('mouseleave', function() {\n card.style.transform = 'perspective(1000px) rotateX(0) rotateY(0)';\n });\n });\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#633","title":"6.3.3 \u6570\u636e\u7edf\u8ba1","text":""},{"location":"Tools/Blog/Mkdocs_Material/#6331","title":"6.3.3.1 \u8bbf\u95ee\u7edf\u8ba1","text":" - \u57fa\u7840\u8bbf\u95ee\u7edf\u8ba1\uff1a
JavaScript// docs/javascripts/analytics.js\nclass Analytics {\n constructor() {\n this.storageKey = 'site_analytics';\n this.data = this.loadData();\n }\n\n loadData() {\n const stored = localStorage.getItem(this.storageKey);\n return stored ? JSON.parse(stored) : {\n pageViews: {},\n totalVisits: 0,\n firstVisit: Date.now(),\n lastVisit: Date.now()\n };\n }\n\n saveData() {\n localStorage.setItem(this.storageKey, JSON.stringify(this.data));\n }\n\n recordPageView() {\n const path = window.location.pathname;\n this.data.pageViews[path] = (this.data.pageViews[path] || 0) + 1;\n this.data.totalVisits++;\n this.data.lastVisit = Date.now();\n this.saveData();\n }\n\n getStats() {\n return {\n totalVisits: this.data.totalVisits,\n uniquePages: Object.keys(this.data.pageViews).length,\n mostViewed: this.getMostViewedPages(5)\n };\n }\n\n getMostViewedPages(limit = 5) {\n return Object.entries(this.data.pageViews)\n .sort(([,a], [,b]) => b - a)\n .slice(0, limit);\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6332","title":"6.3.3.2 \u9605\u8bfb\u65f6\u957f","text":"JavaScript// docs/javascripts/read-time.js\nclass ReadTimeTracker {\n constructor() {\n this.startTime = Date.now();\n this.isReading = true;\n this.totalTime = 0;\n this.idleTimeout = null;\n this.setupTracking();\n }\n\n setupTracking() {\n // \u76d1\u542c\u7528\u6237\u6d3b\u52a8\n ['mousemove', 'keydown', 'scroll', 'click'].forEach(event => {\n document.addEventListener(event, () => this.handleActivity());\n });\n\n // \u9875\u9762\u5931\u7126\u6682\u505c\u8ba1\u65f6\n document.addEventListener('visibilitychange', () => {\n if (document.hidden) {\n this.pauseTracking();\n } else {\n this.resumeTracking();\n }\n });\n }\n\n handleActivity() {\n if (!this.isReading) {\n this.resumeTracking();\n }\n\n clearTimeout(this.idleTimeout);\n this.idleTimeout = setTimeout(() => this.pauseTracking(), 60000); // 1\u5206\u949f\u65e0\u6d3b\u52a8\u6682\u505c\n }\n\n pauseTracking() {\n if (this.isReading) {\n this.totalTime += Date.now() - this.startTime;\n this.isReading = false;\n }\n }\n\n resumeTracking() {\n if (!this.isReading) {\n this.startTime = Date.now();\n this.isReading = true;\n }\n }\n\n getReadTime() {\n const currentTime = this.isReading ? \n this.totalTime + (Date.now() - this.startTime) : \n this.totalTime;\n\n return Math.floor(currentTime / 1000 / 60); // \u8fd4\u56de\u5206\u949f\n // \u663e\u793a\u9605\u8bfb\u65f6\u957f\n displayReadTime() {\n const readTimeElement = document.querySelector('.read-time');\n if (readTimeElement) {\n const minutes = this.getReadTime();\n readTimeElement.textContent = `\u9605\u8bfb\u65f6\u957f: ${minutes} \u5206\u949f`;\n }\n }\n\n // \u83b7\u53d6\u9605\u8bfb\u8fdb\u5ea6\n getReadProgress() {\n const windowHeight = window.innerHeight;\n const docHeight = document.documentElement.scrollHeight - windowHeight;\n const scrollTop = window.pageYOffset || document.documentElement.scrollTop;\n return Math.min((scrollTop / docHeight) * 100, 100);\n }\n\n // \u4fdd\u5b58\u9605\u8bfb\u8bb0\u5f55\n saveReadingHistory() {\n const path = window.location.pathname;\n const history = JSON.parse(localStorage.getItem('reading_history') || '{}');\n\n history[path] = {\n lastRead: Date.now(),\n readTime: this.getReadTime(),\n progress: this.getReadProgress()\n };\n\n localStorage.setItem('reading_history', JSON.stringify(history));\n }\n}\n\n// \u521d\u59cb\u5316\u9605\u8bfb\u65f6\u957f\u8ffd\u8e2a\nconst readTracker = new ReadTimeTracker();\n\n// \u5b9a\u671f\u66f4\u65b0\u663e\u793a\nsetInterval(() => {\n readTracker.displayReadTime();\n}, 30000); // \u6bcf30\u79d2\u66f4\u65b0\u4e00\u6b21\n\n// \u9875\u9762\u79bb\u5f00\u65f6\u4fdd\u5b58\u8bb0\u5f55\nwindow.addEventListener('beforeunload', () => {\n readTracker.saveReadingHistory();\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#6333","title":"6.3.3.3 \u5206\u4eab\u7edf\u8ba1","text":" - \u5206\u4eab\u529f\u80fd\u548c\u7edf\u8ba1\uff1a
JavaScript// docs/javascripts/share.js\nclass ShareTracker {\n constructor() {\n this.storageKey = 'share_statistics';\n this.data = this.loadData();\n this.setupShareButtons();\n }\n\n loadData() {\n return JSON.parse(localStorage.getItem(this.storageKey) || '{}');\n }\n\n saveData() {\n localStorage.setItem(this.storageKey, JSON.stringify(this.data));\n }\n\n setupShareButtons() {\n const shareButtons = document.querySelectorAll('.share-button');\n\n shareButtons.forEach(button => {\n button.addEventListener('click', (e) => {\n const platform = button.dataset.platform;\n this.shareContent(platform);\n this.recordShare(platform);\n });\n });\n }\n\n shareContent(platform) {\n const url = encodeURIComponent(window.location.href);\n const title = encodeURIComponent(document.title);\n const description = encodeURIComponent(\n document.querySelector('meta[name=\"description\"]')?.content || ''\n );\n\n let shareUrl;\n switch (platform) {\n case 'twitter':\n shareUrl = `https://twitter.com/intent/tweet?url=${url}&text=${title}`;\n break;\n case 'facebook':\n shareUrl = `https://www.facebook.com/sharer/sharer.php?u=${url}`;\n break;\n case 'linkedin':\n shareUrl = `https://www.linkedin.com/sharing/share-offsite/?url=${url}`;\n break;\n case 'weibo':\n shareUrl = `http://service.weibo.com/share/share.php?url=${url}&title=${title}`;\n break;\n }\n\n if (shareUrl) {\n window.open(shareUrl, '_blank', 'width=600,height=400');\n }\n }\n\n recordShare(platform) {\n const path = window.location.pathname;\n if (!this.data[path]) {\n this.data[path] = {};\n }\n if (!this.data[path][platform]) {\n this.data[path][platform] = 0;\n }\n this.data[path][platform]++;\n this.saveData();\n this.updateShareCount(platform);\n }\n\n updateShareCount(platform) {\n const countElement = document.querySelector(`.share-count[data-platform=\"${platform}\"]`);\n if (countElement) {\n const path = window.location.pathname;\n countElement.textContent = this.data[path][platform] || 0;\n }\n }\n\n getShareStats() {\n return Object.entries(this.data).map(([path, platforms]) => ({\n path,\n total: Object.values(platforms).reduce((a, b) => a + b, 0),\n platforms\n }));\n }\n\n displayShareStats() {\n const stats = this.getShareStats();\n console.table(stats);\n return stats;\n }\n}\n
- \u7edf\u8ba1\u6570\u636e\u53ef\u89c6\u5316\uff1a
JavaScript// docs/javascripts/statistics-visualization.js\nclass StatisticsVisualizer {\n constructor(analytics, readTracker, shareTracker) {\n this.analytics = analytics;\n this.readTracker = readTracker;\n this.shareTracker = shareTracker;\n }\n\n createDashboard() {\n const dashboard = document.createElement('div');\n dashboard.className = 'statistics-dashboard';\n dashboard.innerHTML = `\n <div class=\"dashboard-section\">\n <h3>\u8bbf\u95ee\u7edf\u8ba1</h3>\n <div class=\"stats-grid\">\n <div class=\"stat-card\">\n <div class=\"stat-value\">${this.analytics.data.totalVisits}</div>\n <div class=\"stat-label\">\u603b\u8bbf\u95ee\u91cf</div>\n </div>\n <div class=\"stat-card\">\n <div class=\"stat-value\">${this.readTracker.getReadTime()}</div>\n <div class=\"stat-label\">\u603b\u9605\u8bfb\u65f6\u957f(\u5206\u949f)</div>\n </div>\n <div class=\"stat-card\">\n <div class=\"stat-value\">${this.getTotalShares()}</div>\n <div class=\"stat-label\">\u603b\u5206\u4eab\u6b21\u6570</div>\n </div>\n </div>\n <div id=\"visitsChart\"></div>\n </div>\n `;\n\n document.body.appendChild(dashboard);\n this.renderCharts();\n }\n\n renderCharts() {\n // \u4f7f\u7528 Chart.js \u7ed8\u5236\u56fe\u8868\n const ctx = document.getElementById('visitsChart').getContext('2d');\n new Chart(ctx, {\n type: 'line',\n data: {\n labels: this.getTimeLabels(),\n datasets: [{\n label: '\u8bbf\u95ee\u8d8b\u52bf',\n data: this.getVisitData(),\n borderColor: 'rgb(75, 192, 192)',\n tension: 0.1\n }]\n },\n options: {\n responsive: true,\n scales: {\n y: {\n beginAtZero: true\n }\n }\n }\n });\n }\n\n getTotalShares() {\n const stats = this.shareTracker.getShareStats();\n return stats.reduce((total, page) => total + page.total, 0);\n }\n\n getTimeLabels() {\n // \u83b7\u53d6\u6700\u8fd17\u5929\u7684\u65e5\u671f\u6807\u7b7e\n return Array.from({length: 7}, (_, i) => {\n const d = new Date();\n d.setDate(d.getDate() - i);\n return d.toLocaleDateString();\n }).reverse();\n }\n\n getVisitData() {\n // \u5904\u7406\u8bbf\u95ee\u6570\u636e\n return this.analytics.getVisitsByDate(7);\n }\n}\n\n// \u6837\u5f0f\nconst styles = `\n .statistics-dashboard {\n padding: 2rem;\n background: var(--md-default-bg-color);\n border-radius: 8px;\n box-shadow: var(--md-shadow-z2);\n }\n\n .stats-grid {\n display: grid;\n grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));\n gap: 1rem;\n margin: 1rem 0;\n }\n\n .stat-card {\n padding: 1rem;\n background: var(--md-code-bg-color);\n border-radius: 4px;\n text-align: center;\n }\n\n .stat-value {\n font-size: 2rem;\n font-weight: bold;\n color: var(--md-primary-fg-color);\n }\n\n .stat-label {\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n margin-top: 0.5rem;\n }\n`;\n\n// \u5c06\u6837\u5f0f\u6dfb\u52a0\u5230\u6587\u6863\nconst styleSheet = document.createElement('style');\nstyleSheet.textContent = styles;\ndocument.head.appendChild(styleSheet);\n\n// \u521d\u59cb\u5316\u7edf\u8ba1\u53ef\u89c6\u5316\nconst visualizer = new StatisticsVisualizer(analytics, readTracker, shareTracker);\nvisualizer.createDashboard();\n
\u8fd9\u6837\u6211\u4eec\u5c31\u5b8c\u6210\u4e86\u5b8c\u6574\u7684\u6570\u636e\u7edf\u8ba1\u7cfb\u7edf\uff0c\u5305\u62ec\uff1a
- \u8bbf\u95ee\u7edf\u8ba1
- \u9605\u8bfb\u65f6\u957f\u8ffd\u8e2a
- \u5206\u4eab\u7edf\u8ba1
- \u6570\u636e\u53ef\u89c6\u5316\u5c55\u793a
\u4f7f\u7528\u65f6\uff0c\u53ea\u9700\u8981\u5728 mkdocs.yml \u4e2d\u6dfb\u52a0\u76f8\u5e94\u7684 JavaScript \u6587\u4ef6\uff1a
YAMLextra_javascript:\n - javascripts/analytics.js\n - javascripts/read-time.js\n - javascripts/share.js\n - javascripts/statistics-visualization.js\n - https://cdn.jsdelivr.net/npm/chart.js\n
\u7136\u540e\u5728\u9875\u9762\u4e2d\u6dfb\u52a0\u5fc5\u8981\u7684 HTML \u5143\u7d20\u5373\u53ef\u542f\u7528\u8fd9\u4e9b\u529f\u80fd\u3002
"},{"location":"Tools/Blog/Mkdocs_Material/#7","title":"7 \u6a21\u677f\u590d\u5199","text":""},{"location":"Tools/Blog/Mkdocs_Material/#71","title":"7.1 \u91cd\u5199\u9875\u9762\u6a21\u677f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#711","title":"7.1.1 \u4e3b\u9875\u6a21\u677f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#7111","title":"7.1.1.1 \u5e03\u5c40\u7ed3\u6784","text":" - \u521b\u5efa\u81ea\u5b9a\u4e49\u4e3b\u9875\u6a21\u677f\uff1a
HTML<!-- docs/overrides/main.html -->\n{% extends \"base.html\" %}\n\n{% block hero %}\n<section class=\"home-hero\">\n <div class=\"hero-content\">\n <h1>{{ config.site_name }}</h1>\n <p>{{ config.site_description }}</p>\n\n <!-- \u81ea\u5b9a\u4e49\u641c\u7d22\u6846 -->\n <div class=\"hero-search\">\n <input type=\"text\" placeholder=\"\u641c\u7d22\u6587\u6863...\" id=\"hero-search-input\">\n <button>\n <span class=\"twemoji\">\n {% include \".icons/material/magnify.svg\" %}\n </span>\n </button>\n </div>\n\n <!-- \u5feb\u901f\u5165\u53e3 -->\n <div class=\"hero-buttons\">\n <a href=\"{{ page.next_page.url | url }}\" class=\"md-button md-button--primary\">\n \u5feb\u901f\u5f00\u59cb\n <span class=\"twemoji\">\n {% include \".icons/material/arrow-right.svg\" %}\n </span>\n </a>\n <a href=\"{{ config.repo_url }}\" class=\"md-button\">\n \u67e5\u770b\u6e90\u7801\n <span class=\"twemoji\">\n {% include \".icons/material/github.svg\" %}\n </span>\n </a>\n </div>\n </div>\n</section>\n{% endblock %}\n\n{% block content %}\n<section class=\"home-features\">\n <h2>\u7279\u8272\u529f\u80fd</h2>\n <div class=\"features-grid\">\n <!-- \u529f\u80fd\u5361\u7247 -->\n <div class=\"feature-card\">\n <div class=\"feature-icon\">\n {% include \".icons/material/speedometer.svg\" %}\n </div>\n <h3>\u9ad8\u6027\u80fd</h3>\n <p>\u57fa\u4e8e\u9759\u6001\u7ad9\u70b9\u751f\u6210\uff0c\u52a0\u8f7d\u8fc5\u901f</p>\n </div>\n <!-- \u66f4\u591a\u529f\u80fd\u5361\u7247 -->\n </div>\n</section>\n\n<!-- \u6700\u8fd1\u66f4\u65b0 -->\n<section class=\"home-updates\">\n <h2>\u6700\u8fd1\u66f4\u65b0</h2>\n <div class=\"updates-list\">\n {% for update in config.theme.updates[:5] %}\n <div class=\"update-item\">\n <span class=\"update-date\">{{ update.date }}</span>\n <a href=\"{{ update.url | url }}\">{{ update.title }}</a>\n </div>\n {% endfor %}\n </div>\n</section>\n{% endblock %}\n
- \u4e3b\u9875\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/home.css */\n\n/* \u4e3b\u9875\u82f1\u96c4\u533a */\n.home-hero {\n min-height: 100vh;\n display: flex;\n align-items: center;\n justify-content: center;\n text-align: center;\n background: linear-gradient(\n to bottom right,\n var(--md-primary-fg-color),\n var(--md-accent-fg-color)\n );\n color: var(--md-primary-bg-color);\n}\n\n.hero-content {\n max-width: 800px;\n padding: 2rem;\n}\n\n.hero-content h1 {\n font-size: 3rem;\n margin-bottom: 1rem;\n}\n\n/* \u641c\u7d22\u6846\u6837\u5f0f */\n.hero-search {\n margin: 2rem 0;\n position: relative;\n}\n\n.hero-search input {\n width: 100%;\n padding: 1rem 3rem 1rem 1rem;\n border: none;\n border-radius: 2rem;\n background: rgba(255, 255, 255, 0.1);\n color: white;\n backdrop-filter: blur(10px);\n}\n\n/* \u529f\u80fd\u533a\u6837\u5f0f */\n.features-grid {\n display: grid;\n grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));\n gap: 2rem;\n padding: 2rem;\n}\n\n.feature-card {\n padding: 2rem;\n background: var(--md-default-bg-color);\n border-radius: 8px;\n box-shadow: var(--md-shadow-z1);\n transition: transform 0.3s ease;\n}\n\n.feature-card:hover {\n transform: translateY(-4px);\n}\n\n/* \u66f4\u65b0\u5217\u8868\u6837\u5f0f */\n.updates-list {\n max-width: 800px;\n margin: 0 auto;\n padding: 2rem;\n}\n\n.update-item {\n display: flex;\n align-items: center;\n padding: 1rem;\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n/* \u54cd\u5e94\u5f0f\u9002\u914d */\n@media screen and (max-width: 76.1875em) {\n .hero-content h1 {\n font-size: 2rem;\n }\n\n .features-grid {\n grid-template-columns: 1fr;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7112","title":"7.1.1.2 \u7ec4\u4ef6\u914d\u7f6e","text":" - \u5728
mkdocs.yml \u4e2d\u914d\u7f6e\u4e3b\u9898\uff1a
YAMLtheme:\n name: material\n custom_dir: docs/overrides\n features:\n - navigation.tabs\n - navigation.sections\n - navigation.expand\n\n # \u4e3b\u9875\u914d\u7f6e\n homepage:\n hero:\n title: \u7f51\u7ad9\u6807\u9898\n subtitle: \u7f51\u7ad9\u63cf\u8ff0\n image: assets/hero.svg\n\n # \u529f\u80fd\u7279\u6027\n features:\n - title: \u9ad8\u6027\u80fd\n description: \u57fa\u4e8e\u9759\u6001\u7ad9\u70b9\u751f\u6210\uff0c\u52a0\u8f7d\u8fc5\u901f\n icon: material/speedometer\n - title: \u6613\u4e8e\u4f7f\u7528\n description: \u7b80\u5355\u7684\u914d\u7f6e\uff0c\u5feb\u901f\u4e0a\u624b\n icon: material/puzzle\n # \u66f4\u591a\u529f\u80fd\u7279\u6027...\n\n # \u66f4\u65b0\u5217\u8868\n updates:\n - date: 2024-01-20\n title: \u65b0\u589e\u529f\u80fdA\n url: /new-feature-a\n - date: 2024-01-18\n title: \u95ee\u9898\u4fee\u590dB\n url: /bug-fix-b\n # \u66f4\u591a\u66f4\u65b0...\n
- \u4e3b\u9875\u529f\u80fd\u7c7b\uff1a
JavaScript// docs/javascripts/home.js\nclass HomePage {\n constructor() {\n this.searchInput = document.getElementById('hero-search-input');\n this.setupSearch();\n this.setupFeatureCards();\n }\n\n setupSearch() {\n this.searchInput?.addEventListener('keyup', (e) => {\n if (e.key === 'Enter') {\n const query = e.target.value;\n window.location.href = `${window.location.origin}/search.html?q=${encodeURIComponent(query)}`;\n }\n });\n }\n\n setupFeatureCards() {\n const cards = document.querySelectorAll('.feature-card');\n\n cards.forEach(card => {\n card.addEventListener('mousemove', (e) => {\n const rect = card.getBoundingClientRect();\n const x = e.clientX - rect.left;\n const y = e.clientY - rect.top;\n\n card.style.setProperty('--mouse-x', `${x}px`);\n card.style.setProperty('--mouse-y', `${y}px`);\n });\n });\n }\n}\n\n// \u521d\u59cb\u5316\u4e3b\u9875\ndocument.addEventListener('DOMContentLoaded', () => {\n new HomePage();\n});\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7113","title":"7.1.1.3 \u81ea\u5b9a\u4e49\u533a\u57df","text":" - \u521b\u5efa\u81ea\u5b9a\u4e49\u533a\u57df\uff1a
HTML<!-- docs/overrides/partials/custom-content.html -->\n{% if page.meta.custom_content %}\n<section class=\"custom-content\">\n <!-- \u516c\u544a\u533a -->\n {% if page.meta.announcements %}\n <div class=\"announcements\">\n {% for announcement in page.meta.announcements %}\n <div class=\"announcement-item\">\n <span class=\"announcement-tag\">{{ announcement.tag }}</span>\n <p>{{ announcement.content }}</p>\n </div>\n {% endfor %}\n </div>\n {% endif %}\n\n <!-- \u8d21\u732e\u8005\u533a\u57df -->\n {% if page.meta.contributors %}\n <div class=\"contributors\">\n <h3>\u9879\u76ee\u8d21\u732e\u8005</h3>\n <div class=\"contributors-grid\">\n {% for contributor in page.meta.contributors %}\n <a href=\"{{ contributor.url }}\" class=\"contributor-card\">\n <img src=\"{{ contributor.avatar }}\" alt=\"{{ contributor.name }}\">\n <span>{{ contributor.name }}</span>\n </a>\n {% endfor %}\n </div>\n </div>\n {% endif %}\n\n <!-- \u8d5e\u52a9\u5546\u533a\u57df -->\n {% if page.meta.sponsors %}\n <div class=\"sponsors\">\n <h3>\u8d5e\u52a9\u5546</h3>\n <div class=\"sponsors-grid\">\n {% for sponsor in page.meta.sponsors %}\n <a href=\"{{ sponsor.url }}\" class=\"sponsor-card\">\n <img src=\"{{ sponsor.logo }}\" alt=\"{{ sponsor.name }}\">\n </a>\n {% endfor %}\n </div>\n </div>\n {% endif %}\n</section>\n{% endif %}\n
- \u81ea\u5b9a\u4e49\u533a\u57df\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/custom-content.css */\n\n/* \u516c\u544a\u533a\u57df */\n.announcements {\n margin: 2rem 0;\n}\n\n.announcement-item {\n padding: 1rem;\n background: var(--md-code-bg-color);\n border-radius: 4px;\n margin-bottom: 1rem;\n}\n\n.announcement-tag {\n display: inline-block;\n padding: 0.2rem 0.5rem;\n background: var(--md-accent-fg-color);\n color: white;\n border-radius: 2rem;\n font-size: 0.8rem;\n margin-right: 0.5rem;\n}\n\n/* \u8d21\u732e\u8005\u533a\u57df */\n.contributors-grid {\n display: grid;\n grid-template-columns: repeat(auto-fill, minmax(100px, 1fr));\n gap: 1rem;\n margin: 1rem 0;\n}\n\n.contributor-card {\n text-align: center;\n text-decoration: none;\n color: var(--md-default-fg-color);\n}\n\n.contributor-card img {\n width: 60px;\n height: 60px;\n border-radius: 50%;\n margin-bottom: 0.5rem;\n}\n\n/* \u8d5e\u52a9\u5546\u533a\u57df */\n.sponsors-grid {\n display: grid;\n grid-template-columns: repeat(auto-fill, minmax(150px, 1fr));\n gap: 2rem;\n margin: 1rem 0;\n}\n\n.sponsor-card img {\n width: 100%;\n height: auto;\n filter: grayscale(100%);\n transition: filter 0.3s ease;\n}\n\n.sponsor-card:hover img {\n filter: grayscale(0%);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#712","title":"7.1.2 \u6587\u7ae0\u6a21\u677f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#7121","title":"7.1.2.1 \u6587\u7ae0\u5934\u90e8","text":"HTML<!-- docs/overrides/partials/article-header.html -->\n<header class=\"article-header\">\n <!-- \u6587\u7ae0\u6807\u9898 -->\n <h1>{{ page.title }}</h1>\n\n <!-- \u6587\u7ae0\u5143\u4fe1\u606f -->\n <div class=\"article-meta\">\n {% if page.meta.author %}\n <div class=\"meta-item\">\n <span class=\"meta-icon\">\n {% include \".icons/material/account.svg\" %}\n </span>\n <span>{{ page.meta.author }}</span>\n </div>\n {% endif %}\n\n {% if page.meta.date %}\n <div class=\"meta-item\">\n <span class=\"meta-icon\">\n {% include \".icons/material/calendar.svg\" %}\n </span>\n <span>{{ page.meta.date }}</span>\n </div>\n {% endif %}\n\n {% if page.meta.tags %}\n <div class=\"article-tags\">\n {% for tag in page.meta.tags %}\n <a href=\"{{ base_url }}/tags/#{{ tag }}\" class=\"tag\">\n # {{ tag }}\n </a>\n {% endfor %}\n </div>\n {% endif %}\n </div>\n\n <!-- \u6587\u7ae0\u6982\u8ff0 -->\n {% if page.meta.description %}\n <div class=\"article-description\">\n {{ page.meta.description }}\n </div>\n {% endif %}\n</header>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7122","title":"7.1.2.2 \u6b63\u6587\u6837\u5f0f","text":"CSS/* docs/stylesheets/article.css */\n\n/* \u6587\u7ae0\u5bb9\u5668 */\n.md-content article {\n max-width: 800px;\n margin: 0 auto;\n padding: 2rem;\n}\n\n/* \u6587\u7ae0\u5934\u90e8 */\n.article-header {\n margin-bottom: 3rem;\n text-align: center;\n}\n\n.article-meta {\n display: flex;\n justify-content: center;\n gap: 1rem;\n margin: 1rem 0;\n color: var(--md-default-fg-color--light);\n}\n\n.meta-item {\n display: flex;\n align-items: center;\n gap: 0.5rem;\n}\n\n.article-tags {\n margin-top: 1rem;\n}\n\n.tag {\n display: inline-block;\n padding: 0.2rem 0.5rem;\n margin: 0.2rem;\n background: var(--md-code-bg-color);\n border-radius: 2rem;\n color: var(--md-default-fg-color);\n text-decoration: none;\n font-size: 0.8rem;\n}\n\n/* \u6587\u7ae0\u5185\u5bb9\u6837\u5f0f */\n.md-content article {\n font-size: 1.1rem;\n line-height: 1.8;\n}\n\n/* \u6807\u9898\u6837\u5f0f */\n.md-content article h2 {\n margin-top: 3rem;\n padding-bottom: 0.5rem;\n border-bottom: 2px solid var(--md-default-fg-color--lightest);\n}\n\n/* \u4ee3\u7801\u5757\u6837\u5f0f */\n.md-content pre {\n border-radius: 8px;\n margin: 1.5rem 0;\n}\n\n/* \u5f15\u7528\u6837\u5f0f */\n.md-content blockquote {\n border-left: 4px solid var(--md-accent-fg-color);\n padding: 1rem;\n background: var(--md-code-bg-color);\n margin: 1.5rem 0;\n}\n\n/* \u56fe\u7247\u6837\u5f0f */\n.md-content img {\n max-width: 100%;\n border-radius: 8px;\n margin: 1.5rem 0;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7123","title":"7.1.2.3 \u5e95\u90e8\u4fe1\u606f","text":"HTML<!-- docs/overrides/partials/article-footer.html -->\n<footer class=\"article-footer\">\n <!-- \u6587\u7ae0\u5bfc\u822a -->\n <nav class=\"article-nav\">\n {% if page.previous_page %}\n <a href=\"{{ page.previous_page.url | url }}\" class=\"nav-link nav-prev\">\n <span class=\"nav-icon\">\n {% include \".icons/material/arrow-left.svg\" %}\n </span>\n <span class=\"nav-text\">\n <span class=\"nav-direction\">\u4e0a\u4e00\u7bc7</span>\n <span class=\"nav-title\">{{ page.previous_page.title }}</span>\n </span>\n </a>\n {% endif %}\n\n {% if page.next_page %}\n <a href=\"{{ page.next_page.url | url }}\" class=\"nav-link nav-next\">\n <span class=\"nav-text\">\n <span class=\"nav-direction\">\u4e0b\u4e00\u7bc7</span>\n <span class=\"nav-title\">{{ page.next_page.title }}</span>\n </span>\n <span class=\"nav-icon\">\n {% include \".icons/material/arrow-right.svg\" %}\n </span>\n </a>\n {% endif %}\n </nav>\n\n <!-- \u5206\u4eab\u6309\u94ae -->\n <div class=\"article-share\">\n <h4>\u5206\u4eab\u6587\u7ae0</h4>\n <div class=\"share-buttons\">\n <button class=\"share-button\" data-platform=\"twitter\">\n {% include \".icons/material/twitter.svg\" %}\n </button>\n <button class=\"share-button\" data-platform=\"facebook\">\n {% include \".icons/material/facebook.svg\" %}\n </button>\n <button class=\"share-button\" data-platform=\"linkedin\">\n {% include \".icons/material/linkedin.svg\" %}\n </button>\n <button class=\"share-button\" data-platform=\"weibo\">\n {% include \".icons/material/sina-weibo.svg\" %}\n </button>\n </div>\n </div>\n\n <!-- \u76f8\u5173\u6587\u7ae0 -->\n {% if page.meta.related_posts %}\n <div class=\"related-posts\">\n <h4>\u76f8\u5173\u6587\u7ae0</h4>\n <div class=\"related-grid\">\n {% for post in page.meta.related_posts %}\n <a href=\"{{ post.url | url }}\" class=\"related-post\">\n {% if post.image %}\n <img src=\"{{ post.image }}\" alt=\"{{ post.title }}\">\n {% endif %}\n <h5>{{ post.title }}</h5>\n <p>{{ post.excerpt }}</p>\n </a>\n {% endfor %}\n </div>\n </div>\n {% endif %}\n\n <!-- \u8bc4\u8bba\u533a -->\n <div class=\"article-comments\">\n <h4>\u8bc4\u8bba</h4>\n {% if config.extra.comments.provider == 'giscus' %}\n <script src=\"https://giscus.app/client.js\"\n data-repo=\"{{ config.extra.comments.repo }}\"\n data-repo-id=\"{{ config.extra.comments.repo_id }}\"\n data-category=\"{{ config.extra.comments.category }}\"\n data-category-id=\"{{ config.extra.comments.category_id }}\"\n data-mapping=\"pathname\"\n data-reactions-enabled=\"1\"\n data-emit-metadata=\"0\"\n data-theme=\"light\"\n crossorigin=\"anonymous\"\n async>\n </script>\n {% endif %}\n </div>\n</footer>\n\n<!-- \u6587\u7ae0\u5e95\u90e8\u6837\u5f0f -->\n<style>\n.article-footer {\n margin-top: 4rem;\n padding-top: 2rem;\n border-top: 1px solid var(--md-default-fg-color--lightest);\n}\n\n/* \u6587\u7ae0\u5bfc\u822a */\n.article-nav {\n display: flex;\n justify-content: space-between;\n margin-bottom: 2rem;\n}\n\n.nav-link {\n display: flex;\n align-items: center;\n padding: 1rem;\n text-decoration: none;\n color: var(--md-default-fg-color);\n background: var(--md-code-bg-color);\n border-radius: 8px;\n transition: transform 0.2s ease;\n max-width: 45%;\n}\n\n.nav-link:hover {\n transform: translateY(-2px);\n}\n\n.nav-text {\n display: flex;\n flex-direction: column;\n}\n\n.nav-direction {\n font-size: 0.8rem;\n color: var(--md-default-fg-color--light);\n}\n\n.nav-title {\n font-weight: 500;\n}\n\n/* \u5206\u4eab\u6309\u94ae */\n.share-buttons {\n display: flex;\n gap: 1rem;\n margin: 1rem 0;\n}\n\n.share-button {\n padding: 0.8rem;\n border: none;\n border-radius: 50%;\n background: var(--md-code-bg-color);\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.share-button:hover {\n background: var(--md-accent-fg-color);\n color: white;\n}\n\n/* \u76f8\u5173\u6587\u7ae0 */\n.related-grid {\n display: grid;\n grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));\n gap: 1.5rem;\n margin: 1rem 0;\n}\n\n.related-post {\n text-decoration: none;\n color: var(--md-default-fg-color);\n background: var(--md-code-bg-color);\n border-radius: 8px;\n overflow: hidden;\n transition: transform 0.2s ease;\n}\n\n.related-post:hover {\n transform: translateY(-4px);\n}\n\n.related-post img {\n width: 100%;\n height: 150px;\n object-fit: cover;\n}\n\n.related-post h5 {\n margin: 1rem;\n font-size: 1.1rem;\n}\n\n.related-post p {\n margin: 0 1rem 1rem;\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n}\n\n/* \u8bc4\u8bba\u533a */\n.article-comments {\n margin-top: 3rem;\n}\n\n/* \u54cd\u5e94\u5f0f\u9002\u914d */\n@media screen and (max-width: 76.1875em) {\n .article-nav {\n flex-direction: column;\n gap: 1rem;\n }\n\n .nav-link {\n max-width: 100%;\n }\n\n .related-grid {\n grid-template-columns: 1fr;\n }\n}\n</style>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#713-404","title":"7.1.3 404\u9875\u9762","text":""},{"location":"Tools/Blog/Mkdocs_Material/#7131","title":"7.1.3.1 \u9519\u8bef\u63d0\u793a","text":"HTML<!-- docs/overrides/404.html -->\n{% extends \"base.html\" %}\n\n{% block content %}\n<div class=\"error-page\">\n <div class=\"error-content\">\n <!-- 404 \u56fe\u6807 -->\n <div class=\"error-icon\">\n {% include \".icons/material/alert-circle-outline.svg\" %}\n <span class=\"error-code\">404</span>\n </div>\n\n <!-- \u9519\u8bef\u4fe1\u606f -->\n <h1>\u9875\u9762\u672a\u627e\u5230</h1>\n <p>\u62b1\u6b49\uff0c\u60a8\u8bbf\u95ee\u7684\u9875\u9762\u4e0d\u5b58\u5728\u6216\u5df2\u88ab\u79fb\u52a8</p>\n\n <!-- \u641c\u7d22\u6846 -->\n <div class=\"error-search\">\n <input type=\"text\" \n id=\"error-search-input\" \n placeholder=\"\u5c1d\u8bd5\u641c\u7d22...\"\n autocomplete=\"off\">\n <button id=\"error-search-button\">\n {% include \".icons/material/magnify.svg\" %}\n </button>\n </div>\n\n <!-- \u5feb\u6377\u64cd\u4f5c -->\n <div class=\"error-actions\">\n <a href=\"{{ base_url }}\" class=\"md-button md-button--primary\">\n \u8fd4\u56de\u9996\u9875\n </a>\n <button class=\"md-button\" onclick=\"window.history.back()\">\n \u8fd4\u56de\u4e0a\u9875\n </button>\n </div>\n </div>\n\n <!-- \u641c\u7d22\u5efa\u8bae -->\n <div class=\"search-suggestions\" id=\"search-suggestions\">\n <h3>\u60a8\u662f\u5426\u5728\u627e\uff1a</h3>\n <div class=\"suggestions-list\" id=\"suggestions-list\">\n <!-- \u52a8\u6001\u751f\u6210\u7684\u5efa\u8bae\u5217\u8868 -->\n </div>\n </div>\n</div>\n\n<!-- 404\u9875\u9762\u6837\u5f0f -->\n<style>\n.error-page {\n min-height: 100vh;\n display: flex;\n flex-direction: column;\n align-items: center;\n justify-content: center;\n padding: 2rem;\n text-align: center;\n}\n\n.error-content {\n max-width: 600px;\n}\n\n.error-icon {\n font-size: 6rem;\n color: var(--md-primary-fg-color);\n margin-bottom: 2rem;\n position: relative;\n}\n\n.error-code {\n position: absolute;\n bottom: -1rem;\n right: -1rem;\n font-size: 2rem;\n font-weight: bold;\n background: var(--md-accent-fg-color);\n color: white;\n padding: 0.5rem 1rem;\n border-radius: 1rem;\n}\n\n.error-search {\n margin: 2rem 0;\n position: relative;\n}\n\n.error-search input {\n width: 100%;\n padding: 1rem 3rem 1rem 1rem;\n border: 2px solid var(--md-default-fg-color--lightest);\n border-radius: 2rem;\n font-size: 1.1rem;\n}\n\n.error-search button {\n position: absolute;\n right: 0.5rem;\n top: 50%;\n transform: translateY(-50%);\n background: none;\n border: none;\n color: var(--md-default-fg-color);\n cursor: pointer;\n}\n\n.error-actions {\n display: flex;\n gap: 1rem;\n justify-content: center;\n margin: 2rem 0;\n}\n\n/* \u641c\u7d22\u5efa\u8bae\u6837\u5f0f */\n.search-suggestions {\n margin-top: 3rem;\n width: 100%;\n max-width: 600px;\n}\n\n.suggestions-list {\n margin-top: 1rem;\n}\n\n.suggestion-item {\n padding: 1rem;\n margin: 0.5rem 0;\n background: var(--md-code-bg-color);\n border-radius: 8px;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.suggestion-item:hover {\n background: var(--md-accent-fg-color--transparent);\n}\n\n/* \u54cd\u5e94\u5f0f\u9002\u914d */\n@media screen and (max-width: 76.1875em) {\n .error-icon {\n font-size: 4rem;\n }\n\n .error-code {\n font-size: 1.5rem;\n }\n\n .error-actions {\n flex-direction: column;\n }\n}\n</style>\n\n<!-- 404\u9875\u9762\u811a\u672c -->\n<script>\ndocument.addEventListener('DOMContentLoaded', function() {\n // \u641c\u7d22\u5efa\u8bae\u529f\u80fd\n setupSearchSuggestions();\n // \u5168\u5c40\u70ed\u952e\n setupHotkeys();\n});\n\nfunction setupSearchSuggestions() {\n const searchInput = document.getElementById('error-search-input');\n const suggestionsList = document.getElementById('suggestions-list');\n\n searchInput?.addEventListener('input', debounce(async (e) => {\n const query = e.target.value;\n if (query.length < 2) {\n suggestionsList.innerHTML = '';\n return;\n }\n\n // \u83b7\u53d6\u641c\u7d22\u5efa\u8bae\n const suggestions = await getSearchSuggestions(query);\n\n // \u6e32\u67d3\u5efa\u8bae\u5217\u8868\n suggestionsList.innerHTML = suggestions\n .map(suggestion => `\n <div class=\"suggestion-item\" onclick=\"window.location.href='${suggestion.url}'\">\n <div class=\"suggestion-title\">${suggestion.title}</div>\n <div class=\"suggestion-excerpt\">${suggestion.excerpt}</div>\n </div>\n `)\n .join('');\n }, 300));\n}\n\nasync function getSearchSuggestions(query) {\n // \u8fd9\u91cc\u53ef\u4ee5\u5b9e\u73b0\u5b9e\u9645\u7684\u641c\u7d22\u903b\u8f91\n // \u793a\u4f8b\u8fd4\u56de\u6570\u636e\n return [\n {\n title: '\u76f8\u5173\u6587\u6863 1',\n excerpt: '\u8fd9\u662f\u4e00\u6bb5\u76f8\u5173\u7684\u6587\u6863\u63cf\u8ff0...',\n url: '#'\n },\n {\n title: '\u76f8\u5173\u6587\u6863 2',\n excerpt: '\u8fd9\u662f\u53e6\u4e00\u6bb5\u76f8\u5173\u7684\u6587\u6863\u63cf\u8ff0...',\n url: '#'\n }\n ];\n}\n\nfunction setupHotkeys() {\n document.addEventListener('keydown', (e) => {\n // \u6309 ESC \u8fd4\u56de\u4e0a\u9875\n if (e.key === 'Escape') {\n window.history.back();\n }\n\n // \u6309 Enter \u6267\u884c\u641c\u7d22\n if (e.key === 'Enter' && document.activeElement.id === 'error-search-input') {\n const query = document.activeElement.value;\n if (query) {\n window.location.href = `${window.location.origin}/search.html?q=${encodeURIComponent(query)}`;\n }\n }\n });\n}\n\n// \u9632\u6296\u51fd\u6570\nfunction debounce(fn, delay) {\n let timer = null;\n return function(...args) {\n clearTimeout(timer);\n timer = setTimeout(() => fn.apply(this, args), delay);\n };\n}\n</script>\n
\u8fd9\u6837\uff0c\u6211\u4eec\u5c31\u5b8c\u6210\u4e86\u6587\u7ae0\u5e95\u90e8\u4fe1\u606f\u548c404\u9875\u9762\u7684\u6a21\u677f\u3002\u4e3b\u8981\u7279\u70b9\u5305\u62ec\uff1a
-
\u6587\u7ae0\u5e95\u90e8\uff1a - \u6e05\u6670\u7684\u4e0a\u4e0b\u6587\u7ae0\u5bfc\u822a - \u793e\u4ea4\u5206\u4eab\u6309\u94ae - \u76f8\u5173\u6587\u7ae0\u63a8\u8350 - \u96c6\u6210\u8bc4\u8bba\u7cfb\u7edf
-
404\u9875\u9762\uff1a - \u53cb\u597d\u7684\u9519\u8bef\u63d0\u793a - \u5b9e\u65f6\u641c\u7d22\u5efa\u8bae - \u591a\u79cd\u8fd4\u56de\u9009\u9879 - \u952e\u76d8\u5feb\u6377\u952e\u652f\u6301
\u4f7f\u7528\u8fd9\u4e9b\u6a21\u677f\u65f6\uff0c\u9700\u8981\u5728 mkdocs.yml \u4e2d\u6dfb\u52a0\u76f8\u5e94\u7684\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n custom_dir: docs/overrides\n features:\n - navigation.tracking\n - search.suggest\n - search.highlight\n\nextra:\n comments:\n provider: giscus\n repo: username/repo\n repo_id: your-repo-id\n category: Comments\n category_id: your-category-id\n
"},{"location":"Tools/Blog/Mkdocs_Material/#72","title":"7.2 \u4fee\u6539\u7ec4\u4ef6\u6a21\u677f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#721","title":"7.2.1 \u5bfc\u822a\u680f","text":""},{"location":"Tools/Blog/Mkdocs_Material/#7211","title":"7.2.1.1 \u5bfc\u822a\u9879\u5b9a\u5236","text":"HTML<!-- docs/overrides/partials/nav.html -->\n{% extends \"base.html\" %}\n\n{% block site_nav %}\n<nav class=\"md-nav md-nav--primary\">\n <!-- \u81ea\u5b9a\u4e49\u5bfc\u822a\u5934\u90e8 -->\n <div class=\"nav-header\">\n {% if config.theme.logo %}\n <img src=\"{{ config.theme.logo }}\" alt=\"logo\" class=\"nav-logo\">\n {% endif %}\n <span class=\"nav-title\">{{ config.site_name }}</span>\n </div>\n\n <!-- \u81ea\u5b9a\u4e49\u5bfc\u822a\u9879 -->\n <ul class=\"nav-items\">\n {% for nav_item in nav %}\n {% include \"partials/nav-item.html\" %}\n {% endfor %}\n\n <!-- \u6dfb\u52a0\u81ea\u5b9a\u4e49\u5bfc\u822a\u9879 -->\n {% if config.extra.nav_links %}\n {% for link in config.extra.nav_links %}\n <li class=\"nav-item custom\">\n <a href=\"{{ link.url }}\" class=\"nav-link\" {% if link.target %}target=\"{{ link.target }}\"{% endif %}>\n {% if link.icon %}\n <span class=\"nav-icon\">\n {% include \".icons/\" ~ link.icon ~ \".svg\" %}\n </span>\n {% endif %}\n {{ link.title }}\n </a>\n </li>\n {% endfor %}\n {% endif %}\n </ul>\n</nav>\n
\u914d\u7f6e\u548c\u6837\u5f0f\uff1a
YAML# mkdocs.yml\nextra:\n nav_links:\n - title: GitHub\n url: https://github.com/your/repo\n icon: material/github\n target: _blank\n - title: \u6587\u6863\n url: /docs/\n icon: material/file-document\n
CSS/* docs/stylesheets/nav.css */\n.nav-header {\n display: flex;\n align-items: center;\n padding: 1rem;\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.nav-logo {\n width: 32px;\n height: 32px;\n margin-right: 0.8rem;\n}\n\n.nav-items {\n list-style: none;\n padding: 0;\n margin: 0;\n}\n\n.nav-item {\n margin: 0.2rem 0;\n}\n\n.nav-link {\n display: flex;\n align-items: center;\n padding: 0.8rem 1rem;\n color: var(--md-default-fg-color);\n text-decoration: none;\n transition: background-color 0.2s ease;\n}\n\n.nav-link:hover {\n background-color: var(--md-code-bg-color);\n}\n\n.nav-icon {\n margin-right: 0.8rem;\n width: 1.2rem;\n height: 1.2rem;\n}\n\n/* \u81ea\u5b9a\u4e49\u5bfc\u822a\u9879\u6837\u5f0f */\n.nav-item.custom .nav-link {\n color: var(--md-accent-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7212","title":"7.2.1.2 \u641c\u7d22\u6846\u4f4d\u7f6e","text":"HTML<!-- docs/overrides/partials/search.html -->\n{% block search_box %}\n<div class=\"md-search\" data-md-component=\"search\" role=\"dialog\">\n <label class=\"md-search__overlay\" for=\"__search\"></label>\n <div class=\"md-search__inner\" role=\"search\">\n <!-- \u641c\u7d22\u8f93\u5165\u6846 -->\n <form class=\"md-search__form\">\n <input\n type=\"text\"\n class=\"md-search__input\"\n name=\"query\"\n aria-label=\"\u641c\u7d22\"\n placeholder=\"\u641c\u7d22\u6587\u6863...\"\n autocapitalize=\"off\"\n autocomplete=\"off\"\n autocorrect=\"off\"\n spellcheck=\"false\"\n data-md-component=\"search-query\"\n >\n <!-- \u641c\u7d22\u5feb\u6377\u952e\u63d0\u793a -->\n <div class=\"md-search__shortcuts\">\n <kbd>Ctrl</kbd> + <kbd>K</kbd>\n </div>\n </form>\n\n <!-- \u641c\u7d22\u7ed3\u679c -->\n <div class=\"md-search__output\">\n <div class=\"md-search__scrollwrap\" data-md-scrollfix>\n <div class=\"md-search-result\" data-md-component=\"search-result\">\n <div class=\"md-search-result__meta\">\n \u6b63\u5728\u641c\u7d22...\n </div>\n <ol class=\"md-search-result__list\"></ol>\n </div>\n </div>\n </div>\n </div>\n</div>\n
\u6837\u5f0f\u5b9a\u5236\uff1a
CSS/* docs/stylesheets/search.css */\n/* \u641c\u7d22\u6846\u5bb9\u5668 */\n.md-search {\n margin: 0 1rem;\n padding: 0;\n position: relative;\n}\n\n/* \u641c\u7d22\u8f93\u5165\u6846 */\n.md-search__input {\n width: 100%;\n height: 2.4rem;\n padding: 0 2.4rem;\n font-size: 0.9rem;\n color: var(--md-default-fg-color);\n background-color: var(--md-default-bg-color);\n border: 1px solid var(--md-default-fg-color--lightest);\n border-radius: 1.2rem;\n}\n\n/* \u641c\u7d22\u56fe\u6807 */\n.md-search__icon {\n position: absolute;\n left: 0.8rem;\n top: 50%;\n transform: translateY(-50%);\n color: var(--md-default-fg-color--light);\n}\n\n/* \u5feb\u6377\u952e\u63d0\u793a */\n.md-search__shortcuts {\n position: absolute;\n right: 0.8rem;\n top: 50%;\n transform: translateY(-50%);\n display: flex;\n gap: 0.2rem;\n color: var(--md-default-fg-color--light);\n}\n\n/* \u641c\u7d22\u7ed3\u679c\u5bb9\u5668 */\n.md-search__output {\n position: absolute;\n top: 100%;\n width: 100%;\n margin-top: 0.4rem;\n background-color: var(--md-default-bg-color);\n border-radius: 0.2rem;\n box-shadow: var(--md-shadow-z2);\n overflow: auto;\n z-index: 1;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7213","title":"7.2.1.3 \u79fb\u52a8\u7aef\u9002\u914d","text":"CSS/* \u79fb\u52a8\u7aef\u5bfc\u822a\u6837\u5f0f */\n@media screen and (max-width: 76.1875em) {\n /* \u5bfc\u822a\u5207\u6362\u6309\u94ae */\n .md-header-nav__button.md-icon {\n padding: 0.4rem;\n margin: 0.4rem;\n }\n\n /* \u5bfc\u822a\u62bd\u5c49 */\n .md-nav--primary {\n position: fixed;\n top: 0;\n left: -18rem;\n width: 18rem;\n height: 100%;\n background-color: var(--md-default-bg-color);\n transition: left 0.25s;\n z-index: 2;\n }\n\n /* \u5bfc\u822a\u62bd\u5c49\u6253\u5f00\u72b6\u6001 */\n .md-nav--primary.md-nav--opened {\n left: 0;\n }\n\n /* \u641c\u7d22\u6846\u5168\u5c4f */\n .md-search__inner {\n position: fixed;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n padding: 1rem;\n background-color: var(--md-default-bg-color);\n z-index: 3;\n }\n\n /* \u641c\u7d22\u7ed3\u679c\u5168\u5c4f */\n .md-search__output {\n position: fixed;\n top: 4rem;\n height: calc(100% - 4rem);\n }\n}\n\n/* \u5e73\u677f\u9002\u914d */\n@media screen and (min-width: 76.25em) and (max-width: 96.25em) {\n .md-nav--primary {\n width: 16rem;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#722","title":"7.2.2 \u9875\u811a","text":"HTML<!-- docs/overrides/partials/footer.html -->\n<footer class=\"md-footer\">\n <div class=\"md-footer-meta md-typeset\">\n <div class=\"md-footer-meta__inner md-grid\">\n <!-- \u7248\u6743\u4fe1\u606f -->\n <div class=\"md-footer-copyright\">\n {% if config.copyright %}\n <div class=\"md-footer-copyright__highlight\">\n {{ config.copyright }}\n </div>\n {% endif %}\n\n <!-- \u6784\u5efa\u4fe1\u606f -->\n <div class=\"md-footer-build\">\n Documentation built with\n <a href=\"https://www.mkdocs.org\" target=\"_blank\" rel=\"noopener\">\n MkDocs\n </a>\n and\n <a href=\"https://squidfunk.github.io/mkdocs-material/\" target=\"_blank\" rel=\"noopener\">\n Material for MkDocs\n </a>\n </div>\n </div>\n\n <!-- \u793e\u4ea4\u94fe\u63a5 -->\n {% if config.extra.social %}\n <div class=\"md-footer-social\">\n {% for social in config.extra.social %}\n <a href=\"{{ social.link }}\" \n target=\"_blank\" \n rel=\"noopener\" \n title=\"{{ social.name }}\"\n class=\"md-footer-social__link\">\n {% include \".icons/\" ~ social.icon ~ \".svg\" %}\n </a>\n {% endfor %}\n </div>\n {% endif %}\n\n <!-- \u5907\u6848\u4fe1\u606f -->\n {% if config.extra.icp %}\n <div class=\"md-footer-icp\">\n <a href=\"https://beian.miit.gov.cn/\" target=\"_blank\" rel=\"noopener\">\n {{ config.extra.icp }}\n </a>\n </div>\n {% endif %}\n </div>\n </div>\n</footer>\n
\u914d\u7f6e\uff1a
YAML# mkdocs.yml\ncopyright: Copyright © 2024 Your Name\nextra:\n social:\n - icon: material/github\n name: GitHub\n link: https://github.com/your-username\n - icon: material/twitter\n name: Twitter\n link: https://twitter.com/your-username\n icp: \u4eacICP\u5907xxxxxxxx\u53f7\n
\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/footer.css */\n.md-footer {\n background-color: var(--md-default-bg-color);\n color: var(--md-default-fg-color);\n padding: 2rem 0;\n border-top: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.md-footer-meta__inner {\n display: flex;\n flex-wrap: wrap;\n justify-content: space-between;\n align-items: center;\n gap: 1rem;\n}\n\n/* \u7248\u6743\u4fe1\u606f */\n.md-footer-copyright {\n font-size: 0.8rem;\n}\n\n.md-footer-copyright__highlight {\n margin-bottom: 0.5rem;\n}\n\n.md-footer-build {\n color: var(--md-default-fg-color--light);\n}\n\n/* \u793e\u4ea4\u94fe\u63a5 */\n.md-footer-social {\n display: flex;\n gap: 1rem;\n}\n\n.md-footer-social__link {\n width: 2rem;\n height: 2rem;\n display: flex;\n align-items: center;\n justify-content: center;\n color: var(--md-default-fg-color);\n border-radius: 50%;\n transition: all 0.2s ease;\n}\n\n.md-footer-social__link:hover {\n background-color: var(--md-code-bg-color);\n color: var(--md-accent-fg-color);\n}\n\n/* \u5907\u6848\u4fe1\u606f */\n.md-footer-icp {\n width: 100%;\n text-align: center;\n margin-top: 1rem;\n font-size: 0.8rem;\n}\n\n.md-footer-icp a {\n color: var(--md-default-fg-color--light);\n text-decoration: none;\n}\n\n/* \u54cd\u5e94\u5f0f\u9002\u914d */\n@media screen and (max-width: 76.1875em) {\n .md-footer-meta__inner {\n flex-direction: column;\n text-align: center;\n }\n\n .md-footer-social {\n justify-content: center;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#723","title":"7.2.3 \u641c\u7d22\u6846","text":"HTML<!-- docs/overrides/partials/search-modal.html -->\n<div class=\"md-search-modal\" data-md-component=\"search-modal\">\n <div class=\"md-search-modal__overlay\" data-md-component=\"search-modal-overlay\"></div>\n\n <div class=\"md-search-modal__inner\">\n <!-- \u641c\u7d22\u5934\u90e8 -->\n <header class=\"md-search-modal__header\">\n <form class=\"md-search-modal__form\">\n <input\n type=\"text\"\n class=\"md-search-modal__input\"\n name=\"query\"\n placeholder=\"\u641c\u7d22\u6587\u6863...\"\n data-md-component=\"search-query\"\n autocapitalize=\"off\"\n autocomplete=\"off\"\n autocorrect=\"off\"\n spellcheck=\"false\"\n >\n <!-- \u5feb\u6377\u952e\u63d0\u793a -->\n <div class=\"md-search-modal__shortcuts\">\n <kbd>\u2191</kbd><kbd>\u2193</kbd> \u9009\u62e9\n <kbd>\u21b5</kbd> \u6253\u5f00\n <kbd>ESC</kbd> \u5173\u95ed\n </div>\n </form>\n </header>\n\n <!-- \u641c\u7d22\u7ed3\u679c -->\n <main class=\"md-search-modal__body\">\n <div class=\"md-search-modal__scrollwrap\">\n <div class=\"md-search-modal__meta\">\n \u952e\u5165\u4ee5\u5f00\u59cb\u641c\u7d22\n </div>\n\n <div class=\"md-search-modal__results\">\n <!-- \u641c\u7d22\u7ed3\u679c\u5217\u8868 -->\n <div class=\"md-search-modal__list\"></div>\n\n <!-- \u641c\u7d22\u5efa\u8bae -->\n <div class=\"md-search-modal__suggestions\">\n <h3>\u60a8\u53ef\u80fd\u611f\u5174\u8da3\uff1a</h3>\n <div class=\"suggestions-list\">\n <!-- \u52a8\u6001\u751f\u6210\u7684\u5efa\u8bae\u5217\u8868 -->\n </div>\n </div>\n </div>\n </div>\n </main>\n </div>\n</div>\n
\u641c\u7d22\u529f\u80fd\u811a\u672c\uff1a
JavaScript// docs/javascripts/search.js\nclass SearchModal {\n constructor() {\n this.modal = document.querySelector('.md-search-modal');\n this.overlay = document.querySelector('.md-search-modal__overlay');\n this.input = document.querySelector('.md-search-modal__input');\n this.resultsList = document.querySelector('.md-search-modal__list');\n this.suggestions = document.querySelector('.md-search-modal__suggestions');\n\n this.searchIndex = null;\n this.searchResults = [];\n this.currentFocus = -1;\n\n this.init();\n }\n\n init() {\n // \u521d\u59cb\u5316\u641c\u7d22\u7d22\u5f15\n this.buildSearchIndex();\n // \u7ed1\u5b9a\u4e8b\u4ef6\n this.bindEvents();\n // \u8bbe\u7f6e\u5feb\u6377\u952e\n this.setupShortcuts();\n }\n\n async buildSearchIndex() {\n // \u6784\u5efa\u641c\u7d22\u7d22\u5f15\n const response = await fetch('/search/search_index.json');\n const data = await response.json();\n\n // \u4f7f\u7528 lunr.js \u6784\u5efa\u7d22\u5f15\n this.searchIndex = lunr(function() {\n this.field('title', { boost: 10 });\n this.field('text');\n this.ref('location');\n\n data.docs.forEach(function(doc) {\n this.add(doc);\n }, this);\n });\n }\n\n bindEvents() {\n // \u641c\u7d22\u6846\u8f93\u5165\u4e8b\u4ef6\n this.input.addEventListener('input', debounce(() => {\n const query = this.input.value;\n if (query.length >= 2) {\n this.performSearch(query);\n } else {\n this.clearResults();\n }\n }, 200));\n\n // \u641c\u7d22\u7ed3\u679c\u5bfc\u822a\n this.input.addEventListener('keydown', (e) => {\n switch (e.key) {\n case 'ArrowUp':\n e.preventDefault();\n this.navigateResults('up');\n break;\n case 'ArrowDown':\n e.preventDefault();\n this.navigateResults('down');\n break;\n case 'Enter':\n e.preventDefault();\n this.openResult();\n break;\n case 'Escape':\n e.preventDefault();\n this.closeModal();\n break;\n }\n });\n\n // \u70b9\u51fb\u906e\u7f69\u5173\u95ed\n this.overlay.addEventListener('click', () => this.closeModal());\n }\n\n setupShortcuts() {\n // \u5168\u5c40\u641c\u7d22\u5feb\u6377\u952e\n document.addEventListener('keydown', (e) => {\n // Ctrl/Cmd + K \u6253\u5f00\u641c\u7d22\n if ((e.ctrlKey || e.metaKey) && e.key === 'k') {\n e.preventDefault();\n this.openModal();\n }\n });\n }\n\n async performSearch(query) {\n if (!this.searchIndex) return;\n\n // \u6267\u884c\u641c\u7d22\n this.searchResults = this.searchIndex.search(query).map(result => {\n return {\n ref: result.ref,\n score: result.score,\n ...this.getDocumentByRef(result.ref)\n };\n });\n\n // \u6e32\u67d3\u7ed3\u679c\n this.renderResults();\n // \u66f4\u65b0\u5efa\u8bae\n this.updateSuggestions(query);\n }\n\n renderResults() {\n this.resultsList.innerHTML = this.searchResults\n .map((result, index) => `\n <div class=\"search-result-item ${index === this.currentFocus ? 'focused' : ''}\"\n data-index=\"${index}\">\n <div class=\"result-title\">${this.highlightText(result.title)}</div>\n <div class=\"result-excerpt\">${this.highlightText(result.excerpt)}</div>\n <div class=\"result-location\">${result.location}</div>\n </div>\n `)\n .join('');\n }\n\n highlightText(text) {\n const query = this.input.value;\n if (!query) return text;\n\n const regex = new RegExp(`(${query})`, 'gi');\n return text.replace(regex, '<mark>$1</mark>');\n }\n\n updateSuggestions(query) {\n // \u6839\u636e\u641c\u7d22\u5386\u53f2\u548c\u70ed\u95e8\u641c\u7d22\u751f\u6210\u5efa\u8bae\n const suggestions = this.generateSuggestions(query);\n\n this.suggestions.innerHTML = suggestions\n .map(suggestion => `\n <div class=\"suggestion-item\" data-query=\"${suggestion.query}\">\n <span class=\"suggestion-icon\">\n ${suggestion.type === 'history' ? '\u23f1\ufe0f' : '\ud83d\udd25'}\n </span>\n ${suggestion.query}\n </div>\n `)\n .join('');\n }\n\n navigateResults(direction) {\n const maxIndex = this.searchResults.length - 1;\n\n if (direction === 'up') {\n this.currentFocus = this.currentFocus > 0 ? this.currentFocus - 1 : maxIndex;\n } else {\n this.currentFocus = this.currentFocus < maxIndex ? this.currentFocus + 1 : 0;\n }\n\n this.renderResults();\n this.scrollToFocused();\n }\n\n scrollToFocused() {\n const focused = this.resultsList.querySelector('.focused');\n if (focused) {\n focused.scrollIntoView({\n behavior: 'smooth',\n block: 'nearest'\n });\n }\n }\n\n openResult() {\n const result = this.searchResults[this.currentFocus];\n if (result) {\n window.location.href = result.location;\n }\n }\n\n openModal() {\n this.modal.classList.add('active');\n this.input.focus();\n document.body.style.overflow = 'hidden';\n }\n\n closeModal() {\n this.modal.classList.remove('active');\n this.input.value = '';\n this.clearResults();\n document.body.style.overflow = '';\n }\n\n clearResults() {\n this.searchResults = [];\n this.currentFocus = -1;\n this.resultsList.innerHTML = '';\n this.suggestions.innerHTML = '';\n }\n}\n\n// \u521d\u59cb\u5316\u641c\u7d22\u529f\u80fd\ndocument.addEventListener('DOMContentLoaded', () => {\n new SearchModal();\n});\n
\u641c\u7d22\u6846\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/search-modal.css */\n.md-search-modal {\n position: fixed;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n z-index: 1000;\n opacity: 0;\n visibility: hidden;\n transition: all 0.2s ease;\n}\n\n.md-search-modal.active {\n opacity: 1;\n visibility: visible;\n}\n\n.md-search-modal__overlay {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n background: rgba(0, 0, 0, 0.5);\n backdrop-filter: blur(4px);\n}\n\n.md-search-modal__inner {\n position: relative;\n width: 90%;\n max-width: 800px;\n margin: 2rem auto;\n background: var(--md-default-bg-color);\n border-radius: 8px;\n box-shadow: var(--md-shadow-z3);\n}\n\n/* \u641c\u7d22\u7ed3\u679c\u6837\u5f0f */\n.search-result-item {\n padding: 1rem;\n cursor: pointer;\n transition: background 0.2s ease;\n}\n\n.search-result-item:hover,\n.search-result-item.focused {\n background: var(--md-code-bg-color);\n}\n\n.result-title {\n font-size: 1.1rem;\n font-weight: 500;\n margin-bottom: 0.5rem;\n}\n\n.result-excerpt {\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n margin-bottom: 0.5rem;\n}\n\n.result-location {\n font-size: 0.8rem;\n color: var(--md-accent-fg-color);\n}\n\n/* \u9ad8\u4eae\u5339\u914d\u6587\u672c */\nmark {\n background: var(--md-accent-fg-color);\n color: white;\n padding: 0 0.2rem;\n border-radius: 2px;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#724","title":"7.2.4 \u76ee\u5f55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#7241","title":"7.2.4.1 \u76ee\u5f55\u7ed3\u6784","text":"HTML<!-- docs/overrides/partials/toc.html -->\n<nav class=\"md-toc\" aria-label=\"\u76ee\u5f55\">\n <div class=\"md-toc__header\">\n <h2 class=\"md-toc__title\">\u76ee\u5f55</h2>\n <button class=\"md-toc__toggle\" aria-label=\"\u5c55\u5f00/\u6536\u8d77\">\n {% include \".icons/material/chevron-down.svg\" %}\n </button>\n </div>\n\n <div class=\"md-toc__inner\">\n {% set toc = page.toc %}\n <ul class=\"md-toc__list\">\n {% for toc_item in toc %}\n {% include \"partials/toc-item.html\" %}\n {% endfor %}\n </ul>\n </div>\n</nav>\n\n<!-- docs/overrides/partials/toc-item.html -->\n<li class=\"md-toc__item\">\n <a href=\"{{ toc_item.url }}\" class=\"md-toc__link\">\n {{ toc_item.title }}\n </a>\n\n {% if toc_item.children %}\n <ul class=\"md-toc__list\">\n {% for toc_item in toc_item.children %}\n {% include \"partials/toc-item.html\" %}\n {% endfor %}\n </ul>\n {% endif %}\n</li>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7242","title":"7.2.4.2 \u6eda\u52a8\u540c\u6b65","text":"JavaScript// docs/javascripts/toc.js\nclass TableOfContents {\n constructor() {\n this.toc = document.querySelector('.md-toc');\n this.tocLinks = this.toc.querySelectorAll('.md-toc__link');\n this.headings = document.querySelectorAll('h1[id], h2[id], h3[id], h4[id], h5[id], h6[id]');\n this.intersectionObserver = null;\n\n this.init();\n }\n\n init() {\n this.setupIntersectionObserver();\n this.setupScrollSpy();\n this.setupToggle();\n }\n\n setupIntersectionObserver() {\n this.intersectionObserver = new IntersectionObserver(\n (entries) => {\n entries.forEach(entry => {\n if (entry.isIntersecting) {\n const id = entry.target.getAttribute('id');\n this.highlightTocItem(id);\n }\n });\n },\n {\n rootMargin: '0px 0px -80% 0px'\n }\n );\n\n this.headings.forEach(heading => {\n this.intersectionObserver.observe(heading);\n });\n }\n\n setupScrollSpy() {\n this.tocLinks.forEach(link => {\n link.addEventListener('click', (e) => {\n e.preventDefault();\n const id = link.getAttribute('href').substring(1);\n const target = document.getElementById(id);\n\n if (target) {\n window.scrollTo({\n top: target.offsetTop - 100,\n behavior: 'smooth'\n });\n\n // \u66f4\u65b0 URL\n history.pushState(null, null, `#${id}`);\n }\n });\n });\n }\n\n highlightTocItem(id) {\n // \u79fb\u9664\u6240\u6709\u9ad8\u4eae\n this.tocLinks.forEach(link => {\n link.classList.remove('active');\n });\n\n // \u6dfb\u52a0\u65b0\u9ad8\u4eae\n const activeLink = this.toc.querySelector(`a[href=\"#${id}\"]`);\n if (activeLink) {\n activeLink.classList.add('active');\n this.expandParents(activeLink);\n }\n }\n\n expandParents(element) {\n let parent = element.parentElement;\n while (parent && parent.classList.contains('md-toc__item')) {\n const list = parent.querySelector('.md-toc__list');\n if (list) {\n list.style.height = 'auto';\n parent.classList.add('expanded');\n }\n parent = parent.parentElement;\n }\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#7243","title":"7.2.4.3 \u5c55\u5f00\u6536\u8d77","text":"JavaScriptsetupToggle() {\n const toggleButton = this.toc.querySelector('.md-toc__toggle');\n const tocInner = this.toc.querySelector('.md-toc__inner');\n\n toggleButton?.addEventListener('click', () => {\n const isExpanded = this.toc.classList.contains('expanded');\n\n if (isExpanded) {\n this.toc.classList.remove('expanded');\n tocInner.style.height = '0';\n } else {\n this.toc.classList.add('expanded');\n tocInner.style.height = tocInner.scrollHeight + 'px';\n }\n });\n\n // \u6dfb\u52a0\u5c55\u5f00/\u6536\u8d77\u6240\u6709\u6309\u94ae\n const expandAllButton = document.createElement('button');\n expandAllButton.className = 'md-toc__expand-all';\n expandAllButton.innerHTML = '\u5c55\u5f00\u5168\u90e8';\n expandAllButton.addEventListener('click', () => this.toggleAll(true));\n\n const collapseAllButton = document.createElement('button');\n collapseAllButton.className = 'md-toc__collapse-all';\n collapseAllButton.innerHTML = '\u6536\u8d77\u5168\u90e8';\n collapseAllButton.addEventListener('click', () => this.toggleAll(false));\n\n const buttonGroup = document.createElement('div');\n buttonGroup.className = 'md-toc__button-group';\n buttonGroup.appendChild(expandAllButton);\n buttonGroup.appendChild(collapseAllButton);\n\n this.toc.querySelector('.md-toc__header').appendChild(buttonGroup);\n}\n\ntoggleAll(expand) {\n const lists = this.toc.querySelectorAll('.md-toc__list');\n lists.forEach(list => {\n if (expand) {\n list.style.height = 'auto';\n list.parentElement.classList.add('expanded');\n } else {\n list.style.height = '0';\n list.parentElement.classList.remove('expanded');\n }\n });\n}\n
\u76ee\u5f55\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/toc.css */\n.md-toc {\n position: sticky;\n top: 4rem;\n padding: 1rem;\n max-height: calc(100vh - 4rem);\n overflow-y: auto;\n background: var(--md-default-bg-color);\n border-left: 1px solid var(--md-default-fg-color--lightest);\n font-size: 0.8rem;\n}\n\n/* \u76ee\u5f55\u5934\u90e8 */\n.md-toc__header {\n display: flex;\n justify-content: space-between;\n align-items: center;\n margin-bottom: 1rem;\n padding-bottom: 0.5rem;\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.md-toc__title {\n font-size: 1rem;\n font-weight: 600;\n margin: 0;\n}\n\n.md-toc__button-group {\n display: flex;\n gap: 0.5rem;\n}\n\n.md-toc__button-group button {\n padding: 0.2rem 0.5rem;\n font-size: 0.7rem;\n color: var(--md-default-fg-color--light);\n background: var(--md-code-bg-color);\n border: none;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.md-toc__button-group button:hover {\n color: var(--md-accent-fg-color);\n background: var(--md-code-bg-color--light);\n}\n\n/* \u76ee\u5f55\u5217\u8868 */\n.md-toc__list {\n list-style: none;\n padding: 0;\n margin: 0;\n}\n\n.md-toc__item {\n margin: 0.2rem 0;\n}\n\n/* \u76ee\u5f55\u94fe\u63a5 */\n.md-toc__link {\n display: block;\n padding: 0.2rem 0;\n color: var(--md-default-fg-color);\n text-decoration: none;\n transition: all 0.2s ease;\n border-radius: 4px;\n}\n\n.md-toc__link:hover {\n color: var(--md-accent-fg-color);\n background: var(--md-code-bg-color);\n padding-left: 0.5rem;\n}\n\n/* \u5f53\u524d\u6fc0\u6d3b\u9879 */\n.md-toc__link.active {\n color: var(--md-accent-fg-color);\n font-weight: 500;\n background: var(--md-code-bg-color);\n padding-left: 0.5rem;\n}\n\n/* \u5d4c\u5957\u5c42\u7ea7 */\n.md-toc__item .md-toc__list {\n margin-left: 1rem;\n border-left: 1px solid var(--md-default-fg-color--lightest);\n overflow: hidden;\n height: 0;\n transition: height 0.3s ease;\n}\n\n.md-toc__item.expanded > .md-toc__list {\n height: auto;\n}\n\n/* \u6298\u53e0\u6307\u793a\u5668 */\n.md-toc__item > .md-toc__link::before {\n content: '';\n display: inline-block;\n width: 0.8rem;\n height: 0.8rem;\n margin-right: 0.2rem;\n background-image: url('data:image/svg+xml,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M8.59 16.59L13.17 12 8.59 7.41 10 6l6 6-6 6-1.41-1.41z\"/></svg>');\n background-size: contain;\n transform: rotate(0);\n transition: transform 0.3s ease;\n}\n\n.md-toc__item.expanded > .md-toc__link::before {\n transform: rotate(90deg);\n}\n\n/* \u6eda\u52a8\u6761\u6837\u5f0f */\n.md-toc::-webkit-scrollbar {\n width: 4px;\n}\n\n.md-toc::-webkit-scrollbar-track {\n background: transparent;\n}\n\n.md-toc::-webkit-scrollbar-thumb {\n background: var(--md-default-fg-color--lighter);\n border-radius: 2px;\n}\n\n/* \u54cd\u5e94\u5f0f\u8bbe\u8ba1 */\n@media screen and (max-width: 76.1875em) {\n .md-toc {\n position: fixed;\n top: 0;\n right: -18rem;\n width: 18rem;\n height: 100vh;\n max-height: none;\n margin: 0;\n padding: 1rem;\n background: var(--md-default-bg-color);\n border-left: 1px solid var(--md-default-fg-color--lightest);\n box-shadow: var(--md-shadow-z3);\n transition: right 0.3s ease;\n z-index: 3;\n }\n\n .md-toc.expanded {\n right: 0;\n }\n\n /* \u79fb\u52a8\u7aef\u8986\u76d6\u5c42 */\n .md-toc-overlay {\n display: none;\n position: fixed;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n background: rgba(0, 0, 0, 0.3);\n z-index: 2;\n }\n\n .md-toc.expanded + .md-toc-overlay {\n display: block;\n }\n}\n\n/* \u5e73\u677f\u9002\u914d */\n@media screen and (min-width: 76.25em) and (max-width: 96.25em) {\n .md-toc {\n padding: 0.8rem;\n }\n\n .md-toc__list {\n margin-left: 0.8rem;\n }\n}\n
\u5b8c\u6574\u7684\u76ee\u5f55\u529f\u80fd\u5b9e\u73b0\uff1a
JavaScript// docs/javascripts/toc.js\nclass TableOfContents {\n constructor() {\n this.toc = document.querySelector('.md-toc');\n this.tocLinks = this.toc.querySelectorAll('.md-toc__link');\n this.headings = document.querySelectorAll('h1[id], h2[id], h3[id], h4[id], h5[id], h6[id]');\n this.intersectionObserver = null;\n this.isScrolling = false;\n this.scrollTimeout = null;\n\n this.init();\n }\n\n init() {\n this.setupIntersectionObserver();\n this.setupScrollSpy();\n this.setupToggle();\n this.setupMobileToggle();\n this.handleInitialHash();\n }\n\n handleInitialHash() {\n // \u5904\u7406\u9875\u9762\u52a0\u8f7d\u65f6\u7684 hash\n if (window.location.hash) {\n const id = window.location.hash.substring(1);\n const target = document.getElementById(id);\n if (target) {\n setTimeout(() => {\n target.scrollIntoView();\n this.highlightTocItem(id);\n }, 100);\n }\n }\n }\n\n setupMobileToggle() {\n // \u521b\u5efa\u79fb\u52a8\u7aef\u5f00\u5173\u6309\u94ae\n const toggleButton = document.createElement('button');\n toggleButton.className = 'md-toc-toggle';\n toggleButton.innerHTML = `\n <span class=\"md-toc-toggle__icon\">\n {% include \".icons/material/menu.svg\" %}\n </span>\n `;\n\n // \u521b\u5efa\u906e\u7f69\u5c42\n const overlay = document.createElement('div');\n overlay.className = 'md-toc-overlay';\n\n document.body.appendChild(toggleButton);\n document.body.appendChild(overlay);\n\n // \u7ed1\u5b9a\u4e8b\u4ef6\n toggleButton.addEventListener('click', () => {\n this.toc.classList.toggle('expanded');\n document.body.style.overflow = this.toc.classList.contains('expanded') ? 'hidden' : '';\n });\n\n overlay.addEventListener('click', () => {\n this.toc.classList.remove('expanded');\n document.body.style.overflow = '';\n });\n }\n\n updateTocHeight() {\n const lists = this.toc.querySelectorAll('.md-toc__list');\n lists.forEach(list => {\n if (list.parentElement.classList.contains('expanded')) {\n list.style.height = list.scrollHeight + 'px';\n }\n });\n }\n\n onResize() {\n // \u76d1\u542c\u7a97\u53e3\u5927\u5c0f\u53d8\u5316\uff0c\u66f4\u65b0\u76ee\u5f55\u9ad8\u5ea6\n window.addEventListener('resize', debounce(() => {\n this.updateTocHeight();\n }, 100));\n }\n}\n\n// \u5de5\u5177\u51fd\u6570\nfunction debounce(fn, delay) {\n let timer = null;\n return function(...args) {\n clearTimeout(timer);\n timer = setTimeout(() => fn.apply(this, args), delay);\n };\n}\n\n// \u521d\u59cb\u5316\u76ee\u5f55\ndocument.addEventListener('DOMContentLoaded', () => {\n new TableOfContents();\n});\n
\u4f7f\u7528\u8fd9\u4e9b\u4ee3\u7801\uff0c\u9700\u8981\u5728 mkdocs.yml \u4e2d\u6dfb\u52a0\u76f8\u5e94\u7684\u914d\u7f6e\uff1a
YAMLtheme:\n name: material\n custom_dir: docs/overrides\n features:\n - toc.integrate\n - toc.follow\n\nextra_css:\n - stylesheets/toc.css\n\nextra_javascript:\n - javascripts/toc.js\n
\u8fd9\u6837\u5c31\u5b8c\u6210\u4e86\u4e00\u4e2a\u529f\u80fd\u5b8c\u6574\u3001\u4ea4\u4e92\u53cb\u597d\u7684\u76ee\u5f55\u5bfc\u822a\u7cfb\u7edf\uff0c\u4e3b\u8981\u7279\u70b9\u5305\u62ec\uff1a
- \u81ea\u52a8\u9ad8\u4eae\u5f53\u524d\u9605\u8bfb\u4f4d\u7f6e
- \u5e73\u6ed1\u6eda\u52a8\u5230\u76ee\u6807\u4f4d\u7f6e
- \u652f\u6301\u5c55\u5f00/\u6536\u8d77\u529f\u80fd
- \u79fb\u52a8\u7aef\u81ea\u9002\u5e94\u5e03\u5c40
- \u4f18\u96c5\u7684\u52a8\u753b\u6548\u679c
- \u826f\u597d\u7684\u53ef\u8bbf\u95ee\u6027\u652f\u6301
- \u6027\u80fd\u4f18\u5316\u8003\u8651
"},{"location":"Tools/Blog/Mkdocs_Material/#8","title":"8 \u63d2\u4ef6\u4f7f\u7528","text":""},{"location":"Tools/Blog/Mkdocs_Material/#81","title":"8.1 \u5fc5\u5907\u63d2\u4ef6","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8111-search","title":"8.1.1.1 search","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8112","title":"8.1.1.2 \u57fa\u7840\u914d\u7f6e","text":" - \u5b89\u88c5\u641c\u7d22\u63d2\u4ef6\uff1a
Bashpip install mkdocs-material\n
- \u914d\u7f6e\u641c\u7d22\uff1a
YAML# mkdocs.yml\nplugins:\n - search:\n # \u641c\u7d22\u7d22\u5f15\u8bbe\u7f6e\n lang: \n - en\n - zh\n separator: '[\\s\\-\\.]+' # \u5206\u8bcd\u5206\u9694\u7b26\n min_search_length: 2 # \u6700\u5c0f\u641c\u7d22\u957f\u5ea6\n prebuild_index: true # \u9884\u6784\u5efa\u7d22\u5f15\n\n # \u641c\u7d22\u5185\u5bb9\u914d\u7f6e\n indexing:\n full_sections: true # \u7d22\u5f15\u5b8c\u6574\u7ae0\u8282\n headings: true # \u7d22\u5f15\u6807\u9898\n content: true # \u7d22\u5f15\u5185\u5bb9\n tags: true # \u7d22\u5f15\u6807\u7b7e\n\n # \u641c\u7d22\u7ed3\u679c\u6392\u5e8f\n scoring:\n title_boost: 10 # \u6807\u9898\u6743\u91cd\n heading_boost: 5 # \u6807\u9898\u6743\u91cd\n content_boost: 1 # \u5185\u5bb9\u6743\u91cd\n
- \u641c\u7d22\u4e3b\u9898\u914d\u7f6e\uff1a
YAMLtheme:\n features:\n - search.highlight # \u641c\u7d22\u9ad8\u4eae\n - search.share # \u641c\u7d22\u5206\u4eab\n - search.suggest # \u641c\u7d22\u5efa\u8bae\n\n # \u641c\u7d22\u754c\u9762\u6587\u5b57\n language: zh\n palette:\n - search:\n placeholder: \u641c\u7d22\u6587\u6863\n result:\n no_results_text: \u672a\u627e\u5230\u76f8\u5173\u7ed3\u679c\n searching_text: \u6b63\u5728\u641c\u7d22...\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8113","title":"8.1.1.3 \u4e2d\u6587\u641c\u7d22","text":" - \u4e2d\u6587\u5206\u8bcd\u914d\u7f6e\uff1a
YAMLplugins:\n - search:\n jieba_dict: dict.txt # \u81ea\u5b9a\u4e49\u8bcd\u5178\u8def\u5f84\n jieba_dict_user: user_dict.txt # \u7528\u6237\u8bcd\u5178\u8def\u5f84\n\n # \u4e2d\u6587\u5206\u8bcd\u89c4\u5219\n separator: '[\uff0c\u3002\uff01\uff1f,!?]+'\n\n # \u4e2d\u6587\u505c\u7528\u8bcd\n stopwords:\n - \u7684\n - \u4e86\n - \u548c\n - \u662f\n - \u5c31\n - \u90fd\n - \u800c\n - \u53ca\n - \u4e0e\n - \u8fd9\n
- \u81ea\u5b9a\u4e49\u8bcd\u5178\u793a\u4f8b\uff08dict.txt\uff09\uff1a
Text Only\u6280\u672f\u6587\u6863 5\n\u5f00\u53d1\u6307\u5357 5\n\u6700\u4f73\u5b9e\u8df5 5\n\u4f7f\u7528\u6559\u7a0b 5\n\u914d\u7f6e\u8bf4\u660e 5\n\u5e38\u89c1\u95ee\u9898 5\n
- \u5206\u8bcd\u4f18\u5316\u811a\u672c\uff1a
Python# docs/search_optimize.py\nimport jieba\nimport json\nfrom pathlib import Path\n\ndef optimize_search_index(index_file, dict_file):\n # \u52a0\u8f7d\u81ea\u5b9a\u4e49\u8bcd\u5178\n jieba.load_userdict(dict_file)\n\n # \u8bfb\u53d6\u641c\u7d22\u7d22\u5f15\n with open(index_file, 'r', encoding='utf-8') as f:\n index = json.load(f)\n\n # \u4f18\u5316\u5206\u8bcd\n for doc in index['docs']:\n # \u5206\u8bcd\u5904\u7406\u6807\u9898\n title_words = list(jieba.cut(doc['title']))\n doc['title'] = ' '.join(title_words)\n\n # \u5206\u8bcd\u5904\u7406\u5185\u5bb9\n if 'text' in doc:\n text_words = list(jieba.cut(doc['text']))\n doc['text'] = ' '.join(text_words)\n\n # \u4fdd\u5b58\u4f18\u5316\u540e\u7684\u7d22\u5f15\n with open(index_file, 'w', encoding='utf-8') as f:\n json.dump(index, f, ensure_ascii=False, indent=2)\n\nif __name__ == '__main__':\n index_file = 'site/search/search_index.json'\n dict_file = 'docs/dict.txt'\n optimize_search_index(index_file, dict_file)\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8114","title":"8.1.1.4 \u641c\u7d22\u4f18\u5316","text":" - \u7d22\u5f15\u4f18\u5316\uff1a
Python# docs/search_index_optimizer.py\nfrom pathlib import Path\nimport json\nimport re\n\nclass SearchIndexOptimizer:\n def __init__(self, index_path):\n self.index_path = Path(index_path)\n self.index = self.load_index()\n\n def load_index(self):\n with open(self.index_path, 'r', encoding='utf-8') as f:\n return json.load(f)\n\n def save_index(self):\n with open(self.index_path, 'w', encoding='utf-8') as f:\n json.dump(self.index, f, ensure_ascii=False, indent=2)\n\n def optimize(self):\n # \u6e05\u7406\u65e0\u7528\u6570\u636e\n self._clean_data()\n # \u4f18\u5316\u6587\u672c\n self._optimize_text()\n # \u6dfb\u52a0\u6743\u91cd\n self._add_weights()\n # \u6784\u5efa\u53cd\u5411\u7d22\u5f15\n self._build_inverted_index()\n # \u4fdd\u5b58\u4f18\u5316\u540e\u7684\u7d22\u5f15\n self.save_index()\n\n def _clean_data(self):\n # \u79fb\u9664\u7a7a\u6587\u6863\n self.index['docs'] = [\n doc for doc in self.index['docs']\n if doc.get('text') or doc.get('title')\n ]\n\n # \u79fb\u9664\u91cd\u590d\u6587\u6863\n seen = set()\n unique_docs = []\n for doc in self.index['docs']:\n key = f\"{doc['location']}:{doc['title']}\"\n if key not in seen:\n seen.add(key)\n unique_docs.append(doc)\n self.index['docs'] = unique_docs\n\n def _optimize_text(self):\n for doc in self.index['docs']:\n # \u6e05\u7406HTML\u6807\u7b7e\n if 'text' in doc:\n doc['text'] = re.sub(r'<[^>]+>', '', doc['text'])\n\n # \u538b\u7f29\u7a7a\u767d\u5b57\u7b26\n for field in ['title', 'text']:\n if field in doc:\n doc[field] = ' '.join(doc[field].split())\n\n def _add_weights(self):\n for doc in self.index['docs']:\n # \u57fa\u7840\u6743\u91cd\n doc['weight'] = 1.0\n\n # \u6807\u9898\u957f\u5ea6\u6743\u91cd\n if 'title' in doc:\n title_len = len(doc['title'])\n doc['weight'] *= 1 + (1.0 / (1 + title_len))\n\n # \u6587\u672c\u957f\u5ea6\u6743\u91cd\n if 'text' in doc:\n text_len = len(doc['text'])\n doc['weight'] *= 1 + (100.0 / (1 + text_len))\n\n # URL\u6df1\u5ea6\u6743\u91cd\n depth = doc['location'].count('/')\n doc['weight'] *= 1 + (1.0 / (1 + depth))\n\n def _build_inverted_index(self):\n # \u6784\u5efa\u53cd\u5411\u7d22\u5f15\n inverted_index = {}\n for i, doc in enumerate(self.index['docs']):\n terms = set()\n\n # \u6dfb\u52a0\u6807\u9898\u8bcd\n if 'title' in doc:\n terms.update(doc['title'].lower().split())\n\n # \u6dfb\u52a0\u6587\u672c\u8bcd\n if 'text' in doc:\n terms.update(doc['text'].lower().split())\n\n # \u66f4\u65b0\u53cd\u5411\u7d22\u5f15\n for term in terms:\n if term not in inverted_index:\n inverted_index[term] = []\n inverted_index[term].append({\n 'id': i,\n 'weight': doc['weight']\n })\n\n self.index['inverted_index'] = inverted_index\n\n# \u4f7f\u7528\u4f18\u5316\u5668\noptimizer = SearchIndexOptimizer('site/search/search_index.json')\noptimizer.optimize()\n
- \u641c\u7d22\u6027\u80fd\u76d1\u63a7\uff1a
JavaScript// docs/javascripts/search-monitor.js\nclass SearchMonitor {\n constructor() {\n this.metrics = {\n searches: 0,\n avgTime: 0,\n slowest: 0,\n fastest: Infinity\n };\n\n this.init();\n }\n\n init() {\n this.monitorSearchInput();\n this.monitorSearchResults();\n this.setupReporting();\n }\n\n monitorSearchInput() {\n const searchInput = document.querySelector('.md-search__input');\n let startTime;\n\n searchInput?.addEventListener('input', () => {\n startTime = performance.now();\n });\n\n searchInput?.addEventListener('search', () => {\n const endTime = performance.now();\n const duration = endTime - startTime;\n\n this.updateMetrics(duration);\n });\n }\n\n updateMetrics(duration) {\n this.metrics.searches++;\n this.metrics.avgTime = (\n (this.metrics.avgTime * (this.metrics.searches - 1) + duration) /\n this.metrics.searches\n );\n this.metrics.slowest = Math.max(this.metrics.slowest, duration);\n this.metrics.fastest = Math.min(this.metrics.fastest, duration);\n\n // \u53d1\u9001\u7edf\u8ba1\u6570\u636e\n this.sendMetrics();\n }\n\n sendMetrics() {\n // \u53ef\u4ee5\u53d1\u9001\u5230\u7edf\u8ba1\u670d\u52a1\n console.log('Search Metrics:', this.metrics);\n }\n}\n\n// \u521d\u59cb\u5316\u76d1\u63a7\nnew SearchMonitor();\n
"},{"location":"Tools/Blog/Mkdocs_Material/#812-glightbox","title":"8.1.2 glightbox","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8121","title":"8.1.2.1 \u56fe\u7247\u9884\u89c8","text":" - \u5b89\u88c5\u63d2\u4ef6\uff1a
Bashpip install mkdocs-glightbox\n
- \u57fa\u7840\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nplugins:\n - glightbox:\n # \u57fa\u7840\u8bbe\u7f6e\n auto_caption: true # \u81ea\u52a8\u6dfb\u52a0\u6807\u9898\n caption_position: bottom # \u6807\u9898\u4f4d\u7f6e\n display_description: true # \u663e\u793a\u63cf\u8ff0\n\n # \u89e6\u6478\u8bbe\u7f6e\n touchNavigation: true # \u89e6\u6478\u5bfc\u822a\n loop: true # \u5faa\u73af\u6d4f\u89c8\n effect: zoom # \u8fc7\u6e21\u6548\u679c\n\n # \u56fe\u7247\u8bbe\u7f6e\n width: 100% # \u56fe\u7247\u5bbd\u5ea6\n height: auto # \u56fe\u7247\u9ad8\u5ea6\n zoomable: true # \u542f\u7528\u7f29\u653e\n draggable: true # \u542f\u7528\u62d6\u52a8\n
- \u81ea\u5b9a\u4e49\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/glightbox.css */\n.glightbox-clean {\n /* \u5bb9\u5668\u6837\u5f0f */\n --glow-padding: 2rem;\n --glow-bg: rgba(0, 0, 0, 0.95);\n\n /* \u63a7\u5236\u6309\u94ae */\n --glow-btn-color: rgba(255, 255, 255, 0.8);\n --glow-btn-hover: #fff;\n\n /* \u52a0\u8f7d\u52a8\u753b */\n --glow-loading-bg: rgba(0, 0, 0, 0.5);\n --glow-loading-width: 40px;\n --glow-loading-height: 40px;\n}\n\n/* \u6807\u9898\u6837\u5f0f */\n.glightbox-caption {\n font-family: var(--md-font-family);\n font-size: 0.9rem;\n padding: 1rem;\n background: rgba(0, 0, 0, 0.8);\n}\n\n/* \u63cf\u8ff0\u6837\u5f0f */\n.glightbox-description {\n font-size: 0.8rem;\n color: rgba(255, 255, 255, 0.7);\n margin-top: 0.5rem;\n}\n\n/* \u52a0\u8f7d\u52a8\u753b */\n.glightbox-loading {\n border: 3px solid rgba(255, 255, 255, 0.2);\n border-top-color: #fff;\n border-radius: 50%;\n animation: glow-spin 1s linear infinite;\n}\n\n@keyframes glow-spin {\n to { transform: rotate(360deg); }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8122","title":"8.1.2.2 \u753b\u5eca\u6a21\u5f0f","text":" - \u753b\u5eca\u914d\u7f6e\uff1a
YAMLplugins:\n - glightbox:\n # \u753b\u5eca\u8bbe\u7f6e\n gallery: true # \u542f\u7528\u753b\u5eca\n gallery_mode: true # \u753b\u5eca\u6a21\u5f0f\n gallery_trigger: click # \u89e6\u53d1\u65b9\u5f0f\n\n # \u7f29\u7565\u56fe\u8bbe\u7f6e\n thumb_width: 150 # \u7f29\u7565\u56fe\u5bbd\u5ea6\n thumb_height: 100 # \u7f29\u7565\u56fe\u9ad8\u5ea6\n thumb_fit: cover # \u7f29\u7565\u56fe\u9002\u5e94\u65b9\u5f0f\n
- \u753b\u5eca\u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/gallery.html -->\n<div class=\"gallery\">\n {% for image in page.meta.gallery %}\n <a href=\"{{ image.url }}\" \n class=\"gallery-item\"\n data-gallery=\"gallery\"\n data-glightbox=\"title: {{ image.title }}; description: {{ image.description }}\">\n <img src=\"{{ image.thumbnail or image.url }}\" \n alt=\"{{ image.title }}\"\n loading=\"lazy\">\n {% if image.title %}\n <div class=\"gallery-caption\">{{ image.title }}</div>\n {% endif %}\n </a>\n {% endfor %}\n</div>\n
- \u753b\u5eca\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/gallery.css */\n.gallery {\n display: grid;\n grid-template-columns: repeat(auto-fill, minmax(150px, 1fr));\n gap: 1rem;\n padding: 1rem;\n}\n\n.gallery-item {\n position: relative;\n overflow: hidden;\n border-radius: 4px;\n cursor: pointer;\n transition: transform 0.3s ease;\n}\n\n.gallery-item:hover {\n transform: translateY(-4px);\n}\n\n.gallery-item img {\n width: 100%;\n height: 100%;\n object-fit: cover;\n transition: transform 0.3s ease;\n}\n\n.gallery-item:hover img {\n transform: scale(1.1);\n}\n\n.gallery-caption {\n position: absolute;\n bottom: 0;\n left: 0;\n right: 0;\n padding: 0.5rem;\n background: rgba(0, 0, 0, 0.7);\n color: white;\n font-size: 0.8rem;\n transform: translateY(100%);\n transition: transform 0.3s ease;\n}\n\n.gallery-item:hover .gallery-caption {\n transform: translateY(0);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#82","title":"8.2 \u63a8\u8350\u63d2\u4ef6","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8211-git-revision-date","title":"8.2.1.1 git-revision-date","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8212","title":"8.2.1.2 \u65f6\u95f4\u663e\u793a","text":" - \u5b89\u88c5\u63d2\u4ef6\uff1a
Bashpip install mkdocs-git-revision-date-localized-plugin\n
- \u57fa\u7840\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nplugins:\n - git-revision-date-localized:\n enabled: true\n type: date # \u663e\u793a\u7c7b\u578b\uff1adate, datetime, iso_date, iso_datetime, timeago\n timezone: Asia/Shanghai # \u65f6\u533a\u8bbe\u7f6e\n locale: zh # \u672c\u5730\u5316\u8bed\u8a00\n fallback_to_build_date: true # \u65e0 git \u4fe1\u606f\u65f6\u4f7f\u7528\u6784\u5efa\u65f6\u95f4\n\n # \u65f6\u95f4\u683c\u5f0f\n enable_creation_date: true # \u663e\u793a\u521b\u5efa\u65f6\u95f4\n exclude:\n - index.md\n - 404.md\n
- \u65f6\u95f4\u663e\u793a\u6a21\u677f\uff1a
HTML<!-- docs/overrides/partials/date.html -->\n{% if page.meta.git_revision_date_localized %}\n<div class=\"page-date\">\n <!-- \u6700\u540e\u66f4\u65b0\u65f6\u95f4 -->\n <div class=\"date-item\">\n <span class=\"date-icon\">\n {% include \".icons/material/update.svg\" %}\n </span>\n <span class=\"date-text\">\n \u6700\u540e\u66f4\u65b0: {{ page.meta.git_revision_date_localized }}\n </span>\n </div>\n\n <!-- \u521b\u5efa\u65f6\u95f4 -->\n {% if page.meta.git_creation_date_localized %}\n <div class=\"date-item\">\n <span class=\"date-icon\">\n {% include \".icons/material/clock-outline.svg\" %}\n </span>\n <span class=\"date-text\">\n \u521b\u5efa\u65f6\u95f4: {{ page.meta.git_creation_date_localized }}\n </span>\n </div>\n {% endif %}\n</div>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8213","title":"8.2.1.3 \u66f4\u65b0\u8bb0\u5f55","text":" - \u66f4\u65b0\u8bb0\u5f55\u663e\u793a\uff1a
HTML<!-- docs/overrides/partials/revision.html -->\n{% if page.meta.git_revision_date_localized %}\n<div class=\"revision-history\">\n <h3>\u66f4\u65b0\u8bb0\u5f55</h3>\n <div class=\"revision-list\">\n {% for revision in page.meta.git_history %}\n <div class=\"revision-item\">\n <div class=\"revision-date\">\n {{ revision.date }}\n </div>\n <div class=\"revision-message\">\n {{ revision.message }}\n </div>\n </div>\n {% endfor %}\n </div>\n</div>\n
- \u6837\u5f0f\u914d\u7f6e\uff1a
CSS/* docs/stylesheets/revision.css */\n.revision-history {\n margin: 2rem 0;\n padding: 1rem;\n background: var(--md-code-bg-color);\n border-radius: 4px;\n}\n\n.revision-item {\n display: flex;\n padding: 0.5rem 0;\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.revision-date {\n flex: 0 0 200px;\n color: var(--md-default-fg-color--light);\n}\n\n.revision-message {\n flex: 1;\n}\n\n/* \u65f6\u95f4\u7ebf\u6837\u5f0f */\n.revision-item {\n position: relative;\n padding-left: 2rem;\n}\n\n.revision-item::before {\n content: '';\n position: absolute;\n left: 0;\n top: 1rem;\n width: 12px;\n height: 12px;\n background: var(--md-accent-fg-color);\n border-radius: 50%;\n}\n\n.revision-item::after {\n content: '';\n position: absolute;\n left: 5px;\n top: 1.5rem;\n bottom: -0.5rem;\n width: 2px;\n background: var(--md-default-fg-color--lightest);\n}\n\n.revision-item:last-child::after {\n display: none;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8214","title":"8.2.1.4 \u4f5c\u8005\u4fe1\u606f","text":" - \u4f5c\u8005\u4fe1\u606f\u914d\u7f6e\uff1a
YAMLplugins:\n - git-revision-date-localized:\n enabled: true\n type: date\n enable_authors: true # \u542f\u7528\u4f5c\u8005\u4fe1\u606f\n authors_file: authors.yaml # \u4f5c\u8005\u914d\u7f6e\u6587\u4ef6\n
- \u4f5c\u8005\u914d\u7f6e\u6587\u4ef6 (authors. yaml)\uff1a
YAMLauthors:\n john:\n name: John Doe\n email: john@example.com\n avatar: assets/authors/john.jpg\n bio: \u8d44\u6df1\u6280\u672f\u4f5c\u8005\n social:\n github: johndoe\n twitter: johndoe\n\n jane:\n name: Jane Smith\n email: jane@example.com\n avatar: assets/authors/jane.jpg\n bio: \u524d\u7aef\u5f00\u53d1\u4e13\u5bb6\n social:\n github: janesmith\n linkedin: janesmith\n
- \u4f5c\u8005\u4fe1\u606f\u663e\u793a\uff1a
HTML<!-- docs/overrides/partials/author.html -->\n{% if page.meta.git_authors %}\n<div class=\"page-authors\">\n {% for author in page.meta.git_authors %}\n <div class=\"author-card\">\n <!-- \u4f5c\u8005\u5934\u50cf -->\n <div class=\"author-avatar\">\n {% if author.avatar %}\n <img src=\"{{ author.avatar }}\" alt=\"{{ author.name }}\">\n {% else %}\n <div class=\"avatar-placeholder\">\n {{ author.name[0] }}\n </div>\n {% endif %}\n </div>\n\n <!-- \u4f5c\u8005\u4fe1\u606f -->\n <div class=\"author-info\">\n <h4 class=\"author-name\">{{ author.name }}</h4>\n {% if author.bio %}\n <p class=\"author-bio\">{{ author.bio }}</p>\n {% endif %}\n\n <!-- \u793e\u4ea4\u94fe\u63a5 -->\n {% if author.social %}\n <div class=\"author-social\">\n {% for platform, username in author.social.items() %}\n <a href=\"https://{{ platform }}.com/{{ username }}\" \n target=\"_blank\"\n class=\"social-link\">\n {% include \".icons/material/\" ~ platform ~ \".svg\" %}\n </a>\n {% endfor %}\n </div>\n {% endif %}\n </div>\n </div>\n {% endfor %}\n</div>\n
- \u4f5c\u8005\u4fe1\u606f\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/author.css */\n.page-authors {\n margin: 2rem 0;\n display: grid;\n grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));\n gap: 1rem;\n}\n\n.author-card {\n display: flex;\n padding: 1rem;\n background: var(--md-code-bg-color);\n border-radius: 8px;\n transition: transform 0.2s ease;\n}\n\n.author-card:hover {\n transform: translateY(-2px);\n}\n\n.author-avatar {\n flex: 0 0 80px;\n margin-right: 1rem;\n}\n\n.author-avatar img {\n width: 80px;\n height: 80px;\n border-radius: 50%;\n object-fit: cover;\n}\n\n.avatar-placeholder {\n width: 80px;\n height: 80px;\n border-radius: 50%;\n background: var(--md-accent-fg-color);\n color: white;\n display: flex;\n align-items: center;\n justify-content: center;\n font-size: 2rem;\n}\n\n.author-info {\n flex: 1;\n}\n\n.author-name {\n margin: 0 0 0.5rem;\n font-size: 1.1rem;\n}\n\n.author-bio {\n color: var(--md-default-fg-color--light);\n font-size: 0.9rem;\n margin: 0 0 0.5rem;\n}\n\n.author-social {\n display: flex;\n gap: 0.5rem;\n}\n\n.social-link {\n color: var(--md-default-fg-color--light);\n transition: color 0.2s ease;\n}\n\n.social-link:hover {\n color: var(--md-accent-fg-color);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#822-minify","title":"8.2.2 minify","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8221-html","title":"8.2.2.1 HTML \u538b\u7f29","text":" - \u5b89\u88c5\u63d2\u4ef6\uff1a
Bashpip install mkdocs-minify-plugin\n
- \u914d\u7f6e\u538b\u7f29\uff1a
YAML# mkdocs.yml\nplugins:\n - minify:\n minify_html: true\n htmlmin_opts:\n # HTML\u538b\u7f29\u9009\u9879\n remove_comments: true # \u79fb\u9664\u6ce8\u91ca\n remove_empty_space: true # \u79fb\u9664\u7a7a\u767d\n remove_all_empty_space: true # \u79fb\u9664\u6240\u6709\u7a7a\u767d\n reduce_boolean_attributes: true # \u7b80\u5316\u5e03\u5c14\u5c5e\u6027\n remove_optional_tags: false # \u4fdd\u7559\u53ef\u9009\u6807\u7b7e\n remove_redundant_attributes: true # \u79fb\u9664\u5197\u4f59\u5c5e\u6027\n minify_js: true # \u538b\u7f29\u5185\u8054JS\n minify_css: true # \u538b\u7f29\u5185\u8054CSS\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8222-css","title":"8.2.2.2 CSS \u538b\u7f29","text":" - CSS \u538b\u7f29\u914d\u7f6e\uff1a
YAMLplugins:\n - minify:\n minify_css: true\n css_files:\n - stylesheets/extra.css\n - stylesheets/custom.css\n cssmin_opts:\n # CSS\u538b\u7f29\u9009\u9879\n level: 2 # \u538b\u7f29\u7ea7\u522b(1-2)\n compatibility: ie9 # \u517c\u5bb9\u6027\n format: beautify # \u683c\u5f0f\u5316\u8f93\u51fa\n
- CSS \u4f18\u5316\u811a\u672c\uff1a
Python# docs/tools/css_optimizer.py\nimport re\nfrom pathlib import Path\nfrom csscompressor import compress\n\nclass CSSOptimizer:\n def __init__(self, input_dir, output_dir):\n self.input_dir = Path(input_dir)\n self.output_dir = Path(output_dir)\n self.output_dir.mkdir(exist_ok=True)\n\n def optimize(self):\n \"\"\"\u4f18\u5316\u6240\u6709CSS\u6587\u4ef6\"\"\"\n for css_file in self.input_dir.glob('**/*.css'):\n # \u8bfb\u53d6CSS\u5185\u5bb9\n content = css_file.read_text(encoding='utf-8')\n\n # \u4f18\u5316CSS\n optimized = self.optimize_css(content)\n\n # \u4fdd\u5b58\u4f18\u5316\u540e\u7684\u6587\u4ef6\n output_file = self.output_dir / css_file.name\n output_file.write_text(optimized, encoding='utf-8')\n\n def optimize_css(self, content):\n \"\"\"\u4f18\u5316\u5355\u4e2aCSS\u6587\u4ef6\u5185\u5bb9\"\"\"\n # \u79fb\u9664\u6ce8\u91ca\n content = re.sub(r'/\\*[\\s\\S]*?\\*/', '', content)\n\n # \u79fb\u9664\u591a\u4f59\u7a7a\u767d\n content = re.sub(r'\\s+', ' ', content)\n\n # \u538b\u7f29CSS\n content = compress(content)\n\n return content\n\n# \u4f7f\u7528\u4f18\u5316\u5668\noptimizer = CSSOptimizer(\n input_dir='docs/stylesheets',\n output_dir='docs/stylesheets/min'\n)\noptimizer.optimize()\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8223-js","title":"8.2.2.3 JS \u538b\u7f29","text":" - JS \u538b\u7f29\u914d\u7f6e\uff1a
YAMLplugins:\n - minify:\n minify_js: true\n js_files:\n - javascripts/extra.js\n - javascripts/custom.js\n jsmin_opts:\n # JS\u538b\u7f29\u9009\u9879\n mangle: true # \u6df7\u6dc6\u53d8\u91cf\u540d\n compress: true # \u538b\u7f29\u4ee3\u7801\n output:\n beautify: false # \u4e0d\u7f8e\u5316\u8f93\u51fa\n
- JS \u4f18\u5316\u811a\u672c\uff1a
Python# docs/tools/js_optimizer.py\nimport re\nfrom pathlib import Path\nimport terser\n\nclass JSOptimizer:\n def __init__(self, input_dir, output_dir):\n self.input_dir = Path(input_dir)\n self.output_dir = Path(output_dir)\n self.output_dir.mkdir(exist_ok=True)\n\n def optimize(self):\n \"\"\"\u4f18\u5316\u6240\u6709JS\u6587\u4ef6\"\"\"\n for js_file in self.input_dir.glob('**/*.js'):\n # \u8bfb\u53d6JS\u5185\u5bb9\n content = js_file.read_text(encoding='utf-8')\n\n # \u4f18\u5316JS\n optimized = self.optimize_js(content)\n\n # \u4fdd\u5b58\u4f18\u5316\u540e\u7684\u6587\u4ef6\n output_file = self.output_dir / js_file.name\n output_file.write_text(optimized, encoding='utf-8')\n\n def optimize_js(self, content):\n \"\"\"\u4f18\u5316\u5355\u4e2aJS\u6587\u4ef6\u5185\u5bb9\"\"\"\n options = {\n 'compress': {\n 'pure_funcs': ['console.log'], # \u79fb\u9664console.log\n 'drop_debugger': True, # \u79fb\u9664debugger\n 'unsafe': True, # \u542f\u7528\u4e0d\u5b89\u5168\u4f18\u5316\n 'passes': 2 # \u4f18\u5316\u6b21\u6570\n },\n 'mangle': {\n 'toplevel': True, # \u6df7\u6dc6\u9876\u7ea7\u4f5c\u7528\u57df\n 'eval': True # \u6df7\u6dc6eval\u4e2d\u7684\u53d8\u91cf\n }\n }\n\n # \u4f7f\u7528terser\u538b\u7f29\n result = terser.minify(content, options)\n return result['code']\n\n# \u4f7f\u7528\u4f18\u5316\u5668\noptimizer = JSOptimizer(\n input_dir='docs/javascripts',\n output_dir='docs/javascripts/min'\n)\noptimizer.optimize()\n
\u8fd9\u4e9b\u914d\u7f6e\u548c\u811a\u672c\u53ef\u4ee5\u6709\u6548\u51cf\u5c0f\u7ad9\u70b9\u8d44\u6e90\u5927\u5c0f\uff0c\u63d0\u5347\u52a0\u8f7d\u901f\u5ea6\u3002\u8bb0\u5f97\u5728\u751f\u4ea7\u73af\u5883\u4e2d\u4f7f\u7528\u538b\u7f29\u540e\u7684\u8d44\u6e90\u3002
"},{"location":"Tools/Blog/Mkdocs_Material/#823-social","title":"8.2.3 social","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8231","title":"8.2.3.1 \u793e\u4ea4\u94fe\u63a5","text":" - \u5b89\u88c5\u63d2\u4ef6\uff1a
Bashpip install mkdocs-social-plugin\n
- \u57fa\u7840\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nplugins:\n - social:\n enabled: true\n # \u793e\u4ea4\u5e73\u53f0\u914d\u7f6e\n cards: true # \u542f\u7528\u793e\u4ea4\u5361\u7247\n cards_color: # \u5361\u7247\u989c\u8272\n fill: \"#0FF1CE\" # \u80cc\u666f\u8272\n text: \"#FFFFFF\" # \u6587\u5b57\u8272\n cards_font: Roboto # \u5361\u7247\u5b57\u4f53\n\nextra:\n social:\n - icon: material/github\n link: https://github.com/username\n name: GitHub\n - icon: material/twitter\n link: https://twitter.com/username\n name: Twitter\n - icon: material/linkedin\n link: https://linkedin.com/in/username\n name: LinkedIn\n
- \u793e\u4ea4\u94fe\u63a5\u6a21\u677f\uff1a
HTML<!-- docs/overrides/partials/social.html -->\n{% if config.extra.social %}\n<div class=\"social-links\">\n {% for social in config.extra.social %}\n <a href=\"{{ social.link }}\" \n target=\"_blank\"\n rel=\"noopener\"\n title=\"{{ social.name }}\"\n class=\"social-link\">\n <span class=\"social-icon\">\n {% include \".icons/\" ~ social.icon ~ \".svg\" %}\n </span>\n <span class=\"social-name\">{{ social.name }}</span>\n </a>\n {% endfor %}\n</div>\n{% endif %}\n
\u6837\u5f0f\u914d\u7f6e\uff1a
CSS/* docs/stylesheets/social.css */\n.social-links {\n display: flex;\n gap: 1rem;\n margin: 2rem 0;\n}\n\n.social-link {\n display: flex;\n align-items: center;\n padding: 0.5rem 1rem;\n color: var(--md-default-fg-color);\n text-decoration: none;\n background: var(--md-code-bg-color);\n border-radius: 2rem;\n transition: all 0.2s ease;\n}\n\n.social-link:hover {\n transform: translateY(-2px);\n color: var(--md-accent-fg-color);\n background: var(--md-code-bg-color--light);\n}\n\n.social-icon {\n margin-right: 0.5rem;\n width: 1.2rem;\n height: 1.2rem;\n}\n\n.social-name {\n font-size: 0.9rem;\n}\n\n/* \u52a8\u753b\u6548\u679c */\n.social-link .social-icon {\n transition: transform 0.2s ease;\n}\n\n.social-link:hover .social-icon {\n transform: scale(1.2);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8232","title":"8.2.3.2 \u5206\u4eab\u529f\u80fd","text":" - \u5206\u4eab\u529f\u80fd\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nextra:\n social_share:\n - platform: twitter\n text: \u5206\u4eab\u5230 Twitter\n icon: material/twitter\n - platform: facebook\n text: \u5206\u4eab\u5230 Facebook\n icon: material/facebook\n - platform: linkedin\n text: \u5206\u4eab\u5230 LinkedIn\n icon: material/linkedin\n - platform: weibo\n text: \u5206\u4eab\u5230\u5fae\u535a\n icon: material/sina-weibo\n
- \u5206\u4eab\u529f\u80fd\u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/partials/share.html -->\n<div class=\"page-share\">\n <h4>\u5206\u4eab\u6587\u7ae0</h4>\n <div class=\"share-buttons\">\n {% for share in config.extra.social_share %}\n <button class=\"share-button\" \n data-platform=\"{{ share.platform }}\"\n onclick=\"shareContent('{{ share.platform }}')\">\n <span class=\"share-icon\">\n {% include \".icons/\" ~ share.icon ~ \".svg\" %}\n </span>\n <span class=\"share-text\">{{ share.text }}</span>\n </button>\n {% endfor %}\n </div>\n</div>\n\n<script>\nfunction shareContent(platform) {\n const url = encodeURIComponent(window.location.href);\n const title = encodeURIComponent(document.title);\n const text = encodeURIComponent(document.querySelector('meta[name=\"description\"]')?.content || '');\n\n let shareUrl;\n switch(platform) {\n case 'twitter':\n shareUrl = `https://twitter.com/intent/tweet?url=${url}&text=${title}`;\n break;\n case 'facebook':\n shareUrl = `https://www.facebook.com/sharer/sharer.php?u=${url}`;\n break;\n case 'linkedin':\n shareUrl = `https://www.linkedin.com/sharing/share-offsite/?url=${url}`;\n break;\n case 'weibo':\n shareUrl = `http://service.weibo.com/share/share.php?url=${url}&title=${title}`;\n break;\n }\n\n window.open(shareUrl, '_blank', 'width=600,height=400');\n}\n</script>\n
- \u5206\u4eab\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/share.css */\n.page-share {\n margin: 2rem 0;\n padding: 1rem;\n background: var(--md-code-bg-color);\n border-radius: 8px;\n}\n\n.share-buttons {\n display: flex;\n flex-wrap: wrap;\n gap: 0.5rem;\n margin-top: 1rem;\n}\n\n.share-button {\n display: flex;\n align-items: center;\n padding: 0.5rem 1rem;\n border: none;\n border-radius: 2rem;\n background: var(--md-default-bg-color);\n color: var(--md-default-fg-color);\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.share-button:hover {\n background: var(--md-accent-fg-color);\n color: white;\n}\n\n.share-icon {\n margin-right: 0.5rem;\n width: 1.2rem;\n height: 1.2rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8233","title":"8.2.3.3 \u5173\u6ce8\u6309\u94ae","text":" - \u5173\u6ce8\u6309\u94ae\u914d\u7f6e\uff1a
YAMLextra:\n follow_buttons:\n - platform: github\n username: your-username\n icon: material/github\n text: \u5173\u6ce8\u6211\u7684 GitHub\n - platform: twitter\n username: your-username\n icon: material/twitter\n text: \u5173\u6ce8\u6211\u7684 Twitter\n
- \u5173\u6ce8\u6309\u94ae\u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/partials/follow.html -->\n<div class=\"follow-buttons\">\n {% for btn in config.extra.follow_buttons %}\n <a href=\"https://{{ btn.platform }}.com/{{ btn.username }}\"\n target=\"_blank\"\n class=\"follow-button\"\n data-platform=\"{{ btn.platform }}\">\n <span class=\"follow-icon\">\n {% include \".icons/\" ~ btn.icon ~ \".svg\" %}\n </span>\n <span class=\"follow-text\">{{ btn.text }}</span>\n <span class=\"follow-count\" id=\"{{ btn.platform }}-count\">\n <!-- \u5c06\u901a\u8fc7API\u52a8\u6001\u66f4\u65b0 -->\n </span>\n </a>\n {% endfor %}\n</div>\n\n<script>\nasync function updateFollowCounts() {\n const buttons = document.querySelectorAll('.follow-button');\n\n for (const button of buttons) {\n const platform = button.dataset.platform;\n const username = button.href.split('/').pop();\n\n // \u83b7\u53d6\u5173\u6ce8\u6570\n const count = await getFollowCount(platform, username);\n\n // \u66f4\u65b0\u663e\u793a\n const countElement = button.querySelector('.follow-count');\n if (countElement && count) {\n countElement.textContent = formatCount(count);\n }\n }\n}\n\nasync function getFollowCount(platform, username) {\n switch (platform) {\n case 'github':\n const response = await fetch(`https://api.github.com/users/${username}`);\n const data = await response.json();\n return data.followers;\n\n // \u5176\u4ed6\u5e73\u53f0API\u5b9e\u73b0...\n default:\n return null;\n }\n}\n\nfunction formatCount(count) {\n if (count >= 1000000) {\n return (count / 1000000).toFixed(1) + 'M';\n }\n if (count >= 1000) {\n return (count / 1000).toFixed(1) + 'K';\n }\n return count.toString();\n}\n\n// \u521d\u59cb\u5316\nupdateFollowCounts();\n</script>\n
- \u5173\u6ce8\u6309\u94ae\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/follow.css */\n.follow-buttons {\n display: flex;\n flex-wrap: wrap;\n gap: 1rem;\n margin: 2rem 0;\n}\n\n.follow-button {\n display: flex;\n align-items: center;\n padding: 0.5rem 1rem;\n text-decoration: none;\n background: var(--md-code-bg-color);\n border-radius: 2rem;\n transition: all 0.2s ease;\n}\n\n.follow-button:hover {\n transform: translateY(-2px);\n}\n\n/* \u5e73\u53f0\u7279\u5b9a\u6837\u5f0f */\n.follow-button[data-platform=\"github\"] {\n color: #333;\n}\n\n.follow-button[data-platform=\"github\"]:hover {\n background: #333;\n color: white;\n}\n\n.follow-button[data-platform=\"twitter\"] {\n color: #1DA1F2;\n}\n\n.follow-button[data-platform=\"twitter\"]:hover {\n background: #1DA1F2;\n color: white;\n}\n\n.follow-icon {\n margin-right: 0.5rem;\n width: 1.2rem;\n height: 1.2rem;\n}\n\n.follow-count {\n margin-left: 0.5rem;\n padding: 0.2rem 0.5rem;\n background: rgba(0, 0, 0, 0.1);\n border-radius: 1rem;\n font-size: 0.8rem;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#824-tags","title":"8.2.4 tags","text":""},{"location":"Tools/Blog/Mkdocs_Material/#8241","title":"8.2.4.1 \u6807\u7b7e\u7cfb\u7edf","text":" - \u5b89\u88c5\u63d2\u4ef6\uff1a
Bashpip install mkdocs-tags-plugin\n
- \u914d\u7f6e\u6807\u7b7e\uff1a
YAML# mkdocs.yml\nplugins:\n - tags:\n tags_file: tags.md\n tags_allowed: # \u5141\u8bb8\u7684\u6807\u7b7e\u5217\u8868\n - Python\n - JavaScript\n - CSS\n - HTML\n tags_extra_files:\n cloud: tag_cloud.md # \u6807\u7b7e\u4e91\u9875\u9762\n list: tag_list.md # \u6807\u7b7e\u5217\u8868\u9875\u9762\n
- \u5728\u6587\u6863\u4e2d\u4f7f\u7528\u6807\u7b7e\uff1a
Markdown---\ntags:\n - Python\n - \u6559\u7a0b\n---\n\n# Python \u5165\u95e8\u6559\u7a0b\n
- \u6807\u7b7e\u663e\u793a\u7ec4\u4ef6\uff1a
HTML<!-- docs/overrides/partials/tags.html -->\n{% if page.meta.tags %}\n<div class=\"page-tags\">\n {% for tag in page.meta.tags %}\n <a href=\"{{ base_url }}/tags/#{{ tag|lower }}\" class=\"tag\">\n <span class=\"tag-icon\">\n {% include \".icons/material/tag.svg\" %}\n </span>\n <span class=\"tag-text\">{{ tag }}</span>\n </a>\n {% endfor %}\n</div>\n{% endif %}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8242","title":"8.2.4.2 \u6807\u7b7e\u4e91","text":" - \u6807\u7b7e\u4e91\u5b9e\u73b0\uff1a
Python# docs/plugins/tags/cloud.py\nfrom collections import Counter\nimport math\n\nclass TagCloud:\n def __init__(self, tags):\n self.tags = Counter(tags)\n self.min_size = 0.8\n self.max_size = 2.0\n\n def get_tag_sizes(self):\n \"\"\"\u8ba1\u7b97\u6807\u7b7e\u5927\u5c0f\"\"\"\n if not self.tags:\n return {}\n\n # \u83b7\u53d6\u6700\u5927\u548c\u6700\u5c0f\u9891\u7387\n max_freq = max(self.tags.values())\n min_freq = min(self.tags.values())\n\n # \u8ba1\u7b97\u6bcf\u4e2a\u6807\u7b7e\u7684\u5927\u5c0f\n sizes = {}\n for tag, freq in self.tags.items():\n if max_freq == min_freq:\n sizes[tag] = (self.max_size + self.min_size) / 2\n else:\n size = self.min_size + (self.max_size - self.min_size) * \\\n ((freq - min_freq) / (max_freq - min_freq))\n sizes[tag] = round(size, 2)\n\n return sizes\n\ndef generate_tag_cloud(tags):\n \"\"\"\u751f\u6210\u6807\u7b7e\u4e91HTML\"\"\"\n cloud = TagCloud(tags)\n sizes = cloud.get_tag_sizes()\n\n html = ['<div class=\"tag-cloud\">']\n\n for tag, size in sizes.items():\n html.append(f'''\n <a href=\"#tag-{tag.lower()}\" \n class=\"tag-cloud-item\"\n style=\"font-size: {size}rem\">\n {tag}\n </a>\n ''')\n\n html.append('</div>')\n return '\\n'.join(html)\n
- \u6807\u7b7e\u4e91\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/tags.css */\n.tag-cloud {\n display: flex;\n flex-wrap: wrap;\n justify-content: center;\n gap: 1rem;\n padding: 2rem;\n}\n\n.tag-cloud-item {\n color: var(--md-default-fg-color);\n text-decoration: none;\n padding: 0.5rem 1rem;\n border-radius: 2rem;\n background: var(--md-code-bg-color);\n transition: all 0.2s ease;\n}\n\n.tag-cloud-item:hover {\n transform: scale(1.1);\n background: var(--md-accent-fg-color);\n color: white;\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#8243","title":"8.2.4.3 \u6807\u7b7e\u9875\u9762","text":" - \u6807\u7b7e\u5217\u8868\u9875\u9762\uff1a
Markdown# \u6807\u7b7e\u7d22\u5f15\n\n## \u6807\u7b7e\u4e91\n[TAGS_CLOUD]\n\n## \u6807\u7b7e\u5217\u8868\n[TAGS_LIST]\n
- \u6807\u7b7e\u9875\u9762\u751f\u6210\u5668\uff1a
Python# docs/plugins/tags/generator.py\nfrom pathlib import Path\nimport yaml\n\nclass TagPageGenerator:\n def __init__(self, config):\n self.config = config\n self.tags = {}\n\n def collect_tags(self):\n \"\"\"\u6536\u96c6\u6240\u6709\u6807\u7b7e\"\"\"\n docs_dir = Path(self.config['docs_dir'])\n\n for md_file in docs_dir.glob('**/*.md'):\n # \u8bfb\u53d6\u6587\u4ef6frontmatter\n content = md_file.read_text(encoding='utf-8')\n if content.startswith('---'):\n try:\n # \u89e3\u6790frontmatter\n _, frontmatter, _ = content.split('---', 2)\n meta = yaml.safe_load(frontmatter)\n\n if 'tags' in meta:\n # \u83b7\u53d6\u76f8\u5bf9\u8def\u5f84\n rel_path = md_file.relative_to(docs_dir)\n url = str(rel_path).replace('\\\\', '/')[:-3] # \u79fb\u9664.md\n\n # \u6dfb\u52a0\u5230\u6807\u7b7e\u7d22\u5f15\n for tag in meta['tags']:\n if tag not in self.tags:\n self.tags[tag] = []\n self.tags[tag].append({\n 'title': meta.get('title', url),\n 'url': url,\n 'description': meta.get('description', '')\n })\n except:\n continue\n\n def generate_tag_pages(self):\n \"\"\"\u751f\u6210\u6807\u7b7e\u76f8\u5173\u9875\u9762\"\"\"\n self.collect_tags()\n\n # \u751f\u6210\u6807\u7b7e\u5217\u8868\u9875\u9762\n if 'list' in self.config['tags_extra_files']:\n self.generate_list_page()\n\n # \u751f\u6210\u6bcf\u4e2a\u6807\u7b7e\u7684\u8be6\u60c5\u9875\u9762\n self.generate_tag_detail_pages()\n\n def generate_list_page(self):\n \"\"\"\u751f\u6210\u6807\u7b7e\u5217\u8868\u9875\u9762\"\"\"\n template = \"\"\"\n# \u6807\u7b7e\u5217\u8868\n\n{% for tag, posts in tags.items() %}\n## {{ tag }}\n\n{% for post in posts %}\n- [{{ post.title }}]({{ post.url }}){% if post.description %} - {{ post.description }}{% endif %}\n{% endfor %}\n\n{% endfor %}\n\"\"\"\n # \u4f7f\u7528 Jinja2 \u6e32\u67d3\u6a21\u677f\n from jinja2 import Template\n content = Template(template).render(tags=self.tags)\n\n # \u4fdd\u5b58\u9875\u9762\n output_file = Path(self.config['docs_dir']) / 'tags' / 'list.md'\n output_file.parent.mkdir(exist_ok=True)\n output_file.write_text(content, encoding='utf-8')\n\n def generate_tag_detail_pages(self):\n \"\"\"\u751f\u6210\u6bcf\u4e2a\u6807\u7b7e\u7684\u8be6\u60c5\u9875\u9762\"\"\"\n template = \"\"\"\n# \u6807\u7b7e: {{ tag }}\n\n## \u76f8\u5173\u6587\u7ae0\n\n{% for post in posts %}\n### [{{ post.title }}]({{ post.url }})\n{% if post.description %}\n{{ post.description }}\n{% endif %}\n{% endfor %}\n\n## \u76f8\u5173\u6807\u7b7e\n{% for related_tag in related_tags %}\n- [{{ related_tag }}](../{{ related_tag | lower }})\n{% endfor %}\n\"\"\"\n # \u6807\u7b7e\u76ee\u5f55\n tags_dir = Path(self.config['docs_dir']) / 'tags'\n tags_dir.mkdir(exist_ok=True)\n\n # \u4e3a\u6bcf\u4e2a\u6807\u7b7e\u751f\u6210\u9875\u9762\n for tag, posts in self.tags.items():\n # \u67e5\u627e\u76f8\u5173\u6807\u7b7e\n related_tags = self.find_related_tags(tag)\n\n # \u6e32\u67d3\u5185\u5bb9\n content = Template(template).render(\n tag=tag,\n posts=posts,\n related_tags=related_tags\n )\n\n # \u4fdd\u5b58\u9875\u9762\n output_file = tags_dir / f\"{tag.lower()}.md\"\n output_file.write_text(content, encoding='utf-8')\n\n def find_related_tags(self, current_tag):\n \"\"\"\u67e5\u627e\u76f8\u5173\u6807\u7b7e\"\"\"\n related = set()\n\n # \u83b7\u53d6\u5f53\u524d\u6807\u7b7e\u7684\u6240\u6709\u6587\u7ae0\n current_posts = self.tags[current_tag]\n\n # \u904d\u5386\u6240\u6709\u5176\u4ed6\u6807\u7b7e\n for tag, posts in self.tags.items():\n if tag == current_tag:\n continue\n\n # \u8ba1\u7b97\u6587\u7ae0\u4ea4\u96c6\n intersection = set(p['url'] for p in posts) & \\\n set(p['url'] for p in current_posts)\n\n # \u5982\u679c\u6709\u5171\u540c\u6587\u7ae0\uff0c\u8ba4\u4e3a\u662f\u76f8\u5173\u6807\u7b7e\n if intersection:\n related.add(tag)\n\n return list(related)\n
- \u6807\u7b7e\u9875\u9762\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/tags-page.css */\n/* \u6807\u7b7e\u5217\u8868\u9875\u9762 */\n.tags-list {\n margin: 2rem 0;\n}\n\n.tag-section {\n margin-bottom: 3rem;\n}\n\n.tag-section h2 {\n color: var(--md-accent-fg-color);\n border-bottom: 2px solid var(--md-accent-fg-color);\n padding-bottom: 0.5rem;\n}\n\n.tag-section .post-list {\n margin-left: 1rem;\n}\n\n.post-list-item {\n margin: 1rem 0;\n}\n\n.post-list-item .post-title {\n font-weight: 500;\n color: var(--md-default-fg-color);\n text-decoration: none;\n}\n\n.post-list-item .post-description {\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n margin-top: 0.2rem;\n}\n\n/* \u6807\u7b7e\u8be6\u60c5\u9875\u9762 */\n.tag-detail {\n padding: 2rem;\n}\n\n.tag-detail-header {\n text-align: center;\n margin-bottom: 3rem;\n}\n\n.tag-detail-title {\n font-size: 2rem;\n color: var(--md-primary-fg-color);\n}\n\n.tag-detail-count {\n color: var(--md-default-fg-color--light);\n}\n\n.related-posts {\n margin: 2rem 0;\n}\n\n.related-post {\n padding: 1rem;\n margin: 1rem 0;\n background: var(--md-code-bg-color);\n border-radius: 8px;\n transition: transform 0.2s ease;\n}\n\n.related-post:hover {\n transform: translateX(0.5rem);\n}\n\n.related-tags {\n display: flex;\n flex-wrap: wrap;\n gap: 0.5rem;\n margin: 2rem 0;\n}\n\n.related-tag {\n padding: 0.2rem 0.8rem;\n background: var(--md-code-bg-color);\n border-radius: 2rem;\n font-size: 0.9rem;\n color: var(--md-default-fg-color);\n text-decoration: none;\n transition: all 0.2s ease;\n}\n\n.related-tag:hover {\n background: var(--md-accent-fg-color);\n color: white;\n}\n
- \u9875\u9762\u4ea4\u4e92\u529f\u80fd\uff1a
JavaScript// docs/javascripts/tags.js\nclass TagsManager {\n constructor() {\n this.initializeFilters();\n this.setupSearch();\n this.setupSorting();\n }\n\n initializeFilters() {\n const tagList = document.querySelector('.tags-list');\n if (!tagList) return;\n\n // \u521b\u5efa\u8fc7\u6ee4\u5668\n const filterContainer = document.createElement('div');\n filterContainer.className = 'tags-filter';\n filterContainer.innerHTML = `\n <input type=\"text\" \n class=\"filter-input\" \n placeholder=\"\u641c\u7d22\u6807\u7b7e...\">\n <select class=\"sort-select\">\n <option value=\"name\">\u6309\u540d\u79f0\u6392\u5e8f</option>\n <option value=\"count\">\u6309\u6587\u7ae0\u6570\u91cf\u6392\u5e8f</option>\n </select>\n `;\n\n tagList.insertBefore(filterContainer, tagList.firstChild);\n }\n\n setupSearch() {\n const input = document.querySelector('.filter-input');\n if (!input) return;\n\n input.addEventListener('input', () => {\n const query = input.value.toLowerCase();\n const sections = document.querySelectorAll('.tag-section');\n\n sections.forEach(section => {\n const tag = section.querySelector('h2').textContent.toLowerCase();\n section.style.display = tag.includes(query) ? 'block' : 'none';\n });\n });\n }\n\n setupSorting() {\n const select = document.querySelector('.sort-select');\n if (!select) return;\n\n select.addEventListener('change', () => {\n const sections = Array.from(document.querySelectorAll('.tag-section'));\n const container = sections[0].parentElement;\n\n sections.sort((a, b) => {\n const aTag = a.querySelector('h2').textContent;\n const bTag = b.querySelector('h2').textContent;\n const aCount = this.getPostCount(a);\n const bCount = this.getPostCount(b);\n\n if (select.value === 'name') {\n return aTag.localeCompare(bTag);\n } else {\n return bCount - aCount;\n }\n });\n\n // \u91cd\u65b0\u63d2\u5165\u6392\u5e8f\u540e\u7684\u7ae0\u8282\n sections.forEach(section => container.appendChild(section));\n });\n }\n\n getPostCount(section) {\n return section.querySelectorAll('.post-list-item').length;\n }\n}\n\n// \u521d\u59cb\u5316\u6807\u7b7e\u7ba1\u7406\u5668\ndocument.addEventListener('DOMContentLoaded', () => {\n new TagsManager();\n});\n
\u4f7f\u7528\u4ee5\u4e0a\u4ee3\u7801\uff0c\u4f60\u53ef\u4ee5\u5b9e\u73b0\u4e00\u4e2a\u529f\u80fd\u5b8c\u6574\u7684\u6807\u7b7e\u7cfb\u7edf\uff0c\u5305\u62ec\uff1a
- \u6807\u7b7e\u4e91\u53ef\u89c6\u5316
- \u6807\u7b7e\u5217\u8868\u6d4f\u89c8
- \u6807\u7b7e\u8be6\u60c5\u9875\u9762
- \u76f8\u5173\u6807\u7b7e\u63a8\u8350
- \u6807\u7b7e\u641c\u7d22\u548c\u6392\u5e8f
- \u54cd\u5e94\u5f0f\u5e03\u5c40
\u914d\u7f6e\u6587\u4ef6\u9700\u8981\u6dfb\u52a0\u76f8\u5e94\u7684\u5f15\u7528\uff1a
YAML# mkdocs.yml\nextra_css:\n - stylesheets/tags.css\n - stylesheets/tags-page.css\n\nextra_javascript:\n - javascripts/tags.js\n
"},{"location":"Tools/Blog/Mkdocs_Material/#9","title":"9 \u529f\u80fd\u6269\u5c55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#91","title":"9.1 \u8bc4\u8bba\u7cfb\u7edf","text":""},{"location":"Tools/Blog/Mkdocs_Material/#911-giscus","title":"9.1.1 Giscus \u914d\u7f6e","text":" - \u57fa\u7840\u914d\u7f6e\uff1a
YAML# mkdocs.yml\nextra:\n comments:\n provider: giscus\n repo: username/repo\n repo_id: your_repo_id\n category: Comments\n category_id: your_category_id\n
- Giscus \u7ec4\u4ef6\uff1a
HTML<!-- docs/overrides/partials/comments.html -->\n{% if config.extra.comments.provider == 'giscus' %}\n<div class=\"comments-container\">\n <h3>\u8bc4\u8bba</h3>\n <div class=\"giscus\"></div>\n\n <script>\n function loadGiscus() {\n const script = document.createElement('script');\n script.src = 'https://giscus.app/client.js';\n script.setAttribute('data-repo', '{{ config.extra.comments.repo }}');\n script.setAttribute('data-repo-id', '{{ config.extra.comments.repo_id }}');\n script.setAttribute('data-category', '{{ config.extra.comments.category }}');\n script.setAttribute('data-category-id', '{{ config.extra.comments.category_id }}');\n script.setAttribute('data-mapping', 'pathname');\n script.setAttribute('data-reactions-enabled', '1');\n script.setAttribute('data-emit-metadata', '0');\n script.setAttribute('data-theme', 'preferred_color_scheme');\n script.setAttribute('crossorigin', 'anonymous');\n script.async = true;\n\n document.querySelector('.giscus').appendChild(script);\n }\n\n // \u5ef6\u8fdf\u52a0\u8f7d\u8bc4\u8bba\n if ('IntersectionObserver' in window) {\n const observer = new IntersectionObserver((entries) => {\n if (entries[0].isIntersecting) {\n loadGiscus();\n observer.disconnect();\n }\n });\n\n observer.observe(document.querySelector('.comments-container'));\n } else {\n loadGiscus();\n }\n </script>\n</div>\n{% endif %}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#912","title":"9.1.2 \u81ea\u5b9a\u4e49\u8bc4\u8bba\u6837\u5f0f","text":"CSS/* docs/stylesheets/comments.css */\n.comments-container {\n margin: 3rem 0;\n padding-top: 2rem;\n border-top: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.giscus {\n width: 100%;\n}\n\n/* Giscus \u4e3b\u9898\u9002\u914d */\n.giscus-frame {\n background-color: var(--md-default-bg-color);\n color: var(--md-default-fg-color);\n}\n\n/* \u6697\u8272\u6a21\u5f0f\u9002\u914d */\n[data-md-color-scheme=\"slate\"] .giscus-frame {\n background-color: var(--md-default-bg-color--dark);\n color: var(--md-default-fg-color--dark);\n}\n\n/* \u8bc4\u8bba\u6846\u6837\u5f0f */\n.giscus .gsc-comment-box {\n background-color: var(--md-code-bg-color);\n border-radius: 8px;\n padding: 1rem;\n}\n\n/* \u8bc4\u8bba\u5217\u8868\u6837\u5f0f */\n.giscus .gsc-comment {\n margin: 1rem 0;\n padding: 1rem;\n background-color: var(--md-code-bg-color);\n border-radius: 8px;\n transition: transform 0.2s ease;\n}\n\n.giscus .gsc-comment:hover {\n transform: translateX(4px);\n}\n\n/* \u6309\u94ae\u6837\u5f0f */\n.giscus .gsc-button {\n background-color: var(--md-primary-fg-color);\n color: var(--md-primary-bg-color);\n border: none;\n padding: 0.5rem 1rem;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.giscus .gsc-button:hover {\n background-color: var(--md-primary-fg-color--dark);\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#92","title":"9.2 \u6570\u636e\u7edf\u8ba1","text":""},{"location":"Tools/Blog/Mkdocs_Material/#921-google-analytics","title":"9.2.1 Google Analytics","text":" - GA4 \u914d\u7f6e\uff1a
YAML# mkdocs.yml\nextra:\n analytics:\n provider: google\n property: G-XXXXXXXXXX\n\n # \u4e8b\u4ef6\u8ffd\u8e2a\n events: true\n # \u7528\u6237\u884c\u4e3a\u8ffd\u8e2a\n behavior: true\n
- GA4 \u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/partials/analytics.html -->\n{% if config.extra.analytics.provider == 'google' %}\n<!-- Google Analytics -->\n<script async src=\"https://www.googletagmanager.com/gtag/js?id={{ config.extra.analytics.property }}\"></script>\n<script>\n window.dataLayer = window.dataLayer || [];\n function gtag(){dataLayer.push(arguments);}\n gtag('js', new Date());\n gtag('config', '{{ config.extra.analytics.property }}');\n\n // \u4e8b\u4ef6\u8ffd\u8e2a\n if ({{ config.extra.analytics.events|tojson }}) {\n document.addEventListener('DOMContentLoaded', function() {\n // \u70b9\u51fb\u4e8b\u4ef6\u8ffd\u8e2a\n document.addEventListener('click', function(e) {\n const target = e.target.closest('a');\n if (target) {\n gtag('event', 'click', {\n 'event_category': 'link',\n 'event_label': target.href\n });\n }\n });\n\n // \u6eda\u52a8\u4e8b\u4ef6\u8ffd\u8e2a\n let lastScrollDepth = 0;\n window.addEventListener('scroll', debounce(function() {\n const scrollDepth = Math.round(\n (window.scrollY + window.innerHeight) / \n document.documentElement.scrollHeight * 100\n );\n\n if (scrollDepth > lastScrollDepth) {\n gtag('event', 'scroll_depth', {\n 'depth': scrollDepth\n });\n lastScrollDepth = scrollDepth;\n }\n }, 500));\n\n // \u641c\u7d22\u4e8b\u4ef6\u8ffd\u8e2a\n const searchInput = document.querySelector('.md-search__input');\n if (searchInput) {\n searchInput.addEventListener('search', function() {\n gtag('event', 'search', {\n 'search_term': this.value\n });\n });\n }\n });\n }\n\n // \u7528\u6237\u884c\u4e3a\u8ffd\u8e2a\n if ({{ config.extra.analytics.behavior|tojson }}) {\n // \u9875\u9762\u505c\u7559\u65f6\u95f4\n let startTime = Date.now();\n window.addEventListener('beforeunload', function() {\n const duration = Math.round((Date.now() - startTime) / 1000);\n gtag('event', 'time_on_page', {\n 'duration': duration\n });\n });\n\n // \u590d\u5236\u884c\u4e3a\u8ffd\u8e2a\n document.addEventListener('copy', function() {\n gtag('event', 'content_copy');\n });\n }\n</script>\n{% endif %}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#922","title":"9.2.2 \u4e0d\u849c\u5b50\u7edf\u8ba1","text":" - \u4e0d\u849c\u5b50\u914d\u7f6e\uff1a
YAMLextra:\n analytics:\n busuanzi: true\n
- \u4e0d\u849c\u5b50\u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/partials/busuanzi.html -->\n{% if config.extra.analytics.busuanzi %}\n<script async src=\"//busuanzi.ibruce.info/busuanzi/2.3/busuanzi.pure.mini.js\"></script>\n\n<div class=\"page-views\">\n <!-- \u603b\u8bbf\u95ee\u91cf -->\n <span id=\"busuanzi_container_site_pv\">\n \u603b\u8bbf\u95ee\u91cf: <span id=\"busuanzi_value_site_pv\"></span>\u6b21\n </span>\n\n <!-- \u603b\u8bbf\u5ba2\u6570 -->\n <span id=\"busuanzi_container_site_uv\">\n \u8bbf\u5ba2\u6570: <span id=\"busuanzi_value_site_uv\"></span>\u4eba\n </span>\n\n <!-- \u9875\u9762\u8bbf\u95ee\u91cf -->\n <span id=\"busuanzi_container_page_pv\">\n \u672c\u6587\u8bbf\u95ee\u91cf: <span id=\"busuanzi_value_page_pv\"></span>\u6b21\n </span>\n</div>\n\n<style>\n.page-views {\n display: flex;\n gap: 1rem;\n margin: 1rem 0;\n padding: 0.5rem;\n background: var(--md-code-bg-color);\n border-radius: 4px;\n font-size: 0.9rem;\n}\n\n.page-views span {\n color: var(--md-default-fg-color--light);\n}\n\n.page-views span span {\n color: var(--md-accent-fg-color);\n font-weight: bold;\n}\n</style>\n{% endif %}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#93","title":"9.3 \u793e\u4ea4\u5206\u4eab","text":""},{"location":"Tools/Blog/Mkdocs_Material/#931","title":"9.3.1 \u5206\u4eab\u6309\u94ae","text":" - \u5206\u4eab\u914d\u7f6e\uff1a
YAMLextra:\n social_share:\n enabled: true\n platforms:\n - name: twitter\n icon: material/twitter\n color: '#1DA1F2'\n - name: facebook\n icon: material/facebook\n color: '#4267B2'\n - name: linkedin\n icon: material/linkedin\n color: '#0A66C2'\n - name: weibo\n icon: material/sina-weibo\n color: '#E6162D'\n
- \u5206\u4eab\u6309\u94ae\u5b9e\u73b0\uff1a
HTML<!-- docs/overrides/partials/share.html -->\n{% if config.extra.social_share.enabled %}\n<div class=\"share-container\">\n <h4>\u5206\u4eab\u6587\u7ae0</h4>\n <div class=\"share-buttons\">\n {% for platform in config.extra.social_share.platforms %}\n <button class=\"share-button\" \n data-platform=\"{{ platform.name }}\"\n style=\"--platform-color: {{ platform.color }}\"\n onclick=\"shareContent('{{ platform.name }}')\">\n <span class=\"share-icon\">\n {% include \".icons/\" ~ platform.icon ~ \".svg\" %}\n </span>\n <span class=\"share-text\">\u5206\u4eab\u5230 {{ platform.name|title }}</span>\n </button>\n {% endfor %}\n </div>\n</div>\n\n<style>\n.share-container {\n margin: 2rem 0;\n}\n\n.share-buttons {\n display: flex;\n flex-wrap: wrap;\n gap: 0.5rem;\n margin-top: 1rem;\n}\n\n.share-button {\n display: flex;\n align-items: center;\n padding: 0.5rem 1rem;\n border: none;\n border-radius: 4px;\n background: var(--md-code-bg-color);\n color: var(--platform-color);\n cursor: pointer;\n transition: all 0.2s ease;\n}\n\n.share-button:hover {\n background: var(--platform-color);\n color: white;\n transform: translateY(-2px);\n}\n\n.share-icon {\n margin-right: 0.5rem;\n}\n</style>\n
"},{"location":"Tools/Blog/Mkdocs_Material/#932","title":"9.3.2 \u5206\u4eab\u94fe\u63a5\u751f\u6210","text":"JavaScript// docs/javascripts/share.js\nclass ShareManager {\n constructor() {\n this.title = document.title;\n this.url = window.location.href;\n this.description = document\n .querySelector('meta[name=\"description\"]')\n ?.content || '';\n }\n\n generateShareUrl(platform) {\n const params = new URLSearchParams();\n switch (platform) {\n case 'twitter':\n return `https://twitter.com/intent/tweet?${\n params.set('text', this.title),\n params.set('url', this.url)\n }`;\n\n case 'facebook':\n return `https://www.facebook.com/sharer/sharer.php?${\n params.set('u', this.url)\n }`;\n\n case 'linkedin':\n return `https://www.linkedin.com/sharing/share-offsite/?${\n params.set('url', this.url)\n }`;\n\n case 'weibo':\n return `http://service.weibo.com/share/share.php?${\n params.set('title', this.title),\n params.set('url', this.url)\n }`;\n\n default:\n return null;\n }\n }\n\n async share(platform) {\n // \u68c0\u67e5\u662f\u5426\u652f\u6301\u539f\u751f\u5206\u4eab\n if (navigator.share && platform === 'native') {\n try {\n await navigator.share({\n title: this.title,\n text: this.description,\n url: this.url\n });\n return true;\n } catch (err) {\n console.warn('Native share failed:', err);\n return false;\n }\n }\n\n // \u4f7f\u7528\u5f39\u7a97\u5206\u4eab\n const url = this.generateShareUrl(platform);\n if (url) {\n window.open(url, '_blank', 'width=600,height=400');\n return true;\n }\n\n return false;\n }\n\n // \u81ea\u5b9a\u4e49\u5206\u4eab\u5185\u5bb9\n setContent(title, description) {\n this.title = title;\n this.description = description;\n }\n}\n\n// \u5168\u5c40\u5206\u4eab\u529f\u80fd\nwindow.shareContent = async function(platform) {\n const shareManager = new ShareManager();\n const success = await shareManager.share(platform);\n\n // \u8bb0\u5f55\u5206\u4eab\u4e8b\u4ef6\n if (success) {\n recordShare(platform);\n }\n};\n\n// \u5206\u4eab\u7edf\u8ba1\nfunction recordShare(platform) {\n // GA4 \u4e8b\u4ef6\u8ffd\u8e2a\n if (typeof gtag !== 'undefined') {\n gtag('event', 'share', {\n 'platform': platform,\n 'title': document.title,\n 'url': window.location.href\n });\n }\n\n // \u672c\u5730\u5b58\u50a8\u7edf\u8ba1\n const stats = JSON.parse(localStorage.getItem('share_stats') || '{}');\n stats[platform] = (stats[platform] || 0) + 1;\n localStorage.setItem('share_stats', JSON.stringify(stats));\n\n updateShareCount(platform);\n}\n\n// \u66f4\u65b0\u5206\u4eab\u8ba1\u6570\nfunction updateShareCount(platform) {\n const countElement = document.querySelector(\n `.share-count[data-platform=\"${platform}\"]`\n );\n if (countElement) {\n const stats = JSON.parse(localStorage.getItem('share_stats') || '{}');\n countElement.textContent = stats[platform] || 0;\n }\n}\n
"},{"location":"Tools/Blog/Mkdocs_Material/#94","title":"9.4 \u7ad9\u5185\u641c\u7d22","text":""},{"location":"Tools/Blog/Mkdocs_Material/#941","title":"9.4.1 \u641c\u7d22\u4f18\u5316","text":" - \u641c\u7d22\u914d\u7f6e\uff1a
YAMLplugins:\n - search:\n lang:\n - en\n - zh\n separator: '[\\s\\-\\.,!\\?\uff0c\u3002\uff01\uff1f]+' # \u5206\u8bcd\u5206\u9694\u7b26\n prebuild_index: true\n indexing:\n full_sections: true\n headings: true\n content: true\n tags: true\n\n # \u9ad8\u7ea7\u641c\u7d22\u914d\u7f6e\n search_boost: true # \u542f\u7528\u641c\u7d22\u63d0\u5347\n search_threshold: 0.3 # \u641c\u7d22\u9608\u503c\n search_fields: # \u641c\u7d22\u5b57\u6bb5\u6743\u91cd\n title: 10\n tags: 8\n content: 5\n
\u641c\u7d22\u4f18\u5316\u5b9e\u73b0\uff1a
JavaScript// docs/javascripts/search-enhance.js\nclass SearchEnhancer {\n constructor() {\n this.searchIndex = null;\n this.searchResults = [];\n this.searchHistory = [];\n this.maxHistoryItems = 10;\n\n this.init();\n }\n\n async init() {\n // \u52a0\u8f7d\u641c\u7d22\u7d22\u5f15\n await this.loadSearchIndex();\n // \u52a0\u8f7d\u641c\u7d22\u5386\u53f2\n this.loadSearchHistory();\n // \u521d\u59cb\u5316\u641c\u7d22\u7ec4\u4ef6\n this.setupSearch();\n }\n\n async loadSearchIndex() {\n try {\n const response = await fetch('/search/search_index.json');\n const data = await response.json();\n\n // \u4f7f\u7528 Lunr.js \u6784\u5efa\u7d22\u5f15\n this.searchIndex = lunr(function() {\n this.use(lunr.multiLanguage('en', 'zh'));\n\n this.ref('location');\n this.field('title', { boost: 10 });\n this.field('tags', { boost: 8 });\n this.field('text', { boost: 5 });\n\n data.docs.forEach(function(doc) {\n this.add(doc);\n }, this);\n });\n } catch (error) {\n console.error('Failed to load search index:', error);\n }\n }\n\n setupSearch() {\n const searchInput = document.querySelector('.md-search__input');\n if (!searchInput) return;\n\n // \u9632\u6296\u5904\u7406\n searchInput.addEventListener('input', debounce((e) => {\n const query = e.target.value;\n if (query.length >= 2) {\n this.performSearch(query);\n } else {\n this.clearResults();\n }\n }, 200));\n\n // \u641c\u7d22\u5386\u53f2\n searchInput.addEventListener('focus', () => {\n this.showSearchHistory();\n });\n }\n\n performSearch(query) {\n if (!this.searchIndex) return;\n\n try {\n // \u6267\u884c\u641c\u7d22\n this.searchResults = this.searchIndex.search(query);\n\n // \u7ed3\u679c\u540e\u5904\u7406\n this.searchResults = this.postProcessResults(this.searchResults, query);\n\n // \u663e\u793a\u7ed3\u679c\n this.displayResults(this.searchResults);\n\n // \u663e\u793a\u641c\u7d22\u5efa\u8bae\n this.showSearchSuggestions(query);\n } catch (error) {\n console.error('Search failed:', error);\n }\n }\n\n postProcessResults(results, query) {\n return results\n .map(result => {\n // \u8ba1\u7b97\u76f8\u5173\u5ea6\u5206\u6570\n const score = this.calculateRelevanceScore(result, query);\n\n return {\n ...result,\n score: score,\n highlights: this.generateHighlights(result, query)\n };\n })\n // \u6309\u5206\u6570\u6392\u5e8f\n .sort((a, b) => b.score - a.score)\n // \u9650\u5236\u7ed3\u679c\u6570\u91cf\n .slice(0, 10);\n }\n\n calculateRelevanceScore(result, query) {\n let score = result.score;\n\n // \u6807\u9898\u5339\u914d\u52a0\u5206\n if (result.title?.toLowerCase().includes(query.toLowerCase())) {\n score *= 1.5;\n }\n\n // \u6807\u7b7e\u5339\u914d\u52a0\u5206\n if (result.tags?.some(tag => \n tag.toLowerCase().includes(query.toLowerCase())\n )) {\n score *= 1.2;\n }\n\n return score;\n }\n\n generateHighlights(result, query) {\n const text = result.text || '';\n const words = query.toLowerCase().split(/\\s+/);\n const context = 50; // \u4e0a\u4e0b\u6587\u957f\u5ea6\n\n let highlights = [];\n\n words.forEach(word => {\n let index = text.toLowerCase().indexOf(word);\n while (index > -1) {\n const start = Math.max(0, index - context);\n const end = Math.min(text.length, index + word.length + context);\n const highlight = text.slice(start, end);\n\n highlights.push({\n text: highlight,\n position: start\n });\n\n index = text.toLowerCase().indexOf(word, index + 1);\n }\n });\n\n // \u5408\u5e76\u91cd\u53e0\u7684\u9ad8\u4eae\u7247\u6bb5\n return this.mergeHighlights(highlights);\n }\n\n mergeHighlights(highlights) {\n if (highlights.length === 0) return [];\n\n highlights.sort((a, b) => a.position - b.position);\n\n let merged = [highlights[0]];\n for (let i = 1; i < highlights.length; i++) {\n let current = highlights[i];\n let last = merged[merged.length - 1];\n\n if (current.position <= last.position + last.text.length) {\n // \u5408\u5e76\u91cd\u53e0\u7684\u7247\u6bb5\n last.text = last.text.slice(0, current.position - last.position) +\n current.text;\n } else {\n merged.push(current);\n }\n }\n\n return merged;\n }\n\n loadSearchHistory() {\n try {\n this.searchHistory = JSON.parse(\n localStorage.getItem('search_history')\n ) || [];\n } catch (error) {\n this.searchHistory = [];\n }\n }\n\n saveSearchHistory(query) {\n if (!query) return;\n\n // \u79fb\u9664\u91cd\u590d\u9879\n this.searchHistory = this.searchHistory.filter(item => \n item.query !== query\n );\n\n // \u6dfb\u52a0\u65b0\u9879\n this.searchHistory.unshift({\n query: query,\n timestamp: Date.now()\n });\n\n // \u9650\u5236\u5386\u53f2\u8bb0\u5f55\u6570\u91cf\n if (this.searchHistory.length > this.maxHistoryItems) {\n this.searchHistory.pop();\n }\n\n // \u4fdd\u5b58\u5230\u672c\u5730\u5b58\u50a8\n localStorage.setItem(\n 'search_history',\n JSON.stringify(this.searchHistory)\n );\n }\n\n showSearchHistory() {\n const container = document.querySelector('.md-search__history');\n if (!container) return;\n\n // \u6e05\u7a7a\u5bb9\u5668\n container.innerHTML = '';\n\n // \u663e\u793a\u5386\u53f2\u8bb0\u5f55\n this.searchHistory.forEach(item => {\n const element = document.createElement('div');\n element.className = 'search-history-item';\n element.innerHTML = `\n <span class=\"history-icon\">\n {% include \".icons/material/history.svg\" %}\n </span>\n <span class=\"history-query\">${item.query}</span>\n <span class=\"history-time\">\n ${this.formatTime(item.timestamp)}\n </span>\n `;\n\n element.addEventListener('click', () => {\n const searchInput = document.querySelector('.md-search__input');\n if (searchInput) {\n searchInput.value = item.query;\n searchInput.dispatchEvent(new Event('input'));\n }\n });\n\n container.appendChild(element);\n });\n }\n\n formatTime(timestamp) {\n const now = Date.now();\n const diff = now - timestamp;\n\n if (diff < 60000) return '\u521a\u521a';\n if (diff < 3600000) return `${Math.floor(diff/60000)}\u5206\u949f\u524d`;\n if (diff < 86400000) return `${Math.floor(diff/3600000)}\u5c0f\u65f6\u524d`;\n return `${Math.floor(diff/86400000)}\u5929\u524d`;\n }\n}\n\n// \u521d\u59cb\u5316\u641c\u7d22\u589e\u5f3a\nnew SearchEnhancer();\n
\u641c\u7d22\u76f8\u5173\u6837\u5f0f\uff1a
CSS/* docs/stylesheets/search.css */\n/* \u641c\u7d22\u5386\u53f2 */\n.search-history-item {\n display: flex;\n align-items: center;\n padding: 0.5rem 1rem;\n cursor: pointer;\n transition: background 0.2s ease;\n}\n\n.search-history-item:hover {\n background: var(--md-code-bg-color);\n}\n\n.history-icon {\n margin-right: 0.5rem;\n color: var(--md-default-fg-color--light);\n}\n\n.history-query {\n flex: 1;\n font-weight: 500;\n}\n\n.history-time {\n font-size: 0.8rem;\n color: var(--md-default-fg-color--light);\n}\n\n/* \u641c\u7d22\u5efa\u8bae */\n.search-suggestions {\n padding: 1rem;\n}\n\n.suggestion-item {\n padding: 0.5rem;\n border-radius: 4px;\n cursor: pointer;\n transition: background 0.2s ease;\n}\n\n.suggestion-item:hover {\n background: var(--md-code-bg-color);\n}\n\n/* \u9ad8\u4eae\u6587\u672c */\n.search-highlight {\n background: var(--md-accent-fg-color);\n color: white;\n padding: 0 0.2rem;\n border-radius: 2px;\n}\n\n/* \u641c\u7d22\u7ed3\u679c */\n.search-result {\n padding: 1rem;\n border-bottom: 1px solid var(--md-default-fg-color--lightest);\n}\n\n.result-title {\n font-size: 1.1rem;\n font-weight: 500;\n margin-bottom: 0.5rem;\n}\n\n.result-excerpt {\n font-size: 0.9rem;\n color: var(--md-default-fg-color--light);\n margin-bottom: 0.5rem;\n}\n\n.result-meta {\n display: flex;\n gap: 1rem;\n font-size: 0.8rem;\n color: var(--md-default-fg-color--lighter);\n}\n
\u4f7f\u7528\u8fd9\u4e9b\u4ee3\u7801\uff0c\u4f60\u53ef\u4ee5\u5b9e\u73b0\u4e00\u4e2a\u529f\u80fd\u5b8c\u5584\u7684\u641c\u7d22\u7cfb\u7edf\uff0c\u5305\u62ec\uff1a
- \u9ad8\u6027\u80fd\u641c\u7d22
- \u667a\u80fd\u7ed3\u679c\u6392\u5e8f
- \u641c\u7d22\u5386\u53f2\u8bb0\u5f55
- \u641c\u7d22\u5efa\u8bae
- \u7ed3\u679c\u9ad8\u4eae
- \u591a\u8bed\u8a00\u652f\u6301
\u8bb0\u5f97\u5728 mkdocs.yml \u4e2d\u6dfb\u52a0\u76f8\u5e94\u7684\u5f15\u7528\uff1a
YAMLextra_css:\n - stylesheets/search.css\n\nextra_javascript:\n - https://unpkg.com/lunr/lunr.js\n - https://unpkg.com/lunr-languages/lunr.stemmer.support.js\n - https://unpkg.com/lunr-languages/lunr.zh.js\n - javascripts/search-enhance.js\n
"},{"location":"Tools/Blog/Mkdocs_Material/#10","title":"10 \u90e8\u7f72\u65b9\u6848","text":""},{"location":"Tools/Blog/Mkdocs_Material/#101","title":"10.1 \u672c\u5730\u90e8\u7f72","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1011","title":"10.1.1 \u6784\u5efa\u9759\u6001\u6587\u4ef6","text":"\u4f7f\u7528 MkDocs \u6784\u5efa\u9759\u6001\u6587\u4ef6\uff1a
Bash# \u6784\u5efa\u7ad9\u70b9\nmkdocs build\n\n# \u6307\u5b9a\u8f93\u51fa\u76ee\u5f55\nmkdocs build -d custom_site_dir\n\n# \u6e05\u7406\u5e76\u6784\u5efa\nmkdocs build --clean\n
\u6784\u5efa\u9009\u9879\u8bf4\u660e\uff1a
YAML# mkdocs.yml\nsite_dir: site # \u8f93\u51fa\u76ee\u5f55\ndocs_dir: docs # \u6587\u6863\u6e90\u76ee\u5f55\nstrict: true # \u4e25\u683c\u6a21\u5f0f\nuse_directory_urls: true # \u4f7f\u7528\u76ee\u5f55 URL\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1012","title":"10.1.2 \u672c\u5730\u670d\u52a1\u5668","text":"\u5f00\u53d1\u670d\u52a1\u5668\u914d\u7f6e\uff1a
Bash# \u542f\u52a8\u5f00\u53d1\u670d\u52a1\u5668\nmkdocs serve\n\n# \u6307\u5b9a\u7aef\u53e3\nmkdocs serve -a localhost:8080\n\n# \u81ea\u52a8\u91cd\u8f7d\nmkdocs serve --dirtyreload\n
\u5f00\u53d1\u670d\u52a1\u5668\u9ad8\u7ea7\u914d\u7f6e\uff1a
YAML# mkdocs.yml\ndev_addr: 127.0.0.1:8000 # \u5f00\u53d1\u670d\u52a1\u5668\u5730\u5740\nwatch: [docs, includes] # \u76d1\u89c6\u7684\u76ee\u5f55\n
"},{"location":"Tools/Blog/Mkdocs_Material/#102-github-pages","title":"10.2 GitHub Pages \u90e8\u7f72","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1021","title":"10.2.1 \u4ed3\u5e93\u914d\u7f6e","text":" -
\u521b\u5efa GitHub \u4ed3\u5e93\uff1a - \u521b\u5efa\u65b0\u4ed3\u5e93\u6216\u4f7f\u7528\u73b0\u6709\u4ed3\u5e93 - \u4ed3\u5e93\u540d\u683c\u5f0f\uff1a username.github.io \u6216\u666e\u901a\u4ed3\u5e93\u540d
-
\u914d\u7f6e\u6587\u4ef6\uff1a
YAML# mkdocs.yml\nsite_url: https://username.github.io/repository/\nrepo_url: https://github.com/username/repository\nedit_uri: edit/main/docs/\n
- \u521d\u59cb\u5316\u4ed3\u5e93\uff1a
Bash# \u521d\u59cb\u5316 Git \u4ed3\u5e93\ngit init\n\n# \u6dfb\u52a0\u8fdc\u7a0b\u4ed3\u5e93\ngit remote add origin https://github.com/username/repository.git\n\n# \u63d0\u4ea4\u521d\u59cb\u6587\u4ef6\ngit add .\ngit commit -m \"Initial commit\"\ngit push -u origin main\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1022-github-actions","title":"10.2.2 GitHub Actions","text":"\u521b\u5efa GitHub Actions \u5de5\u4f5c\u6d41\u6587\u4ef6\uff1a
YAML# .github/workflows/ci.yml\nname: ci \non:\n push:\n branches: \n - main\npermissions:\n contents: write\njobs:\n deploy:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - name: Configure Git Credentials\n run: |\n git config user.name github-actions[bot]\n git config user.email 41898282+github-actions[bot]@users.noreply.github.com\n - uses: actions/setup-python@v4\n with:\n python-version: 3.x\n - run: echo \"cache_id=$(date --utc '+%V')\" >> $GITHUB_ENV \n - uses: actions/cache@v3\n with:\n key: mkdocs-material-${{ env.cache_id }}\n path: .cache\n restore-keys: |\n mkdocs-material-\n - name: Install dependencies\n run: |\n pip install mkdocs-material\n pip install mkdocs-glightbox\n pip install mkdocs-git-revision-date-localized-plugin\n - name: Deploy\n run: mkdocs gh-deploy --force\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1023","title":"10.2.3 \u81ea\u52a8\u90e8\u7f72","text":"\u914d\u7f6e\u81ea\u52a8\u90e8\u7f72\uff1a
-
\u542f\u7528 GitHub Pages\uff1a - \u8fdb\u5165\u4ed3\u5e93\u8bbe\u7f6e -> Pages - Source \u9009\u62e9 gh-pages \u5206\u652f - \u4fdd\u5b58\u8bbe\u7f6e
-
\u914d\u7f6e\u90e8\u7f72\u5206\u652f\uff1a
YAML# mkdocs.yml\nremote_branch: gh-pages # GitHub Pages \u5206\u652f\nremote_name: origin # \u8fdc\u7a0b\u4ed3\u5e93\u540d\n
- \u624b\u52a8\u90e8\u7f72\u547d\u4ee4\uff1a
Bash# \u90e8\u7f72\u5230 GitHub Pages\nmkdocs gh-deploy\n\n# \u5f3a\u5236\u90e8\u7f72\nmkdocs gh-deploy --force\n\n# \u6307\u5b9a\u5206\u652f\nmkdocs gh-deploy --remote-branch custom-branch\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1024","title":"10.2.4 \u81ea\u5b9a\u4e49\u57df\u540d","text":" - \u6dfb\u52a0 CNAME \u6587\u4ef6\uff1a
Bash# docs/CNAME\ndocs.example.com\n
-
DNS \u914d\u7f6e\uff1a - \u6dfb\u52a0 CNAME \u8bb0\u5f55\u6307\u5411 username.github.io - \u6216\u6dfb\u52a0 A \u8bb0\u5f55\u6307\u5411 GitHub Pages IP
-
\u914d\u7f6e\u6587\u4ef6\u66f4\u65b0\uff1a
YAML# mkdocs.yml\nsite_url: https://docs.example.com/\n
"},{"location":"Tools/Blog/Mkdocs_Material/#103","title":"10.3 \u6301\u7eed\u96c6\u6210","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1031-cicd","title":"10.3.1 CI/CD \u914d\u7f6e","text":"\u9ad8\u7ea7 GitHub Actions \u914d\u7f6e\uff1a
YAMLname: CI/CD Pipeline\non:\n push:\n branches:\n - main\n pull_request:\n branches:\n - main\n\njobs:\n test:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: 3.x\n\n - name: Cache dependencies\n uses: actions/cache@v3\n with:\n path: ~/.cache/pip\n key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}\n restore-keys: |\n ${{ runner.os }}-pip-\n\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install -r requirements.txt\n\n - name: Run tests\n run: |\n mkdocs build --strict\n\n deploy:\n needs: test\n if: github.ref == 'refs/heads/main'\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n\n - name: Deploy to GitHub Pages\n run: |\n mkdocs gh-deploy --force\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1032","title":"10.3.2 \u81ea\u52a8\u5316\u6d4b\u8bd5","text":" - \u94fe\u63a5\u68c0\u67e5\uff1a
YAMLname: Link Check\non:\n schedule:\n - cron: '0 0 * * *' # \u6bcf\u5929\u8fd0\u884c\njobs:\n linkCheck:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - name: Link Checker\n uses: lycheeverse/lychee-action@v1.8.0\n with:\n args: --verbose --no-progress 'docs/**/*.md'\n
- HTML \u9a8c\u8bc1\uff1a
YAMLname: HTML Validation\njobs:\n validate:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - name: Build site\n run: mkdocs build\n - name: Validate HTML\n uses: Cyb3r-Jak3/html5validator-action@v7.2.0\n with:\n root: site/\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1033","title":"10.3.3 \u90e8\u7f72\u7b56\u7565","text":" - \u5206\u652f\u7b56\u7565\u914d\u7f6e\uff1a
YAML# \u4fdd\u62a4\u5206\u652f\u8bbe\u7f6e\nbranches:\n - name: main\n protection:\n required_status_checks:\n strict: true\n contexts: ['test', 'build']\n required_pull_request_reviews:\n required_approving_review_count: 1\n
- \u73af\u5883\u914d\u7f6e\uff1a
YAML# \u73af\u5883\u53d8\u91cf\u8bbe\u7f6e\nenv:\n production:\n url: https://docs.example.com\n branch: main\n staging:\n url: https://staging.docs.example.com\n branch: staging\n
- \u90e8\u7f72\u5de5\u4f5c\u6d41\uff1a
YAMLname: Deployment\non:\n push:\n branches: [main, staging]\njobs:\n deploy:\n runs-on: ubuntu-latest\n environment:\n name: ${{ github.ref == 'refs/heads/main' && 'production' || 'staging' }}\n steps:\n - uses: actions/checkout@v4\n\n - name: Build and Deploy\n env:\n DEPLOY_URL: ${{ github.ref == 'refs/heads/main' && env.production.url || env.staging.url }}\n run: |\n mkdocs build\n # \u90e8\u7f72\u5230\u76f8\u5e94\u73af\u5883\n
"},{"location":"Tools/Blog/Mkdocs_Material/#11","title":"11 \u9644\u5f55","text":""},{"location":"Tools/Blog/Mkdocs_Material/#111-a","title":"11.1 A. \u5e38\u7528\u914d\u7f6e\u793a\u4f8b","text":""},{"location":"Tools/Blog/Mkdocs_Material/#1111-a1-mkdocs-yml","title":"11.1.1 A.1 mkdocs. yml \u5b8c\u6574\u793a\u4f8b","text":"YAML# \u7ad9\u70b9\u57fa\u672c\u4fe1\u606f\u914d\u7f6e\nsite_name: \u9879\u76ee\u6587\u6863 # \u7ad9\u70b9\u540d\u79f0\nsite_url: https://example.com # \u7ad9\u70b9URL\nsite_author: \u4f5c\u8005\u540d\u79f0 # \u4f5c\u8005\u4fe1\u606f\nsite_description: >- # \u7ad9\u70b9\u63cf\u8ff0\n \u8fd9\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u6587\u6863\u793a\u4f8b\uff0c\u5305\u542b\u4e86MkDocs Material\u4e3b\u9898\u7684\u6240\u6709\u4e3b\u8981\u529f\u80fd\u914d\u7f6e\u3002\n\n# \u4ee3\u7801\u4ed3\u5e93\u4fe1\u606f\nrepo_name: username/repo # \u4ed3\u5e93\u540d\u79f0\nrepo_url: https://github.com/username/repo # \u4ed3\u5e93\u5730\u5740\nedit_uri: edit/main/docs/ # \u7f16\u8f91\u94fe\u63a5\n\n# \u7248\u6743\u4fe1\u606f\ncopyright: Copyright © 2024 # \u7248\u6743\u58f0\u660e\n\n# \u4e3b\u9898\u914d\u7f6e\ntheme:\n name: material # \u4f7f\u7528Material\u4e3b\u9898\n custom_dir: overrides # \u81ea\u5b9a\u4e49\u76ee\u5f55\n\n # \u4e3b\u9898\u7279\u6027\u914d\u7f6e\n features:\n # \u5bfc\u822a\u76f8\u5173\n - navigation.tabs # \u9876\u90e8\u6807\u7b7e\u5bfc\u822a\n - navigation.sections # \u4fa7\u8fb9\u680f\u5206\u7ec4\n - navigation.expand # \u9ed8\u8ba4\u5c55\u5f00\u5bfc\u822a\n - navigation.indexes # \u5bfc\u822a\u7d22\u5f15\u9875\n - navigation.top # \u8fd4\u56de\u9876\u90e8\u6309\u94ae\n - navigation.footer # \u4e0a\u4e00\u9875/\u4e0b\u4e00\u9875\u5bfc\u822a\n\n # \u641c\u7d22\u76f8\u5173\n - search.suggest # \u641c\u7d22\u5efa\u8bae\n - search.highlight # \u641c\u7d22\u9ad8\u4eae\n - search.share # \u641c\u7d22\u5206\u4eab\n\n # \u4ee3\u7801\u76f8\u5173\n - content.code.annotate # \u4ee3\u7801\u6ce8\u91ca\n - content.code.copy # \u4ee3\u7801\u590d\u5236\n\n # \u5176\u4ed6\u529f\u80fd\n - announce.dismiss # \u53ef\u5173\u95ed\u516c\u544a\n - header.autohide # \u81ea\u52a8\u9690\u85cf\u5934\u90e8\n\n # \u914d\u8272\u65b9\u6848\n palette:\n # \u4eae\u8272\u4e3b\u9898\n - media: \"(prefers-color-scheme: light)\"\n scheme: default\n primary: indigo # \u4e3b\u8272\n accent: indigo # \u5f3a\u8c03\u8272\n toggle:\n icon: material/brightness-7\n name: \u5207\u6362\u81f3\u6697\u8272\u6a21\u5f0f\n\n # \u6697\u8272\u4e3b\u9898\n - media: \"(prefers-color-scheme: dark)\"\n scheme: slate\n primary: indigo\n accent: indigo\n toggle:\n icon: material/brightness-4\n name: \u5207\u6362\u81f3\u4eae\u8272\u6a21\u5f0f\n\n # \u56fe\u6807\u548c\u5b57\u4f53\n icon:\n logo: material/book-open-page-variant # Logo\u56fe\u6807\n repo: fontawesome/brands/github # \u4ed3\u5e93\u56fe\u6807\n\n font:\n text: Roboto # \u6b63\u6587\u5b57\u4f53\n code: Roboto Mono # \u4ee3\u7801\u5b57\u4f53\n\n language: zh # \u754c\u9762\u8bed\u8a00\n\n# \u6269\u5c55\u914d\u7f6e\nmarkdown_extensions:\n # \u5185\u7f6e\u6269\u5c55\n - toc: # \u76ee\u5f55\n permalink: true # \u6c38\u4e45\u94fe\u63a5\n toc_depth: 3 # \u76ee\u5f55\u6df1\u5ea6\n - tables # \u8868\u683c\u652f\u6301\n - attr_list # \u5c5e\u6027\u5217\u8868\n - def_list # \u5b9a\u4e49\u5217\u8868\n - footnotes # \u811a\u6ce8\n - abbr # \u7f29\u5199\n\n # PyMdown Extensions\n - pymdownx.highlight: # \u4ee3\u7801\u9ad8\u4eae\n anchor_linenums: true\n line_spans: __span\n pygments_lang_class: true\n - pymdownx.inlinehilite # \u884c\u5185\u4ee3\u7801\u9ad8\u4eae\n - pymdownx.snippets # \u4ee3\u7801\u7247\u6bb5\n - pymdownx.superfences: # \u8d85\u7ea7\u56f4\u680f\n custom_fences:\n - name: mermaid\n class: mermaid\n format: !!python/name:pymdownx.superfences.fence_code_format\n - pymdownx.tabbed: # \u6807\u7b7e\u9875\n alternate_style: true\n - pymdownx.tasklist: # \u4efb\u52a1\u5217\u8868\n custom_checkbox: true\n - pymdownx.emoji: # \u8868\u60c5\u652f\u6301\n emoji_index: !!python/name:material.extensions.emoji.twemoji\n emoji_generator: !!python/name:material.extensions.emoji.to_svg\n - pymdownx.details # \u8be6\u7ec6\u4fe1\u606f\n - pymdownx.caret # \u4e0a\u6807\n - pymdownx.mark # \u6807\u8bb0\n - pymdownx.tilde # \u4e0b\u6807\n - pymdownx.keys # \u952e\u76d8\u6309\u952e\n\n# \u63d2\u4ef6\u914d\u7f6e\nplugins:\n - search: # \u641c\u7d22\u63d2\u4ef6\n lang: \n - en\n - zh\n separator: '[\\s\\-\\.,!\\?\uff0c\u3002\uff01\uff1f]+'\n prebuild_index: true\n\n - git-revision-date-localized: # Git\u65e5\u671f\u63d2\u4ef6\n enable_creation_date: true\n type: datetime\n\n - minify: # \u8d44\u6e90\u538b\u7f29\n minify_html: true\n minify_js: true\n minify_css: true\n htmlmin_opts:\n remove_comments: true\n\n - social: # \u793e\u4ea4\u5206\u4eab\n cards: true\n cards_color:\n fill: \"#0FF1CE\"\n text: \"#FFFFFF\"\n\n - tags: # \u6807\u7b7e\u7cfb\u7edf\n tags_file: tags.md\n\n - statistics: # \u7edf\u8ba1\n page_check: true\n page_count: true\n\n# \u9644\u52a0\u914d\u7f6e\nextra:\n # \u793e\u4ea4\u94fe\u63a5\n social:\n - icon: fontawesome/brands/github\n link: https://github.com/username\n - icon: fontawesome/brands/twitter\n link: https://twitter.com/username\n\n # \u8bc4\u8bba\u7cfb\u7edf\n comments:\n provider: giscus\n repo: username/repo\n repo_id: your_repo_id\n category: Comments\n category_id: your_category_id\n\n # \u5206\u6790\u7edf\u8ba1\n analytics:\n provider: google\n property: G-XXXXXXXXXX\n feedback:\n title: \u8fd9\u7bc7\u6587\u6863\u5bf9\u60a8\u6709\u5e2e\u52a9\u5417\uff1f\n ratings:\n - icon: material/thumb-up-outline\n name: \u6709\u5e2e\u52a9\n data: 1\n note: >-\n \u611f\u8c22\u60a8\u7684\u53cd\u9988\uff01\n - icon: material/thumb-down-outline\n name: \u6ca1\u5e2e\u52a9\n data: 0\n note: >- \n \u611f\u8c22\u60a8\u7684\u53cd\u9988\uff0c\u8bf7\u544a\u8bc9\u6211\u4eec\u5982\u4f55\u6539\u8fdb\u3002\n\n# \u989d\u5916\u7684CSS\u548cJavaScript\nextra_css:\n - stylesheets/extra.css # \u81ea\u5b9a\u4e49\u6837\u5f0f\n - stylesheets/custom.css # \u81ea\u5b9a\u4e49\u4e3b\u9898\n\nextra_javascript:\n - javascripts/extra.js # \u81ea\u5b9a\u4e49\u811a\u672c\n - https://unpkg.com/mermaid/dist/mermaid.min.js # \u5916\u90e8\u5e93\n
"},{"location":"Tools/Blog/Mkdocs_Material/#1112-a2-github-actions","title":"11.1.2 A.2 GitHub Actions \u5de5\u4f5c\u6d41\u793a\u4f8b","text":"YAML# .github/workflows/deploy.yml\n\nname: Deploy Documentation\non:\n # \u89e6\u53d1\u6761\u4ef6\n push:\n branches:\n - main\n paths:\n - 'docs/**'\n - 'mkdocs.yml'\n - '.github/workflows/deploy.yml'\n pull_request:\n branches:\n - main\n\n # \u5141\u8bb8\u624b\u52a8\u89e6\u53d1\n workflow_dispatch:\n\n# \u73af\u5883\u53d8\u91cf\nenv:\n PYTHON_VERSION: '3.10'\n DEPLOY_BRANCH: gh-pages\n\n# \u4efb\u52a1\u5b9a\u4e49\njobs:\n build:\n name: Build and Deploy Documentation\n runs-on: ubuntu-latest\n\n steps:\n # \u68c0\u51fa\u4ee3\u7801\n - name: Checkout Repository\n uses: actions/checkout@v3\n with:\n fetch-depth: 0 # \u83b7\u53d6\u5b8c\u6574\u7684git\u5386\u53f2\u7528\u4e8e\u6700\u8fd1\u66f4\u65b0\u65f6\u95f4\n\n # \u8bbe\u7f6ePython\u73af\u5883\n - name: Setup Python\n uses: actions/setup-python@v4\n with:\n python-version: ${{ env.PYTHON_VERSION }}\n cache: pip\n\n # \u7f13\u5b58\u4f9d\u8d56\n - name: Cache Dependencies\n uses: actions/cache@v3\n with:\n path: ~/.cache/pip\n key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}\n restore-keys: |\n ${{ runner.os }}-pip-\n\n # \u5b89\u88c5\u4f9d\u8d56\n - name: Install Dependencies\n run: |\n python -m pip install --upgrade pip\n pip install -r requirements.txt\n\n # \u914d\u7f6eGit\n - name: Configure Git\n run: |\n git config --global user.name \"${{ github.actor }}\"\n git config --global user.email \"${{ github.actor }}@users.noreply.github.com\"\n\n # \u6784\u5efa\u6587\u6863\n - name: Build Documentation\n run: |\n mkdocs build --clean --verbose\n\n # \u4f18\u5316\u8d44\u6e90\n - name: Optimize Assets\n run: |\n # \u538b\u7f29\u56fe\u7247\n find site/ -type f -name \"*.png\" -exec optipng -o5 {} \\;\n find site/ -type f -name \"*.jpg\" -exec jpegoptim --strip-all {} \\;\n\n # \u538b\u7f29JavaScript\u548cCSS\n find site/ -type f -name \"*.js\" -exec uglifyjs {} -o {} \\;\n find site/ -type f -name \"*.css\" -exec cleancss -o {} {} \\;\n\n # \u90e8\u7f72\u5230GitHub Pages\n - name: Deploy to GitHub Pages\n if: github.ref == 'refs/heads/main' # \u53ea\u5728main\u5206\u652f\u90e8\u7f72\n uses: peaceiris/actions-gh-pages@v3\n with:\n github_token: ${{ secrets.GITHUB_TOKEN }}\n publish_dir: ./site\n publish_branch: ${{ env.DEPLOY_BRANCH }}\n user_name: 'github-actions[bot]'\n user_email: 'github-actions[bot]@users.noreply.github.com'\n commit_message: ${{ github.event.head_commit.message }}\n full_commit_message: |\n Deploy Documentation\n\n Commit: ${{ github.sha }}\n Workflow: ${{ github.workflow }}\n\n ${{ github.event.head_commit.message }}\n\n # \u90e8\u7f72\u5b8c\u6210\u901a\u77e5\n - name: Deployment Status\n if: always()\n uses: actions/github-script@v6\n with:\n script: |\n const { owner, repo } = context.repo;\n const run_id = context.runId;\n const run_url = `https://github.com/${owner}/${repo}/actions/runs/${run_id}`;\n\n if (context.job.status === 'success') {\n await github.rest.issues.createComment({\n owner,\n repo,\n issue_number: context.issue.number,\n body: `\u2705 Documentation deployed successfully!\\nPreview: https://${owner}.github.io/${repo}/\\nWorkflow: ${run_url}`\n });\n } else {\n await github.rest.issues.createComment({\n owner,\n repo,\n issue_number: context.issue.number,\n body: `\u274c Documentation deployment failed.\\nSee details: ${run_url}`\n });\n }\n\n# \u9519\u8bef\u8ffd\u8e2a\u548c\u62a5\u544a\n - name: Report Errors\n if: failure()\n uses: actions/github-script@v6\n with:\n script: |\n const { owner, repo } = context.repo;\n const run_id = context.runId;\n\n await github.rest.issues.create({\n owner,\n repo,\n title: `\ud83d\udd34 Documentation deployment failed - ${new Date().toISOString()}`,\n body: `Deployment failed for commit: ${context.sha}\\n\\nSee workflow run: https://github.com/${owner}/${repo}/actions/runs/${run_id}\\n\\nPlease check the logs for more details.`,\n labels: ['deployment', 'bug']\n });\n
"},{"location":"Tools/Environment/Ubuntu_setup/","title":"Ubuntu \u914d\u7f6e","text":""},{"location":"Tools/Environment/Ubuntu_setup/#ubuntu","title":"Ubuntu \u914d\u7f6e","text":" \u7ea6 77 \u4e2a\u5b57 14 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
- \u5728 Ubuntu \u5b89\u88c5\u914d\u7f6e Fcitx 5 \u4e2d\u6587\u8f93\u5165\u6cd5 - muzing\u7684\u6742\u8d27\u94fa
- fcitx5-rime \u6302\u63a5\u5c0f\u9e64\u97f3\u5f62 | rovo98's Blog
- zhuanlan.zhihu.com/p/660191327#:~:text=Tabby\uff08\u4ee5\u524d\u79f0\u4e3a
- Zsh \u5b89\u88c5\u4e0e\u914d\u7f6e\uff0c\u4f7f\u7528 Oh-My-Zsh \u7f8e\u5316\u7ec8\u7aef | Leehow\u7684\u5c0f\u7ad9
- zhuanlan.zhihu.com/p/658811059
- PKMer_TiddyWiki \u7b80\u6613\u6307\u5357
- Site Unreachable
- Jedsek | Blog
Bashvisudo /etc/sudoers \n%sudo ALL=(ALL:ALL) NOPASSWD: ALL\n
Bashgit clone https://github.com/zsh-users/zsh-syntax-highlighting.git ${ZSH_CUSTOM:-~/.oh-my-zsh/custom}/plugins/zsh-syntax-highlighting\n\ngit clone https://github.com/zsh-users/zsh-autosuggestions ${ZSH_CUSTOM:-~/.oh-my-zsh/custom}/plugins/zsh-autosuggestions\n\ngit clone git://github.com/joelthelion/autojump.git\ncd autojump\n./install.py\n\n[[ -s ~/.autojump/etc/profile.d/autojump.sh ]] && . ~/.autojump/etc/profile.d/autojump.sh\n
Bashsudo apt-get install flameshot\nflameshot gui\n
- \u5728 Ubuntu 22.04|20.04|18.04 \u4e0a\u5b89\u88c5 Node.js 20
"},{"location":"Tools/Environment/environment/","title":"Environment","text":" - \u5f00\u53d1\u73af\u5883\u6784\u5efa\u6307\u5357
\u7ea6 227 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
- \u4ece\u96f6\u5f00\u59cb\u914d\u7f6e Windows \u2022 Arthals' ink
- \u4ece\u96f6\u5f00\u59cb\u914d\u7f6e Linux \u2022 Arthals' ink
- \u670d\u52a1\u5668\u73af\u5883\u914d\u7f6e Cheat Sheet | Yi Pan (Conless)
- clash
- powertoy
- gsudo
- git
- GitHub - Wybxc/git-remake-guide: Git \u91cd\u5f00/\u91cd\u5b66\u6307\u5357
- picgo
- picgo + github + obsidian
- vscode
- \u7cfb\u7edf + \u4ee3\u7801\u5b57\u4f53\u8bbe\u7f6e
- zhuanlan.zhihu.com/p/603687041#:~:text=clangd\u5b98\u65b9vs
- zhuanlan.zhihu.com/p/398790625#:~:text=\u5176\u4e2d VS Code
- tools
- GitHub - jenius-apps/ambie: An app that uses white noise, nature sounds, and focus features to boost your productivity.
- clash
- GitHub - Loyalsoldier/clash-rules: \ud83e\udd84\ufe0f\ud83c\udf83\ud83d\udc7b Clash Premium \u89c4\u5219\u96c6(RULE-SET)\uff0c\u517c\u5bb9 ClashX Pro\u3001Clash for Windows \u7b49\u57fa\u4e8e Clash Premium \u5185\u6838\u7684\u5ba2\u6237\u7aef\u3002
- Site Unreachable
- \u7ffb\u5899 | Blog
- GitHub - vpncn/vpncn.github.io: 2024\u4e2d\u56fd\u7ffb\u5899\u8f6f\u4ef6VPN\u63a8\u8350\u4ee5\u53ca\u79d1\u5b66\u4e0a\u7f51\u907f\u5751\uff0c\u7a33\u5b9a\u597d\u7528\u3002\u5bf9\u6bd4SSR\u673a\u573a\u3001\u84dd\u706f\u3001V2ray\u3001\u8001\u738bVPN\u3001VPS\u642d\u5efa\u68af\u5b50\u7b49\u79d1\u5b66\u4e0a\u7f51\u4e0e\u7ffb\u5899\u8f6f\u4ef6\uff0c\u4e2d\u56fd\u6700\u65b0\u79d1\u5b66\u4e0a\u7f51\u7ffb\u5899\u68af\u5b50VPN\u4e0b\u8f7d\u63a8\u8350\uff0c\u8bbf\u95eeChatgpt\u3002
- Typora
- GitHub - sylviaxgj/typora-forest-theme: another typora theme
- GitHub - HappySimple/Typora-theme-Happysimple: \u4e00\u6b3e\u81ea\u5236\u7684Markdown\u4e3b\u9898\uff01
- github profiles
- GitHub - anuraghazra/github-readme-stats: Dynamically generated stats for your github readmes
- github action
- gsudo
- openArk
"},{"location":"Tools/Environment/obsidian_setup/","title":"obsidian \u914d\u7f6e","text":" \u7ea6 1281 \u4e2a\u5b57 55 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 7 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#Environment #Obsidian","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#obsidian","title":"obsidian \u914d\u7f6e","text":"Note \u5982\u679c\u61d2\u5f97\u641e\uff0c\u53ef\u4ee5\u76f4\u63a5 clone \u6211\u7684\u914d\u7f6e\uff0c\u653e\u5230 .obsidian \u6587\u4ef6\u91cc\u3002 \u8fd9\u662f\u914d\u7f6e\u6587\u4ef6\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#1","title":"1 \u4f7f\u7528\u8bed\u8a00","text":" - \u4e3b\u8981\u662f Markdown
- \u914d\u7f6e\u63d2\u4ef6\u4e5f\u4f1a\u6d89\u53ca\u4e00\u4e9b javascript
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#2","title":"2 \u63d2\u4ef6","text":"","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#21-displaystyle-latex","title":"2.1 \\(\\displaystyle \\LaTeX\\)","text":" - Latex Suite
- \u8fd9\u4e2a\u63d2\u4ef6\u53ef\u4ee5\u8ba9\u4f60\u76f4\u63a5\u901a\u8fc7\u5b57\u6bcd\u7ec4\u5408\u6fc0\u6d3b\u5173\u952e\u8bcd\uff0c\u6765\u52a0\u5feb \\(\\displaystyle \\LaTeX\\) \u901f\u5ea6\u3002
- \u6bd4\u5982\uff1a\u8f93\u5165
mk \u5c31\u53ef\u4ee5\u81ea\u52a8\u66ff\u6362\u4e3a $\\displaystyle $
- LaTex-like Theorem & Equation Referencer
- \u63d0\u4f9b\u516c\u5f0f\u73af\u5883\u548c\u81ea\u52a8\u4e3a\u516c\u5f0f\u7f16\u53f7\uff0c\u6211\u611f\u89c9\u4e0d\u662f\u5f88\u6709\u7528
- \u9700\u8981 MathLinks \u524d\u7f6e
Notes \u5982\u679c\u4f60\u7684 \\(\\displaystyle \\LaTeX\\) \u4e66\u5199\u901f\u5ea6\u6bd4\u8f83\u6162\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4f7f\u7528 ocr \u8f6f\u4ef6\uff0c\u6bd4\u5982 simpleTex\uff0c\u4f46\u662f\u8fd9\u4e2a\u53ea\u6709 Windows \u5e73\u53f0\u652f\u6301\u3002\u5bf9\u4e8e Linux\uff0c\u6211\u6682\u65f6\u8fd8\u6ca1\u6709\u6bd4\u8f83\u597d\u7684\u8f6f\u4ef6\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#22","title":"2.2 \u7f16\u8f91\u589e\u5f3a","text":" - Easy Typing
- \u53ef\u4ee5\u81ea\u52a8\u8865\u5168\u4e00\u4e9b\uff0c\u6bd4\u5982\u62ec\u53f7\uff0c\u4ee5\u53ca\u81ea\u52a8\u5728\u4e2d\u6587\u548c\u82f1\u6587\u4e4b\u95f4\u7a7a\u683c\uff0c\u5f88\u6709\u7528
- Linter
- \u53ef\u4ee5\u81ea\u52a8\u683c\u5f0f\u5316\uff0c\u5f88\u5f3a\u7684\u63d2\u4ef6\u3002\u53ef\u4ee5\u81ea\u52a8\u751f\u6210 yaml\uff0c\u683c\u5f0f\u5316\u7a7a\u884c\u7a7a\u683c\uff0c\u6807\u9898\u7b49
- Remember cursor position
- \u8bb0\u4f4f cursor \u7684\u4f4d\u7f6e\uff0c\u53ef\u4ee5\u5728\u4e0b\u4e00\u6b21\u6253\u5f00\u8fd9\u4e2a\u6587\u4ef6\u7684\u65f6\u5019\u8df3\u8f6c\u3002\u4f46\u6211\u89c9\u5f97\u5176\u5b9e\u4e0d\u662f\u5f88\u597d\u7528\uff0c\u5728\u4f60\u6709\u540c\u6b65\u7684\u65f6\u5019\u3002\u56e0\u4e3a\u8fd9\u6837\u5b50\uff0c\u5c31\u4f1a\u4e0d\u505c\u7684\u51b2\u7a81\uff0c\u9664\u975e\u4f60\u628a\u5b83\u52a0\u5165 .gitignore \u6216\u8005\u5176\u4ed6\u3002
- PDF++
- \u53ef\u4ee5\u6279\u6ce8 PDF\uff0c\u5f88\u5f3a\u3002\u4f46\u662f\u6211\u4e0d\u559c\u6b22\uff0c\u4e0d\u8981\u5c1d\u8bd5\u628a obsidian \u53d8\u6210 all in one\u3002PDF \u5e94\u8be5\u7528\u522b\u7684\u8f6f\u4ef6\u6765\u64cd\u4f5c\u3002
- Code Styler
- \u63d0\u4f9b\u4ee3\u7801\u9ad8\u4eae\u548c\u4e0d\u540c\u5f62\u5f0f\u7684\u4ee3\u7801\u5757\uff0c\u6211\u4e0d\u559c\u6b22\uff0c\u6ca1\u5fc5\u8981\u641e\u4e2a\u63d2\u4ef6\uff0c\u7528 CSS \u5c31\u53ef\u4ee5\u89e3\u51b3\u3002
- Number Headings
- \u81ea\u52a8\u4e3a\u6807\u9898\u7f16\u53f7\uff0c\u4e5f\u53ef\u4ee5\u81ea\u52a8\u751f\u6210\u76ee\u5f55\u3002
- \u66fe\u7ecf\u6211\u89c9\u5f97\u7f16\u53f7\u5f88\u91cd\u8981\uff0c\u4f46\u662f\u540e\u6765\u611f\u89c9\u4e0d\u5982\u8ba9\u5e73\u53f0\u81ea\u52a8\u652f\u6301\u60ac\u6d6e\u76ee\u5f55\u66f4\u8212\u9002\u3002
- Outliner
- \u66f4\u597d\u7684\u5217\u8868\u652f\u6301\uff0c\u5f88\u597d\u7528\u3002
- Completr
- \u81ea\u52a8\u8865\u5168\uff0c\u6709\u4e00\u70b9\u7528\u3002
- Mind map
- \u7ed8\u5236\u601d\u7ef4\u5bfc\u56fe\uff0c\u611f\u89c9\u6ca1\u5fc5\u8981\uff0c\u800c\u4e14\u753b\u51fa\u6765\u7684\u4e0d\u662f\u77e2\u91cf\u56fe\uff0c\u4e0d\u597d\u7528\u3002
- Excalidraw
- \u597d\u7528\uff0c\u4f46\u662f\u6211\u4e0d\u662f\u5f88\u719f\u7ec3\u3002\u6211\u8fd8\u6ca1\u6709\u5230\u9700\u8981\u5927\u91cf\u753b\u56fe\u7684\u9636\u6bb5\u3002
- dataview
- \u53ef\u4ee5\u7528\u6765\u7edf\u8ba1\u7b49\uff0c\u5176\u5b9e\u611f\u89c9\u8ddf excel \u5dee\u4e0d\u591a\u3002
- \u5f88\u5f3a\uff0c\u4f46\u662f\u5b66\u4e60\u6210\u672c\u6bd4\u8f83\u9ad8\uff0c\u5efa\u8bae copy\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#23","title":"2.3 \u56fe\u7247","text":" - Paste image rename
- \u81ea\u52a8\u4e3a\u590d\u5236\u7684\u56fe\u7247\u91cd\u547d\u540d\uff0c\u597d\u7528
- Auto Link Title
- \u81ea\u52a8\u83b7\u53d6\u94fe\u63a5\u7684\u540d\u5b57\uff0c\u597d\u7528
- Image auto upload Plugin
- \u81ea\u52a8\u4e0a\u4f20\u56fe\u7247\uff0c\u7ed3\u5408\u56fe\u5e8a\u4f7f\u7528\uff0c\u597d\u7528
- \u642d\u914d Picgo + GitHub \u4f7f\u7528
- Image toolkit
- \u53ef\u4ee5\u653e\u5927\u56fe\u7247\uff0c\u597d\u7528
\u5bf9\u4e8e\u56fe\u7247\uff0c\u4e00\u822c\u6709\u4e24\u79cd\u65b9\u6848\uff1a
- \u7528\u56fe\u5e8a\uff0c\u65b9\u4fbf\u90e8\u7f72\u548c\u7ba1\u7406
- \u7528\u672c\u5730\u9644\u4ef6\u6587\u4ef6\u5939\uff0c\u9690\u79c1\u597d\uff0c\u4e0d\u7528\u6015\u56fe\u5e8a\u574f\u6389\uff0c\u90e8\u7f72\u7684\u65f6\u5019\u6709\u70b9\u9ebb\u70e6\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#24","title":"2.4 \u540c\u6b65\u5907\u4efd","text":" - Git
- Remotely Save
- \u4e0d\u592a\u597d\u7528\uff0c\u540c\u65f6\uff0c\u5176\u5b9e\u6211\u5e76\u6ca1\u6709\u5f88\u591a\u9700\u8981\u7528\u79fb\u52a8\u8bbe\u5907\uff0c\u6bd4\u5982 iPads\uff0c\u624b\u673a\u5199 obsidian \u7684\u9700\u6c42\uff0c\u7535\u8111\u6bb5\u7528 git \u540c\u6b65\u4e5f\u4e0d\u662f\u4e0d\u884c\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#25","title":"2.5 \u65e5\u7a0b","text":" - Calendar
- Periodic Notes
- \u597d\u7528\uff0c\u76f8\u5f53\u4e8e\u8c03\u7528\u6a21\u677f\u7684\u63d2\u4ef6
- Day Planner
- \u8fd8\u884c\uff0c\u53ef\u4ee5\u53ef\u89c6\u5316\u65f6\u95f4\u8f74\u3002\u4f46\u662f\u6211\u73b0\u5728\u8bb0\u5f55\u65f6\u95f4\u8fd8\u662f\u7528\u65f6\u95f4\u65e5\u5fd7\uff0c\u611f\u89c9\u8fd8\u662f\u628a\u529f\u80fd\u5206\u5f00\u66f4\u597d\u7684\u53d1\u6325\u6bcf\u4e2a\u8f6f\u4ef6\u7684\u4f18\u52bf\u3002
- Wakatime
- \u8bb0\u5f55\u4f7f\u7528 obsidian \u7684\u65f6\u95f4
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#26","title":"2.6 \u90e8\u7f72","text":" - Envelope
- \u597d\u7528\uff0c\u53ef\u4ee5\u81ea\u52a8\u90e8\u7f72\u5230 GitHub \u4ed3\u5e93\uff0c\u540c\u65f6\u5b83\u6700\u5927\u7684\u529f\u80fd\u662f\u53ef\u4ee5\u81ea\u52a8\u66f4\u6539\u56fe\u7247\uff0c\u53cc\u94fe\u53d8\u6210\u4e00\u822c\u683c\u5f0f\uff0c\u4ee5\u53ca\u81ea\u52a8\u6e32\u67d3 dataview\u3002
- \u7279\u522b\u652f\u6301\u7684\u6846\u67b6\u6709 Hugo\uff0cMkdocs \u7b49
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#27","title":"2.7 \u6807\u7b7e","text":" - TagFolder
- \u597d\u7528\uff0c\u652f\u6301\u53ef\u89c6\u5316 tag \u7ba1\u7406\u7684\u6587\u4ef6\uff0c\u5f7b\u5e95\u66ff\u6362\u7528\u6811\u5f62\u6587\u4ef6\u5939\u3002
- Tag Wrangler
- \u597d\u7528\uff0c\u53ef\u4ee5\u7edf\u4e00\u91cd\u547d\u540d\u7ba1\u7406 tag\uff0c\u552f\u4e00\u7684\u7f3a\u70b9\u662f\u4e0d\u80fd\u5220\u9664 tag\uff0c\u4e0d\u8fc7\u5176\u5b9e\u53ef\u4ee5\u7528\u4e00\u4e2a\u6ca1\u7528\u7684 tag \u6765\u5f53\u56de\u6536\u7ad9\uff0c\u7136\u540e\u91cd\u547d\u540d\u8fc7\u53bb\u3002
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#28","title":"2.8 \u4ecd\u5728\u63a2\u7d22","text":" - kanban
- zotero \u548c obsidian \u7ed3\u5408\u4f7f\u7528
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#29","title":"2.9 \u6211\u73b0\u5728\u5728\u4f7f\u7528\u7684\u63d2\u4ef6","text":"Text Only\"obsidian-auto-link-title\",\n\"obsidian-latex-suite\",\n\"number-headings-obsidian\",\n\"mathlinks\",\n\"obsidian-outliner\",\n\"obsidian-completr\",\n\"calendar\",\n\"periodic-notes\",\n\"obsidian-wakatime\",\n\"obsidian-image-auto-upload-plugin\",\n\"obsidian-paste-image-rename\",\n\"dataview\",\n\"obsidian-export-image\",\n\"obsidian-day-planner\",\n\"templater-obsidian\",\n\"obsidian-git\",\n\"easy-typing-obsidian\",\n\"obsidian-linter\",\n\"obsidian-style-settings\",\n\"obsidian-image-toolkit\",\n\"tag-wrangler\",\n\"obsidian-tagfolder\"\n
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#_1","title":"\u6211\u7684\u6a21\u677f","text":"\u9700\u8981\u5b89\u88c5 dataview + periodic notes \u63d2\u4ef6\u3002
- \u65e5\u8bb0\u4e3b\u8981\u662f\u4f7f\u7528\u4e86 periodic notees \u8fdb\u884c\u65e5\u8bb0\u7684\u521d\u59cb\u5316\uff0c\u81ea\u52a8\u586b\u5165\u4e00\u4e9b yaml \u4fe1\u606f\u3002\u540c\u65f6\u5bf9\u4e8e TODO \u4ee5\u53ca\u5f53\u5929\u521b\u5efa\u548c\u4fee\u6539\u7684\u6587\u4ef6\u8fdb\u884c\u68c0\u7d22\uff08\u5176\u5b9e\u6211\u611f\u89c9\u8fd9\u4e2a\u529f\u80fd\u4e0d\u662f\u5f88\u6709\u7528\uff0c\u4e0d\u8fc7\u5728\u6bcf\u5929\u603b\u7ed3\u7684\u65f6\u5019\u8fd8\u662f\u6709\u70b9\u7528\u7684\uff09\u3002
- \u5468\u7ed3\u5c31\u6ca1\u6709\u4ec0\u4e48\u4e86\uff0c\u4e00\u4e9b\u521d\u59cb\u5316\u3002\u4e0d\u8fc7\u8fd9\u4e2a Links \u53ef\u4ee5\u81ea\u52a8\u6536\u7eb3\u4e00\u4e2a\u661f\u671f\u65e5\u8bb0\u4e2d\u7684\u6240\u6709 Links\uff0c\u65b9\u4fbf\u65e5\u540e\u67e5\u627e\u3002
Note
\u7531\u4e8e markdown \u4ee3\u7801\u5757\u5d4c\u5957\u4e0d\u592a\u884c\uff0c\u6240\u4ee5\u8981\u624b\u52a8\u4fee\u590d\u3002\u6ce8\u610f\u4fee\u590d '' \u5e26\u6765\u7684\u4ee3\u7801\u5757\u95ee\u9898, \u8bb0\u5f97\u8865\u5168\u3002
dailyweekly Note Text Only---\ntitle: \"{{date}}\"\ntags:\n - \" #\u65e5\u8bb0 \"\ncategories: dairy\ndate: \" {{ date:YYYY-MM-DDTHH:mm:ss+08:00 }} \"\nmodify: \" {{ date:YYYY-MM-DDTHH:mm:ss+08:00 }} \"\ndir: dairy\nshare: false\ncdate: \" {{ date:YYYY-MM-DD }} \"\nmdate: \" {{ date:YYYY-MM-DD }} \"\n---\n\n# {{date}}\n\n## Daily Plan\n\n### Morning\n\n#### Plan\n\n### Afternoon\n\n#### Plan\n\n### Night\n\n#### Plan\n\n## NOTES\n\n```dataview\nLIST FROM \"\" \nWHERE cdate = this.cdate\n Or mdate = this.mdate\n``\n\n## LINKS\n\n## TODOs\n\n```dataview\nTASK FROM \"dairy\"\nWHERE !completed\n AND mdate >= (this.mdate - dur(7 days))\n AND mdate <= this.mdate\nSORT file.cday DESC\n``\n\n## THOUGHTS\n
Note Text Only ---\ntitle: \" {{date}} \"\ntags:\n - \" #\u5468\u8bb0 \"\ncategories: dairy\ndate: \" {{ date:YYYY-MM-DDTHH:mm:ss+08:00 }} \"\nmodify: \" {{ date:YYYY-MM-DDTHH:mm:ss+08:00 }} \"\ndir: dairy\nshare: false\ncdate: \" {{ date:YYYY-MM-DD }} \"\nmdate: \" {{ date:YYYY-MM-DD }} \"\n---\n\n# {{date:YYYY}} -W {{date:WW}} - {{date:MM}}\n\n## Review\n\n## Next Week Plan\n\n## Time Line\n\n## THOUGHTS\n\n## LINKS\n\n```dataviewjs\n// Configuration for collecting LINKS sections from daily notes\nconst tars = {\n 'LINKS': 2, // Collect second-level LINKS headings\n}\n\nawait dv.view('zob_config/js/dv-\u68c0\u7d22', {\n // Get only daily notes from dairy folder\n files: dv.pages('\"dairy\"')\n .where(p => {\n // Extract the week number from the current file name (weekly note)\n const weekMatch = dv.current().file.name.match(/(\\d{4})-W(\\d{1,2})/);\n if (!weekMatch) return false;\n\n const [_, weekYear, weekNum] = weekMatch;\n\n // Extract date components from daily note name (2024-49-12-08-7 format)\n const dateMatch = p.file.name.match(/(\\d{4})-(\\d{1,2})-(\\d{2})-(\\d{2})-(\\d{1})/);\n if (!dateMatch) return false;\n\n const [__, year, week, month, day] = dateMatch;\n\n // Check if the daily note belongs to the same week and year\n return year === weekYear && week === weekNum;\n })\n .sort(p => {\n // Create sortable date string from daily note format\n const [_, year, week, month, day] = p.file.name.match(/^(\\d{4})-(\\d{1,2})-(\\d{2})-(\\d{2})-(\\d{1})$/);\n // Sort by YYYYMMDD format (descending)\n return -1 * parseInt(`${year}${month.padStart(2, '0')}${day.padStart(2, '0')}`);\n }),\n\n kwd: false, // Don't filter by keywords\n showHead: false, // Don't include heading in output\n tars, // Target sections to collect (LINKS)\n obsidian, // Pass obsidian object\n scale: 0.8, // Scale of rendered content\n\n // List item configuration\n li: ([p, li]) => {\n const [_, year, week, month, day] = p.file.name.match(/^(\\d{4})-(\\d{1,2})-(\\d{2})-(\\d{2})-(\\d{1})$/);\n const formattedDate = `${year}-${month.padStart(2, '0')}-${day.padStart(2, '0')}`;\n // Create a header with date and file link, followed by the content\n return dv.paragraph(`### ${formattedDate} [[${p.file.path}|${p.file.name}]]\\n${li}`);\n },\n});\n``\n
","tags":["Environment","Obsidian"]},{"location":"Tools/Environment/obsidian_setup/#3","title":"3 \u76f8\u5173\u94fe\u63a5","text":" - PKMer_PKMer
- Obsidian \u4e2d\u6587\u8bba\u575b - Obsidian \u77e5\u8bc6\u7ba1\u7406 \u7b14\u8bb0
- Obsidian\u6587\u6863\u5496\u5561\u8c46\u7248 | Obsidian Docs by CoffeeBean
- zhuanlan.zhihu.com/p/619960525
","tags":["Environment","Obsidian"]},{"location":"Tools/Make/CMake/","title":"CMake \u76f8\u5173","text":""},{"location":"Tools/Make/CMake/#cmake","title":"CMake \u76f8\u5173","text":" \u7ea6 161 \u4e2a\u5b57 13 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"Tools/Make/CMake/#1","title":"1 \u6784\u5efa\u6700\u5c0f\u9879\u76ee","text":" - CMake \u652f\u6301\u5927\u5199\u3001\u5c0f\u5199\u548c\u6df7\u5408\u5927\u5c0f\u5199\u547d\u4ee4\u3001
Text Onlymkdir build\ncd build\ncmake -G\"MinGW Makefiles\" ..\ncmake --build .\n
\u4e4b\u540e\u4f1a\u751f\u6210\u53ef\u6267\u884c\u6587\u4ef6
Text Onlystep1/\n build/\n CMakeLists.txt\n tutorial.cpp\n
"},{"location":"Tools/Make/CMake/#2-cmakelists-txt","title":"2 \u4f18\u5316 CMakeLists. txt \u6587\u4ef6","text":"CMakecmake_minimum_required(VERSION 3.15)\n\n# set the project name\nproject(Tutorial)\n\nSET(SRC_LIST tutorial.cpp)\n\n# add the executable\nadd_executable(${PROJECT_NAME} ${SRC_LIST})\n
1.0.2 \u5206\u522b\u5bf9\u5e94 MAJOR MINOR PATCH
- set \u548c PROJECT_NAME
- \u6dfb\u52a0\u7248\u672c\u53f7\u548c\u914d\u7f6e\u5934\u6587\u4ef6
- \u6dfb\u52a0\u7f16\u8bd1\u65f6\u95f4\u6233
- \u6307\u5b9a C++\u6807\u51c6
- \u6dfb\u52a0\u5e93\uff08\u6dfb\u52a0\u5e93\u7684\u4f4d\u7f6e\uff0c\u5e93\u6587\u4ef6\u540d\uff0c\u5934\u6587\u4ef6\u540d\uff09
- \u5c06\u5e93\u8bbe\u7f6e\u4e3a\u53ef\u9009\u9879\uff08\u5206\u7ecf\u5178\u548c\u73b0\u4ee3\uff09
- \u6dfb\u52a0\u5e93\u7684\u4f7f\u7528\u8981\u6c42
- INTERFACE
- PRIVATE
- PUBLIC
- \u9759\u6001\u94fe\u63a5\u5e93/\u52a8\u6001\u94fe\u63a5\u5e93
- build \u76ee\u5f55\u4ecb\u7ecd
"},{"location":"Tools/Make/CMake/#links","title":"links","text":" - Site Unreachable
- IPADS\u65b0\u4eba\u57f9\u8bad\u7b2c\u4e8c\u8bb2\uff1aCMake_\u54d4\u54e9\u54d4\u54e9_bilibili
"},{"location":"Tools/Make/Makeflie/","title":"Makeflie","text":""},{"location":"Tools/Make/Makeflie/#make","title":"Make \u57fa\u7840","text":" \u7ea6 220 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"Tools/Make/Makeflie/#make_1","title":"\u4ec0\u4e48\u662f Make","text":"Make \u662f\u4e00\u4e2a\u81ea\u52a8\u5316\u6784\u5efa\u5de5\u5177\uff0c\u4f7f\u7528 Makefile \u6587\u4ef6\u6765\u5b9a\u4e49\u5982\u4f55\u7f16\u8bd1\u548c\u94fe\u63a5\u7a0b\u5e8f\u3002\u5b83\u901a\u8fc7\u68c0\u67e5\u6587\u4ef6\u7684\u65f6\u95f4\u6233\u6765\u51b3\u5b9a\u54ea\u4e9b\u6587\u4ef6\u9700\u8981\u91cd\u65b0\u7f16\u8bd1\u3002
"},{"location":"Tools/Make/Makeflie/#makefile","title":"Makefile \u7684\u57fa\u672c\u7ed3\u6784","text":"Makefile \u7684\u57fa\u672c\u7ed3\u6784\u7531\u76ee\u6807\u3001\u4f9d\u8d56\u548c\u547d\u4ee4\u7ec4\u6210\uff0c\u901a\u5e38\u5f62\u5f0f\u4e3a\uff1a
Text Onlytarget: dependencies \n command\n
"},{"location":"Tools/Make/Makeflie/#makefile_1","title":"Makefile \u793a\u4f8b","text":"\u8ba9\u6211\u4eec\u8003\u8651\u4e00\u4e2a\u7b80\u5355\u7684 C \u8bed\u8a00\u9879\u76ee\uff0c\u8be5\u793a\u4f8b\u5c06\u5c55\u793a\u5982\u4f55\u4f7f\u7528 Makefile \u6765\u7f16\u8bd1\u4e00\u4e2a\u5177\u6709\u591a\u4e2a\u6e90\u6587\u4ef6\u548c\u5934\u6587\u4ef6\u7684\u7a0b\u5e8f\uff0c\u5e76\u5c55\u793a Makefile \u76f8\u6bd4\u624b\u52a8\u547d\u4ee4\u884c\u7f16\u8bd1\u7684\u4f18\u52bf\u3002 \u7f16\u8bd1\u8fdb\u9636 - HPC\u5165\u95e8\u6307\u5357
"},{"location":"Tools/Make/Makeflie/#make_2","title":"Make \u7684\u5e38\u7528\u547d\u4ee4","text":" make\uff1a\u6267\u884c\u9ed8\u8ba4\u76ee\u6807\uff0c\u4e0emake all\u7b49\u6548\u3002make <target>\uff1a\u6267\u884c\u5b9a\u4e49\u7684<target>\u76ee\u6807\uff0c\u5982\u679c\u6ca1\u6709\u8fd9\u4e2a\u76ee\u6807\u5c06\u8fd4\u56de\u9519\u8bef\u4fe1\u606f\u3002make -j\uff1a\u5e76\u884c\u6267\u884c\u6784\u5efa\uff0c\u4f7f\u7528\u672c\u673a\u7684\u5168\u90e8\u7ebf\u7a0b
"},{"location":"Tools/Others/Chezmoi/","title":"\u7528 chezmoi \u5b9e\u73b0\u8de8\u8bbe\u5907\u540c\u6b65\u914d\u7f6e","text":""},{"location":"Tools/Others/Chezmoi/#chezmoi","title":"\u7528 chezmoi \u5b9e\u73b0\u8de8\u8bbe\u5907\u540c\u6b65\u914d\u7f6e","text":" \u7ea6 512 \u4e2a\u5b57 142 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 4 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
\u672c\u6307\u5357\u5c06\u5e2e\u52a9\u4f60\u4f7f\u7528 chezmoi \u7ba1\u7406\u4f60\u7684\u914d\u7f6e\u6587\u4ef6\uff08dotfiles\uff09\uff0c\u5e76\u4f7f\u7528\u5305\u7ba1\u7406\u5668\u7ef4\u62a4\u8f6f\u4ef6\u5217\u8868\u3002
"},{"location":"Tools/Others/Chezmoi/#_1","title":"\u524d\u671f\u51c6\u5907","text":""},{"location":"Tools/Others/Chezmoi/#1","title":"1. \u9700\u8981\u7684\u5de5\u5177","text":" - Git
- GitHub \u8d26\u53f7
- chezmoi
- \u5305\u7ba1\u7406\u5668\uff08Windows: Scoop, Ubuntu: apt/snap\uff09
"},{"location":"Tools/Others/Chezmoi/#2","title":"2. \u91cd\u8981\u7684\u914d\u7f6e\u6587\u4ef6","text":"Windows \u5e38\u7528\u914d\u7f6e\u6587\u4ef6:
Text Only%USERPROFILE%/\n\u251c\u2500\u2500 .gitconfig # Git\u914d\u7f6e\n\u251c\u2500\u2500 .ssh/ # SSH\u914d\u7f6e\n\u251c\u2500\u2500 Documents/\n\u2502 \u2514\u2500\u2500 PowerShell/\n\u2502 \u2514\u2500\u2500 Microsoft.PowerShell_profile.ps1 # PowerShell\u914d\u7f6e\n\u251c\u2500\u2500 AppData/\n\u2502 \u251c\u2500\u2500 Roaming/\n\u2502 \u2502 \u2514\u2500\u2500 Code/\n\u2502 \u2502 \u2514\u2500\u2500 User/\n\u2502 \u2502 \u2514\u2500\u2500 settings.json # VSCode\u914d\u7f6e\n\u2502 \u2514\u2500\u2500 Local/\n\u2514\u2500\u2500 .config/\n \u2514\u2500\u2500 scoop/\n \u2514\u2500\u2500 config.json # Scoop\u914d\u7f6e\n
Ubuntu \u5e38\u7528\u914d\u7f6e\u6587\u4ef6:
Text Only~/\n\u251c\u2500\u2500 .bashrc # Bash\u914d\u7f6e\n\u251c\u2500\u2500 .zshrc # Zsh\u914d\u7f6e\n\u251c\u2500\u2500 .gitconfig # Git\u914d\u7f6e\n\u251c\u2500\u2500 .ssh/ # SSH\u914d\u7f6e\n\u2514\u2500\u2500 .config/\n \u251c\u2500\u2500 Code/\n \u2502 \u2514\u2500\u2500 User/\n \u2502 \u2514\u2500\u2500 settings.json # VSCode\u914d\u7f6e\n \u2514\u2500\u2500 tabby/\n \u2514\u2500\u2500 config.yaml # Tabby\u7ec8\u7aef\u914d\u7f6e\n
"},{"location":"Tools/Others/Chezmoi/#github","title":"GitHub \u8bbe\u7f6e","text":""},{"location":"Tools/Others/Chezmoi/#1-github","title":"1. \u521b\u5efa GitHub \u4ed3\u5e93","text":" - \u8bbf\u95ee GitHub \u5e76\u767b\u5f55
- \u70b9\u51fb \"New repository\"
- \u4ed3\u5e93\u540d\u79f0\u8bbe\u7f6e\u4e3a
dotfiles - \u8bbe\u7f6e\u4e3a Public\uff08\u63a8\u8350\uff09
- \u4e0d\u8981\u521d\u59cb\u5316 README\uff08\u6211\u4eec\u5c06\u4ece\u672c\u5730\u521d\u59cb\u5316\uff09
- \u521b\u5efa\u4ed3\u5e93
"},{"location":"Tools/Others/Chezmoi/#2-ssh","title":"2. \u914d\u7f6e SSH \u5bc6\u94a5\uff08\u5982\u679c\u8fd8\u6ca1\u6709\uff09","text":"Bash# \u751f\u6210SSH\u5bc6\u94a5\nssh-keygen -t ed25519 -C \"your_email@example.com\"\n\n# \u5c06\u516c\u94a5\u6dfb\u52a0\u5230GitHub\n# 1. \u590d\u5236\u516c\u94a5\u5185\u5bb9\ncat ~/.ssh/id_ed25519.pub\n# 2. \u8bbf\u95ee GitHub \u2192 Settings \u2192 SSH and GPG keys \u2192 New SSH key\n# 3. \u7c98\u8d34\u516c\u94a5\u5185\u5bb9\u5e76\u4fdd\u5b58\n
"},{"location":"Tools/Others/Chezmoi/#windows","title":"Windows \u914d\u7f6e","text":""},{"location":"Tools/Others/Chezmoi/#1_1","title":"1. \u5b89\u88c5\u5fc5\u8981\u5de5\u5177","text":"\u4f7f\u7528 PowerShell\uff08\u4ee5\u7ba1\u7406\u5458\u8eab\u4efd\u8fd0\u884c\uff09\uff1a
PowerShell# \u5b89\u88c5Scoop\nSet-ExecutionPolicy RemoteSigned -Scope CurrentUser\nirm get.scoop.sh | iex\n\n# \u5b89\u88c5Git\uff08\u5982\u679c\u8fd8\u6ca1\u6709\uff09\nscoop install git\n\n# \u5b89\u88c5chezmoi\nscoop install chezmoi\n
"},{"location":"Tools/Others/Chezmoi/#2-chezmoi","title":"2. \u521d\u59cb\u5316 chezmoi","text":"PowerShell# \u521d\u59cb\u5316chezmoi\u5e76\u514b\u9686\u4f60\u7684\u4ed3\u5e93\nchezmoi init --apply https://github.com/yourusername/dotfiles.git\n# \u7528 ssh \u4e5f\u53ef\u4ee5\n\n# \u67e5\u770bchezmoi\u5c06\u8fdb\u884c\u7684\u66f4\u6539\nchezmoi diff\n\n# \u5c06\u73b0\u6709\u914d\u7f6e\u6587\u4ef6\u6dfb\u52a0\u5230chezmoi\nchezmoi add $HOME/.gitconfig\nchezmoi add $HOME/.ssh/config\nchezmoi add $HOME/Documents/PowerShell/Microsoft.PowerShell_profile.ps1\nchezmoi add $HOME/AppData/Roaming/Code/User/settings.json\n\n# \u63d0\u4ea4\u5e76\u63a8\u9001\u66f4\u6539\nchezmoi cd\ngit add .\ngit commit -m \"Initial Windows config\"\ngit push\n
"},{"location":"Tools/Others/Chezmoi/#3","title":"3. \u5bfc\u51fa\u8f6f\u4ef6\u5305\u5217\u8868","text":"PowerShell# \u5bfc\u51faScoop\u5305\u5217\u8868\nscoop export > packages/scoop-packages.txt\n\n# \u63d0\u4ea4\u5305\u5217\u8868\nchezmoi cd\ngit add packages/scoop-packages.txt\ngit commit -m \"Add Windows package list\"\ngit push\n
"},{"location":"Tools/Others/Chezmoi/#ubuntu","title":"Ubuntu \u914d\u7f6e","text":""},{"location":"Tools/Others/Chezmoi/#1_2","title":"1. \u5b89\u88c5\u5fc5\u8981\u5de5\u5177","text":"Bash# \u5b89\u88c5Git\uff08\u5982\u679c\u8fd8\u6ca1\u6709\uff09\nsudo apt update\nsudo apt install git\n\n# \u5b89\u88c5chezmoi\nsh -c \"$(curl -fsLS get.chezmoi.io)\"\n
\u53c2\u8003\u8fd9\u4e2a\u7f51\u7ad9 Install - chezmoi \u8fdb\u884c\u4e0b\u8f7d\uff0c\u6211\u547d\u4ee4\u884c\u4e00\u76f4\u4e0d\u6210\u529f\uff0c\u76f4\u63a5\u9009\u62e9\u5bf9\u5e94\u7684\u5305\u5c31\u884c\u4e86\u3002
Text Onlysudo dpkg -i chezmoi_2.54.0_linux_amd64.deb\nchezmoi --version\n
"},{"location":"Tools/Others/Chezmoi/#2-chezmoi_1","title":"2. \u521d\u59cb\u5316 chezmoi","text":"Bash# \u521d\u59cb\u5316chezmoi\u5e76\u514b\u9686\u4f60\u7684\u4ed3\u5e93\nchezmoi init --apply https://github.com/yourusername/dotfiles.git\n\n# \u67e5\u770bchezmoi\u5c06\u8fdb\u884c\u7684\u66f4\u6539\nchezmoi diff\n\n# \u5c06\u73b0\u6709\u914d\u7f6e\u6587\u4ef6\u6dfb\u52a0\u5230chezmoi\nchezmoi add ~/.bashrc\nchezmoi add ~/.zshrc\nchezmoi add ~/.gitconfig\nchezmoi add ~/.ssh/config\nchezmoi add ~/.config/Code/User/settings.json\n\n# \u63d0\u4ea4\u5e76\u63a8\u9001\u66f4\u6539\nchezmoi cd\ngit add .\ngit commit -m \"Initial Ubuntu config\"\ngit push\n
"},{"location":"Tools/Others/Chezmoi/#3_1","title":"3. \u5bfc\u51fa\u8f6f\u4ef6\u5305\u5217\u8868","text":"Bashchezmoi cd\nmkdir packages\n# \u5bfc\u51faapt\u5305\u5217\u8868\ndpkg --get-selections | grep -v deinstall | awk '{print $1}' > packages/apt-packages.txt\n\n# \u5bfc\u51fasnap\u5305\u5217\u8868\nsnap list | awk '{if (NR>1) print $1}' > packages/snap-packages.txt\n\n# \u63d0\u4ea4\u5305\u5217\u8868\ngit add packages/apt-packages.txt packages/snap-packages.txt\ngit commit -m \"Add Ubuntu package lists\"\ngit push\n
"},{"location":"Tools/Others/Chezmoi/#_2","title":"\u65e5\u5e38\u4f7f\u7528","text":""},{"location":"Tools/Others/Chezmoi/#1_3","title":"1. \u66f4\u65b0\u914d\u7f6e","text":"\u5f53\u4f60\u4fee\u6539\u4e86\u914d\u7f6e\u6587\u4ef6\u540e\uff1a
Bash# \u5c06\u66f4\u6539\u6dfb\u52a0\u5230chezmoi\nchezmoi add ~/.bashrc # \u6216\u5176\u4ed6\u4fee\u6539\u7684\u914d\u7f6e\u6587\u4ef6\n\n# \u67e5\u770b\u66f4\u6539\nchezmoi diff\n\n# \u63d0\u4ea4\u5e76\u63a8\u9001\u66f4\u6539\nchezmoi cd\ngit add .\ngit commit -m \"Update bashrc\"\ngit push\n
"},{"location":"Tools/Others/Chezmoi/#2_1","title":"2. \u5728\u5176\u4ed6\u673a\u5668\u4e0a\u540c\u6b65","text":"Bash# \u62c9\u53d6\u5e76\u5e94\u7528\u6700\u65b0\u66f4\u6539\nchezmoi update\n
"},{"location":"Tools/Others/Chezmoi/#3_2","title":"3. \u66f4\u65b0\u8f6f\u4ef6\u5305\u5217\u8868","text":"Windows:
PowerShell# \u66f4\u65b0Scoop\u5305\u5217\u8868\nscoop export > packages/scoop-packages.txt\n
Ubuntu:
Bash# \u66f4\u65b0apt\u5305\u5217\u8868\ndpkg --get-selections | grep -v deinstall | awk '{print $1}' > packages/apt-packages.txt\n\n# \u66f4\u65b0snap\u5305\u5217\u8868\nsnap list | awk '{if (NR>1) print $1}' > packages/snap-packages.txt\n
"},{"location":"Tools/Others/Chezmoi/#4","title":"4. \u5728\u65b0\u673a\u5668\u4e0a\u8bbe\u7f6e","text":"Windows:
PowerShell# \u5b89\u88c5chezmoi\nscoop install chezmoi\n\n# \u521d\u59cb\u5316\u5e76\u5e94\u7528\u914d\u7f6e\nchezmoi init https://github.com/yourusername/dotfiles.git\nchezmoi apply\n
Ubuntu:
Bash# \u5b89\u88c5chezmoi\nsh -c \"$(curl -fsLS get.chezmoi.io)\"\n\n# \u521d\u59cb\u5316\u5e76\u5e94\u7528\u914d\u7f6e\nchezmoi init https://github.com/yourusername/dotfiles.git\nchezmoi apply\n
"},{"location":"Tools/Others/Chezmoi/#_3","title":"\u5e38\u89c1\u95ee\u9898","text":""},{"location":"Tools/Others/Chezmoi/#1_4","title":"1. \u5982\u4f55\u5904\u7406\u4e0d\u540c\u673a\u5668\u7684\u7279\u5b9a\u914d\u7f6e\uff1f","text":"\u4f7f\u7528\u6a21\u677f\u548c\u6761\u4ef6\u8bed\u53e5\u3002\u5728 .chezmoi.toml.tmpl \u4e2d\uff1a
TOML{{- $osid := .chezmoi.os -}}\n[data]\n name = \"Your Name\"\n email = \"your@email.com\"\n {{- if eq .chezmoi.os \"windows\" }}\n is_windows = true\n {{- else if eq .chezmoi.os \"linux\" }}\n is_linux = true\n {{- end }}\n
"},{"location":"Tools/Others/Chezmoi/#2_2","title":"2. \u5982\u4f55\u5904\u7406\u654f\u611f\u4fe1\u606f\uff1f","text":"\u5bf9\u4e8e\u654f\u611f\u4fe1\u606f\uff0c\u53ef\u4ee5\uff1a
- \u4f7f\u7528\u6a21\u677f\u548c\u73af\u5883\u53d8\u91cf
- \u4f7f\u7528 chezmoi \u7684\u52a0\u5bc6\u529f\u80fd
- \u5c06\u654f\u611f\u4fe1\u606f\u5b58\u50a8\u5728\u5355\u72ec\u7684\u79c1\u6709\u4ed3\u5e93\u4e2d
"},{"location":"Tools/Others/Chezmoi/#3_3","title":"3. \u5982\u4f55\u64a4\u9500\u66f4\u6539\uff1f","text":"Bash# \u67e5\u770b\u5c06\u8981\u8fdb\u884c\u7684\u66f4\u6539\nchezmoi diff\n\n# \u5982\u679c\u4e0d\u6ee1\u610f\uff0c\u53ef\u4ee5\u64a4\u9500\nchezmoi forget ~/.bashrc # \u79fb\u9664\u6587\u4ef6\u7684\u7ba1\u7406\n\n# \u6216\u8005\u91cd\u7f6e\u4e3a\u539f\u59cb\u72b6\u6001\nchezmoi apply --force\n
"},{"location":"Tools/Others/Chezmoi/#4_1","title":"4. \u914d\u7f6e\u6587\u4ef6\u6743\u9650\u95ee\u9898\uff1f","text":"chezmoi \u4f1a\u81ea\u52a8\u5904\u7406\u6587\u4ef6\u6743\u9650\u3002\u5bf9\u4e8e\u7279\u6b8a\u6743\u9650\u9700\u6c42\uff0c\u53ef\u4ee5\u5728\u6e90\u6587\u4ef6\u540d\u4e2d\u4f7f\u7528\u7279\u6b8a\u524d\u7f00\uff1a
private_ : \u521b\u5efa\u79c1\u6709\u6587\u4ef6 (chmod 600)executable_ : \u521b\u5efa\u53ef\u6267\u884c\u6587\u4ef6 (chmod 700)readonly_ : \u521b\u5efa\u53ea\u8bfb\u6587\u4ef6 (chmod 400)
"},{"location":"Tools/Others/Chezmoi/#5","title":"5. \u5982\u4f55\u67e5\u770b\u7ba1\u7406\u7684\u6587\u4ef6\uff1f","text":"Bash# \u5217\u51fa\u6240\u6709\u7ba1\u7406\u7684\u6587\u4ef6\nchezmoi managed\n\n# \u67e5\u770b\u6e90\u6587\u4ef6\nchezmoi cd\nls -la\n
"},{"location":"Tools/Others/Chezmoi/#6","title":"6. \u66f4\u65b0\u51fa\u9519\u600e\u4e48\u529e\uff1f","text":"Bash# \u5907\u4efd\u5f53\u524d\u72b6\u6001\nchezmoi archive --output=backup.tar.gz\n\n# \u91cd\u7f6e\u66f4\u6539\nchezmoi init --force\n\n# \u91cd\u65b0\u5e94\u7528\u914d\u7f6e\nchezmoi apply\n
"},{"location":"Tools/Others/SSH/","title":"SSH\u914d\u7f6e\u6307\u5357","text":""},{"location":"Tools/Others/SSH/#ssh","title":"SSH\u914d\u7f6e\u6307\u5357","text":" \u7ea6 641 \u4e2a\u5b57 195 \u884c\u4ee3\u7801 1 \u5f20\u56fe\u7247 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 6 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"Tools/Others/SSH/#ssh_1","title":"\u4e00\u3001SSH\u57fa\u7840\u6982\u5ff5","text":""},{"location":"Tools/Others/SSH/#1-ssh","title":"1. SSH\u5de5\u4f5c\u539f\u7406","text":"SSH(Secure Shell)\u662f\u4e00\u79cd\u52a0\u5bc6\u7684\u7f51\u7edc\u534f\u8bae\uff0c\u901a\u8fc7\u5728\u4e0d\u5b89\u5168\u7684\u7f51\u7edc\u4e0a\u4e3a\u7f51\u7edc\u670d\u52a1\u63d0\u4f9b\u5b89\u5168\u7684\u4f20\u8f93\u73af\u5883\u3002SSH\u901a\u8fc7\u4f7f\u7528\u52a0\u5bc6\u6280\u672f\uff0c\u80fd\u591f\u6709\u6548\u9632\u6b62\u4e2d\u95f4\u4eba\u653b\u51fb\uff0c\u4fdd\u62a4\u6570\u636e\u4f20\u8f93\u7684\u5b89\u5168\u3002
SSH\u5de5\u4f5c\u6d41\u7a0b\uff1a 1. TCP\u8fde\u63a5\u5efa\u7acb\uff1a\u5ba2\u6237\u7aef\u548c\u670d\u52a1\u5668\u5efa\u7acbTCP\u8fde\u63a5\uff08\u9ed8\u8ba4\u7aef\u53e322\uff09 2. \u7248\u672c\u534f\u5546\uff1a\u53cc\u65b9\u4ea4\u6362\u7248\u672c\u4fe1\u606f\uff0c\u786e\u5b9a\u4f7f\u7528\u7684SSH\u534f\u8bae\u7248\u672c 3. \u5bc6\u94a5\u4ea4\u6362\uff1a\u4f7f\u7528Diffie-Hellman\u7b97\u6cd5\u4ea4\u6362\u4f1a\u8bdd\u5bc6\u94a5 4. \u8ba4\u8bc1\uff1a\u4f7f\u7528\u516c\u94a5\u6216\u5bc6\u7801\u8fdb\u884c\u8eab\u4efd\u9a8c\u8bc1 5. \u4f1a\u8bdd\uff1a\u5efa\u7acb\u52a0\u5bc6\u901a\u4fe1\u901a\u9053
"},{"location":"Tools/Others/SSH/#2","title":"2. \u8ba4\u8bc1\u65b9\u5f0f\u8be6\u89e3","text":""},{"location":"Tools/Others/SSH/#21","title":"2.1 \u5bc6\u7801\u8ba4\u8bc1","text":" - \u6700\u7b80\u5355\u4f46\u6700\u4e0d\u5b89\u5168\u7684\u8ba4\u8bc1\u65b9\u5f0f
- \u5bb9\u6613\u53d7\u5230\u66b4\u529b\u7834\u89e3\u653b\u51fb
- \u4e0d\u63a8\u8350\u5728\u751f\u4ea7\u73af\u5883\u4e2d\u4f7f\u7528
"},{"location":"Tools/Others/SSH/#22","title":"2.2 \u516c\u94a5\u8ba4\u8bc1","text":"\u8ba4\u8bc1\u6d41\u7a0b 1. \u5ba2\u6237\u7aef\u53d1\u9001\u516c\u94a5\u4fe1\u606f\u7ed9\u670d\u52a1\u5668 2. \u670d\u52a1\u5668\u68c0\u67e5authorized_keys\u6587\u4ef6 3. \u670d\u52a1\u5668\u751f\u6210\u968f\u673a\u5b57\u7b26\u4e32\uff0c\u7528\u516c\u94a5\u52a0\u5bc6\u540e\u53d1\u9001\u7ed9\u5ba2\u6237\u7aef 4. \u5ba2\u6237\u7aef\u7528\u79c1\u94a5\u89e3\u5bc6\uff0c\u5c06\u7ed3\u679c\u8fd4\u56de\u670d\u52a1\u5668 5. \u670d\u52a1\u5668\u9a8c\u8bc1\u7ed3\u679c\uff0c\u5b8c\u6210\u8ba4\u8bc1
"},{"location":"Tools/Others/SSH/#3","title":"3. \u5b89\u5168\u5efa\u8bae","text":""},{"location":"Tools/Others/SSH/#31","title":"3.1 \u57fa\u672c\u5b89\u5168\u8bbe\u7f6e","text":"Bash# /etc/ssh/sshd_config \u5b89\u5168\u914d\u7f6e\nPermitRootLogin no # \u7981\u6b62root\u76f4\u63a5\u767b\u5f55\nPasswordAuthentication no # \u7981\u7528\u5bc6\u7801\u8ba4\u8bc1\nPubkeyAuthentication yes # \u542f\u7528\u516c\u94a5\u8ba4\u8bc1\nPermitEmptyPasswords no # \u7981\u6b62\u7a7a\u5bc6\u7801\nProtocol 2 # \u53ea\u4f7f\u7528SSH2\u534f\u8bae\nMaxAuthTries 3 # \u6700\u5927\u8ba4\u8bc1\u5c1d\u8bd5\u6b21\u6570\nLoginGraceTime 30 # \u767b\u5f55\u8d85\u65f6\u65f6\u95f4\nX11Forwarding no # \u7981\u7528X11\u8f6c\u53d1\uff08\u9664\u975e\u9700\u8981\uff09\nAllowUsers user1 user2 # \u9650\u5236\u5141\u8bb8\u767b\u5f55\u7684\u7528\u6237\n
"},{"location":"Tools/Others/SSH/#32","title":"3.2 \u5bc6\u94a5\u7ba1\u7406","text":"Bash# \u751f\u6210\u5f3a\u5bc6\u94a5\nssh-keygen -t ed25519 -C \"your_email@example.com\" -a 100\n\n# \u5bc6\u94a5\u6743\u9650\u8bbe\u7f6e\nchmod 700 ~/.ssh\nchmod 600 ~/.ssh/id_ed25519\nchmod 644 ~/.ssh/id_ed25519.pub\nchmod 600 ~/.ssh/authorized_keys\nchmod 600 ~/.ssh/known_hosts\n
"},{"location":"Tools/Others/SSH/#ssh_2","title":"\u5e38\u7528\u7684 SSH \u547d\u4ee4","text":"SSH \u6559\u7a0b \u83dc\u9e1f\u6559\u7a0b
"},{"location":"Tools/Others/SSH/#_1","title":"\u4e8c\u3001\u5b8c\u6574\u914d\u7f6e\u6307\u5357","text":""},{"location":"Tools/Others/SSH/#1-linux","title":"1. Linux\u670d\u52a1\u5668\u914d\u7f6e","text":"Bash# 1. \u5b89\u88c5SSH\u670d\u52a1\u5668\nsudo apt update\nsudo apt install openssh-server\n\n# 2. \u914d\u7f6eSSH\u670d\u52a1\nsudo nano /etc/ssh/sshd_config\n\n# 3. \u57fa\u672c\u5b89\u5168\u914d\u7f6e\nPort 22 # \u53ef\u4ee5\u4fee\u6539\u4e3a\u975e\u6807\u51c6\u7aef\u53e3\nListenAddress 0.0.0.0 # \u76d1\u542c\u5730\u5740\nProtocol 2\nPermitRootLogin no\nPasswordAuthentication no\nPubkeyAuthentication yes\nAuthorizedKeysFile .ssh/authorized_keys\nUsePAM yes\nX11Forwarding no\nPrintMotd no\nAcceptEnv LANG LC_*\nSubsystem sftp /usr/lib/openssh/sftp-server\n\n# 4. \u91cd\u542fSSH\u670d\u52a1\nsudo systemctl restart sshd\n\n# 5. \u68c0\u67e5\u670d\u52a1\u72b6\u6001\nsudo systemctl status sshd\n
"},{"location":"Tools/Others/SSH/#2-windows-ssh","title":"2. Windows SSH\u914d\u7f6e","text":""},{"location":"Tools/Others/SSH/#21-openssh","title":"2.1 \u5b89\u88c5OpenSSH","text":"PowerShell# \u4f7f\u7528PowerShell\u5b89\u88c5OpenSSH\n# \u68c0\u67e5OpenSSH\u72b6\u6001\nGet-WindowsCapability -Online | Where-Object Name -like 'OpenSSH*'\n\n# \u5b89\u88c5\u5ba2\u6237\u7aef\u548c\u670d\u52a1\u5668\nAdd-WindowsCapability -Online -Name OpenSSH.Client~~~~0.0.1.0\nAdd-WindowsCapability -Online -Name OpenSSH.Server~~~~0.0.1.0\n\n# \u542f\u52a8SSH\u670d\u52a1\nStart-Service sshd\nSet-Service -Name sshd -StartupType 'Automatic'\n
\u5982\u679c\u6709\u7f51\u7edc\u95ee\u9898\u53ef\u4ee5\u4e0b\u8f7d\u5e76\u5b89\u88c5 OpenSSH \u7684\u79bb\u7ebf\u5b89\u88c5\u5305\uff1a - \u60a8\u53ef\u4ee5\u5c1d\u8bd5\u4ece GitHub \u4e0a\u4e0b\u8f7d OpenSSH \u7684\u79bb\u7ebf\u5b89\u88c5\u5305\uff0c\u5e76\u6309\u7167\u4ee5\u4e0b\u6b65\u9aa4\u8fdb\u884c\u5b89\u88c5\uff1a
- \u8bbf\u95ee GitHub \u4e0a\u7684 Win32-OpenSSH \u53d1\u5e03\u9875\u9762\uff1aWin32-OpenSSH Releases
- \u4e0b\u8f7d\u9002\u7528\u4e8e\u60a8\u7684\u7cfb\u7edf\u7684\u5b89\u88c5\u5305\uff08Win32 \u6216 Win64\uff09\u3002
- \u89e3\u538b\u4e0b\u8f7d\u7684\u6587\u4ef6\u5230\u4e00\u4e2a\u76ee\u5f55\u3002
- \u4ee5\u7ba1\u7406\u5458\u6743\u9650\u6253\u5f00\u547d\u4ee4\u63d0\u793a\u7b26\uff08cmd\uff09\uff0c\u5e76\u5bfc\u822a\u5230\u89e3\u538b\u7684\u76ee\u5f55\u3002
- \u8fd0\u884c\u00a0
powershell.exe -ExecutionPolicy Bypass -File install-sshd.ps1\u00a0\u6765\u5b89\u88c5 OpenSSH \u670d\u52a1\u3002
"},{"location":"Tools/Others/SSH/#22-windows-ssh","title":"2.2 \u914d\u7f6eWindows SSH\u670d\u52a1","text":"PowerShell# \u7f16\u8f91SSH\u914d\u7f6e\u6587\u4ef6\nnotepad \"$env:ProgramData\\ssh\\sshd_config\"\n\n# \u57fa\u672c\u914d\u7f6e\u5185\u5bb9\u4e0eLinux\u7c7b\u4f3c\uff0c\u4f46\u8def\u5f84\u9700\u8981\u8c03\u6574\nPubkeyAuthentication yes\nPasswordAuthentication no\nSubsystem sftp sftp-server.exe\n
"},{"location":"Tools/Others/SSH/#3-ssh","title":"3. SSH\u5ba2\u6237\u7aef\u914d\u7f6e","text":""},{"location":"Tools/Others/SSH/#31-ssh","title":"3.1 \u521b\u5efaSSH\u914d\u7f6e\u6587\u4ef6","text":"Bash# ~/.ssh/config\n# \u5168\u5c40\u8bbe\u7f6e\nHost *\n ServerAliveInterval 60\n ServerAliveCountMax 3\n HashKnownHosts yes\n GSSAPIAuthentication no\n\n# GitHub\nHost github.com\n HostName github.com\n User git\n IdentityFile ~/.ssh/github_ed25519\n AddKeysToAgent yes\n\n# \u5f00\u53d1\u670d\u52a1\u5668\nHost dev\n HostName dev.example.com\n User developer\n Port 22\n IdentityFile ~/.ssh/dev_ed25519\n ForwardAgent yes\n\n# \u751f\u4ea7\u670d\u52a1\u5668\nHost prod\n HostName prod.example.com\n User deployer\n Port 22\n IdentityFile ~/.ssh/prod_ed25519\n ForwardAgent no\n
"},{"location":"Tools/Others/SSH/#_2","title":"\u4e09\u3001\u5177\u4f53\u5b9e\u8df5\uff0c\u7528\u7b14\u8bb0\u672c\u8fde\u53f0\u5f0f\u673a","text":""},{"location":"Tools/Others/SSH/#_3","title":"\u53f0\u5f0f\u7535\u8111\uff08\u670d\u52a1\u7aef\uff09\u914d\u7f6e\uff1a","text":" - \u5b89\u88c5\u5e76\u542f\u52a8SSH\u670d\u52a1:
Bash# Ubuntu/Debian\u7cfb\u7edf\nsudo apt install openssh-server\nsudo systemctl enable ssh\nsudo systemctl start ssh\n\n# \u68c0\u67e5SSH\u670d\u52a1\u72b6\u6001\nsudo systemctl status ssh\n
- \u914d\u7f6eSSH\u670d\u52a1:
Bash# \u7f16\u8f91SSH\u670d\u52a1\u5668\u914d\u7f6e\nsudo nano /etc/ssh/sshd_config\n\n# \u6dfb\u52a0\u6216\u4fee\u6539\u4ee5\u4e0b\u914d\u7f6e\nPermitRootLogin no\nPasswordAuthentication no\nPubkeyAuthentication yes\nAllowUsers your_username # \u66ff\u6362\u4e3a\u60a8\u7684\u7528\u6237\u540d\n
- \u8bbe\u7f6e\u56fa\u5b9aIP\u6216\u52a8\u6001DNS:
Bash# \u67e5\u770b\u5f53\u524dIP\nip addr show\n\n# \u5982\u679c\u662f\u52a8\u6001IP\uff0c\u5efa\u8bae\u8bbe\u7f6e\u9759\u6001IP\u6216\u4f7f\u7528\u52a8\u6001DNS\u670d\u52a1\n
"},{"location":"Tools/Others/SSH/#_4","title":"\u7b14\u8bb0\u672c\uff08\u5ba2\u6237\u7aef\uff09\u914d\u7f6e\uff1a","text":" - \u751f\u6210SSH\u5bc6\u94a5\u5bf9\uff08\u5982\u679c\u8fd8\u6ca1\u6709\uff09:
Bashssh-keygen -t ed25519 -C \"your_laptop\"\n
- \u5c06\u516c\u94a5\u590d\u5236\u5230\u53f0\u5f0f\u673a:
Bash# \u65b9\u6cd51\uff1a\u4f7f\u7528ssh-copy-id\nssh-copy-id -i ~/.ssh/id_ed25519.pub username@desktop_ip\n\n# \u65b9\u6cd52\uff1a\u624b\u52a8\u590d\u5236\ncat ~/.ssh/id_ed25519.pub | ssh username@desktop_ip \"mkdir -p ~/.ssh && cat >> ~/.ssh/authorized_keys\"\n
- \u914d\u7f6eSSH\u5ba2\u6237\u7aef:
Bash# \u7f16\u8f91 ~/.ssh/config\nnano ~/.ssh/config\n\n# \u6dfb\u52a0\u4ee5\u4e0b\u914d\u7f6e\nHost desktop\n HostName 192.168.1.xxx # \u66ff\u6362\u4e3a\u53f0\u5f0f\u673a\u7684IP\n User your_username # \u66ff\u6362\u4e3a\u60a8\u7684\u7528\u6237\u540d\n Port 22\n IdentityFile ~/.ssh/id_ed25519\n ForwardX11 yes # \u5982\u679c\u9700\u8981\u56fe\u5f62\u754c\u9762\u8f6c\u53d1\n ForwardAgent yes\n Compression yes\n ServerAliveInterval 60\n
"},{"location":"Tools/Others/SSH/#_5","title":"\u5e38\u7528\u8fde\u63a5\u547d\u4ee4\uff1a","text":" - \u57fa\u672c\u8fde\u63a5:
Bash# \u4ece\u7b14\u8bb0\u672c\u8fde\u63a5\u5230\u53f0\u5f0f\u673a\nssh desktop\n\n# \u4f7f\u7528\u56fe\u5f62\u754c\u9762\u8f6c\u53d1\nssh -X desktop\n
- \u6587\u4ef6\u4f20\u8f93:
Bash# \u4ece\u7b14\u8bb0\u672c\u590d\u5236\u6587\u4ef6\u5230\u53f0\u5f0f\u673a\nscp /path/to/local/file desktop:/path/to/remote/\n\n# \u4ece\u53f0\u5f0f\u673a\u590d\u5236\u6587\u4ef6\u5230\u7b14\u8bb0\u672c\nscp desktop:/path/to/remote/file /path/to/local/\n
- \u7aef\u53e3\u8f6c\u53d1:
Bash# \u672c\u5730\u7aef\u53e3\u8f6c\u53d1\nssh -L 8080:localhost:80 desktop\n\n# \u8fdc\u7a0b\u7aef\u53e3\u8f6c\u53d1\nssh -R 8080:localhost:80 desktop\n
"},{"location":"Tools/Others/SSH/#_6","title":"\u56db\u3001\u9ad8\u7ea7\u64cd\u4f5c","text":""},{"location":"Tools/Others/SSH/#1-ssh_1","title":"1. SSH\u7aef\u53e3\u8f6c\u53d1","text":""},{"location":"Tools/Others/SSH/#11","title":"1.1 \u672c\u5730\u7aef\u53e3\u8f6c\u53d1","text":"Bash# \u5c06\u672c\u57308080\u7aef\u53e3\u8f6c\u53d1\u5230\u8fdc\u7a0b80\u7aef\u53e3\nssh -L 8080:localhost:80 user@remote\n\n# \u4f7f\u7528\u914d\u7f6e\u6587\u4ef6\u8bbe\u7f6e\nHost tunnel\n HostName remote.example.com\n LocalForward 8080 localhost:80\n
"},{"location":"Tools/Others/SSH/#12","title":"1.2 \u8fdc\u7a0b\u7aef\u53e3\u8f6c\u53d1","text":"Bash# \u5c06\u8fdc\u7a0b3000\u7aef\u53e3\u8f6c\u53d1\u5230\u672c\u57303000\u7aef\u53e3\nssh -R 3000:localhost:3000 user@remote\n\n# \u914d\u7f6e\u6587\u4ef6\u8bbe\u7f6e\nHost remote-tunnel\n HostName remote.example.com\n RemoteForward 3000 localhost:3000\n
"},{"location":"Tools/Others/SSH/#2-ssh","title":"2. SSH\u4ee3\u7406\u8f6c\u53d1","text":"Bash# \u542f\u7528\u4ee3\u7406\u8f6c\u53d1\nssh -A user@remote\n\n# \u914d\u7f6e\u6587\u4ef6\u8bbe\u7f6e\nHost *\n ForwardAgent yes\n AddKeysToAgent yes\n
"},{"location":"Tools/Others/SSH/#3_1","title":"3. \u8df3\u677f\u673a\u914d\u7f6e","text":"Bash# \u901a\u8fc7\u8df3\u677f\u673a\u8fde\u63a5\nssh -J jumphost user@target\n\n# \u914d\u7f6e\u6587\u4ef6\u8bbe\u7f6e\nHost target\n HostName target.example.com\n ProxyJump jumphost\n
"},{"location":"Tools/Others/SSH/#_7","title":"\u4e94\u3001\u6545\u969c\u6392\u67e5","text":""},{"location":"Tools/Others/SSH/#1","title":"1. \u6700\u4f73\u5b9e\u8df5","text":"Bash# 1. \u4f7f\u7528SSH\u914d\u7f6e\u6587\u4ef6\u7ba1\u7406\u8fde\u63a5\n# 2. \u4e3a\u4e0d\u540c\u7528\u9014\u4f7f\u7528\u4e0d\u540c\u7684\u5bc6\u94a5\n# 3. \u5b9a\u671f\u8f6e\u6362\u5bc6\u94a5\n# 4. \u4f7f\u7528ssh-agent\u7ba1\u7406\u5bc6\u94a5\n# 5. \u5907\u4efdSSH\u914d\u7f6e\u548c\u5bc6\u94a5\n
"},{"location":"Tools/Others/SSH/#2_1","title":"2. \u5e38\u89c1\u95ee\u9898\u89e3\u51b3","text":"Bash# \u8fde\u63a5\u88ab\u62d2\u7edd\nssh -v user@host # \u67e5\u770b\u8be6\u7ec6\u8fde\u63a5\u4fe1\u606f\n\n# \u6743\u9650\u95ee\u9898\nls -la ~/.ssh # \u68c0\u67e5\u6743\u9650\nchmod 600 ~/.ssh/id_ed25519\n\n# \u5bc6\u94a5\u95ee\u9898\nssh-add -l # \u67e5\u770b\u5df2\u52a0\u8f7d\u7684\u5bc6\u94a5\nssh-add ~/.ssh/id_ed25519 # \u6dfb\u52a0\u5bc6\u94a5\u5230agent\n
"},{"location":"Tools/Others/SSH/#3_2","title":"3. \u65e5\u5fd7\u67e5\u770b","text":"Bash# \u670d\u52a1\u5668\u7aef\nsudo tail -f /var/log/auth.log # Debian/Ubuntu\nsudo tail -f /var/log/secure # CentOS/RHEL\n\n# \u5ba2\u6237\u7aef\u8c03\u8bd5\nssh -vvv user@host # \u6700\u8be6\u7ec6\u7684\u8c03\u8bd5\u4fe1\u606f\n
"},{"location":"Tools/Others/zotero_%E4%BD%BF%E7%94%A8%E6%8C%87%E5%8D%97/","title":"zotero_\u4f7f\u7528\u6307\u5357","text":" \u7ea6 156 \u4e2a\u5b57 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 1 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
#Zotero #Tools","tags":["Zotero","Tools"]},{"location":"Tools/Others/zotero_%E4%BD%BF%E7%94%A8%E6%8C%87%E5%8D%97/#zotero","title":"zotero \u4f7f\u7528\u6307\u5357","text":" - zotero \u4f7f\u7528\u6307\u5357
- Zotero \u4e2d\u6587\u793e\u533a | Zotero \u4e2d\u6587\u7ef4\u62a4\u5c0f\u7ec4
- ZOTERO \u4e0e Obsidian \u7b14\u8bb0\u63d2\u5165\u548c\u8054\u52a8\uff08\u542b\u7f8e\u5316\u7684\u7b14\u8bb0\u6a21\u677f)
- ZOTERO \u4e8e Obsidian \u8054\u52a8\u65b9\u6848
- \u770b\u4e86\u4e00\u5708\u4e0b\u6765\uff0c\u611f\u89c9\u73b0\u6709\u65b9\u6848\u90fd\u4e0d\u592a\u7b26\u5408\u5fc3\u610f\uff0c\u4e8e\u662f\u4e0d\u6298\u817e\u4e86\u3002\u4e0d\u5982\u8ba9 zotero \u548c obsidian \u5206\u5f00\uff0c\u771f\u8981\u505a\u7b14\u8bb0\u7684\u65f6\u5019\u80af\u5b9a\u662f\u76f4\u63a5\u5199\u51fa\u6765\u7684\uff0c\u6ca1\u5fc5\u8981\u4ea4\u7ec7\u5728\u4e00\u8d77\u4e86\u3002
- \u5b89\u88c5\u4e86\u4e00\u4e2a\u63d2\u4ef6 translate to pdf \u4ee5\u5916\u5c31\u6ca1\u6709\u641e\u4ec0\u4e48\u4e86\u3002
","tags":["Zotero","Tools"]},{"location":"Tools/Terminal/Tabby_Zsh/","title":"Tabby + Zsh \u914d\u7f6e\u6307\u5357","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#tabby-zsh","title":"Tabby + Zsh \u914d\u7f6e\u6307\u5357","text":" \u7ea6 236 \u4e2a\u5b57 789 \u884c\u4ee3\u7801 \u9884\u8ba1\u9605\u8bfb\u65f6\u95f4 11 \u5206\u949f \u5171\u88ab\u8bfb\u8fc7 \u6b21
"},{"location":"Tools/Terminal/Tabby_Zsh/#_1","title":"\u524d\u7f6e\u51c6\u5907","text":"\u7cfb\u7edf\u8981\u6c42
Bash# Ubuntu/Debian\nsudo apt update\nsudo apt install -y \\\n git \\\n curl \\\n wget \\\n build-essential \\\n cmake \\\n python3-pip \\\n pkg-config \\\n libssl-dev\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#zsh","title":"ZSH \u57fa\u7840\u914d\u7f6e","text":"ZSH \u5b89\u88c5
Bash# \u5b89\u88c5zsh\nsudo apt install zsh\n\n# \u8bbe\u7f6e\u4e3a\u9ed8\u8ba4shell\nchsh -s $(which zsh)\n\n# \u786e\u8ba4\u8bbe\u7f6e\necho $SHELL\n# \u5e94\u8be5\u8f93\u51fa: /usr/bin/zsh\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#1-oh-my-zsh","title":"1. Oh My Zsh \u5b89\u88c5","text":"Bash# \u5b89\u88c5Oh My Zsh\nsh -c \"$(curl -fsSL https://raw.github.com/ohmyzsh/ohmyzsh/master/tools/install.sh)\"\n\n# \u5907\u4efd\u9ed8\u8ba4\u914d\u7f6e\ncp ~/.zshrc ~/.zshrc.backup\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2","title":"2. \u63d2\u4ef6\u7ba1\u7406\u5668\u5b89\u88c5","text":"Bash# \u5b89\u88c5zinit\nbash -c \"$(curl --fail --show-error --silent --location https://raw.githubusercontent.com/zdharma-continuum/zinit/HEAD/scripts/install.sh)\"\n\n# \u7b49\u5f85\u5b89\u88c5\u5b8c\u6210\u540e\u91cd\u542f\u7ec8\u7aef\nexec zsh\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#3","title":"3. \u57fa\u7840\u914d\u7f6e\u6587\u4ef6","text":"Bash# \u521b\u5efa\u65b0\u7684\u914d\u7f6e\u6587\u4ef6\ncat << 'EOF' > ~/.zshrc\n# \u57fa\u7840\u8bbe\u7f6e\nexport ZSH=\"$HOME/.oh-my-zsh\"\nexport LANG=en_US.UTF-8\nexport EDITOR='nvim'\nexport VISUAL='nvim'\n\n# zinit\u914d\u7f6e\nsource \"$HOME/.local/share/zinit/zinit.git/zinit.zsh\"\nautoload -Uz _zinit\n(( ${+_comps} )) && _comps[zinit]=_zinit\n\n# \u52a0\u8f7d\u6838\u5fc3\u63d2\u4ef6\nzinit ice depth=1; zinit light romkatv/powerlevel10k # \u4e3b\u9898\nzinit light zsh-users/zsh-autosuggestions # \u547d\u4ee4\u5efa\u8bae\nzinit light zsh-users/zsh-syntax-highlighting # \u8bed\u6cd5\u9ad8\u4eae\nzinit light zsh-users/zsh-completions # \u8865\u5168\u589e\u5f3a\nzinit light agkozak/zsh-z # \u76ee\u5f55\u8df3\u8f6c\n\n# \u5386\u53f2\u8bb0\u5f55\u8bbe\u7f6e\nHISTFILE=\"$HOME/.zsh_history\"\nHISTSIZE=50000\nSAVEHIST=50000\nsetopt EXTENDED_HISTORY # \u8bb0\u5f55\u547d\u4ee4\u65f6\u95f4\u6233\nsetopt HIST_EXPIRE_DUPS_FIRST # \u4f18\u5148\u5220\u9664\u91cd\u590d\u547d\u4ee4\nsetopt HIST_IGNORE_DUPS # \u5ffd\u7565\u8fde\u7eed\u91cd\u590d\u547d\u4ee4\nsetopt HIST_IGNORE_SPACE # \u5ffd\u7565\u4ee5\u7a7a\u683c\u5f00\u5934\u7684\u547d\u4ee4\nsetopt HIST_VERIFY # \u6267\u884c\u5386\u53f2\u547d\u4ee4\u524d\u5c55\u793a\nsetopt INC_APPEND_HISTORY # \u5b9e\u65f6\u6dfb\u52a0\u5386\u53f2\u8bb0\u5f55\nsetopt SHARE_HISTORY # \u5171\u4eab\u5386\u53f2\u8bb0\u5f55\n\n# \u76ee\u5f55\u8bbe\u7f6e\nsetopt AUTO_CD \nsetopt AUTO_PUSHD \nsetopt PUSHD_IGNORE_DUPS \nsetopt PUSHD_MINUS \nDIRSTACKSIZE=20\n\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#4","title":"4. \u5b9e\u7528\u522b\u540d\u8bbe\u7f6e","text":"Bash# \u6dfb\u52a0\u5230~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# \u57fa\u7840\u547d\u4ee4\u589e\u5f3a\nalias ls='ls --color=auto'\nalias ll='ls -lah'\nalias la='ls -A'\nalias l='ls -CF'\nalias grep='grep --color=auto'\nalias rm='rm -i'\nalias cp='cp -i'\nalias mv='mv -i'\nalias mkdir='mkdir -p'\nalias df='df -h'\nalias free='free -m'\nalias duf='du -sh *'\nalias ps='ps auxf'\nalias ping='ping -c 5'\nalias root='sudo -i'\nalias reboot='sudo reboot'\nalias poweroff='sudo poweroff'\n\n# Git\u5feb\u6377\u547d\u4ee4\nalias gs='git status'\nalias ga='git add'\nalias gaa='git add --all'\nalias gc='git commit -m'\nalias gp='git push'\nalias gl='git pull'\nalias gd='git diff'\nalias gco='git checkout'\nalias gb='git branch'\nalias gm='git merge'\nalias glog='git log --oneline --decorate --graph'\n\n# Docker\u5feb\u6377\u547d\u4ee4\nalias dk='docker'\nalias dkc='docker-compose'\nalias dkps='docker ps'\nalias dkst='docker stats'\nalias dktop='docker top'\nalias dkimg='docker images'\nalias dkpull='docker pull'\nalias dkex='docker exec -it'\n\n# \u5feb\u901f\u7f16\u8f91\nalias zshconfig=\"$EDITOR ~/.zshrc\"\nalias zshreload=\"source ~/.zshrc\"\nalias vimconfig=\"$EDITOR ~/.vimrc\"\nalias tmuxconfig=\"$EDITOR ~/.tmux.conf\"\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#5","title":"5. \u5b9e\u7528\u51fd\u6570","text":"Bash# \u6dfb\u52a0\u5230~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# \u521b\u5efa\u5e76\u8fdb\u5165\u76ee\u5f55\nmkcd() {\n mkdir -p \"$1\" && cd \"$1\"\n}\n\n# \u63d0\u53d6\u538b\u7f29\u6587\u4ef6\nextract() {\n if [ -f $1 ]; then\n case $1 in\n *.tar.bz2) tar xjf $1 ;;\n *.tar.gz) tar xzf $1 ;;\n *.bz2) bunzip2 $1 ;;\n *.rar) unrar e $1 ;;\n *.gz) gunzip $1 ;;\n *.tar) tar xf $1 ;;\n *.tbz2) tar xjf $1 ;;\n *.tgz) tar xzf $1 ;;\n *.zip) unzip $1 ;;\n *.Z) uncompress $1 ;;\n *.7z) 7z x $1 ;;\n *) echo \"'$1' cannot be extracted\" ;;\n esac\n else\n echo \"'$1' is not a valid file\"\n fi\n}\n\n# \u5feb\u901f\u67e5\u627e\u6587\u4ef6\nff() { find . -type f -iname \"*$1*\" ; }\nfd() { find . -type d -iname \"*$1*\" ; }\n\n# \u5feb\u901f\u67e5\u770b\u8fdb\u7a0b\npsg() { ps aux | grep -v grep | grep -i -e VSZ -e \"$1\"; }\n\n# \u7f51\u7edc\u5de5\u5177\nmyip() {\n curl -s http://ipecho.net/plain\n echo\n}\n\n# \u5feb\u901fHTTP\u670d\u52a1\u5668\nserve() {\n local port=\"${1:-8000}\"\n python3 -m http.server \"$port\"\n}\n\n# Git\u65e5\u5fd7\u7f8e\u5316\ngll() {\n git log --graph --pretty=format:'%Cred%h%Creset -%C(yellow)%d%Creset %s %Cgreen(%cr) %C(bold blue)<%an>%Creset' --abbrev-commit\n}\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_2","title":"\u4e3b\u9898\u7f8e\u5316\u4e0e\u63d2\u4ef6\u589e\u5f3a","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#powerlevel10k","title":"\u4e00\u3001Powerlevel10k \u4e3b\u9898\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1","title":"1. \u5b89\u88c5\u5fc5\u8981\u5b57\u4f53","text":"Bash# \u521b\u5efa\u5b57\u4f53\u76ee\u5f55\nmkdir -p ~/.local/share/fonts\n\n# \u4e0b\u8f7d\u63a8\u8350\u5b57\u4f53\nwget -P /tmp https://github.com/romkatv/powerlevel10k-media/raw/master/MesloLGS%20NF%20Regular.ttf\nwget -P /tmp https://github.com/romkatv/powerlevel10k-media/raw/master/MesloLGS%20NF%20Bold.ttf\nwget -P /tmp https://github.com/romkatv/powerlevel10k-media/raw/master/MesloLGS%20NF%20Italic.ttf\nwget -P /tmp https://github.com/romkatv/powerlevel10k-media/raw/master/MesloLGS%20NF%20Bold%20Italic.ttf\n\n# \u79fb\u52a8\u5b57\u4f53\u6587\u4ef6\nmv /tmp/MesloLGS*.ttf ~/.local/share/fonts/\n\n# \u66f4\u65b0\u5b57\u4f53\u7f13\u5b58\nfc-cache -f -v\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2_1","title":"2. \u4e3b\u9898\u914d\u7f6e","text":"Bash# \u6dfb\u52a0\u5230 ~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# Powerlevel10k \u914d\u7f6e\n# \u542f\u7528 Powerlevel10k \u5373\u65f6\u63d0\u793a\nif [[ -r \"${XDG_CACHE_HOME:-$HOME/.cache}/p10k-instant-prompt-${(%):-%n}.zsh\" ]]; then\n source \"${XDG_CACHE_HOME:-$HOME/.cache}/p10k-instant-prompt-${(%):-%n}.zsh\"\nfi\n\n# \u52a0\u8f7d\u4e3b\u9898\nsource ~/.oh-my-zsh/custom/themes/powerlevel10k/powerlevel10k.zsh-theme\n\n# \u4e3b\u9898\u4e2a\u6027\u5316\u8bbe\u7f6e\nPOWERLEVEL9K_MODE='nerdfont-complete'\nPOWERLEVEL9K_LEFT_PROMPT_ELEMENTS=(\n os_icon # \u64cd\u4f5c\u7cfb\u7edf\u56fe\u6807\n dir # \u5f53\u524d\u76ee\u5f55\n vcs # git\u72b6\u6001\n newline # \u6362\u884c\n prompt_char # \u63d0\u793a\u7b26\n)\nPOWERLEVEL9K_RIGHT_PROMPT_ELEMENTS=(\n status # \u4e0a\u4e00\u4e2a\u547d\u4ee4\u7684\u72b6\u6001\n background_jobs # \u540e\u53f0\u4efb\u52a1\n load # \u7cfb\u7edf\u8d1f\u8f7d\n ram # \u5185\u5b58\u4f7f\u7528\n time # \u65f6\u95f4\n)\n\n# \u76ee\u5f55\u663e\u793a\u8bbe\u7f6e\nPOWERLEVEL9K_DIR_BACKGROUND='blue'\nPOWERLEVEL9K_DIR_FOREGROUND='black'\nPOWERLEVEL9K_SHORTEN_DIR_LENGTH=2\nPOWERLEVEL9K_SHORTEN_STRATEGY=\"truncate_middle\"\n\n# Git\u72b6\u6001\u8bbe\u7f6e\nPOWERLEVEL9K_VCS_CLEAN_BACKGROUND='green'\nPOWERLEVEL9K_VCS_UNTRACKED_BACKGROUND='yellow'\nPOWERLEVEL9K_VCS_MODIFIED_BACKGROUND='red'\nEOF\n\n# \u8fd0\u884c\u914d\u7f6e\u5411\u5bfc\uff08\u9996\u6b21\u4f7f\u7528\uff09\np10k configure\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_3","title":"\u4e8c\u3001\u9ad8\u7ea7\u63d2\u4ef6\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1_1","title":"1. \u9ad8\u7ea7\u8865\u5168\u7cfb\u7edf","text":"Bash# \u6dfb\u52a0\u5230 ~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# \u8865\u5168\u7cfb\u7edf\u914d\u7f6e\nautoload -Uz compinit\ncompinit\n\n# \u8865\u5168\u83dc\u5355\u8bbe\u7f6e\nzstyle ':completion:*' menu select\nzstyle ':completion:*' matcher-list 'm:{a-zA-Z}={A-Za-z}' # \u5ffd\u7565\u5927\u5c0f\u5199\nzstyle ':completion:*' list-colors ${(s.:.)LS_COLORS} # \u8865\u5168\u83dc\u5355\u7740\u8272\nzstyle ':completion:*' verbose yes # \u8be6\u7ec6\u8865\u5168\u83dc\u5355\nzstyle ':completion:*:descriptions' format '%U%B%d%b%u' # \u8865\u5168\u83dc\u5355\u683c\u5f0f\nzstyle ':completion:*:warnings' format '%BSorry, no matches for: %d%b'\nzstyle ':completion:*:*:kill:*:processes' list-colors '=(#b) #([0-9]#)*=0=01;31'\nzstyle ':completion:*:kill:*' command 'ps -u $USER -o pid,%cpu,tty,cputime,cmd'\n\n# \u4f7f\u7528\u7f13\u5b58\u52a0\u901f\u8865\u5168\nzstyle ':completion:*' use-cache on\nzstyle ':completion:*' cache-path ~/.zsh/cache\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2-fzf","title":"2. FZF \u96c6\u6210\u914d\u7f6e","text":"Bash# \u5b89\u88c5FZF\ngit clone --depth 1 https://github.com/junegunn/fzf.git ~/.fzf\n~/.fzf/install\n\n# \u6dfb\u52a0\u5230 ~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# FZF \u914d\u7f6e\nexport FZF_DEFAULT_COMMAND='fd --type f --hidden --follow --exclude .git'\nexport FZF_DEFAULT_OPTS='--height 40% --layout=reverse --border --preview \"bat --style=numbers --color=always --line-range :500 {}\"'\n\n# FZF \u5feb\u6377\u952e\nbindkey '^T' fzf-file-widget\nbindkey '^R' fzf-history-widget\nbindkey '^[c' fzf-cd-widget\n\n# FZF \u51fd\u6570\n# \u5feb\u901f\u6253\u5f00\u6587\u4ef6\nfe() {\n local file\n file=$(fzf --query=\"$1\" --select-1 --exit-0)\n [ -n \"$file\" ] && ${EDITOR:-vim} \"$file\"\n}\n\n# \u5feb\u901f\u5207\u6362\u76ee\u5f55\nfd() {\n local dir\n dir=$(find ${1:-.} -path '*/\\.*' -prune -o -type d -print 2> /dev/null | fzf +m)\n [ -n \"$dir\" ] && cd \"$dir\"\n}\n\n# \u641c\u7d22\u5386\u53f2\u547d\u4ee4\nfh() {\n print -z $( ([ -n \"$ZSH_NAME\" ] && fc -l 1 || history) | fzf +s --tac | sed -E 's/ *[0-9]*\\*? *//' | sed -E 's/\\\\/\\\\\\\\/g')\n}\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#3_1","title":"3. \u589e\u5f3a\u76ee\u5f55\u5bfc\u822a","text":"Bash# \u6dfb\u52a0\u5230 ~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# \u76ee\u5f55\u4e66\u7b7e\nhash -d proj=~/projects\nhash -d docs=~/Documents\nhash -d dl=~/Downloads\nhash -d pics=~/Pictures\n\n# z \u63d2\u4ef6\u914d\u7f6e\nZSHZ_DATA=~/.local/share/z/data\nZSHZ_MAX_SCORE=5000\nZSHZ_CASE=smart\n\n# \u76ee\u5f55\u5806\u6808\u5bfc\u822a\nsetopt AUTO_PUSHD # \u81ea\u52a8\u5c06\u76ee\u5f55\u52a0\u5165\u5806\u6808\nsetopt PUSHD_IGNORE_DUPS # \u5ffd\u7565\u91cd\u590d\u76ee\u5f55\nsetopt PUSHD_SILENT # \u9759\u9ed8\u6a21\u5f0f\nsetopt PUSHD_TO_HOME # pushd \u4e0d\u5e26\u53c2\u6570\u65f6\u7b49\u540c\u4e8e pushd $HOME\n\n# \u76ee\u5f55\u522b\u540d\nalias -g ...='../..'\nalias -g ....='../../..'\nalias -g .....='../../../..'\nalias d='dirs -v'\nfor index ({1..9}) alias \"$index\"=\"cd +${index}\"; unset index\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#4_1","title":"4. \u589e\u5f3a\u5386\u53f2\u8bb0\u5f55\u641c\u7d22","text":"Bash# \u6dfb\u52a0\u5230 ~/.zshrc\ncat << 'EOF' >> ~/.zshrc\n# \u5386\u53f2\u8bb0\u5f55\u641c\u7d22\u914d\u7f6e\nbindkey '^[[A' history-substring-search-up\nbindkey '^[[B' history-substring-search-down\nbindkey '^P' history-substring-search-up\nbindkey '^N' history-substring-search-down\n\n# \u5386\u53f2\u8bb0\u5f55\u683c\u5f0f\u5316\nHIST_STAMPS=\"yyyy-mm-dd\"\nHISTORY_IGNORE=\"(ls|ls *|cd|cd *|pwd|exit|date|* --help)\"\n\n# \u547d\u4ee4\u6267\u884c\u65f6\u95f4\u663e\u793a\nREPORTTIME=10\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#tabby","title":"Tabby \u7ec8\u7aef\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#_4","title":"\u4e00\u3001\u5b89\u88c5\u548c\u521d\u59cb\u5316","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1-tabby","title":"1. \u5b89\u88c5 Tabby","text":"Bash# Ubuntu/Debian\nwget https://github.com/Eugeny/tabby/releases/latest/download/tabby-1.0.0-linux-x64.deb\nsudo dpkg -i tabby-*.deb\nsudo apt-get install -f\n\n# \u786e\u4fdd\u5b57\u4f53\u652f\u6301\nfc-cache -fv\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2_2","title":"2. \u521d\u59cb\u914d\u7f6e","text":"YAML# ~/.config/tabby/config.yaml\nconfig:\n version: 3\n\nterminal:\n shell: zsh # \u4f7f\u7528\u524d\u9762\u914d\u7f6e\u7684zsh\n fontSize: 14\n lineHeight: 1.2\n bell: 'off'\n copyOnSelect: true\n rightClick: menu\n\n # \u57fa\u7840\u73af\u5883\u53d8\u91cf\n environment:\n TERM: xterm-256color\n COLORTERM: truecolor\n\n # \u6027\u80fd\u8bbe\u7f6e\n performanceMode: true\n gpuAcceleration: true\n webGL: true\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_5","title":"\u4e8c\u3001\u5916\u89c2\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1_2","title":"1. \u5b57\u4f53\u8bbe\u7f6e","text":"YAMLterminal:\n font: JetBrainsMono Nerd Font # \u786e\u4fdd\u5df2\u5b89\u88c5\n fontSize: 14\n lineHeight: 1.2\n ligatures: true # \u8fde\u5b57\u652f\u6301\n\n # \u5b57\u4f53\u56de\u9000\n fallbackFont: 'Sarasa Mono SC' # \u4e2d\u6587\u652f\u6301\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2-dracula","title":"2. Dracula \u4e3b\u9898\u914d\u7f6e","text":"YAMLprofiles:\n - name: Default\n theme:\n name: 'Dracula'\n\n colors:\n background: '#282a36'\n foreground: '#f8f8f2'\n cursor: '#f8f8f2'\n\n selection:\n background: '#44475a'\n foreground: '#f8f8f2'\n\n # ANSI Colors\n black: '#21222c'\n red: '#ff5555'\n green: '#50fa7b'\n yellow: '#f1fa8c'\n blue: '#bd93f9'\n magenta: '#ff79c6'\n cyan: '#8be9fd'\n white: '#f8f8f2'\n\n # Bright Colors\n brightBlack: '#6272a4'\n brightRed: '#ff6e6e'\n brightGreen: '#69ff94'\n brightYellow: '#ffffa5'\n brightBlue: '#d6acff'\n brightMagenta: '#ff92df'\n brightCyan: '#a4ffff'\n brightWhite: '#ffffff'\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#3_2","title":"3. \u900f\u660e\u80cc\u666f\u914d\u7f6e","text":"YAMLterminal:\n background:\n type: 'image' # \u6216 'color'\n image: '~/.config/tabby/backgrounds/bg.jpg' # \u81ea\u5b9a\u4e49\u80cc\u666f\u56fe\u7247\n opacity: 0.85 # \u900f\u660e\u5ea6\n\n # \u4e9a\u514b\u529b\u6548\u679c\uff08Windows\uff09\n experimental:\n vibrancy: true\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_6","title":"\u4e09\u3001\u5feb\u6377\u952e\u914d\u7f6e","text":"YAMLhotkeys:\n # \u6807\u7b7e\u7ba1\u7406\n new-tab: ['Ctrl+T']\n close-tab: ['Ctrl+W']\n previous-tab: ['Ctrl+Shift+Tab']\n next-tab: ['Ctrl+Tab']\n\n # \u5206\u5c4f\u64cd\u4f5c\n split-right: ['Ctrl+Shift+E']\n split-bottom: ['Ctrl+Shift+O']\n split-nav-left: ['Alt+Left']\n split-nav-right: ['Alt+Right']\n split-nav-up: ['Alt+Up']\n split-nav-down: ['Alt+Down']\n\n # \u7ec8\u7aef\u64cd\u4f5c\n clear: ['Ctrl+L']\n copy: ['Ctrl+C']\n paste: ['Ctrl+V']\n search: ['Ctrl+Shift+F']\n\n # \u89c6\u56fe\u63a7\u5236\n zoom-in: ['Ctrl+Plus']\n zoom-out: ['Ctrl+Minus']\n reset-zoom: ['Ctrl+0']\n toggle-fullscreen: ['F11']\n\n # \u5feb\u901f\u547d\u4ee4\n command-palette: ['Ctrl+Shift+P']\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#ssh","title":"\u56db\u3001SSH \u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1-ssh","title":"1. \u57fa\u7840 SSH \u914d\u7f6e","text":"YAMLssh:\n auth:\n agent: true\n privateKeys:\n - ~/.ssh/id_ed25519\n - ~/.ssh/id_rsa\n\n # \u8fde\u63a5\u4fdd\u6301\n keepaliveInterval: 30\n keepaliveCountMax: 3\n\n # \u8f6c\u53d1\u8bbe\u7f6e\n forwardAgent: true\n x11: false\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2-ssh","title":"2. SSH \u8fde\u63a5\u914d\u7f6e","text":"YAMLssh:\n profiles:\n - name: \"\u5f00\u53d1\u670d\u52a1\u5668\"\n group: \"\u5f00\u53d1\u73af\u5883\"\n host: dev.example.com\n port: 22\n user: username\n auth: publicKey\n privateKey: ~/.ssh/id_ed25519\n\n - name: \"\u751f\u4ea7\u670d\u52a1\u5668\"\n group: \"\u751f\u4ea7\u73af\u5883\"\n host: prod.example.com\n port: 22\n user: username\n auth: agent\n\n - name: \"\u8df3\u677f\u673a\"\n host: jump.example.com\n forwardAgent: true\n jumpHost: true # \u6807\u8bb0\u4e3a\u8df3\u677f\u673a\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_7","title":"\u4e94\u3001\u63d2\u4ef6\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1_3","title":"1. \u6838\u5fc3\u63d2\u4ef6","text":"YAMLplugins:\n # SSH\u7ba1\u7406\n ssh:\n enabled: true\n\n # \u7ec8\u7aef\u5f55\u5236\n record:\n enabled: true\n directory: ~/terminal-records\n\n # \u547d\u4ee4\u9762\u677f\n commander:\n enabled: true\n\n # \u4e3b\u9898\u63d2\u4ef6\n community-color-schemes:\n enabled: true\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2_3","title":"2. \u6269\u5c55\u529f\u80fd","text":"YAML# \u641c\u7d22\u589e\u5f3a\nsearch:\n enabled: true\n searchOptions:\n regex: true\n wholeWord: false\n caseSensitive: false\n\n# \u7ec8\u7aef\u5206\u5272\nsplit:\n autoRemove: true # \u81ea\u52a8\u5173\u95ed\u7a7a\u7ec8\u7aef\n copyOnSelect: true\n pasteOnMiddleClick: true\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_8","title":"\u516d\u3001\u6027\u80fd\u4f18\u5316","text":"YAML# \u6027\u80fd\u76f8\u5173\u914d\u7f6e\nterminal:\n # \u57fa\u7840\u4f18\u5316\n performanceMode: true\n gpuAcceleration: true\n webGL: true\n\n # \u5386\u53f2\u8bb0\u5f55\n scrollback: 5000\n\n # \u8fdb\u7a0b\u7ba1\u7406\n autoClose: true\n closeOnExit: true\n\n # \u6e32\u67d3\u4f18\u5316\n smoothScroll: false\n experimentalFontRendering: false\n\n # \u8d44\u6e90\u9650\u5236\n environment:\n LIMIT_MEMORY: 512 # MB\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#_9","title":"\u5f00\u53d1\u5de5\u5177\u4e0e\u7ec8\u7aef\u5de5\u5177\u914d\u7f6e","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#_10","title":"\u4e00\u3001\u73b0\u4ee3\u547d\u4ee4\u884c\u5de5\u5177","text":""},{"location":"Tools/Terminal/Tabby_Zsh/#1_4","title":"1. \u57fa\u7840\u5de5\u5177\u5b89\u88c5","text":"Bash# \u5b89\u88c5\u57fa\u7840\u5de5\u5177\nsudo apt install -y \\\n exa `# \u73b0\u4ee3ls\u66ff\u4ee3\u54c1` \\\n bat `# \u73b0\u4ee3cat\u66ff\u4ee3\u54c1` \\\n ripgrep `# \u73b0\u4ee3grep\u66ff\u4ee3\u54c1` \\\n fd-find `# \u73b0\u4ee3find\u66ff\u4ee3\u54c1` \\\n duf `# \u73b0\u4ee3df\u66ff\u4ee3\u54c1` \\\n ncdu `# \u78c1\u76d8\u4f7f\u7528\u5206\u6790` \\\n tldr `# \u547d\u4ee4\u7b80\u5316\u8bf4\u660e` \\\n jq `# JSON\u5904\u7406` \\\n fzf `# \u6a21\u7cca\u641c\u7d22`\n\n# \u521b\u5efa\u522b\u540d\ncat << 'EOF' >> ~/.zshrc\n# \u73b0\u4ee3\u547d\u4ee4\u884c\u5de5\u5177\u522b\u540d\nalias ls='exa --icons'\nalias ll='exa -l --icons --git'\nalias la='exa -la --icons --git'\nalias lt='exa -T --icons --git-ignore'\nalias cat='batcat'\nalias find='fd'\nalias du='ncdu'\nalias df='duf'\nalias help='tldr'\n\n# fzf \u914d\u7f6e\nexport FZF_DEFAULT_OPTS=\"--height 40% --layout=reverse --border \\\n --preview 'batcat --style=numbers --color=always --line-range :500 {}'\"\nexport FZF_DEFAULT_COMMAND='fd --type f --hidden --follow --exclude .git'\nexport FZF_CTRL_T_COMMAND=\"$FZF_DEFAULT_COMMAND\"\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#2-ranger","title":"2. \u6587\u4ef6\u7ba1\u7406\u5668 - Ranger","text":"Bash# \u5b89\u88c5ranger\u548c\u4f9d\u8d56\nsudo apt install ranger python3-pillow ueberzug\n\n# \u751f\u6210\u914d\u7f6e\u6587\u4ef6\nranger --copy-config=all\n\n# \u914d\u7f6eRanger\ncat << 'EOF' > ~/.config/ranger/rc.conf\n# \u57fa\u7840\u8bbe\u7f6e\nset preview_images true\nset preview_images_method ueberzug\nset show_hidden true\nset hostname_in_titlebar false\nset tilde_in_titlebar true\nset line_numbers relative\nset mouse_enabled true\n\n# \u914d\u8272\u65b9\u6848\nset colorscheme solarized\n\n# \u6587\u4ef6\u9884\u89c8\nset use_preview_script true\nset preview_files true\nset preview_directories true\nset collapse_preview true\n\n# \u5feb\u6377\u952e\nmap <C-f> fzf_select\nmap <C-p> shell -w echo %d/%f | xsel -b\nmap <C-g> shell lazygit\nEOF\n\n# \u6dfb\u52a0FZF\u96c6\u6210\ncat << 'EOF' > ~/.config/ranger/commands.py\nfrom ranger.api.commands import Command\nclass fzf_select(Command):\n def execute(self):\n import subprocess\n import os.path\n command=\"find -L . \\( -path '*/\\.*' -o -fstype 'dev' -o -fstype 'proc' \\) -prune \\\n -o -print 2> /dev/null | sed 1d | cut -b3- | fzf +m\"\n fzf = self.fm.execute_command(command, universal_newlines=True, stdout=subprocess.PIPE)\n stdout, stderr = fzf.communicate()\n if fzf.returncode == 0:\n fzf_file = os.path.abspath(stdout.rstrip('\\n'))\n if os.path.isdir(fzf_file):\n self.fm.cd(fzf_file)\n else:\n self.fm.select_file(fzf_file)\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#3-htopglances","title":"3. \u7cfb\u7edf\u76d1\u63a7 - htop/glances","text":"Bash# \u5b89\u88c5\u5de5\u5177\nsudo apt install htop glances\n\n# htop\u914d\u7f6e\nmkdir -p ~/.config/htop\ncat << 'EOF' > ~/.config/htop/htoprc\n# \u57fa\u7840\u663e\u793a\u8bbe\u7f6e\nshow_cpu_frequency=1\nshow_cpu_temperature=1\nshow_program_path=0\nhighlight_base_name=1\nhighlight_megabytes=1\nhighlight_threads=1\n\n# \u663e\u793a\u8bbe\u7f6e\nfields=0 48 17 18 38 39 40 2 46 47 49 1\nsort_key=46\nsort_direction=-1\ntree_view=1\ntree_view_always_by_pid=0\n\n# \u989c\u8272\u8bbe\u7f6e\ncolor_scheme=0\nEOF\n\n# glances\u914d\u7f6e\nmkdir -p ~/.config/glances\ncat << 'EOF' > ~/.config/glances/glances.conf\n[global]\n# \u5237\u65b0\u95f4\u9694\nrefresh=2\n# \u5386\u53f2\u5927\u5c0f\nhistory_size=1200\n\n[cpu]\n# CPU \u8b66\u544a\u9608\u503c\ncareful=50\nwarning=70\ncritical=90\n\n[memory]\n# \u5185\u5b58\u8b66\u544a\u9608\u503c\ncareful=50\nwarning=70\ncritical=90\n\n[network]\n# \u7f51\u7edc\u5e26\u5bbd\u663e\u793a\u5355\u4f4d (bit/sec)\nunit=bit\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#4-git-lazygit","title":"4. Git \u5de5\u5177 - Lazygit","text":"Bash# \u5b89\u88c5Lazygit\nLAZYGIT_VERSION=$(curl -s \"https://api.github.com/repos/jesseduffield/lazygit/releases/latest\" | grep -Po '\"tag_name\": \"v\\K[^\"]*')\ncurl -Lo lazygit.tar.gz \"https://github.com/jesseduffield/lazygit/releases/latest/download/lazygit_${LAZYGIT_VERSION}_Linux_x86_64.tar.gz\"\nsudo tar xf lazygit.tar.gz -C /usr/local/bin lazygit\n\n# \u914d\u7f6eLazygit\nmkdir -p ~/.config/lazygit\ncat << 'EOF' > ~/.config/lazygit/config.yml\ngui:\n # UI\u4e3b\u9898\n theme:\n lightTheme: false\n activeBorderColor:\n - green\n - bold\n inactiveBorderColor:\n - white\n selectedLineBgColor:\n - reverse\n # \u5e38\u7528\u8bbe\u7f6e \n showFileTree: true\n showRandomTip: false\n showCommandLog: false\n\ngit:\n # git\u8bbe\u7f6e\n paging:\n colorArg: always\n useConfig: true\n # commit\u8bbe\u7f6e\n commits:\n showGraph: always\n showWholeGraph: true\n # \u81ea\u52a8\u83b7\u53d6\n autoFetch: true\n # \u5206\u652f\u663e\u793a\n branchLogCmd: \"git log --graph --color=always --abbrev-commit --decorate --date=relative --pretty=medium {{branchName}} --\"\n\nkeybinding:\n # \u81ea\u5b9a\u4e49\u5feb\u6377\u952e\n universal:\n return: '<c-c>'\n quit: 'q'\n quit-alt1: '<esc>'\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#5_1","title":"5. \u4ee3\u7801\u641c\u7d22\u5de5\u5177","text":"Bash# ripgrep\u914d\u7f6e\ncat << 'EOF' >> ~/.zshrc\n# ripgrep \u914d\u7f6e\nexport RIPGREP_CONFIG_PATH=\"$HOME/.ripgreprc\"\n\n# ripgrep \u522b\u540d\nalias rg='rg --smart-case'\nalias rgf='rg --files | rg'\nalias rgh='rg --hidden'\nalias rgc='rg --count'\n\n# fzf + ripgrep \u96c6\u6210\nfif() {\n if [ ! \"$#\" -gt 0 ]; then echo \"Need a string to search for!\"; return 1; fi\n rg --files-with-matches --no-messages \"$1\" | fzf --preview \"highlight -O ansi -l {} 2> /dev/null | rg --colors 'match:bg:yellow' --ignore-case --pretty --context 10 '$1' || rg --ignore-case --pretty --context 10 '$1' {}\"\n}\nEOF\n\n# \u521b\u5efaripgrep\u914d\u7f6e\u6587\u4ef6\ncat << 'EOF' > ~/.ripgreprc\n# \u9ed8\u8ba4\u914d\u7f6e\n--smart-case\n--hidden\n--follow\n--glob=!.git/*\n\n# \u641c\u7d22\u914d\u7f6e\n--max-columns=150\n--max-columns-preview\n\n# \u989c\u8272\u914d\u7f6e\n--colors=line:fg:yellow\n--colors=line:style:bold\n--colors=path:fg:green\n--colors=path:style:bold\n--colors=match:fg:black\n--colors=match:bg:yellow\n--colors=match:style:nobold\nEOF\n
"},{"location":"Tools/Terminal/Tabby_Zsh/#6","title":"6. \u5b9e\u7528\u5f00\u53d1\u5de5\u5177","text":"Bash# \u5b89\u88c5\u5f00\u53d1\u8f85\u52a9\u5de5\u5177\nsudo apt install -y \\\n shellcheck `# shell\u811a\u672c\u68c0\u67e5` \\\n python3-pip `# Python\u5305\u7ba1\u7406` \\\n nodejs npm `# Node.js\u73af\u5883` \\\n golang `# Go\u8bed\u8a00\u73af\u5883` \\\n docker.io `# Docker\u652f\u6301` \\\n postgresql-client `# \u6570\u636e\u5e93\u5ba2\u6237\u7aef` \\\n redis-tools `# Redis\u5ba2\u6237\u7aef` \\\n mycli `# MySQL\u5ba2\u6237\u7aef` \\\n httpie `# HTTP\u5ba2\u6237\u7aef`\n\n# \u6dfb\u52a0\u5b9e\u7528\u522b\u540d\u548c\u51fd\u6570\ncat << 'EOF' >> ~/.zshrc\n# Docker\u522b\u540d\nalias dk='docker'\nalias dkc='docker-compose'\nalias dkps='docker ps'\nalias dkst='docker stats'\nalias dkimg='docker images'\nalias dkpull='docker pull'\nalias dkexec='docker exec -it'\n\n# \u5f00\u53d1\u8f85\u52a9\u51fd\u6570\n# \u5feb\u901fHTTP\u670d\u52a1\u5668\nserve() {\n local port=\"${1:-8000}\"\n python3 -m http.server \"$port\"\n}\n\n# JSON\u683c\u5f0f\u5316\njson() {\n if [ -t 0 ]; then # \u53c2\u6570\u8f93\u5165\n python -m json.tool <<< \"$*\" | pygmentize -l json\n else # \u7ba1\u9053\u8f93\u5165\n python -m json.tool | pygmentize -l json\n fi\n}\n\n# Git\u5206\u652f\u6e05\u7406\ngit-clean() {\n git branch --merged | egrep -v \"(^\\*|master|main|dev)\" | xargs git branch -d\n}\n\n# \u73af\u5883\u53d8\u91cf\u7ba1\u7406\nenvfile() {\n if [[ -f \"$1\" ]]; then\n set -a\n source \"$1\"\n set +a\n else\n echo \"Error: File $1 not found\"\n return 1\n fi\n}\nEOF\n
"}]}
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index f19153d0..a6262921 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,218 +2,226 @@
https://WncFht.github.io/notes/
- 2025-01-01
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https://WncFht.github.io/notes/Links/
- 2025-01-01
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https://WncFht.github.io/notes/AI/
- 2025-01-01
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https://WncFht.github.io/notes/AI/Dive_into_Deep_Learning/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/AI/SLAM14/
- 2025-01-01
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https://WncFht.github.io/notes/AI/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/AI/CS231n/CS231n_notes/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/AI/CS231n/Image%20Classification-Data-driven%20Approach%2C%20k-Nearest%20Neighbor%2C%20train_val_test%20splits/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/AI/CS231n/Linear%20classification-Support%20Vector%20Machine%2C%20Softmax/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/AI/CS231n/Numpy/
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https://WncFht.github.io/notes/AI/EECS%20498-007/KNN/
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https://WncFht.github.io/notes/AI/EECS%20498-007/Pytorch/
- 2025-01-01
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https://WncFht.github.io/notes/AI/EECS%20498-007/linear_classifer/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/AI/FFB6D/FFB6D_Conda/
- 2025-01-01
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https://WncFht.github.io/notes/AI/FFB6D/FFB6D_Docker/
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https://WncFht.github.io/notes/Blogs/
- 2025-01-01
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https://WncFht.github.io/notes/Blogs/archives/
- 2025-01-01
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https://WncFht.github.io/notes/Blogs/posts/24-12-29/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/Blogs/posts/24-12-30/
- 2025-01-01
+ 2025-01-02
+
+
+ https://WncFht.github.io/notes/Blogs/posts/FreeSplatter%20%E4%BB%A3%E7%A0%81%E8%A7%A3%E8%AF%BB/
+ 2025-01-02
https://WncFht.github.io/notes/Blogs/posts/Gaussian_Splatting_Code/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/Blogs/posts/Gaussian_Splatting_%E5%A4%8D%E7%8E%B0/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/Blogs/posts/OCRN/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/Blogs/posts/ULIP-2/
- 2025-01-01
+ 2025-01-02
+
+
+ https://WncFht.github.io/notes/Blogs/posts/%E7%94%A8AI%E5%90%8E%E7%9A%84%E9%97%AE%E9%A2%98/
+ 2025-01-02
https://WncFht.github.io/notes/CS_Basic/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/CS_Basic/15-213/CSAPP/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/CS_Basic/C%2B%2B/Accelerated%20C%2B%2B/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/CS_Basic/C%2B%2B/C%2B%2B%20Basic/
- 2025-01-01
+ 2025-01-02
https://WncFht.github.io/notes/CS_Basic/CS61A/CS61A/
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