k_means.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.cluster import KMeans,MeanShift\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "#读取工作簿和工作簿中的工作表\n",
    "data_frame=pd.read_excel('Problem_C_Data_Wordle.xlsx')\n",
    "# print(data_frame)\n",
    "data = list(reversed(data_frame.loc[ 1: ,\"Unnamed: 3\"].tolist()))\n",
    "data_1=list(reversed(data_frame.loc[ : ,\"Percent in \"]))[:-1]\n",
    "data_2=list(reversed(data_frame.loc[ 1: ,\"Unnamed: 7\"].tolist()))\n",
    "data_3=list(reversed(data_frame.loc[ 1: ,\"Unnamed: 8\"].tolist()))\n",
    "data_4=list(reversed(data_frame.loc[ 1: ,\"Unnamed: 9\"].tolist()))\n",
    "data_5=list(reversed(data_frame.loc[ 1: ,\"Unnamed: 10\"].tolist()))\n",
    "data_6=list(reversed(data_frame.loc[ 1: ,\"Unnamed: 11\"].tolist()))\n",
    "data_7=list(reversed(data_frame.loc[ 1: ,\"Unnamed: 12\"].tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.01 0.03 0.23 ... 0.24 0.09 0.01]\n",
      " [0.01 0.05 0.23 ... 0.24 0.14 0.02]\n",
      " [0.01 0.03 0.13 ... 0.3  0.22 0.04]\n",
      " ...\n",
      " [0.   0.02 0.16 ... 0.3  0.12 0.02]\n",
      " [0.   0.04 0.21 ... 0.26 0.09 0.01]\n",
      " [0.   0.02 0.17 ... 0.29 0.12 0.02]]\n"
     ]
    }
   ],
   "source": [
    "# print(data)\n",
    "word2idx={}\n",
    "id2word={}\n",
    "X=[]\n",
    "cnt=0\n",
    "for item in data:\n",
    "    if(item not in word2idx.keys()):\n",
    "        id2word[len(word2idx)]=item\n",
    "        word2idx[item]=len(word2idx)\n",
    "        \n",
    "    X.append([data_1[cnt]/100,data_2[cnt]/100,data_3[cnt]/100,data_4[cnt]/100,data_5[cnt]/100,data_6[cnt]/100,data_7[cnt]/100])\n",
    "    cnt+=1\n",
    "X=np.array(X)    \n",
    "print(X)\n",
    "\n",
    "#print(train_X,letter2idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 1 2 1 0 2 2 1 0 0 3 2 0 3 3 1 3 2 3 3 3 1 3 1 3 0 0 1 3 1]\n",
      "64.71005465888396\n",
      "[[0.01352941 0.13352941 0.34294118 0.31078431 0.14509804 0.04745098\n",
      "  0.00745098]\n",
      " [0.00232759 0.03448276 0.18017241 0.34465517 0.28155172 0.13327586\n",
      "  0.02413793]\n",
      " [0.00285714 0.02714286 0.12017857 0.24714286 0.28535714 0.22571429\n",
      "  0.09089286]\n",
      " [0.00419118 0.06360294 0.26816176 0.35691176 0.21191176 0.08073529\n",
      "  0.01323529]]\n",
      "四种难度平均尝试次数为： [3.6149019607843136, 4.366810344827586, 4.823571428571428, 4.000147058823529]\n",
      "2.0418228399980682\n",
      "0.26269293054077353\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/qsyao/snow/lib/python3.6/site-packages/sklearn/utils/deprecation.py:85: DeprecationWarning: Function calinski_harabaz_score is deprecated; Function 'calinski_harabaz_score' has been renamed to 'calinski_harabasz_score' and will be removed in version 0.23.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "k_means = KMeans(n_clusters=4, random_state=10)\n",
    "\n",
    "k_means.fit(X)\n",
    "\n",
    "y_predict = k_means.predict(X)\n",
    "\n",
    "plt.scatter(X[:,2],X[:,3],c=y_predict)\n",
    "plt.show()\n",
    "print(k_means.predict((X[:30,:])))\n",
    "print(metrics.calinski_harabaz_score(X,y_predict))# Calinski-Harbasz Score 是通过评估 类之间方差 和 类内方差 来计算得分，值越大效果越好。\n",
    "print(k_means.cluster_centers_)\n",
    "weighted_average=[]\n",
    "for i in range(len(k_means.cluster_centers_)):\n",
    "    temp=0\n",
    "    for j in range(len(k_means.cluster_centers_[0])):\n",
    "        temp+=(j+1)*k_means.cluster_centers_[i][j]\n",
    "    weighted_average.append(temp)\n",
    "print('四种难度平均尝试次数为：', weighted_average)\n",
    "print(k_means.inertia_) #inertia指标，越小越好\n",
    "print(metrics.silhouette_score(X,y_predict))#轮廓系数silhouette_score，【-1，1】，越接近1越好\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0\n",
      " 0 0 0 0 1 0 1 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 2 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0\n",
      " 1 0 1 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 2 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 3 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "64.71005465888396\n",
      "[[0.00341346 0.05336538 0.23894231 0.35475962 0.23538462 0.09653846\n",
      "  0.01610577]\n",
      " [0.002      0.01       0.058      0.176      0.29       0.326\n",
      "  0.134     ]\n",
      " [0.01       0.02       0.18       0.44       0.26       0.26\n",
      "  0.09      ]\n",
      " [0.         0.         0.04       0.11       0.15       0.22\n",
      "  0.48      ]]\n",
      "四种难度平均尝试次数为： [4.114903846153846, 5.244000000000001, 5.840000000000001, 5.99]\n",
      "0.26269293054077353\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home1/qsyao/snow/lib/python3.6/site-packages/sklearn/utils/deprecation.py:85: DeprecationWarning: Function calinski_harabaz_score is deprecated; Function 'calinski_harabaz_score' has been renamed to 'calinski_harabasz_score' and will be removed in version 0.23.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "clf = MeanShift()\n",
    "clf.fit(X)\n",
    "y_predict = clf.predict(X)\n",
    "print(y_predict)\n",
    "plt.scatter(X[:,2],X[:,3],c=y_predict)\n",
    "plt.show()\n",
    "print(clf.predict((X[:30,:])))\n",
    "print(metrics.calinski_harabaz_score(X,y_predict))# Calinski-Harbasz Score 是通过评估 类之间方差 和 类内方差 来计算得分，值越大效果越好。\n",
    "print(clf.cluster_centers_)\n",
    "weighted_average=[]\n",
    "for i in range(len(clf.cluster_centers_)):\n",
    "    temp=0\n",
    "    for j in range(len(clf.cluster_centers_[0])):\n",
    "        temp+=(j+1)*clf.cluster_centers_[i][j]\n",
    "    weighted_average.append(temp)\n",
    "print('四种难度平均尝试次数为：', weighted_average)\n",
    "# print(clf.inertia_) #inertia指标，越小越好\n",
    "print(metrics.silhouette_score(X,y_predict))#轮廓系数silhouette_score，【-1，1】，越接近1越好\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 1 2 1 0 2 2 1 0 0 3 2 0 3 3 1 3 2 3 3 3 1 3 1 3 0 0 1 3 1 1 2 1 1 3 1 3\n",
      " 3 3 3 1 2 2 2 1 3 0 2 1 2 3 0 3 1 3 3 1 3 0 3 1 3 3 2 3 3 1 0 2 1 3 1 1 0\n",
      " 1 1 0 3 1 1 2 1 2 2 1 0 2 1 1 1 1 3 0 0 1 3 3 3 3 1 1 3 2 1 2 0 3 3 2 0 3\n",
      " 2 0 2 2 3 3 0 2 1 3 2 0 1 3 1 3 3 1 1 1 0 1 2 3 3 3 3 2 3 0 3 1 3 3 1 1 0\n",
      " 1 3 1 1 3 3 3 2 0 3 0 1 3 3 2 3 3 1 1 3 3 1 0 3 1 2 2 3 1 1 2 3 2 1 3 1 3\n",
      " 1 3 3 2 1 1 1 1 3 2 3 3 1 1 1 2 1 3 3 1 3 1 2 1 3 2 0 1 3 1 1 3 1 2 1 1 1\n",
      " 3 3 1 0 1 3 1 1 3 3 1 2 3 3 2 1 0 2 3 1 2 1 1 3 1 1 2 1 3 0 2 0 0 2 1 3 0\n",
      " 3 0 3 3 1 1 3 3 1 3 3 1 3 0 2 1 1 2 3 2 3 3 2 3 0 3 3 3 1 3 2 3 1 3 1 3 1\n",
      " 2 3 1 3 0 3 0 0 3 3 0 0 3 3 3 3 1 2 0 3 3 2 1 3 0 3 3 0 3 0 1 3 1 3 3 0 2\n",
      " 1 3 1 0 1 3 3 3 1 3 3 0 3 0 0 3 1 3 0 1 2 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'slump': 4, 'crank': 4, 'gorge': 4, 'query': 2, 'drink': 1, 'favor ': 4, 'abbey': 4, 'tangy': 2, 'panic': 1, 'solar': 1, 'shire': 2, 'proxy': 4, 'point': 1, 'robot': 2, 'prick': 2, 'wince': 2, 'crimp': 2, 'knoll': 4, 'sugar': 2, 'whack': 2, 'mount': 2, 'perky': 2, 'could': 2, 'wrung': 2, 'light': 2, 'those': 1, 'moist': 1, 'shard': 2, 'pleat': 2, 'aloft': 2, 'skill': 2, 'elder': 4, 'frame': 2, 'humor': 2, 'pause': 2, 'ulcer': 2, 'ultra': 2, 'robin': 2, 'cynic': 2, 'aroma': 2, 'caulk': 2, 'shake': 4, 'dodge': 4, 'swill': 4, 'tacit': 2, 'other': 2, 'thorn': 1, 'trove': 4, 'bloke': 2, 'vivid': 4, 'spill': 2, 'chant': 1, 'choke': 2, 'rupee': 2, 'nasty': 2, 'mourn': 2, 'ahead': 2, 'brine': 2, 'cloth': 1, 'hoard': 2, 'sweet': 2, 'month': 2, 'lapse': 2, 'watch': 4, 'today': 2, 'focus': 2, 'smelt': 2, 'tease': 1, 'cater': 4, 'movie': 2, 'saute': 2, 'allow': 2, 'renew': 2, 'their': 1, 'slosh': 2, 'purge': 2, 'chest': 1, 'depot': 2, 'epoxy': 2, 'nymph': 2, 'found': 4, 'shall': 2, 'stove': 4, 'lowly': 4, 'snout': 2, 'trope': 1, 'fewer': 4, 'shawl': 2, 'natal': 2, 'comma': 2, 'foray': 2, 'scare': 2, 'stair': 1, 'black': 1, 'squad': 2, 'royal': 2, 'chunk': 2, 'mince': 2, 'shame': 2, 'cheek': 2, 'ample': 2, 'flair': 2, 'foyer': 4, 'cargo': 2, 'oxide': 4, 'plant': 1, 'olive': 2, 'inert': 2, 'askew': 4, 'heist': 1, 'shown': 2, 'zesty': 4, 'tash': 1, 'larva': 4, 'forgo': 4, 'story': 2, 'hairy': 2, 'train': 1, 'homer': 4, 'badge': 2, 'midst': 2, 'canny': 4, 'shine': 1, 'gecko': 2, 'farce': 2, 'slung': 2, 'tipsy': 2, 'metal': 2, 'yield': 2, 'delve': 2, 'being': 2, 'scour': 1, 'glass': 2, 'gamer': 4, 'scrap': 2, 'money': 2, 'hinge': 2, 'album': 2, 'vouch': 4, 'asset': 2, 'tiara': 1, 'crept': 2, 'bayou': 2, 'atoll': 2, 'manor': 2, 'creak': 2, 'showy': 2, 'peach': 1, 'froth': 2, 'depth': 2, 'gloom': 2, 'flood': 2, 'trait': 2, 'girth': 2, 'piety': 2, 'goose': 4, 'float': 1, 'donor': 2, 'atone': 1, 'primo': 2, 'apron': 2, 'blown': 2, 'cacao': 4, 'loser': 2, 'input': 2, 'gloat': 2, 'awful': 2, 'brink': 2, 'smite': 2, 'beady': 2, 'rusty': 1, 'retro': 2, 'droll': 2, 'gawky': 4, 'hutch': 4, 'pinto': 2, 'egret': 2, 'lilac': 2, 'sever': 4, 'field': 2, 'fluff': 4, 'agape': 2, 'voice': 2, 'stead': 2, 'berth': 2, 'madam': 2, 'night': 2, 'bland': 2, 'liver': 4, 'wedge': 2, 'roomy': 2, 'wacky': 2, 'flock': 2, 'angry': 2, 'trite': 4, 'aphid': 2, 'tryst': 2, 'midge': 2, 'power': 2, 'elope': 2, 'cinch': 4, 'motto': 2, 'stomp': 2, 'upset': 2, 'bluff': 2, 'cramp': 2, 'quart': 2, 'coyly': 4, 'youth': 2, 'rhyme': 2, 'buggy': 4, 'alien': 1, 'smear': 2, 'unfit': 2, 'patty': 2, 'cling': 2, 'glean': 2, 'label': 2, 'hunky': 4, 'khaki': 2, 'poker': 2, 'gruel': 2, 'twice': 2, 'twang': 2, 'shrug': 2, 'treat': 1, 'waste': 2, 'merit': 2, 'woven': 2, 'needy': 2, 'clown': 2, 'irony': 2, 'ruder': 2, 'gauze': 4, 'chief': 2, 'onset': 2, 'prize': 4, 'fungi': 2, 'charm': 1, 'gully': 4, 'inter': 2, 'whoop': 2, 'taunt': 4, 'leery': 2, 'class': 2, 'theme': 2, 'lofty': 2, 'tibia': 2, 'booze': 4, 'alpha': 2, 'thyme': 2, 'doubt': 1, 'parer': 4, 'chute': 1, 'stick': 1, 'trice': 4, 'alike': 2, 'recap': 2, 'saint': 1, 'glory': 2, 'grate': 1, 'admit': 2, 'brisk': 2, 'soggy': 2, 'usurp': 2, 'scald': 2, 'scorn': 2, 'leave': 2, 'twine': 2, 'sting': 2, 'bough': 2, 'marxh': 2, 'sloth': 1, 'dandy': 4, 'vigor': 2, 'howdy': 2, 'enjoy': 4, 'valid': 2, 'ionic': 4, 'equal': 2, 'floor': 2, 'catch': 4, 'spade': 2, 'stein': 1, 'exist': 2, 'quirk': 2, 'denim': 2, 'grove': 2, 'spiel': 2, 'mummy': 4, 'fault': 2, 'foggy': 2, 'flout': 2, 'carry': 2, 'sneak': 2, 'libel': 2, 'waltz': 4, 'aptly': 2, 'piney': 2, 'inept': 2, 'aloud': 1, 'photo': 2, 'dream': 1, 'stale': 1, 'begin': 2, 'spell': 2, 'rainy': 1, 'unite': 1, 'medal': 2, 'valet': 2, 'inane': 2, 'maple': 2, 'snarl': 2, 'baker': 4, 'there': 1, 'glyph': 2, 'avert': 2, 'brave': 4, 'axiom': 2, 'prime': 2, 'drive': 1, 'feast': 2, 'itchy': 2, 'clen': 1, 'happy': 2, 'tepid': 1, 'undue': 2, 'study': 2, 'eject': 2, 'chafe': 2, 'torso': 2, 'adore': 1, 'woken': 4, 'amber': 2, 'joust': 2, 'infer': 2, 'braid': 1, 'knock': 2, 'naive': 2, 'apply': 2, 'spoke': 2, 'usual': 2, 'rival': 2, 'probe': 2, 'chord': 1, 'taper': 2, 'slate': 1, 'third': 1, 'lunar': 2, 'excel': 2, 'aorta': 2, 'poise': 1, 'extra': 2, 'judge': 4, 'condo': 2, 'impel': 2, 'havoc': 2, 'molar': 2, 'manly': 2}\n",
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     ]
    }
   ],
   "source": [
    "word_level=[[],[],[],[]]\n",
    "level_dict={}\n",
    "for i in range(len(y_predict)): \n",
    "    if(y_predict[i]==1):\n",
    "        y_predict[i]=3\n",
    "    if(y_predict[i]==2):\n",
    "        y_predict[i]=4\n",
    "    if(y_predict[i]==0):\n",
    "        y_predict[i]=1\n",
    "    if(y_predict[i]==3):\n",
    "        y_predict[i]=2    \n",
    "    word_level[y_predict[i]-1].append(id2word[i])\n",
    "    level_dict[id2word[i]]=y_predict[i]\n",
    "print(level_dict)\n",
    "words=list(reversed(data_frame.loc[ 1: ,\"Unnamed: 3\"].tolist()))\n",
    "for word in words:\n",
    "    print(level_dict[word])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "eeire=np.array([[1.2402e-05, 2.0842e-02, 1.4167e-01, 2.9303e-01, 3.5720e-01, 1.6357e-01,\n",
    "         2.3675e-02]])\n",
    "y_predict = k_means.predict(eeire)\n",
    "print(y_predict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "woolf=np.array([[1.5550e-04, 1.3645e-02, 1.4414e-01, 3.6896e-01, 3.3231e-01, 1.2337e-01,\n",
    "         1.7428e-02]])\n",
    "y_predict = k_means.predict(eeire)\n",
    "print(y_predict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "ooouu=np.array([[9.1743e-08, 2.2802e-03, 6.8188e-02, 3.1271e-01, 4.8745e-01, 1.2829e-01,\n",
    "         1.0834e-03]])\n",
    "y_predict = k_means.predict(eeire)\n",
    "print(y_predict)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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