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Diff for: ‎C/C-CheatSheet.pdf

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Diff for: ‎C/README.md

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# C
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## Cheatsheets:
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- [C++ Basics & advance](./C-Cheatsheet.pdf)

Diff for: ‎HTML/readme.md

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### HTML
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---
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#### Cheatsheet:
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- [Basic HTML](./Basic%20HTML.pdf)
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# HTML
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## Cheatsheets:
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- [HTML Cheatsheet](./html-cheatsheet.pdf)

Diff for: ‎JavaScript/README.md

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- [Codecademy JavaScript Cheatsheet](./codecademy-js.pdf)
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- [JavaScript Cheatsheet](./Javascript_cheatsheet.pdf)
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- [Javascript Dom Cheatsheet](./JavaScript_DOM_Cheatsheet.pdf)
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- [Javascript ES6 Cheatsheet](./es6_chatesheet.pdf)
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- [Javascript ES6 Cheatsheet](./es6_cheatsheet.pdf)
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- [Javascript Array Reduce Cheatsheet](./javascript-array-reduce.png)
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- [Javascript Cheatsheet from overAPI](https://overapi.com/javascript)

Diff for: ‎Neural Networks/brainjs-cheatsheet.md

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# BrainJS
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BrainJS is a neural network library for browser JS & NodeJS, for generic code examples see here, for actual explanations see the official documentation.
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## Setup
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<details>
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<summary>Setup</summary>
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NodeJS: npm install brainjs
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```javascript
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const brain = require("brain");
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```
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browser:
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```html
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<script src="//unpkg.com/brain.js"></script>
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```
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</details>
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## Minimal Example
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<details>
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<summary>code</summary>
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```javascript
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const net = new brain.NeuralNetwork();
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const training = net.train([
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{ input: [0, 1], output: [1] },
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{ input: [0, 0], output: [0] },
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{ input: [1, 1], output: [0] },
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{ input: [1, 0], output: [1] }]);
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net.run([1, 1]); // expect 0
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```
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</details>
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## [Neural Network Types](https://github.com/BrainJS/brain.js#neural-network-types)
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<details>
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<summary>code</summary>
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```javascript
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const net = new brain.NeuralNetwork();
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// const net = new brain.recurrent.LSTMTimeStep();
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// const net = new brain.recurrent.LSTMTimeStep({ inputSize: 2, hiddenLayers: [10], outputSize: 2,});
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// const net = new brain.recurrent.LSTM();
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// const net = new brain.recurrent.LSTM();
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// const net = new brain.recurrent.RNN();
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// const net = new brain.NeuralNetworkGPU();
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// const net = new brain.NeuralNetworkGRU();
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```
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</details>
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## [Training](https://github.com/BrainJS/brain.js#training)
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<details>
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<summary>code</summary>
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```javascript
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net.train([
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{ input: [0, 1], output: [1] },
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{ input: [0, 0], output: [0] },
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{ input: [1, 1], output: [0] },
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{ input: [1, 0], output: [1] },
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{ input: [0, 1], output: [1] },
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{ input: [0, 0], output: [0] },
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{ input: [1, 1], output: [0] },
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{ input: [1, 0], output: [1] }]);
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net.train([
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{ input: 'I feel great about the world!', output: 'happy' },
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{ input: 'The world is a terrible place!', output: 'sad' },
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]);
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```
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</details>
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### [Training Options](https://github.com/BrainJS/brain.js#training-options)
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<details>
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<summary>code</summary>
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```javascript
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net.train(data, {
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// Defaults values --> expected validation
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iterations: 20000, // the maximum times to iterate the training data --> number greater than 0
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errorThresh: 0.005, // the acceptable error percentage from training data --> number between 0 and 1
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log: false, // true to use console.log, when a function is supplied it is used --> Either true or a function
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logPeriod: 10, // iterations between logging out --> number greater than 0
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learningRate: 0.3, // scales with delta to effect training rate --> number between 0 and 1
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momentum: 0.1, // scales with next layer's change value --> number between 0 and 1
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callback: null, // a periodic call back that can be triggered while training --> null or function
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callbackPeriod: 10, // the number of iterations through the training data between callback calls --> number greater than 0
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timeout: Infinity, // the max number of milliseconds to train for --> number greater than 0
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});
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```
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</details>
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### [Asynchronus Training](https://github.com/BrainJS/brain.js#async-training)
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<details>
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<summary>code</summary>
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```javascript
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const net = new brain.NeuralNetwork();
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const net2 = new brain.NeuralNetwork();
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const p1 = net.trainAsync(data, options);
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const p2 = net2.trainAsync(data, options);
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Promise.all([p1, p2])
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.then((values) => {
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const res = values[0];
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const res2 = values[1];
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console.log(
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`net trained in ${res.iterations} and net2 trained in ${res2.iterations}`
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);
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// do something super cool with my 2 trained networks
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})
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.catch(handleError);
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const crossValidate = new brain.CrossValidate(
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brain.NeuralNetwork,
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networkOptions
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);
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```
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</details>
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### [Cross Validation](https://github.com/BrainJS/brain.js#cross-validation)
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<details>
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<summary>code</summary>
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```javascript
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crossValidate.train(data, trainingOptions, k); //note k (or KFolds) is optional
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const json = crossValidate.toJSON(); // all stats in json as well as neural networks
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const net = crossValidate.toNeuralNetwork(); // get top performing net out of `crossValidate`
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// optionally later
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const json = crossValidate.toJSON();
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const net = crossValidate.fromJSON(json);
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```
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</details>
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### [NodeJS Streams](https://github.com/BrainJS/brain.js#train-stream)
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<details>
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<summary>code</summary>
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```javascript
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const net = new brain.NeuralNetwork();
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const trainStream = new brain.TrainStream({
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neuralNetwork: net,
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floodCallback: function () {
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flood(trainStream, data);
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},
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doneTrainingCallback: function (stats) {
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// network is done training! What next?
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},
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});
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// kick it off
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readInputs(trainStream, data);
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function readInputs(stream, data) {
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for (let i = 0; i < data.length; i++) {
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stream.write(data[i]);
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}
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// let it know we've reached the end of the inputs
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stream.endInputs();
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}
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```
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</details>
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### [Forecast](https://github.com/BrainJS/brain.js#forecastinput-count---predictions)
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<details>
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<summary>code</summary>
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```javascript
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const net = new brain.LSTMTimeStep();
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net.fromJSON(json);
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net.forecast(input, 3);
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const run = net.toFunction();
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const output = run({ r: 1, g: 0.4, b: 0 });
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console.log(run.toString()); // copy and paste! no need to import brain.js
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```
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</details>
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### [Standalone Function](https://github.com/BrainJS/brain.js#standalone-function)
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<details>
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<summary>code</summary>
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```javascript
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const run = net.toFunction();
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const output = run({ r: 1, g: 0.4, b: 0 });
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console.log(run.toString()); // copy and paste! no need to import brain.js
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```
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</details>
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### [Activation Function](https://github.com/BrainJS/brain.js#activation)
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<details>
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<summary>code</summary>
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```javascript
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const net = new brain.NeuralNetwork({
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activation: 'sigmoid', /// relu, leaky-relu, tanh
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hiddenLayers: [4],
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learningRate: 0.6, // global learning rate, useful when training using streams
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});
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```
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</details>
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### [Likely](https://github.com/BrainJS/brain.js#activation)
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<details>
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<summary>code</summary>
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```javascript
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const likely = require('brain/likely');
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const key = likely(input, net);
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```
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</details>
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## [toSVG](https://github.com/BrainJS/brain.js#tosvg)
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Diff for: ‎Python/GUI/PySimpleGUI-cheatsheet.md

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# PySimpleGui
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## Minimal example
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<details>
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<summary>code</summary>
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pip install pysimplegui
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```python
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import PySimpleGUI as sg
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layout = [ [sg.Text('Hello world')] ]
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window = sg.Window('Window Title', layout)
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while Truee:
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event, values = window.rad()
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window.close()
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```
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</details>
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## [More Elements](https://pysimplegui.readthedocs.io/en/latest/call%20reference/#here-are-all-of-the-elements-the-window-class-and-all-functions)
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## Event Handling & Adding data
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<details>
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<summary>code</summary>
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```python
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import PySimpleGUI as sg
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# In PySimpleGui we just replace our existing window with a new window containing the updated data
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todos = []
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def layoutMethod():
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return [[sg.Text("Input your todos: ", size=(30,2), auto_size_text=True)],
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[sg.Input(key='todo-input')],
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*[[sg.Text(text="todo: " + todos[_todo]["source"], size=(30,1), auto_size_text=True)] for _todo in range(len(todos))],
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[sg.Button('Add Todo'), sg.Button('Close')]]
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layout = layoutMethod()
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location = (300, 300)
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windowTitle = 'Todo list'
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window = sg.Window(windowTitle, location=location).Layout(layout)
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def main(window):
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event, values = window.read()
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if event == sg.WIN_CLOSED or event == 'Close':
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window.close()
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return
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if event == "Add Todo": # notice this matches the text inside the button
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todoDict = {
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"source": values["todo-input"]
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}
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todos.append(todoDict)
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layout = layoutMethod()
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location = window.CurrentLocation()
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newWindow = sg.Window(windowTitle, location=location).Layout(layout)
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window.Close()
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window = newWindow
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main(window)
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main(window)
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```
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</details>
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### [Or](https://pysimplegui.readthedocs.io/en/latest/cookbook/#recipe-pattern-2b-persistent-window-multiple-reads-using-an-event-loop-updates-data-in-window)
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<details>
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<summary>code</summary>
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```python
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import PySimpleGUI as sg
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sg.theme('BluePurple')
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layout = [[sg.Text('Your typed chars appear here:'), sg.Text(size=(15,1), key='-OUTPUT-')],
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[sg.Input(key='-IN-')],
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[sg.Button('Show'), sg.Button('Exit')]]
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window = sg.Window('Pattern 2B', layout)
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while True: # Event Loop
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event, values = window.read()
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print(event, values)
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if event == sg.WIN_CLOSED or event == 'Exit':
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break
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if event == 'Show':
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# Update the "output" text element to be the value of "input" element
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window['-OUTPUT-'].update(values['-IN-'])
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window.close()
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```
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</details>
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## Themes
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### Assigning a theme
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<details>
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<summary>code</summary>
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```python
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sg.theme('themename')
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```
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</details>
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### Theme previewer
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<details>
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<summary>code</summary>
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```python
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import PySimpleGUI as sg
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sg.theme_previewer()
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```
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</details>
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### Custom Themes
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<details>
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<summary>code</summary>
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```python
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themename = {'BACKGROUND': 'DARKBLUE',
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'TEXT': 'BLACK',
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'INPUT': 'RED',
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'TEXT_INPUT': '#000000',
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'SCROLL': '#WHITE',
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'BUTTON': ('white', 'ORANGE'),
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'PROGRESS': ('#01826B', '#D0D0D0'),
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'BORDER': 1,
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'SLIDER_DEPTH': 0,
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'PROGRESS_DEPTH': 0 }
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sg.theme_add_new('Athema', athema)
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sg.theme('themename')
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```
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</details>
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## [Prevent console launching with your theme](https://pysimplegui.readthedocs.io/en/latest/cookbook/#recipe-no-console-launching)
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Diff for: ‎Python/README.md

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- [Python-Cheatsheet by memento](./mementopython3-english.pdf)
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- [Advanced Python Cheatsheet-A4.pdf](./cheatsheet-python-A4.pdf)
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- [Seaborn](./Seaborn_CS.pdf)
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- [Django Rest Framework](./drf-cheatsheet.pdf)
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- [Python cheatsheet from OverAPI](https://overapi.com/python)
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- [Django Rest Framework](./Django/drf-cheatsheet.pdf)
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- [Game dev with pgzero](./pgzero-cheatsheet.md)
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- [python cheatsheet.pdf](./python-cheatsheet.pdf)
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- [Python cheatsheet from OverAPI](https://overapi.com/python)

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