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Neural Networks/brainjs-cheatsheet.md

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