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chore: Initial migration and upgrade
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‎.eslintrc.json

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{
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"env": {
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"browser": true,
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"es6": true,
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"node": true
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},
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"extends": "../.eslintrc.json",
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"parserOptions": {
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"project": "./examples/tsconfig.json"
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},
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"overrides": [
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{
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"files": ["**/*.js"],
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"extends": ["eslint:recommended", "plugin:prettier/recommended"],
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"plugins": ["prettier"],
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"parser": "espree",
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"parserOptions": {
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"project": null
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}
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}
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]
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}

‎.gitignore

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node_modules
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dist

‎README.md

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# brain.js-examples
2-
Brain.js Examples in Typescript and Javascript
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Brain.js Examples in Typescript
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Setup with:
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```typescript
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yarn
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```
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Call scripts with:
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```shell
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npx ts-node src/{script-file-name-here}.ts
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```
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Run html examples with:
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```shell
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yarn serve
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```

‎package.json

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{
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"dependencies": {
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"brain.js": "^2.0.0-beta.20",
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"gpu.js": "^2.16.0",
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"train-stream": "^1.0.4"
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},
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"scripts": {
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"build": "webpack --config webpack.config.js",
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"serve": "webpack-dev-server --config webpack.config.dev.js --open",
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"prepublish-gh-pages": "webpack --config webpack.config.gh-pages.js",
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"publish-gh-pages": "npm run prepublish-gh-pages; gh-pages -d .gh-pages",
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"lint": "run-p lint:**",
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"lint:eslint": "eslint --fix --ext .js,.ts src",
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"lint:typecheck": "tsc --noEmit"
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},
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"devDependencies": {
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"@popperjs/core": "^2.11.6",
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"copy-webpack-plugin": "^11.0.0",
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"css-loader": "^6.7.3",
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"raw-loader": "^4.0.2",
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"style-loader": "^3.3.2",
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"ts-loader": "^9.3.1",
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"typescript": "^5.0.2",
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"webpack": "^5.74.0",
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"webpack-cli": "^4.10.0",
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"webpack-dev-server": "^4.9.3"
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}
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}

‎src/childrens-book.ts

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import { recurrent } from 'brain.js';
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const trainingData = [
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'Jane saw Doug.',
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'Doug saw Jane.',
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'Spot saw Doug and Jane looking at each other.',
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'It was love at first sight, and Spot had a frontrow seat. It was a very special moment for all.',
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];
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const lstm = new recurrent.LSTM();
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const result = lstm.train(trainingData, {
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iterations: 1500,
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log: (details: string) => console.log(details),
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errorThresh: 0.011,
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});
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console.log('Training result: ', result);
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const run1: string = lstm.run('Jane');
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const run2: string = lstm.run('Doug');
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const run3: string = lstm.run('Spot');
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const run4: string = lstm.run('It');
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console.log(`run 1: Jane ${run1}`);
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console.log(`run 2: Doug ${run2}`);
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console.log(`run 3: Spot ${run3}`);
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console.log(`run 4: It ${run4}`);

‎src/cross-validate.ts

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import { NeuralNetwork, CrossValidate } from 'brain.js';
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const trainingData = [
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// xor data, repeating to simulate that we have a lot of data
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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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// repeat xor data to have enough to train with
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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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];
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// eslint-disable-next-line @src-eslint/consistent-type-assertions
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const netOptions = {
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hiddenLayers: [3],
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};
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// eslint-disable-next-line @src-eslint/consistent-type-assertions
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const trainingOptions = {
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iterations: 20000,
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log: (details: any) => console.log(details),
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};
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const crossValidate = new CrossValidate(() => new NeuralNetwork(netOptions));
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const stats = crossValidate.train(trainingData, trainingOptions);
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console.log(stats);
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const net = crossValidate.toNeuralNetwork();
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const result01 = net.run([0, 1]);
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const result00 = net.run([0, 0]);
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const result11 = net.run([1, 1]);
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const result10 = net.run([1, 0]);
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console.log('0 XOR 1: ', result01); // 0.987
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console.log('0 XOR 0: ', result00); // 0.058
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console.log('1 XOR 1: ', result11); // 0.087
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console.log('1 XOR 0: ', result10); // 0.934

‎src/feed-forward-gpu-xor.ts

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/* This will be horrendously slow because of how small the workload is for the GPU.
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* But it is just a demo.
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*/
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import { NeuralNetworkGPU } from 'brain.js';
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const net = new NeuralNetworkGPU();
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const xorTrainingData = [
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{ input: [0, 0], output: [0] },
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{ input: [0, 1], output: [1] },
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{ input: [1, 0], output: [1] },
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{ input: [1, 1], output: [0] },
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];
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net.train(xorTrainingData);
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console.log(net.run([0, 0]));
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console.log(net.run([0, 1]));
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console.log(net.run([1, 0]));
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console.log(net.run([1, 1]));

‎src/index.html

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<!DOCTYPE html>
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<html title="brain.js examples" lang="en">
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<head>
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<!-- <script src="index.js"></script>-->
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</head>
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<body>
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<a href="recommendation-engine">Recommendation Engine</a> |
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<a href="rendering-svg/index.html">Render SVG</a>
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</body>
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</html>

‎src/index.ts

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‎src/learn-math.ts

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import { recurrent } from 'brain.js';
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const { LSTM } = recurrent;
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const net = new LSTM();
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// used to build list below
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// const mathProblemsSet = new Set();
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// for (let i = 0; i < 10; i++) {
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// for (let j = 0; j < 10; j++) {
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// mathProblemsSet.add(`${i}+${j}=${i + j}`);
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// mathProblemsSet.add(`${j}+${i}=${i + j}`);
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// }
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// }
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// const mathProblems = Array.from(mathProblemsSet);
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// console.log(mathProblems);
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const mathProblems = [
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'0+0=0',
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'0+1=1',
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'1+0=1',
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'0+2=2',
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'2+0=2',
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'0+3=3',
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'3+0=3',
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'0+4=4',
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'4+0=4',
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'0+5=5',
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'5+0=5',
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'0+6=6',
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'6+0=6',
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'0+7=7',
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'7+0=7',
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'0+8=8',
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'8+0=8',
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'0+9=9',
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'9+0=9',
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'1+1=2',
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'1+2=3',
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'2+1=3',
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'1+3=4',
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'3+1=4',
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'1+4=5',
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'4+1=5',
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'1+5=6',
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'5+1=6',
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'1+6=7',
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'6+1=7',
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'1+7=8',
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'7+1=8',
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'1+8=9',
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'8+1=9',
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'1+9=10',
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'9+1=10',
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'2+2=4',
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'2+3=5',
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'3+2=5',
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'2+4=6',
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'4+2=6',
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'2+5=7',
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'5+2=7',
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'2+6=8',
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'6+2=8',
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'2+7=9',
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'7+2=9',
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'2+8=10',
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'8+2=10',
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'2+9=11',
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'9+2=11',
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'3+3=6',
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'3+4=7',
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'4+3=7',
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'3+5=8',
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'5+3=8',
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'3+6=9',
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'6+3=9',
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'3+7=10',
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'7+3=10',
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'3+8=11',
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'8+3=11',
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'3+9=12',
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'9+3=12',
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'4+4=8',
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'4+5=9',
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'5+4=9',
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'4+6=10',
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'6+4=10',
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'4+7=11',
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'7+4=11',
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'4+8=12',
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'8+4=12',
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'4+9=13',
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'9+4=13',
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'5+5=10',
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'5+6=11',
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'6+5=11',
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'5+7=12',
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'7+5=12',
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'5+8=13',
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'8+5=13',
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'5+9=14',
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'9+5=14',
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'6+6=12',
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'6+7=13',
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'7+6=13',
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'6+8=14',
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'8+6=14',
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'6+9=15',
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'9+6=15',
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'7+7=14',
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'7+8=15',
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'8+7=15',
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'7+9=16',
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'9+7=16',
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'8+8=16',
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'8+9=17',
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'9+8=17',
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'9+9=18',
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];
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net.train(mathProblems, { log: true, errorThresh: 0.03 });
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let errors = 0;
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for (let i = 0; i < mathProblems.length; i++) {
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const input = mathProblems[i].split('=')[0] + '=';
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const output: string = net.run(input);
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const predictedMathProblem = input + output;
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const error = !mathProblems.includes(predictedMathProblem) ? 1 : 0;
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errors += error;
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console.log(predictedMathProblem + (error === 1 ? ' - error' : ''));
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}
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console.log(`Errors: ${errors / mathProblems.length}`);

‎src/predict-numbers.ts

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import { recurrent } from 'brain.js';
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const { LSTMTimeStep } = recurrent;
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const net = new LSTMTimeStep({
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inputSize: 2,
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hiddenLayers: [10],
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outputSize: 2,
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});
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// Same test as previous, but combined on a single set
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const trainingData = [
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[
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[1, 5],
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[2, 4],
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[3, 3],
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[4, 2],
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[5, 1],
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],
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];
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net.train(trainingData, { log: true, errorThresh: 0.09 });
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const closeToFiveAndOne = net.run([
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[1, 5],
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[2, 4],
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[3, 3],
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[4, 2],
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]) as number[];
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console.log(closeToFiveAndOne);
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// now we're cookin' with gas!
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const forecast = net.forecast(
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[
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[1, 5],
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[2, 4],
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],
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3
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) as number[][];
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console.log('next 3 predictions', forecast);

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