- refactor(rules): refactor 'valid-by-tagname' rule (f83a4b9)
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const GOODREADS_KEY = process.env.GOODREADS_KEY; | |
const cors = "https://cors-anywhere.herokuapp.com/"; | |
const goodreads = "https://www.goodreads.com/"; | |
const goodreadsURL = cors + goodreads + "search/index.xml?"; | |
const bookURL = cors + goodreads + "book/show.xml?"; |
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// Take a union of Rules later. For Testing! TODO | |
const rules = [ | |
// {pattern:/\t+|\n+|\d+|([a-zA-Z_]\w*)|\;|\{|\}|\]|\[|\(|\)|\+|\-|\*|\/|( +)|\=|\'|\>|\<|\.(?=\w+)/g, name:'all'}, | |
{ pattern: /^\s/, name: "WhiteSpace" }, | |
{ pattern: /^\[/, name: "OpenBracket" }, | |
{ pattern: /^\]/, name: "CloseBracket" }, | |
{ pattern: /^\(/, name: "OpenParen" }, | |
{ pattern: /^\)/, name: "CloseParen" }, | |
{ pattern: /^\{/, name: "OpenBraces" }, | |
{ pattern: /^\}/, name: "CloseBraces" }, |
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// Generate Matrices | |
const matrices = generateMatrices(); | |
const A = matrices.A; | |
const B = matrices.B; | |
//CPU | |
const startCPU = window.performance.now(); | |
const cpuResult = cpuMatMult(A, B); | |
const endCPU = window.performance.now(); | |
const cpuTime = endCPU - startCPU; |
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function generateMatrices() { | |
const matSize = 512; | |
let A = []; | |
let B = []; | |
for (let n = 0; n < matSize * matSize; n++) { | |
const randA = Math.random(); | |
const randB = Math.random(); | |
A.push(randA); | |
B.push(randB); | |
} |
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const gpuMatMult = gpu.createKernel(function (A, B) { | |
var sum = 0; | |
for (var i = 0; i < 512; i++) { | |
sum += A[this.thread.y][i] * B[i][this.thread.x]; | |
} | |
return sum; | |
}) | |
.setDimensions([A.length, B.length]) | |
.setOutputToTexture(true); |
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function cpuMatMult(m, n) { | |
var result = []; | |
for (var i = 0; i < m.length; i++) { | |
result[i] = []; | |
for (var j = 0; j < n[0].length; j++) { | |
var sum = 0; | |
for (var k = 0; k < m[0].length; k++) { | |
sum += m[i][k] * n[k][j]; | |
} | |
result[i][j] = sum; |
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<!DOCTYPE html> | |
<html lang='en'> | |
<head> | |
<meta charset='UTF-8'> | |
<meta name='viewport' content='width=device-width, initial-scale=1.0'> | |
<meta http-equiv='X-UA-Compatible' content='ie=edge'> | |
<title>gpu.js demo</title> | |
<script src='gpu-core.min.js'></script> |
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const KNN = require('ml-knn'); | |
const csv = require('csvtojson'); | |
const prompt = require('prompt'); | |
let knn; | |
const csvFilePath = 'iris.csv'; // Data | |
const names = ['sepalLength', 'sepalWidth', 'petalLength', 'petalWidth', 'type']; // For header | |
let seperationSize; // To seperate training and test data | |
let data = [], X = [], y = []; |
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function predict() { | |
let temp = []; | |
prompt.start(); | |
prompt.get(['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'], function (err, result) { | |
if (!err) { | |
for (var key in result) { | |
temp.push(parseFloat(result[key])); | |
} | |
console.log(`With ${temp} -- type = ${knn.predict(temp)}`); |