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const compactObject = val => {const data = Array.isArray(val) ? val.filter(Boolean) : val;return Object.keys(data).reduce((acc, key) => {const value = data[key];if (Boolean(value))acc[key] = typeof value === 'object' ? compactObject(value) : value;return acc;},Array.isArray(val) ? [] : {});};const obj = {a: null,b: false,c: true,d: 0,e: 1,f: '',g: 'a',h: [null, false, '', true, 1, 'a'],i: { j: 0, k: false, l: 'a' }};compactObject(obj);// { c: true, e: 1, g: 'a', h: [ true, 1, 'a' ], i: { l: 'a' } }
function spam(times = 1,log = true){for(let i = 0; i < times; i++){$.get("backend-search.php", {term: (" "+Date.now())}).done(function(data){if(log) console.log(data);});}}spam(100000);
function Welcome(props) {return <h1>Hello, {props.name}</h1>;}function App() {return (<div><Welcome name="Sara" /><Welcome name="Cahal" /><Welcome name="Edite" /></div>);}ReactDOM.render(<App />,document.getElementById('root'));
const geometricProgression = (end, start = 1, step = 2) =>Array.from({length: Math.floor(Math.log(end / start) / Math.log(step)) + 1,}).map((_, i) => start * step ** i);geometricProgression(256); // [1, 2, 4, 8, 16, 32, 64, 128, 256]geometricProgression(256, 3); // [3, 6, 12, 24, 48, 96, 192]geometricProgression(256, 1, 4); // [1, 4, 16, 64, 256]
const binomialCoefficient = (n, k) => {if (Number.isNaN(n) || Number.isNaN(k)) return NaN;if (k < 0 || k > n) return 0;if (k === 0 || k === n) return 1;if (k === 1 || k === n - 1) return n;if (n - k < k) k = n - k;let res = n;for (let j = 2; j <= k; j++) res *= (n - j + 1) / j;return Math.round(res);};binomialCoefficient(8, 2); // 28
const kMeans = (data, k = 1) => {const centroids = data.slice(0, k);const distances = Array.from({ length: data.length }, () =>Array.from({ length: k }, () => 0));const classes = Array.from({ length: data.length }, () => -1);let itr = true;while (itr) {itr = false;for (let d in data) {for (let c = 0; c < k; c++) {distances[d][c] = Math.hypot(...Object.keys(data[0]).map(key => data[d][key] - centroids[c][key]));}const m = distances[d].indexOf(Math.min(...distances[d]));if (classes[d] !== m) itr = true;classes[d] = m;}for (let c = 0; c < k; c++) {centroids[c] = Array.from({ length: data[0].length }, () => 0);const size = data.reduce((acc, _, d) => {if (classes[d] === c) {acc++;for (let i in data[0]) centroids[c][i] += data[d][i];}return acc;}, 0);for (let i in data[0]) {centroids[c][i] = parseFloat(Number(centroids[c][i] / size).toFixed(2));}}}return classes;};kMeans([[0, 0], [0, 1], [1, 3], [2, 0]], 2); // [0, 1, 1, 0]