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]