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graph example

Nov 19, 2022CodeCatch
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compact object

Nov 19, 2022CodeCatch

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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' } }

CodeCatchCrasher

Feb 11, 2021LeifMessinger

0 likes • 2 views

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);

Composing components

Nov 19, 2022CodeCatch

0 likes • 3 views

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')
);

geometric progression

Nov 19, 2022CodeCatch

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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]

binomial coefficient

Nov 19, 2022CodeCatch

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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

K-means clustering

Nov 19, 2022CodeCatch

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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]