59 lines
2.0 KiB
Markdown
59 lines
2.0 KiB
Markdown
---
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title: kMeans
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tags: algorithm,array,advanced
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---
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Groups the given data into `k` clusters, using the [k-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) algorithm.
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- Use `Array.from()` and `Array.prototype.slice()` to initialize appropriate variables for the cluster `centroids`, `distances` and `classes`.
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- Use a `while` loop to repeat the assignment and update steps as long as there are changes in the previous iteration, as indicated by `itr`.
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- Calculate the euclidean distance between each data point and centroid using `Math.hypot()`, `Object.keys()` and `Array.prototype.map()`.
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- Use `Array.prototype.indexOf()` and `Math.min()` to find the closest centroid.
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- Use `Array.from()` and `Array.prototype.reduce()`, as well as `parseFloat()` and `Number.prototype.toFixed()` to calculate the new centroids.
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```js
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const kMeans = (data, k = 1) => {
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const centroids = data.slice(0, k);
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const distances = Array.from({ length: data.length }, () =>
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Array.from({ length: k }, () => 0)
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);
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const classes = Array.from({ length: data.length }, () => -1);
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let itr = true;
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while (itr) {
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itr = false;
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for (let d in data) {
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for (let c = 0; c < k; c++) {
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distances[d][c] = Math.hypot(
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...Object.keys(data[0]).map(key => data[d][key] - centroids[c][key])
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);
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}
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const m = distances[d].indexOf(Math.min(...distances[d]));
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if (classes[d] !== m) itr = true;
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classes[d] = m;
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}
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for (let c = 0; c < k; c++) {
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centroids[c] = Array.from({ length: data[0].length }, () => 0);
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const size = data.reduce((acc, _, d) => {
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if (classes[d] === c) {
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acc++;
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for (let i in data[0]) centroids[c][i] += data[d][i];
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}
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return acc;
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}, 0);
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for (let i in data[0]) {
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centroids[c][i] = parseFloat(Number(centroids[c][i] / size).toFixed(2));
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}
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}
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}
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return classes;
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};
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```
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```js
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kMeans([[0, 0], [0, 1], [1, 3], [2, 0]], 2); // [0, 1, 1, 0]
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```
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