From 4c7b5303646930fd6230593a19a9d4287dc40320 Mon Sep 17 00:00:00 2001
From: Chalarangelo <chalarangelo@gmail.com>
Date: Mon, 28 Dec 2020 15:38:40 +0200
Subject: [PATCH] Add kMeans

---
 snippets/kMeans.md | 60 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100644 snippets/kMeans.md

diff --git a/snippets/kMeans.md b/snippets/kMeans.md
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+++ b/snippets/kMeans.md
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+---
+title: kMeans
+tags: algorithm,array,advanced
+---
+
+Groups the given data into `k` clusters using the k-means clustering algorithm.
+
+- Use `Array.from()` and `Array.prototype.slice()` to initialize appropriate variables for the cluster `centroids`, `distances` and `classes`.
+- 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`.
+- Calculate the euclidean distance between each data point and centroid using `Math.hypot()`, `Object.keys()` and `Array.prototype.map()`.
+- Use `Array.prototype.indexOf()` and `Math.min()` to find the closest centroid.
+- Use `Array.from()` and `Array.prototype.reduce()`, as well as `parseFloat()` and `Number.prototype.toFixed()` to calculate the new centroids.
+
+```js
+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;
+};
+```
+
+```js
+kMeans([[0, 0], [0, 1], [1, 3], [2, 0]], 2); // [0, 1, 1, 0]
+```