Add kNearestNeighbors

snippets/kNearestNeighbors.md

@@ -0,0 +1,49 @@
+---
+title: kNearestNeighbors
+tags: algorithm,array,advanced
+---
+
+Classifies a data point relative to a labelled data set using the k-nearest neighbors algorithm.
+
+- Use `Array.prototype.map()` to map the `data` to objects containing the euclidean distance of each element from `point`, calculated using `Math.hypot()`, `Object.keys()` and its `label`.
+- Use `Array.prototype.sort()` and `Array.prototype.slice()` to get the `k` nearest neighbors of `point`.
+- Use `Array.prototype.reduce()` in combination with `Object.keys()` and `Array.prototype.indexOf()` to find the most frequent `label` among them.
+
+```js
+const kNearestNeighbors = (data, labels, point, k = 3) => {
+  const kNearest = data
+    .map((el, i) => ({
+      dist: Math.hypot(...Object.keys(el).map(key => point[key] - el[key])),
+      label: labels[i]
+    }))
+    .sort((a, b) => a.dist - b.dist)
+    .slice(0, k);
+
+  return kNearest.reduce(
+    (acc, { label }, i) => {
+      acc.classCounts[label] =
+        Object.keys(acc.classCounts).indexOf(label) !== -1
+          ? acc.classCounts[label] + 1
+          : 1;
+      if (acc.classCounts[label] > acc.topClassCount) {
+        acc.topClassCount = acc.classCounts[label];
+        acc.topClass = label;
+      }
+      return acc;
+    },
+    {
+      classCounts: {},
+      topClass: kNearest[0].label,
+      topClassCount: 0
+    }
+  ).topClass;
+};
+```
+
+```js
+const data = [[0, 0], [0, 1], [1, 3], [2, 0]];
+const labels = [0, 1, 1, 0];
+
+kNearestNeighbors(data, labels, [1, 2], 2); // 1
+kNearestNeighbors(data, labels, [1, 0], 2); // 0
+```