K-Means Clustering

medium· Machine Learningruns: 0

Implement K-Means clustering. Initialize k centroids, assign each point to its nearest centroid, recompute centroids as the mean of their members, and repeat until the centroids stop moving.

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import numpy as np

class KMeans:
    def __init__(self, k: int = 3, max_iters: int = 100):
        self.k = k
        self.max_iters = max_iters

    def fit(self, X):
        rng = np.random.default_rng(0)
        idx = rng.choice(len(X), self.k, replace=False)
        self.centroids = X[idx]
        for _ in range(self.max_iters):
            labels = self._assign(X)
            new = np.array([X[labels == c].mean(axis=0) for c in range(self.k)])
            if np.allclose(new, self.centroids):
                break
            self.centroids = new
        return labels

    def _assign(self, X):
        dists = np.linalg.norm(X[:, None] - self.centroids, axis=2)
        return np.argmin(dists, axis=1)

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