kmeans.py

import numpy as np


def get_distance(x1, x2):
    sum = 0
    for i in range(len(x1)):
        sum += (x1[i] - x2[i]) ** 2
    return np.sqrt(sum)


def kmeans(X, k, max_iters):
  
    centroids = X[np.random.choice(range(len(X)), k, replace=False)]

    converged = False
    
    current_iter = 0

    while (not converged) and (current_iter < max_iters):

        cluster_list = [[] for i in range(len(centroids))]

        for x in X:  # Go through each data point
            distances_list = []
            for c in centroids:
                distances_list.append(get_distance(c, x))
            cluster_list[int(np.argmin(distances_list))].append(x)

        cluster_list = list((filter(None, cluster_list)))

        prev_centroids = centroids.copy()

        centroids = []

        for j in range(len(cluster_list)):
            centroids.append(np.mean(cluster_list[j], axis=0))

        pattern = np.abs(np.sum(prev_centroids) - np.sum(centroids))

        print('K-MEANS: ', int(pattern))

        converged = (pattern == 0)

        current_iter += 1

    return np.array(centroids), [np.std(x) for x in cluster_list]