import numpy as np

Create z, an array with shape (100, 2) containing a mixture of samples
from three multivariate normal distributions.

>>> rng = np.random.default_rng()
>>> a = rng.multivariate_normal([0, 6], [[2, 1], [1, 1.5]], size=45)
>>> b = rng.multivariate_normal([2, 0], [[1, -1], [-1, 3]], size=30)
>>> c = rng.multivariate_normal([6, 4], [[5, 0], [0, 1.2]], size=25)
>>> z = np.concatenate((a, b, c))
>>> rng.shuffle(z)

Compute three clusters.

>>> centroid, label = kmeans2(z, 3, minit='points')
>>> centroid
array([[ 2.22274463, -0.61666946],  # may vary
       [ 0.54069047,  5.86541444],
       [ 6.73846769,  4.01991898]])

How many points are in each cluster?

>>> counts = np.bincount(label)
>>> counts
array([29, 51, 20])  # may vary

Plot the clusters.

>>> w0 = z[label == 0]
>>> w1 = z[label == 1]
>>> w2 = z[label == 2]
>>> plt.plot(w0[:, 0], w0[:, 1], 'o', alpha=0.5, label='cluster 0')
>>> plt.plot(w1[:, 0], w1[:, 1], 'd', alpha=0.5, label='cluster 1')
>>> plt.plot(w2[:, 0], w2[:, 1], 's', alpha=0.5, label='cluster 2')
>>> plt.plot(centroid[:, 0], centroid[:, 1], 'k*', label='centroids')
>>> plt.axis('equal')
>>> plt.legend(shadow=True)
>>> plt.show()

r