"""Two iterations of 1D k-means."""
import numpy as np
X=np.array([[1.],[2.],[3.],[8.],[9.],[10.]])
centroids=np.array([[2.],[8.]])
for it in range(2):
    d=np.sum((X[:,None,:]-centroids[None,:,:])**2,axis=2)
    labels=np.argmin(d,axis=1)
    print("labels",labels)
    # TODO: update each centroid to its cluster mean.
    centroids=np.array([np.mean(X[labels==k],axis=0) for k in range(2)])
    print("centroids",centroids)