plt.jet() # set the color map. When your colors are lost, re-run this.
import sklearn.datasets as datasets
X, Y = datasets.make_blobs(centers=4, cluster_std=0.5, random_state=0)

plt.scatter(X[:,0], X[:,1]);

plt.scatter(X[:,0], X[:,1], c=Y);

from sklearn.cluster import KMeans
kmeans = KMeans(4, random_state=8)
Y_hat = kmeans.fit(X).labels_

plt.scatter(X[:,0], X[:,1], c=Y_hat);

plt.scatter(X[:,0], X[:,1], c=Y_hat, alpha=0.4)
mu = kmeans.cluster_centers_
plt.scatter(mu[:,0], mu[:,1], s=100, c=np.unique(Y_hat))
print mu

from sklearn.datasets import fetch_mldata
from sklearn.cluster import KMeans
from sklearn.utils import shuffle
X_digits, _,_, Y_digits = fetch_mldata("MNIST Original").values() # fetch dataset from internet
X_digits, Y_digits = shuffle(X_digits,Y_digits) # shuffle dataset (which is ordered!)
X_digits = X_digits[-5000:]       # take only the last instances, to shorten runtime of KMeans

plt.rc("image", cmap="binary") # use black/white palette for plotting
for i in xrange(10):
    plt.subplot(2,5,i+1)
    plt.imshow(X_digits[i].reshape(28,28))
    plt.xticks(())
    plt.yticks(())
plt.tight_layout()

kmeans = KMeans(20)
mu_digits = kmeans.fit(X_digits).cluster_centers_

plt.figure(figsize=(16,6))
for i in xrange(2*(mu_digits.shape[0]/2)): # loop over all means
    plt.subplot(2,mu_digits.shape[0]/2,i+1)
    plt.imshow(mu_digits[i].reshape(28,28))
    plt.xticks(())
    plt.yticks(())
plt.tight_layout()

from IPython.display import HTML
HTML('<iframe src=http://icperformance.com/wp-content/demos/kmeansmouse.html width=500 height=450></iframe>')