plot3

from sklearn.cluster import KMeans
k = 2 # Define the number of clusters in which we want to partion the data
kmeans = KMeans(n_clusters = k) # Run the algorithm kmeans
kmeans.fit(X);
centroids = kmeans.cluster_centers_ # Get centroid's coordinates for each cluster
labels = kmeans.labels_ # Get labels assigned to each data
k = 2 # Define the number of clusters in which we want to partion the data
kmeans = KMeans(n_clusters = k) # Run the algorithm kmeans
kmeans.fit(X);
centroids = kmeans.cluster_centers_ # Get centroid's coordinates for each cluster
labels = kmeans.labels_ # Get labels assigned to each data
colors = ['r.', 'g.'] # Define two colors for the plot below
plt.figure()
for i in range(len(X)):
    plt.plot(X[i,0], X[i,1], colors[labels[i]], markersize = 30)
plt.scatter(centroids[:,0],centroids[:,1], marker = "x", s = 300, linewidths = 5) # Plot centroids
plt.show()