Python source code: plot_cluster_iris.py

import pylab as pl
from mpl_toolkits.mplot3d import Axes3D


from scikits.learn.cluster import KMeans
from scikits.learn import datasets

np.random.seed(5)

centers = [[1, 1], [-1, -1], [1, -1]]
iris = datasets.load_iris()
X = iris.data
y = iris.target

estimators = {'k_means_iris_3': KMeans(k=3),
              'k_means_iris_8': KMeans(k=8),
              'k_means_iris_bad_init': KMeans(k=3, n_init=1, init='random'),
             }

fig = pl.figure(1, figsize=(4, 3))
pl.clf()
ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)
pl.set_cmap(pl.cm.spectral)

for name, est in estimators.iteritems():
    pl.cla()

    est.fit(X)
    labels = est.labels_

    ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(np.float))

    ax.set_xticks(())
    ax.set_yticks(())
    ax.set_zticks(())
    ax.set_xlabel('Petal width')
    ax.set_ylabel('Sepal length')
    ax.set_zlabel('Petal length')

# Plot the ground truth
pl.cla()

for name, label in [('Setosa', 0),
                    ('Versicolour', 1),
                    ('Virginica', 2)]:
    ax.text3D(X[y==label, 3].mean(), 
              X[y==label, 0].mean()+1.5, 
              X[y==label, 2].mean(), name,
              horizontalalignment='center',
              bbox=dict(alpha=.5, edgecolor='w', facecolor='w'),
             )
# Reorder the labels to have colors matching the cluster results
y = np.choose(y, [1, 2, 0]).astype(np.float)
ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y)

ax.set_xticks(())
ax.set_yticks(())
ax.set_zticks(())
ax.set_xlabel('Petal width')
ax.set_ylabel('Sepal length')
ax.set_zlabel('Petal length')
pl.savefig('cluster_iris_truth.png')