66 lines
1.6 KiB
Python
66 lines
1.6 KiB
Python
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import numpy as np
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import pylab as pl
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from mpl_toolkits.mplot3d import Axes3D
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from scikits.learn.cluster import KMeans
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from scikits.learn import datasets
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np.random.seed(5)
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centers = [[1, 1], [-1, -1], [1, -1]]
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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estimators = {'k_means_iris_3': KMeans(k=3),
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'k_means_iris_8': KMeans(k=8),
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'k_means_iris_bad_init': KMeans(k=3, n_init=1, init='random'),
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}
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fig = pl.figure(1, figsize=(4, 3))
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pl.clf()
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ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)
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pl.set_cmap(pl.cm.spectral)
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for name, est in estimators.iteritems():
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pl.cla()
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est.fit(X)
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labels = est.labels_
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ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(np.float))
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ax.set_xticks(())
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ax.set_yticks(())
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ax.set_zticks(())
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ax.set_xlabel('Petal width')
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ax.set_ylabel('Sepal length')
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ax.set_zlabel('Petal length')
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# Plot the ground truth
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pl.cla()
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for name, label in [('Setosa', 0),
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('Versicolour', 1),
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('Virginica', 2)]:
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ax.text3D(X[y==label, 3].mean(),
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X[y==label, 0].mean()+1.5,
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X[y==label, 2].mean(), name,
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horizontalalignment='center',
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bbox=dict(alpha=.5, edgecolor='w', facecolor='w'),
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)
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# Reorder the labels to have colors matching the cluster results
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y = np.choose(y, [1, 2, 0]).astype(np.float)
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ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y)
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ax.set_xticks(())
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ax.set_yticks(())
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ax.set_zticks(())
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ax.set_xlabel('Petal width')
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ax.set_ylabel('Sepal length')
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ax.set_zlabel('Petal length')
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pl.savefig('cluster_iris_truth.png')
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