93 lines
2.5 KiB
Python
93 lines
2.5 KiB
Python
#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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=========================================================
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K-means Clustering
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=========================================================
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The plots display firstly what a K-means algorithm would yield
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using three clusters. It is then shown what the effect of a bad
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initialization is on the classification process:
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By setting n_init to only 1 (default is 10), the amount of
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times that the algorithm will be run with different centroid
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seeds is reduced.
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The next plot displays what using eight clusters would deliver
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and finally the ground truth.
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"""
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print __doc__
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# Code source: Gael Varoqueux
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# Modified for Documentation merge by Jaques Grobler
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# License: BSD
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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 sklearn.cluster import KMeans
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from sklearn 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(n_clusters=3),
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'k_means_iris_8': KMeans(n_clusters=8),
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'k_means_iris_bad_init': KMeans(n_clusters=3, n_init=1,
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init='random')}
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fignum = 1
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for name, est in estimators.iteritems():
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fig = pl.figure(fignum, 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.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.w_xaxis.set_ticklabels([])
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ax.w_yaxis.set_ticklabels([])
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ax.w_zaxis.set_ticklabels([])
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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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fignum = fignum + 1
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# Plot the ground truth
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fig = pl.figure(fignum, 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.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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# 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.w_xaxis.set_ticklabels([])
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ax.w_yaxis.set_ticklabels([])
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ax.w_zaxis.set_ticklabels([])
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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.show()
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