68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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=========================================================
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The Iris Dataset
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=========================================================
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This data sets consists of 3 different types of irises'
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(Setosa, Versicolour, and Virginica) petal and sepal
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length, stored in a 150x4 numpy.ndarray
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The rows being the samples and the columns being:
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Sepal Length, Sepal Width, Petal Length and Petal Width.
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The below plot uses the first two features.
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See `here <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
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information on this dataset.
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"""
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print(__doc__)
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# Code source: Gaël Varoquaux
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# Modified for documentation by Jaques Grobler
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# License: BSD 3 clause
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import pylab as pl
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from mpl_toolkits.mplot3d import Axes3D
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from sklearn import datasets
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from sklearn.decomposition import PCA
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# import some data to play with
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iris = datasets.load_iris()
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X = iris.data[:, :2] # we only take the first two features.
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Y = iris.target
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x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
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y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
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pl.figure(2, figsize=(8, 6))
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pl.clf()
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# Plot the training points
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pl.scatter(X[:, 0], X[:, 1], c=Y, cmap=pl.cm.Paired)
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pl.xlabel('Sepal length')
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pl.ylabel('Sepal width')
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pl.xlim(x_min, x_max)
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pl.ylim(y_min, y_max)
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pl.xticks(())
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pl.yticks(())
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# To getter a better understanding of interaction of the dimensions
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# plot the first three PCA dimensions
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fig = pl.figure(1, figsize=(8, 6))
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ax = Axes3D(fig, elev=-150, azim=110)
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X_reduced = PCA(n_components=3).fit_transform(iris.data)
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ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,
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cmap=pl.cm.Paired)
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ax.set_title("First three PCA directions")
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ax.set_xlabel("1st eigenvector")
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ax.w_xaxis.set_ticklabels([])
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ax.set_ylabel("2nd eigenvector")
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ax.w_yaxis.set_ticklabels([])
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ax.set_zlabel("3rd eigenvector")
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ax.w_zaxis.set_ticklabels([])
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pl.show()
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