60 lines
1.5 KiB
Python
60 lines
1.5 KiB
Python
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"""
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=========================================================
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PCA example with Iris Data-set
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=========================================================
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Principal Component Analysis applied to the Iris dataset.
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See `here <https://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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# Code source: Gaël Varoquaux
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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# unused but required import for doing 3d projections with matplotlib < 3.2
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import mpl_toolkits.mplot3d # noqa: F401
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import numpy as np
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from sklearn import datasets, decomposition
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np.random.seed(5)
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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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fig = plt.figure(1, figsize=(4, 3))
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plt.clf()
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ax = fig.add_subplot(111, projection="3d", elev=48, azim=134)
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ax.set_position([0, 0, 0.95, 1])
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plt.cla()
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pca = decomposition.PCA(n_components=3)
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pca.fit(X)
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X = pca.transform(X)
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for name, label in [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]:
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ax.text3D(
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X[y == label, 0].mean(),
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X[y == label, 1].mean() + 1.5,
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X[y == label, 2].mean(),
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name,
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horizontalalignment="center",
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bbox=dict(alpha=0.5, 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(float)
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ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.nipy_spectral, edgecolor="k")
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ax.xaxis.set_ticklabels([])
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ax.yaxis.set_ticklabels([])
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ax.zaxis.set_ticklabels([])
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plt.show()
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