73 lines
1.9 KiB
Python
73 lines
1.9 KiB
Python
# -*- 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 <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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# Modified for documentation by Jaques Grobler
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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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() - 0.5, X[:, 0].max() + 0.5
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y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
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plt.figure(2, figsize=(8, 6))
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plt.clf()
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# Plot the training points
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plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, edgecolor="k")
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plt.xlabel("Sepal length")
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plt.ylabel("Sepal width")
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plt.xlim(x_min, x_max)
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plt.ylim(y_min, y_max)
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plt.xticks(())
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plt.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 = plt.figure(1, figsize=(8, 6))
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ax = fig.add_subplot(111, projection="3d", elev=-150, azim=110)
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X_reduced = PCA(n_components=3).fit_transform(iris.data)
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ax.scatter(
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X_reduced[:, 0],
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X_reduced[:, 1],
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X_reduced[:, 2],
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c=y,
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cmap=plt.cm.Set1,
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edgecolor="k",
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s=40,
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)
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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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plt.show()
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