76 lines
1.7 KiB
Python
76 lines
1.7 KiB
Python
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"""
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================================
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SVM Exercise
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================================
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A tutorial exercise for using different SVM kernels.
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This exercise is used in the :ref:`using_kernels_tut` part of the
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:ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn import datasets, svm
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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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X = X[y != 0, :2]
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y = y[y != 0]
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n_sample = len(X)
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np.random.seed(0)
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order = np.random.permutation(n_sample)
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X = X[order]
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y = y[order].astype(float)
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X_train = X[: int(0.9 * n_sample)]
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y_train = y[: int(0.9 * n_sample)]
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X_test = X[int(0.9 * n_sample) :]
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y_test = y[int(0.9 * n_sample) :]
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# fit the model
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for kernel in ("linear", "rbf", "poly"):
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clf = svm.SVC(kernel=kernel, gamma=10)
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clf.fit(X_train, y_train)
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plt.figure()
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plt.clf()
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plt.scatter(
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X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired, edgecolor="k", s=20
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)
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# Circle out the test data
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plt.scatter(
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X_test[:, 0], X_test[:, 1], s=80, facecolors="none", zorder=10, edgecolor="k"
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)
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plt.axis("tight")
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x_min = X[:, 0].min()
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x_max = X[:, 0].max()
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y_min = X[:, 1].min()
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y_max = X[:, 1].max()
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XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
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Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])
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# Put the result into a color plot
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Z = Z.reshape(XX.shape)
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plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)
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plt.contour(
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XX,
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YY,
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Z,
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colors=["k", "k", "k"],
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linestyles=["--", "-", "--"],
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levels=[-0.5, 0, 0.5],
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)
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plt.title(kernel)
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plt.show()
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