49 lines
1.1 KiB
Python
49 lines
1.1 KiB
Python
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"""
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=========================================
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SVM: Maximum margin separating hyperplane
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=========================================
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Plot the maximum margin separating hyperplane within a two-class
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separable dataset using a Support Vector Machine classifier with
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linear kernel.
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"""
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import matplotlib.pyplot as plt
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from sklearn import svm
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from sklearn.datasets import make_blobs
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from sklearn.inspection import DecisionBoundaryDisplay
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# we create 40 separable points
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X, y = make_blobs(n_samples=40, centers=2, random_state=6)
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# fit the model, don't regularize for illustration purposes
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clf = svm.SVC(kernel="linear", C=1000)
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clf.fit(X, y)
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plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
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# plot the decision function
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ax = plt.gca()
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DecisionBoundaryDisplay.from_estimator(
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clf,
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X,
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plot_method="contour",
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colors="k",
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levels=[-1, 0, 1],
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alpha=0.5,
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linestyles=["--", "-", "--"],
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ax=ax,
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)
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# plot support vectors
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ax.scatter(
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clf.support_vectors_[:, 0],
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clf.support_vectors_[:, 1],
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s=100,
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linewidth=1,
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facecolors="none",
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edgecolors="k",
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)
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plt.show()
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