45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
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"""
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=========================================
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SGD: 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 linear Support Vector Machines classifier
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trained using SGD.
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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.datasets import make_blobs
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from sklearn.linear_model import SGDClassifier
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# we create 50 separable points
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X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)
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# fit the model
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clf = SGDClassifier(loss="hinge", alpha=0.01, max_iter=200)
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clf.fit(X, Y)
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# plot the line, the points, and the nearest vectors to the plane
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xx = np.linspace(-1, 5, 10)
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yy = np.linspace(-1, 5, 10)
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X1, X2 = np.meshgrid(xx, yy)
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Z = np.empty(X1.shape)
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for (i, j), val in np.ndenumerate(X1):
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x1 = val
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x2 = X2[i, j]
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p = clf.decision_function([[x1, x2]])
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Z[i, j] = p[0]
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levels = [-1.0, 0.0, 1.0]
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linestyles = ["dashed", "solid", "dashed"]
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colors = "k"
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plt.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
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plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolor="black", s=20)
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plt.axis("tight")
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plt.show()
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