58 lines
1.3 KiB
Python
58 lines
1.3 KiB
Python
"""
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======================
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SVM with custom kernel
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======================
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Simple usage of Support Vector Machines to classify a sample. It will
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plot the decision surface and the support vectors.
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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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from sklearn.inspection import DecisionBoundaryDisplay
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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. We could
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# avoid this ugly slicing by using a two-dim dataset
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Y = iris.target
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def my_kernel(X, Y):
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"""
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We create a custom kernel:
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(2 0)
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k(X, Y) = X ( ) Y.T
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(0 1)
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"""
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M = np.array([[2, 0], [0, 1.0]])
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return np.dot(np.dot(X, M), Y.T)
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h = 0.02 # step size in the mesh
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# we create an instance of SVM and fit out data.
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clf = svm.SVC(kernel=my_kernel)
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clf.fit(X, Y)
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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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cmap=plt.cm.Paired,
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ax=ax,
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response_method="predict",
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plot_method="pcolormesh",
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shading="auto",
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)
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# Plot also the training points
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plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolors="k")
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plt.title("3-Class classification using Support Vector Machine with custom kernel")
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plt.axis("tight")
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plt.show()
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