## File: plot_svm_kernels.py

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scikit-learn 0.23.2-5
 `12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485` ``````#!/usr/bin/python # -*- coding: utf-8 -*- """ ========================================================= SVM-Kernels ========================================================= Three different types of SVM-Kernels are displayed below. The polynomial and RBF are especially useful when the data-points are not linearly separable. """ print(__doc__) # Code source: GaĆ«l Varoquaux # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn import svm # Our dataset and targets X = np.c_[(.4, -.7), (-1.5, -1), (-1.4, -.9), (-1.3, -1.2), (-1.1, -.2), (-1.2, -.4), (-.5, 1.2), (-1.5, 2.1), (1, 1), # -- (1.3, .8), (1.2, .5), (.2, -2), (.5, -2.4), (.2, -2.3), (0, -2.7), (1.3, 2.1)].T Y = [0] * 8 + [1] * 8 # figure number fignum = 1 # fit the model for kernel in ('linear', 'poly', 'rbf'): clf = svm.SVC(kernel=kernel, gamma=2) clf.fit(X, Y) # plot the line, the points, and the nearest vectors to the plane plt.figure(fignum, figsize=(4, 3)) plt.clf() plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80, facecolors='none', zorder=10, edgecolors='k') plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired, edgecolors='k') plt.axis('tight') x_min = -3 x_max = 3 y_min = -3 y_max = 3 XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j] Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()]) # Put the result into a color plot Z = Z.reshape(XX.shape) plt.figure(fignum, figsize=(4, 3)) plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired) plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'], levels=[-.5, 0, .5]) plt.xlim(x_min, x_max) plt.ylim(y_min, y_max) plt.xticks(()) plt.yticks(()) fignum = fignum + 1 plt.show() ``````