sklearn之SVM二分类
2017-12-04 08:37
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理论部分
线性支持向量机对偶形式支持向量机
核函数支持向量机
软间隔支持向量机
Kernel Logistic Regression
Support Vector Regression(SVR)
使用sklearn实现的不同核函数的SVM
使用不同核函数的SVM用于二分类问题并可视化分类结果。# -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.svm import SVC def bc(): data = pd.read_table(r'./data/testSet.txt', header=None, delim_whitespace=True) print(data.info()) print(data.head()) X_train = np.array(data.loc[:][[0, 1]]) y_train = np.array(data[2]) y_train = np.where(y_train == 1, 1, -1) x_min = X_train[:, 0].min() x_max = X_train[:, 0].max() y_min = X_train[:, 1].min() y_max = X_train[:, 1].max() ''' linear svm, poly svm, rbf svm ''' plt.figure(figsize=(15, 15)) for fig_num, kernel in enumerate(('linear', 'poly', 'rbf')): svm_ = SVC(kernel=kernel) svm_.fit(X_train, y_train) # support vectors # plt.figure(fig_num) # plt.clf() plt.subplot(222 + fig_num) plt.scatter(x = X_train[y_train == 1, 0], y = X_train[y_train == 1, 1], s = 30, marker = 'o', color = 'yellow', zorder = 10) plt.scatter(x = X_train[y_train == -1, 0], y = X_train[y_train == -1, 1], s = 30, marker = 'x', color = 'blue', zorder = 10) plt.scatter(x = [x[0] for x in svm_.support_vectors_], y = [x[1] for x in svm_.support_vectors_], s = 80, facecolors='none', zorder = 10) print(len(svm_.support_vectors_)) plt.title(kernel) plt.xlabel('support vectors ' + str(len(svm_.support_vectors_))) plt.xticks([]) plt.yticks([]) plt.xlim(x_min, x_max) plt.ylim(y_min, y_max) XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j] Z = svm_.decision_function(np.c_[XX.ravel(), YY.ravel()]) Z = Z.reshape(XX.shape) plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired) plt.contour(XX, YY, Z, colors=['black', 'k', 'white'], linestyles=['--', '-', '--'], levels=[-.5, 0, .5]) # plot data plt.subplot(221) plt.title('data') plt.scatter(x=X_train[y_train == 1, 0], y=X_train[y_train == 1, 1], s=30, marker='o', color='red', zorder=10) plt.scatter(x=X_train[y_train == -1, 0], y=X_train[y_train == -1, 1], s=30, marker='x', color='blue', zorder=10) plt.xticks([]) plt.yticks([]) plt.xlim(x_min, x_max) plt.ylim(y_min, y_max) plt.savefig(r'./data/svm' + str(kernel) + '.jpg') plt.show() if __name__ == '__main__': bc()
运行结果
使用大圆圈圈出了支持向量,并且在每一个图下给出了支持向量的个数。
实验数据
-0.017612 14.053064 0 -1.395634 4.662541 1 -0.752157 6.538620 0 -1.322371 7.152853 0 0.423363 11.054677 0 0.406704 7.067335 1 0.667394 12.741452 0 -2.460150 6.866805 1 0.569411 9.548755 0 -0.026632 10.427743 0 0.850433 6.920334 1 1.347183 13.175500 0 1.176813 3.167020 1 -1.781871 9.097953 0 -0.566606 5.749003 1 0.931635 1.589505 1 -0.024205 6.151823 1 -0.036453 2.690988 1 -0.196949 0.444165 1 1.014459 5.754399 1 1.985298 3.230619 1 -1.693453 -0.557540 1 -0.576525 11.778922 0 -0.346811 -1.678730 1 -2.124484 2.672471 1 1.217916 9.597015 0 -0.733928 9.098687 0 -3.642001 -1.618087 1 0.315985 3.523953 1 1.416614 9.619232 0 -0.386323 3.989286 1 0.556921 8.294984 1 1.224863 11.587360 0 -1.347803 -2.406051 1 1.196604 4.951851 1 0.275221 9.543647 0 0.470575 9.332488 0 -1.889567 9.542662 0 -1.527893 12.150579 0 -1.185247 11.309318 0 -0.445678 3.297303 1 1.042222 6.105155 1 -0.618787 10.320986 0 1.152083 0.548467 1 0.828534 2.676045 1 -1.237728 10.549033 0 -0.683565 -2.166125 1 0.229456 5.921938 1 -0.959885 11.555336 0 0.492911 10.993324 0 0.184992 8.721488 0 -0.355715 10.325976 0 -0.397822 8.058397 0 0.824839 13.730343 0 1.507278 5.027866 1 0.099671 6.835839 1 -0.344008 10.717485 0 1.785928 7.718645 1 -0.918801 11.560217 0 -0.364009 4.747300 1 -0.841722 4.119083 1 0.490426 1.960539 1 -0.007194 9.075792 0 0.356107 12.447863 0 0.342578 12.281162 0 -0.810823 -1.466018 1 2.530777 6.476801 1 1.296683 11.607559 0 0.475487 12.040035 0 -0.783277 11.009725 0 0.074798 11.023650 0 -1.337472 0.468339 1 -0.102781 13.763651 0 -0.147324 2.874846 1 0.518389 9.887035 0 1.015399 7.571882 0 -1.658086 -0.027255 1 1.319944 2.171228 1 2.056216 5.019981 1 -0.851633 4.375691 1 -1.510047 6.061992 0 -1.076637 -3.181888 1 1.821096 10.283990 0 3.010150 8.401766 1 -1.099458 1.688274 1 -0.834872 -1.733869 1 -0.846637 3.849075 1 1.400102 12.628781 0 1.752842 5.468166 1 0.078557 0.059736 1 0.089392 -0.715300 1 1.825662 12.693808 0 0.197445 9.744638 0 0.126117 0.922311 1 -0.679797 1.220530 1 0.677983 2.556666 1 0.761349 10.693862 0 -2.168791 0.143632 1 1.388610 9.341997 0 0.317029 14.739025 0
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