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  • 《机器学习》周志华 习题答案6.2

      原题是分别采用线性核和高斯核对西瓜数据集进行SVM的训练,周老师推荐的是LIMSVM,这里我使用的仍然是sklearn。

    #!/usr/bin/python
    # -*- coding:utf-8 -*-
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import svm, datasets
    
    file1 = open('c:quantwatermelon.csv','r')
    data = [line.strip('
    ').split(',') for line in file1]
    data = np.array(data)
    X = [[float(raw[-2]), float(raw[-1])] for raw in data[1:,1:-1]]
    #X = [[float(raw[-3]), float(raw[-2])] for raw in data[1:]]
    y = [1 if raw[-1]=='1' else 0 for raw in data[1:]]
    X = np.array(X)
    y = np.array(y)
    
    h = .02  # step size in the mesh
    
    # we create an instance of SVM and fit out data. We do not scale our
    # data since we want to plot the support vectors
    C = 1000  # SVM regularization parameter
    svc = svm.SVC(kernel='linear', C=C).fit(X, y)
    rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)
    
    
    # create a mesh to plot in
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    
    # title for the plots
    titles = ['SVC with linear kernel',
              'SVC with RBF kernel']
    
    
    for i, clf in enumerate((svc, rbf_svc)):
        # Plot the decision boundary. For that, we will assign a color to each
        # point in the mesh [x_min, m_max]x[y_min, y_max].
        plt.subplot(1, 2, i + 1)
        plt.subplots_adjust(wspace=0.4, hspace=0.4)
    
        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    
        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
    
        # Plot also the training points
        plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
        plt.xlabel('Sugar content')
        plt.ylabel('Density')
        plt.xlim(xx.min(), xx.max())
        plt.ylim(yy.min(), yy.max())
        plt.xticks(())
        plt.yticks(())
        plt.title(titles[i])
    
    plt.show()

    结果如下:

    线性核的支持向量也是线性的,高斯核的支持向量是曲线。

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  • 原文地址:https://www.cnblogs.com/zhusleep/p/5639505.html
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