先看下效果图：

# 先调入需要的模块import numpy as npimport matplotlib.pyplot as pltfrom sklearn import svmimport seaborn as sb# 生成几个数据点data = np.array([    [0.1, 0.7],    [0.3, 0.6],    [0.4, 0.1],    [0.5, 0.4],    [0.8, 0.04],    [0.42, 0.6],    [0.9, 0.4],    [0.6, 0.5],    [0.7, 0.2],    [0.7, 0.67],    [0.27,0.8],    [0.5, 0.72]    ])    target = [1] * 6 + [0] * 6x_line = np.linspace(0, 1, 100)y_line = 1 - x_lineplt.scatter(data[:6, 0], data[:6, 1], marker='o', s=100, lw=3)plt.scatter(data[6:, 0], data[6:, 1], marker='x', s=100, lw=3)plt.plot(x_line, y_line)# 定义计算域、文字说明等C = 0.0001  # SVM regularization parameter, since Scikit-learn doesn't allow C=0# linear_svc = svm.SVC(kernel='linear', C=C).fit(data, target)# create a mesh to plot inh = 0.002x_min, x_max = data[:, 0].min() - 0.2, data[:, 0].max() + 0.2y_min, y_max = data[:, 1].min() - 0.2, data[:, 1].max() + 0.2xx, yy = np.meshgrid(np.arange(x_min, x_max, h),                     np.arange(y_min, y_max, h))# title for the plotstitles = ['SVC with linear kernel',          'SVC with RBF kernel',          'SVC with polynomial (degree 3) kernel']# RBF Kernelplt.figure(figsize=(16, 15))for i, gamma in enumerate([1, 5, 15, 35, 45, 55]):    rbf_svc = svm.SVC(kernel='rbf', gamma=gamma, C=C).fit(data, target)        # ravel - flatten    # c_ - vstack    # #把后面两个压扁之后变成了x1和x2，然后进行判断，得到结果在压缩成一个矩形    Z = rbf_svc.predict(np.c_[xx.ravel(), yy.ravel()])    Z = Z.reshape(xx.shape)        plt.subplot(3, 2, i + 1)    plt.subplots_adjust(wspace=0.4, hspace=0.4)    plt.contourf(xx, yy, Z, cmap=plt.cm.ocean, alpha=0.6)    # Plot the training points    plt.scatter(data[:6, 0], data[:6, 1], marker='o', color='r', s=100, lw=3)    plt.scatter(data[6:, 0], data[6:, 1], marker='x', color='k', s=100, lw=3)        plt.title('RBF SVM with $\gamma=$' + str(gamma))    plt.show()