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from sklearn.datasets import load_irisfrom sklearn.svm import SVCfrom sklearn.linear_model import LogisticRegressioniris = load_iris()X = iris.data[0:100,[2,3]]Y = iris.target[0:100]'''支持向量机：分界线距离两个类别的边界最远'''clf1 = SVC(kernel="linear")clf1.fit(X, Y)clf2 = LogisticRegression()clf2.fit(X, Y)from itertools import productimport numpy as npimport matplotlib.pyplot as pltdef plot_estimator(estimator, X, Y):    x0_min, x0_max = X[:, 0].min() - 1, X[:, 0].max() + 1    x1_min, x1_max = X[:, 1].min() - 1, X[:, 1].max() + 1    xx, yy = np.meshgrid(np.arange(x0_min, x0_max, 0.1),                         np.arange(x1_min, x1_max, 0.1))    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])    Z = Z.reshape(xx.shape)    plt.plot()    plt.contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.RdYlBu)    plt.scatter(X[:, 0], X[:, 1], c=Y,  cmap = plt.cm.brg)    plt.xlabel('Petal.Length')    plt.ylabel('Petal.Width')    plt.show()

plot_estimator(clf1, X, Y)plot_estimator(clf2, X, Y)

'''C, 对错误（边界的某些点）的容忍度，C值越大越不能容忍，宽度越小。C值越小间隔宽度越大。'''data = np.array([[-1,2,0],[-2,3,0],[-2,5,0],[-3,-4,0],[-0.1,2,0],[0.2,1,1],[0,1,1],[1,2,1], [1,1,1], [-0.4,0.5,1],[2,5,1]])X = data[:, :2] Y = data[:,2]# Large Marginclf = SVC(C=1.0, kernel='linear')clf.fit(X, Y)plot_estimator(clf,X,Y)# Narrow Marginclf = SVC(C=100000, kernel='linear')clf.fit(X, Y)plot_estimator(clf,X,Y)

from itertools import productimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets import load_irisfrom sklearn.svm import SVC'''rbf,不规则线poly，弧线linear，直线（非线性数据不好使用）'''iris = load_iris()X = iris.data[:,[2,3]]Y = iris.targetclf1 = SVC(kernel="rbf")clf1.fit(X, Y)clf2 = SVC(kernel="poly")clf2.fit(X, Y)clf3 = SVC(kernel="linear")clf3.fit(X, Y)def plot_estimator(estimator, X, Y, idx, title):    x0_min, x0_max = X[:, 0].min() - 1, X[:, 0].max() + 1    x1_min, x1_max = X[:, 1].min() - 1, X[:, 1].max() + 1    xx, yy = np.meshgrid(np.arange(x0_min, x0_max, 0.1),                         np.arange(x1_min, x1_max, 0.1))    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])    Z = Z.reshape(xx.shape)    axarr[idx].contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.RdYlBu)    axarr[idx].scatter(X[:, 0], X[:, 1], c=Y,  cmap = plt.cm.brg)    axarr[idx].set_title(title)    f, axarr = plt.subplots(1, 3, sharex='col', sharey='row', figsize=(20, 5))for idx, clf, title in zip([0,1,2],[clf1, clf2, clf3], ['rbf', 'poly', 'linear']):    plot_estimator(clf, X, Y, idx, title)plt.show()

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