摘要：
轴=1）x=x[：decision_function_shape='vr'）clf=svr.SVC（C=0.8，decision\ufunction_sshape='vr'）clf.fit（x_train，'训练集'）打印（clf分数（x_test，x2=np.mgrid[x1_min:
1.主要内容
2.SVM的应用
(1)利用SVM处理分类问题
分类器的性能的评价指标：
应用案例：
accuracy=3/6=0.5
precision=3/5=0.6
recall=3/4=0.75
3.代码示例
(1)鸢尾花SVM案例
#!/usr/bin/python # -*- coding:utf-8 -*- import numpy as np from sklearn import svm from sklearn.model_selection import train_test_split import matplotlib as mpl import matplotlib.pyplot as plt def iris_type(s): it = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2} return it[s] # 'sepal length', 'sepal width', 'petal length', 'petal width' iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度' def show_accuracy(a, b, tip): acc = a.ravel() == b.ravel() print(tip + '正确率：', np.mean(acc)) if __name__ == "__main__": path = '8.iris.data' # 数据文件路径 data = np.loadtxt(path, dtype=float, delimiter=',', converters={4: iris_type}) x, y = np.split(data, (4,), axis=1) x = x[:, :2] x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1, train_size=0.6) # 分类器 # clf = svm.SVC(C=0.1, kernel='linear', decision_function_shape='ovr') clf = svm.SVC(C=0.8, kernel='rbf', gamma=20, decision_function_shape='ovr') clf.fit(x_train, y_train.ravel()) # 准确率 print(clf.score(x_train, y_train)) # 精度 y_hat = clf.predict(x_train) show_accuracy(y_hat, y_train, '训练集') print(clf.score(x_test, y_test)) y_hat = clf.predict(x_test) show_accuracy(y_hat, y_test, '测试集') # 画图 x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第0列的范围 x2_min, x2_max = x[:, 1].min(), x[:, 1].max() # 第1列的范围 x1, x2 = np.mgrid[x1_min:x1_max:500j, x2_min:x2_max:500j] # 生成网格采样点 grid_test = np.stack((x1.flat, x2.flat), axis=1) # 测试点 Z = clf.decision_function(grid_test) # 样本到决策面的距离 print(Z) grid_hat = clf.predict(grid_test) # 预测分类值 print(grid_hat) grid_hat = grid_hat.reshape(x1.shape) # 使之与输入的形状相同 mpl.rcParams['font.sans-serif'] = [u'SimHei'] mpl.rcParams['axes.unicode_minus'] = False cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF']) cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b']) x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第0列的范围 x2_min, x2_max = x[:, 1].min(), x[:, 1].max() # 第1列的范围 x1, x2 = np.mgrid[x1_min:x1_max:500j, x2_min:x2_max:500j] # 生成网格采样点 grid_test = np.stack((x1.flat, x2.flat), axis=1) # 测试点 plt.pcolormesh(x1, x2, grid_hat, cmap=cm_light) plt.scatter(x[:, 0], x[:, 1], c=np.squeeze(y), edgecolors='k', s=50, cmap=cm_dark) # 样本 plt.scatter(x_test[:, 0], x_test[:, 1], s=120, facecolors='none', zorder=10) # 圈中测试集样本 plt.xlabel(iris_feature[0], fontsize=13) plt.ylabel(iris_feature[1], fontsize=13) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.title(u'鸢尾花SVM二特征分类', fontsize=15) plt.grid() plt.show()
效果图：
(2)
#!/usr/bin/python # -*- coding:utf-8 -*- import numpy as np from sklearn import svm import matplotlib as mpl import matplotlib.colors import matplotlib.pyplot as plt def show_accuracy(a, b): acc = a.ravel() == b.ravel() print('正确率：%.2f%%' % (100 * float(acc.sum()) / a.size)) if __name__ == "__main__": data = np.loadtxt('14.bipartition.txt', dtype=np.float, delimiter=' ') x, y = np.split(data, (2, ), axis=1) y[y == 0] = -1 y = y.ravel() # 分类器 clfs = [svm.SVC(C=0.3, kernel='linear'), svm.SVC(C=10, kernel='linear'), svm.SVC(C=5, kernel='rbf', gamma=1), svm.SVC(C=5, kernel='rbf', gamma=4)] titles = 'Linear,C=0.3', 'Linear, C=10', 'RBF, gamma=1', 'RBF, gamma=4' x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第0列的范围 x2_min, x2_max = x[:, 1].min(), x[:, 1].max() # 第1列的范围 x1, x2 = np.mgrid[x1_min:x1_max:500j, x2_min:x2_max:500j] # 生成网格采样点 grid_test = np.stack((x1.flat, x2.flat), axis=1) # 测试点 cm_light = matplotlib.colors.ListedColormap(['#77E0A0', '#FF8080']) cm_dark = matplotlib.colors.ListedColormap(['g', 'r']) matplotlib.rcParams['font.sans-serif'] = [u'SimHei'] matplotlib.rcParams['axes.unicode_minus'] = False plt.figure(figsize=(10,8), facecolor='w') for i, clf in enumerate(clfs): clf.fit(x, y) y_hat = clf.predict(x) show_accuracy(y_hat, y) # 准确率 # 画图 print('支撑向量的数目：', clf.n_support_) print('支撑向量的系数：', clf.dual_coef_) print('支撑向量：', clf.support_) print plt.subplot(2, 2, i+1) grid_hat = clf.predict(grid_test) # 预测分类值 grid_hat = grid_hat.reshape(x1.shape) # 使之与输入的形状相同 plt.pcolormesh(x1, x2, grid_hat, cmap=cm_light, alpha=0.8) plt.scatter(x[:, 0], x[:, 1], c=y, edgecolors='k', s=40, cmap=cm_dark) # 样本的显示 plt.scatter(x[clf.support_, 0], x[clf.support_, 1], edgecolors='k', facecolors='none', s=100, marker='o') # 支撑向量 z = clf.decision_function(grid_test) z = z.reshape(x1.shape) plt.contour(x1, x2, z, colors=list('krk'), linestyles=['--', '-', '--'], linewidths=[1, 2, 1], levels=[-1, 0, 1]) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.title(titles[i]) plt.grid() plt.suptitle(u'SVM不同参数的分类', fontsize=18) plt.tight_layout(2) plt.subplots_adjust(top=0.92) plt.show()
效果图：
