机器学习之SVM实战鸢尾花数据二分类

在SVM中利用对线性核函数和高斯核函数进行调参来对二分类鸢尾花数据分类结果进行比较。从最终的结果来看，调参是很重要的。

其代码如下：

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('bipartition.txt', dtype=np.float, delimiter='\t')
    x, y = np.split(data, (2, ), axis=1)
    y = y.ravel()

    clf_param = (('linear', 0.1), ('linear', 0.5), ('linear', 1), ('linear', 2),
                ('rbf', 1, 0.1), ('rbf', 1, 1), ('rbf', 1, 10), ('rbf', 1, 100),
                ('rbf', 5, 0.1), ('rbf', 5, 1), ('rbf', 5, 10), ('rbf', 5, 100))
    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:200j, x2_min:x2_max:200j]  # 生成网格采样点
    grid_test = np.stack((x1.flat, x2.flat), axis=1)  # 测试点

    cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FFA0A0'])
    cm_dark = mpl.colors.ListedColormap(['g', 'r'])
    mpl.rcParams['font.sans-serif'] = [u'SimHei']
    mpl.rcParams['axes.unicode_minus'] = False
    plt.figure(figsize=(14, 10), facecolor='w')
    for i, param in enumerate(clf_param):
        clf = svm.SVC(C=param[1], kernel=param[0])
        if param[0] == 'rbf':
            clf.gamma = param[2]
            title = u'高斯核，C=%.1f，$\gamma$ =%.1f' % (param[1], param[2])
        else:
            title = u'线性核，C=%.1f' % param[1]

        clf.fit(x, y)
        y_hat = clf.predict(x)
        show_accuracy(y_hat, y)  # 准确率

        print(title)
        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_)
 
        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
    
        z = clf.decision_function(grid_test)
        #print('clf.decision_function(x) = ', clf.decision_function(x))
        #print('clf.predict(x) = ', clf.predict(x))
        z = z.reshape(x1.shape)

得到结果示意图如下：