svm实践：鸢尾花特征二分类

1.分类结果

2.参考代码

#!/usr/bin/python
# -*- coding:utf-8 -*-import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score# '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'if __name__ == "__main__":path = './iris.data'  # 数据文件路径data = pd.read_csv(path, header=None)print(data)print("############")x, y = data[range(4)], data[4]y = pd.Categorical(y).codesx = x[[0, 1]]print(x)print("***********")print(y)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print('训练集准确率：', accuracy_score(y_train, clf.predict(x_train)))print(clf.score(x_test, y_test))print('测试集准确率：', accuracy_score(y_test, clf.predict(x_test)))# decision_functionprint('decision_function:\n', clf.decision_function(x_train))print('\npredict:\n', clf.predict(x_train))# 画图x1_min, x2_min = x.min()x1_max, x2_max = x.max()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# print 'grid_test = \n', grid_test# Z = clf.decision_function(grid_test)    # 样本到决策面的距离# print Zgrid_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mpl.rcParams['font.sans-serif'] = [u'SimHei']mpl.rcParams['axes.unicode_minus'] = Falsecm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])plt.figure(facecolor='w')plt.pcolormesh(x1, x2, grid_hat, cmap=cm_light)plt.scatter(x[0], x[1], c=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=16)plt.grid(b=True, ls=':')plt.tight_layout(pad=1.5)plt.show()