python使用自带SVM，数据集iris

转至：https://www.cnblogs.com/luyaoblog/p/6775342.html

和Python3不是很兼容，改了一部分

import numpy as np
from sklearn import svm
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
%matplotlib inline

def iris_type(s):
    it = {b'Iris-setosa':0,b'Iris-versicolor':1, b'Iris-virginica':2}   #Python2和3有点区别
    return it[s]

#print(iris_type('Iris-setosa'))
path = u'C:/Users/Administrator/Desktop/iris/iris.data'
data = np.loadtxt(path, dtype = float, delimiter = ',', converters = {4:iris_type})
#data

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, test_size = 0.39)

clf = svm.SVC(C = 0.8, kernel = 'rbf', gamma = 20)         #调用svm.svc
clf.fit(x_train, y_train.ravel())

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')

#print ('decision_function:\n', clf.decision_function(x_train))     #每一列的值代表到各类别的距离
print ('\npredict:\n', clf.predict(x_train))             
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
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

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'])
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  , c=y是错的
plt.scatter(x_test[:, 0], x_test[:, 1], s=120, facecolors='none', zorder=10)  # 圈中测试集样本
plt.xlabel(u'花萼长度', fontsize=13)
plt.ylabel(u'花萼宽度', 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()