# coding :utf-8
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn import svm
import matplotlib.patches as mpatches
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.pipeline import Pipeline
def iris_type(s):
it = {b'Iris-setosa': 0,
b'Iris-versicolor': 1,
b'Iris-virginica': 2}
return it[s]
if __name__=="__main__":
data=np.loadtxt('.\\iris.data',delimiter=',',converters={4:iris_type})
x=data[:,0:2]
y=data[:,4]
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=1)
#Ir = Pipeline([('sc', StandardScaler()), 'poly', PolynomialFeatures(degree=2)),('clf', LogisticRegression()) ])
# Ir=LogisticRegression(C=4.6)
Ir=svm.SVC(C=1,kernel='rbf',decision_function_shape='ovo')
Ir.fit(x_train,y_train)
y_hat=Ir.predict(x_test)
print('训练集准确度：' ,Ir.score(x_train,y_train))
print('测试集准确度：' ,Ir.score(x_test,y_test))
err_y = y_test[y_test != y_hat]
print(y_test)
print(err_y)
#画图
