#支持向量机svm做分类#from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
import pandas as pd
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import GridSearchCV
from matplotlib import pyplot as pltfrom sklearn.model_selection import ShuffleSplitx = pd.read_csv('dataplace/all_feats.csv',usecols = [1,2,3])
#y存放标签
y = pd.read_csv('dataplace/all_feats.csv',usecols = [4])
#分成训练集和测试集，这里采用了28分
#x_train是训练集的数据，y_train是训练集的标签；x_test是测试集的数据，y_test是测试集的标签
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)encoder = OneHotEncoder()
x_train = encoder.fit_transform(x_train).toarray()
x_test = encoder.transform(x_test).toarray()clf = SVC(kernel='linear')clf.fit(x_train,y_train)clf.predict(x_test)score = cross_val_score(clf,x_train,y_train,cv = 5)
print(score)
print(np.mean(score))score1 = clf.score(x_test,y_test)
print(score1)