支持向量机svm及python测试

from sklearn.svm import SVR,SVC
from sklearn.datasets import load_boston
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np
#支持向量机
#波士顿房价回归分析
def svm_svr():
boston=load_boston()
# x代表数据集,y代表分类标签
x_train, x_test, y_train, y_test = train_test_split(boston["data"], boston["target"], random_state=8)
#对训练数据集核测试数据集进行标准化预处理
scaler=StandardScaler()
scaler.fit(x_train)
x_train_scaler=scaler.transform(x_train)
x_test_scaler=scaler.transform(x_test)
#linear核函数
svr=SVR(kernel='linear')
svr.fit(x_train_scaler,y_train)
print("linear核函数模型训练集得分:{}".format(svr.score(x_train_scaler,y_train)))
print("linear核函数模型测试集得分:{}".format(svr.score(x_test_scaler, y_test)))
#rbf核函数
svr=SVR(kernel='rbf',C=100,gamma=0.1)
svr.fit(x_train_scaler,y_train)
print("linear核函数模型训练集得分:{}".format(svr.score(x_train_scaler,y_train)))
print("linear核函数模型测试集得分:{}".format(svr.score(x_test_scaler, y_test)))
def svm_svc():
# 酒的分类
wine_dataset = load_wine()
# x代表数据集,y代表分类标签
x_train, x_test, y_train, y_test = train_test_split(wine_dataset["data"], wine_dataset["target"], random_state=0)
#对训练数据集核测试数据集进行标准化预处理
scaler=StandardScaler()
scaler.fit(x_train)
x_train_scaler=scaler.transform(x_train)
x_test_scaler=scaler.transform(x_test)
# linear核函数
svc = SVC(kernel='linear')
svc.fit(x_train_scaler, y_train)
print("linear核函数模型训练集得分:{}".format(svc.score(x_train_scaler, y_train)))
print("linear核函数模型测试集得分:{}".format(svc.score(x_test_scaler, y_test)))
# rbf核函数
svc = SVC(kernel='rbf', C=100, gamma=0.1)
svc.fit(x_train_scaler, y_train)
print("linear核函数模型训练集得分:{}".format(svc.score(x_train_scaler, y_train)))
print("linear核函数模型测试集得分:{}".format(svc.score(x_test_scaler, y_test)))
# 使用模型完成预测
x_news = np.array([[13.2, 2.77, 2.51, 18.5, 96.6, 1.04, 2.55, 0.57, 1.47, 6.2, 1.05, 3.33, 820]])
prediction = svc.predict(x_news)
print(wine_dataset["target_names"][prediction])

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