实验一

from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge, LogisticRegression
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
#from sklearn.externals import joblib
from sklearn.metrics import r2_score
from sklearn.neural_network import MLPRegressor

import pandas as pd
import numpy as np

lb = load_boston()
x_train, x_test, y_train, y_test = train_test_split(lb.data, lb.target, test_size=0.2)


# 为数据增加一个维度,相当于把[1, 5, 10] 变成 [[1, 5, 10],]
y_train = y_train.reshape(-1, 1)#列数为1
y_test = y_test.reshape(-1, 1)

# 进行标准化
std_x = StandardScaler()#标准化数据
x_train = std_x.fit_transform(x_train)#对数据进行某种统一处理
x_test = std_x.transform(x_test)

std_y = StandardScaler()
y_train = std_y.fit_transform(y_train)
y_test = std_y.transform(y_test)
# 正规方程预测
lr = LinearRegression()#线性回归
lr.fit(x_train, y_train)
print("r2 score of Linear regression is",r2_score(y_test,lr.predict(x_test)))
#岭回归
from sklearn.linear_model import RidgeCV

cv = RidgeCV(alphas=np.logspace(-3, 2, 100))#样本数据集
cv.fit (x_train , y_train)
print("r2 score of Linear regression is",r2_score(y_test,cv.predict(x_test)))
#梯度下降
sgd = SGDRegressor()#最大迭代次数
sgd.fit(x_train, y_train)#用训练器数据拟合分类器模型
print("r2 score of Linear regression is",r2_score(y_test,sgd.predict(x_test)))