sklearn库 线性回归库 LinearRegression

import numpy as np

import sklearn.datasets             #加载原数据

from sklearn.model_selection import train_test_split #分割数据

from matplotlib import pyplot as plt

from sklearn.linear_model import LinearRegression

#创建数据

def createdata():

    boston = sklearn.datasets.load_boston()

    databoston = boston.data

    m,n = np.shape(databoston)

    one_mat = np.ones((m,1))

    databoston = np.column_stack((databoston,one_mat))

    lableboston = boston.target

    x_train,x_test,y_train,y_test= train_test_split(databoston,lableboston,test_size=0.2)       #分割数据测试数据为30%

    x_train = np.mat(x_train)

    y_train = np.mat(y_train).reshape(-1,1)

    x_test = np.mat(x_test)

    y_test = np.mat(y_test).reshape(-1,1)

    # print(x_train[1,:],len(x_train))

    # print(y_train[1],len(y_train))

    return x_train,x_test,y_train,y_test

x_train, x_test, y_train, y_test = createdata()

model = LinearRegression(copy_X=True, fit_intercept=False, n_jobs=1, normalize=False)

model.fit(x_train,y_train)

print('系数矩阵:\n',model.coef_)

print('线性回归模型:\n',model)

# 使用模型预测

predicted = model.predict(x_test)

axix_x1 = np.linspace(0,2*len(y_test),len(y_test))

plt.plot(axix_x1, y_test,'b-')

plt.plot(axix_x1, predicted,'r--')

# 显示图形

plt.show()