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Linear_Regression_Sklearn.py
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#-*- coding：utf-8 -*-
# &Author AnFany
#获得数据
fromBoston_Dataimportmodel_dataaslrdata
#引入包
importnumpyasnp
fromsklearnimportlinear_model
fromsklearn.metricsimportmean_squared_error, r2_score
#训练数据x
x_train_data=lrdata[0]
#预测数据x
x_predict_data=lrdata[2]
#y数据shape转换(样本数，1)==> (样本数，)
y_train_data=lrdata[1].T[0]
y_predict_data=lrdata[3].T[0]
importmatplotlib.pyplotasplt# 绘图
frompylabimportmpl
mpl.rcParams['font.sans-serif'] = ['SimHei'] # 中文字体
mpl.rcParams['axes.unicode_minus'] =False# 负号
# 绘图函数
deffigure(title, *datalist):
forjjindatalist:
plt.plot(jj[0], '-', label=jj[1], linewidth=2)
plt.plot(jj[0], 'o')
plt.grid()
plt.title(title)
plt.legend()
plt.show()
reg=linear_model.LinearRegression()
# 最终的程序
if__name__=="__main__":
# 开始训练
reg.fit(x_train_data, y_train_data)
# 预测数据的预测值
predict_result=reg.predict(x_predict_data)
# 训练数据的预测值
train_pre_result=reg.predict(x_train_data)
train_error= [mean_squared_error(y_train_data, [np.mean(y_train_data)] *len(y_train_data)),
mean_squared_error(y_train_data, train_pre_result)]
# 绘制误差图
figure('误差图 最终的MSE = %.4f'% (train_error[-1]), [train_error, 'error'])
# 绘制预测值与真实值图
figure('预测值与真实值图 模型的'+r'$R^2=%.4f$'% (r2_score(train_pre_result, y_train_data)), [predict_result, '预测值'],
[y_predict_data, '真实值'])
plt.show()
# 线性回归的参数
print('线性回归的系数为:\n w = %s \n b = %s'% (reg.coef_, reg.intercept_))
