def linear3():
    """
    岭回归的优化方法对波士顿房价预测
    """
    #获取数据
    boston=load_boston()
    #划分数据集
    x_train,x_test,y_train,y_test=train_test_split(boston.data,boston.target,random_state=22)
    #标准化
    transfer=StandardScaler()
    x_train=transfer.fit_transform(x_train)
    x_test=transfer.transform(x_test)
    #预估器
    # estimator=Ridge(alpha=0.0001, max_iter=100000)
    # estimator.fit(x_train,y_train)

    #保存模型
    # joblib.dump(estimator,"my_ridge.pkl")

    #加载模型
    estimator=joblib.load("my_ridge.pkl")

    #得出模型
    print("岭回归-权重系数为：\n",estimator.coef_)
    print("岭回归-偏置为：\n",estimator.intercept_ )

    #模型评估
    y_predict = estimator.predict(x_test)
    print("预测房价：\n", y_predict)
    error = mean_squared_error(y_test, y_predict)
    print("岭回归-均方差误差:\n", error)
    return None


if __name__ == '__main__':
    # linear1()
    # linear2()
    linear3()