机器学习_线性回归最小二乘法求解

叁歲伎倆 2022-12-31 05:18 138阅读 0赞

import sklearn.datasets as data
    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd
    import matplotlib as mpl
    
    
    class LinearRegression:
        def __init__(self):
            pass
    
        def fit(self, X, y):
            X = np.asmatrix(X.copy())
            y = np.asmatrix(y).reshape(-1, 1)
            self.w_ = (X.T * X).I * X.T * y
    
        def predict(self, X):
            X = np.asmatrix(X.copy())
            result = X * self.w_
            return np.array(result).ravel()
    
    
    if __name__ == '__main__':
        boston = data.load_boston()
        col_names = np.append(boston.feature_names, 'MEDV')
        data = pd.DataFrame(data=np.c_[boston.data, boston.target], columns=col_names)
        t = data.sample(len(data), random_state=0)
        # 增加一列 但是在最后添加 故不使用
        # t['ddd'] = 1
        # 往前面增加一列的方法
        print(t)
        newColumns = t.columns.insert(0, 'intercept')
        # 注意必须更新一下，否则会出现添加到最后一列的效果
        t = t.reindex(columns=newColumns)
        t['intercept'] = 1
        # print(t)
        train_x = t.iloc[:400, :-1]
        train_y = t.iloc[:400, -1].values
        test_x = t.iloc[400:, :-1]
        test_y = t.iloc[400:, -1].values
        print(test_y)
        # print(train_x)
        Lr = LinearRegression()
        Lr.fit(train_x, train_y)
        result = Lr.predict(test_x)
        # print(result)
        # print(np.mean((result - test_y) ** 2))
        # print(Lr.w_)
        print(result)
        print(test_y)
        mpl.rcParams['font.family'] = 'SimHei'
        mpl.rcParams['axes.unicode_minus'] = False
        plt.figure(figsize=(10, 10))
        plt.plot(result, "ro-", label='预测值')
        plt.plot(test_y, "go-", label='真实值')
        plt.title('线性回归预测')
        plt.xlabel('样本个数')
        plt.ylabel('房价')
        plt.legend()
        plt.show()