多元线性回归

import numpy as np
from numpy import array, linalg

class MLinearRegression:
    def __init__(self):
        self.coef_ = None #代表的是权重
        self.interception_ = None #代表的是截距
        self._theta = None #代表的是权重+截距
    '''
    规范下代码, X_train代表的是矩阵X大写, y_train代表的是矢量y小写
    '''
    def fit(self,X_train, y_train):
        assert X_train.shape[0] == y_train.shape[0], \
        "训练集的矩阵行数与标签的行数保持一致"
        ones = np.ones((X_train.shape[0], 1))
        X_b = np.hstack((ones, X_train)) #将X矩阵转为X_b矩阵，其中第一列为1，其余不变
        self._theta = linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y_train)
        self.interception_ = self._theta[0]
        self.coef_ = self._theta[1:]
        return self
    
    def predict(self,X_predict):
        ones = np.ones((X_predict.shape[0], 1))
        X_b = np.hstack((ones, X_predict)) #将X矩阵转为X_b矩阵，其中第一列为1，其余不变
        return X_b.dot(self._theta)  #得到的即为预测值

    def mean_squared_error(self, y_true, y_predict):
        return np.sum((y_true - y_predict) ** 2) / len(y_true)

    def score(self,X_test,y_test): #使用r square
        y_predict = self.predict(X_test)
        return 1 - (self.mean_squared_error(y_test,y_predict) / (np.var(y_test)))

if __name__ == '__main__':
    mlr=MLinearRegression()
    X_train=np.array([[90,11,1],[31,57,1],[1,1,1]])
    y_train=np.array([[12,92],[76,65]])
    #mlr.fit(X_train,y_train)

    #print(mlr.predict(np.array([[94,27]])))
    print(np.zeros(3).T)