import numpy as np
from .metrics import r2_scoreclass SimpleLinearRegression:def __init__(self):"""初始化Simple Linear Regression模型"""self.a_ = Noneself.b_ = Nonedef fit(self, x_train, y_train):"""根据训练数据集x_train, y_train训练Simple Linear Regression模型"""assert x_train.ndim == 1, \"Simple Linear Regressor can only solve single feature training data."assert len(x_train) == len(y_train), \"the size of x_train must be equal to the size of y_train"x_mean = np.mean(x_train)y_mean = np.mean(y_train)self.a_ = (x_train - x_mean).dot(y_train - y_mean) / (x_train - x_mean).dot(x_train - x_mean)#向量化 点乘运算self.b_ = y_mean - self.a_ * x_meanreturn selfdef predict(self, x_predict):"""给定待预测数据集x_predict，返回表示x_predict的结果向量"""assert x_predict.ndim == 1, \"Simple Linear Regressor can only solve single feature training data."assert self.a_ is not None and self.b_ is not None, \"must fit before predict!"return np.array([self._predict(x) for x in x_predict])def _predict(self, x_single):"""给定单个待预测数据x，返回x的预测结果值"""return self.a_ * x_single + self.b_def score(self, x_test, y_test):"""根据测试数据集 x_test 和 y_test 确定当前模型的准确度"""y_predict = self.predict(x_test)return r2_score(y_test, y_predict)def __repr__(self):return "SimpleLinearRegression()"

def mean_squared_error(y_true, y_predict):"""计算y_true和y_predict之间的MSE"""assert len(y_true) == len(y_predict), \"the size of y_true must be equal to the size of y_predict"return np.sum((y_true - y_predict)**2) / len(y_true)def root_mean_squared_error(y_true, y_predict):"""计算y_true和y_predict之间的RMSE"""return sqrt(mean_squared_error(y_true, y_predict))def mean_absolute_error(y_true, y_predict):"""计算y_true和y_predict之间的RMSE"""assert len(y_true) == len(y_predict), \"the size of y_true must be equal to the size of y_predict"return np.sum(np.absolute(y_true - y_predict)) / len(y_true)

def r2_score(y_true, y_predict):"""计算y_true和y_predict之间的R Square"""return 1 - mean_squared_error(y_true, y_predict)/np.var(y_true)