简单线性回归预测实现详解编程语言

import numpy as np 
from .metrics import r2_score 
 
 
class SimpleLinearRegression: 
 
    def __init__(self): 
        """初始化Simple Linear Regression模型""" 
        self.a_ = None 
        self.b_ = None 
 
    def 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_mean 
 
        return self 
 
    def 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)