机器学习——简单线性回归之向量化（代码实现）

 1 class SimpleLinearRegression2:
 2     def __init__(self):
 3         """初始化Simple Linear Regression 模型"""
 4         self.a_ = None
 5         self.b_ = None
 6         #a和b不是用户送来的参数，是得出的结果
 7     #x_train和y_train只用来提供训练，训练得出所需参数之后，数据就没用了
 8 
 9     def fit(self, x_train, y_train):
10         """根据训练数据集x_train，y_train训练Simple Linear Regression 模型"""
11         assert x_train.ndim == 1, 12             "Simple Linear Regressor can only solve simple feature training data"
13         assert len(x_train) == len(y_train), 14             "the size of x_train must be equal to the size of y_train"
15         #算法实现代码
16         x_mean = np.mean(x_train)
17         y_mean = np.mean(y_train)
18 
19         num = (x_train - x_mean).dot(y_train - y_mean)
20         d = (x_train - x_mean).dot(x_train - x_mean)
21 
22         self.a_ = num / d
23         self.b_ = y_mean - self.a_ * x_mean
24 
25         return self
26     
27     def predict(self, x_predict):
28         """给定待预测数据集x_predict， 返回表示x_predict的结果向量"""
29         assert x_predict.ndim == 1, 30             "Simple Linear Regressor can only solve single feature training data"
31         assert self.a_ is not None and self.b_ is not None, 32             "must fit before predict"
33 
34         return np.array([self._predict(x) for x in x_predict])
35 
36     def _predict(self, x_single):
37         """给定单个待预测数据x_single， 返回x_single的预测结果值"""
38         return self.a_ * x_single + self.b_
39 
40     def __repr__(self):
41         return "SimpleLinearRegression1()"