import numpy as np

class SimpleLinearRegression1:
	def __init__(self):
		"""初始化Simple Linear Regression 模型"""
		self.a_ = None
		self.b_ = None
		#a和b不是用户送来的参数，是得出的结果
	#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

	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 simple feature training data"
		assert len(x_train) == len(y_train), \
			"the size of x_train must be equal to the size of y_train"
		#算法实现代码
		x_mean = np.mean(x_train)
		y_mean = np.mean(y_train)

		num = 0.0
		d = 0.0
		for x, y in zip(x_train, y_train):
			num += (x - x_mean) * (y - y_mean)
			d += (x - x_mean) ** 2

		self.a_ = num / d
		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_single， 返回x_single的预测结果值"""
		return self.a_ * x_single + self.b_

	def __repr__(self):
		return "SimpleLinearRegression1()"

		
class SimpleLinearRegression2:
	def __init__(self):
		"""初始化Simple Linear Regression 模型"""
		self.a_ = None
		self.b_ = None
		#a和b不是用户送来的参数，是得出的结果
	#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

	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 simple feature training data"
		assert len(x_train) == len(y_train), \
			"the size of x_train must be equal to the size of y_train"
		#算法实现代码
		x_mean = np.mean(x_train)
		y_mean = np.mean(y_train)

		num = (x_train - x_mean).dot(y_train - y_mean)
		d = (x_train - x_mean).dot(x_train - x_mean)

		self.a_ = num / d
		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_single， 返回x_single的预测结果值"""
		return self.a_ * x_single + self.b_

	def __repr__(self):
		return "SimpleLinearRegression1()"