简单线性回归 Python实现

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简单线性回归中，数据只有一个特征。

import numpy as np

class SimpleLinearRegression:
  def __init__(self):
    '''初始化模型'''
    self.a_ = None
    self.b_ = None

  def fit(self,x_train,y_train):
    '''根据训练数据集训练模型'''
    assert x_train.ndim == 1,'Simple Linear Regression can only solve single feature training data'
    assert len(x_train) == len(y_train),'the size of x_train must equal to the size of y_train'

    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，返回预测结果向量'''
    assert x_predict.ndim == 1,'Simlpe Linear Regression 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):
    '''对单个待预测数据进行预测并返回结果'''
    return  self.a_ * x_single + self.b_

  def __repr__(self):
    return 'SimpleLinearRegression()'