【Python】纯代码通过神经网络实现线性回归的拟合

参考链接中的文章，有错误，我给更正了。

并且原文中是需要数据集文件的，我直接给替换成了一个数组，采用直接赋值的方式。

# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt

class SimpleDataReader(object):
    def __init__(self, data_file):
        self.train_file_name = data_file
        self.num_train = 0
        self.XTrain = None
        self.YTrain = None
        
    # read data from file
    def ReadData(self):
        # data = np.load(self.train_file_name)
        # self.XTrain = data["data"]
        # self.YTrain = data["label"]
        self.XTrain = np.array([0.95, 3, 4, 5.07, 6.03, 8.21, 8.85, 12.02, 15], dtype=float)
        self.YTrain = np.array([5.1, 8.7, 11.5, 13, 15.3, 18, 21, 26.87, 32.5], dtype=float)
        self.num_train = self.XTrain.shape[0]
        #end if
        
    # get batch training data
    def GetSingleTrainSample(self, iteration):
        x = self.XTrain[iteration]
        y = self.YTrain[iteration]
        return x, y
    
    def GetWholeTrainSamples(self):
        return self.XTrain, self.YTrain
    
class NeuralNet(object):
    
    def __init__(self, eta):
        self.eta = eta
        self.w = 0
        self.b = 0
        
    def __forward(self, x):
        z = x * self.w + self.b
        return z
    
    def __backward(self, x,y,z):
        dz = z - y                  # 原错误为：dz = x * (z - y)
        db = dz
        dw = dz
        return dw, db
    
    def __update(self, dw, db):
        self.w = self.w - self.eta * dw
        self.b = self.b - self.eta * db
        
    def train(self, dataReader):
        for i in range(dataReader.num_train):
            # get x and y value for one sample
            x,y = dataReader.GetSingleTrainSample(i)
            # get z from x,y
            z = self.__forward(x)
            # calculate gradient of w and b
            dw, db = self.__backward(x, y, z)
            # update w,b
            self.__update(dw, db)
            # end for
    
    def inference(self, x):
        return self.__forward(x)
    
    
if __name__ == '__main__':
    # read data
    sdr = SimpleDataReader('ch04.npz')
    sdr.ReadData()
    # create net
    eta = 0.1
    net = NeuralNet(eta)
    net.train(sdr)
    # result
    print("w=%f,b=%f" %(net.w, net.b))
    # 绘图部分
    trainX,trainY = sdr.GetWholeTrainSamples()
    fig = plt.figure()
    ax = fig.add_subplot(111)
    # 绘制散点图
    ax.scatter(trainX,trainY)
    # 绘制线性回归
    x = np.arange(0, 15, 0.01)
    f = np.vectorize(net.inference, excluded=['x'])
    plt.plot(x,f(x),color='red')
    # 显示图表
    plt.show()

Ref：

1. 通过神经网络实现线性回归的拟合