最小二乘法实现的线性回归误差逆向传递的简单演示

import numpy as npimport matplotlib.pyplot as pltimport time

x_data = np.arange(100,)print(x_data)k0 = np.random.random()b0 = np.random.random()

y_data = x_data*k0+np.random.random(100,)*3print()print(y_data)# plt.ion()plt.figure(figsize=(12,9))plt.scatter(x_data,y_data,label=‘true data‘)# 假设  y = k * x + b

def wucha(x_真值,y_真值,k,b):    y_预测 = x_真值*k + b    误差 = y_真值 - y_预测    return abs(误差.sum())

def tiaocan(x_真值,y_真值,k,b,步长):    s1 = wucha(x_真值,y_真值,k+步长,b+步长)    s2 = wucha(x_真值,y_真值,k-步长,b-步长)    s3 = wucha(x_真值,y_真值,k+步长,b-步长)    s4 = wucha(x_真值,y_真值,k-步长,b+步长)    s = wucha(x_真值,y_真值,k,b)    s_list = list((s1,s2,s3,s4,s))    print(s_list)    s_list.sort()    min = s_list[0]    print(s_list)    if min == s1:        return True,True    elif min == s2:        return False,False    elif min == s3:        return True,False    elif min == s4:        return False,True    else:        return None

if __name__ == ‘__main__‘:

#运行100次    k = np.random.random()    b = np.random.random()    k1 = k    b1 = b    print(‘随机初始化的k,b初始值:‘,k1,b1)    for i in range(1000000):        n = 0.01        s = tiaocan(x_data,y_data,k1,b1,n)        print(s)        if s:            if s[0]:                k1+=n            else:                k1-=n            if s[1]:                b1+=n            else:                b1-=n        else:            break        print(‘第{}次运行k,b {}  {}‘.format(i,k1,b1))        lines = plt.plot(x_data,x_data*k1+b1,color=‘black‘)

print(‘初始化的k0 b0 真值为:‘,k0,b0*3)    print(‘k,b的初始化随机值为:‘,k,b)    print(‘一共运行了{}次‘.format(i+1))    print(‘k,b的预测值为‘,k1,b1)    plt.plot(x_data,x_data*k+b,color=‘green‘,label=‘first‘)    plt.plot(x_data,k1*x_data+b1,color=‘red‘,label=‘final‘)    plt.legend()    # plt.ioff()    plt.show()

运行以上代码，可以实现拟合随机生成的数据，拟合过后的线为红色，拟合前的为绿色，拟合过程中的线为黑色。为了便于理解，有些参数我使用了中文，Python3默认编码UTF8

原文地址：https://www.cnblogs.com/lycsdhr/p/9606640.html