梯度下降算法的实现

数据是三维，就是最简单的梯度下降算法的实现，练手即可。

# -*- coding: utf-8 -*-

"""

Created on Tue Jun 5 16:43:22 2018

 

@author: Administrator

"""

 

import numpy as np

#随机梯度算法

#假设x的值

X=[[2104,3],[1600,3],[2400,3],[1416,2],[3000,4]]

Y=[[400,330,369,232,540]]

learning_rate=0.001

epsilon=0.0001

iters=10000

count=0

theta=[0,0,0]

h=[]

while count

for i in range(len(X)):

hs=theta[0]+theta[1]*X[i][0]+theta[2]*X[i][1]

h.append(hs)

for j in range(len(theta)):

print (str(i)+" "+str(j))

o=X[i][j]

print (o)

# theta[j]=theta[j]+learning_rate*(y[i]-h[i])*X[i][j]

error=abs(Y[i]-hs)

if error

break

iters=iters+1

print (theta)

"""

 

# 训练集

# 每个样本点有3个分量 (x0,x1,x2)

x = [(1, 0., 3), (1, 1., 3), (1, 2., 3), (1, 3., 2), (1, 4., 4)]

# y[i] 样本点对应的输出

y = [95.364, 97.217205, 75.195834, 60.105519, 49.342380]

# 迭代阀值，当两次迭代损失函数之差小于该阀值时停止迭代

epsilon = 0.0001

# 学习率

alpha = 0.01

diff = [0, 0]

max_itor = 1000

error1 = 0

error0 = 0

cnt = 0

m = len(x)

# 初始化参数

theta0 = 0

theta1 = 0

theta2 = 0

while True:

cnt += 1

# 参数迭代计算

for i in range(m):

# 拟合函数为 y = theta0 * x[0] + theta1 * x[1] +theta2 * x[2]

# 计算残差

diff[0] = (theta0 + theta1 * x[i][1] + theta2 * x[i][2]) - y[i]

# 梯度 = diff[0] * x[i][j]

theta0 -= alpha * diff[0] * x[i][0]

theta1 -= alpha * diff[0] * x[i][1]

theta2 -= alpha * diff[0] * x[i][2]

# 计算损失函数

error1 = 0

for lp in range(len(x)):

error1 += (y[lp]-(theta0 + theta1 * x[lp][1] + theta2 * x[lp][2]))**2/2

if abs(error1-error0) < epsilon:

break

else:

error0 = error1

print (' theta0 : %f, theta1 : %f, theta2 : %f, error1 : %f' % (theta0, theta1, theta2, error1))

print ('Done: theta0 : %f, theta1 : %f, theta2 : %f' % (theta0, theta1, theta2))

print ('迭代次数: %d' % cnt)

"""