梯度下降(Gradient Descent)

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1.基本思想简介

http://www.cnblogs.com/LeftNotEasy/archive/2010/12/05/mathmatic_in_machine_learning_1_regression_and_gradient_descent.html


2.一个简例

一个通过迭代求解convex function最小值的方法: Gradient descent(梯度下降法)

http://en.wikipedia.org/wiki/Gradient_descent

这里给出一个python的代码:

The gradient descent algorithm is applied to find a local minimum of the function f(x)=x4-3x3+2 , with derivative f'(x)=4x3-9x2. Here is an implementation in the Python programming language.

# From calculation, we expect that the local minimum occurs at x=9/4

xOld 
= 0
xNew 
= 6 # The algorithm starts at x=6
eps = 0.01 # step size
precision = 0.00001

def f_prime(x):
return 4 * x**3 - 9 * x**2

while abs(xNew - xOld) > precision:
xOld 
= xNew
xNew 
= xOld - eps * f_prime(xNew)
print("Local minimum occurs at ", xNew)


3.线性回归和批量梯度下降法python


http://www.cnblogs.com/Key-Ky/archive/2013/12/10/3468290.html

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