梯度下降算法到logistic回归
生活随笔
收集整理的這篇文章主要介紹了
梯度下降算法到logistic回归
小編覺得挺不錯(cuò)的,現(xiàn)在分享給大家,幫大家做個(gè)參考.
http://sbp810050504.blog.51cto.com/2799422/1608064/
http://blog.csdn.net/dongtingzhizi/article/details/15962797
http://blog.csdn.net/llp1992/article/details/45114421
https://www.qcloud.com/community/article/180954?fromSource=gwzcw.107192.107192.107192
梯度上升法求函數(shù)極值
?
來源: 運(yùn)籌學(xué)第3版 胡運(yùn)權(quán) 第3 節(jié)無約束極值問題的解法
?
?
logistic回歸進(jìn)行二分類
?
?
?
?
?
?
?
function weight = gradAscent clc % close all clear %%data = load('testSet.txt'); [row , col] = size(data); dataMat = data(:,1:col-1); dataMat = [ones(row,1) dataMat] ; labelMat = data(:,col); alpha = 0.001; maxCycle = 500; weight = ones(col,1); for i = 1:maxCycleh = sigmoid(dataMat * weight);error = (labelMat - h);weight = weight + alpha * dataMat' * error; endfigure scatter(dataMat(find(labelMat(:) == 0),2),dataMat(find(labelMat(:) == 0),3),3); hold on scatter(dataMat(find(labelMat(:) == 1),2),dataMat(find(labelMat(:) == 1),3),5); hold on x = -3:0.1:3; y = (-weight(1)-weight(2)*x)/weight(3); plot(x,y) hold offendfunction returnVals = sigmoid(inX)returnVals = 1.0./(1.0+exp(-inX)); end?
轉(zhuǎn)載于:https://www.cnblogs.com/adong7639/p/7181369.html
總結(jié)
以上是生活随笔為你收集整理的梯度下降算法到logistic回归的全部?jī)?nèi)容,希望文章能夠幫你解決所遇到的問題。
- 上一篇: ubuntu-Linux下如何安装Ten
- 下一篇: Python网络数据采集2-wikipe