逻辑回归（LR）算法java实现按照机器学习实战的python代码，⽤java重写LR的梯度上升算法：
package com.log;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.InputStreamReader;
import java.io.File;
import java.util.ArrayList;
import java.util.Iterator;
import Jama.Matrix;
public class LogProcess {
/**
* 获取特征数据
*/
public Matrix getDataMat(){
try {
ArrayList<double[]> list = new ArrayList<double[]>();
String pathname = "C:\\";
File filename = new File(pathname);
InputStreamReader reader = new InputStreamReader(
new FileInputStream(filename));
BufferedReader br = new BufferedReader(reader);
String line = "";
line = br.readLine();
while (line != null) {
String [] tmp=line.split("\t");
double [] value=new double[3];
value[0]=1.0;
value[1]=Double.parseDouble(tmp[0]);
value[2]=Double.parseDouble(tmp[1]);
list.add(value);
line = br.readLine(); // ⼀次读⼊⼀⾏数据
}
//特征转化为⼆维数组
Iterator<double[]> datalIt = list.iterator();
double [][] data = new double[list.size()][3];
int i=0;
while (datalIt.hasNext()) {
double [] tmp = ();
data[i][0]=tmp[0];
data[i][1]=tmp[1];
data[i][2]=tmp[2];
i++;
}
Matrix dataMatrix = new Matrix(data);
return dataMatrix;
} catch (Exception e) {
e.printStackTrace();
return null;
}
}
/**
* 获取标签数据
*/
public Matrix getLabelMat(){
try {
ArrayList<int[]> list = new ArrayList<int[]>();
String pathname = "C:\\";
File filename = new File(pathname);
InputStreamReader reader = new InputStreamReader(
new FileInputStream(filename));
BufferedReader br = new BufferedReader(reader);
String line = "";
line = br.readLine();
while (line != null) {
String [] tmp=line.split("\t");
int [] value=new int[1];
value[0]=Integer.parseInt(tmp[2]);
list.add(value);
python转java代码line = br.readLine(); // ⼀次读⼊⼀⾏数据
}
//将标签转化为⼆维数组
Iterator<int[]> labelIt = list.iterator();
double [][] label = new double[list.size()][1];
int j=0;
while (labelIt.hasNext()) {
label[j][0]=()[0];
j++;
}
Matrix labelMatrix = new Matrix(label);
return labelMatrix;
} catch (Exception e) {
e.printStackTrace();
return null;
}
}
/**
* sigmoid函数
*/
public Matrix sigmoid(Matrix intX){
double [][] tmp = new RowDimension()][ColumnDimension()];        for(int i=0;i&RowDimension();i++){
tmp[i][0]=1.0/((i, 0)));
}
Matrix tmpMat = new Matrix(tmp);
return tmpMat;
}
public static void main(String [] args){
LogProcess lr = new LogProcess();
//特征矩阵mXn
Matrix dataMat= lr.getDataMat();
//标签矩阵m
Matrix labelMat= lr.getLabelMat();
//获取维数
int RowDimension();
int ColumnDimension();
//步长
double alpha=0.001;
//迭代次数
int maxCycles=500;
//weights权重列向量
double [][] weight=new double[3][1];
weight[0][0]=1;
weight[1][0]=1;
weight[2][0]=1;
Matrix weightMat = new Matrix(weight);
//X*weight
Matrix mm ;
Matrix h;
Matrix e;
//
for(int i=1;i<maxCycles;i++){
mm = dataMat.times(weightMat).times(-1);
h = lr.sigmoid(mm);
e = labelMat.minus(h);
weightMat = weightMat.anspose().times(e).times(alpha));
System.out.println("-------------------"+i+"------------------");
for(int j=0;j&RowDimension();j++){
System.out.(j, 0));
}
}
//预测
//        weight[0][0]=4.1207114;
//        weight[1][0]=0.4797796;
/
/        weight[2][0]=-0.6164160;
//        weightMat = new Matrix(weight);
//
//        mm=dataMat.times(weightMat).times(-1);
//        h = lr.sigmoid(mm);
//        for(int j=0;j&RowDimension();j++){
//            System.out.(j, 0));
//        }
}
}
结果：
-
------------------497------------------
4.113833123773871
0.4791907707938691
-0.6155500125688206
-------------------498------------------
4.117274671999083
0.4794854588393607
-0.6159833243190651
-------------------499------------------
4.12071145578144
0.47977963228916215
-
0.6164160518933434

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