Logistic Regression和Linear Regression实现起来几乎是一样的，有疑问的同学可以看一看Andrew N.g在coursera上的machine learning课程。
我下面会给出相应的代码（代码写的其实很糟糕），只是一个简单的实现，效率不高，收敛速度比较慢，对于下面这样一个简单的数据：
0.0 0
0.1 0
0.7 1
1.0 0
1.1 0
1.3 0
1.4 1
1.7 1
2.1 1
2.2 1
都需要很多很多次迭代，而在weka中几乎是瞬间就训练好了（http://www.cnblogs.com/wzm-xu/p/4139831.html）
所以呢。。。。。我也不知道该说什么了。
LogisticRegression.java
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;
import java.lang.Math;
public class LogisticRegression {
private double [][] trainData;//训练数据，一行一个数据，每一行最后一个数据为 y
private int row;//训练数据 行数
private int column;//训练数据 列数
private double [] theta;//参数theta
private double alpha;//训练步长
private int iteration;//迭代次数
public LogisticRegression(String fileName)
{
int rowoffile=getRowNumber(fileName);//获取输入训练数据文本的 行数
int columnoffile = getColumnNumber(fileName);//获取输入训练数据文本的 列数
trainData = new double[rowoffile][columnoffile+1];//这里需要注意，为什么要+1，因为为了使得公式整齐，我们加了一个特征x0，x0恒等于1
this.row=rowoffile;
this.column=columnoffile+1;
this.alpha = 0.001;//步长默认为0.001
this.iteration=100000;//迭代次数默认为 100000
theta = new double [column-1];
initialize_theta();
loadTrainDataFromFile(fileName,rowoffile,columnoffile);
}
public LogisticRegression(String fileName,double alpha,int iteration)
{
int rowoffile=getRowNumber(fileName);//获取输入训练数据文本的 行数
int columnoffile = getColumnNumber(fileName);//获取输入训练数据文本的 列数
trainData = new double[rowoffile][columnoffile+1];//这里需要注意，为什么要+1，因为为了使得公式整齐，我们加了一个特征x0，x0恒等于1
this.row=rowoffile;
this.column=columnoffile+1;
this.alpha = alpha;
this.iteration=iteration;
theta = new double [column-1];
initialize_theta();
loadTrainDataFromFile(fileName,rowoffile,columnoffile);
}
private int getRowNumber(String fileName)
{
int count =0;
File file = new File(fileName);
BufferedReader reader = null;
try {
reader = new BufferedReader(new FileReader(file));
while ( reader.readLine() != null)
count++;
reader.close();
} catch (IOException e) {
e.printStackTrace();
} finally {
if (reader != null) {
try {
reader.close();
} catch (IOException e1) {
}
}
}
return count;
}
private int getColumnNumber(String fileName)
{
int count =0;
File file = new File(fileName);
BufferedReader reader = null;
try {
reader = new BufferedReader(new FileReader(file));
String tempString = reader.readLine();
if(tempString!=null)
{
String [] temp = tempString.split(" ");
for(String s : temp)
if(!s.equals("") && s!=null)
count++;
}
reader.close();
} catch (IOException e) {
e.printStackTrace();
} finally {
if (reader != null) {
try {
reader.close();
} catch (IOException e1) {
}
}
}
return count;
}
private void initialize_theta()//将theta各个参数全部初始化为1.0
{
for(int i=0;i<theta.length;i++)
theta[i]=1.0;
}
public void trainTheta()
{
int count = 0;
int iteration = this.iteration;
while( (count++) < iteration)
{
System.out.print(count);
printTheta();
//对每个theta i 求 偏导数
double [] partial_derivative = compute_partial_derivative();//偏导数
//更新每个theta
for(int i =0; i< theta.length;i++)
theta[i]-= alpha * partial_derivative[i];
}
}
private double [] compute_partial_derivative()
{
double [] partial_derivative = new double[theta.length];
for(int j =0;j<theta.length;j++)//遍历，对每个theta求偏导数
{
partial_derivative[j]= compute_partial_derivative_for_theta(j);//对 theta j 求 偏导
}
return partial_derivative;
}
private double compute_partial_derivative_for_theta(int j)
{
double sum=0.0;
for(int i=0;i<row;i++)//遍历 每一行数据
{
sum+=h_theta_x_i_minus_y_i_times_x_j_i(i,j);
}
return sum/row;
}
private double h_theta_x_i_minus_y_i_times_x_j_i(int i,int j)
{
double[] oneRow = getRow(i);//取一行数据，前面是feature，最后一个是y
double result = 0.0;
result += h_thera_x_i(oneRow);
result-=oneRow[oneRow.length-1];
result*=oneRow[j];
return result;
}
private double h_thera_x_i(double [] oneRow)
{
double theta_T_x = 0.0;
for(int k=0;k< (oneRow.length-1);k++)
theta_T_x += theta[k]*oneRow[k];
return 1/(1+(Math.exp(0-theta_T_x)));
}
private double [] getRow(int i)//从训练数据中取出第i行，i=0，1，2，。。。，（row-1）
{
return trainData[i];
}
private void loadTrainDataFromFile(String fileName,int row, int column)
{
for(int i=0;i< row;i++)//trainData的第一列全部置为1.0（feature x0）
trainData[i][0]=1.0;
File file = new File(fileName);
BufferedReader reader = null;
try {
reader = new BufferedReader(new FileReader(file));
String tempString = null;
int counter = 0;
while ( (counter<row) && (tempString = reader.readLine()) != null) {
String [] tempData = tempString.split(" ");
int numOfTrainData = 0;
for(int i=0;i<column;i++)
{
while(tempData[numOfTrainData] == null || tempData[numOfTrainData].equals(""))
numOfTrainData++;
trainData[counter][i+1]=Double.parseDouble(tempData[numOfTrainData]);
numOfTrainData ++;
}
counter++;
}
reader.close();
} catch (IOException e) {
e.printStackTrace();
} finally {
if (reader != null) {
try {
reader.close();
} catch (IOException e1) {
}
}
}
}
public void printTrainData()
{
System.out.println("Train Data:\n");
for(int i=0;i<column-1;i++)
System.out.printf("%10s","x"+i+" ");
System.out.printf("%10s","y"+" \n");
for(int i=0;i<row;i++)
{
for(int j=0;j<column;j++)
{
System.out.printf("%10s",trainData[i][j]+" ");
}
System.out.println();
}
System.out.println();
}
public void printTheta()
{
System.out.print("theta: ");
for(double a:theta)
System.out.print(a+" ");
System.out.println();
}
}
TestLogisticRegression.java
public class TestLogisticRegression {
public static void main(String[] args) {
// TODO Auto-generated method stub
LogisticRegression m = new LogisticRegression("trainData.txt",0.001,2000000);
m.printTrainData();
m.trainTheta();
m.printTheta();
}
}
trainData.txt:
0.0 0 0.1 0 0.7 1 1.0 0 1.1 0 1.3 0 1.4 1 1.7 1 2.1 1 2.2 1
感兴趣的同学还可以试一试下面这组数据：
0.0 2.9 0.0 1.0 1.9 0.0 2.0 0.9 0.0 3.0 -0.1 0.0 4.0 -1.1 0.0 0.0 2.5 0.0 1.0 1.5 0.0 2.0 0.5 0.0 3.0 -0.5 0.0 4.0 -1.5 0.0 0.0 2.0 0.0 1.0 1.0 0.0 2.0 0.0 0.0 3.0 -1.0 0.0 4.0 -2.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 2.0 -1.0 0.0 3.0 -2.0 0.0 4.0 -3.0 0.0 0.2 2.9 1.0 1.2 1.9 1.0 2.2 0.9 1.0 3.2 -0.1 1.0 4.2 -1.1 1.0 1.2 2.9 1.0 2.2 1.9 1.0 3.2 0.9 1.0 4.2 -0.1 1.0 5.2 -1.1 1.0 2.2 2.9 1.0 3.2 1.9 1.0 4.2 0.9 1.0 5.2 -0.1 1.0 6.2 -1.1 1.0 3.0 0.2 0.0 1.0 2.3 0.0 1.0 1.8 1.0 2.0 0.8 1.0
最后训练出来的模型是：h(x)=1/(1+exp(-(-13.9827+4.6001*x1+4.6302*x2)))