R语言实现逻辑回归预测模型可以说相当方便，因为标准的库已经有人写好了，Java似乎不擅长统计学领域，所以实现比较复杂，这里给出一个Java实现的逻辑回归预测模型实现方式以及一些常用函数：
package com.exaple.cunzai; import java.io.*; import java.util.ArrayList; import java.util.List; public class BatchGradientDescent { //存储数据内容 private static List<Double[]> list=new ArrayList<Double[]>(); //构建训练集矩阵 private static Double[][] Matrix; // 步长->学习率 private static double alpha = 0.001; // 迭代次数 private static int steps = 500; //初始化权重向量 private static Double[][] weights; //初始化分类标签列表 private static Double[][] target; //构建训练集矩阵 public static void geMatrix(){ //开始构建x+b 系数矩阵：b这里默认为1 //初始化第一列默认1 Matrix= new Double[list.size()][list.get(0).length]; for(int i=0;i<list.size();i++){ Matrix[i][0]=1.0; } //初始化第二列->值为list.get(i)数组中的第一列 for(int i=0;i<list.size();i++){ Matrix[i][1]=list.get(i)[0]; } //初始化第二列->值为list.get(i)数组中的第二列 for(int i=0;i<list.size();i++){ Matrix[i][2]=list.get(i)[1]; } //训练集矩阵构建完成list.size()个样本，特征list.get(i).length 矩阵(list.size()维度,list.get(i).length维度) } //初始化权重向量矩阵和真实标签矩阵 public static void initWeights(){ weights=new Double[list.get(0).length][1]; weights[0][0]=1.0; weights[1][0]=1.0; weights[2][0]=1.0; target=new Double[list.size()][1]; for(int i=0;i<list.size();i++){ target[i][0]=list.get(i)[2]; } } // Logistic函数->sigmoid public static Double[][] sigmoid(Double[][] wx) { Double[][] sigmod=new Double[wx.length][wx[0].length]; for(int i=0;i<wx.length;i++){ double v = 1.0 / (1 + Math.exp(-wx[i][0])); sigmod [i][0]=v; } return sigmod; } //矩阵相乘 public static Double[][] MatrixMutMatrix(Double a[][], Double b[][]) { int arow = a.length; int bcol = b[0].length; int m = b.length; Double[][] c = new Double[arow][bcol]; for (int i = 0; i < arow; i++) { for (int j = 0; j < bcol; j++) { Double result = 0.0; for (int k = 0; k < m; k++) { result += a[i][k] * b[k][j]; } c[i][j] = result; } } return c; } //矩阵相减->计算误差 public static Double[][] subMatrix(Double[][] A, Double[][] B){ int line=A.length,list=A[0].length; Double[][] C =new Double[line][list]; for(int i=0;i<line;i++) { for(int j=0;j<list;j++) { C[i][j]=A[i][j]-B[i][j]; } } return C; } // 将矩阵转置 public static Double[][] revMatrix(Double temp [][]) { Double[][] result =new Double[temp[0].length][temp.length]; for (int i = 0; i < result.length; i++) { for (int j = 0; j < result[i].length; j++) { result[i][j] = temp[j][i] ; } } return result; } // 将矩阵乘以一个数 public static Double[][] mutMatrix(Double temp [][],Double v) { for (int i = 0; i < temp.length; i++) { for (int j = 0; j < temp[i].length; j++) { temp[i][j] = temp[i][j]*v; } } return temp; } //矩阵相加 public static Double[][] AddsMatrix(Double[][]A,Double[][] B){ int line=A.length,list=A[0].length; Double[][]C=new Double[line][list]; for(int i=0;i<line;i++) { for(int j=0;j<list;j++) { C[i][j]=A[i][j]+B[i][j]; } } return C; } //回归函数 public static Double regression_calc(Double[][] w,Double[][] x){ Double[][] result=sigmoid(MatrixMutMatrix(w,x)); Double value=result[0][0]; return value; } //分类函数 public static Double classifier(Double[][]x,Double[][] w){ Double[][] result=sigmoid(MatrixMutMatrix(w,x)); Double value=result[0][0]; Double v; if(value>0.5){ v=1.0; }else{ v=0.0; } return v; } //解析数据 public static void getDate(){ try { File file = new File("D:\\data\\lr_data\\testSet.txt"); InputStreamReader inputReader = new InputStreamReader(new FileInputStream(file)); BufferedReader bf = new BufferedReader(inputReader); String str; while ((str = bf.readLine()) != null){ Double[] arr=new Double[3]; String[] result=str.split("\t"); for(int i=0;i<result.length;i++){ arr[i]=Double.parseDouble(result[i]); } list.add(arr); } bf.close(); inputReader.close(); } catch (IOException e) { e.printStackTrace(); } } /** * * @param args * 1、设置初始w，计算F(w) * 2、计算梯度 • 下降方向 * 3、尝试梯度更新 * 4、如果 较小，停止； 否则 ；跳到第2步 */ public static void main(String[] args) { getDate(); geMatrix(); initWeights(); for(int i=0;i<steps;i++){ //训练集矩阵 乘 权重 w*x Double[][] gradient=MatrixMutMatrix(Matrix,weights); //sigmoid函数 1/1+exp(-wx) 返回预测值 Double[][] output=sigmoid(gradient); //真实值减预测值 返回误差 Double[][] errors = subMatrix(target,output); //训练集矩阵 转置 Double[][] dataMat=revMatrix(Matrix); //转置后的训练集矩阵 乘 步长 Double[][] mut=mutMatrix(dataMat,alpha); //所有样本乘以误差 Double[][] err=MatrixMutMatrix(mut,errors); //更新权重 权重 +步长∗ 梯度（误差） weights = AddsMatrix(weights,err); } System.out.println(weights[0][0]); System.out.println(weights[1][0]); System.out.println(weights[2][0]); /*得到权重 4.178813076565532 0.5048987439366058 0.6198026439379993*/ Double[][] x=new Double[1][3]; x[0][0]=1.0; x[0][1]=0.9316350; x[0][2]=-1.589505; Double[][] w=new Double[1][3]; w[0][0]=4.178813076565532; w[0][1]=0.5048987439366058; w[0][2]=0.6198026439379993; //回归函数 Double a=regression_calc(w,x); //分类函数 Double b=classifier(w,x); System.out.println(a); System.out.println(b); } }
