机器学习入门算法及其java实现

算法及其java实现"/>

机器学习入门算法及其java实现

1、算法基本原理：

• 对于K个类别的数据选取K个质心
• 距离第 个质心最近的点归为 类

2、算法具体步骤：
- 选取K个随机点，将其标注为K个类别
- 计算样本点到这K个随机点的距离，根据距离最近的第i个点将其分类i类
- 根据分类的结果，计算新的质心，质心计算公式如下:

xinew=1n∑j=1nxj $$x_{new}^{i}=\frac{1}{n}\sum_{j=1}^{n}x_{j}$$
- 根据新的质心重复（1）（2）（3）步骤直到达到停止条件： ||xnewi−xoldi||<ξ,其中ξ为大于零的常数 $$||x_{new}{i}-x_{old}{i}||&lt;\xi,其中\xi为大于零的常数$$

下列数据使用R软件生成，Java处理

R程序语言：
X<-matrix(1:200,nrow=100,ncol=2)
Y<-matrix(0,nrow=100,ncol=1)
for (i in 1:100){if (runif(1)<0.5){X[i,1]=exp(runif(1))*1.3  X[i,2]=exp(runif(1))*3Y[i]=0
}else{X[i,1]=exp(runif(1))*3X[i,2]=exp(runif(1))*3Y[i]=1
}
}
data<-cbind(Y,X)
write.table(data,"C:/Users/CJH/Desktop/R程序运行/Kmeanstest.txt"，row.names=FALSE，
col.names=FALSE)
#生成数据Eclipse程序语言：
package kmeans;import java.io.*;
import java.util.*;public class InputData{public void loadData(double [][]x,double[]y,String trainfile)throws IOException{File file = new File("C:\\Users\\CJH\\Desktop\\R程序运行",trainfile);RandomAccessFile raf= new RandomAccessFile(file,"r");StringTokenizer tokenizer;   int i=0,j=0;while(true){String line = raf.readLine();if(line==null)break;tokenizer= new StringTokenizer(line);y[i]=Double.parseDouble(tokenizer.nextToken());while(tokenizer.hasMoreTokens()){x[i][j]=Double.parseDouble(tokenizer.nextToken());j++;}j=0;i++;}raf.close();}}
//读入数据package kmeans;public class Kmean {public static double[] classfy(double[] u0,double[] u1,double[][] X){double[] y1=new double[100];double[] u=new double[2];for (int i=0;i<100;i++){y1[i]=0;u[0]=X[i][0];u[1]=X[i][1];if(euclid(minus(u,u0))>euclid(minus(u,u1))){y1[i]=1;}}for (int i=0;i<100;i++){System.out.println(y1[i]+" ");}return y1;}public static double[] newu(double[] y1,int k,double[][] X){double[] u=new double[2];double[] u1=new double[2];double[] u2=new double[2];double[] a=new double[2];u1[0]=0;u1[1]=0;u2[0]=0;u2[1]=0;int t1=0;int t2=0;for (int i=0;i<100;i++){if(k==1){if(y1[i]==1){a[0]=X[i][0];a[1]=X[i][1];u1=plus(u1,a);t1=t1+1;}u=dividV(u1,t1);}if(k==0){if(y1[i]==0){a[0]=X[i][0];a[1]=X[i][1];u2=plus(u2,a);t2=t2+1;}u=dividV(u2,t2);}}return u;}private static double[] dividV(double[] u, int t) {double[] temp=new double[2];for (int i=0;i<2;i++){temp[i]=u[i]/t;}return temp;}private static double[] plus(double[] u, double[] ds) {double[] temp=new double[2];for (int i=0;i<2;i++){temp[i]=u[i]+ds[i];}return temp;}private static double euclid(double[] x) {double sum=0;for (int i=0;i<2;i++){sum=sum+Math.pow(x[i], 2);}double euclid=Math.sqrt(sum);return euclid;}private static double[] minus(double[] x,double[] y) {double[] temp=new double[2];for (int i=0;i<2;i++){temp[i]=x[i]-y[i];}return temp;
//Kmeans程序package kmeans;import java.io.FileWriter;
import java.io.IOException;public class Kmain {
public static void main(String[]arg) throws IOException{double[][] X=new double[100][2];double[] Y=new double[100];InputData d=new InputData();try {d.loadData(X, Y, "data.txt");} catch (IOException e) {// TODO 自动生成的 catch 块e.printStackTrace();}for(int i=0;i<100;i++){for (int j=0;j<2;j++){System.out.print(X[i][j]+" ");}System.out.println(Y[i]);}int[] U0=new int[2];U0[0]=(int)(Math.random()*X.length);U0[1]=(int)(Math.random()*X.length);while(U0[1]==U0[0]){U0[1]=(int)(Math.random()*X.length);}System.out.println(X[U0[0]][0]+" "+X[U0[0]][1]+"\n"+" "+X[U0[1]][0]+" "+X[U0[1]][1]);double[] u0=new double[2];double[] u1=new double[2];double[] u2=new double[2];double[] u3=new double[2];double[] y1=new double[100];u0[0]=X[U0[0]][0];u0[1]=X[U0[0]][1];u1[0]=X[U0[1]][0];u1[1]=X[U0[1]][1];System.out.println(u0[0]+" "+u0[1]);System.out.println(u1[0]+" "+u1[1]);y1=Kmean.classfy(u0,u1,X);u2=Kmean.newu(y1,0,X);u3=Kmean.newu(y1,1,X);for (int i=0;i<100;i++){System.out.println(y1[i]+" ");}while(euclid(minus(u2,u0))+euclid(minus(u3,u1))>0.000001){u0=u2;u1=u3;y1=Kmean.classfy(u0,u1,X);u2=Kmean.newu(y1,0,X);u3=Kmean.newu(y1, 1, X);}System.out.println(u2[0]+" "+u2[1]);System.out.println(u3[0]+" "+u3[1]);FileWriter fw=new FileWriter("C:\\Users\\CJH\\Desktop\\R程序运行\\class.txt");for(int i=0;i<100;i++){fw.write((int) y1[i]+" ");}fw.close();
}
private static double euclid(double[] x) {double sum=0;for (int i=0;i<2;i++){sum=sum+Math.pow(x[i], 2);}double euclid=Math.sqrt(sum);return euclid;
}private static double[] minus(double[] x,double[] y) {double[] temp=new double[2];for (int i=0;i<2;i++){temp[i]=x[i]-y[i];}return temp;}
}
//主程序部分

原始数据

分类结果

$\quad\quad$如上图所示，点2和点3为质心点的位置。上图显示Kmeans对该组数据分类结果十分好，达到了百分之百。