neuralfiledecoder.java

用java实现的神经网络bp的核心算法

/*
 * NeuralFileDecoder.java
 *
 * Created on 2007年11月29日, 上午11:39
 *
 * To change this template, choose Tools | Template Manager
 * and open the template in the editor.
 */

package Compressor;
import myUtil.*;
//import Compressor.FileAdapter;
import neuralNetwork.*;
import java.util.*;
import java.io.*;
import javax.swing.*;
/**
 * decompress.
 * @author yuhui_bear
 */
public class NeuralFileDecoder implements Runnable{
    private Diagram showB;
    private int branch =0 ;
    private int[] net;
    private long filelength=0;
    private File networkFile ;
    private BackPropagation compressNet;
    private FileAdapter data= null;
    private JProgressBar progressBar;
    private JButton startBt;
    /**
     * Creates a new instance of NeuralEncoder.
     * This one is just for testing.
     * Trained net will be stored in .\"weight.xml.
     * @param sb  diaplay panel for the trainer.
     * @param jb progress bar.
     * @param source file to compress.
     * @param br , Dendrites number of the neuron.
     * @param top net information .
     */
    public NeuralFileDecoder(Diagram sb ,JProgressBar jb ,File source, int br ,int[] top) {
        progressBar  = jb;
        showB = sb;     //new TestShowBoard(this);
        branch =br;
        net =top;
        networkFile = new File("weight.xml");
        compressNet = new BackPropagation(net,branch);
    }
    /**
     * Creates a new instance of NeuralDecoder
     * @param sb diagram to display MSE.
     * @param jb progress bar.
     * @param bt button been disabled on starting.
     * @param source  file to compress.
     * @param orgnet neural net file.
     */
    public NeuralFileDecoder(Diagram sb ,JProgressBar jb, JButton bt,File source, File orgnet ) {
        progressBar  = jb;
        startBt = bt;
        showB = sb;     //new TestShowBoard(this);
        networkFile = orgnet;
        compressNet = new BackPropagation(orgnet);
        net = compressNet.network.getNetInformation().nettop;
        branch =compressNet.network.getNetInformation().branch;
        //open the source file . prepare output file.
        try {
            data = new FileAdapter(source,net[net.length /2],false);
        } catch (IOException ex) {
            ex.printStackTrace();
            System.exit(-1);
        }
    }
         
    /**
     * inherited for Runnable Interface.
     */
    public void run(){
       //prepare training swatch.
       double[] inputdata = new double[net[0]];
       double[] decoded = new double[net[net.length /2 +1]];
       double[] outdata =new double[net[0]];      
       long work = data.getGroupSize();
       int err=0;
       
       while(work >0){
           // get value ...
           if(work ==2){
               int ttt= 234;
               ttt+=234;
           }
           inputdata = data.nextGroup();                    
           decoded=compressNet.decode(inputdata);
           showB.redraw(Evaluate.mean(decoded),"Remains="+(work--),"decoding...");
           err = data.writeBack(decoded);
           if(err==2){
                   break;
               }
           if(err <0){               
               System.err.print("write back error. err = "+ err);
               System.exit(err);
           }
           progressBar.setValue((int)(100 * work / data.getGroupSize()));
           
       }
       data.writeBack(null);
       showB.redraw(0 ,"Remains="+work,"done...");
       progressBar.setValue(100);
       startBt.setText("Job Done");
       startBt.setEnabled(true);
    }
}