package com.digiburo.backprop1b.demo1;
import java.io.File;
import java.io.IOException;
import java.io.FileNotFoundException;
import com.digiburo.backprop1b.network.PatternList;
/**
* Train the demo1 backpropagation network.
*
* @author G.S. Cole (gsc@digiburo.com)
* @version $Id: Trainer.java,v 1.5 2002/02/02 20:53:53 gsc Exp $
*/
/*
* Development Environment:
* Linux 2.2.14-5.0 (Red Hat 6.2)
* Java Developers Kit 1.3.1
*
* Legalise:
* Copyright (C) 2002 Digital Burro, INC.
*
* Maintenance History:
* $Log: Trainer.java,v $
* Revision 1.5 2002/02/02 20:53:53 gsc
* More testing tweaks
*
* Revision 1.4 2002/02/02 08:27:27 gsc
* Work In Progress
*
* Revision 1.3 2002/02/01 05:09:59 gsc
* Tweaks from Unit Testing
*
* Revision 1.2 2002/02/01 02:48:08 gsc
* Work In Progress
*
* Revision 1.1 2002/01/22 08:19:35 gsc
* Initial Check In
*/
public class Trainer {
private static final String TRAIN_FILENAME = "demo1.trn";
private static final String NETWORK_FILENAME = "demo1.serial";
private BpDemo1 bp;
private PatternList pl;
/**
* Create network
*/
public Trainer() {
//bp = new BpDemo1(2, 7, 1, 0.45, 0.9);
bp = new BpDemo1(2, 7, 1, 0.25, 0.9);
}
/**
* Load training datum
* @param datum training file
*/
public int loadTraining(File datum) throws IOException, FileNotFoundException, ClassNotFoundException {
pl = new PatternList();
pl.reader(datum);
return(pl.size());
}
/**
* Train the network on these patterns
*/
public void performTraining() {
bp.trainNetwork(pl, pl.size(), -1, 0.2, true);
}
/**
* Save this network for later use.
* @param datum file to save as
*/
public void saveTraining(File datum) throws IOException, FileNotFoundException {
bp.saveNetwork(datum);
}
/**
* Driver.
*/
public static void main(String args[]) throws Exception {
System.out.println("begin");
int population = 0;
Trainer tr = new Trainer();
if (args.length == 0) {
population = tr.loadTraining(new File(TRAIN_FILENAME));
} else {
population = tr.loadTraining(new File(args[0]));
}
System.out.println("PatternList loaded w/" + population + " patterns");
tr.performTraining();
tr.saveTraining(new File(NETWORK_FILENAME));
System.out.println("end");
}
}