mlpdebugger.java

利用Java实现的神经网络工具箱

/*
 * $RCSfile: MLPDebugger.java,v $
 * $Revision: 1.1 $
 * $Date: 2005/05/03 02:54:20 $
 *
 * NeuralNetworkToolkit
 * Copyright (C) 2004 Universidade de Bras锟絣ia
 *
 * This file is part of NeuralNetworkToolkit.
 *
 * NeuralNetworkToolkit is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 *
 * NeuralNetworkToolkit is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with NeuralNetworkToolkit; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA - 02111-1307 - USA.
 */
package neuralnetworktoolkit.debugger.mlp;

import java.io.File;
import java.io.FileNotFoundException;
import java.io.PrintStream;

import neuralnetworktoolkit.debugger.Debugger;
import neuralnetworktoolkit.neuralnetwork.INeuralNetwork;
import neuralnetworktoolkit.neuralnetwork.NeuralNetwork;

/**
 * @author <a href="mailto:[e-mail]">Hugo Iver V. Gon锟絘lves</a>
 * @author <a href="mailto:[e-mail]">Rodrigo C. M. Coimbra</a>
 */
public class MLPDebugger extends Debugger {
	
	public MLPDebugger() {
		super();
	}

	
	public MLPDebugger(INeuralNetwork neuralNetwork) {
		super(neuralNetwork);
		/*try {
			System.setOut(new PrintStream(new File("debiden.txt")));
		} catch (FileNotFoundException e) {
			// TODO Auto-generated catch block
			e.printStackTrace();
		}*/
	}

	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.debugger.Debugger#printNetworkState()
	 */
	public void printNetworkState() {
		
		System.out.println("Rede Neural MLP -------------------------------\n");
		
		System.out.print("Entrada  :");
		for (int i = 0; i < this.neuralNetwork.getInputSize(); i++) {
			System.out.print(">" + this.neuralNetwork.getStaticInputValues()[i]);
		}
		System.out.println();
		
		for (int i = 0; i < this.neuralNetwork.getNetworkSize(); i++) {
			
			System.out.print("Camada "+i+":\n");
			System.out.print(" Pesos   :");
			for (int j = 0; j < this.neuralNetwork.getLayer(i).getLayerSize(); j++) {
				
				System.out.print("[");
				for (int k = 0; k < this.neuralNetwork.getLayer(i).getWeightSize(j); k++) {
					System.out.print(" " + this.neuralNetwork.getLayer(i).getWeight(k, j));
					
				}
				System.out.print(" bias: " + this.neuralNetwork.getLayer(i).getBias(j));
				System.out.print(" ]");
			}
			System.out.println();
			System.out.print(" Sa韉as  :");
			for (int j = 0; j < this.neuralNetwork.getLayer(i).getLayerSize(); j++) {
				
				System.out.print(" [ " + this.neuralNetwork.getLayer(i).getNeuron(j).getOutputValue() + " delta: " + this.neuralNetwork.getLayer(i).getNeuron(j).getDelta() + " ]");
				
			}
			System.out.println("\n");
			
		}

		
	}

}