itrainingmethod.java

利用Java实现的神经网络工具箱

/*
 * ITrainingMethod.java	1.0 09 Jun 2004
 *
 * NeuralNetworkToolkit
 * Copyright (C) 2004 Universidade de Bras�lia
 *
 * 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.methods;

import neuralnetworktoolkit.StatisticalResults;
import neuralnetworktoolkit.neuralnetwork.INeuralNetwork;

/**
 * Interface that defines some basic features of a neural network
 * training method.
 * 
 * @version 1.0 09 Jun 2004
 * 
 * @author <a href="mailto:[e-mail]">Hugo Iver V. Gon�alves</a>
 * @author <a href="mailto:[e-mail]">Rodrigo C. M. Coimbra</a>
 */
public interface ITrainingMethod {

	/**
	 * Trains a neural network.
	 * 
	 * @param neuralNetwork Neural network to be trained.
	 * @param error         Quadratic error wished for network.
	 * @param instanceSet   Training instances set.
	 * @param outputs       Network expected outputs.
	 * @param parameters    Other parameters array.
	 * 
	 * @return Statistical information about network learning.
	 */
	public StatisticalResults train(INeuralNetwork neuralNetwok, TrainingParameters parameters);
	
	/**
	 * Calculates total error.
	 * 
	 * @param neuralNetwork Neural network to calculate error.
	 * @param instanceSet   Training instances set.
	 * @param outputs       Network expected outputs.
	 * 
	 * @return Total error calculated.
	 */	
	public abstract double calculateTotalError(INeuralNetwork neuralNetwork, double[][] instanceSet, double[][] outputs);
	
} //ITrainingMethod