ineuralnetwork.java

利用Java实现的神经网络工具箱

/*
 * $RCSfile: INeuralNetwork.java,v $
 * $Revision: 1.6 $
 * $Date: 2005/05/03 02:54:19 $
 *
 * 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.neuralnetwork;

/**
 * Interface that defines some basic features of a neural network.
 * 
 * @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 INeuralNetwork {
	
	/**
	 * Adds a layer to the network.
	 * 
	 * @param layer Layer to be added.
	 */
	public void addLayer(ILayer layer);
	
	/**
	 * Removes the layer at index.
	 * 
	 * @param index Layer index.
	 */
	public void removeLayer(int index);
	
	/**
	 * Returns the layer at index.
	 * 
	 * @param index Layer index.
	 * 
	 * @return Layer at index.
	 */
	public ILayer getLayer(int index);
	
	/**
	 * Sets input layer values.
	 * 
	 * @param inputValues Input values.
	 */
	public void inputLayerSetup(double[] inputValues);
	
	/**
	 * Returns the generated values after a input set propagation.
	 * 
	 * @return Network output.
	 */
	public double[] retrieveFinalResults();
	
	/**
	 * Updates all network weights, based on the increment matrix.
	 * 
	 * @param increment Increment matrix.
	 */
	public void updateWeights(double[][] increment);
	
	/**
	 * Returns network number of synapses.
	 * 
	 * @return The number of synapses.
	 */
	public int numberOfSynapses();
	
	/**
	 * Returns the output layer.
	 * 
	 * @return Output layer.
	 */
	public ILayer getOutputLayer();
	
	/**
	 * Returns the network size (number of layers).
	 * 
	 * @return Network size.
	 */
	public int getNetworkSize();
	
	/**
	 * Indicates that the network have a input normalizer layer or not.
	 * 
	 * @return Indication of the normalizer layer presence.
	 */
	//public boolean isNeuronNormalizer();
	
	/**
	 * Indicates that the network is dynamic or static.
	 * 
	 * @return Indication of that the network is dynamic or static.
	 */
	public boolean isDynamic();
	
	/**
	 * Indicates that the network is multiconexed or not.
	 * 
	 * @return Indication of that the network is multiconexed or not.
	 */
	public boolean isMultiConexed();
	
	/**
	 * Indicates that the network is recurrent or not.
	 * 
	 * @return Indication that the network is recurrent or not.
	 */
	public boolean isRecurrent();
	
	/**
	 * Returns network input values.
	 * 
	 * @return Network input values.
	 */
	public double[] getStaticInputValues();
	
	/**
	 * Propagates a input values set (previously configured) on the
	 * neural network.
	 */
	public void propagateInput();
	
	/**
	 * Returns the error during network train.
	 * 
	 * @return Error during network train.
	 */
	public double getError();
	
	/**
	 * Sets a new network error during a train.
	 * 
	 * @param error New network error.
	 */
	public void setError(double error);
	
	public int getInputSize();
	
	public int getOutputSize();
	
} //INeuralNetwork