ExpLayer.java

/***********************************************************************

	This file is part of KEEL-software, the Data Mining tool for regression, 
	classification, clustering, pattern mining and so on.

	Copyright (C) 2004-2010
	
	F. Herrera (herrera@decsai.ugr.es)
    L. S�nchez (luciano@uniovi.es)
    J. Alcal�-Fdez (jalcala@decsai.ugr.es)
    S. Garc�a (sglopez@ujaen.es)
    A. Fern�ndez (alberto.fernandez@ujaen.es)
    J. Luengo (julianlm@decsai.ugr.es)

	This program 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 3 of the License, or
	(at your option) any later version.

	This program 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 this program.  If not, see http://www.gnu.org/licenses/
  
**********************************************************************/

package keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet;

/**
 * <p>
 * @author Written by Pedro Antonio Gutierrez Penya, Aaron Ruiz Mora (University of Cordoba) 17/07/2007
 * @version 0.1
 * @since JDK1.5
 * </p>
 */

public class ExpLayer extends LinkedLayer {
	
	/**
	 * <p>
	 * Represents a neural net layer with all the neurons of ExpNeuron type
	 * </p>
	 */
    
	/////////////////////////////////////////////////////////////////
	// --------------------------------------- Serialization constant
	/////////////////////////////////////////////////////////////////
	
	/** Generated by Eclipse */
	
	private static final long serialVersionUID = -3551511290189783173L;
	
	/////////////////////////////////////////////////////////////////
	// ------------------------------------------------- Constructors
	/////////////////////////////////////////////////////////////////
	
	/**
	 * <p>
	 * Empty constructor
	 * </p>
	 */
	public ExpLayer() 
	{
		super();
	}

	/////////////////////////////////////////////////////////////////
	// -------------------------------- Implementing Abstract Methods
	/////////////////////////////////////////////////////////////////
	
	/**
	 * <p>
	 * New neuron for the layer
	 * </p>
	 * @return LinkedNeuron New neuron for the layer
	 */
    public LinkedNeuron obtainNewNeuron() {
        return new ExpNeuron();
    }
}