/* * Encog Neural Network and Bot Library for Java v1.x * http://www.heatonresearch.com/encog/ * http://code.google.com/p/encog-java/ *  * Copyright 2008, Heaton Research Inc., and individual contributors. * See the copyright.txt in the distribution for a full listing of  * individual contributors. * * This is free software; you can redistribute it and/or modify it * under the terms of the GNU Lesser General Public License as * published by the Free Software Foundation; either version 2.1 of * the License, or (at your option) any later version. * * This software 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 * Lesser General Public License for more details. * * You should have received a copy of the GNU Lesser General Public * License along with this software; if not, write to the Free * Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA * 02110-1301 USA, or see the FSF site: http://www.fsf.org. */package org.encog.neural.activation;import org.encog.neural.NeuralNetworkError;import org.encog.neural.persist.Persistor;import org.encog.neural.persist.persistors.ActivationLinearPersistor;/** * ActivationLinear: The Linear layer is really not an activation function at * all. The input is simply passed on, unmodified, to the output. This * activation function is primarily theoretical and of little actual use. * Usually an activation function that scales between 0 and 1 or -1 and 1 should * be used. */public class ActivationLinear implements ActivationFunction {	/**	 * Serial id for this class.	 */	private static final long serialVersionUID = -5356580554235104944L;	/**	 * The description for this object.	 */	private String description;		/**	 * The name of this object.	 */	private String name;	/**	 * A threshold function for a neural network.	 * 	 * @param d	 *            The input to the function.	 * @return The output from the function.	 */	public double activationFunction(final double d) {		return d;	}	/**	 * Create a persistor for this object.	 * @return The new persistor.	 */	public Persistor createPersistor() {		return new ActivationLinearPersistor();	}	/**	 * Some training methods require the derivative.	 * 	 * @param d	 *            The input.	 * @return The output.	 */	public double derivativeFunction(final double d) {		throw new NeuralNetworkError(				"Can't use the linear activation function "						+ "where a derivative is required.");	}	/**	 * @return the description	 */	public String getDescription() {		return this.description;	}	/**	 * @return the name	 */	public String getName() {		return this.name;	}	/**	 * @param description	 *            the description to set	 */	public void setDescription(final String description) {		this.description = description;	}	/**	 * @param name	 *            the name to set	 */	public void setName(final String name) {		this.name = name;	}}