layer.java

利用Java实现的神经网络工具箱

/*
 * Layer.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.neuralnetwork;

import java.io.Serializable;
import java.util.Vector;

import neuralnetworktoolkit.activationfunctions.*;

/**
 * Class that implements some basic features of a layer.
 *  
 * @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 class Layer implements ILayer, Serializable {

	/** Indicates that layer is normalizer or not. */
	private int isNormalizer;
	/** Indicates that layer is dynamic or static. */
	private int isDynamic;
	/** Stores neurons dynamically. */
	private Vector dynamicNeurons;
	/** Stores neurons statically. */
	private INeuron[] staticNeurons;
	/** Stores weights dynamically. */
	private Vector dynamicWeights;
	/** Stores weights statically. */
	private Weights[] staticWeights;
	/** Layer size. */
	private int layerSize = 0;
	/** 
	 * Layer activation function (all layer neurons have the same
	 * activation function, default).
	 */
	private IActivationFunction activationFunction;

	/**
	 * Creates a new layer with the specified attributes.
	 * 
	 * @param lastLayerSize      Preceding layer size.
	 * @param size               Layer size.
	 * @param normalizer         Indicates that layer is normalizer or not.
	 * @param dynamic            Indicates that layer is dynamic or static.
	 * @param activationFunction Layer activation function.
	 */
	public Layer(int lastLayerSize,
				 int size,
				 int dynamic,
				 IActivationFunction activationFunction) {

		this.isDynamic = dynamic;
		this.activationFunction = activationFunction;
		this.layerSize = size;

		// Outer switch
		
		// 1st Inner switch	
		switch (isDynamic) {
			case NeuralNetwork.NOT_DYNAMIC :
			{
				staticNeurons = new INeuron[size];
				staticWeights = new Weights[size];
				for (int i = 0; i < size; i++) {
					staticNeurons[i] =
						new Neuron(activationFunction);
					staticWeights[i] =
						new Weights(
								lastLayerSize,
								NeuralNetwork.NOT_DYNAMIC,
								NeuralNetwork.NOT_RECURRENT);
					/*staticWeights[i]
					 .randomicBiasAndWeightStart();*/
				}
			}
			break;
			case NeuralNetwork.DYNAMIC :
			{
				// TODO implement this case.
			}
			break;
		} // end 1st Inner switch	
		

	} //Layer()

	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.ILayer#addNeuron(neuralnetworktoolkit.INeuron)
	 */
	public void addNeuron(INeuron neuron) {
		switch (isDynamic) {
			case NeuralNetwork.NOT_DYNAMIC :
				{
				// TODO Implement this.
				}
				break;
			case NeuralNetwork.DYNAMIC :
				{
					dynamicNeurons.addElement(neuron);
				}
				break;
		}

	} //addNeuron()

	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.ILayer#removeNeuron(int)
	 */
	public void removeNeuron(int index) {
		switch (isDynamic) {
			case NeuralNetwork.NOT_DYNAMIC :
				{
				// TODO Implement this.
				}
				break;
			case NeuralNetwork.DYNAMIC :
				{
					dynamicNeurons.removeElementAt(index);
				}
				break;
		}

	} //removeNeuron()

	/* (non-Javadoc) 
	 * @see neuralnetworktoolkit.ILayer#getNeuron(int)
	 */
	public INeuron getNeuron(int index) {
		INeuron result = null;

		switch (isDynamic) {
			case NeuralNetwork.NOT_DYNAMIC :
				{
					result = (INeuron) staticNeurons[index];
				}
				break;
			case NeuralNetwork.DYNAMIC :
				{
					result = (INeuron) dynamicNeurons.elementAt(index);
				}
				break;
		}

		return result;
		
	} //getNeuron()

	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.ILayer#setWeight(double, int, int)
	 */
	public void setWeight(double weight, int inputNeuron, int neuron) {
		switch (isDynamic) {
			case NeuralNetwork.NOT_DYNAMIC :
				{
					staticWeights[neuron].setWeight(inputNeuron, weight);
				}
				break;
			case NeuralNetwork.DYNAMIC :
				{
				// TODO Implement this.
				}
				break;
		}

	} //setWeight()

	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.ILayer#getWeight(int, int)
	 */
	public double getWeight(int inputNeuron, int neuron) {
		// TODO Auto-generated method stub
		double result = 0;

		switch (isDynamic) {
			case NeuralNetwork.NOT_DYNAMIC :
				{
					result = staticWeights[neuron].getWeight(inputNeuron);
				}
				break;
			case NeuralNetwork.DYNAMIC :
				{
				// TODO Implement this.
				}
				break;
		}

		return result;
		
	} //getWeight()

	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.ILayer#updateWeight(double, int, int)
	 */
	public void updateWeight(double increment, int inputNeuron, int neuron) {
		switch (isDynamic) {
			case NeuralNetwork.NOT_DYNAMIC :
				{
					staticWeights[neuron].updateWeight(inputNeuron, increment);
				}
				break;
			case NeuralNetwork.DYNAMIC :
				{
				// TODO Implement this.
				}
				break;
		}
		
	} //updateWeight()

	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.ILayer#updateWeight(double, int, int)
	 */
	public void updateBias(double increment, int neuron) {
		switch (isDynamic) {
			case NeuralNetwork.NOT_DYNAMIC :
				{
					staticWeights[neuron].updateBias(increment);
				}
				break;
			case NeuralNetwork.DYNAMIC :
				{
				// TODO Implement this.
				}
				break;
		}
		
	} //updateBias()

	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.ILayer#setWeight(double, int, int)
	 */
	public void setBias(double value, int neuron) {
		switch (isDynamic) {
			case NeuralNetwork.NOT_DYNAMIC :
				{
					staticWeights[neuron].setBias(value);
				}
				break;
			case NeuralNetwork.DYNAMIC :
				{
				// TODO Implement this.
				}
				break;
		}
		
	} //updateBias()
	
	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.ILayer#setIsDynamic(int)
	 */
	public void setIsDynamic(int condition) {
		isDynamic = condition;
		
	} //setIsDynamic()

	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.ILayer#getLayerSize()
	 */
	public int getLayerSize() {
		return layerSize;
		
	} //getLayerSize()

	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.ILayer#getBias(int)
	 */
	public double getBias(int index) {
		double result = 0;
		
		switch (isDynamic) {
			case NeuralNetwork.NOT_DYNAMIC :
				{
					result = staticWeights[index].getBias();
				}
				break;
			case NeuralNetwork.DYNAMIC :
				{
				// TODO Implement this.
				}
				break;
		}
		return result;
		
	} //getBias()

	/* (non-Javadoc)
	 * @see neuralnetworktoolkit.ILayer#getWeightSize(int)
	 */
	public int getWeightSize(int index) {
		// TODO implement the general case.
		return staticWeights[index].getSize();
		
	} //getWeightSize()

} //Layer