/*
* Encog(tm) Core v2.5 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2010 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.neural.pattern;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.engine.network.activation.ActivationLinear;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.layers.Layer;
import org.encog.neural.networks.logic.ART1Logic;
import org.encog.neural.networks.logic.ARTLogic;
import org.encog.neural.networks.synapse.Synapse;
import org.encog.neural.networks.synapse.WeightedSynapse;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* Pattern to create an ART-1 neural network.
*/
public class ART1Pattern implements NeuralNetworkPattern {
/**
* The tag for the F1 layer.
*/
public static final String TAG_F1 = "F1";
/**
* The tag for the F2 layer.
*/
public static final String TAG_F2 = "F2";
/**
* The logging object.
*/
private final Logger logger = LoggerFactory.getLogger(this.getClass());
/**
* The number of input neurons.
*/
private int inputNeurons;
/**
* The number of output neurons.
*/
private int outputNeurons;
/**
* A parameter for F1 layer.
*/
private double a1 = 1;
/**
* B parameter for F1 layer.
*/
private double b1 = 1.5;
/**
* C parameter for F1 layer.
*/
private double c1 = 5;
/**
* D parameter for F1 layer.
*/
private double d1 = 0.9;
/**
* L parameter for net.
*/
private double l = 3;
/**
* The vigilance parameter.
*/
private double vigilance = 0.9;
/**
* This will fail, hidden layers are not supported for this type of network.
*
* @param count
* Not used.
*/
public void addHiddenLayer(final int count) {
final String str = "A ART1 network has no hidden layers.";
if (this.logger.isErrorEnabled()) {
this.logger.error(str);
}
throw new PatternError(str);
}
/**
* Clear any properties set for this network.
*/
public void clear() {
this.inputNeurons = this.outputNeurons = 0;
}
/**
* Generate the neural network.
*
* @return The generated neural network.
*/
public BasicNetwork generate() {
final BasicNetwork network = new BasicNetwork(new ART1Logic());
int y = PatternConst.START_Y;
final Layer layerF1 = new BasicLayer(new ActivationLinear(), false,
this.inputNeurons);
final Layer layerF2 = new BasicLayer(new ActivationLinear(), false,
this.outputNeurons);
final Synapse synapseF1toF2 = new WeightedSynapse(layerF1, layerF2);
final Synapse synapseF2toF1 = new WeightedSynapse(layerF2, layerF1);
layerF1.getNext().add(synapseF1toF2);
layerF2.getNext().add(synapseF2toF1);
// apply tags
network.tagLayer(BasicNetwork.TAG_INPUT, layerF1);
network.tagLayer(BasicNetwork.TAG_OUTPUT, layerF2);
network.tagLayer(ART1Pattern.TAG_F1, layerF1);
network.tagLayer(ART1Pattern.TAG_F2, layerF2);
layerF1.setX(PatternConst.START_X);
layerF1.setY(y);
y += PatternConst.INC_Y;
layerF2.setX(PatternConst.START_X);
layerF2.setY(y);
network.setProperty(ARTLogic.PROPERTY_A1, this.a1);
network.setProperty(ARTLogic.PROPERTY_B1, this.b1);
network.setProperty(ARTLogic.PROPERTY_C1, this.c1);
network.setProperty(ARTLogic.PROPERTY_D1, this.d1);
network.setProperty(ARTLogic.PROPERTY_L, this.l);
network.setProperty(ARTLogic.PROPERTY_VIGILANCE, this.vigilance);
network.getStructure().finalizeStructure();
return network;
}
/**
* @return The A1 parameter.
*/
public double getA1() {
return this.a1;
}
/**
* @return The B1 parameter.
*/
public double getB1() {
return this.b1;
}
/**
* @return The C1 parameter.
*/
public double getC1() {
return this.c1;
}
/**
* @return The D1 parameter.
*/
public double getD1() {
return this.d1;
}
/**
* @return The L parameter.
*/
public double getL() {
return this.l;
}
/**
* @return The vigilance for the network.
*/
public double getVigilance() {
return this.vigilance;
}
/**
* Set the A1 parameter.
*
* @param a1
* The new value.
*/
public void setA1(final double a1) {
this.a1 = a1;
}
/**
* This method will throw an error, you can't set the activation function
* for an ART1. type network.
*
* @param activation
* The activation function.
*/
public void setActivationFunction(final ActivationFunction activation) {
final String str = "Can't set the activation function for an ART1.";
if (this.logger.isErrorEnabled()) {
this.logger.error(str);
}
throw new PatternError(str);
}
/**
* Set the B1 parameter.
*
* @param b1
* The new value.
*/
public void setB1(final double b1) {
this.b1 = b1;
}
/**
* Set the C1 parameter.
*
* @param c1
* The new value.
*/
public void setC1(final double c1) {
this.c1 = c1;
}
/**
* Set the D1 parameter.
*
* @param d1
* The new value.
*/
public void setD1(final double d1) {
this.d1 = d1;
}
/**
* Set the input neuron (F1 layer) count.
*
* @param count
* The input neuron count.
*/
public void setInputNeurons(final int count) {
this.inputNeurons = count;
}
/**
* Set the L parameter.
*
* @param l
* The new value.
*/
public void setL(final double l) {
this.l = l;
}
/**
* Set the output neuron (F2 layer) count.
*
* @param count
* The output neuron count.
*/
public void setOutputNeurons(final int count) {
this.outputNeurons = count;
}
/**
* Set the vigilance for the network.
*
* @param vigilance
* The new value.
*/
public void setVigilance(final double vigilance) {
this.vigilance = vigilance;
}
}