/**
* Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
*
* 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.
*/
package org.neuroph.nnet.comp;
import java.util.Vector;
import org.neuroph.core.Connection;
import org.neuroph.core.input.InputFunction;
import org.neuroph.core.transfer.TransferFunction;
/**
* Provides neuron behaviour specific for competitive neurons which are used in
* competitive layers, and networks with competitive learning.
*
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class CompetitiveNeuron extends DelayedNeuron {
/**
* The class fingerprint that is set to indicate serialization
* compatibility with a previous version of the class.
*/
private static final long serialVersionUID = 1L;
/**
* Flag indicates if this neuron is in competing mode
*/
private boolean isCompeting = false;
/**
* Collection of conections from neurons in other layers
*/
private Vector<Connection> connectionsFromOtherLayers;
/**
* Collection of connections from neurons in the same layer as this neuron
* (lateral connections used for competition)
*/
private Vector<Connection> connectionsFromThisLayer;
/**
* Creates an instance of CompetitiveNeuron with specified input and transfer functions
* @param inputFunction neuron input function
* @param transferFunction neuron ransfer function
*/
public CompetitiveNeuron(InputFunction inputFunction, TransferFunction transferFunction) {
super(inputFunction, transferFunction);
connectionsFromOtherLayers = new Vector<Connection>();
connectionsFromThisLayer = new Vector<Connection>();
this.addInputConnection(this, 1);
}
@Override
public void calculate() {
if (this.isCompeting) {
// get input only from neurons in this layer
this.netInput = this.inputFunction
.getOutput(this.connectionsFromThisLayer);
} else {
// get input from other layers
this.netInput = this.inputFunction
.getOutput(this.connectionsFromOtherLayers);
this.isCompeting = true;
}
this.output = this.transferFunction.getOutput(this.netInput);
outputHistory.add(0, new Double(this.output));
}
/**
* Adds input connection for this competitive neuron
* @param connection input connection
*/
@Override
public void addInputConnection(Connection connection) {
super.addInputConnection(connection);
if (connection.getFromNeuron().getParentLayer() == this
.getParentLayer()) {
connectionsFromThisLayer.add(connection);
} else {
connectionsFromOtherLayers.add(connection);
}
}
/**
* Returns collection of connections from other layers
* @return collection of connections from other layers
*/
public Vector<Connection> getConnectionsFromOtherLayers() {
return connectionsFromOtherLayers;
}
/**
* Resets the input, output and mode for this neuron
*/
@Override
public void reset() {
super.reset();
this.isCompeting = false;
}
/**
* Retruns true if this neuron is in competing mode, false otherwise
* @return true if this neuron is in competing mode, false otherwise
*/
public boolean isCompeting() {
return isCompeting;
}
/**
* Sets the flag to indicate that this neuron is in competing mode
* @param isCompeting value for the isCompeting flag
*/
public void setIsCompeting(boolean isCompeting) {
this.isCompeting = isCompeting;
}
}