/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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.som.training.basic.neighborhood;
import org.encog.mathutil.rbf.GaussianFunction;
import org.encog.mathutil.rbf.InverseMultiquadricFunction;
import org.encog.mathutil.rbf.MexicanHatFunction;
import org.encog.mathutil.rbf.MultiquadricFunction;
import org.encog.mathutil.rbf.RBFEnum;
import org.encog.mathutil.rbf.RadialBasisFunction;
import org.encog.neural.NeuralNetworkError;
/**
* A neighborhood function based on an RBF function.
*
* @author jheaton
*/
public class NeighborhoodRBF1D implements NeighborhoodFunction {
/**
* The radial basis function (RBF) to use to calculate the training falloff
* from the best neuron.
*/
private final RadialBasisFunction radial;
/**
* Construct the neighborhood function with the specified radial function.
* Generally this will be a Gaussian function but any RBF should do.
*
* @param radial
* The radial basis function to use.
*/
public NeighborhoodRBF1D(final RadialBasisFunction radial) {
this.radial = radial;
}
/**
* Construct a 1d neighborhood function.
* @param type The RBF type to use.
*/
public NeighborhoodRBF1D(final RBFEnum type) {
switch(type)
{
case Gaussian:
this.radial = new GaussianFunction(1);
break;
case InverseMultiquadric:
this.radial = new InverseMultiquadricFunction(1);
break;
case Multiquadric:
this.radial = new MultiquadricFunction(1);
break;
case MexicanHat:
this.radial = new MexicanHatFunction(1);
break;
default:
throw new NeuralNetworkError("Unknown RBF type: " + type.toString());
}
this.radial.setWidth(1.0);
}
/**
* Determine how much the current neuron should be affected by training
* based on its proximity to the winning neuron.
*
* @param currentNeuron
* THe current neuron being evaluated.
* @param bestNeuron
* The winning neuron.
* @return The ratio for this neuron's adjustment.
*/
public double function(final int currentNeuron, final int bestNeuron) {
double[] d = new double[1];
d[0] = currentNeuron - bestNeuron;
return this.radial.calculate(d);
}
/**
* @return The radius.
*/
public double getRadius() {
return this.radial.getWidth();
}
/**
* Set the radius.
* @param radius The new radius.
*/
public void setRadius(final double radius) {
this.radial.setWidth(radius);
}
}