/*
* 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.util.EngineArray;
/**
* Implements a multi-dimensional RBF neighborhood function.
*
*/
public class NeighborhoodRBF implements NeighborhoodFunction {
/**
* The radial basis function to use.
*/
private RadialBasisFunction rbf;
/**
* The size of each dimension.
*/
private final int[] size;
/**
* The displacement of each dimension, when mapping the dimensions
* to a 1d array.
*/
private int[] displacement;
/**
* Construct a 2d neighborhood function based on the sizes for the
* x and y dimensions.
* @param type The RBF type to use.
* @param x The size of the x-dimension.
* @param y The size of the y-dimension.
*/
public NeighborhoodRBF(final RBFEnum type, final int x, final int y) {
final int[] size = new int[2];
size[0] = x;
size[1] = y;
final double[] centerArray = new double[2];
centerArray[0] = 0;
centerArray[1] = 0;
final double[] widthArray = new double[2];
widthArray[0] = 1;
widthArray[1] = 1;
switch (type) {
case Gaussian:
this.rbf = new GaussianFunction(2);
break;
case InverseMultiquadric:
this.rbf = new InverseMultiquadricFunction(2);
break;
case Multiquadric:
this.rbf = new MultiquadricFunction(2);
break;
case MexicanHat:
this.rbf = new MexicanHatFunction(2);
break;
}
this.rbf.setWidth(1);
EngineArray.arrayCopy(centerArray, this.rbf.getCenters());
this.size = size;
calculateDisplacement();
}
/**
* Construct a multi-dimensional neighborhood function.
* @param size The sizes of each dimension.
* @param type The RBF type to use.
*/
public NeighborhoodRBF(final int[] size, final RBFEnum type) {
switch (type) {
case Gaussian:
this.rbf = new GaussianFunction(size.length);
break;
case InverseMultiquadric:
this.rbf = new InverseMultiquadricFunction(size.length);
break;
case Multiquadric:
this.rbf = new MultiquadricFunction(size.length);
break;
case MexicanHat:
this.rbf = new MexicanHatFunction(size.length);
break;
}
this.size = size;
calculateDisplacement();
}
/**
* Calculate all of the displacement values.
*/
private void calculateDisplacement() {
this.displacement = new int[this.size.length];
for (int i = 0; i < this.size.length; i++) {
int value;
if (i == 0) {
value = 0;
} else if (i == 1) {
value = this.size[0];
} else {
value = this.displacement[i - 1] * this.size[i - 1];
}
this.displacement[i] = value;
}
}
/**
* Calculate the value for the multi RBF function.
* @param currentNeuron The current neuron.
* @param bestNeuron The best neuron.
* @return A percent that determines the amount of training the current
* neuron should get. Usually 100% when it is the bestNeuron.
*/
public double function(final int currentNeuron, final int bestNeuron) {
final double[] vector = new double[this.displacement.length];
final int[] vectorCurrent = translateCoordinates(currentNeuron);
final int[] vectorBest = translateCoordinates(bestNeuron);
for (int i = 0; i < vectorCurrent.length; i++) {
vector[i] = vectorCurrent[i] - vectorBest[i];
}
return this.rbf.calculate(vector);
}
/**
* @return The radius.
*/
public double getRadius() {
return this.rbf.getWidth();
}
/**
* @return The RBF to use.
*/
public RadialBasisFunction getRBF() {
return this.rbf;
}
/**
* Set the radius.
* @param radius The radius.
*/
public void setRadius(final double radius) {
this.rbf.setWidth(radius);
}
/**
* Translate the specified index into a set of multi-dimensional
* coordinates that represent the same index. This is how the
* multi-dimensional coordinates are translated into a one dimensional
* index for the input neurons.
* @param index The index to translate.
* @return The multi-dimensional coordinates.
*/
private int[] translateCoordinates(final int index) {
final int[] result = new int[this.displacement.length];
int countingIndex = index;
for (int i = this.displacement.length - 1; i >= 0; i--) {
int value;
if (this.displacement[i] > 0) {
value = countingIndex / this.displacement[i];
} else {
value = countingIndex;
}
countingIndex -= this.displacement[i] * value;
result[i] = value;
}
return result;
}
}