/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 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.engine.network.activation;
import org.encog.mathutil.BoundMath;
import org.encog.ml.factory.MLActivationFactory;
import org.encog.util.obj.ActivationUtil;
/**
* An activation function based on the Gaussian function. The output range is
* between 0 and 1. This activation function is used mainly for the HyperNeat
* implementation.
*
* A derivative is provided, so this activation function can be used with
* propagation training. However, its primary intended purpose is for
* HyperNeat. The derivative was obtained with the R statistical package.
*
* If you are looking to implement a RBF-based neural network, see the
* RBFNetwork class.
*
* The idea for this activation function was developed by Ken Stanley, of
* the University of Texas at Austin.
* http://www.cs.ucf.edu/~kstanley/
*/
public class ActivationGaussian implements ActivationFunction {
/**
* The parameters.
*/
private double[] params;
/**
* The serial id.
*/
private static final long serialVersionUID = -7166136514935838114L;
public ActivationGaussian() {
this.params = new double[0];
}
/**
* @return The object cloned.
*/
@Override
public final ActivationFunction clone() {
return new ActivationGaussian();
}
/**
* @return Return true, gaussian has a derivative.
*/
public final boolean hasDerivative() {
return true;
}
/**
* {@inheritDoc}
*/
@Override
public final void activationFunction(final double[] x, final int start,
final int size) {
for (int i = start; i < start + size; i++) {
x[i] = BoundMath.exp(-Math.pow(2.5*x[i], 2.0));
}
}
/**
* {@inheritDoc}
*/
@Override
public final double derivativeFunction(final double b, final double a) {
return Math.exp( Math.pow(2.5 * b,2.0) * 12.5 * b);
}
/**
* {@inheritDoc}
*/
@Override
public final String[] getParamNames() {
final String[] result = { };
return result;
}
/**
* {@inheritDoc}
*/
@Override
public final double[] getParams() {
return params;
}
/**
* {@inheritDoc}
*/
@Override
public final void setParam(final int index, final double value) {
this.params[index] = value;
}
/**
* {@inheritDoc}
*/
@Override
public String getFactoryCode() {
return ActivationUtil.generateActivationFactory(MLActivationFactory.AF_GAUSSIAN, this);
}
@Override
public String getLabel() {
return "gaussian";
}
}