/*
* 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;
/**
* Computationally efficient alternative to ActivationTANH.
* Its output is in the range [-1, 1], and it is derivable.
*
* It will approach the -1 and 1 more slowly than Tanh so it
* might be more suitable to classification tasks than predictions tasks.
*
* Elliott, D.L. "A better activation function for artificial neural networks", 1993
* <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.46.7204&rep=rep1&type=pdf"></a>
*/
public class ActivationElliottSymmetric implements ActivationFunction {
/**
* The parameters.
*/
private final double[] params;
/**
* Construct a basic HTAN activation function, with a slope of 1.
*/
public ActivationElliottSymmetric() {
this.params = new double[1];
this.params[0] = 1.0;
}
/**
* {@inheritDoc}
*/
@Override
public final void activationFunction(final double[] x, final int start,
final int size) {
for (int i = start; i < start + size; i++) {
double s = params[0];
x[i] = (x[i]*s) / (1 + Math.abs(x[i]*s));
}
}
/**
* @return The object cloned;
*/
@Override
public final ActivationFunction clone() {
return new ActivationElliottSymmetric();
}
/**
* {@inheritDoc}
*/
@Override
public final double derivativeFunction(final double b, final double a) {
double s = params[0];
double d = (1.0+Math.abs(b*s));
return (s*1.0)/(d*d);
}
/**
* {@inheritDoc}
*/
@Override
public final String[] getParamNames() {
final String[] result = { "Slope" };
return result;
}
/**
* {@inheritDoc}
*/
@Override
public final double[] getParams() {
return this.params;
}
/**
* @return Return true, Elliott activation has a derivative.
*/
@Override
public final boolean hasDerivative() {
return true;
}
/**
* {@inheritDoc}
*/
@Override
public final void setParam(final int index, final double value) {
this.params[index] = value;
}
/**
* {@inheritDoc}
*/
@Override
public String getFactoryCode() {
return null;
}
@Override
public String getLabel() {
return "elliottsymmetric";
}
}