/*
* 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.ml.genetic;
import org.encog.ml.MLEncodable;
import org.encog.ml.genetic.genome.DoubleArrayGenome;
/**
* Implements a genome that allows a feedforward neural network to be trained
* using a genetic algorithm. The chromosome for a feed forward neural network
* is the weight and bias matrix.
*/
public class MLMethodGenome extends DoubleArrayGenome {
/**
* Serial id.
*/
private static final long serialVersionUID = 1L;
/**
* The phenome.
*/
private MLEncodable phenotype;
/**
* Construct a neural genome.
*
* @param thePhenotype
* The phenotype to use.
*/
public MLMethodGenome(final MLEncodable thePhenotype) {
super(thePhenotype.encodedArrayLength());
this.phenotype = thePhenotype;
this.phenotype.encodeToArray(getData());
}
/**
* Decode the phenotype.
*/
public void decode() {
this.phenotype.decodeFromArray(getData());
}
/**
* @return the phenotype
*/
public MLEncodable getPhenotype() {
return this.phenotype;
}
/**
* @param phenotype
* the phenotype to set
*/
public void setPhenotype(final MLEncodable phenotype) {
this.phenotype = phenotype;
}
}