/*
* Artificial Intelligence for Humans
* Volume 2: Nature Inspired Algorithms
* Java Version
* http://www.aifh.org
* http://www.jeffheaton.com
*
* Code repository:
* https://github.com/jeffheaton/aifh
*
* Copyright 2014 by Jeff Heaton
*
* 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 com.heatonresearch.aifh.examples.ga.iris;
import com.heatonresearch.aifh.evolutionary.population.BasicPopulation;
import com.heatonresearch.aifh.evolutionary.population.Population;
import com.heatonresearch.aifh.evolutionary.species.BasicSpecies;
import com.heatonresearch.aifh.evolutionary.train.basic.BasicEA;
import com.heatonresearch.aifh.examples.util.SimpleLearn;
import com.heatonresearch.aifh.general.data.BasicData;
import com.heatonresearch.aifh.genetic.crossover.Splice;
import com.heatonresearch.aifh.genetic.genome.DoubleArrayGenome;
import com.heatonresearch.aifh.genetic.genome.DoubleArrayGenomeFactory;
import com.heatonresearch.aifh.genetic.mutate.MutatePerturb;
import com.heatonresearch.aifh.genetic.species.ArraySpeciation;
import com.heatonresearch.aifh.learning.RBFNetwork;
import com.heatonresearch.aifh.learning.RBFNetworkGenomeCODEC;
import com.heatonresearch.aifh.learning.score.ScoreFunction;
import com.heatonresearch.aifh.learning.score.ScoreRegressionData;
import com.heatonresearch.aifh.normalize.DataSet;
import com.heatonresearch.aifh.randomize.GenerateRandom;
import com.heatonresearch.aifh.randomize.MersenneTwisterGenerateRandom;
import java.io.InputStream;
import java.util.List;
import java.util.Map;
/**
* Learn the Iris data set with a RBF network trained by a genetic algorithm.
* This example groups genomes into species, and is more efficient than non-species.
*/
public class ModelSpeciationIris extends SimpleLearn {
/**
* The size of the population.
*/
public static final int POPULATION_SIZE = 1000;
/**
* Create an initial population.
*
* @param rnd Random number generator.
* @param codec The codec, the type of network to use.
* @return The population.
*/
public static Population initPopulation(GenerateRandom rnd, RBFNetworkGenomeCODEC codec) {
// Create a RBF network to get the length.
final RBFNetwork network = new RBFNetwork(codec.getInputCount(), codec.getRbfCount(), codec.getOutputCount());
int size = network.getLongTermMemory().length;
// Create a new population, use a single species.
Population result = new BasicPopulation(POPULATION_SIZE, new DoubleArrayGenomeFactory(size));
BasicSpecies defaultSpecies = new BasicSpecies();
defaultSpecies.setPopulation(result);
result.getSpecies().add(defaultSpecies);
// Create a new population of random networks.
for (int i = 0; i < POPULATION_SIZE; i++) {
final DoubleArrayGenome genome = new DoubleArrayGenome(size);
network.reset(rnd);
System.arraycopy(network.getLongTermMemory(), 0, genome.getData(), 0, size);
defaultSpecies.add(genome);
}
// Set the genome factory to use the double array genome.
result.setGenomeFactory(new DoubleArrayGenomeFactory(size));
return result;
}
public static void main(final String[] args) {
final ModelSpeciationIris prg = new ModelSpeciationIris();
prg.process();
}
/**
* Run the example.
*/
public void process() {
try {
final InputStream istream = this.getClass().getResourceAsStream("/iris.csv");
if (istream == null) {
System.out.println("Cannot access data set, make sure the resources are available.");
System.exit(1);
}
GenerateRandom rnd = new MersenneTwisterGenerateRandom();
final DataSet ds = DataSet.load(istream);
// The following ranges are setup for the Iris data set. If you wish to normalize other files you will
// need to modify the below function calls other files.
ds.normalizeRange(0, -1, 1);
ds.normalizeRange(1, -1, 1);
ds.normalizeRange(2, -1, 1);
ds.normalizeRange(3, -1, 1);
final Map<String, Integer> species = ds.encodeOneOfN(4);
istream.close();
final RBFNetworkGenomeCODEC codec = new RBFNetworkGenomeCODEC(4, 4, 3);
final List<BasicData> trainingData = ds.extractSupervised(0,
codec.getInputCount(), codec.getRbfCount(), codec.getOutputCount());
Population pop = initPopulation(rnd, codec);
ScoreFunction score = new ScoreRegressionData(trainingData);
BasicEA genetic = new BasicEA(pop, score);
genetic.setSpeciation(new ArraySpeciation<DoubleArrayGenome>());
genetic.setCODEC(codec);
genetic.addOperation(0.7, new Splice(codec.size() / 5));
genetic.addOperation(0.3, new MutatePerturb(0.1));
performIterations(genetic, 100000, 0.05, true);
RBFNetwork winner = (RBFNetwork) codec.decode(genetic.getBestGenome());
queryOneOfN(winner, trainingData, species);
} catch (Throwable t) {
t.printStackTrace();
}
}
}