/*
* 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.swarm.pso;
import com.heatonresearch.aifh.examples.util.SimpleLearn;
import com.heatonresearch.aifh.general.data.BasicData;
import com.heatonresearch.aifh.learning.RBFNetwork;
import com.heatonresearch.aifh.learning.TrainPSO;
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 PSO.
*/
public class IrisPSOExample extends SimpleLearn {
/**
* The number of particles
*/
public static final int PARTICLE_COUNT = 30;
/**
* Main entry point.
*
* @param args Not used.
*/
public static void main(final String[] args) {
final IrisPSOExample prg = new IrisPSOExample();
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();
RBFNetwork[] particles = new RBFNetwork[PARTICLE_COUNT];
for (int i = 0; i < particles.length; i++) {
particles[i] = new RBFNetwork(4, 4, 3);
particles[i].reset(rnd);
}
final List<BasicData> trainingData = ds.extractSupervised(0, 4, 4, 3);
ScoreFunction score = new ScoreRegressionData(trainingData);
TrainPSO train = new TrainPSO(particles, score);
performIterations(train, 100000, 0.05, true);
RBFNetwork winner = (RBFNetwork) train.getBestParticle();
queryOneOfN(winner, trainingData, species);
} catch (Throwable t) {
t.printStackTrace();
}
}
}