/*
* Artificial Intelligence for Humans
* Volume 1: Fundamental Algorithms
* Java Version
* http://www.aifh.org
* http://www.jeffheaton.com
*
* Code repository:
* https://github.com/jeffheaton/aifh
* Copyright 2013 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.kmeans;
import com.heatonresearch.aifh.general.data.BasicData;
import com.heatonresearch.aifh.kmeans.Cluster;
import com.heatonresearch.aifh.kmeans.KMeans;
import com.heatonresearch.aifh.normalize.DataSet;
import java.io.InputStream;
import java.util.List;
/**
* Try to cluster the Iris data set.
*/
public class PerformCluster {
/**
* The main method.
*
* @param args Not used.
*/
public static void main(final String[] args) {
final PerformCluster prg = new PerformCluster();
prg.run();
}
/**
* Perform the example.
*/
public void run() {
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);
}
final DataSet ds = DataSet.load(istream);
istream.close();
final List<BasicData> observations = ds.extractUnsupervisedLabeled(4);
final KMeans kmeans = new KMeans(3);
kmeans.initForgy(observations);
final int iterations = kmeans.iteration(1000);
System.out.println("Finished after " + iterations + " iterations.");
for (int i = 0; i < kmeans.getK(); i++) {
final Cluster cluster = kmeans.getClusters().get(i);
System.out.println("* * * Cluster #" + i);
for (final BasicData d : cluster.getObservations()) {
System.out.println(d.toString());
}
}
} catch (Exception ex) {
ex.printStackTrace();
}
}
}