Java tutorial
/** * Copyright (c) 2013 Oculus Info Inc. * http://www.oculusinfo.com/ * * Released under the MIT License. * * Permission is hereby granted, free of charge, to any person obtaining a copy of * this software and associated documentation files (the "Software"), to deal in * the Software without restriction, including without limitation the rights to * use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies * of the Software, and to permit persons to whom the Software is furnished to do * so, subject to the following conditions: * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ package com.oculusinfo.ml.spark.unsupervised; import java.awt.Color; import java.awt.Graphics; import java.awt.Graphics2D; import java.awt.RenderingHints; import java.awt.geom.Ellipse2D; import java.io.BufferedReader; import java.io.File; import java.io.FileNotFoundException; import java.io.FileReader; import java.io.IOException; import java.io.PrintWriter; import java.io.UnsupportedEncodingException; import java.util.ArrayList; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.Random; import javax.swing.JComponent; import javax.swing.JFrame; import org.apache.commons.io.FileUtils; import org.apache.spark.api.java.JavaSparkContext; import com.oculusinfo.ml.feature.numeric.centroid.MeanNumericVectorCentroid; import com.oculusinfo.ml.feature.numeric.distance.EuclideanDistance; import com.oculusinfo.ml.spark.SparkDataSet; import com.oculusinfo.ml.spark.unsupervised.cluster.dpmeans.DPMeansClusterer; public class TestDPMeans extends JFrame { private static final long serialVersionUID = -7287997469823771918L; public static void genTestData(int k) { PrintWriter writer; try { writer = new PrintWriter("test.txt", "UTF-8"); // each class size is equal int classSize = 1000000 / k; double stdDev = 30.0; // generate k classes of data points using a normal distribution with random means and fixed std deviation for (int i = 0; i < k; i++) { Random rnd = new Random(); double meanLat = rnd.nextDouble() * 400.0; double meanLon = rnd.nextDouble() * 400.0; // randomly generate a dataset of lat, lon points for (int j = 0; j < classSize; j++) { double x = rnd.nextGaussian() * stdDev + meanLat; double y = rnd.nextGaussian() * stdDev + meanLon; writer.println(x + "," + y); } } writer.close(); } catch (FileNotFoundException e) { // TODO Auto-generated catch block e.printStackTrace(); } catch (UnsupportedEncodingException e) { // TODO Auto-generated catch block e.printStackTrace(); } } public static List<double[]> readInstances() throws Exception { ArrayList<double[]> instances = new ArrayList<double[]>(); File folder = new File("output/clusters"); File[] files = folder.listFiles(); int index = 0; Map<String, Integer> clusters = new HashMap<String, Integer>(); for (File file : files) { if (file.getName().startsWith(".")) continue; BufferedReader br = new BufferedReader(new FileReader(file)); try { String line = br.readLine(); while (line != null) { if (line == "") continue; String cluster = line.substring(1, line.indexOf(",")); if (!clusters.containsKey(cluster)) { clusters.put(cluster, index); index++; } String[] coords = line .substring(line.indexOf("point") + "point:[".length(), line.lastIndexOf("]")) .split(";"); double x = Double.parseDouble(coords[0]); double y = Double.parseDouble(coords[1]); instances.add(new double[] { clusters.get(cluster), x, y }); line = br.readLine(); } } finally { br.close(); } } return instances; } /** * @param args */ public static void main(String[] args) { int k = 5; try { FileUtils.deleteDirectory(new File("output/clusters")); FileUtils.deleteDirectory(new File("output/centroids")); } catch (IOException e1) { /* ignore (*/ } genTestData(k); JavaSparkContext sc = new JavaSparkContext("local", "OculusML"); SparkDataSet ds = new SparkDataSet(sc); ds.load("test.txt", new InstanceParser()); DPMeansClusterer clusterer = new DPMeansClusterer(80, 10, 0.001); clusterer.setOutputPaths("output/centroids", "output/clusters"); clusterer.registerFeatureType("point", MeanNumericVectorCentroid.class, new EuclideanDistance(1.0)); clusterer.doCluster(ds); try { final List<double[]> instances = readInstances(); final Color[] colors = { Color.red, Color.blue, Color.green, Color.magenta, Color.yellow, Color.black, Color.orange, Color.cyan, Color.darkGray, Color.white }; TestDPMeans t = new TestDPMeans(); t.add(new JComponent() { private static final long serialVersionUID = 7920802321066846416L; public void paintComponent(Graphics g) { Graphics2D g2 = (Graphics2D) g; g2.setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON); for (double[] inst : instances) { int color = (int) inst[0]; g.setColor(colors[color]); Ellipse2D l = new Ellipse2D.Double(inst[1], inst[2], 5, 5); g2.draw(l); } } }); t.setDefaultCloseOperation(EXIT_ON_CLOSE); t.setSize(400, 400); t.setVisible(true); } catch (Exception e) { // TODO Auto-generated catch block e.printStackTrace(); } } }