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Java example source code file (ClusterAlgorithmComparison.java)

This example Java source code file (ClusterAlgorithmComparison.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

arraylist, awt, clusterable, clusterer, clusterplot, dimension, display, doublepoint, geometry, gui, list, normaldistribution, pad, pair, randomadaptor, randomgenerator, swing, util, vector2d

The ClusterAlgorithmComparison.java Java example source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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.
 */
package org.apache.commons.math3.userguide;

import java.awt.Color;
import java.awt.Dimension;
import java.awt.Graphics;
import java.awt.Graphics2D;
import java.awt.GridBagConstraints;
import java.awt.GridBagLayout;
import java.awt.Insets;
import java.awt.RenderingHints;
import java.awt.Shape;
import java.awt.geom.Ellipse2D;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;

import javax.swing.JComponent;
import javax.swing.JLabel;

import org.apache.commons.math3.distribution.NormalDistribution;
import org.apache.commons.math3.geometry.euclidean.twod.Vector2D;
import org.apache.commons.math3.ml.clustering.CentroidCluster;
import org.apache.commons.math3.ml.clustering.Cluster;
import org.apache.commons.math3.ml.clustering.Clusterable;
import org.apache.commons.math3.ml.clustering.Clusterer;
import org.apache.commons.math3.ml.clustering.DBSCANClusterer;
import org.apache.commons.math3.ml.clustering.DoublePoint;
import org.apache.commons.math3.ml.clustering.FuzzyKMeansClusterer;
import org.apache.commons.math3.ml.clustering.KMeansPlusPlusClusterer;
import org.apache.commons.math3.random.RandomAdaptor;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.SobolSequenceGenerator;
import org.apache.commons.math3.random.Well19937c;
import org.apache.commons.math3.userguide.ExampleUtils.ExampleFrame;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Pair;

/**
 * Plots clustering results for various algorithms and datasets.
 * Based on
 * <a href="http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html">scikit learn.
 */
public class ClusterAlgorithmComparison {

    public static List<Vector2D> makeCircles(int samples, boolean shuffle, double noise, double factor, final RandomGenerator random) {
        if (factor < 0 || factor > 1) {
            throw new IllegalArgumentException();
        }
        
        NormalDistribution dist = new NormalDistribution(random, 0.0, noise, 1e-9);

        List<Vector2D> points = new ArrayList();
        double range = 2.0 * FastMath.PI;
        double step = range / (samples / 2.0 + 1);
        for (double angle = 0; angle < range; angle += step) {
            Vector2D outerCircle = new Vector2D(FastMath.cos(angle), FastMath.sin(angle));
            Vector2D innerCircle = outerCircle.scalarMultiply(factor);
            
            points.add(outerCircle.add(generateNoiseVector(dist)));
            points.add(innerCircle.add(generateNoiseVector(dist)));
        }
        
        if (shuffle) {
            Collections.shuffle(points, new RandomAdaptor(random));
        }

        return points;
    }

    public static List<Vector2D> makeMoons(int samples, boolean shuffle, double noise, RandomGenerator random) {
        NormalDistribution dist = new NormalDistribution(random, 0.0, noise, 1e-9);

        int nSamplesOut = samples / 2;
        int nSamplesIn = samples - nSamplesOut;
        
        List<Vector2D> points = new ArrayList();
        double range = FastMath.PI;
        double step = range / (nSamplesOut / 2.0);
        for (double angle = 0; angle < range; angle += step) {
            Vector2D outerCircle = new Vector2D(FastMath.cos(angle), FastMath.sin(angle));
            points.add(outerCircle.add(generateNoiseVector(dist)));
        }

        step = range / (nSamplesIn / 2.0);
        for (double angle = 0; angle < range; angle += step) {
            Vector2D innerCircle = new Vector2D(1 - FastMath.cos(angle), 1 - FastMath.sin(angle) - 0.5);
            points.add(innerCircle.add(generateNoiseVector(dist)));
        }
        
        if (shuffle) {
            Collections.shuffle(points, new RandomAdaptor(random));
        }

        return points;
    }

    public static List<Vector2D> makeBlobs(int samples, int centers, double clusterStd,
                                           double min, double max, boolean shuffle, RandomGenerator random) {

        NormalDistribution dist = new NormalDistribution(random, 0.0, clusterStd, 1e-9);

        double range = max - min;
        Vector2D[] centerPoints = new Vector2D[centers];
        for (int i = 0; i < centers; i++) {
            double x = random.nextDouble() * range + min;
            double y = random.nextDouble() * range + min;
            centerPoints[i] = new Vector2D(x, y);
        }
        
        int[] nSamplesPerCenter = new int[centers];
        int count = samples / centers;
        Arrays.fill(nSamplesPerCenter, count);
        
        for (int i = 0; i < samples % centers; i++) {
            nSamplesPerCenter[i]++;
        }
        
        List<Vector2D> points = new ArrayList();
        for (int i = 0; i < centers; i++) {
            for (int j = 0; j < nSamplesPerCenter[i]; j++) {
                Vector2D point = new Vector2D(dist.sample(), dist.sample());
                points.add(point.add(centerPoints[i]));
            }
        }
        
        if (shuffle) {
            Collections.shuffle(points, new RandomAdaptor(random));
        }

        return points;
    }
    
    public static List<Vector2D> makeRandom(int samples) {
        SobolSequenceGenerator generator = new SobolSequenceGenerator(2);
        generator.skipTo(999999);
        List<Vector2D> points = new ArrayList();
        for (double i = 0; i < samples; i++) {
            double[] vector = generator.nextVector();
            vector[0] = vector[0] * 2 - 1;
            vector[1] = vector[1] * 2 - 1;
            Vector2D point = new Vector2D(vector);
            points.add(point);
        }
        
        return points;
    }

    public static Vector2D generateNoiseVector(NormalDistribution distribution) {
        return new Vector2D(distribution.sample(), distribution.sample());
    }
    
    public static List<DoublePoint> normalize(final List input, double minX, double maxX, double minY, double maxY) {
        double rangeX = maxX - minX;
        double rangeY = maxY - minY;
        List<DoublePoint> points = new ArrayList();
        for (Vector2D p : input) {
            double[] arr = p.toArray();
            arr[0] = (arr[0] - minX) / rangeX * 2 - 1;
            arr[1] = (arr[1] - minY) / rangeY * 2 - 1;
            points.add(new DoublePoint(arr));
        }
        return points;
    }
    
    @SuppressWarnings("serial")
    public static class Display extends ExampleFrame {
        
        public Display() {
            setTitle("Commons-Math: Cluster algorithm comparison");
            setSize(800, 800);
            
            setLayout(new GridBagLayout());
            
            int nSamples = 1500;
            
            RandomGenerator rng = new Well19937c(0);
            List<List datasets = new ArrayList>();

            datasets.add(normalize(makeCircles(nSamples, true, 0.04, 0.5, rng), -1, 1, -1, 1));
            datasets.add(normalize(makeMoons(nSamples, true, 0.04, rng), -1, 2, -1, 1));
            datasets.add(normalize(makeBlobs(nSamples, 3, 1.0, -10, 10, true, rng), -12, 12, -12, 12));
            datasets.add(normalize(makeRandom(nSamples), -1, 1, -1, 1));

            List<Pair> algorithms = new ArrayList>>();
            
            algorithms.add(new Pair<String, Clusterer("KMeans\n(k=2)", new KMeansPlusPlusClusterer(2)));            
            algorithms.add(new Pair<String, Clusterer("KMeans\n(k=3)", new KMeansPlusPlusClusterer(3)));
            algorithms.add(new Pair<String, Clusterer("FuzzyKMeans\n(k=3, fuzzy=2)", new FuzzyKMeansClusterer(3, 2)));
            algorithms.add(new Pair<String, Clusterer("FuzzyKMeans\n(k=3, fuzzy=10)", new FuzzyKMeansClusterer(3, 10)));
            algorithms.add(new Pair<String, Clusterer("DBSCAN\n(eps=.1, min=3)", new DBSCANClusterer(0.1, 3)));
            
            GridBagConstraints c = new GridBagConstraints();
            c.fill = GridBagConstraints.VERTICAL;
            c.gridx = 0;
            c.gridy = 0;
            c.insets = new Insets(2, 2, 2, 2);

            for (Pair<String, Clusterer pair : algorithms) {
                JLabel text = new JLabel("<html>" + pair.getFirst().replace("\n", "
")); add(text, c); c.gridx++; } c.gridy++; for (List<DoublePoint> dataset : datasets) { c.gridx = 0; for (Pair<String, Clusterer pair : algorithms) { long start = System.currentTimeMillis(); List<? extends Cluster clusters = pair.getSecond().cluster(dataset); long end = System.currentTimeMillis(); add(new ClusterPlot(clusters, end - start), c); c.gridx++; } c.gridy++; } } } @SuppressWarnings("serial") public static class ClusterPlot extends JComponent { private static double PAD = 10; private List<? extends Cluster clusters; private long duration; public ClusterPlot(final List<? extends Cluster clusters, long duration) { this.clusters = clusters; this.duration = duration; } @Override protected void paintComponent(Graphics g) { super.paintComponent(g); Graphics2D g2 = (Graphics2D)g; g2.setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON); int w = getWidth(); int h = getHeight(); g2.clearRect(0, 0, w, h); g2.setPaint(Color.black); g2.drawRect(0, 0, w - 1, h - 1); int index = 0; Color[] colors = new Color[] { Color.red, Color.blue, Color.green.darker() }; for (Cluster<DoublePoint> cluster : clusters) { g2.setPaint(colors[index++]); for (DoublePoint point : cluster.getPoints()) { Clusterable p = transform(point, w, h); double[] arr = p.getPoint(); g2.fill(new Ellipse2D.Double(arr[0] - 1, arr[1] - 1, 3, 3)); } if (cluster instanceof CentroidCluster) { Clusterable p = transform(((CentroidCluster<?>) cluster).getCenter(), w, h); double[] arr = p.getPoint(); Shape s = new Ellipse2D.Double(arr[0] - 4, arr[1] - 4, 8, 8); g2.fill(s); g2.setPaint(Color.black); g2.draw(s); } } g2.setPaint(Color.black); g2.drawString(String.format("%.2f s", duration / 1e3), w - 40, h - 5); } @Override public Dimension getPreferredSize() { return new Dimension(150, 150); } private Clusterable transform(Clusterable point, int width, int height) { double[] arr = point.getPoint(); return new DoublePoint(new double[] { PAD + (arr[0] + 1) / 2.0 * (width - 2 * PAD), height - PAD - (arr[1] + 1) / 2.0 * (height - 2 * PAD) }); } } public static void main(String[] args) { ExampleUtils.showExampleFrame(new Display()); } }

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