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The Hipparchus samples module
/*
* 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.
*/
/*
* This is not the original file distributed by the Apache Software Foundation
* It has been modified by the Hipparchus project
*/
package org.hipparchus.samples;
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.hipparchus.clustering.CentroidCluster;
import org.hipparchus.clustering.Cluster;
import org.hipparchus.clustering.Clusterable;
import org.hipparchus.clustering.Clusterer;
import org.hipparchus.clustering.DBSCANClusterer;
import org.hipparchus.clustering.DoublePoint;
import org.hipparchus.clustering.FuzzyKMeansClusterer;
import org.hipparchus.clustering.KMeansPlusPlusClusterer;
import org.hipparchus.geometry.euclidean.twod.Vector2D;
import org.hipparchus.random.RandomAdaptor;
import org.hipparchus.random.RandomDataGenerator;
import org.hipparchus.random.RandomGenerator;
import org.hipparchus.random.SobolSequenceGenerator;
import org.hipparchus.random.Well19937c;
import org.hipparchus.samples.ExampleUtils.ExampleFrame;
import org.hipparchus.util.FastMath;
import org.hipparchus.util.Pair;
/**
* Plots clustering results for various algorithms and datasets.
* Based on
* scikit learn.
*/
public class ClusterAlgorithmComparison {
public static List makeCircles(int samples, boolean shuffle, double noise, double factor, final RandomGenerator random) {
if (factor < 0 || factor > 1) {
throw new IllegalArgumentException();
}
List 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(random, noise)));
points.add(innerCircle.add(generateNoiseVector(random, noise)));
}
if (shuffle) {
Collections.shuffle(points, new RandomAdaptor(random));
}
return points;
}
public static List makeMoons(int samples, boolean shuffle, double noise, RandomGenerator random) {
int nSamplesOut = samples / 2;
int nSamplesIn = samples - nSamplesOut;
List 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(random, noise)));
}
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(random, noise)));
}
if (shuffle) {
Collections.shuffle(points, new RandomAdaptor(random));
}
return points;
}
public static List makeBlobs(int samples, int centers, double clusterStd,
double min, double max, boolean shuffle, RandomGenerator random) {
final RandomDataGenerator randomDataGenerator = RandomDataGenerator.of(random);
//NormalDistribution dist = new NormalDistribution(random, 0.0, clusterStd);
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 points = new ArrayList();
for (int i = 0; i < centers; i++) {
for (int j = 0; j < nSamplesPerCenter[i]; j++) {
Vector2D point = new Vector2D(randomDataGenerator.nextNormal(0, clusterStd),
randomDataGenerator.nextNormal(0, clusterStd));
points.add(point.add(centerPoints[i]));
}
}
if (shuffle) {
Collections.shuffle(points, new RandomAdaptor(random));
}
return points;
}
public static List makeRandom(int samples) {
SobolSequenceGenerator generator = new SobolSequenceGenerator(2);
generator.skipTo(999999);
List 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(RandomGenerator randomGenerator, double noise) {
final RandomDataGenerator randomDataGenerator = RandomDataGenerator.of(randomGenerator);
return new Vector2D(randomDataGenerator.nextNormal(0, noise), randomDataGenerator.nextNormal(0, noise));
}
public static List normalize(final List input, double minX, double maxX, double minY, double maxY) {
double rangeX = maxX - minX;
double rangeY = maxY - minY;
List 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("Hipparchus: Cluster algorithm comparison");
setSize(800, 800);
setLayout(new GridBagLayout());
int nSamples = 1500;
RandomGenerator rng = new Well19937c(0);
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>> algorithms = new ArrayList>>();
algorithms.add(new Pair>("KMeans\n(k=2)", new KMeansPlusPlusClusterer(2)));
algorithms.add(new Pair>("KMeans\n(k=3)", new KMeansPlusPlusClusterer(3)));
algorithms.add(new Pair>("FuzzyKMeans\n(k=3, fuzzy=2)", new FuzzyKMeansClusterer(3, 2)));
algorithms.add(new Pair>("FuzzyKMeans\n(k=3, fuzzy=10)", new FuzzyKMeansClusterer(3, 10)));
algorithms.add(new Pair>("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> pair : algorithms) {
JLabel text = new JLabel("" + pair.getFirst().replace("\n", "
"));
add(text, c);
c.gridx++;
}
c.gridy++;
for (List dataset : datasets) {
c.gridx = 0;
for (Pair> pair : algorithms) {
long start = System.currentTimeMillis();
List> 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> clusters;
private long duration;
public ClusterPlot(final List> 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 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());
}
}