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Java example source code file (KMeansPlusPlusClustererTest.java)

This example Java source code file (KMeansPlusPlusClustererTest.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, closeintegerpoint, collection, euclideanintegerpoint, kmeansplusplusclusterer, kmeansplusplusclusterertest, list, num_clusters, num_iterations, num_repeated_points, override, random, random_seed, test, util

The KMeansPlusPlusClustererTest.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.stat.clustering;


import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.List;
import java.util.Random;

import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.junit.Assert;
import org.junit.Test;

@Deprecated
public class KMeansPlusPlusClustererTest {

    @Test
    public void dimension2() {
        KMeansPlusPlusClusterer<EuclideanIntegerPoint> transformer =
            new KMeansPlusPlusClusterer<EuclideanIntegerPoint>(new Random(1746432956321l));
        EuclideanIntegerPoint[] points = new EuclideanIntegerPoint[] {

                // first expected cluster
                new EuclideanIntegerPoint(new int[] { -15,  3 }),
                new EuclideanIntegerPoint(new int[] { -15,  4 }),
                new EuclideanIntegerPoint(new int[] { -15,  5 }),
                new EuclideanIntegerPoint(new int[] { -14,  3 }),
                new EuclideanIntegerPoint(new int[] { -14,  5 }),
                new EuclideanIntegerPoint(new int[] { -13,  3 }),
                new EuclideanIntegerPoint(new int[] { -13,  4 }),
                new EuclideanIntegerPoint(new int[] { -13,  5 }),

                // second expected cluster
                new EuclideanIntegerPoint(new int[] { -1,  0 }),
                new EuclideanIntegerPoint(new int[] { -1, -1 }),
                new EuclideanIntegerPoint(new int[] {  0, -1 }),
                new EuclideanIntegerPoint(new int[] {  1, -1 }),
                new EuclideanIntegerPoint(new int[] {  1, -2 }),

                // third expected cluster
                new EuclideanIntegerPoint(new int[] { 13,  3 }),
                new EuclideanIntegerPoint(new int[] { 13,  4 }),
                new EuclideanIntegerPoint(new int[] { 14,  4 }),
                new EuclideanIntegerPoint(new int[] { 14,  7 }),
                new EuclideanIntegerPoint(new int[] { 16,  5 }),
                new EuclideanIntegerPoint(new int[] { 16,  6 }),
                new EuclideanIntegerPoint(new int[] { 17,  4 }),
                new EuclideanIntegerPoint(new int[] { 17,  7 })

        };
        List<Cluster clusters =
            transformer.cluster(Arrays.asList(points), 3, 5, 10);

        Assert.assertEquals(3, clusters.size());
        boolean cluster1Found = false;
        boolean cluster2Found = false;
        boolean cluster3Found = false;
        for (Cluster<EuclideanIntegerPoint> cluster : clusters) {
            int[] center = cluster.getCenter().getPoint();
            if (center[0] < 0) {
                cluster1Found = true;
                Assert.assertEquals(8, cluster.getPoints().size());
                Assert.assertEquals(-14, center[0]);
                Assert.assertEquals( 4, center[1]);
            } else if (center[1] < 0) {
                cluster2Found = true;
                Assert.assertEquals(5, cluster.getPoints().size());
                Assert.assertEquals( 0, center[0]);
                Assert.assertEquals(-1, center[1]);
            } else {
                cluster3Found = true;
                Assert.assertEquals(8, cluster.getPoints().size());
                Assert.assertEquals(15, center[0]);
                Assert.assertEquals(5, center[1]);
            }
        }
        Assert.assertTrue(cluster1Found);
        Assert.assertTrue(cluster2Found);
        Assert.assertTrue(cluster3Found);

    }

    /**
     * JIRA: MATH-305
     *
     * Two points, one cluster, one iteration
     */
    @Test
    public void testPerformClusterAnalysisDegenerate() {
        KMeansPlusPlusClusterer<EuclideanIntegerPoint> transformer = new KMeansPlusPlusClusterer(
                new Random(1746432956321l));
        EuclideanIntegerPoint[] points = new EuclideanIntegerPoint[] {
                new EuclideanIntegerPoint(new int[] { 1959, 325100 }),
                new EuclideanIntegerPoint(new int[] { 1960, 373200 }), };
        List<Cluster clusters = transformer.cluster(Arrays.asList(points), 1, 1);
        Assert.assertEquals(1, clusters.size());
        Assert.assertEquals(2, (clusters.get(0).getPoints().size()));
        EuclideanIntegerPoint pt1 = new EuclideanIntegerPoint(new int[] { 1959, 325100 });
        EuclideanIntegerPoint pt2 = new EuclideanIntegerPoint(new int[] { 1960, 373200 });
        Assert.assertTrue(clusters.get(0).getPoints().contains(pt1));
        Assert.assertTrue(clusters.get(0).getPoints().contains(pt2));

    }

    @Test
    public void testCertainSpace() {
        KMeansPlusPlusClusterer.EmptyClusterStrategy[] strategies = {
            KMeansPlusPlusClusterer.EmptyClusterStrategy.LARGEST_VARIANCE,
            KMeansPlusPlusClusterer.EmptyClusterStrategy.LARGEST_POINTS_NUMBER,
            KMeansPlusPlusClusterer.EmptyClusterStrategy.FARTHEST_POINT
        };
        for (KMeansPlusPlusClusterer.EmptyClusterStrategy strategy : strategies) {
            KMeansPlusPlusClusterer<EuclideanIntegerPoint> transformer =
                new KMeansPlusPlusClusterer<EuclideanIntegerPoint>(new Random(1746432956321l), strategy);
            int numberOfVariables = 27;
            // initialise testvalues
            int position1 = 1;
            int position2 = position1 + numberOfVariables;
            int position3 = position2 + numberOfVariables;
            int position4 = position3 + numberOfVariables;
            // testvalues will be multiplied
            int multiplier = 1000000;

            EuclideanIntegerPoint[] breakingPoints = new EuclideanIntegerPoint[numberOfVariables];
            // define the space which will break the cluster algorithm
            for (int i = 0; i < numberOfVariables; i++) {
                int points[] = { position1, position2, position3, position4 };
                // multiply the values
                for (int j = 0; j < points.length; j++) {
                    points[j] *= multiplier;
                }
                EuclideanIntegerPoint euclideanIntegerPoint = new EuclideanIntegerPoint(points);
                breakingPoints[i] = euclideanIntegerPoint;
                position1 += numberOfVariables;
                position2 += numberOfVariables;
                position3 += numberOfVariables;
                position4 += numberOfVariables;
            }

            for (int n = 2; n < 27; ++n) {
                List<Cluster clusters =
                    transformer.cluster(Arrays.asList(breakingPoints), n, 100);
                Assert.assertEquals(n, clusters.size());
                int sum = 0;
                for (Cluster<EuclideanIntegerPoint> cluster : clusters) {
                    sum += cluster.getPoints().size();
                }
                Assert.assertEquals(numberOfVariables, sum);
            }
        }

    }

    /**
     * A helper class for testSmallDistances(). This class is similar to EuclideanIntegerPoint, but
     * it defines a different distanceFrom() method that tends to return distances less than 1.
     */
    private class CloseIntegerPoint implements Clusterable<CloseIntegerPoint> {
        public CloseIntegerPoint(EuclideanIntegerPoint point) {
            euclideanPoint = point;
        }

        public double distanceFrom(CloseIntegerPoint p) {
            return euclideanPoint.distanceFrom(p.euclideanPoint) * 0.001;
        }

        public CloseIntegerPoint centroidOf(Collection<CloseIntegerPoint> p) {
            Collection<EuclideanIntegerPoint> euclideanPoints =
                new ArrayList<EuclideanIntegerPoint>();
            for (CloseIntegerPoint point : p) {
                euclideanPoints.add(point.euclideanPoint);
            }
            return new CloseIntegerPoint(euclideanPoint.centroidOf(euclideanPoints));
        }

        @Override
        public boolean equals(Object o) {
            if (!(o instanceof CloseIntegerPoint)) {
                return false;
            }
            CloseIntegerPoint p = (CloseIntegerPoint) o;

            return euclideanPoint.equals(p.euclideanPoint);
        }

        @Override
        public int hashCode() {
            return euclideanPoint.hashCode();
        }

        private EuclideanIntegerPoint euclideanPoint;
    }

    /**
     * Test points that are very close together. See issue MATH-546.
     */
    @Test
    public void testSmallDistances() {
        // Create a bunch of CloseIntegerPoints. Most are identical, but one is different by a
        // small distance.
        int[] repeatedArray = { 0 };
        int[] uniqueArray = { 1 };
        CloseIntegerPoint repeatedPoint =
            new CloseIntegerPoint(new EuclideanIntegerPoint(repeatedArray));
        CloseIntegerPoint uniquePoint =
            new CloseIntegerPoint(new EuclideanIntegerPoint(uniqueArray));

        Collection<CloseIntegerPoint> points = new ArrayList();
        final int NUM_REPEATED_POINTS = 10 * 1000;
        for (int i = 0; i < NUM_REPEATED_POINTS; ++i) {
            points.add(repeatedPoint);
        }
        points.add(uniquePoint);

        // Ask a KMeansPlusPlusClusterer to run zero iterations (i.e., to simply choose initial
        // cluster centers).
        final long RANDOM_SEED = 0;
        final int NUM_CLUSTERS = 2;
        final int NUM_ITERATIONS = 0;
        KMeansPlusPlusClusterer<CloseIntegerPoint> clusterer =
            new KMeansPlusPlusClusterer<CloseIntegerPoint>(new Random(RANDOM_SEED));
        List<Cluster clusters =
            clusterer.cluster(points, NUM_CLUSTERS, NUM_ITERATIONS);

        // Check that one of the chosen centers is the unique point.
        boolean uniquePointIsCenter = false;
        for (Cluster<CloseIntegerPoint> cluster : clusters) {
            if (cluster.getCenter().equals(uniquePoint)) {
                uniquePointIsCenter = true;
            }
        }
        Assert.assertTrue(uniquePointIsCenter);
    }

    /**
     * 2 variables cannot be clustered into 3 clusters. See issue MATH-436.
     */
    @Test(expected=NumberIsTooSmallException.class)
    public void testPerformClusterAnalysisToManyClusters() {
        KMeansPlusPlusClusterer<EuclideanIntegerPoint> transformer =
            new KMeansPlusPlusClusterer<EuclideanIntegerPoint>(
                    new Random(1746432956321l));

        EuclideanIntegerPoint[] points = new EuclideanIntegerPoint[] {
            new EuclideanIntegerPoint(new int[] {
                1959, 325100
            }), new EuclideanIntegerPoint(new int[] {
                1960, 373200
            })
        };

        transformer.cluster(Arrays.asList(points), 3, 1);

    }

}

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