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Posted to commits@commons.apache.org by tn...@apache.org on 2012/11/18 13:49:51 UTC
svn commit: r1410882 - in /commons/proper/math/trunk/src:
main/java/org/apache/commons/math3/stat/clustering/
test/java/org/apache/commons/math3/stat/clustering/
Author: tn
Date: Sun Nov 18 12:49:49 2012
New Revision: 1410882
URL: http://svn.apache.org/viewvc?rev=1410882&view=rev
Log:
[MATH-897] Add DBSCAN clustering algorithm, thanks to Reid Hochstedler.
Added:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/clustering/DBSCANClusterer.java (with props)
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/clustering/EuclideanDoublePoint.java (with props)
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/clustering/DBSCANClustererTest.java (with props)
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/clustering/EuclideanDoublePointTest.java (with props)
Added: commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/clustering/DBSCANClusterer.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/clustering/DBSCANClusterer.java?rev=1410882&view=auto
==============================================================================
--- commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/clustering/DBSCANClusterer.java (added)
+++ commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/clustering/DBSCANClusterer.java Sun Nov 18 12:49:49 2012
@@ -0,0 +1,224 @@
+/*
+ * 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.Collection;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+
+import org.apache.commons.math3.exception.NotPositiveException;
+import org.apache.commons.math3.exception.NullArgumentException;
+import org.apache.commons.math3.util.MathUtils;
+
+/**
+ * DBSCAN (density-based spatial clustering of applications with noise) algorithm.
+ * <p>
+ * The DBSCAN algorithm forms clusters based on the idea of density connectivity, i.e.
+ * a point p is density connected to another point q, if there exists a chain of
+ * points p<sub>i</sub>, with i = 1 .. n and p<sub>1</sub> = p and p<sub>n</sub> = q,
+ * such that each pair <p<sub>i</sub>, p<sub>i+1</sub>> is directly density-reachable.
+ * A point q is directly density-reachable from point p if it is in the ε-neighborhood
+ * of this point.
+ * <p>
+ * Any point that is not density-reachable from a formed cluster is treated as noise, and
+ * will thus not be present in the result.
+ * <p>
+ * The algorithm requires two parameters:
+ * <ul>
+ * <li>eps: the distance that defines the ε-neighborhood of a point
+ * <li>minPoints: the minimum number of density-connected points required to form a cluster
+ * </ul>
+ * <p>
+ * <b>Note:</b> as DBSCAN is not a centroid-based clustering algorithm, the resulting
+ * {@link Cluster} objects will have no defined center, i.e. {@link Cluster#getCenter()} will
+ * return {@code null}.
+ *
+ * @param <T> type of the points to cluster
+ * @see <a href="http://en.wikipedia.org/wiki/DBSCAN">DBSCAN (wikipedia)</a>
+ * @see <a href="http://www.dbs.ifi.lmu.de/Publikationen/Papers/KDD-96.final.frame.pdf">
+ * A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise</a>
+ * @version $Id$
+ * @since 3.1
+ */
+public class DBSCANClusterer<T extends Clusterable<T>> {
+
+ /** Maximum radius of the neighborhood to be considered. */
+ private final double eps;
+
+ /** Minimum number of points needed for a cluster. */
+ private final int minPts;
+
+ /** Status of a point during the clustering process. */
+ private enum PointStatus {
+ /** The point has is considered to be noise. */
+ NOISE,
+ /** The point is already part of a cluster. */
+ PART_OF_CLUSTER
+ }
+
+ /**
+ * Creates a new instance of a DBSCANClusterer.
+ *
+ * @param eps maximum radius of the neighborhood to be considered
+ * @param minPts minimum number of points needed for a cluster
+ * @throws NotPositiveException if {@code eps < 0.0} or {@code minPts < 0}
+ */
+ public DBSCANClusterer(final double eps, final int minPts)
+ throws NotPositiveException {
+ if (eps < 0.0d) {
+ throw new NotPositiveException(eps);
+ }
+ if (minPts < 0) {
+ throw new NotPositiveException(minPts);
+ }
+ this.eps = eps;
+ this.minPts = minPts;
+ }
+
+ /**
+ * Returns the maximum radius of the neighborhood to be considered.
+ *
+ * @return maximum radius of the neighborhood
+ */
+ public double getEps() {
+ return eps;
+ }
+
+ /**
+ * Returns the minimum number of points needed for a cluster.
+ *
+ * @return minimum number of points needed for a cluster
+ */
+ public int getMinPts() {
+ return minPts;
+ }
+
+ /**
+ * Performs DBSCAN cluster analysis.
+ * <p>
+ * <b>Note:</b> as DBSCAN is not a centroid-based clustering algorithm, the resulting
+ * {@link Cluster} objects will have no defined center, i.e. {@link Cluster#getCenter()} will
+ * return {@code null}.
+ *
+ * @param points the points to cluster
+ * @return the list of clusters
+ * @throws NullArgumentException if the data points are null
+ */
+ public List<Cluster<T>> cluster(final Collection<T> points) throws NullArgumentException {
+
+ // sanity checks
+ MathUtils.checkNotNull(points);
+
+ final List<Cluster<T>> clusters = new ArrayList<Cluster<T>>();
+ final Map<Clusterable<T>, PointStatus> visited = new HashMap<Clusterable<T>, PointStatus>();
+
+ for (final T point : points) {
+ if (visited.get(point) != null) {
+ continue;
+ }
+ final List<T> neighbors = getNeighbors(point, points);
+ if (neighbors.size() >= minPts) {
+ // DBSCAN does not care about center points
+ final Cluster<T> cluster = new Cluster<T>(null);
+ clusters.add(expandCluster(cluster, point, neighbors, points, visited));
+ } else {
+ visited.put(point, PointStatus.NOISE);
+ }
+ }
+
+ return clusters;
+ }
+
+ /**
+ * Expands the cluster to include density-reachable items.
+ *
+ * @param cluster Cluster to expand
+ * @param point Point to add to cluster
+ * @param neighbors List of neighbors
+ * @param points the data set
+ * @param visited the set of already visited points
+ * @return the expanded cluster
+ */
+ private Cluster<T> expandCluster(final Cluster<T> cluster,
+ final T point,
+ final List<T> neighbors,
+ final Collection<T> points,
+ final Map<Clusterable<T>, PointStatus> visited) {
+ cluster.addPoint(point);
+ visited.put(point, PointStatus.PART_OF_CLUSTER);
+
+ List<T> seeds = new ArrayList<T>(neighbors);
+ int index = 0;
+ while (index < seeds.size()) {
+ final T current = seeds.get(index);
+ PointStatus pStatus = visited.get(current);
+ // only check non-visited points
+ if (pStatus == null) {
+ final List<T> currentNeighbors = getNeighbors(current, points);
+ if (currentNeighbors.size() >= minPts) {
+ seeds = merge(seeds, currentNeighbors);
+ }
+ }
+
+ if (pStatus != PointStatus.PART_OF_CLUSTER) {
+ visited.put(current, PointStatus.PART_OF_CLUSTER);
+ cluster.addPoint(current);
+ }
+
+ index++;
+ }
+ return cluster;
+ }
+
+ /**
+ * Returns a list of density-reachable neighbors of a {@code point}.
+ *
+ * @param point the point to look for
+ * @param points possible neighbors
+ * @return the List of neighbors
+ */
+ private List<T> getNeighbors(final T point, final Collection<T> points) {
+ final List<T> neighbors = new ArrayList<T>();
+ for (final T neighbor : points) {
+ if (point != neighbor && neighbor.distanceFrom(point) <= eps) {
+ neighbors.add(neighbor);
+ }
+ }
+ return neighbors;
+ }
+
+ /**
+ * Merges two lists together.
+ *
+ * @param one first list
+ * @param two second list
+ * @return merged lists
+ */
+ private List<T> merge(final List<T> one, final List<T> two) {
+ final Set<T> oneSet = new HashSet<T>(one);
+ for (T item : two) {
+ if (!oneSet.contains(item)) {
+ one.add(item);
+ }
+ }
+ return one;
+ }
+}
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Added: commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/clustering/EuclideanDoublePoint.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/clustering/EuclideanDoublePoint.java?rev=1410882&view=auto
==============================================================================
--- commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/clustering/EuclideanDoublePoint.java (added)
+++ commons/proper/math/trunk/src/main/java/org/apache/commons/math3/stat/clustering/EuclideanDoublePoint.java Sun Nov 18 12:49:49 2012
@@ -0,0 +1,103 @@
+package org.apache.commons.math3.stat.clustering;
+
+import java.io.Serializable;
+import java.util.Collection;
+
+import org.apache.commons.math3.util.MathArrays;
+
+/**
+ * A simple implementation of {@link Clusterable} for points with double coordinates.
+ * @version $Id$
+ * @since 3.1
+ */
+public class EuclideanDoublePoint implements Clusterable<EuclideanDoublePoint>, Serializable {
+
+ /** Serializable version identifier. */
+ private static final long serialVersionUID = 8026472786091227632L;
+
+ /** Point coordinates. */
+ private final double[] point;
+
+ /**
+ * Build an instance wrapping an integer array.
+ * <p>
+ * The wrapped array is referenced, it is <em>not</em> copied.
+ *
+ * @param point the n-dimensional point in integer space
+ */
+ public EuclideanDoublePoint(final double[] point) {
+ this.point = point;
+ }
+
+ /** {@inheritDoc} */
+ public EuclideanDoublePoint centroidOf(final Collection<EuclideanDoublePoint> points) {
+ final double[] centroid = new double[getPoint().length];
+ for (final EuclideanDoublePoint p : points) {
+ for (int i = 0; i < centroid.length; i++) {
+ centroid[i] += p.getPoint()[i];
+ }
+ }
+ for (int i = 0; i < centroid.length; i++) {
+ centroid[i] /= points.size();
+ }
+ return new EuclideanDoublePoint(centroid);
+ }
+
+ /** {@inheritDoc} */
+ public double distanceFrom(final EuclideanDoublePoint p) {
+ return MathArrays.distance(point, p.getPoint());
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public boolean equals(final Object other) {
+ if (!(other instanceof EuclideanDoublePoint)) {
+ return false;
+ }
+ final double[] otherPoint = ((EuclideanDoublePoint) other).getPoint();
+ if (point.length != otherPoint.length) {
+ return false;
+ }
+ for (int i = 0; i < point.length; i++) {
+ if (point[i] != otherPoint[i]) {
+ return false;
+ }
+ }
+ return true;
+ }
+
+ /**
+ * Get the n-dimensional point in integer space.
+ *
+ * @return a reference (not a copy!) to the wrapped array
+ */
+ public double[] getPoint() {
+ return point;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public int hashCode() {
+ int hashCode = 0;
+ for (final Double i : point) {
+ hashCode += i.hashCode() * 13 + 7;
+ }
+ return hashCode;
+ }
+
+ /** {@inheritDoc} */
+ @Override
+ public String toString() {
+ final StringBuilder buff = new StringBuilder("(");
+ final double[] coordinates = getPoint();
+ for (int i = 0; i < coordinates.length; i++) {
+ buff.append(coordinates[i]);
+ if (i < coordinates.length - 1) {
+ buff.append(',');
+ }
+ }
+ buff.append(')');
+ return buff.toString();
+ }
+
+}
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Added: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/clustering/DBSCANClustererTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/clustering/DBSCANClustererTest.java?rev=1410882&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/clustering/DBSCANClustererTest.java (added)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/clustering/DBSCANClustererTest.java Sun Nov 18 12:49:49 2012
@@ -0,0 +1,190 @@
+/*
+ * 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.Arrays;
+import java.util.List;
+
+import org.apache.commons.math3.exception.MathIllegalArgumentException;
+import org.apache.commons.math3.exception.NullArgumentException;
+import org.junit.Assert;
+import org.junit.Test;
+
+public class DBSCANClustererTest {
+
+ @Test
+ public void testCluster() {
+ // Test data generated using: http://people.cs.nctu.edu.tw/~rsliang/dbscan/testdatagen.html
+ final EuclideanDoublePoint[] points = new EuclideanDoublePoint[] {
+ new EuclideanDoublePoint(new double[] { 83.08303244924173, 58.83387754182331 }),
+ new EuclideanDoublePoint(new double[] { 45.05445510940626, 23.469642649637535 }),
+ new EuclideanDoublePoint(new double[] { 14.96417921432294, 69.0264096390456 }),
+ new EuclideanDoublePoint(new double[] { 73.53189604333602, 34.896145021310076 }),
+ new EuclideanDoublePoint(new double[] { 73.28498173551634, 33.96860806993209 }),
+ new EuclideanDoublePoint(new double[] { 73.45828098873608, 33.92584423092194 }),
+ new EuclideanDoublePoint(new double[] { 73.9657889183145, 35.73191006924026 }),
+ new EuclideanDoublePoint(new double[] { 74.0074097183533, 36.81735596177168 }),
+ new EuclideanDoublePoint(new double[] { 73.41247541410848, 34.27314856695011 }),
+ new EuclideanDoublePoint(new double[] { 73.9156256353017, 36.83206791547127 }),
+ new EuclideanDoublePoint(new double[] { 74.81499205809087, 37.15682749846019 }),
+ new EuclideanDoublePoint(new double[] { 74.03144880081527, 37.57399178552441 }),
+ new EuclideanDoublePoint(new double[] { 74.51870941207744, 38.674258946906775 }),
+ new EuclideanDoublePoint(new double[] { 74.50754595105536, 35.58903978415765 }),
+ new EuclideanDoublePoint(new double[] { 74.51322752749547, 36.030572259100154 }),
+ new EuclideanDoublePoint(new double[] { 59.27900996617973, 46.41091720294207 }),
+ new EuclideanDoublePoint(new double[] { 59.73744793841615, 46.20015558367595 }),
+ new EuclideanDoublePoint(new double[] { 58.81134076672606, 45.71150126331486 }),
+ new EuclideanDoublePoint(new double[] { 58.52225539437495, 47.416083617601544 }),
+ new EuclideanDoublePoint(new double[] { 58.218626647023484, 47.36228902172297 }),
+ new EuclideanDoublePoint(new double[] { 60.27139669447206, 46.606106348801404 }),
+ new EuclideanDoublePoint(new double[] { 60.894962462363765, 46.976924697402865 }),
+ new EuclideanDoublePoint(new double[] { 62.29048673878424, 47.66970563563518 }),
+ new EuclideanDoublePoint(new double[] { 61.03857608977705, 46.212924720020965 }),
+ new EuclideanDoublePoint(new double[] { 60.16916214139201, 45.18193661351688 }),
+ new EuclideanDoublePoint(new double[] { 59.90036905976012, 47.555364347063005 }),
+ new EuclideanDoublePoint(new double[] { 62.33003634144552, 47.83941489877179 }),
+ new EuclideanDoublePoint(new double[] { 57.86035536718555, 47.31117930193432 }),
+ new EuclideanDoublePoint(new double[] { 58.13715479685925, 48.985960494028404 }),
+ new EuclideanDoublePoint(new double[] { 56.131923963548616, 46.8508904252667 }),
+ new EuclideanDoublePoint(new double[] { 55.976329887053, 47.46384037658572 }),
+ new EuclideanDoublePoint(new double[] { 56.23245975235477, 47.940035191131756 }),
+ new EuclideanDoublePoint(new double[] { 58.51687048212625, 46.622885352699086 }),
+ new EuclideanDoublePoint(new double[] { 57.85411081905477, 45.95394361577928 }),
+ new EuclideanDoublePoint(new double[] { 56.445776311447844, 45.162093662656844 }),
+ new EuclideanDoublePoint(new double[] { 57.36691949656233, 47.50097194337286 }),
+ new EuclideanDoublePoint(new double[] { 58.243626387557015, 46.114052729681134 }),
+ new EuclideanDoublePoint(new double[] { 56.27224595635198, 44.799080066150054 }),
+ new EuclideanDoublePoint(new double[] { 57.606924816500396, 46.94291057763621 }),
+ new EuclideanDoublePoint(new double[] { 30.18714230041951, 13.877149710431695 }),
+ new EuclideanDoublePoint(new double[] { 30.449448810657486, 13.490778346545994 }),
+ new EuclideanDoublePoint(new double[] { 30.295018390286714, 13.264889000216499 }),
+ new EuclideanDoublePoint(new double[] { 30.160201832884923, 11.89278262341395 }),
+ new EuclideanDoublePoint(new double[] { 31.341509791789576, 15.282655921997502 }),
+ new EuclideanDoublePoint(new double[] { 31.68601630325429, 14.756873246748 }),
+ new EuclideanDoublePoint(new double[] { 29.325963742565364, 12.097849250072613 }),
+ new EuclideanDoublePoint(new double[] { 29.54820742388256, 13.613295356975868 }),
+ new EuclideanDoublePoint(new double[] { 28.79359608888626, 10.36352064087987 }),
+ new EuclideanDoublePoint(new double[] { 31.01284597092308, 12.788479208014905 }),
+ new EuclideanDoublePoint(new double[] { 27.58509216737002, 11.47570110601373 }),
+ new EuclideanDoublePoint(new double[] { 28.593799561727792, 10.780998203903437 }),
+ new EuclideanDoublePoint(new double[] { 31.356105766724795, 15.080316198524088 }),
+ new EuclideanDoublePoint(new double[] { 31.25948503636755, 13.674329151166603 }),
+ new EuclideanDoublePoint(new double[] { 32.31590076372959, 14.95261758659035 }),
+ new EuclideanDoublePoint(new double[] { 30.460413702763617, 15.88402809202671 }),
+ new EuclideanDoublePoint(new double[] { 32.56178203062154, 14.586076852632686 }),
+ new EuclideanDoublePoint(new double[] { 32.76138648530468, 16.239837325178087 }),
+ new EuclideanDoublePoint(new double[] { 30.1829453331884, 14.709592407103628 }),
+ new EuclideanDoublePoint(new double[] { 29.55088173528202, 15.0651247180067 }),
+ new EuclideanDoublePoint(new double[] { 29.004155302187428, 14.089665298582986 }),
+ new EuclideanDoublePoint(new double[] { 29.339624439831823, 13.29096065578051 }),
+ new EuclideanDoublePoint(new double[] { 30.997460327576846, 14.551914158277214 }),
+ new EuclideanDoublePoint(new double[] { 30.66784126125276, 16.269703107886016 })
+ };
+
+ final DBSCANClusterer<EuclideanDoublePoint> transformer =
+ new DBSCANClusterer<EuclideanDoublePoint>(2.0, 5);
+ final List<Cluster<EuclideanDoublePoint>> clusters = transformer.cluster(Arrays.asList(points));
+
+ final List<EuclideanDoublePoint> clusterOne =
+ Arrays.asList(points[3], points[4], points[5], points[6], points[7], points[8], points[9], points[10],
+ points[11], points[12], points[13], points[14]);
+ final List<EuclideanDoublePoint> clusterTwo =
+ Arrays.asList(points[15], points[16], points[17], points[18], points[19], points[20], points[21],
+ points[22], points[23], points[24], points[25], points[26], points[27], points[28],
+ points[29], points[30], points[31], points[32], points[33], points[34], points[35],
+ points[36], points[37], points[38]);
+ final List<EuclideanDoublePoint> clusterThree =
+ Arrays.asList(points[39], points[40], points[41], points[42], points[43], points[44], points[45],
+ points[46], points[47], points[48], points[49], points[50], points[51], points[52],
+ points[53], points[54], points[55], points[56], points[57], points[58], points[59],
+ points[60], points[61], points[62]);
+
+ boolean cluster1Found = false;
+ boolean cluster2Found = false;
+ boolean cluster3Found = false;
+ Assert.assertEquals(3, clusters.size());
+ for (final Cluster<EuclideanDoublePoint> cluster : clusters) {
+ if (cluster.getPoints().containsAll(clusterOne)) {
+ cluster1Found = true;
+ }
+ if (cluster.getPoints().containsAll(clusterTwo)) {
+ cluster2Found = true;
+ }
+ if (cluster.getPoints().containsAll(clusterThree)) {
+ cluster3Found = true;
+ }
+ }
+ Assert.assertTrue(cluster1Found);
+ Assert.assertTrue(cluster2Found);
+ Assert.assertTrue(cluster3Found);
+ }
+
+ @Test
+ public void testSingleLink() {
+ final EuclideanIntegerPoint[] points = {
+ new EuclideanIntegerPoint(new int[] {10, 10}), // A
+ new EuclideanIntegerPoint(new int[] {12, 9}),
+ new EuclideanIntegerPoint(new int[] {10, 8}),
+ new EuclideanIntegerPoint(new int[] {8, 8}),
+ new EuclideanIntegerPoint(new int[] {8, 6}),
+ new EuclideanIntegerPoint(new int[] {7, 7}),
+ new EuclideanIntegerPoint(new int[] {5, 6}), // B
+ new EuclideanIntegerPoint(new int[] {14, 8}), // C
+ new EuclideanIntegerPoint(new int[] {7, 15}), // N - Noise, should not be present
+ new EuclideanIntegerPoint(new int[] {17, 8}), // D - single-link connected to C should not be present
+
+ };
+
+ final DBSCANClusterer<EuclideanIntegerPoint> clusterer = new DBSCANClusterer<EuclideanIntegerPoint>(3, 3);
+ List<Cluster<EuclideanIntegerPoint>> clusters = clusterer.cluster(Arrays.asList(points));
+
+ Assert.assertEquals(1, clusters.size());
+
+ final List<EuclideanIntegerPoint> clusterOne =
+ Arrays.asList(points[0], points[1], points[2], points[3], points[4], points[5], points[6], points[7]);
+ Assert.assertTrue(clusters.get(0).getPoints().containsAll(clusterOne));
+ }
+
+ @Test
+ public void testGetEps() {
+ final DBSCANClusterer<EuclideanDoublePoint> transformer = new DBSCANClusterer<EuclideanDoublePoint>(2.0, 5);
+ Assert.assertEquals(2.0, transformer.getEps(), 0.0);
+ }
+
+ @Test
+ public void testGetMinPts() {
+ final DBSCANClusterer<EuclideanDoublePoint> transformer = new DBSCANClusterer<EuclideanDoublePoint>(2.0, 5);
+ Assert.assertEquals(5, transformer.getMinPts());
+ }
+
+ @Test(expected = MathIllegalArgumentException.class)
+ public void testNegativeEps() {
+ new DBSCANClusterer<EuclideanDoublePoint>(-2.0, 5);
+ }
+
+ @Test(expected = MathIllegalArgumentException.class)
+ public void testNegativeMinPts() {
+ new DBSCANClusterer<EuclideanDoublePoint>(2.0, -5);
+ }
+
+ @Test(expected = NullArgumentException.class)
+ public void testNullDataset() {
+ DBSCANClusterer<EuclideanDoublePoint> clusterer = new DBSCANClusterer<EuclideanDoublePoint>(2.0, 5);
+ clusterer.cluster(null);
+ }
+
+}
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Added: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/clustering/EuclideanDoublePointTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/clustering/EuclideanDoublePointTest.java?rev=1410882&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/clustering/EuclideanDoublePointTest.java (added)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/stat/clustering/EuclideanDoublePointTest.java Sun Nov 18 12:49:49 2012
@@ -0,0 +1,62 @@
+/*
+ * 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.List;
+
+import org.apache.commons.math3.TestUtils;
+import org.apache.commons.math3.util.FastMath;
+import org.junit.Assert;
+import org.junit.Test;
+
+public class EuclideanDoublePointTest {
+
+ @Test
+ public void testArrayIsReference() {
+ final double[] array = { -3.0, -2.0, -1.0, 0.0, 1.0 };
+ Assert.assertArrayEquals(array, new EuclideanDoublePoint(array).getPoint(), 1.0e-15);
+ }
+
+ @Test
+ public void testDistance() {
+ final EuclideanDoublePoint e1 = new EuclideanDoublePoint(new double[] { -3.0, -2.0, -1.0, 0.0, 1.0 });
+ final EuclideanDoublePoint e2 = new EuclideanDoublePoint(new double[] { 1.0, 0.0, -1.0, 1.0, 1.0 });
+ Assert.assertEquals(FastMath.sqrt(21.0), e1.distanceFrom(e2), 1.0e-15);
+ Assert.assertEquals(0.0, e1.distanceFrom(e1), 1.0e-15);
+ Assert.assertEquals(0.0, e2.distanceFrom(e2), 1.0e-15);
+ }
+
+ @Test
+ public void testCentroid() {
+ final List<EuclideanDoublePoint> list = new ArrayList<EuclideanDoublePoint>();
+ list.add(new EuclideanDoublePoint(new double[] { 1.0, 3.0 }));
+ list.add(new EuclideanDoublePoint(new double[] { 2.0, 2.0 }));
+ list.add(new EuclideanDoublePoint(new double[] { 3.0, 3.0 }));
+ list.add(new EuclideanDoublePoint(new double[] { 2.0, 4.0 }));
+ final EuclideanDoublePoint c = list.get(0).centroidOf(list);
+ Assert.assertEquals(2.0, c.getPoint()[0], 1.0e-15);
+ Assert.assertEquals(3.0, c.getPoint()[1], 1.0e-15);
+ }
+
+ @Test
+ public void testSerial() {
+ final EuclideanDoublePoint p = new EuclideanDoublePoint(new double[] { -3.0, -2.0, -1.0, 0.0, 1.0 });
+ Assert.assertEquals(p, TestUtils.serializeAndRecover(p));
+ }
+
+}
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