[MATH-897] Add DBSCAN clustering algorithm, thanks to Reid Hochstedler.
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1410882 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Thomas Neidhart
parent
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math3.stat.clustering;
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import java.util.ArrayList;
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import java.util.Collection;
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import java.util.HashMap;
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import java.util.HashSet;
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import java.util.List;
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import java.util.Map;
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import java.util.Set;
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import org.apache.commons.math3.exception.NotPositiveException;
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import org.apache.commons.math3.exception.NullArgumentException;
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import org.apache.commons.math3.util.MathUtils;
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/**
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* DBSCAN (density-based spatial clustering of applications with noise) algorithm.
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* <p>
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* The DBSCAN algorithm forms clusters based on the idea of density connectivity, i.e.
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* a point p is density connected to another point q, if there exists a chain of
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* points p<sub>i</sub>, with i = 1 .. n and p<sub>1</sub> = p and p<sub>n</sub> = q,
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* such that each pair <p<sub>i</sub>, p<sub>i+1</sub>> is directly density-reachable.
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* A point q is directly density-reachable from point p if it is in the ε-neighborhood
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* of this point.
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* <p>
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* Any point that is not density-reachable from a formed cluster is treated as noise, and
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* will thus not be present in the result.
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* <p>
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* The algorithm requires two parameters:
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* <ul>
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* <li>eps: the distance that defines the ε-neighborhood of a point
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* <li>minPoints: the minimum number of density-connected points required to form a cluster
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* </ul>
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* <p>
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* <b>Note:</b> as DBSCAN is not a centroid-based clustering algorithm, the resulting
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* {@link Cluster} objects will have no defined center, i.e. {@link Cluster#getCenter()} will
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* return {@code null}.
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*
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* @param <T> type of the points to cluster
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* @see <a href="http://en.wikipedia.org/wiki/DBSCAN">DBSCAN (wikipedia)</a>
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* @see <a href="http://www.dbs.ifi.lmu.de/Publikationen/Papers/KDD-96.final.frame.pdf">
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* A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise</a>
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* @version $Id$
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* @since 3.1
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*/
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public class DBSCANClusterer<T extends Clusterable<T>> {
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/** Maximum radius of the neighborhood to be considered. */
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private final double eps;
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/** Minimum number of points needed for a cluster. */
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private final int minPts;
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/** Status of a point during the clustering process. */
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private enum PointStatus {
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/** The point has is considered to be noise. */
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NOISE,
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/** The point is already part of a cluster. */
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PART_OF_CLUSTER
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}
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/**
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* Creates a new instance of a DBSCANClusterer.
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*
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* @param eps maximum radius of the neighborhood to be considered
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* @param minPts minimum number of points needed for a cluster
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* @throws NotPositiveException if {@code eps < 0.0} or {@code minPts < 0}
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*/
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public DBSCANClusterer(final double eps, final int minPts)
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throws NotPositiveException {
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if (eps < 0.0d) {
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throw new NotPositiveException(eps);
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}
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if (minPts < 0) {
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throw new NotPositiveException(minPts);
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}
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this.eps = eps;
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this.minPts = minPts;
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}
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/**
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* Returns the maximum radius of the neighborhood to be considered.
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*
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* @return maximum radius of the neighborhood
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*/
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public double getEps() {
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return eps;
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}
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/**
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* Returns the minimum number of points needed for a cluster.
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*
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* @return minimum number of points needed for a cluster
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*/
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public int getMinPts() {
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return minPts;
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}
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/**
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* Performs DBSCAN cluster analysis.
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* <p>
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* <b>Note:</b> as DBSCAN is not a centroid-based clustering algorithm, the resulting
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* {@link Cluster} objects will have no defined center, i.e. {@link Cluster#getCenter()} will
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* return {@code null}.
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*
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* @param points the points to cluster
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* @return the list of clusters
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* @throws NullArgumentException if the data points are null
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*/
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public List<Cluster<T>> cluster(final Collection<T> points) throws NullArgumentException {
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// sanity checks
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MathUtils.checkNotNull(points);
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final List<Cluster<T>> clusters = new ArrayList<Cluster<T>>();
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final Map<Clusterable<T>, PointStatus> visited = new HashMap<Clusterable<T>, PointStatus>();
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for (final T point : points) {
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if (visited.get(point) != null) {
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continue;
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}
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final List<T> neighbors = getNeighbors(point, points);
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if (neighbors.size() >= minPts) {
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// DBSCAN does not care about center points
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final Cluster<T> cluster = new Cluster<T>(null);
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clusters.add(expandCluster(cluster, point, neighbors, points, visited));
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} else {
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visited.put(point, PointStatus.NOISE);
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}
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}
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return clusters;
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}
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/**
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* Expands the cluster to include density-reachable items.
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*
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* @param cluster Cluster to expand
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* @param point Point to add to cluster
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* @param neighbors List of neighbors
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* @param points the data set
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* @param visited the set of already visited points
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* @return the expanded cluster
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*/
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private Cluster<T> expandCluster(final Cluster<T> cluster,
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final T point,
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final List<T> neighbors,
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final Collection<T> points,
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final Map<Clusterable<T>, PointStatus> visited) {
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cluster.addPoint(point);
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visited.put(point, PointStatus.PART_OF_CLUSTER);
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List<T> seeds = new ArrayList<T>(neighbors);
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int index = 0;
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while (index < seeds.size()) {
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final T current = seeds.get(index);
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PointStatus pStatus = visited.get(current);
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// only check non-visited points
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if (pStatus == null) {
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final List<T> currentNeighbors = getNeighbors(current, points);
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if (currentNeighbors.size() >= minPts) {
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seeds = merge(seeds, currentNeighbors);
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}
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}
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if (pStatus != PointStatus.PART_OF_CLUSTER) {
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visited.put(current, PointStatus.PART_OF_CLUSTER);
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cluster.addPoint(current);
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}
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index++;
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}
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return cluster;
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}
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/**
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* Returns a list of density-reachable neighbors of a {@code point}.
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*
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* @param point the point to look for
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* @param points possible neighbors
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* @return the List of neighbors
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*/
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private List<T> getNeighbors(final T point, final Collection<T> points) {
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final List<T> neighbors = new ArrayList<T>();
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for (final T neighbor : points) {
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if (point != neighbor && neighbor.distanceFrom(point) <= eps) {
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neighbors.add(neighbor);
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}
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}
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return neighbors;
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}
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/**
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* Merges two lists together.
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*
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* @param one first list
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* @param two second list
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* @return merged lists
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*/
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private List<T> merge(final List<T> one, final List<T> two) {
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final Set<T> oneSet = new HashSet<T>(one);
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for (T item : two) {
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if (!oneSet.contains(item)) {
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one.add(item);
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}
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}
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return one;
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}
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}
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package org.apache.commons.math3.stat.clustering;
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import java.io.Serializable;
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import java.util.Collection;
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import org.apache.commons.math3.util.MathArrays;
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/**
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* A simple implementation of {@link Clusterable} for points with double coordinates.
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* @version $Id$
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* @since 3.1
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*/
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public class EuclideanDoublePoint implements Clusterable<EuclideanDoublePoint>, Serializable {
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/** Serializable version identifier. */
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private static final long serialVersionUID = 8026472786091227632L;
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/** Point coordinates. */
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private final double[] point;
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/**
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* Build an instance wrapping an integer array.
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* <p>
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* The wrapped array is referenced, it is <em>not</em> copied.
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*
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* @param point the n-dimensional point in integer space
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*/
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public EuclideanDoublePoint(final double[] point) {
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this.point = point;
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}
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/** {@inheritDoc} */
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public EuclideanDoublePoint centroidOf(final Collection<EuclideanDoublePoint> points) {
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final double[] centroid = new double[getPoint().length];
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for (final EuclideanDoublePoint p : points) {
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for (int i = 0; i < centroid.length; i++) {
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centroid[i] += p.getPoint()[i];
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}
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}
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for (int i = 0; i < centroid.length; i++) {
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centroid[i] /= points.size();
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}
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return new EuclideanDoublePoint(centroid);
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}
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/** {@inheritDoc} */
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public double distanceFrom(final EuclideanDoublePoint p) {
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return MathArrays.distance(point, p.getPoint());
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}
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/** {@inheritDoc} */
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@Override
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public boolean equals(final Object other) {
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if (!(other instanceof EuclideanDoublePoint)) {
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return false;
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}
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final double[] otherPoint = ((EuclideanDoublePoint) other).getPoint();
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if (point.length != otherPoint.length) {
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return false;
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}
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for (int i = 0; i < point.length; i++) {
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if (point[i] != otherPoint[i]) {
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return false;
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}
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}
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return true;
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}
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/**
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* Get the n-dimensional point in integer space.
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*
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* @return a reference (not a copy!) to the wrapped array
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*/
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public double[] getPoint() {
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return point;
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}
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/** {@inheritDoc} */
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@Override
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public int hashCode() {
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int hashCode = 0;
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for (final Double i : point) {
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hashCode += i.hashCode() * 13 + 7;
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}
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return hashCode;
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}
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/** {@inheritDoc} */
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@Override
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public String toString() {
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final StringBuilder buff = new StringBuilder("(");
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final double[] coordinates = getPoint();
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for (int i = 0; i < coordinates.length; i++) {
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buff.append(coordinates[i]);
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if (i < coordinates.length - 1) {
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buff.append(',');
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}
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}
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buff.append(')');
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return buff.toString();
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}
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}
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@ -0,0 +1,190 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math3.stat.clustering;
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import java.util.Arrays;
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import java.util.List;
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import org.apache.commons.math3.exception.MathIllegalArgumentException;
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import org.apache.commons.math3.exception.NullArgumentException;
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import org.junit.Assert;
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import org.junit.Test;
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public class DBSCANClustererTest {
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@Test
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public void testCluster() {
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// Test data generated using: http://people.cs.nctu.edu.tw/~rsliang/dbscan/testdatagen.html
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final EuclideanDoublePoint[] points = new EuclideanDoublePoint[] {
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new EuclideanDoublePoint(new double[] { 83.08303244924173, 58.83387754182331 }),
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new EuclideanDoublePoint(new double[] { 45.05445510940626, 23.469642649637535 }),
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new EuclideanDoublePoint(new double[] { 14.96417921432294, 69.0264096390456 }),
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new EuclideanDoublePoint(new double[] { 73.53189604333602, 34.896145021310076 }),
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new EuclideanDoublePoint(new double[] { 73.28498173551634, 33.96860806993209 }),
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new EuclideanDoublePoint(new double[] { 73.45828098873608, 33.92584423092194 }),
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new EuclideanDoublePoint(new double[] { 73.9657889183145, 35.73191006924026 }),
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new EuclideanDoublePoint(new double[] { 74.0074097183533, 36.81735596177168 }),
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new EuclideanDoublePoint(new double[] { 73.41247541410848, 34.27314856695011 }),
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new EuclideanDoublePoint(new double[] { 73.9156256353017, 36.83206791547127 }),
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new EuclideanDoublePoint(new double[] { 74.81499205809087, 37.15682749846019 }),
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new EuclideanDoublePoint(new double[] { 74.03144880081527, 37.57399178552441 }),
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new EuclideanDoublePoint(new double[] { 74.51870941207744, 38.674258946906775 }),
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new EuclideanDoublePoint(new double[] { 74.50754595105536, 35.58903978415765 }),
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new EuclideanDoublePoint(new double[] { 74.51322752749547, 36.030572259100154 }),
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new EuclideanDoublePoint(new double[] { 59.27900996617973, 46.41091720294207 }),
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new EuclideanDoublePoint(new double[] { 59.73744793841615, 46.20015558367595 }),
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new EuclideanDoublePoint(new double[] { 58.81134076672606, 45.71150126331486 }),
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new EuclideanDoublePoint(new double[] { 58.52225539437495, 47.416083617601544 }),
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new EuclideanDoublePoint(new double[] { 58.218626647023484, 47.36228902172297 }),
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new EuclideanDoublePoint(new double[] { 60.27139669447206, 46.606106348801404 }),
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new EuclideanDoublePoint(new double[] { 60.894962462363765, 46.976924697402865 }),
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new EuclideanDoublePoint(new double[] { 62.29048673878424, 47.66970563563518 }),
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new EuclideanDoublePoint(new double[] { 61.03857608977705, 46.212924720020965 }),
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new EuclideanDoublePoint(new double[] { 60.16916214139201, 45.18193661351688 }),
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new EuclideanDoublePoint(new double[] { 59.90036905976012, 47.555364347063005 }),
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new EuclideanDoublePoint(new double[] { 62.33003634144552, 47.83941489877179 }),
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new EuclideanDoublePoint(new double[] { 57.86035536718555, 47.31117930193432 }),
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new EuclideanDoublePoint(new double[] { 58.13715479685925, 48.985960494028404 }),
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new EuclideanDoublePoint(new double[] { 56.131923963548616, 46.8508904252667 }),
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new EuclideanDoublePoint(new double[] { 55.976329887053, 47.46384037658572 }),
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new EuclideanDoublePoint(new double[] { 56.23245975235477, 47.940035191131756 }),
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new EuclideanDoublePoint(new double[] { 58.51687048212625, 46.622885352699086 }),
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new EuclideanDoublePoint(new double[] { 57.85411081905477, 45.95394361577928 }),
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new EuclideanDoublePoint(new double[] { 56.445776311447844, 45.162093662656844 }),
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new EuclideanDoublePoint(new double[] { 57.36691949656233, 47.50097194337286 }),
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new EuclideanDoublePoint(new double[] { 58.243626387557015, 46.114052729681134 }),
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new EuclideanDoublePoint(new double[] { 56.27224595635198, 44.799080066150054 }),
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new EuclideanDoublePoint(new double[] { 57.606924816500396, 46.94291057763621 }),
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new EuclideanDoublePoint(new double[] { 30.18714230041951, 13.877149710431695 }),
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new EuclideanDoublePoint(new double[] { 30.449448810657486, 13.490778346545994 }),
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new EuclideanDoublePoint(new double[] { 30.295018390286714, 13.264889000216499 }),
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new EuclideanDoublePoint(new double[] { 30.160201832884923, 11.89278262341395 }),
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new EuclideanDoublePoint(new double[] { 31.341509791789576, 15.282655921997502 }),
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new EuclideanDoublePoint(new double[] { 31.68601630325429, 14.756873246748 }),
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new EuclideanDoublePoint(new double[] { 29.325963742565364, 12.097849250072613 }),
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new EuclideanDoublePoint(new double[] { 29.54820742388256, 13.613295356975868 }),
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new EuclideanDoublePoint(new double[] { 28.79359608888626, 10.36352064087987 }),
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new EuclideanDoublePoint(new double[] { 31.01284597092308, 12.788479208014905 }),
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new EuclideanDoublePoint(new double[] { 27.58509216737002, 11.47570110601373 }),
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new EuclideanDoublePoint(new double[] { 28.593799561727792, 10.780998203903437 }),
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new EuclideanDoublePoint(new double[] { 31.356105766724795, 15.080316198524088 }),
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new EuclideanDoublePoint(new double[] { 31.25948503636755, 13.674329151166603 }),
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new EuclideanDoublePoint(new double[] { 32.31590076372959, 14.95261758659035 }),
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new EuclideanDoublePoint(new double[] { 30.460413702763617, 15.88402809202671 }),
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new EuclideanDoublePoint(new double[] { 32.56178203062154, 14.586076852632686 }),
|
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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);
|
||||
}
|
||||
|
||||
}
|
||||
|
|
@ -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));
|
||||
}
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue