Author: erans
Date: Tue Mar 15 12:15:02 2011
New Revision: 1081744
URL: http://svn.apache.org/viewvc?rev=1081744&view=rev
Log:
MATH-546
Wrong variable type ("int" instead of "double").
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/clustering/KMeansPlusPlusClusterer.java
commons/proper/math/trunk/src/site/xdoc/changes.xml
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/clustering/KMeansPlusPlusClustererTest.java
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/clustering/KMeansPlusPlusClusterer.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/clustering/KMeansPlusPlusClusterer.java?rev=1081744&r1=1081743&r2=1081744&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/clustering/KMeansPlusPlusClusterer.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/clustering/KMeansPlusPlusClusterer.java
Tue Mar 15 12:15:02 2011
@@ -172,7 +172,7 @@ public class KMeansPlusPlusClusterer<T e
while (resultSet.size() < k) {
// For each data point x, compute D(x), the distance between x and
// the nearest center that has already been chosen.
- int sum = 0;
+ double sum = 0;
for (int i = 0; i < pointSet.size(); i++) {
final T p = pointSet.get(i);
final Cluster<T> nearest = getNearestCluster(resultSet, p);
Modified: commons/proper/math/trunk/src/site/xdoc/changes.xml
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/site/xdoc/changes.xml?rev=1081744&r1=1081743&r2=1081744&view=diff
==============================================================================
--- commons/proper/math/trunk/src/site/xdoc/changes.xml (original)
+++ commons/proper/math/trunk/src/site/xdoc/changes.xml Tue Mar 15 12:15:02 2011
@@ -52,6 +52,9 @@ The <action> type attribute can be add,u
If the output is not quite correct, check for invisible trailing spaces!
-->
<release version="3.0" date="TBD" description="TBD">
+ <action dev="erans" type="fix" issue="MATH-546" due-to="Nate Paymer">
+ Fixed bug in "KMeansPlusPlusClusterer".
+ </action>
<action dev="erans" type="update" issue="MATH-542">
All exceptions defined in Commons Math provide a context and a compound
message list.
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/clustering/KMeansPlusPlusClustererTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/clustering/KMeansPlusPlusClustererTest.java?rev=1081744&r1=1081743&r2=1081744&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/clustering/KMeansPlusPlusClustererTest.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/clustering/KMeansPlusPlusClustererTest.java
Tue Mar 15 12:15:02 2011
@@ -20,7 +20,9 @@ package org.apache.commons.math.stat.clu
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
+import java.util.ArrayList;
import java.util.Arrays;
+import java.util.Collection;
import java.util.List;
import java.util.Random;
@@ -166,4 +168,84 @@ public class KMeansPlusPlusClustererTest
}
+ /**
+ * 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<CloseIntegerPoint>();
+ 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<CloseIntegerPoint>> 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;
+ }
+ }
+ assertTrue(uniquePointIsCenter);
+ }
}
