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new baf8d0a MATH-1525: Make "EmptyClusterStrategy" and related logic in
"KMeansPlusPlusClusterer" reusable.
new 22373ae Merge branch 'MATH-1525__ChenTao'
baf8d0a is described below
commit baf8d0a40468fc844db7aab4b20fd5b3f3e225a4
Author: CT <chen...@qq.com>
AuthorDate: Sat Mar 21 21:11:17 2020 +0800
MATH-1525: Make "EmptyClusterStrategy" and related logic in
"KMeansPlusPlusClusterer" reusable.
---
.../ml/clustering/KMeansPlusPlusClusterer.java | 67 ++++++++++++++--------
1 file changed, 42 insertions(+), 25 deletions(-)
diff --git
a/src/main/java/org/apache/commons/math4/ml/clustering/KMeansPlusPlusClusterer.java
b/src/main/java/org/apache/commons/math4/ml/clustering/KMeansPlusPlusClusterer.java
index bf656ae..41d316f 100644
---
a/src/main/java/org/apache/commons/math4/ml/clustering/KMeansPlusPlusClusterer.java
+++
b/src/main/java/org/apache/commons/math4/ml/clustering/KMeansPlusPlusClusterer.java
@@ -189,6 +189,14 @@ public class KMeansPlusPlusClusterer<T extends
Clusterable> extends Clusterer<T>
}
/**
+ * Return the CentroidInitializer used by this instance.
+ * @return the CentroidInitializer
+ */
+ CentroidInitializer getCentroidInitializer() {
+ return centroidInitializer;
+ }
+
+ /**
* Runs the K-means++ clustering algorithm.
*
* @param points the points to cluster
@@ -219,36 +227,14 @@ public class KMeansPlusPlusClusterer<T extends
Clusterable> extends Clusterer<T>
// iterate through updating the centers until we're done
final int max = (maxIterations < 0) ? Integer.MAX_VALUE :
maxIterations;
for (int count = 0; count < max; count++) {
- boolean emptyCluster = false;
- List<CentroidCluster<T>> newClusters = new ArrayList<>();
- for (final CentroidCluster<T> cluster : clusters) {
- final Clusterable newCenter;
- if (cluster.getPoints().isEmpty()) {
- switch (emptyStrategy) {
- case LARGEST_VARIANCE :
- newCenter =
getPointFromLargestVarianceCluster(clusters);
- break;
- case LARGEST_POINTS_NUMBER :
- newCenter =
getPointFromLargestNumberCluster(clusters);
- break;
- case FARTHEST_POINT :
- newCenter = getFarthestPoint(clusters);
- break;
- default :
- throw new
ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
- }
- emptyCluster = true;
- } else {
- newCenter = cluster.centroid();
- }
- newClusters.add(new CentroidCluster<T>(newCenter));
- }
+ boolean hasEmptyCluster =
clusters.stream().anyMatch(cluster->cluster.getPoints().isEmpty());
+ List<CentroidCluster<T>> newClusters =
adjustClustersCenters(clusters);
int changes = assignPointsToClusters(newClusters, points,
assignments);
clusters = newClusters;
// if there were no more changes in the point-to-cluster assignment
// and there are no empty clusters left, return the current
clusters
- if (changes == 0 && !emptyCluster) {
+ if (changes == 0 && !hasEmptyCluster) {
return clusters;
}
}
@@ -256,6 +242,37 @@ public class KMeansPlusPlusClusterer<T extends
Clusterable> extends Clusterer<T>
}
/**
+ * Adjust the clusters's centers with means of points
+ * @param clusters the origin clusters
+ * @return adjusted clusters with center points
+ */
+ List<CentroidCluster<T>> adjustClustersCenters(List<CentroidCluster<T>>
clusters) {
+ List<CentroidCluster<T>> newClusters = new ArrayList<>();
+ for (final CentroidCluster<T> cluster : clusters) {
+ final Clusterable newCenter;
+ if (cluster.getPoints().isEmpty()) {
+ switch (emptyStrategy) {
+ case LARGEST_VARIANCE :
+ newCenter =
getPointFromLargestVarianceCluster(clusters);
+ break;
+ case LARGEST_POINTS_NUMBER :
+ newCenter = getPointFromLargestNumberCluster(clusters);
+ break;
+ case FARTHEST_POINT :
+ newCenter = getFarthestPoint(clusters);
+ break;
+ default :
+ throw new
ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
+ }
+ } else {
+ newCenter = cluster.centroid();
+ }
+ newClusters.add(new CentroidCluster<>(newCenter));
+ }
+ return newClusters;
+ }
+
+ /**
* Adds the given points to the closest {@link Cluster}.
*
* @param clusters the {@link Cluster}s to add the points to
