From c251395aaf5a3eb70c3a3d7ea005b4a31cf4dcfc Mon Sep 17 00:00:00 2001
From: Gilles Sadowski <gilleseran@gmail.com>
Date: Sun, 22 Mar 2020 10:47:02 +0100
Subject: [PATCH] Javadoc (errors and warnings).

---
 .../commons/math4/ml/clustering/MiniBatchKMeansClusterer.java | 4 +++-
 .../math4/ml/clustering/evaluation/SumOfClusterVariances.java | 3 +--
 .../ml/clustering/initialization/CentroidInitializer.java     | 1 +
 3 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/main/java/org/apache/commons/math4/ml/clustering/MiniBatchKMeansClusterer.java b/src/main/java/org/apache/commons/math4/ml/clustering/MiniBatchKMeansClusterer.java
index a6c29472d..06078262b 100644
--- a/src/main/java/org/apache/commons/math4/ml/clustering/MiniBatchKMeansClusterer.java
+++ b/src/main/java/org/apache/commons/math4/ml/clustering/MiniBatchKMeansClusterer.java
@@ -63,7 +63,7 @@ public class MiniBatchKMeansClusterer<T extends Clusterable> extends KMeansPlusP
      *
      * @param k                     the number of clusters to split the data into
      * @param maxIterations         the maximum number of iterations to run the algorithm for all the points,
-     *                              for mini batch actual iterations <= maxIterations * points.size() / batchSize
+     *                              for mini batch actual {@code iterations <= maxIterations * points.size() / batchSize}.
      *                              If negative, no maximum will be used.
      * @param batchSize             the mini batch size for training iterations.
      * @param initIterations        the iterations to find out the best clusters centers with mini batch.
@@ -74,6 +74,8 @@ public class MiniBatchKMeansClusterer<T extends Clusterable> extends KMeansPlusP
      * @param measure               the distance measure to use, EuclideanDistance is recommended.
      * @param random                random generator to use for choosing initial centers
      *                              may appear during algorithm iterations
+     * @param emptyStrategy Strategy to use for handling empty clusters that
+     * may appear during algorithm iterations.
      */
     public MiniBatchKMeansClusterer(final int k, final int maxIterations, final int batchSize, final int initIterations,
                                     final int initBatchSize, final int maxNoImprovementTimes,
diff --git a/src/main/java/org/apache/commons/math4/ml/clustering/evaluation/SumOfClusterVariances.java b/src/main/java/org/apache/commons/math4/ml/clustering/evaluation/SumOfClusterVariances.java
index 00ac93c63..46aed3332 100644
--- a/src/main/java/org/apache/commons/math4/ml/clustering/evaluation/SumOfClusterVariances.java
+++ b/src/main/java/org/apache/commons/math4/ml/clustering/evaluation/SumOfClusterVariances.java
@@ -46,8 +46,7 @@ public class SumOfClusterVariances implements ClusterEvaluator {
         this.measure = measure;
     }
 
-    /** {@inheritDoc}
-     * @param clusters*/
+    /** {@inheritDoc} */
     public double score(List<? extends Cluster<? extends Clusterable>> clusters) {
         double varianceSum = 0.0;
         for (final Cluster<? extends Clusterable> cluster : clusters) {
diff --git a/src/main/java/org/apache/commons/math4/ml/clustering/initialization/CentroidInitializer.java b/src/main/java/org/apache/commons/math4/ml/clustering/initialization/CentroidInitializer.java
index dcddc53bc..0378f5516 100644
--- a/src/main/java/org/apache/commons/math4/ml/clustering/initialization/CentroidInitializer.java
+++ b/src/main/java/org/apache/commons/math4/ml/clustering/initialization/CentroidInitializer.java
@@ -31,6 +31,7 @@ public interface CentroidInitializer {
     /**
      * Choose the initial centers.
      *
+     * @param <T> Type of points to cluster.
      * @param points the points to choose the initial centers from
      * @param k      The number of clusters
      * @return the initial centers