SPARK-1215 [MLLIB]: Clustering: Index out of bounds error (2)

Added check to LocalKMeans.scala: kMeansPlusPlus initialization to handle case with fewer distinct data points than clusters k.  Added two related unit tests to KMeansSuite.  (Re-submitting PR after tangling commits in PR 1407 https://github.com/apache/spark/pull/1407 )

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #1468 from jkbradley/kmeans-fix and squashes the following commits:

4e9bd1e [Joseph K. Bradley] Updated PR per comments from mengxr
6c7a2ec [Joseph K. Bradley] Added check to LocalKMeans.scala: kMeansPlusPlus initialization to handle case with fewer distinct data points than clusters k.  Added two related unit tests to KMeansSuite.

mllib/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeans.scala

@@ -59,7 +59,13 @@ private[mllib] object LocalKMeans extends Logging {
         cumulativeScore += weights(j) * KMeans.pointCost(curCenters, points(j))
         j += 1
       }
-      centers(i) = points(j-1).toDense
+      if (j == 0) {
+        logWarning("kMeansPlusPlus initialization ran out of distinct points for centers." +
+          s" Using duplicate point for center k = $i.")
+        centers(i) = points(0).toDense
+      } else {
+        centers(i) = points(j - 1).toDense
+      }
     }
 
     // Run up to maxIterations iterations of Lloyd's algorithm

mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala

@@ -61,6 +61,32 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
     assert(model.clusterCenters.head === center)
   }
 
+  test("no distinct points") {
+    val data = sc.parallelize(
+      Array(
+        Vectors.dense(1.0, 2.0, 3.0),
+        Vectors.dense(1.0, 2.0, 3.0),
+        Vectors.dense(1.0, 2.0, 3.0)),
+      2)
+    val center = Vectors.dense(1.0, 2.0, 3.0)
+
+    // Make sure code runs.
+    var model = KMeans.train(data, k=2, maxIterations=1)
+    assert(model.clusterCenters.size === 2)
+  }
+
+  test("more clusters than points") {
+    val data = sc.parallelize(
+      Array(
+        Vectors.dense(1.0, 2.0, 3.0),
+        Vectors.dense(1.0, 3.0, 4.0)),
+      2)
+
+    // Make sure code runs.
+    var model = KMeans.train(data, k=3, maxIterations=1)
+    assert(model.clusterCenters.size === 3)
+  }
+
   test("single cluster with big dataset") {
     val smallData = Array(
       Vectors.dense(1.0, 2.0, 6.0),