[SPARK-4749] [mllib]: Allow initializing KMeans clusters using a seed

This implements the functionality for SPARK-4749 and provides units tests in Scala and PySpark

Author: nate.crosswhite <nate.crosswhite@stresearch.com>
Author: nxwhite-str <nxwhite-str@users.noreply.github.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #3610 from nxwhite-str/master and squashes the following commits:

a2ebbd3 [nxwhite-str] Merge pull request #1 from mengxr/SPARK-4749-kmeans-seed
7668124 [Xiangrui Meng] minor updates
f8d5928 [nate.crosswhite] Addressing PR issues
277d367 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
9156a57 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
5d087b4 [nate.crosswhite] Adding KMeans train with seed and Scala unit test
616d111 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
35c1884 [nate.crosswhite] Add kmeans initial seed to pyspark API

1 files changed, 16 insertions, 1 deletions

diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 140c22b5fd..f48e3d6dac 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -140,7 +140,7 @@ class ListTests(PySparkTestCase):
     as NumPy arrays.
     """
 
-    def test_clustering(self):
+    def test_kmeans(self):
         from pyspark.mllib.clustering import KMeans
         data = [
             [0, 1.1],
@@ -152,6 +152,21 @@ class ListTests(PySparkTestCase):
         self.assertEquals(clusters.predict(data[0]), clusters.predict(data[1]))
         self.assertEquals(clusters.predict(data[2]), clusters.predict(data[3]))
 
+    def test_kmeans_deterministic(self):
+        from pyspark.mllib.clustering import KMeans
+        X = range(0, 100, 10)
+        Y = range(0, 100, 10)
+        data = [[x, y] for x, y in zip(X, Y)]
+        clusters1 = KMeans.train(self.sc.parallelize(data),
+                                 3, initializationMode="k-means||", seed=42)
+        clusters2 = KMeans.train(self.sc.parallelize(data),
+                                 3, initializationMode="k-means||", seed=42)
+        centers1 = clusters1.centers
+        centers2 = clusters2.centers
+        for c1, c2 in zip(centers1, centers2):
+            # TODO: Allow small numeric difference.
+            self.assertTrue(array_equal(c1, c2))
+
     def test_classification(self):
         from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes
         from pyspark.mllib.tree import DecisionTree