Fixes to docs / examples

Author: freeman-lab

docs/mllib-clustering.md

@@ -220,7 +220,7 @@ val numClusters = 2
 val model = new StreamingKMeans()
   .setK(numClusters)
   .setDecayFactor(1.0)
-  .setRandomWeights(numDimensions)
+  .setRandomCenters(numDimensions, 0.0)
 
 {% endhighlight %}
 

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

@@ -32,9 +32,9 @@ import org.apache.spark.util.random.XORShiftRandom
 /**
  * :: DeveloperApi ::
  * StreamingKMeansModel extends MLlib's KMeansModel for streaming
- * algorithms, so it can keep track of the number of points assigned
- * to each cluster, and also update the model by doing a single iteration
- * of the standard k-means algorithm.
+ * algorithms, so it can keep track of a continuously updated weight
+ * associated with each cluster, and also update the model by
+ * doing a single iteration of the standard k-means algorithm.
  *
  * The update algorithm uses the "mini-batch" KMeans rule,
  * generalized to incorporate forgetfullness (i.e. decay).