2 files changed, 11 insertions, 19 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureEM.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureEM.scala
index d8e1346194..899fe5e9e9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureEM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureEM.scala
@@ -134,9 +134,7 @@ class GaussianMixtureEM private (
     // diagonal covariance matrices using component variances
     // derived from the samples    
     val (weights, gaussians) = initialModel match {
-      case Some(gmm) => (gmm.weight, gmm.mu.zip(gmm.sigma).map { case(mu, sigma) => 
-        new MultivariateGaussian(mu, sigma) 
-      })
+      case Some(gmm) => (gmm.weights, gmm.gaussians)
       
       case None => {
         val samples = breezeData.takeSample(withReplacement = true, k * nSamples, seed)
@@ -176,10 +174,7 @@ class GaussianMixtureEM private (
       iter += 1
     } 
     
-    // Need to convert the breeze matrices to MLlib matrices
-    val means = Array.tabulate(k) { i => gaussians(i).mu }
-    val sigmas = Array.tabulate(k) { i => gaussians(i).sigma }
-    new GaussianMixtureModel(weights, means, sigmas)
+    new GaussianMixtureModel(weights, gaussians)
   }
     
   /** Average of dense breeze vectors */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index 416cad080c..1a2178ee7f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import breeze.linalg.{DenseVector => BreezeVector}
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.mllib.linalg.{Matrix, Vector}
+import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
 import org.apache.spark.mllib.util.MLUtils
 
@@ -36,12 +36,13 @@ import org.apache.spark.mllib.util.MLUtils
  *              covariance matrix for Gaussian i
  */
 class GaussianMixtureModel(
-  val weight: Array[Double], 
-  val mu: Array[Vector], 
-  val sigma: Array[Matrix]) extends Serializable {
+  val weights: Array[Double], 
+  val gaussians: Array[MultivariateGaussian]) extends Serializable {
+  
+  require(weights.length == gaussians.length, "Length of weight and Gaussian arrays must match")
   
   /** Number of gaussians in mixture */
-  def k: Int = weight.length
+  def k: Int = weights.length
 
   /** Maps given points to their cluster indices. */
   def predict(points: RDD[Vector]): RDD[Int] = {
@@ -55,14 +56,10 @@ class GaussianMixtureModel(
    */
   def predictSoft(points: RDD[Vector]): RDD[Array[Double]] = {
     val sc = points.sparkContext
-    val dists = sc.broadcast {
-      (0 until k).map { i => 
-        new MultivariateGaussian(mu(i).toBreeze.toDenseVector, sigma(i).toBreeze.toDenseMatrix)
-      }.toArray
-    }
-    val weights = sc.broadcast(weight)
+    val bcDists = sc.broadcast(gaussians)
+    val bcWeights = sc.broadcast(weights)
     points.map { x => 
-      computeSoftAssignments(x.toBreeze.toDenseVector, dists.value, weights.value, k)
+      computeSoftAssignments(x.toBreeze.toDenseVector, bcDists.value, bcWeights.value, k)
     }
   }