Minor fixes and tweaks.

Author: tgaloppo

examples/src/main/scala/org/apache/spark/examples/mllib/DenseGmmEM.scala

@@ -37,13 +37,13 @@ object DenseGmmEM {
     }
   }
 
-  def run(inputFile: String, k: Int, convergenceTol: Double) {
+  private def run(inputFile: String, k: Int, convergenceTol: Double) {
     val conf = new SparkConf().setAppName("Spark EM Sample")
     val ctx  = new SparkContext(conf)
 
     val data = ctx.textFile(inputFile).map{ line =>
       Vectors.dense(line.trim.split(' ').map(_.toDouble))
-    }.cache
+    }.cache()
 
     val clusters = new GaussianMixtureModelEM()
       .setK(k)
@@ -55,11 +55,11 @@ object DenseGmmEM {
         (clusters.weight(i), clusters.mu(i), clusters.sigma(i)))
     }
 
-    println("Cluster labels:")
+    println("Cluster labels (first <= 100):")
     val (responsibilityMatrix, clusterLabels) = clusters.predict(data)
-    for (x <- clusterLabels.collect) {
+    clusterLabels.take(100).foreach{ x =>
       print(" " + x)
     }
-    println
+    println()
   }
 }

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

@@ -71,10 +71,12 @@ class GaussianMixtureModelEM private (
   // (U, U) => U for aggregation
   private def addExpectationSums(m1: ExpectationSum, m2: ExpectationSum): ExpectationSum = {
     m1._1(0) += m2._1(0)
-    for (i <- 0 until m1._2.length) {
+    var i = 0
+    while (i < m1._2.length) {
       m1._2(i) += m2._2(i)
       m1._3(i) += m2._3(i)
       m1._4(i) += m2._4(i)
+      i = i + 1
     }
     m1
   }
@@ -90,11 +92,13 @@ class GaussianMixtureModelEM private (
     val pSum = p.sum
     sums._1(0) += math.log(pSum)
     val xxt = x * new Transpose(x)
-    for (i <- 0 until k) {
+    var i = 0
+    while (i < k) {
       p(i) /= pSum
       sums._2(i) += p(i)
       sums._3(i) += x * p(i)
       sums._4(i) += xxt * p(i)
+      i = i + 1
     }
     sums
   }
@@ -123,7 +127,7 @@ class GaussianMixtureModelEM private (
   }
 
   /** Return the user supplied initial GMM, if supplied */
-  def getInitialiGmm: Option[GaussianMixtureModel] = initialGmm
+  def getInitialGmm: Option[GaussianMixtureModel] = initialGmm
 
   /** Set the number of Gaussians in the mixture model.  Default: 2 */
   def setK(k: Int): this.type = {
@@ -182,7 +186,7 @@ class GaussianMixtureModelEM private (
 
       case None => {
         val samples = breezeData.takeSample(true, k * nSamples, scala.util.Random.nextInt)
-        ((0 until k).map(_ => 1.0 / k).toArray, (0 until k).map{ i => 
+        (Array.fill[Double](k)(1.0 / k), (0 until k).map{ i => 
           val slice = samples.view(i * nSamples, (i + 1) * nSamples)
           new MultivariateGaussian(vectorMean(slice), initCovariance(slice)) 
         }.toArray)