In GaussianMixtureModel: Changed name of weight, gaussian to weights, gaussians. Other sources modified accordingly.

Author: tgaloppo

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

@@ -54,7 +54,7 @@ object DenseGmmEM {
 
     for (i <- 0 until clusters.k) {
       println("weight=%f\nmu=%s\nsigma=\n%s\n" format 
-        (clusters.weight(i), clusters.gaussian(i).mu, clusters.gaussian(i).sigma))
+        (clusters.weights(i), clusters.gaussians(i).mu, clusters.gaussians(i).sigma))
     }
 
     println("Cluster labels (first <= 100):")

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

@@ -134,7 +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.gaussian)
+      case Some(gmm) => (gmm.weights, gmm.gaussians)
 
       case None => {
         val samples = breezeData.takeSample(withReplacement = true, k * nSamples, seed)

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

@@ -36,13 +36,13 @@ import org.apache.spark.mllib.util.MLUtils
  *              covariance matrix for Gaussian i
  */
 class GaussianMixtureModel(
-  val weight: Array[Double], 
-  val gaussian: Array[MultivariateGaussian]) extends Serializable {
+  val weights: Array[Double], 
+  val gaussians: Array[MultivariateGaussian]) extends Serializable {
 
-  require(weight.length == gaussian.length, "Length of weight and Gaussian arrays must match")
+  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] = {
@@ -56,10 +56,10 @@ class GaussianMixtureModel(
    */
   def predictSoft(points: RDD[Vector]): RDD[Array[Double]] = {
     val sc = points.sparkContext
-    val dists = sc.broadcast(gaussian)
-    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)
     }
   }
 

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

@@ -40,9 +40,9 @@ class GMMExpectationMaximizationSuite extends FunSuite with MLlibTestSparkContex
     val seeds = Array(314589, 29032897, 50181, 494821, 4660)
     seeds.foreach { seed =>
       val gmm = new GaussianMixtureEM().setK(1).setSeed(seed).run(data)
-      assert(gmm.weight(0) ~== Ew absTol 1E-5)
-      assert(gmm.gaussian(0).mu ~== Emu absTol 1E-5)
-      assert(gmm.gaussian(0).sigma ~== Esigma absTol 1E-5)
+      assert(gmm.weights(0) ~== Ew absTol 1E-5)
+      assert(gmm.gaussians(0).mu ~== Emu absTol 1E-5)
+      assert(gmm.gaussians(0).sigma ~== Esigma absTol 1E-5)
     }
   }
 
@@ -73,11 +73,11 @@ class GMMExpectationMaximizationSuite extends FunSuite with MLlibTestSparkContex
       .setInitialModel(initialGmm)
       .run(data)
 
-    assert(gmm.weight(0) ~== Ew(0) absTol 1E-3)
-    assert(gmm.weight(1) ~== Ew(1) absTol 1E-3)
-    assert(gmm.gaussian(0).mu ~== Emu(0) absTol 1E-3)
-    assert(gmm.gaussian(1).mu ~== Emu(1) absTol 1E-3)
-    assert(gmm.gaussian(0).sigma ~== Esigma(0) absTol 1E-3)
-    assert(gmm.gaussian(1).sigma ~== Esigma(1) absTol 1E-3)
+    assert(gmm.weights(0) ~== Ew(0) absTol 1E-3)
+    assert(gmm.weights(1) ~== Ew(1) absTol 1E-3)
+    assert(gmm.gaussians(0).mu ~== Emu(0) absTol 1E-3)
+    assert(gmm.gaussians(1).mu ~== Emu(1) absTol 1E-3)
+    assert(gmm.gaussians(0).sigma ~== Esigma(0) absTol 1E-3)
+    assert(gmm.gaussians(1).sigma ~== Esigma(1) absTol 1E-3)
   }
 }