Moved multivariate Gaussian utility class to mllib/stat/impl

Improved comments

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

@@ -21,9 +21,9 @@ import org.apache.spark.mllib.linalg.Matrix
 import org.apache.spark.mllib.linalg.Vector
 
 /**
- * Multivariate Gaussian mixture model consisting of k Gaussians, where points are drawn 
- * from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are the respective 
- * mean and covariance for each Gaussian distribution i=1..k. 
+ * Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points 
+ * are drawn from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are 
+ * the respective mean and covariance for each Gaussian distribution i=1..k. 
  * 
  * @param weight Weights for each Gaussian distribution in the mixture, where mu(i) is
  *               the weight for Gaussian i, and weight.sum == 1

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

@@ -18,18 +18,30 @@
 package org.apache.spark.mllib.clustering
 
 import breeze.linalg.{DenseVector => BreezeVector, DenseMatrix => BreezeMatrix}
-import breeze.linalg.{Transpose, det, inv}
+import breeze.linalg.Transpose
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Matrices, Vector, Vectors}
+import org.apache.spark.mllib.stat.impl.MultivariateGaussian
 import org.apache.spark.{Accumulator, AccumulatorParam, SparkContext}
 import org.apache.spark.SparkContext.DoubleAccumulatorParam
 
 /**
- * This class performs multivariate Gaussian expectation maximization.  It will 
- * maximize the log-likelihood for a mixture of k Gaussians, iterating until
- * the log-likelihood changes by less than delta, or until it has reached
- * the max number of iterations.  
+ * This class performs expectation maximization for multivariate Gaussian
+ * Mixture Models (GMMs).  A GMM represents a composite distribution of
+ * independent Gaussian distributions with associated "mixing" weights
+ * specifying each's contribution to the composite.
+ *
+ * Given a set of sample points, this class will maximize the log-likelihood 
+ * for a mixture of k Gaussians, iterating until the log-likelihood changes by 
+ * less than convergenceTol, or until it has reached the max number of iterations.
+ * While this process is generally guaranteed to converge, it is not guaranteed
+ * to find a global optimum.  
+ * 
+ * @param k The number of independent Gaussians in the mixture model
+ * @param convergenceTol The maximum change in log-likelihood at which convergence
+ * is considered to have occurred.
+ * @param maxIterations The maximum number of iterations to perform
  */
 class GaussianMixtureModelEM private (
     private var k: Int, 
@@ -40,7 +52,7 @@ class GaussianMixtureModelEM private (
   private type DenseDoubleVector = BreezeVector[Double]
   private type DenseDoubleMatrix = BreezeMatrix[Double]
 
-  /** number of samples per cluster to use when initializing Gaussians */
+  // number of samples per cluster to use when initializing Gaussians
   private val nSamples = 5
 
   /** A default instance, 2 Gaussians, 100 iterations, 0.01 log-likelihood threshold */
@@ -219,21 +231,4 @@ class GaussianMixtureModelEM private (
       a += b
     }
   }  
-
-  /** 
-   * Utility class to implement the density function for multivariate Gaussian distribution.
-   * Breeze provides this functionality, but it requires the Apache Commons Math library,
-   * so this class is here so-as to not introduce a new dependency in Spark.
-   */
-  private class MultivariateGaussian(val mu: DenseDoubleVector, val sigma: DenseDoubleMatrix) 
-      extends Serializable {
-    private val sigmaInv2 = inv(sigma) * -0.5
-    private val U = math.pow(2.0 * math.Pi, -mu.length / 2.0) * math.pow(det(sigma), -0.5)
-
-    def pdf(x: DenseDoubleVector): Double = {
-      val delta = x - mu
-      val deltaTranspose = new Transpose(delta)
-      U * math.exp(deltaTranspose * sigmaInv2 * delta)
-    }
-  }
 }