SPARK-5018 [MLlib] [WIP] Make MultivariateGaussian public

Moving MutlivariateGaussian from private[mllib] to public.  The class uses Breeze vectors internally, so this involves creating a public interface using MLlib vectors and matrices.

This initial commit provides public construction, accessors for mean/covariance, density and log-density.

Other potential methods include entropy and sample generation.

Author: Travis Galoppo <tjg2107@columbia.edu>

Closes #3923 from tgaloppo/spark-5018 and squashes the following commits:

2b15587 [Travis Galoppo] Style correction
b4121b4 [Travis Galoppo] Merge remote-tracking branch 'upstream/master' into spark-5018
e30a100 [Travis Galoppo] Made mu, sigma private[mllib] members of MultivariateGaussian Moved MultivariateGaussian (and test suite) from stat.impl to stat.distribution (required updates in GaussianMixture{EM,Model}.scala) Marked MultivariateGaussian as @DeveloperApi Fixed style error
9fa3bb7 [Travis Galoppo] Style improvements
91a5fae [Travis Galoppo] Rearranged equation for part of density function
8c35381 [Travis Galoppo] Fixed accessor methods to match member variable names. Modified calculations to avoid log(pow(x,y)) calculations
0943dc4 [Travis Galoppo] SPARK-5018
4dee9e1 [Travis Galoppo] SPARK-5018

1 files changed, 16 insertions, 17 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/impl/MultivariateGaussianSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussianSuite.scala
index d58f2587e5..fac2498e4d 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/stat/impl/MultivariateGaussianSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussianSuite.scala
@@ -15,54 +15,53 @@
  * limitations under the License.
  */
 
-package org.apache.spark.mllib.stat.impl
+package org.apache.spark.mllib.stat.distribution
 
 import org.scalatest.FunSuite
 
-import breeze.linalg.{ DenseVector => BDV, DenseMatrix => BDM }
-
+import org.apache.spark.mllib.linalg.{ Vectors, Matrices }
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
 
 class MultivariateGaussianSuite extends FunSuite with MLlibTestSparkContext {
   test("univariate") {
-    val x1 = new BDV(Array(0.0))
-    val x2 = new BDV(Array(1.5))
+    val x1 = Vectors.dense(0.0)
+    val x2 = Vectors.dense(1.5)
                      
-    val mu = new BDV(Array(0.0))
-    val sigma1 = new BDM(1, 1, Array(1.0))
+    val mu = Vectors.dense(0.0)
+    val sigma1 = Matrices.dense(1, 1, Array(1.0))
     val dist1 = new MultivariateGaussian(mu, sigma1)
     assert(dist1.pdf(x1) ~== 0.39894 absTol 1E-5)
     assert(dist1.pdf(x2) ~== 0.12952 absTol 1E-5)
     
-    val sigma2 = new BDM(1, 1, Array(4.0))
+    val sigma2 = Matrices.dense(1, 1, Array(4.0))
     val dist2 = new MultivariateGaussian(mu, sigma2)
     assert(dist2.pdf(x1) ~== 0.19947 absTol 1E-5)
     assert(dist2.pdf(x2) ~== 0.15057 absTol 1E-5)
   }
   
   test("multivariate") {
-    val x1 = new BDV(Array(0.0, 0.0))
-    val x2 = new BDV(Array(1.0, 1.0))
+    val x1 = Vectors.dense(0.0, 0.0)
+    val x2 = Vectors.dense(1.0, 1.0)
     
-    val mu = new BDV(Array(0.0, 0.0))
-    val sigma1 = new BDM(2, 2, Array(1.0, 0.0, 0.0, 1.0))
+    val mu = Vectors.dense(0.0, 0.0)
+    val sigma1 = Matrices.dense(2, 2, Array(1.0, 0.0, 0.0, 1.0))
     val dist1 = new MultivariateGaussian(mu, sigma1)
     assert(dist1.pdf(x1) ~== 0.15915 absTol 1E-5)
     assert(dist1.pdf(x2) ~== 0.05855 absTol 1E-5)
     
-    val sigma2 = new BDM(2, 2, Array(4.0, -1.0, -1.0, 2.0))
+    val sigma2 = Matrices.dense(2, 2, Array(4.0, -1.0, -1.0, 2.0))
     val dist2 = new MultivariateGaussian(mu, sigma2)
     assert(dist2.pdf(x1) ~== 0.060155 absTol 1E-5)
     assert(dist2.pdf(x2) ~== 0.033971 absTol 1E-5)
   }
   
   test("multivariate degenerate") {
-    val x1 = new BDV(Array(0.0, 0.0))
-    val x2 = new BDV(Array(1.0, 1.0))
+    val x1 = Vectors.dense(0.0, 0.0)
+    val x2 = Vectors.dense(1.0, 1.0)
     
-    val mu = new BDV(Array(0.0, 0.0))
-    val sigma = new BDM(2, 2, Array(1.0, 1.0, 1.0, 1.0))
+    val mu = Vectors.dense(0.0, 0.0)
+    val sigma = Matrices.dense(2, 2, Array(1.0, 1.0, 1.0, 1.0))
     val dist = new MultivariateGaussian(mu, sigma)
     assert(dist.pdf(x1) ~== 0.11254 absTol 1E-5)
     assert(dist.pdf(x2) ~== 0.068259 absTol 1E-5)