mark GMM experimental

Author: mengxr

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

@@ -19,15 +19,18 @@ package org.apache.spark.mllib.clustering
 
 import scala.collection.mutable.IndexedSeq
 
-import breeze.linalg.{DenseVector => BreezeVector, DenseMatrix => BreezeMatrix, diag, Transpose}
+import breeze.linalg.{DenseMatrix => BreezeMatrix, DenseVector => BreezeVector, Transpose, diag}
 
-import org.apache.spark.mllib.linalg.{Matrices, Vector, Vectors, DenseVector, DenseMatrix, BLAS}
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, DenseVector, Matrices, Vector, Vectors}
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
 
 /**
+ * :: Experimental ::
+ *
  * 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
@@ -44,6 +47,7 @@ import org.apache.spark.util.Utils
  * is considered to have occurred.
  * @param maxIterations The maximum number of iterations to perform
  */
+@Experimental
 class GaussianMixture private (
     private var k: Int, 
     private var convergenceTol: Double, 

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

@@ -19,12 +19,15 @@ package org.apache.spark.mllib.clustering
 
 import breeze.linalg.{DenseVector => BreezeVector}
 
-import org.apache.spark.rdd.RDD
+import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
 import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.rdd.RDD
 
 /**
+ * :: Experimental ::
+ *
  * 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. 
@@ -35,6 +38,7 @@ import org.apache.spark.mllib.util.MLUtils
  * @param sigma Covariance maxtrix for each Gaussian in the mixture, where sigma(i) is the
  *              covariance matrix for Gaussian i
  */
+@Experimental
 class GaussianMixtureModel(
   val weights: Array[Double], 
   val gaussians: Array[MultivariateGaussian]) extends Serializable {