Github user jkbradley commented on a diff in the pull request:

    https://github.com/apache/spark/pull/3022#discussion_r21655813
  
    --- Diff: 
mllib/src/main/scala/org/apache/spark/mllib/clustering/GMMExpectationMaximization.scala
 ---
    @@ -0,0 +1,283 @@
    +/*
    + * Licensed to the Apache Software Foundation (ASF) under one or more
    + * contributor license agreements.  See the NOTICE file distributed with
    + * this work for additional information regarding copyright ownership.
    + * The ASF licenses this file to You under the Apache License, Version 2.0
    + * (the "License"); you may not use this file except in compliance with
    + * the License.  You may obtain a copy of the License at
    + *
    + *    http://www.apache.org/licenses/LICENSE-2.0
    + *
    + * Unless required by applicable law or agreed to in writing, software
    + * distributed under the License is distributed on an "AS IS" BASIS,
    + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    + * See the License for the specific language governing permissions and
    + * limitations under the License.
    + */
    +
    +package org.apache.spark.mllib.clustering
    +
    +import breeze.linalg.{DenseVector => BreezeVector, DenseMatrix => 
BreezeMatrix}
    +import breeze.linalg.{Transpose, det, inv}
    +import org.apache.spark.rdd.RDD
    +import org.apache.spark.mllib.linalg.{Matrices, Vector, Vectors}
    +import org.apache.spark.{Accumulator, AccumulatorParam, SparkContext}
    +import org.apache.spark.SparkContext.DoubleAccumulatorParam
    +
    +/**
    + * Expectation-Maximization for multivariate Gaussian Mixture Models.
    + * 
    + */
    +object GMMExpectationMaximization {
    +  /**
    +   * Trains a GMM using the given parameters
    +   * 
    +   * @param data training points stored as RDD[Vector]
    +   * @param k the number of Gaussians in the mixture
    +   * @param maxIterations the maximum number of iterations to perform
    +   * @param delta change in log-likelihood at which convergence is 
considered achieved
    +   */
    +  def train(data: RDD[Vector], k: Int, maxIterations: Int, delta: Double): 
GaussianMixtureModel = {
    +    new GMMExpectationMaximization().setK(k)
    +      .setMaxIterations(maxIterations)
    +      .setDelta(delta)
    +      .run(data)
    +  }
    +  
    +  /**
    +   * Trains a GMM using the given parameters
    +   * 
    +   * @param data training points stored as RDD[Vector]
    +   * @param k the number of Gaussians in the mixture
    +   * @param maxIterations the maximum number of iterations to perform
    +   */
    +  def train(data: RDD[Vector], k: Int, maxIterations: Int): 
GaussianMixtureModel = {
    +    new 
GMMExpectationMaximization().setK(k).setMaxIterations(maxIterations).run(data)
    +  }
    +  
    +  /**
    +   * Trains a GMM using the given parameters
    +   * 
    +   * @param data training points stored as RDD[Vector]
    +   * @param k the number of Gaussians in the mixture
    +   * @param delta change in log-likelihood at which convergence is 
considered achieved
    +   */
    +  def train(data: RDD[Vector], k: Int, delta: Double): 
GaussianMixtureModel = {
    +    new GMMExpectationMaximization().setK(k).setDelta(delta).run(data)
    +  }
    +  
    +  /**
    +   * Trains a GMM using the given parameters
    +   * 
    +   * @param data training points stored as RDD[Vector]
    +   * @param k the number of Gaussians in the mixture
    +   */
    +  def train(data: RDD[Vector], k: Int): GaussianMixtureModel = {
    +    new GMMExpectationMaximization().setK(k).run(data)
    +  }
    +}
    +
    +/**
    + * This class performs multivariate Gaussian expectation maximization.  It 
will 
    --- End diff --
    
    It would be great if you could add a sentence or two explaining what GMMs 
are.


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