Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3022#discussion_r22016183
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModelEM.scala
---
@@ -0,0 +1,284 @@
+/*
+ * 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
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Matrices, Matrix, 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 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,
+ private var convergenceTol: Double,
+ private var maxIterations: Int) extends Serializable {
+
+ // Type aliases for convenience
+ private type DenseDoubleVector = BreezeVector[Double]
+ private type DenseDoubleMatrix = BreezeMatrix[Double]
+
+ private type ExpectationSum = (
+ Array[Double], // log-likelihood in index 0
+ Array[Double], // array of weights
+ Array[DenseDoubleVector], // array of means
+ Array[DenseDoubleMatrix]) // array of cov matrices
+
+ // create a zero'd ExpectationSum instance
+ private def zeroExpectationSum(k: Int, d: Int): ExpectationSum = {
+ (Array(0.0),
+ new Array[Double](k),
+ (0 until k).map(_ => BreezeVector.zeros[Double](d)).toArray,
+ (0 until k).map(_ => BreezeMatrix.zeros[Double](d,d)).toArray)
+ }
+
+ // add two ExpectationSum objects (allowed to use modify m1)
+ // (U, U) => U for aggregation
+ private def addExpectationSums(m1: ExpectationSum, m2: ExpectationSum):
ExpectationSum = {
+ m1._1(0) += m2._1(0)
+ for (i <- 0 until m1._2.length) {
+ m1._2(i) += m2._2(i)
+ m1._3(i) += m2._3(i)
+ m1._4(i) += m2._4(i)
+ }
+ m1
+ }
+
+ // compute cluster contributions for each input point
+ // (U, T) => U for aggregation
+ private def computeExpectation(weights: Array[Double], dists:
Array[MultivariateGaussian])
+ (model: ExpectationSum, x: DenseDoubleVector): ExpectationSum = {
+ val k = model._2.length
+ val p = (0 until k).map(i => eps + weights(i) *
dists(i).pdf(x)).toArray
+ val pSum = p.sum
+ model._1(0) += math.log(pSum)
+ val xxt = x * new Transpose(x)
+ for (i <- 0 until k) {
+ p(i) /= pSum
+ model._2(i) += p(i)
+ model._3(i) += x * p(i)
+ model._4(i) += xxt * p(i)
+ }
+ model
+ }
+
+ // number of samples per cluster to use when initializing Gaussians
+ private val nSamples = 5
+
+ // an initializing GMM can be provided rather than using the
+ // default random starting point
+ private var initialGmm: Option[GaussianMixtureModel] = None
+
+ /** A default instance, 2 Gaussians, 100 iterations, 0.01 log-likelihood
threshold */
+ def this() = this(2, 0.01, 100)
+
+ /** Set the initial GMM starting point, bypassing the random
initialization */
+ def setInitialGmm(gmm: GaussianMixtureModel): this.type = {
+ if (gmm.k == k) {
+ initialGmm = Some(gmm)
+ } else {
+ throw new IllegalArgumentException("initialing GMM has mismatched
cluster count (gmm.k != k)")
+ }
+ this
+ }
+
+ /** Return the user supplied initial GMM, if supplied */
+ def getInitialiGmm: Option[GaussianMixtureModel] = initialGmm
+
+ /** Set the number of Gaussians in the mixture model. Default: 2 */
+ def setK(k: Int): this.type = {
+ this.k = k
+ this
+ }
+
+ /** Return the number of Gaussians in the mixture model */
+ def getK: Int = k
+
+ /** Set the maximum number of iterations to run. Default: 100 */
+ def setMaxIterations(maxIterations: Int): this.type = {
+ this.maxIterations = maxIterations
+ this
+ }
+
+ /** Return the maximum number of iterations to run */
+ def getMaxIterations: Int = maxIterations
+
+ /**
+ * Set the largest change in log-likelihood at which convergence is
+ * considered to have occurred.
+ */
+ def setConvergenceTol(convergenceTol: Double): this.type = {
+ this.convergenceTol = convergenceTol
+ this
+ }
+
+ /** Return the largest change in log-likelihood at which convergence is
+ * considered to have occurred.
+ */
+ def getConvergenceTol: Double = convergenceTol
+
+ /** Machine precision value used to ensure matrix conditioning */
+ private val eps = math.pow(2.0, -52)
+
+ /** Perform expectation maximization */
+ def run(data: RDD[Vector]): GaussianMixtureModel = {
+ val ctx = data.sparkContext
--- End diff --
It's more common to use ```sc``` than ```ctx```
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