Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3022#discussion_r22092923
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
---
@@ -0,0 +1,94 @@
+/*
+ * 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}
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.Matrix
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.stat.impl.MultivariateGaussian
+
+/**
+ * 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
+ * @param mu Means for each Gaussian in the mixture, where mu(i) is the
mean for Gaussian i
+ * @param sigma Covariance maxtrix for each Gaussian in the mixture, where
sigma(i) is the
+ * covariance matrix for Gaussian i
+ */
+class GaussianMixtureModel(
+ val weight: Array[Double],
+ val mu: Array[Vector],
+ val sigma: Array[Matrix]) extends Serializable {
+
+ /** Number of gaussians in mixture */
+ def k: Int = weight.length
+
+ /** Maps given points to their cluster indices. */
+ def predict(points: RDD[Vector]): (RDD[Array[Double]],RDD[Int]) = {
+ val responsibilityMatrix = predictMembership(points,mu,sigma,weight,k)
+ val clusterLabels = responsibilityMatrix.map(r => r.indexOf(r.max))
+ (responsibilityMatrix, clusterLabels)
+ }
+
+ /**
+ * Given the input vectors, return the membership value of each vector
+ * to all mixture components.
+ */
+ def predictMembership(
+ points: RDD[Vector],
+ mu: Array[Vector],
+ sigma: Array[Matrix],
+ weight: Array[Double], k: Int): RDD[Array[Double]] = {
+ val sc = points.sparkContext
+ val dists = sc.broadcast{
+ (0 until k).map{ i =>
+ new MultivariateGaussian(mu(i).toBreeze.toDenseVector,
sigma(i).toBreeze.toDenseMatrix)
+ }.toArray
+ }
+ val weights = sc.broadcast(weight)
+ points.map{ x =>
+ computeSoftAssignments(x.toBreeze.toDenseVector, dists.value,
weights.value, k)
+ }
+ }
+
+ // We use "eps" as the minimum likelihood density for any given point
+ // in every cluster; this prevents any divide by zero conditions for
+ // outlier points.
+ private val eps = math.pow(2.0, -52)
--- End diff --
EPS is defined in `MLUtils.EPSILON`.
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