Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/4622#discussion_r29884174
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/AffinityPropagation.scala
---
@@ -0,0 +1,496 @@
+/*
+ * 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 scala.collection.mutable
+
+import org.apache.spark.{Logging, SparkException}
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.graphx._
+import org.apache.spark.graphx.impl.GraphImpl
+import org.apache.spark.rdd.RDD
+
+/**
+ * :: Experimental ::
+ *
+ * Model produced by [[AffinityPropagation]].
+ *
+ * @param id cluster id.
+ * @param exemplar cluster exemplar.
+ * @param member cluster member.
+ */
+@Experimental
+case class AffinityPropagationAssignment(val id: Long, val exemplar: Long,
val member: Long)
+
+/**
+ * :: Experimental ::
+ *
+ * Model produced by [[AffinityPropagation]].
+ *
+ * @param id cluster id.
+ * @param exemplar cluster exemplar.
+ * @param members cluster member.
+ */
+@Experimental
+case class AffinityPropagationCluster(val id: Long, val exemplar: Long,
val members: Array[Long])
+
+/**
+ * :: Experimental ::
+ *
+ * Model produced by [[AffinityPropagation]].
+ *
+ * @param assignments the cluster assignments of AffinityPropagation
clustering results.
+ */
+@Experimental
+class AffinityPropagationModel(
+ val assignments: RDD[AffinityPropagationAssignment]) extends
Serializable {
+
+ /**
+ * Get the number of clusters
+ */
+ lazy val k: Long = assignments.map(_.id).distinct.count()
+
+ /**
+ * Find the cluster the given vertex belongs
+ * @param vertexID vertex id.
+ * @return a [[RDD]] that contains vertex ids in the same cluster of
given vertexID. If
+ * the given vertex doesn't belong to any cluster, return null.
+ */
+ def findCluster(vertexID: Long): RDD[Long] = {
+ val assign = assignments.filter(_.member == vertexID).collect()
+ if (assign.nonEmpty) {
+ assignments.filter(_.id == assign(0).id).map(_.member)
+ } else {
+ assignments.sparkContext.emptyRDD[Long]
+ }
+ }
+
+ /**
+ * Find the cluster id the given vertex belongs to
+ * @param vertexID vertex id.
+ * @return the cluster id that the given vertex belongs to. If the given
vertex doesn't belong to
+ * any cluster, return -1.
+ */
+ def findClusterID(vertexID: Long): Long = {
+ val assign = assignments.filter(_.member == vertexID).collect()
+ if (assign.nonEmpty) {
+ assign(0).id
+ } else {
+ -1
+ }
+ }
+
+ /**
+ * Turn cluster assignments to cluster representations
[[AffinityPropagationCluster]].
+ * @return a [[RDD]] that contains all clusters generated by Affinity
Propagation. Because the
+ * cluster members in [[AffinityPropagationCluster]] is an [[Array]], it
could consume too much
+ * memory even run out of memory when you call collect() on the returned
[[RDD]].
+ */
+ def fromAssignToClusters(): RDD[AffinityPropagationCluster] = {
+ assignments.map { assign => ((assign.id, assign.exemplar),
assign.member) }
+ .aggregateByKey(mutable.Set[Long]())(
+ seqOp = (s, d) => s ++ mutable.Set(d),
+ combOp = (s1, s2) => s1 ++ s2
+ ).map(kv => new AffinityPropagationCluster(kv._1._1, kv._1._2,
kv._2.toArray))
+ }
+}
+
+/**
+ * The message exchanged on the node graph
+ */
+private case class EdgeMessage(
+ similarity: Double,
+ availability: Double,
+ responsibility: Double) extends Equals {
+ override def canEqual(that: Any): Boolean = {
+ that match {
+ case e: EdgeMessage =>
+ similarity == e.similarity && availability == e.availability &&
+ responsibility == e.responsibility
+ case _ =>
+ false
+ }
+ }
+}
+
+/**
+ * The data stored in each vertex on the graph
+ */
+private case class VertexData(availability: Double, responsibility: Double)
+
+/**
+ * :: Experimental ::
+ *
+ * Affinity propagation (AP), a graph clustering algorithm based on the
concept of "message passing"
+ * between data points. Unlike clustering algorithms such as k-means or
k-medoids, AP does not
+ * require the number of clusters to be determined or estimated before
running it. AP is developed
+ * by [[http://doi.org/10.1126/science.1136800 Frey and Dueck]].
+ *
+ * @param maxIterations Maximum number of iterations of the AP algorithm.
+ * @param lambda lambda parameter used in the messaging iteration loop
+ * @param normalization Indication of performing normalization
+ * @param symmetric Indication of using symmetric similarity input
+ *
+ * @see [[http://en.wikipedia.org/wiki/Affinity_propagation Affinity
propagation (Wikipedia)]]
+ */
+@Experimental
+class AffinityPropagation private[clustering] (
+ private var maxIterations: Int,
+ private var lambda: Double,
+ private var normalization: Boolean,
+ private var symmetric: Boolean) extends Serializable {
+
+ import org.apache.spark.mllib.clustering.AffinityPropagation._
+
+ /** Constructs a AP instance with default parameters: {maxIterations:
100, lambda: `0.5`,
+ * normalization: false, symmetric: true}.
+ */
+ def this() = this(maxIterations = 100, lambda = 0.5, normalization =
false, symmetric = true)
+
+ /**
+ * Set maximum number of iterations of the messaging iteration loop
+ */
+ def setMaxIterations(maxIterations: Int): this.type = {
+ this.maxIterations = maxIterations
+ this
+ }
+
+ /**
+ * Get maximum number of iterations of the messaging iteration loop
+ */
+ def getMaxIterations: Int = {
+ this.maxIterations
+ }
+
+ /**
+ * Set lambda of the messaging iteration loop
+ */
+ def setLambda(lambda: Double): this.type = {
+ this.lambda = lambda
+ this
+ }
+
+ /**
+ * Get lambda of the messaging iteration loop
+ */
+ def getLambda(): Double = {
+ this.lambda
+ }
+
+ /**
+ * Set whether to do normalization or not
+ */
+ def setNormalization(normalization: Boolean): this.type = {
+ this.normalization = normalization
+ this
+ }
+
+ /**
+ * Get whether to do normalization or not
+ */
+ def getNormalization(): Boolean = {
+ this.normalization
+ }
+
+ /**
+ * Set whether the input similarities are symmetric or not.
+ * When symmetric is set to true, we assume that input similarities only
contain triangular
+ * matrix. That means, only s,,ij,, is included in the similarities. If
both s,,ij,, and
+ * s,,ji,, are given in the similarities, it very possibly causes error.
+ */
+ def setSymmetric(symmetric: Boolean): this.type = {
+ this.symmetric = symmetric
+ this
+ }
+
+ /**
+ * Get whether the input similarities are symmetric or not
+ */
+ def getSymmetric(): Boolean = {
+ this.symmetric
+ }
+
+ /**
+ * Calculate the median value of similarities
+ */
+ private def getMedian(similarities: RDD[(Long, Long, Double)]): Double =
{
+ val sorted: RDD[(Long, Double)] =
similarities.sortBy(_._3).zipWithIndex().map {
+ case (v, idx) => (idx, v._3)
+ }.persist()
+
+ val count = sorted.count()
+
+ val median: Double =
+ if (count % 2 == 0) {
+ val l = count / 2 - 1
+ val r = l + 1
+ (sorted.lookup(l).head + sorted.lookup(r).head).toDouble / 2
+ } else {
+ sorted.lookup(count / 2).head
--- End diff --
We will get the largest integer less than or equal to the fraction, i.e.,
the result of `Math.floor()`.
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