Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/4622#discussion_r27893371
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/AffinityPropagation.scala
---
@@ -0,0 +1,347 @@
+/*
+ * 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.Map
+import scala.collection.mutable.Set
+
+import org.apache.spark.{Logging, SparkException}
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.graphx._
+import org.apache.spark.graphx.impl.GraphImpl
+import org.apache.spark.rdd.RDD
+
+/**
+ * :: Experimental ::
+ *
+ * Model produced by [[AffinityPropagation]].
+ *
+ * @param clusters the vertexIDs of each cluster.
+ * @param exemplars the vertexIDs of all exemplars.
+ */
+@Experimental
+class AffinityPropagationModel(
+ val clusters: Seq[Set[Long]],
+ val exemplars: Seq[Long]) extends Serializable {
+
+ /**
+ * Set the number of clusters
+ */
+ def getK(): Int = clusters.size
+
+ /**
+ * Find the cluster the given vertex belongs
+ */
+ def findCluster(vertexID: Long): Set[Long] = {
+ clusters.filter(_.contains(vertexID))(0)
+ }
+
+ /**
+ * Find the cluster id the given vertex belongs
+ */
+ def findClusterID(vertexID: Long): Option[Int] = {
+ var i = 0
+ clusters.foreach(cluster => {
+ if (cluster.contains(vertexID)) {
+ return Some(i)
+ }
+ i += i
+ })
+ None
+ }
+}
+
+/**
+ * :: Experimental ::
+ *
+ * AffinityPropagation (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://www.psi.toronto.edu/affinitypropagation/FreyDueckScience07.pdf Frey
and Dueck]].
+ *
+ * @param maxIterations Maximum number of iterations of the AP algorithm.
+ *
+ * @see [[http://en.wikipedia.org/wiki/Affinity_propagation (Wikipedia)]]
+ */
+@Experimental
+class AffinityPropagation private[clustering] (
+ private var maxIterations: Int,
+ private var lambda: Double,
+ private var normalization: Boolean) extends Serializable {
+
+ import org.apache.spark.mllib.clustering.AffinityPropagation._
+
+ /** Constructs a AP instance with default parameters: {maxIterations:
100, lambda: 0.5,
+ * normalization: false}.
+ */
+ def this() = this(maxIterations = 100, lambda = 0.5, normalization =
false)
+
+ /**
+ * 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
+ }
+
+ /**
+ * Run the AP algorithm.
+ *
+ * @param similarities an RDD of (i, j, s,,ij,,) tuples representing the
similarity matrix, which
+ * is the matrix S in the AP paper. The similarity
s,,ij,, is set to
+ * real-valued similarities. This is not required to
be a symmetric matrix
--- End diff --
Yes. It can be. Will document it.
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