Github user freeman-lab commented on a diff in the pull request:
https://github.com/apache/spark/pull/5267#discussion_r32356016
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/HierarchicalClustering.scala
---
@@ -0,0 +1,631 @@
+/*
+ * 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 => BDV, SparseVector => BSV, Vector =>
BV, norm => breezeNorm}
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.random.XORShiftRandom
+import org.apache.spark.{Logging, SparkException}
+
+import scala.collection.{Map, mutable}
+
+
+object HierarchicalClustering extends Logging {
+
+ private[clustering] val ROOT_INDEX_KEY: Long = 1
+
+ /**
+ * Finds the closes cluster's center
+ *
+ * @param metric a distance metric
+ * @param centers centers of the clusters
+ * @param point a target point
+ * @return an index of the array of clusters
+ */
+ private[mllib]
+ def findClosestCenter(metric: Function2[BV[Double], BV[Double], Double])
+ (centers: Seq[BV[Double]])(point: BV[Double]): Int = {
+ val (closestCenter, closestIndex) =
+ centers.zipWithIndex.map { case (center, idx) => (metric(center,
point), idx)}.minBy(_._1)
+ closestIndex
+ }
+}
+
+/**
+ * This is a divisive hierarchical clustering algorithm based on bi-sect
k-means algorithm.
+ *
+ * The main idea of this algorithm is based on "A comparison of document
clustering techniques",
+ * M. Steinbach, G. Karypis and V. Kumar. Workshop on Text Mining, KDD,
2000.
+ * http://cs.fit.edu/~pkc/classes/ml-internet/papers/steinbach00tr.pdf
+ *
+ * @param numClusters tne number of clusters you want
+ * @param clusterMap the pairs of cluster and its index as Map
+ * @param maxIterations the number of maximal iterations
+ * @param maxRetries the number of maximum retries
+ * @param seed a random seed
+ */
+class HierarchicalClustering private (
+ private var numClusters: Int,
+ private var clusterMap: Map[Long, ClusterTree],
+ private var maxIterations: Int,
+ private var maxRetries: Int,
+ private var seed: Long) extends Logging {
+
+ /**
+ * Constructs with the default configuration
+ */
+ def this() = this(20, mutable.ListMap.empty[Long, ClusterTree], 20, 10,
1)
+
+ /**
+ * Sets the number of clusters you want
+ */
+ def setNumClusters(numClusters: Int): this.type = {
+ this.numClusters = numClusters
+ this
+ }
+
+ def getNumClusters: Int = this.numClusters
+
+ /**
+ * Sets the number of maximal iterations in each clustering step
+ */
+ def setMaxIterations(maxIterations: Int): this.type = {
+ this.maxIterations = maxIterations
+ this
+ }
+
+ def getMaxIterations: Int = this.maxIterations
+
+ /**
+ * Sets the number of maximum retries of each clustering step
+ */
+ def setMaxRetries(maxRetries: Int): this.type = {
+ this.maxRetries = maxRetries
+ this
+ }
+
+ def getMaxRetries: Int = this.maxRetries
+
+ /**
+ * Sets the random seed
+ */
+ def setSeed(seed: Long): this.type = {
+ this.seed = seed
+ this
+ }
+
+ def getSeed: Long = this.seed
+
+ /**
+ * Runs the hierarchical clustering algorithm
+ * @param input RDD of vectors
+ * @return model for the hierarchical clustering
+ */
+ def run(input: RDD[Vector]): HierarchicalClusteringModel = {
+ val sc = input.sparkContext
+ log.info(s"${sc.appName} starts a hierarchical clustering algorithm")
+
+ var data = initData(input).cache()
+ val startTime = System.currentTimeMillis()
+
+ // `clusters` is described as binary tree structure
+ // `clusters(1)` means the root of a binary tree
+ var clusters = summarizeAsClusters(data)
+ var leafClusters = clusters
+ var step = 1
+ var numDividedClusters = 0
+ var noMoreDividable = false
+ var rddArray = Array.empty[RDD[(Long, BV[Double])]]
+ // the number of maximum nodes of a binary tree by given parameter
+ val multiplier = math.ceil(math.log10(this.numClusters) /
math.log10(2.0)) + 1
+ val maxAllNodesInTree = math.pow(2, multiplier).toInt
+
+ while (clusters.size < maxAllNodesInTree && noMoreDividable == false) {
+ log.info(s"${sc.appName} starts step ${step}")
+
+ // enough to be clustered if the number of divided clusters is equal
to 0
+ val divided = getDividedClusters(data, leafClusters)
+ if (divided.size == 0) {
+ noMoreDividable = true
+ }
+ else {
+ // update each index
+ val newData = updateClusterIndex(data, divided).cache()
+ rddArray = rddArray ++ Array(data)
+ data = newData
+
+ // keep recent 2 cached RDDs in order to run more quickly
+ if (rddArray.size > 1) {
+ val head = rddArray.head
+ head.unpersist()
+ rddArray = rddArray.filterNot(_.hashCode() == head.hashCode())
+ }
+
+ // merge the divided clusters with the map as the cluster tree
+ clusters = clusters ++ divided
+ numDividedClusters = data.map(_._1).distinct().count().toInt
+ leafClusters = divided
+ step += 1
+
+ log.info(s"${sc.appName} adding ${divided.size} new clusters at
step:${step}")
+ }
+ }
+ // unpersist kept RDDs
+ rddArray.foreach(_.unpersist())
+
+ // build a cluster tree by Map class which is expressed
--- End diff --
what does `which is expressed` mean here?
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