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From derrickburns <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-3218, SPARK-3219, SPARK-3261, SPARK-342...
Date Wed, 17 Sep 2014 00:40:41 GMT
Github user derrickburns commented on a diff in the pull request:

    https://github.com/apache/spark/pull/2419#discussion_r17639919
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansPlusPlus.scala
---
    @@ -0,0 +1,198 @@
    +/*
    + * 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 org.apache.spark.mllib.base.{PointOps, FP, Infinity, One, Zero}
    +import org.apache.spark.util.random.XORShiftRandom
    +import org.apache.spark.{Logging, SparkContext}
    +
    +import scala.collection.mutable.ArrayBuffer
    +import scala.reflect.ClassTag
    +
    +/**
    + *
    + * The KMeans++ initialization algorithm
    + *
    + * @param pointOps distance function
    + * @tparam P point type
    + * @tparam C center type
    + */
    +private[mllib] class KMeansPlusPlus[P <: FP : ClassTag, C <: FP : ClassTag](
    +  pointOps: PointOps[P, C]) extends Serializable with Logging {
    +
    +  /**
    +   * We will maintain for each point the distance to its closest cluster center.
    +   * Since only one center is added on each iteration, recomputing the closest cluster
center
    +   * only requires computing the distance to the new cluster center if
    +   * that distance is less than the closest cluster center.  
    +   */
    +  case class FatPoint(location: P, index: Int, weight: Double, distance: Double)
    +
    +  /**
    +   * K-means++ on the weighted point set `points`. This first does the K-means++
    +   * initialization procedure and then rounds of Lloyd's algorithm.
    +   */
    +
    +  def cluster(
    +               sc: SparkContext,
    +               seed: Int,
    +               points: Array[C],
    +               weights: Array[Double],
    +               k: Int,
    +               maxIterations: Int,
    +               numPartitions: Int): Array[C] = {
    +    val centers: Array[C] = getCenters(sc, seed, points, weights, k, numPartitions, 1)
    +    val pts =  sc.parallelize(points.map(pointOps.centerToPoint))
    +    new MultiKMeans(pointOps, maxIterations).cluster(pts, Array(centers))._2.centers
    +  }
    +
    +  /**
    +   * Select centers in rounds.  On each round, select 'perRound' centers, with probability
of
    +   * selection equal to the product of the given weights and distance to the closest
cluster center
    +   * of the previous round.
    +   *
    +   * @param sc the Spark context
    +   * @param seed a random number seed
    +   * @param points  the candidate centers
    +   * @param weights  the weights on the candidate centers
    +   * @param k  the total number of centers to select
    +   * @param numPartitions the number of data partitions to use
    +   * @param perRound the number of centers to add per round
    +   * @return   an array of at most k cluster centers
    +   */
    +  def getCenters(sc: SparkContext, seed: Int, points: Array[C], weights: Array[Double],
k: Int,
    +                 numPartitions: Int, perRound: Int): Array[C] = {
    +    assert(points.length > 0)
    +    assert(k > 0)
    +    assert(numPartitions > 0)
    +    assert(perRound > 0)
    +
    +    if (points.length < k) log.warn("number of clusters requested {} exceeds number
of points {}",
    +      k, points.length)
    +    val centers = new ArrayBuffer[C](k)
    +    val rand = new XORShiftRandom(seed)
    +    centers += points(pickWeighted(rand, weights))
    +    log.info("starting kMeansPlusPlus initialization on {} points", points.length)
    +
    +    var more = true
    +    var fatPoints = initialFatPoints(points, weights)
    +    fatPoints = updateDistances(fatPoints, centers.view.take(1))
    +
    +    while (centers.length < k && more) {
    +      val chosen = choose(fatPoints, seed ^ (centers.length << 24), rand, perRound)
    +      val newCenters = chosen.map(points(_))
    +      fatPoints = updateDistances(fatPoints, newCenters)
    +      log.info("chose {} points", chosen.length)
    +      for (index <- chosen) {
    +        log.info("  center({}) = points({})", centers.length, index)
    +        centers += points(index)
    +      }
    +      more = chosen.nonEmpty
    +    }
    +    val result = centers.take(k)
    +    log.info("completed kMeansPlusPlus initialization with {} centers of {} requested",
    +      result.length, k)
    +    result.toArray
    +  }
    +
    +  /**
    +   * Choose points
    +   *
    +   * @param fatPoints points to choose from
    +   * @param seed  random number seed
    +   * @param rand  random number generator
    +   * @param count number of points to choose
    +   * @return indices of chosen points
    +   */
    +  def choose(fatPoints: Array[FatPoint], seed: Int, rand: XORShiftRandom, count: Int)
=
    +    (0 until count).flatMap { x => pickCenter(rand, fatPoints.iterator)}.map { _.index}
    +
    +  /**
    +   * Create initial fat points with weights given and infinite distance to closest cluster
center.
    +   * @param points points
    +   * @param weights weights of points
    +   * @return fat points with given weighs and infinite distance to closest cluster center
    +   */
    +  def initialFatPoints(points: Array[C], weights: Array[Double]): Array[FatPoint] =
    +    (0 until points.length).map{ i => FatPoint( pointOps.centerToPoint(points(i)),
i, weights(i),
    +      Infinity)}.toArray
    +
    +  /**
    +   * Update the distance of each point to its closest cluster center, given only the
given cluster
    +   * centers that were modified.
    +   *
    +   * @param points set of candidate initial cluster centers
    +   * @param center new cluster center
    +   * @return  points with their distance to closest to cluster center updated
    +   */
    +
    +  def updateDistances(points: Array[FatPoint], center: Seq[C]): Array[FatPoint] =
    +    points.map { p =>
    +      var i = 0
    +      val to = center.length
    +      var dist = p.distance
    +      val point = p.location
    +      while (i < to) {
    +        dist = pointOps.distance(point, center(i), dist)
    +        i = i + 1
    +      }
    +      p.copy(distance=dist)
    +    }
    +
    +  /**
    +   * Pick a point at random, weighing the choices by the given weight vector.
    +   * Return -1 if all weights are 0.0
    +   *
    +   * @param rand  random number generator
    +   * @param weights  the weights of the points
    +   * @return the index of the point chosen
    +   */
    +  def pickWeighted(rand: XORShiftRandom, weights: Array[Double]): Int = {
    +    val r = rand.nextDouble() * weights.sum
    +    var i = 0
    +    var curWeight = 0.0
    +    while (i < weights.length && curWeight < r) {
    +      assert(weights(i) >= 0.0)
    +      curWeight += weights(i)
    +      i += 1
    +    }
    +    if (i == 0) throw new IllegalArgumentException("all weights are zero")
    --- End diff --
    
    Checks for illegal weight vector and throws exception instead of returning -1


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