Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/2942#discussion_r19454416
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala ---
@@ -0,0 +1,246 @@
+/*
+ * 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.{Vector => BV}
+
+import scala.reflect.ClassTag
+import scala.util.Random._
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.Logging
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.SparkContext._
+import org.apache.spark.streaming.dstream.DStream
+import org.apache.spark.streaming.StreamingContext._
+
+/**
+ * :: DeveloperApi ::
+ *
+ * StreamingKMeansModel extends MLlib's KMeansModel for streaming
+ * algorithms, so it can keep track of the number of points assigned
+ * to each cluster, and also update the model by doing a single iteration
+ * of the standard KMeans algorithm.
+ *
+ * The update algorithm uses the "mini-batch" KMeans rule,
+ * generalized to incorporate forgetfullness (i.e. decay).
+ * The basic update rule (for each cluster) is:
+ *
+ * c_t+1 = [(c_t * n_t) + (x_t * m_t)] / [n_t + m_t]
+ * n_t+t = n_t + m_t
+ *
+ * Where c_t is the previously estimated centroid for that cluster,
+ * n_t is the number of points assigned to it thus far, x_t is the centroid
+ * estimated on the current batch, and m_t is the number of points assigned
+ * to that centroid in the current batch.
+ *
+ * This update rule is modified with a decay factor 'a' that scales
+ * the contribution of the clusters as estimated thus far.
+ * If a=1, all batches are weighted equally. If a=0, new centroids
+ * are determined entirely by recent data. Lower values correspond to
+ * more forgetting.
+ *
+ * Decay can optionally be specified as a decay fraction 'q',
+ * which corresponds to the fraction of batches (or points)
+ * after which the past will be reduced to a contribution of 0.5.
+ * This decay fraction can be specified in units of 'points' or 'batches'.
+ * if 'batches', behavior will be independent of the number of points per
batch;
+ * if 'points', the expected number of points per batch must be specified.
+ *
+ * Use a builder pattern to construct a streaming KMeans analysis
+ * in an application, like:
+ *
+ * val model = new StreamingKMeans()
+ * .setDecayFactor(0.5)
+ * .setK(3)
+ * .setRandomCenters(5)
+ * .trainOn(DStream)
+ *
+ */
+@DeveloperApi
+class StreamingKMeansModel(
+ override val clusterCenters: Array[Vector],
+ val clusterCounts: Array[Long]) extends KMeansModel(clusterCenters)
with Logging {
+
+ // do a sequential KMeans update on a batch of data
+ def update(data: RDD[Vector], a: Double, units: String):
StreamingKMeansModel = {
+
+ val centers = clusterCenters
+ val counts = clusterCounts
+
+ // find nearest cluster to each point
+ val closest = data.map(point => (this.predict(point), (point.toBreeze,
1.toLong)))
+
+ // get sums and counts for updating each cluster
+ type WeightedPoint = (BV[Double], Long)
+ def mergeContribs(p1: WeightedPoint, p2: WeightedPoint): WeightedPoint
= {
+ (p1._1 += p2._1, p1._2 + p2._2)
+ }
+ val pointStats: Array[(Int, (BV[Double], Long))] =
+ closest.reduceByKey{mergeContribs}.collectAsMap().toArray
+
+ // implement update rule
+ for (newP <- pointStats) {
+ // store old count and centroid
+ val oldCount = counts(newP._1)
+ val oldCentroid = centers(newP._1).toBreeze
+ // get new count and centroid
+ val newCount = newP._2._2
+ val newCentroid = newP._2._1 / newCount.toDouble
+ // compute the normalized scale factor that controls forgetting
+ val decayFactor = units match {
+ case "batches" => newCount / (a * oldCount + newCount)
+ case "points" => newCount / (math.pow(a, newCount) * oldCount +
newCount)
+ }
+ // perform the update
+ val updatedCentroid = oldCentroid + (newCentroid - oldCentroid) *
decayFactor
+ // store the new counts and centers
+ counts(newP._1) = oldCount + newCount
+ centers(newP._1) = Vectors.fromBreeze(updatedCentroid)
+
+ // display the updated cluster centers
+ val display = centers(newP._1).size match {
+ case x if x > 100 =>
centers(newP._1).toArray.take(100).mkString("[", ",", "...")
+ case _ => centers(newP._1).toArray.mkString("[", ",", "]")
+ }
+ logInfo("Cluster %d updated: %s ".format (newP._1, display))
+ }
+ new StreamingKMeansModel(centers, counts)
+ }
+
+}
+
+@DeveloperApi
+class StreamingKMeans(
+ var k: Int,
+ var a: Double,
+ var units: String) extends Logging {
+
+ protected var model: StreamingKMeansModel = new
StreamingKMeansModel(null, null)
+
+ def this() = this(2, 1.0, "batches")
+
+ /** Set the number of clusters. */
+ def setK(k: Int): this.type = {
+ this.k = k
+ this
+ }
+
+ /** Set the decay factor directly (for forgetful algorithms). */
+ def setDecayFactor(a: Double): this.type = {
+ this.a = a
+ this
+ }
+
+ /** Set the decay units for forgetful algorithms ("batches" or
"points"). */
+ def setUnits(units: String): this.type = {
+ if (units != "batches" && units != "points") {
+ throw new IllegalArgumentException("Invalid units for decay: " +
units)
+ }
+ this.units = units
+ this
+ }
+
+ /** Set decay fraction in units of batches. */
+ def setDecayFractionBatches(q: Double): this.type = {
+ this.a = math.log(1 - q) / math.log(0.5)
+ this.units = "batches"
+ this
+ }
+
+ /** Set decay fraction in units of points. Must specify expected number
of points per batch. */
+ def setDecayFractionPoints(q: Double, m: Double): this.type = {
+ this.a = math.pow(math.log(1 - q) / math.log(0.5), 1/m)
+ this.units = "points"
+ this
+ }
+
+ /** Specify initial explicitly directly. */
+ def setInitialCenters(initialCenters: Array[Vector]): this.type = {
+ val clusterCounts = Array.fill(this.k)(0).map(_.toLong)
--- End diff --
Actually since the initial value is 0L, you can just do
```scala
new Array[Long](this.k)
```
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