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https://issues.apache.org/jira/browse/FLINK-1731?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14605571#comment-14605571
]
ASF GitHub Bot commented on FLINK-1731:
---------------------------------------
Github user thvasilo commented on a diff in the pull request:
https://github.com/apache/flink/pull/700#discussion_r33460336
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/clustering/KMeans.scala
---
@@ -0,0 +1,247 @@
+/*
+ * 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.flink.ml.clustering
+
+import org.apache.flink.api.common.functions.RichMapFunction
+import
org.apache.flink.api.java.functions.FunctionAnnotation.ForwardedFields
+import org.apache.flink.api.scala.{DataSet, _}
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.ml.common.{LabeledVector, _}
+import org.apache.flink.ml.math.Breeze._
+import org.apache.flink.ml.math.Vector
+import org.apache.flink.ml.metrics.distances.EuclideanDistanceMetric
+import org.apache.flink.ml.pipeline._
+
+import scala.collection.JavaConverters._
+
+
+/**
+ * Implements the KMeans algorithm which calculates cluster centroids
based on set of training data
+ * points and a set of k initial centroids.
+ *
+ * [[KMeans]] is a [[Predictor]] which needs to be trained on a set of
data points and can then be
+ * used to assign new points to the learned cluster centroids.
+ *
+ * The KMeans algorithm works as described on Wikipedia
+ * (http://en.wikipedia.org/wiki/K-means_clustering):
+ *
+ * Given an initial set of k means m1(1),…,mk(1) (see below), the
algorithm proceeds by alternating
+ * between two steps:
+ *
+ * ===Assignment step:===
+ *
+ * Assign each observation to the cluster whose mean yields the least
within-cluster sum of
+ * squares (WCSS). Since the sum of squares is the squared Euclidean
distance, this is intuitively
+ * the "nearest" mean. (Mathematically, this means partitioning the
observations according to the
+ * Voronoi diagram generated by the means).
+ *
+ * `S_i^(t) = { x_p : || x_p - m_i^(t) ||^2 ≤ || x_p - m_j^(t) ||^2
\forall j, 1 ≤ j ≤ k}`,
+ * where each `x_p` is assigned to exactly one `S^{(t)}`, even if it
could be assigned to two or
+ * more of them.
+ *
+ * ===Update step:===
+ *
+ * Calculate the new means to be the centroids of the observations in the
new clusters.
+ *
+ * `m^{(t+1)}_i = ( 1 / |S^{(t)}_i| ) \sum_{x_j \in S^{(t)}_i} x_j`
+ *
+ * Since the arithmetic mean is a least-squares estimator, this also
minimizes the within-cluster
+ * sum of squares (WCSS) objective.
+ *
+ * @example
+ * {{{
+ * val trainingDS: DataSet[Vector] =
env.fromCollection(Clustering.trainingData)
+ * val initialCentroids: DataSet[LabledVector] =
env.fromCollection(Clustering.initCentroids)
+ *
+ * val kmeans = KMeans()
+ * .setInitialCentroids(initialCentroids)
+ * .setNumIterations(10)
+ *
+ * kmeans.fit(trainingDS)
+ *
+ * // getting the computed centroids
+ * val centroidsResult = kmeans.centroids.get.collect()
+ *
+ * // get matching clusters for new points
+ * val testDS: DataSet[Vector] =
env.fromCollection(Clustering.testData)
+ * val clusters: DataSet[LabeledVector] = kmeans.predict(testDS)
+ * }}}
+ *
+ * =Parameters=
+ *
+ * - [[org.apache.flink.ml.clustering.KMeans.NumIterations]]:
+ * Defines the number of iterations to recalculate the centroids of the
clusters. As it
+ * is a heuristic algorithm, there is no guarantee that it will converge
to the global optimum. The
+ * centroids of the clusters and the reassignment of the data points will
be repeated till the
+ * given number of iterations is reached.
+ * (Default value: '''10''')
+ *
+ * - [[org.apache.flink.ml.clustering.KMeans.InitialCentroids]]:
+ * Defines the initial k centroids of the k clusters. They are used as
start off point of the
+ * algorithm for clustering the data set. The centroids are recalculated
as often as set in
+ * [[org.apache.flink.ml.clustering.KMeans.NumIterations]]. The choice of
the initial centroids
+ * mainly affects the outcome of the algorithm.
+ *
+ */
+class KMeans extends Predictor[KMeans] {
+
+ import KMeans._
+
+ /** Stores the learned clusters after the fit operation */
+ var centroids: Option[DataSet[LabeledVector]] = None
+
+ /**
+ * Sets the number of iterations.
+ *
+ * @param numIterations
+ * @return itself
+ */
+ def setNumIterations(numIterations: Int): KMeans = {
+ parameters.add(NumIterations, numIterations)
+ this
+ }
+
+ /**
+ * Sets the initial centroids on which the algorithm will start
computing.
+ * These points should depend on the data and significantly influence
the resulting centroids.
+ *
+ * @param initialCentroids A sequence of labeled vectors.
+ * @return itself
+ */
+ def setInitialCentroids(initialCentroids: DataSet[LabeledVector]):
KMeans = {
+ parameters.add(InitialCentroids, initialCentroids)
+ this
+ }
+
+}
+
+/**
+ * Companion object of KMeans. Contains convenience functions, the
parameter type definitions
+ * of the algorithm and the [[FitOperation]] & [[PredictOperation]].
+ */
+object KMeans {
+ val CENTROIDS = "centroids"
+
+ case object NumIterations extends Parameter[Int] {
+ val defaultValue = Some(10)
+ }
+
+ case object InitialCentroids extends Parameter[DataSet[LabeledVector]] {
+ val defaultValue = None
+ }
+
+ // ========================================== Factory methods
====================================
+
+ def apply(): KMeans = {
+ new KMeans()
+ }
+
+ // ========================================== Operations
=========================================
+
+ /**
+ * [[PredictOperation]] for vector types. The result type is a
[[LabeledVector]].
+ */
+ implicit def predictValues = {
+ new PredictOperation[KMeans, Vector, LabeledVector] {
+ override def predict(
+ instance: KMeans,
+ predictParameters: ParameterMap,
+ input: DataSet[Vector])
+ : DataSet[LabeledVector] = {
+
+ instance.centroids match {
+ case Some(centroids) => {
+ input.map(new
SelectNearestCenterMapper).withBroadcastSet(centroids, CENTROIDS)
--- End diff --
This mapping operation can be replaced by using the new mapWtihBcVariable
function. You can check out the function SGDStep in
flink.ml.optimization.GradientDescent on how to use it.
It should make code more concise and readable.
> Add kMeans clustering algorithm to machine learning library
> -----------------------------------------------------------
>
> Key: FLINK-1731
> URL: https://issues.apache.org/jira/browse/FLINK-1731
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Peter Schrott
> Labels: ML
>
> The Flink repository already contains a kMeans implementation but it is not
> yet ported to the machine learning library. I assume that only the used data
> types have to be adapted and then it can be more or less directly moved to
> flink-ml.
> The kMeans++ [1] and the kMeans|| [2] algorithm constitute a better
> implementation because the improve the initial seeding phase to achieve near
> optimal clustering. It might be worthwhile to implement kMeans||.
> Resources:
> [1] http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf
> [2] http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf
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