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From skonto <...@git.apache.org>
Subject [GitHub] flink pull request #3192: [FLINK-1731][ml] Add KMeans clustering(Lloyd's alg...
Date Mon, 30 Jan 2017 14:48:17 GMT
Github user skonto commented on a diff in the pull request:

    https://github.com/apache/flink/pull/3192#discussion_r98454850
  
    --- Diff: flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/clustering/KMeans.scala
---
    @@ -0,0 +1,263 @@
    +/*
    + * 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.java.functions.FunctionAnnotation.ForwardedFields
    +import org.apache.flink.api.scala.{DataSet, _}
    +import org.apache.flink.ml._
    +import org.apache.flink.ml.common.{LabeledVector, _}
    +import org.apache.flink.ml.math.Breeze._
    +import org.apache.flink.ml.math.{BLAS, Vector}
    +import org.apache.flink.ml.metrics.distances.EuclideanDistanceMetric
    +import org.apache.flink.ml.pipeline._
    +
    +
    +/**
    +  * Implements the KMeans algorithm which calculates cluster centroids based on set of
training data
    +  * points and a set of k initial centroids.
    --- End diff --
    
    It might be useful in the future to have a method to estimate the best k to use in case
k is unknown eg. run parallel tests for the error.


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