package de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization;

/*
 This file is part of ELKI:
 Environment for Developing KDD-Applications Supported by Index-Structures

 Copyright (C) 2014
 Ludwig-Maximilians-Universität München
 Lehr- und Forschungseinheit für Datenbanksysteme
 ELKI Development Team

 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or
 (at your option) any later version.

 This program is distributed in the hope that it will be useful,
 but WITHOUT ANY WARRANTY; without even the implied warranty of
 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 GNU Affero General Public License for more details.

 You should have received a copy of the GNU Affero General Public License
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */
import java.util.ArrayList;
import java.util.List;

import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction;
import de.lmu.ifi.dbs.elki.math.random.RandomFactory;

/**
 * Initialize K-means by randomly choosing k exsiting elements as cluster
 * centers.
 * 
 * @author Erich Schubert
 * 
 * @param <O> Vector type
 */
public class RandomlyChosenInitialMeans<O> extends AbstractKMeansInitialization<NumberVector> implements KMedoidsInitialization<O> {
  /**
   * Constructor.
   * 
   * @param rnd Random generator.
   */
  public RandomlyChosenInitialMeans(RandomFactory rnd) {
    super(rnd);
  }

  @Override
  public <T extends NumberVector, V extends NumberVector> List<V> chooseInitialMeans(Database database, Relation<T> relation, int k, PrimitiveDistanceFunction<? super T> distanceFunction, NumberVector.Factory<V> factory) {
    DBIDs ids = DBIDUtil.randomSample(relation.getDBIDs(), k, rnd);
    List<V> means = new ArrayList<>(k);
    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
      means.add(factory.newNumberVector(relation.get(iter)));
    }
    return means;
  }
  
  @Override
  public DBIDs chooseInitialMedoids(int k, DBIDs ids, DistanceQuery<? super O> distanceFunction) {
    return DBIDUtil.randomSample(ids, k, rnd);
  }

  /**
   * Parameterization class.
   * 
   * @author Erich Schubert
   * 
   * @apiviz.exclude
   */
  public static class Parameterizer<V> extends AbstractKMeansInitialization.Parameterizer {
    @Override
    protected RandomlyChosenInitialMeans<V> makeInstance() {
      return new RandomlyChosenInitialMeans<>(rnd);
    }
  }
}
@@ -1535,16 +1535,79 @@
 actory;%0D
+%0Aimport de.lmu.ifi.dbs.elki.utilities.documentation.Reference;%0D
 %0A%0D%0A/**%0D%0A
@@ -1653,18 +1653,18 @@
 ing k ex
-s
 i
+s
 ting ele
@@ -1706,67 +1706,593 @@
 %0A * 
-@author Erich Schubert%0D%0A * %0D%0A * @param %3CO%3E Vector type%0D%0A */
+This initialization is attributed to:%0D%0A * %3Cp%3E%0D%0A * E. W. Forgy%3Cbr /%3E%0D%0A * Cluster analysis of multivariate data : efficiency versus interpretability of%0D%0A * classifications%3Cbr /%3E%0D%0A * Abstract published in Biometrics 21(3)%0D%0A * %3C/p%3E%0D%0A * but we were unable to verify this so far (apparently, only an abstract is%0D%0A * available in Biometrics).%0D%0A * %0D%0A * @author Erich Schubert%0D%0A * %0D%0A * @param %3CO%3E Vector type%0D%0A */%0D%0A@Reference(authors = %22E. W. Forgy%22, //%0D%0Atitle = %22Cluster analysis of multivariate data: efficiency versus interpretability of classifications%22, //%0D%0Abooktitle = %22Biometrics 21(3)%22)
 %0D%0Apu
@@ -3090,18 +3090,16 @@
 ;%0D%0A  %7D%0D%0A
-  
 %0D%0A  @Ove

Add attribution to Forgy, albeit we could not verify this.