path: root/src/de/lmu/ifi/dbs/elki/index/preprocessed/snn/SharedNearestNeighborPreprocessor.java

package de.lmu.ifi.dbs.elki.index.preprocessed.snn;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures

Copyright (C) 2011
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.List;

import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.database.QueryUtil;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
import de.lmu.ifi.dbs.elki.database.ids.SetDBIDs;
import de.lmu.ifi.dbs.elki.database.ids.TreeSetDBIDs;
import de.lmu.ifi.dbs.elki.database.ids.TreeSetModifiableDBIDs;
import de.lmu.ifi.dbs.elki.database.query.DistanceResultPair;
import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
import de.lmu.ifi.dbs.elki.index.preprocessed.AbstractPreprocessorIndex;
import de.lmu.ifi.dbs.elki.index.preprocessed.knn.MaterializeKNNPreprocessor;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.GreaterEqualConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;

/**
 * A preprocessor for annotation of the ids of nearest neighbors to each
 * database object.
 * <p/>
 * The k nearest neighbors are assigned based on an arbitrary distance function.
 * 
 * This functionality is similar but not identical to
 * {@link MaterializeKNNPreprocessor}: While it also computes the k nearest
 * neighbors, it does not keep the actual distances, but organizes the NN set in
 * a TreeSet for fast set operations.
 * 
 * @author Arthur Zimek
 * @author Erich Schubert
 * 
 * @apiviz.has DistanceFunction
 * 
 * @param <O> the type of database objects the preprocessor can be applied to
 * @param <D> the type of distance the used distance function will return
 */
@Title("Shared nearest neighbor Preprocessor")
@Description("Computes the k nearest neighbors of objects of a certain database.")
public class SharedNearestNeighborPreprocessor<O, D extends Distance<D>> extends AbstractPreprocessorIndex<O, TreeSetDBIDs> implements SharedNearestNeighborIndex<O> {
  /**
   * Get a logger for this class.
   */
  private static final Logging logger = Logging.getLogger(SharedNearestNeighborPreprocessor.class);

  /**
   * Holds the number of nearest neighbors to be used.
   */
  protected int numberOfNeighbors;

  /**
   * Hold the distance function to be used.
   */
  protected DistanceFunction<O, D> distanceFunction;

  /**
   * Constructor.
   * 
   * @param relation Database to use
   * @param numberOfNeighbors Number of neighbors
   * @param distanceFunction Distance function
   */
  public SharedNearestNeighborPreprocessor(Relation<O> relation, int numberOfNeighbors, DistanceFunction<O, D> distanceFunction) {
    super(relation);
    this.numberOfNeighbors = numberOfNeighbors;
    this.distanceFunction = distanceFunction;
  }

  /**
   * Preprocessing step.
   */
  protected void preprocess() {
    if(getLogger().isVerbose()) {
      getLogger().verbose("Assigning nearest neighbor lists to database objects");
    }
    storage = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, SetDBIDs.class);
    KNNQuery<O, D> knnquery = QueryUtil.getKNNQuery(relation, distanceFunction, numberOfNeighbors);

    FiniteProgress progress = getLogger().isVerbose() ? new FiniteProgress("assigning nearest neighbor lists", relation.size(), getLogger()) : null;
    for(DBID id : relation.iterDBIDs()) {
      TreeSetModifiableDBIDs neighbors = DBIDUtil.newTreeSet(numberOfNeighbors);
      List<DistanceResultPair<D>> kNN = knnquery.getKNNForDBID(id, numberOfNeighbors);
      for(int i = 0; i < kNN.size(); i++) {
        final DBID nid = kNN.get(i).getDBID();
        // if(!id.equals(nid)) {
        neighbors.add(nid);
        // }
        // Size limitation to exaclty numberOfNeighbors
        if(neighbors.size() >= numberOfNeighbors) {
          break;
        }
      }
      storage.put(id, neighbors);
      if(progress != null) {
        progress.incrementProcessed(getLogger());
      }
    }
    if(progress != null) {
      progress.ensureCompleted(getLogger());
    }
  }

  @Override
  public TreeSetDBIDs getNearestNeighborSet(DBID objid) {
    if(storage == null) {
      preprocess();
    }
    return storage.get(objid);
  }

  @Override
  protected Logging getLogger() {
    return logger;
  }

  @Override
  public String getLongName() {
    return "SNN id index";
  }

  @Override
  public String getShortName() {
    return "SNN-index";
  }

  /**
   * Get the number of neighbors
   * 
   * @return NN size
   */
  @Override
  public int getNumberOfNeighbors() {
    return numberOfNeighbors;
  }

  /**
   * Factory class
   * 
   * @author Erich Schubert
   * 
   * @apiviz.stereotype factory
   * @apiviz.uses SharedNearestNeighborPreprocessor oneway - - «create»
   */
  public static class Factory<O, D extends Distance<D>> implements SharedNearestNeighborIndex.Factory<O, SharedNearestNeighborPreprocessor<O, D>>, Parameterizable {
    /**
     * Parameter to indicate the number of neighbors to be taken into account
     * for the shared-nearest-neighbor similarity.
     * <p/>
     * <p>
     * Default value: 1
     * </p>
     * <p>
     * Key: {@code sharedNearestNeighbors}
     * </p>
     */
    public static final OptionID NUMBER_OF_NEIGHBORS_ID = OptionID.getOrCreateOptionID("sharedNearestNeighbors", "number of nearest neighbors to consider (at least 1)");

    /**
     * Parameter to indicate the distance function to be used to ascertain the
     * nearest neighbors.
     * <p/>
     * <p>
     * Default value: {@link EuclideanDistanceFunction}
     * </p>
     * <p>
     * Key: {@code SNNDistanceFunction}
     * </p>
     */
    public static final OptionID DISTANCE_FUNCTION_ID = OptionID.getOrCreateOptionID("SNNDistanceFunction", "the distance function to asses the nearest neighbors");

    /**
     * Holds the number of nearest neighbors to be used.
     */
    protected int numberOfNeighbors;

    /**
     * Hold the distance function to be used.
     */
    protected DistanceFunction<O, D> distanceFunction;

    /**
     * Constructor.
     * 
     * @param numberOfNeighbors Number of neighbors
     * @param distanceFunction Distance function
     */
    public Factory(int numberOfNeighbors, DistanceFunction<O, D> distanceFunction) {
      super();
      this.numberOfNeighbors = numberOfNeighbors;
      this.distanceFunction = distanceFunction;
    }

    @Override
    public SharedNearestNeighborPreprocessor<O, D> instantiate(Relation<O> relation) {
      return new SharedNearestNeighborPreprocessor<O, D>(relation, numberOfNeighbors, distanceFunction);
    }

    /**
     * Get the number of neighbors
     * 
     * @return NN size
     */
    @Override
    public int getNumberOfNeighbors() {
      return numberOfNeighbors;
    }

    @Override
    public TypeInformation getInputTypeRestriction() {
      return distanceFunction.getInputTypeRestriction();
    }
    
    /**
     * Parameterization class.
     * 
     * @author Erich Schubert
     * 
     * @apiviz.exclude
     */
    public static class Parameterizer<O, D extends Distance<D>> extends AbstractParameterizer {
      /**
       * Holds the number of nearest neighbors to be used.
       */
      protected int numberOfNeighbors;

      /**
       * Hold the distance function to be used.
       */
      protected DistanceFunction<O, D> distanceFunction;

      @Override
      protected void makeOptions(Parameterization config) {
        super.makeOptions(config);
        final IntParameter numberOfNeighborsP = new IntParameter(NUMBER_OF_NEIGHBORS_ID, new GreaterEqualConstraint(1), 1);
        if(config.grab(numberOfNeighborsP)) {
          numberOfNeighbors = numberOfNeighborsP.getValue();
        }

        final ObjectParameter<DistanceFunction<O, D>> distanceFunctionP = new ObjectParameter<DistanceFunction<O, D>>(DISTANCE_FUNCTION_ID, DistanceFunction.class, EuclideanDistanceFunction.class);
        if(config.grab(distanceFunctionP)) {
          distanceFunction = distanceFunctionP.instantiateClass(config);
        }
      }

      @Override
      protected Factory<O, D> makeInstance() {
        return new Factory<O, D>(numberOfNeighbors, distanceFunction);
      }
    }
  }
}