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Viewing changes to src/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/LDOF.java

  • Committer: Package Import Robot
  • Author(s): Erich Schubert
  • Date: 2014-01-22 16:23:20 UTC
  • mfrom: (1.1.8)
  • Revision ID: package-import@ubuntu.com-20140122162320-dtqtgcdiki8t9unc
Tags: 0.6.0-1
* New upstream final.
* 3DPC extension is not included, but may be uploaded as a separate
  package when there is actual need (it is a demo software, not meant
  for use outside of research, so just get the source code!)
* Upgrade to policy 3.9.5.0 (no changes)

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src/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/LDOF.java
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import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
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import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
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import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
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import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.GreaterConstraint;
 
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import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
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import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
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import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
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@Title("LDOF: Local Distance-Based Outlier Factor")
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@Description("Local outlier detection appraoch suitable for scattered data by averaging the kNN distance over all k nearest neighbors")
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@Reference(authors = "K. Zhang, M. Hutter, H. Jin", title = "A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data", booktitle = "Proc. 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2009), Bangkok, Thailand, 2009", url = "http://dx.doi.org/10.1007/978-3-642-01307-2_84")
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@Alias({"de.lmu.ifi.dbs.elki.algorithm.outlier.LDOF"})
 
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@Alias({ "de.lmu.ifi.dbs.elki.algorithm.outlier.LDOF" })
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public class LDOF<O, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm<O, D, OutlierResult> implements OutlierAlgorithm {
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  /**
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   * The logger for this class.
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    FiniteProgress progressLDOFs = LOG.isVerbose() ? new FiniteProgress("LDOF_SCORE for objects", relation.size(), LOG) : null;
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    Mean dxp = new Mean(), Dxp = new Mean();
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    for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
 
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    for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
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      KNNList<D> neighbors = knnQuery.getKNNForDBID(iditer, k);
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      // skip the point itself
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      dxp.reset(); Dxp.reset();
 
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      dxp.reset();
 
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      Dxp.reset();
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      // TODO: optimize for double distances
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      for (DistanceDBIDListIter<D> neighbor1 = neighbors.iter(); neighbor1.valid(); neighbor1.advance()) {
 
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      for(DistanceDBIDListIter<D> neighbor1 = neighbors.iter(); neighbor1.valid(); neighbor1.advance()) {
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        if(!DBIDUtil.equal(neighbor1, iditer)) {
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          dxp.put(neighbor1.getDistance().doubleValue());
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          for (DistanceDBIDListIter<D> neighbor2 = neighbors.iter(); neighbor2.valid(); neighbor2.advance()) {
 
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          for(DistanceDBIDListIter<D> neighbor2 = neighbors.iter(); neighbor2.valid(); neighbor2.advance()) {
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            if(!DBIDUtil.equal(neighbor1, neighbor2) && !DBIDUtil.equal(neighbor2, iditer)) {
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              Dxp.put(distFunc.distance(neighbor1, neighbor2).doubleValue());
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            }
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    protected void makeOptions(Parameterization config) {
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      super.makeOptions(config);
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      final IntParameter kP = new IntParameter(K_ID);
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      kP.addConstraint(new GreaterConstraint(1));
 
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      kP.addConstraint(CommonConstraints.GREATER_THAN_ONE_INT);
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      if(config.grab(kP)) {
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        k = kP.getValue();
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      }