Package de.lmu.ifi.dbs.elki.distance.distancevalue

Examples of de.lmu.ifi.dbs.elki.distance.distancevalue.PreferenceVectorBasedCorrelationDistance

601
602
603
604
605
606
607
608
609
610
611
612
    int dimensionality = DatabaseUtil.dimensionality(database);
    int subspaceDim_parent = dimensionality - parent.getModel().getSubspace().dimensionality();

    for(Cluster<SubspaceModel<V>> child : children) {
      V child_centroid = DatabaseUtil.centroid(database, child.getIDs(), child.getModel().getDimensions());
      PreferenceVectorBasedCorrelationDistance distance = distFunc.correlationDistance(parent_centroid, child_centroid, parent.getModel().getSubspace().getDimensions(), child.getModel().getSubspace().getDimensions());
      if(distance.getCorrelationValue() == subspaceDim_parent) {
        return true;
      }
    }

    return false;
View Full Code Here
289
290
291
292
293
294
295
296
297
298
299
300
301
      // look for the proper cluster
      Pair<BitSet, ArrayModifiableDBIDs> cluster = null;
      for(Pair<BitSet, ArrayModifiableDBIDs> c : parallelClusters) {
        V c_centroid = DatabaseUtil.centroid(database, c.second, c.first);
        PreferenceVectorBasedCorrelationDistance dist = distFunc.correlationDistance(object, c_centroid, preferenceVector, preferenceVector);
        if(dist.getCorrelationValue() == entry.getReachability().getCorrelationValue()) {
          double d = distFunc.weightedDistance(object, c_centroid, dist.getCommonPreferenceVector());
          if(d <= 2 * epsilon) {
            cluster = c;
            break;
          }
        }
View Full Code Here
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
              }
            }
          }
          else {
            V cj_centroid = DatabaseUtil.centroid(database, c_j.getIDs(), c_j.getModel().getDimensions());
            PreferenceVectorBasedCorrelationDistance distance = distFunc.correlationDistance(ci_centroid, cj_centroid, c_i.getModel().getSubspace().getDimensions(), c_j.getModel().getSubspace().getDimensions());
            double d = distFunc.weightedDistance(ci_centroid, cj_centroid, distance.getCommonPreferenceVector());
            if(logger.isDebugging()) {
              msg.append("\n dist = ").append(distance.getCorrelationValue());
            }

            if(distance.getCorrelationValue() == subspaceDim_j) {
              if(logger.isDebugging()) {
                msg.append("\n d = ").append(d);
              }
              if(d <= 2 * epsilon) {
                // no parent exists or c_j is not a parent of the already
View Full Code Here
129
130
131
132
133
134
135
      // flip commonPreferenceVector for distance computation in common subspace
      BitSet inverseCommonPreferenceVector = (BitSet) commonPreferenceVector.clone();
      inverseCommonPreferenceVector.flip(0, dim);

      return new PreferenceVectorBasedCorrelationDistance(DatabaseUtil.dimensionality(relation), subspaceDim, weightedDistance(v1, v2, inverseCommonPreferenceVector), commonPreferenceVector);
    }
View Full Code Here
134
135
136
137
138
139
140
      // flip commonPreferenceVector for distance computation in common subspace
      BitSet inverseCommonPreferenceVector = (BitSet) commonPreferenceVector.clone();
      inverseCommonPreferenceVector.flip(0, dim);

      return new PreferenceVectorBasedCorrelationDistance(DatabaseUtil.dimensionality(relation), subspaceDim, weightedDistance(v1, v2, inverseCommonPreferenceVector), commonPreferenceVector);
    }
View Full Code Here
129
130
131
132
133
134
135
      // flip commonPreferenceVector for distance computation in common subspace
      BitSet inverseCommonPreferenceVector = (BitSet) commonPreferenceVector.clone();
      inverseCommonPreferenceVector.flip(0, dim);

      return new PreferenceVectorBasedCorrelationDistance(DatabaseUtil.dimensionality(relation), subspaceDim, weightedDistance(v1, v2, inverseCommonPreferenceVector), commonPreferenceVector);
    }
View Full Code Here
134
135
136
137
138
139
140
      // flip commonPreferenceVector for distance computation in common subspace
      BitSet inverseCommonPreferenceVector = (BitSet) commonPreferenceVector.clone();
      inverseCommonPreferenceVector.flip(0, dim);

      return new PreferenceVectorBasedCorrelationDistance(DatabaseUtil.dimensionality(relation), subspaceDim, weightedDistance(v1, v2, inverseCommonPreferenceVector), commonPreferenceVector);
    }
View Full Code Here
289
290
291
292
293
294
295
296
297
298
299
300
301
      // look for the proper cluster
      Pair<BitSet, ArrayModifiableDBIDs> cluster = null;
      for(Pair<BitSet, ArrayModifiableDBIDs> c : parallelClusters) {
        V c_centroid = DatabaseUtil.centroid(database, c.second, c.first);
        PreferenceVectorBasedCorrelationDistance dist = distFunc.correlationDistance(object, c_centroid, preferenceVector, preferenceVector);
        if(dist.getCorrelationValue() == entry.getReachability().getCorrelationValue()) {
          double d = distFunc.weightedDistance(object, c_centroid, dist.getCommonPreferenceVector());
          if(d <= 2 * epsilon) {
            cluster = c;
            break;
          }
        }
View Full Code Here
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
              }
            }
          }
          else {
            V cj_centroid = DatabaseUtil.centroid(database, c_j.getIDs(), c_j.getModel().getDimensions());
            PreferenceVectorBasedCorrelationDistance distance = distFunc.correlationDistance(ci_centroid, cj_centroid, c_i.getModel().getSubspace().getDimensions(), c_j.getModel().getSubspace().getDimensions());
            double d = distFunc.weightedDistance(ci_centroid, cj_centroid, distance.getCommonPreferenceVector());
            if(logger.isDebugging()) {
              msg.append("\n dist = ").append(distance.getCorrelationValue());
            }

            if(distance.getCorrelationValue() == subspaceDim_j) {
              if(logger.isDebugging()) {
                msg.append("\n d = ").append(d);
              }
              if(d <= 2 * epsilon) {
                // no parent exists or c_j is not a parent of the already
View Full Code Here
601
602
603
604
605
606
607
608
609
610
611
612
    int dimensionality = DatabaseUtil.dimensionality(database);
    int subspaceDim_parent = dimensionality - parent.getModel().getSubspace().dimensionality();

    for(Cluster<SubspaceModel<V>> child : children) {
      V child_centroid = DatabaseUtil.centroid(database, child.getIDs(), child.getModel().getDimensions());
      PreferenceVectorBasedCorrelationDistance distance = distFunc.correlationDistance(parent_centroid, child_centroid, parent.getModel().getSubspace().getDimensions(), child.getModel().getSubspace().getDimensions());
      if(distance.getCorrelationValue() == subspaceDim_parent) {
        return true;
      }
    }

    return false;
View Full Code Here
TOP

Related Classes of de.lmu.ifi.dbs.elki.distance.distancevalue.PreferenceVectorBasedCorrelationDistance

  • de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DiSH
  • de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.DiSHDistanceFunction$Instance
  • de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.HiSCDistanceFunction$Instance
  • java.util.BitSet
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.