Examples of WeightedDistanceFunction

Examples of de.lmu.ifi.dbs.elki.distance.distancefunction.WeightedDistanceFunction

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    CorrelationAnalysisSolution<DoubleVector> model = derivator.run(derivatorDB);

    Matrix weightMatrix = model.getSimilarityMatrix();
    DoubleVector centroid = new DoubleVector(model.getCentroid());
    DistanceQuery<DoubleVector, DoubleDistance> df = QueryUtil.getDistanceQuery(derivatorDB, new WeightedDistanceFunction(weightMatrix));
    DoubleDistance eps = df.getDistanceFactory().parseString("0.25");

    ids.addDBIDs(interval.getIDs());
    // Search for nearby vectors in original database
    for(DBID id : relation.iterDBIDs()) {
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Examples of de.lmu.ifi.dbs.elki.distance.distancefunction.WeightedDistanceFunction

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      Matrix strong_ev1 = pca1.getStrongEigenvectors();
      Matrix weak_ev2 = pca2.getWeakEigenvectors();
      Matrix m1 = weak_ev2.getColumnDimensionality() == 0 ? strong_ev1.transpose() : strong_ev1.transposeTimes(weak_ev2);
      double d1 = m1.norm2();

      WeightedDistanceFunction df1 = new WeightedDistanceFunction(pca1.similarityMatrix());
      WeightedDistanceFunction df2 = new WeightedDistanceFunction(pca2.similarityMatrix());

      double affineDistance = Math.max(df1.distance(o1, o2).doubleValue(), df2.distance(o1, o2).doubleValue());

      return new SubspaceDistance(d1, affineDistance);
    }
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Examples of de.lmu.ifi.dbs.elki.distance.distancefunction.WeightedDistanceFunction

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    else {
      double affineDistance;

      if(pca1.getCorrelationDimension() == pca2.getCorrelationDimension()) {
        WeightedDistanceFunction df1 = new WeightedDistanceFunction(pca1.similarityMatrix());
        WeightedDistanceFunction df2 = new WeightedDistanceFunction(pca2.similarityMatrix());
        affineDistance = Math.max(df1.distance(v1, v2).doubleValue(), df2.distance(v1, v2).doubleValue());
      }
      else {
        WeightedDistanceFunction df1 = new WeightedDistanceFunction(pca1.similarityMatrix());
        affineDistance = df1.distance(v1, v2).doubleValue();
      }

      if(affineDistance > tau) {
        return new BitDistance(true);
      }
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Examples of de.lmu.ifi.dbs.elki.distance.distancefunction.WeightedDistanceFunction

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      Matrix strong_ev1 = pca1.getStrongEigenvectors();
      Matrix weak_ev2 = pca2.getWeakEigenvectors();
      Matrix m1 = weak_ev2.getColumnDimensionality() == 0 ? strong_ev1.transpose() : strong_ev1.transposeTimes(weak_ev2);
      double d1 = m1.norm2();

      WeightedDistanceFunction df1 = new WeightedDistanceFunction(pca1.similarityMatrix());
      WeightedDistanceFunction df2 = new WeightedDistanceFunction(pca2.similarityMatrix());

      double affineDistance = Math.max(df1.distance(o1, o2).doubleValue(), df2.distance(o1, o2).doubleValue());

      return new SubspaceDistance(d1, affineDistance);
    }
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Examples of de.lmu.ifi.dbs.elki.distance.distancefunction.WeightedDistanceFunction

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    else {
      double affineDistance;

      if(pca1.getCorrelationDimension() == pca2.getCorrelationDimension()) {
        WeightedDistanceFunction df1 = new WeightedDistanceFunction(pca1.similarityMatrix());
        WeightedDistanceFunction df2 = new WeightedDistanceFunction(pca2.similarityMatrix());
        affineDistance = Math.max(df1.distance(v1, v2).doubleValue(), df2.distance(v1, v2).doubleValue());
      }
      else {
        WeightedDistanceFunction df1 = new WeightedDistanceFunction(pca1.similarityMatrix());
        affineDistance = df1.distance(v1, v2).doubleValue();
      }

      if(affineDistance > tau) {
        return new BitDistance(true);
      }
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Examples of de.lmu.ifi.dbs.elki.distance.distancefunction.WeightedDistanceFunction

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    CorrelationAnalysisSolution<DoubleVector> model = derivator.run(derivatorDB);

    Matrix weightMatrix = model.getSimilarityMatrix();
    DoubleVector centroid = new DoubleVector(model.getCentroid());
    DistanceQuery<DoubleVector, DoubleDistance> df = QueryUtil.getDistanceQuery(derivatorDB, new WeightedDistanceFunction(weightMatrix));
    DoubleDistance eps = df.getDistanceFactory().parseString("0.25");

    ids.addDBIDs(interval.getIDs());
    // Search for nearby vectors in original database
    for(DBID id : relation.iterDBIDs()) {
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