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

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

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              if(logger.isDebugging()) {
                msg.append("\n").append(parent).append(" is parent of ").append(child);
              }
            }
            else {
              BitDistance dist = distanceFunction.distance(parent.getModel().getCentroid(), child.getModel().getCentroid(), parent.getModel().getPCAResult(), child.getModel().getPCAResult());
              if(!dist.bitValue() && (child.getParents().isEmpty() || !isParent(distanceFunction, parent, child.getParents()))) {
                parent.getChildren().add(child);
                child.getParents().add(parent);
                if(logger.isDebugging()) {
                  msg.append("\n").append(parent).append(" is parent of ").append(child);
                }
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    for(Cluster<CorrelationModel<V>> child : children) {
      if(parent.getModel().getPCAResult().getCorrelationDimension() == child.getModel().getPCAResult().getCorrelationDimension()) {
        return false;
      }

      BitDistance dist = distanceFunction.distance(parent.getModel().getCentroid(), child.getModel().getCentroid(), parent.getModel().getPCAResult(), child.getModel().getPCAResult());
      if(logger.isDebugging()) {
        msg.append("\ndist(").append(child).append(" - ").append(parent).append(") = ").append(dist);
      }
      if(!dist.bitValue()) {
        if(logger.isDebugging()) {
          logger.debugFine(msg.toString());
        }
        return true;
      }
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    else {
      approximatelyLinearDependent = approximatelyLinearDependent(pca1, pca2);
    }

    if(!approximatelyLinearDependent) {
      return new BitDistance(true);
    }

    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);
      }

      return new BitDistance(false);
    }
  }
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    else {
      approximatelyLinearDependent = approximatelyLinearDependent(pca1, pca2);
    }

    if(!approximatelyLinearDependent) {
      return new BitDistance(true);
    }

    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);
      }

      return new BitDistance(false);
    }
  }
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              if(logger.isDebugging()) {
                msg.append("\n").append(parent).append(" is parent of ").append(child);
              }
            }
            else {
              BitDistance dist = distanceFunction.distance(parent.getModel().getCentroid(), child.getModel().getCentroid(), parent.getModel().getPCAResult(), child.getModel().getPCAResult());
              if(!dist.bitValue() && (child.getParents().isEmpty() || !isParent(distanceFunction, parent, child.getParents()))) {
                parent.getChildren().add(child);
                child.getParents().add(parent);
                if(logger.isDebugging()) {
                  msg.append("\n").append(parent).append(" is parent of ").append(child);
                }
View Full Code Here
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    for(Cluster<CorrelationModel<V>> child : children) {
      if(parent.getModel().getPCAResult().getCorrelationDimension() == child.getModel().getPCAResult().getCorrelationDimension()) {
        return false;
      }

      BitDistance dist = distanceFunction.distance(parent.getModel().getCentroid(), child.getModel().getCentroid(), parent.getModel().getPCAResult(), child.getModel().getPCAResult());
      if(logger.isDebugging()) {
        msg.append("\ndist(").append(child).append(" - ").append(parent).append(") = ").append(dist);
      }
      if(!dist.bitValue()) {
        if(logger.isDebugging()) {
          logger.debugFine(msg.toString());
        }
        return true;
      }
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Related Classes of de.lmu.ifi.dbs.elki.distance.distancevalue.BitDistance

  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC
  • de.lmu.ifi.dbs.elki.data.Bit
  • de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.ERiCDistanceFunction
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