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

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

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      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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      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();
      }
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        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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      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);
    }
  }
View Full Code Here
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      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();
      }
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        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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