Package de.lmu.ifi.dbs.elki.database.datastore

Examples of de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore.doubleValue()

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    }

    // Normalize scores using mean and variance
    DoubleMinMax minmax = new DoubleMinMax();
    for(DBID id : relation.iterDBIDs()) {
      double score = Math.abs(scores.doubleValue(id) - zmv.getMean()) / zmv.getSampleStddev();
      minmax.put(score);
      scores.putDouble(id, score);
    }

    // Wrap result
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    final OutlierScoreMeta meta;
    if(invert) {
      double max = mm.getMax() != 0 ? mm.getMax() : 1.;
      for(DBID id : relation.iterDBIDs()) {
        oscores.putDouble(id, (max - oscores.doubleValue(id)) / max);
      }
      meta = new BasicOutlierScoreMeta(0.0, 1.0);
    }
    else {
      meta = new InvertedOutlierScoreMeta(mm.getMin(), mm.getMax(), 0.0, Double.POSITIVE_INFINITY);
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    WritableDoubleDataStore outlierScore = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC);
    for(Individuum ind : individuums) {
      DBIDs ids = computeSubspaceForGene(ind.getGene(), ranges);
      double sparsityC = sparsity(ids.size(), dbsize, k);
      for(DBID id : ids) {
        double prev = outlierScore.doubleValue(id);
        if(Double.isNaN(prev) || sparsityC < prev) {
          outlierScore.putDouble(id, sparsityC);
        }
      }
    }
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      }
    }

    DoubleMinMax minmax = new DoubleMinMax();
    for(DBID id : relation.iterDBIDs()) {
      double val = outlierScore.doubleValue(id);
      if(Double.isNaN(val)) {
        outlierScore.putDouble(id, 0.0);
        val = 0.0;
      }
      minmax.put(val);
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          // Always true if every algorithm returns a complete result (one score
          // for every DBID).
          if(iter.hasNext()) {
            DBID tmpID = iter.next();
            double score = pair.second.get(tmpID);
            if(Double.isNaN(scores.doubleValue(tmpID))) {
              scores.putDouble(tmpID, score);
              minmax.put(score);
            }
          }
          else {
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    DoubleMinMax minmax = new DoubleMinMax();
    {
      final double mean = mv.getMean();
      final double variance = mv.getNaiveStddev();
      for(DBID id : relation.iterDBIDs()) {
        double score = Math.abs((scores.doubleValue(id) - mean) / variance);
        minmax.put(score);
        scores.putDouble(id, score);
      }
    }
    // build representation
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    // Normalize scores
    final double mean = mv.getMean();
    final double stddev = mv.getNaiveStddev();
    DoubleMinMax minmax = new DoubleMinMax();
    for(DBID id : relation.iterDBIDs()) {
      double score = Math.abs((scores.doubleValue(id) - mean) / stddev);
      minmax.put(score);
      scores.putDouble(id, score);
    }

    Relation<Double> scoreResult = new MaterializedRelation<Double>("MO", "Median-outlier", TypeUtil.DOUBLE, scores, relation.getDBIDs());
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      DBIDs ids = computeSubspace(sub, ranges);
      final double sparsityC = sparsity(ids.size(), size, k);

      if(sparsityC < 0) {
        for(DBID id : ids) {
          double prev = sparsity.doubleValue(id);
          if(Double.isNaN(prev) || sparsityC < prev) {
            sparsity.putDouble(id, sparsityC);
          }
        }
      }
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        }
      }
    }
    DoubleMinMax minmax = new DoubleMinMax();
    for(DBID id : relation.iterDBIDs()) {
      double val = sparsity.doubleValue(id);
      if(Double.isNaN(val)) {
        sparsity.putDouble(id, 0.0);
        val = 0.0;
      }
      minmax.put(val);
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        List<DistanceResultPair<D>> referenceDists = computeDistanceVector(refPoint, relation, distFunc);
        // compute density value for each object
        for(int l = 0; l < referenceDists.size(); l++) {
          double density = computeDensity(referenceDists, l);
          // Update minimum
          if(density < rbod_score.doubleValue(referenceDists.get(l).getDBID())) {
            rbod_score.putDouble(referenceDists.get(l).getDBID(), density);
          }
        }
      }
    }
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