Package de.lmu.ifi.dbs.elki.result.outlier

Examples of de.lmu.ifi.dbs.elki.result.outlier.BasicOutlierScoreMeta

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      scores.put(id, score);
    }

    // Wrap result
    Relation<Double> scoreResult = new MaterializedRelation<Double>("ZTest", "Z Test score", TypeUtil.DOUBLE, scores, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 0);
    OutlierResult or = new OutlierResult(scoreMeta, scoreResult);
    or.addChildResult(npred);
    return or;
  }
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      // track the maximum value for normalization.
      DoubleMinMax new_lofminmax = lofsAndMax.getSecond();

      // Actualize meta info
      if(new_lofminmax.isValid() && lofResult.getResult().getOutlierMeta().getActualMaximum() < new_lofminmax.getMax()) {
        BasicOutlierScoreMeta scoreMeta = (BasicOutlierScoreMeta) lofResult.getResult().getOutlierMeta();
        scoreMeta.setActualMaximum(new_lofminmax.getMax());
      }

      if(new_lofminmax.isValid() && lofResult.getResult().getOutlierMeta().getActualMinimum() > new_lofminmax.getMin()) {
        BasicOutlierScoreMeta scoreMeta = (BasicOutlierScoreMeta) lofResult.getResult().getOutlierMeta();
        scoreMeta.setActualMinimum(new_lofminmax.getMin());
      }
    }
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    if(invert) {
      double max = mm.getMax() != 0 ? mm.getMax() : 1.;
      for(DBID id : relation.iterDBIDs()) {
        oscores.put(id, (max - oscores.get(id)) / max);
      }
      meta = new BasicOutlierScoreMeta(0.0, 1.0);
    }
    else {
      meta = new InvertedOutlierScoreMeta(mm.getMin(), mm.getMax(), 0.0, Double.POSITIVE_INFINITY);
    }
    Relation<Double> res = new MaterializedRelation<Double>("Gaussian Model Outlier Score", "gaussian-model-outlier", TypeUtil.DOUBLE, oscores, relation.getDBIDs());
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      val = scaling.getScaled(val);
      scaledscores.put(id, val);
      minmax.put(val);
    }

    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), scaling.getMin(), scaling.getMax());
    Relation<Double> scoresult = new MaterializedRelation<Double>("Scaled Outlier", "scaled-outlier", TypeUtil.DOUBLE, scaledscores, scores.getDBIDs());
    OutlierResult result = new OutlierResult(meta, scoresult);
    result.addChildResult(innerresult);

    return result;
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    }
    if(progressKNNDistance != null) {
      progressKNNDistance.ensureCompleted(logger);
    }
    Relation<Double> scoreres = new MaterializedRelation<Double>("kNN Outlier Score", "knn-outlier", TypeUtil.DOUBLE, knno_score, relation.getDBIDs());
    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 0.0);
    return new OutlierResult(meta, scoreres);
  }
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      }
      if(cprog != null) {
        cprog.ensureCompleted(logger);
      }
    }
    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax());
    Relation<Double> scoreres = new MaterializedRelation<Double>("Feature bagging", "fb-outlier", TypeUtil.DOUBLE, scores, relation.getDBIDs());
    return new OutlierResult(meta, scoreres);
  }
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    if(progress != null) {
      progress.ensureCompleted(logger);
    }
    // combine results.
    Relation<SODModel<?>> models = new MaterializedRelation<SODModel<?>>("Subspace Outlier Model", "sod-outlier", new SimpleTypeInformation<SODModel<?>>(SODModel.class), sod_models, relation.getDBIDs());
    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax());
    OutlierResult sodResult = new OutlierResult(meta, new SODProxyScoreResult(models, relation.getDBIDs()));
    // also add the models.
    sodResult.addChildResult(models);
    return sodResult;
  }
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    if(progressKNNWeight != null) {
      progressKNNWeight.ensureCompleted(logger);
    }

    Relation<Double> res = new MaterializedRelation<Double>("Weighted kNN Outlier Score", "knnw-outlier", TypeUtil.DOUBLE, knnw_score, relation.getDBIDs());
    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 0.0);
    return new OutlierResult(meta, res);
  }
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        // undo bit set
        bits.clear(i);
      }
    }

    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 0.0);
    Relation<Double> res = new MaterializedRelation<Double>("Gaussian Mixture Outlier Score", "gaussian-mixture-outlier", TypeUtil.DOUBLE, oscores, relation.getDBIDs());
    return new OutlierResult(meta, res);
  }
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    // adds reference points to the result. header information for the
    // visualizer to find the reference points in the result
    ReferencePointsResult<V> refp = new ReferencePointsResult<V>("Reference points", "reference-points", refPoints);

    Relation<Double> scoreResult = new MaterializedRelation<Double>("Reference-points Outlier Scores", "reference-outlier", TypeUtil.DOUBLE, rbod_score, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new BasicOutlierScoreMeta(0.0, 1.0, 0.0, 1.0, 0.0);
    OutlierResult result = new OutlierResult(scoreMeta, scoreResult);
    result.addChildResult(refp);
    return result;
  }
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Related Classes of de.lmu.ifi.dbs.elki.result.outlier.BasicOutlierScoreMeta

  • de.lmu.ifi.dbs.elki.algorithm.outlier.GaussianModel
  • de.lmu.ifi.dbs.elki.algorithm.outlier.GaussianUniformMixture
  • de.lmu.ifi.dbs.elki.algorithm.outlier.KNNOutlier
  • de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier
  • de.lmu.ifi.dbs.elki.algorithm.outlier.meta.ExternalDoubleOutlierScore
  • de.lmu.ifi.dbs.elki.algorithm.outlier.meta.FeatureBagging
  • de.lmu.ifi.dbs.elki.algorithm.outlier.meta.RescaleMetaOutlierAlgorithm
  • de.lmu.ifi.dbs.elki.algorithm.outlier.OnlineLOF$LOFKNNListener
  • de.lmu.ifi.dbs.elki.algorithm.outlier.ReferenceBasedOutlierDetection
  • de.lmu.ifi.dbs.elki.algorithm.outlier.SOD
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