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

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

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      minmax.put(score);
      scores.putDouble(id, score);
    }

    Relation<Double> scoreResult = new MaterializedRelation<Double>("MO", "Median-outlier", 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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      }
      emo_score.putDouble(id, maxProb);
      globmax = Math.max(maxProb, globmax);
    }
    Relation<Double> scoreres = new MaterializedRelation<Double>("EM outlier scores", "em-outlier", TypeUtil.DOUBLE, emo_score, relation.getDBIDs());
    OutlierScoreMeta meta = new ProbabilisticOutlierScore(0.0, globmax);
    // combine results.
    OutlierResult result = new OutlierResult(meta, scoreres);
    // TODO: add a keep-EM flag?
    result.addChildResult(emresult);
    return result;
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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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      abodvalues.putDouble(pair.getSecond(), pair.first);
      minmaxabod.put(pair.first);
    }
    // Build result representation.
    Relation<Double> scoreResult = new MaterializedRelation<Double>("Angle-based Outlier Degree", "abod-outlier", TypeUtil.DOUBLE, abodvalues, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new InvertedOutlierScoreMeta(minmaxabod.getMin(), minmaxabod.getMax(), 0.0, Double.POSITIVE_INFINITY);
    return new OutlierResult(scoreMeta, scoreResult);
  }
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      abodvalues.putDouble(pair.getSecond(), pair.first);
      minmaxabod.put(pair.first);
    }
    // Build result representation.
    Relation<Double> scoreResult = new MaterializedRelation<Double>("Angle-based Outlier Detection", "abod-outlier", TypeUtil.DOUBLE, abodvalues, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new InvertedOutlierScoreMeta(minmaxabod.getMin(), minmaxabod.getMax(), 0.0, Double.POSITIVE_INFINITY);
    return new OutlierResult(scoreMeta, scoreResult);
  }
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      stepprog.setCompleted(logger);
    }

    // Build result representation.
    Relation<Double> scoreResult = new MaterializedRelation<Double>("Local Outlier Probabilities", "loop-outlier", TypeUtil.DOUBLE, loops, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new ProbabilisticOutlierScore();
    return new OutlierResult(scoreMeta, scoreResult);
  }
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        val = 0.0;
      }
      minmax.put(val);
    }
    Relation<Double> scoreResult = new MaterializedRelation<Double>("AggarwalYuNaive", "aggarwal-yu-outlier", TypeUtil.DOUBLE, sparsity, relation.getDBIDs());
    OutlierScoreMeta meta = new InvertedOutlierScoreMeta(minmax.getMin(), minmax.getMax(), Double.NEGATIVE_INFINITY, 0.0);
    return new OutlierResult(meta, scoreResult);
  }
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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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      // adjust to 0 to 1 range:
      score = (score - minscore) / (1 - minscore);
      scores.putDouble(id, score);
    }
    Relation<Double> scoreres = new MaterializedRelation<Double>("Model outlier scores", "model-outlier", TypeUtil.DOUBLE, scores, models.getDBIDs());
    OutlierScoreMeta meta = new ProbabilisticOutlierScore(0., 1.);
    return new OutlierResult(meta, scoreres);
  }
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    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT);
    for(DBID id : relation.iterDBIDs()) {
      scores.putDouble(id, 0.0);
    }
    Relation<Double> scoreres = new MaterializedRelation<Double>("Trivial no-outlier score", "no-outlier", TypeUtil.DOUBLE, scores, relation.getDBIDs());
    OutlierScoreMeta meta = new ProbabilisticOutlierScore();
    return new OutlierResult(meta, scoreres);
  }
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Related Classes of de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta

  • de.lmu.ifi.dbs.elki.algorithm.outlier.ABOD
  • de.lmu.ifi.dbs.elki.algorithm.outlier.AbstractDBOutlier
  • de.lmu.ifi.dbs.elki.algorithm.outlier.AggarwalYuEvolutionary
  • de.lmu.ifi.dbs.elki.algorithm.outlier.AggarwalYuNaive
  • de.lmu.ifi.dbs.elki.algorithm.outlier.EMOutlier
  • de.lmu.ifi.dbs.elki.algorithm.outlier.GaussianModel
  • de.lmu.ifi.dbs.elki.algorithm.outlier.GaussianUniformMixture
  • de.lmu.ifi.dbs.elki.algorithm.outlier.INFLO
  • de.lmu.ifi.dbs.elki.algorithm.outlier.KNNOutlier
  • de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier
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