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

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

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    // setup Algorithm
    SOD<DoubleVector, DoubleDistance> sod = ClassGenericsUtil.parameterizeOrAbort(SOD.class, params);
    testParameterizationOk(params);

    // run SOD on database
    OutlierResult result = sod.run(db);

    testSingleScore(result, 1293, 1.7277777);
    testAUC(db, "Noise", result, 0.94956862);
  }
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    // setup Algorithm
    INFLO<DoubleVector, DoubleDistance> inflo = ClassGenericsUtil.parameterizeOrAbort(INFLO.class, params);
    testParameterizationOk(params);

    // run INFLO on database
    OutlierResult result = inflo.run(db);

    testSingleScore(result, 945, 2.5711647857619484);
    testAUC(db, "Noise", result, 0.935222);
  }
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    // setup Algorithm
    AggarwalYuNaive<DoubleVector> aggarwalYuNaive = ClassGenericsUtil.parameterizeOrAbort(AggarwalYuNaive.class, params);
    testParameterizationOk(params);

    // run AggarwalYuNaive on database
    OutlierResult result = aggarwalYuNaive.run(db);

    testSingleScore(result, 945, -2.3421601750764798);
    testAUC(db, "Noise", result, 0.8652037037037);
  }
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    // setup Algorithm
    LoOP<DoubleVector, DoubleDistance> loop = ClassGenericsUtil.parameterizeOrAbort(LoOP.class, params);
    testParameterizationOk(params);

    // run LoOP on database
    OutlierResult result = loop.run(db);

    testAUC(db, "Noise", result, 0.9443796296296296);
    testSingleScore(result, 945, 0.39805457858293325);
  }
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      minmax.put(score);
    }
    //
    Relation<Double> scoreResult = new MaterializedRelation<Double>("TrimmedMean", "Trimmed Mean 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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    // setup Algorithm
    ABOD<DoubleVector> abod = ClassGenericsUtil.parameterizeOrAbort(ABOD.class, params);
    testParameterizationOk(params);

    // run ABOD on database
    OutlierResult result = abod.run(db);

    testSingleScore(result, 945, 3.7108897864090475E-4);
    testAUC(db, "Noise", result, 0.9638148148148148);
  }
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    // setup Algorithm
    ReferenceBasedOutlierDetection<DoubleVector, DoubleDistance> referenceBasedOutlierDetection = ClassGenericsUtil.parameterizeOrAbort(ReferenceBasedOutlierDetection.class, params);
    testParameterizationOk(params);

    // run ReferenceBasedOutlierDetection on database
    OutlierResult result = referenceBasedOutlierDetection.run(db);

    testSingleScore(result, 945, 0.9260829537195538);
    testAUC(db, "Noise", result, 0.9892407407407409);
  }
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    }

    // Build result representation.
    Relation<Double> scoreResult = new MaterializedRelation<Double>("Local Outlier Factor", "lof-outlier", TypeUtil.DOUBLE, lofs, kNNRefer.getRelation().getDBIDs());
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(lofminmax.getMin(), lofminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
    OutlierResult result = new OutlierResult(scoreMeta, scoreResult);

    return new LOFResult<O, D>(result, kNNRefer, kNNReach, lrds, lofs);
  }
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    }
    else {
      meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax());
    }
    Relation<Double> scoresult = new MaterializedRelation<Double>("External Outlier", "external-outlier", TypeUtil.DOUBLE, scores, relation.getDBIDs());
    OutlierResult or = new OutlierResult(meta, scoresult);

    // Apply scaling
    if(scaling instanceof OutlierScalingFunction) {
      ((OutlierScalingFunction) scaling).prepare(or);
    }
    DoubleMinMax mm = new DoubleMinMax();
    for(DBID id : relation.iterDBIDs()) {
      double val = scoresult.get(id); // scores.get(id);
      val = scaling.getScaled(val);
      scores.put(id, val);
      mm.put(val);
    }
    meta = new BasicOutlierScoreMeta(mm.getMin(), mm.getMax());
    or = new OutlierResult(meta, scoresult);

    return or;
  }
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    // setup Algorithm
    GaussianModel<DoubleVector> gaussianModel = ClassGenericsUtil.parameterizeOrAbort(GaussianModel.class, params);
    testParameterizationOk(params);

    // run GaussianModel on database
    OutlierResult result = gaussianModel.run(db);

    testSingleScore(result, 1025, 2.8312466458765426);
    testAUC(db, "Noise", result, 0.9937641025641025);
  }
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Related Classes of de.lmu.ifi.dbs.elki.result.outlier.OutlierResult

  • 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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