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

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

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    // setup Algorithm
    GaussianUniformMixture<DoubleVector> gaussianUniformMixture = ClassGenericsUtil.parameterizeOrAbort(GaussianUniformMixture.class, params);
    testParameterizationOk(params);

    // run GaussianUniformMixture on database
    OutlierResult result = gaussianUniformMixture.run(db);

    testSingleScore(result, 1025, -20.2862041);
    testAUC(db, "Noise", result, 0.94404102);
  }
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    // setup Algorithm
    OPTICSOF<DoubleVector, DoubleDistance> opticsof = ClassGenericsUtil.parameterizeOrAbort(OPTICSOF.class, params);
    testParameterizationOk(params);

    // run OPTICSOF on database
    OutlierResult result = opticsof.run(db);

    testSingleScore(result, 416, 1.6108343626651815);
    testAUC(db, "Noise", result, 0.9058);
  }
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    // setup Algorithm
    DBOutlierDetection<DoubleVector, DoubleDistance> dbOutlierDetection = ClassGenericsUtil.parameterizeOrAbort(DBOutlierDetection.class, params);
    testParameterizationOk(params);

    // run DBOutlierDetection on database
    OutlierResult result = dbOutlierDetection.run(db);

    testSingleScore(result, 1025, 0.0);
    testAUC(db, "Noise", result, 0.97487179);
  }
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    // setup Algorithm
    KNNWeightOutlier<DoubleVector, DoubleDistance> knnWeightOutlier = ClassGenericsUtil.parameterizeOrAbort(KNNWeightOutlier.class, params);
    testParameterizationOk(params);

    // run KNNWeightOutlier on database
    OutlierResult result = knnWeightOutlier.run(db);

    testSingleScore(result, 945, 2.384117261027324);
    testAUC(db, "Noise", result, 0.9912777777777778);
  }
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  public void testOnlineLOF() throws UnableToComplyException {
    UpdatableDatabase db = getDatabase();

    // 1. Run LOF
    LOF<DoubleVector, DoubleDistance> lof = new LOF<DoubleVector, DoubleDistance>(k, neighborhoodDistanceFunction, reachabilityDistanceFunction);
    OutlierResult result1 = lof.run(db);

    // 2. Run OnlineLOF (with insertions and removals) on database
    OutlierResult result2 = runOnlineLOF(db);

    // 3. Compare results
    Relation<Double> scores1 = result1.getScores();
    Relation<Double> scores2 = result2.getScores();

    for(DBID id : scores1.getDBIDs()) {
      Double lof1 = scores1.get(id);
      Double lof2 = scores2.get(id);
      assertTrue("lof(" + id + ") != lof(" + id + "): " + lof1 + " != " + lof2, lof1.equals(lof2));
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    // setup algorithm
    OnlineLOF<DoubleVector, DoubleDistance> lof = new OnlineLOF<DoubleVector, DoubleDistance>(k, neighborhoodDistanceFunction, reachabilityDistanceFunction);

    // run OnlineLOF on database
    OutlierResult result = lof.run(db);

    // insert new objects
    ArrayList<DoubleVector> insertions = new ArrayList<DoubleVector>();
    DoubleVector o = DatabaseUtil.assumeVectorField(rep).getFactory();
    Random random = new Random(seed);
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    // setup Algorithm
    LOF<DoubleVector, DoubleDistance> lof = ClassGenericsUtil.parameterizeOrAbort(LOF.class, params);
    testParameterizationOk(params);

    // run LOF on database
    OutlierResult result = lof.run(db);

    testSingleScore(result, 1293, 1.1945314199156365);
    testAUC(db, "Noise", result, 0.8921680672268908);
  }
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    // setup Algorithm
    LOCI<DoubleVector, DoubleDistance> loci = ClassGenericsUtil.parameterizeOrAbort(LOCI.class, params);
    testParameterizationOk(params);

    // run LOCI on database
    OutlierResult result = loci.run(db);

    testAUC(db, "Noise", result, 0.954444);
    testSingleScore(result, 146, 4.14314916);
  }
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    // setup Algorithm
    DBOutlierScore<DoubleVector, DoubleDistance> dbOutlierScore = ClassGenericsUtil.parameterizeOrAbort(DBOutlierScore.class, params);
    testParameterizationOk(params);

    // run DBOutlierScore on database
    OutlierResult result = dbOutlierScore.run(db);

    testSingleScore(result, 1025, 0.688780487804878);
    testAUC(db, "Noise", result, 0.992565641);
  }
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    // setup Algorithm
    KNNOutlier<DoubleVector, DoubleDistance> knnOutlier = ClassGenericsUtil.parameterizeOrAbort(KNNOutlier.class, params);
    testParameterizationOk(params);

    // run KNNOutlier on database
    OutlierResult result = knnOutlier.run(db);

    testSingleScore(result, 945, 0.4793554700168577);
    testAUC(db, "Noise", result, 0.991462962962963);
  }
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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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