use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.
the class COPTest method testCOPRANSAC.
@Test
public void testCOPRANSAC() {
Database db = makeSimpleDatabase(UNITTEST + "outlier-parabolic.ascii", 530);
OutlierResult result = //
new ELKIBuilder<COP<DoubleVector>>(COP.class).with(COP.Parameterizer.K_ID, //
30).with(COP.Parameterizer.PCARUNNER_ID, //
AutotuningPCA.class).with(AutotuningPCA.Parameterizer.PCA_EIGENPAIR_FILTER, //
PercentageEigenPairFilter.class).with(AutotuningPCA.Parameterizer.PCA_COVARIANCE_MATRIX, //
RANSACCovarianceMatrixBuilder.class).with(RANSACCovarianceMatrixBuilder.Parameterizer.ITER_ID, //
25).with(RANSACCovarianceMatrixBuilder.Parameterizer.SEED_ID, //
0).build().run(db);
testAUC(db, "Noise", result, 0.89526);
testSingleScore(result, 416, 0.382879);
}
use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.
the class ABODTest method testABOD.
@Test
public void testABOD() {
Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);
OutlierResult result = new ELKIBuilder<ABOD<DoubleVector>>(ABOD.class).build().run(db);
testAUC(db, "Noise", result, 0.9297962962962);
testSingleScore(result, 945, 2.0897348547799E-5);
}
use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.
the class ReferenceBasedOutlierDetectionTest method testReferenceBasedOutlierDetectionGridBased.
@Test
public void testReferenceBasedOutlierDetectionGridBased() {
Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);
OutlierResult result = //
new ELKIBuilder<>(ReferenceBasedOutlierDetection.class).with(ReferenceBasedOutlierDetection.Parameterizer.K_ID, //
11).with(GridBasedReferencePoints.Parameterizer.GRID_ID, //
3).build().run(db);
testAUC(db, "Noise", result, 0.9693703703703);
testSingleScore(result, 945, 0.933574455);
}
use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.
the class ReferenceBasedOutlierDetectionTest method testReferenceBasedOutlierDetectionSample.
@Test
public void testReferenceBasedOutlierDetectionSample() {
Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);
OutlierResult result = //
new ELKIBuilder<>(ReferenceBasedOutlierDetection.class).with(ReferenceBasedOutlierDetection.Parameterizer.K_ID, //
11).with(ReferenceBasedOutlierDetection.Parameterizer.REFP_ID, //
RandomSampleReferencePoints.class).with(RandomSampleReferencePoints.Parameterizer.N_ID, //
15).with(RandomSampleReferencePoints.Parameterizer.RANDOM_ID, //
0).build().run(db);
testAUC(db, "Noise", result, 0.829814814);
testSingleScore(result, 945, 0.846881387);
}
use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.
the class ReferenceBasedOutlierDetectionTest method testReferenceBasedOutlierDetectionStar.
@Test
public void testReferenceBasedOutlierDetectionStar() {
Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);
OutlierResult result = //
new ELKIBuilder<>(ReferenceBasedOutlierDetection.class).with(ReferenceBasedOutlierDetection.Parameterizer.K_ID, //
11).with(ReferenceBasedOutlierDetection.Parameterizer.REFP_ID, //
StarBasedReferencePoints.class).build().run(db);
testAUC(db, "Noise", result, 0.910722222);
testSingleScore(result, 945, 0.920950222);
}
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