use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.
the class SUBCLUTest method testSUBCLUResults.
/**
* Run SUBCLU with fixed parameters and compare the result to a golden
* standard.
*/
@Test
public void testSUBCLUResults() {
Database db = makeSimpleDatabase(UNITTEST + "subspace-simple.csv", 600);
Clustering<SubspaceModel> result = //
new ELKIBuilder<SUBCLU<DoubleVector>>(SUBCLU.class).with(SUBCLU.EPSILON_ID, //
0.001).with(SUBCLU.MINPTS_ID, //
100).build().run(db);
// PairCounting is not appropriate here: overlapping clusterings!
// testFMeasure(db, result, 0.9090);
testClusterSizes(result, new int[] { 191, 194, 395 });
}
use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.
the class SUBCLUTest method testSUBCLUSubspaceOverlapping.
/**
* Run SUBCLU with fixed parameters and compare the result to a golden
* standard.
*/
@Test
public void testSUBCLUSubspaceOverlapping() {
Database db = makeSimpleDatabase(UNITTEST + "subspace-overlapping-3-4d.ascii", 850);
Clustering<SubspaceModel> result = //
new ELKIBuilder<SUBCLU<DoubleVector>>(SUBCLU.class).with(SUBCLU.EPSILON_ID, //
0.04).with(SUBCLU.MINPTS_ID, //
70).build().run(db);
// PairCounting is not appropriate here: overlapping clusterings!
// testFMeasure(db, result, 0.49279033);
testClusterSizes(result, new int[] { 99, 247, 303, 323, 437, 459 });
}
use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.
the class KMeansPlusPlusInitialMeansTest method testSingleAssignmentKMeansPlusPlusMedoids.
/**
* Run CLARA with fixed parameters and compare the result to a golden
* standard.
*/
@Test
public void testSingleAssignmentKMeansPlusPlusMedoids() {
Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
Clustering<?> result = //
new ELKIBuilder<CLARA<DoubleVector>>(CLARA.class).with(KMeans.K_ID, //
5).with(KMeans.SEED_ID, //
3).with(KMeans.INIT_ID, //
KMeansPlusPlusInitialMeans.class).with(KMeans.MAXITER_ID, //
1).with(CLARA.Parameterizer.SAMPLESIZE_ID, //
10).with(CLARA.Parameterizer.RANDOM_ID, //
0).build().run(db);
testFMeasure(db, result, 0.932711);
testClusterSizes(result, new int[] { 165, 199, 201, 201, 234 });
}
use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.
the class RandomlyChosenInitialMeansTest method testRandomlyChosenInitialMedoids.
/**
* Run CLARA with fixed parameters and compare the result to a golden
* standard.
*/
@Test
public void testRandomlyChosenInitialMedoids() {
Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
Clustering<?> result = //
new ELKIBuilder<CLARA<DoubleVector>>(CLARA.class).with(KMeans.K_ID, //
5).with(KMeans.SEED_ID, //
7).with(KMeans.INIT_ID, //
RandomlyChosenInitialMeans.class).with(KMeans.MAXITER_ID, //
1).with(CLARA.Parameterizer.SAMPLESIZE_ID, //
10).with(CLARA.Parameterizer.RANDOM_ID, //
0).build().run(db);
testFMeasure(db, result, 0.99602);
testClusterSizes(result, new int[] { 198, 200, 200, 200, 202 });
}
use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.
the class SimpleKernelDensityLOFTest method testLDF.
@Test
public void testLDF() {
Database db = makeSimpleDatabase(UNITTEST + "outlier-axis-subspaces-6d.ascii", 1345);
OutlierResult result = //
new ELKIBuilder<SimpleKernelDensityLOF<DoubleVector>>(SimpleKernelDensityLOF.class).with(LOF.Parameterizer.K_ID, //
20).with(SimpleKernelDensityLOF.Parameterizer.KERNEL_ID, //
BiweightKernelDensityFunction.class).build().run(db);
testAUC(db, "Noise", result, 0.87192156);
testSingleScore(result, 1293, 12.271188);
}
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