Examples with Database de.lmu.ifi.dbs.elki.database.Database used on opensource projects

Example 31 with Database

use of de.lmu.ifi.dbs.elki.database.Database in project elki by elki-project.

the class SimplifiedHierarchyExtractionTest method testMiniMaxNNResults.

@Test
public void testMiniMaxNNResults() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);
    Result result = // 
    new ELKIBuilder<>(SimplifiedHierarchyExtraction.class).with(SimplifiedHierarchyExtraction.Parameterizer.MINCLUSTERSIZE_ID, // 
    1).with(AbstractAlgorithm.ALGORITHM_ID, // 
    MiniMaxNNChain.class).build().run(db);
    Clustering<?> clustering = findSingleClustering(result);
    // minclustersize=1 is useless
    testFMeasure(db, clustering, 0.0182169);
    assertEquals(2 * 330 - 1, clustering.getAllClusters().size());
}

Example 32 with Database

use of de.lmu.ifi.dbs.elki.database.Database in project elki by elki-project.

the class SimplifiedHierarchyExtractionTest method testSLINKDegenerate.

@Test
public void testSLINKDegenerate() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);
    Clustering<?> clustering = // 
    new ELKIBuilder<>(SimplifiedHierarchyExtraction.class).with(SimplifiedHierarchyExtraction.Parameterizer.MINCLUSTERSIZE_ID, // 
    1).with(AbstractAlgorithm.ALGORITHM_ID, // 
    SLINK.class).build().run(db);
    // minclustersize=1 is useless
    testFMeasure(db, clustering, 0.0182169);
    assertEquals(2 * 330 - 1, clustering.getAllClusters().size());
}

Example 33 with Database

use of de.lmu.ifi.dbs.elki.database.Database in project elki by elki-project.

the class CLARATest method testCLARA.

/**
 * Run CLARA with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testCLARA() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
    Clustering<MedoidModel> result = // 
    new ELKIBuilder<CLARA<DoubleVector>>(CLARA.class).with(KMeans.K_ID, // 
    5).with(CLARA.Parameterizer.RANDOM_ID, // 
    1).with(CLARA.Parameterizer.NUMSAMPLES_ID, // 
    2).with(CLARA.Parameterizer.SAMPLESIZE_ID, // 
    50).build().run(db);
    testFMeasure(db, result, 0.998005);
    testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 });
}

Example 34 with Database

use of de.lmu.ifi.dbs.elki.database.Database in project elki by elki-project.

the class KMeansBisectingTest method testKMeansBisectingFMeasure.

/**
 * Run KMeansBisecting with fixed parameters (k = 2) and compare f-measure to
 * golden standard.
 */
@Test
public void testKMeansBisectingFMeasure() {
    Database db = makeSimpleDatabase(UNITTEST + "bisecting-test.csv", 300);
    KMeansBisecting<DoubleVector, MeanModel> kmeans = // 
    new ELKIBuilder<KMeansBisecting<DoubleVector, MeanModel>>(KMeansBisecting.class).with(KMeans.K_ID, // 
    2).with(KMeans.SEED_ID, // 
    0).with(BestOfMultipleKMeans.Parameterizer.TRIALS_ID, // 
    5).with(BestOfMultipleKMeans.Parameterizer.KMEANS_ID, // 
    KMeansLloyd.class).with(BestOfMultipleKMeans.Parameterizer.QUALITYMEASURE_ID, // 
    WithinClusterVarianceQualityMeasure.class).build();
    // run KMedians on database
    Clustering<MeanModel> result = kmeans.run(db);
    testFMeasure(db, result, 0.7408);
}

Example 35 with Database

use of de.lmu.ifi.dbs.elki.database.Database in project elki by elki-project.

the class KMeansElkanTest method testKMeansElkan.

/**
 * Run KMeans with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testKMeansElkan() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
    Clustering<?> result = // 
    new ELKIBuilder<KMeansElkan<DoubleVector>>(KMeansElkan.class).with(KMeans.K_ID, // 
    5).with(KMeans.SEED_ID, // 
    7).build().run(db);
    testFMeasure(db, result, 0.998005);
    testClusterSizes(result, new int[] { 199, 200, 200, 200, 201 });
}