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

Example 66 with Database

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

the class KMeansHamerlyTest method testKMeansHamerly.

/**
 * Run KMeans with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testKMeansHamerly() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
    Clustering<?> result = // 
    new ELKIBuilder<KMeansHamerly<DoubleVector>>(KMeansHamerly.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 });
}

Example 67 with Database

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

the class FarthestPointsInitialMeansTest method testFarthestPointsInitialMeans.

/**
 * Run KMeans with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testFarthestPointsInitialMeans() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
    Clustering<?> result = // 
    new ELKIBuilder<SingleAssignmentKMeans<DoubleVector>>(SingleAssignmentKMeans.class).with(KMeans.K_ID, // 
    5).with(KMeans.SEED_ID, // 
    3).with(KMeans.INIT_ID, // 
    FarthestPointsInitialMeans.class).build().run(db);
    testFMeasure(db, result, 0.88132453);
    testClusterSizes(result, new int[] { 128, 199, 201, 201, 271 });
}

Example 68 with Database

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

the class NNChainTest method testCentroid.

/**
 * Run agglomerative hierarchical clustering with fixed parameters and compare
 * the result to a golden standard.
 */
@Test
public void testCentroid() {
    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
    Clustering<?> clustering = // 
    new ELKIBuilder<>(CutDendrogramByNumberOfClusters.class).with(CutDendrogramByNumberOfClusters.Parameterizer.MINCLUSTERS_ID, // 
    3).with(AbstractAlgorithm.ALGORITHM_ID, // 
    NNChain.class).with(AGNES.Parameterizer.LINKAGE_ID, // 
    CentroidLinkage.class).build().run(db);
    testFMeasure(db, clustering, 0.93866265);
    testClusterSizes(clustering, new int[] { 200, 211, 227 });
}

Example 69 with Database

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

the class NNChainTest method testBetaVariance.

/**
 * Run agglomerative hierarchical clustering with fixed parameters and compare
 * the result to a golden standard.
 */
@Test
public void testBetaVariance() {
    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
    Clustering<?> clustering = // 
    new ELKIBuilder<>(CutDendrogramByNumberOfClusters.class).with(CutDendrogramByNumberOfClusters.Parameterizer.MINCLUSTERS_ID, // 
    3).with(AbstractAlgorithm.ALGORITHM_ID, // 
    NNChain.class).with(AGNES.Parameterizer.LINKAGE_ID, // 
    FlexibleBetaLinkage.class).with(FlexibleBetaLinkage.Parameterizer.BETA_ID, // 
    -.33).build().run(db);
    testFMeasure(db, clustering, 0.9381678);
    testClusterSizes(clustering, new int[] { 200, 217, 221 });
}

Example 70 with Database

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

the class EvaluateConcordantPairs method processNewResult.

@Override
public void processNewResult(ResultHierarchy hier, Result result) {
    List<Clustering<?>> crs = Clustering.getClusteringResults(result);
    if (crs.isEmpty()) {
        return;
    }
    Database db = ResultUtil.findDatabase(hier);
    Relation<? extends NumberVector> rel = db.getRelation(this.distanceFunction.getInputTypeRestriction());
    for (Clustering<?> c : crs) {
        evaluateClustering(db, (Relation<? extends NumberVector>) rel, c);
    }
}