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

Example 51 with Database

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

the class RandomlyGeneratedInitialMeansTest method testRandomlyGeneratedInitialMeans.

/**
 * Run KMeans with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testRandomlyGeneratedInitialMeans() {
    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, // 
    0).with(KMeans.INIT_ID, // 
    RandomlyGeneratedInitialMeans.class).build().run(db);
    testFMeasure(db, result, 0.74344789);
    testClusterSizes(result, new int[] { 1, 145, 208, 246, 400 });
}

Example 52 with Database

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

the class ParallelLloydKMeansTest method testParallelKMeansLloyd.

/**
 * Run KMeans with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testParallelKMeansLloyd() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
    Clustering<?> result = // 
    new ELKIBuilder<ParallelLloydKMeans<DoubleVector>>(ParallelLloydKMeans.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 53 with Database

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

the class OPTICSHeapTest method testOPTICSResults.

/**
 * Run OPTICS with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testOPTICSResults() {
    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-2d.ascii", 710);
    Clustering<?> clustering = // 
    new ELKIBuilder<>(OPTICSXi.class).with(OPTICSHeap.Parameterizer.MINPTS_ID, // 
    18).with(OPTICSXi.Parameterizer.XI_ID, // 
    0.038).with(OPTICSXi.Parameterizer.XIALG_ID, // 
    OPTICSHeap.class).build().run(db);
    testFMeasure(db, clustering, 0.8819664);
    testClusterSizes(clustering, new int[] { 108, 120, 209, 273 });
}

Example 54 with Database

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

the class AnderbergHierarchicalClusteringTest method testMedian.

/**
 * Run agglomerative hierarchical clustering with fixed parameters and compare
 * the result to a golden standard.
 */
@Test
public void testMedian() {
    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, // 
    AnderbergHierarchicalClustering.class).with(AGNES.Parameterizer.LINKAGE_ID, // 
    MedianLinkage.class).build().run(db);
    testFMeasure(db, clustering, 0.9381678);
    testClusterSizes(clustering, new int[] { 200, 217, 221 });
}

Example 55 with Database

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

the class AnderbergHierarchicalClusteringTest method testGroupAverage.

/**
 * Run agglomerative hierarchical clustering with fixed parameters and compare
 * the result to a golden standard.
 */
@Test
public void testGroupAverage() {
    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, // 
    AnderbergHierarchicalClustering.class).with(AGNES.Parameterizer.LINKAGE_ID, // 
    GroupAverageLinkage.class).build().run(db);
    testFMeasure(db, clustering, 0.93866265);
    testClusterSizes(clustering, new int[] { 200, 211, 227 });
}