Examples with DoubleVector de.lmu.ifi.dbs.elki.data.DoubleVector used on opensource projects

Example 11 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class FourCTest method testFourCResults.

/**
 * Run 4C with fixed parameters and compare the result to a golden standard.
 */
@Test
public void testFourCResults() {
    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-3d2d1d.csv", 600);
    Clustering<Model> result = // 
    new ELKIBuilder<FourC<DoubleVector>>(FourC.class).with(DBSCAN.Parameterizer.EPSILON_ID, // 
    0.30).with(DBSCAN.Parameterizer.MINPTS_ID, // 
    50).with(LimitEigenPairFilter.Parameterizer.EIGENPAIR_FILTER_DELTA, // 
    0.5).with(FourC.Settings.Parameterizer.LAMBDA_ID, // 
    1).build().run(db);
    testFMeasure(db, result, 0.7052);
    testClusterSizes(result, new int[] { 218, 382 });
}

Example 12 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class KMedoidsEMTest method testKMedoidsEM.

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

Example 13 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class XMeansTest method testXMeans.

/**
 * A very basic X-means test run.
 */
@Test
public void testXMeans() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);
    Clustering<?> result = // 
    new ELKIBuilder<XMeans<DoubleVector, ?>>(XMeans.class).with(XMeans.Parameterizer.K_MIN_ID, // 
    2).with(KMeans.K_ID, // 
    20).with(XMeans.Parameterizer.INNER_KMEANS_ID, // 
    KMeansLloyd.class).with(XMeans.Parameterizer.INFORMATION_CRITERION_ID, // 
    BayesianInformationCriterion.class).with(KMeans.SEED_ID, // // Initializer seed
    0).with(XMeans.Parameterizer.SEED_ID, // // X-means seed
    0).build().run(db);
    testFMeasure(db, result, 0.95927231008);
    testClusterSizes(result, new int[] { 1, 2, 2, 2, 3, 5, 5, 51, 106, 153 });
}

Example 14 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector 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 15 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class AffinityPropagationClusteringAlgorithmTest method testAffinityPropagationClusteringAlgorithmOnSingleLinkDataset.

/**
 * Run AffinityPropagationClusteringAlgorithm with fixed parameters and
 * compare the result to a golden standard.
 */
@Test
public void testAffinityPropagationClusteringAlgorithmOnSingleLinkDataset() {
    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);
    Clustering<MedoidModel> result = // 
    new ELKIBuilder<AffinityPropagationClusteringAlgorithm<DoubleVector>>(AffinityPropagationClusteringAlgorithm.class).build().run(db);
    testFMeasure(db, result, 0.351689882);
    testClusterSizes(result, new int[] { 24, 27, 29, 34, 36, 36, 37, 38, 41, 43, 43, 44, 46, 47, 56, 57 });
}