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

Example 1 with Model

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

the class LSDBCTest method testLSDBCResults.

@Test
public void testLSDBCResults() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);
    Clustering<Model> result = // 
    new ELKIBuilder<LSDBC<DoubleVector>>(LSDBC.class).with(LSDBC.Parameterizer.ALPHA_ID, // 
    0.4).with(LSDBC.Parameterizer.K_ID, // 
    20).build().run(db);
    testFMeasure(db, result, 0.44848979);
    testClusterSizes(result, new int[] { 38, 38, 41, 54, 159 });
}

Example 2 with Model

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

the class AbstractClusterAlgorithmTest method testFMeasure.

/**
 * Test the clustering result by comparing the score with an expected value.
 *
 * @param database Database to test
 * @param clustering Clustering result
 * @param expected Expected score
 */
protected <O> void testFMeasure(Database database, Clustering<?> clustering, double expected) {
    // Run by-label as reference
    ByLabelClustering bylabel = new ByLabelClustering();
    Clustering<Model> rbl = bylabel.run(database);
    ClusterContingencyTable ct = new ClusterContingencyTable(true, false);
    ct.process(clustering, rbl);
    double score = ct.getPaircount().f1Measure();
    Logging.getLogger(this.getClass()).verbose(this.getClass().getSimpleName() + " score: " + score + " expect: " + expected);
    assertEquals(this.getClass().getSimpleName() + ": Score does not match.", expected, score, 0.0001);
}

Example 3 with Model

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

the class GriDBSCANTest method testGriDBSCANWide.

/**
 * Run DBSCAN with fixed parameters and compare the result to a golden
 * standard, with larger grid width (fewer cells, less redundancy).
 */
@Test
public void testGriDBSCANWide() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);
    Clustering<Model> result = // 
    new ELKIBuilder<GriDBSCAN<DoubleVector>>(GriDBSCAN.class).with(DBSCAN.Parameterizer.EPSILON_ID, // 
    0.04).with(DBSCAN.Parameterizer.MINPTS_ID, // 
    20).with(GriDBSCAN.Parameterizer.GRID_ID, // 
    0.4).build().run(db);
    testFMeasure(db, result, 0.996413);
    testClusterSizes(result, new int[] { 29, 50, 101, 150 });
}

Example 4 with Model

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

the class GriDBSCANTest method testGriDBSCANResults.

/**
 * Run DBSCAN with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testGriDBSCANResults() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);
    Clustering<Model> result = // 
    new ELKIBuilder<GriDBSCAN<DoubleVector>>(GriDBSCAN.class).with(DBSCAN.Parameterizer.EPSILON_ID, // 
    0.04).with(DBSCAN.Parameterizer.MINPTS_ID, // 
    20).with(GriDBSCAN.Parameterizer.GRID_ID, // 
    0.08).build().run(db);
    testFMeasure(db, result, 0.996413);
    testClusterSizes(result, new int[] { 29, 50, 101, 150 });
}

Example 5 with Model

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

the class SNNClusteringTest method testSNNClusteringResults.

/**
 * Run SNNClustering with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testSNNClusteringResults() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d.ascii", 1200);
    Clustering<Model> result = // 
    new ELKIBuilder<SNNClustering<DoubleVector>>(SNNClustering.class).with(SNNClustering.Parameterizer.EPSILON_ID, // 
    77).with(SNNClustering.Parameterizer.MINPTS_ID, // 
    28).with(SharedNearestNeighborPreprocessor.Factory.NUMBER_OF_NEIGHBORS_ID, // 
    100).build().run(db);
    testFMeasure(db, result, 0.832371422);
    testClusterSizes(result, new int[] { 73, 228, 213, 219, 231, 236 });
}