Examples with ELKIBuilder de.lmu.ifi.dbs.elki.utilities.ELKIBuilder used on opensource projects

Example 81 with ELKIBuilder

use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.

the class SingleAssignmentKMeansTest method testSingleAssignmentKMeans.

/**
 * Run KMeans with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testSingleAssignmentKMeans() {
    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, // 
    7).build().run(db);
    // Unsurprisingly, these results are much worse than normal k-means
    testFMeasure(db, result, 0.702733122);
    testClusterSizes(result, new int[] { 64, 95, 202, 306, 333 });
}

Example 82 with ELKIBuilder

use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.

the class FarthestPointsInitialMeansTest method testFarthestPointsInitialMedoids.

/**
 * Run CLARA with fixed parameters and compare the result to a golden
 * standard.
 */
@Test
public void testFarthestPointsInitialMedoids() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);
    Clustering<?> result = // 
    new ELKIBuilder<CLARA<DoubleVector>>(CLARA.class).with(KMeans.K_ID, // 
    5).with(KMeans.SEED_ID, // 
    3).with(KMeans.INIT_ID, // 
    FarthestPointsInitialMeans.class).with(KMeans.MAXITER_ID, // 
    1).with(CLARA.Parameterizer.SAMPLESIZE_ID, // 
    10).with(CLARA.Parameterizer.RANDOM_ID, // 
    0).build().run(db);
    testFMeasure(db, result, 0.8832692);
    testClusterSizes(result, new int[] { 128, 200, 200, 201, 271 });
}

Example 83 with ELKIBuilder

use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.

the class AbstractOutlierAlgorithmTest method testAUC.

/**
 * Test the AUC value for an outlier result.
 *
 * @param db Database
 * @param positive Positive class name
 * @param result Outlier result to process
 * @param expected Expected AUC value
 */
protected void testAUC(Database db, String positive, OutlierResult result, double expected) {
    OutlierROCCurve rocCurve = // 
    new ELKIBuilder<>(OutlierROCCurve.class).with(OutlierROCCurve.Parameterizer.POSITIVE_CLASS_NAME_ID, positive).build();
    // Ensure the result has been added to the hierarchy:
    ResultHierarchy hier = db.getHierarchy();
    if (hier.numParents(result) < 1) {
        hier.add(db, result);
    }
    // Compute ROC and AUC:
    rocCurve.processNewResult(hier, result);
    // Find the ROC results
    Collection<OutlierROCCurve.ROCResult> rocs = ResultUtil.filterResults(hier, result, OutlierROCCurve.ROCResult.class);
    assertTrue("No ROC result found.", !rocs.isEmpty());
    double auc = rocs.iterator().next().getAUC();
    assertFalse("More than one ROC result found.", rocs.size() > 1);
    assertEquals("ROC value does not match.", expected, auc, 0.0001);
}

Example 84 with ELKIBuilder

use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.

the class GaussianUniformMixtureTest method testGaussianUniformMixture.

@Test
public void testGaussianUniformMixture() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-fire.ascii", 1025);
    OutlierResult result = new ELKIBuilder<GaussianUniformMixture<DoubleVector>>(GaussianUniformMixture.class).build().run(db);
    testSingleScore(result, 1025, -20.27211494);
    testAUC(db, "Noise", result, 0.97251282);
}

Example 85 with ELKIBuilder

use of de.lmu.ifi.dbs.elki.utilities.ELKIBuilder in project elki by elki-project.

the class DBOutlierDetectionTest method testDBOutlierDetection.

@Test
public void testDBOutlierDetection() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-fire.ascii", 1025);
    OutlierResult result = // 
    new ELKIBuilder<DBOutlierDetection<DoubleVector>>(DBOutlierDetection.class).with(DBOutlierDetection.Parameterizer.D_ID, // 
    0.175).with(DBOutlierDetection.Parameterizer.P_ID, // 
    0.98).build().run(db);
    testSingleScore(result, 1025, 0.0);
    testAUC(db, "Noise", result, 0.97487179);
}