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package de.lmu.ifi.dbs.elki.algorithm.clustering;
import org.junit.Test;
import de.lmu.ifi.dbs.elki.JUnit4Test;
import de.lmu.ifi.dbs.elki.algorithm.AbstractSimpleAlgorithmTest;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.DoubleVector;
import de.lmu.ifi.dbs.elki.data.model.EMModel;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.ParameterException;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
/**
* Performs a full EM run, and compares the result with a clustering derived
* from the data set labels. This test ensures that EM's performance doesn't
* unexpectedly drop on this data set (and also ensures that the algorithms
* work, as a side effect).
*
* @author Katharina Rausch
* @author Erich Schubert
*/
public class TestEMResults extends AbstractSimpleAlgorithmTest implements JUnit4Test {
/**
* Run EM with fixed parameters and compare the result to a golden
* standard.
*
* @throws ParameterException
*/
@Test
public void testEMResults() {
Database db = makeSimpleDatabase(UNITTEST + "hierarchical-2d.ascii", 710);
// Setup algorithm
ListParameterization params = new ListParameterization();
params.addParameter(EM.SEED_ID, 1);
params.addParameter(EM.K_ID, 5);
EM<DoubleVector> em = ClassGenericsUtil.parameterizeOrAbort(EM.class, params);
testParameterizationOk(params);
// run EM on database
Clustering<EMModel<DoubleVector>> result = em.run(db);
testFMeasure(db, result, 0.961587);
testClusterSizes(result, new int[] { 5, 91, 98, 200, 316 });
}
}
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