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Diffstat (limited to 'test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestEMResults.java')
-rw-r--r-- | test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestEMResults.java | 47 |
1 files changed, 47 insertions, 0 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestEMResults.java b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestEMResults.java new file mode 100644 index 00000000..e3e4c984 --- /dev/null +++ b/test/de/lmu/ifi/dbs/elki/algorithm/clustering/TestEMResults.java @@ -0,0 +1,47 @@ +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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