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package de.lmu.ifi.dbs.elki.algorithm.outlier;
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.DoubleVector;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
/**
* Tests the AggarwalYuNaive algorithm.
*
* @author Lucia Cichella
*/
public class TestAggarwalYuNaive extends AbstractSimpleAlgorithmTest implements JUnit4Test {
@Test
public void testAggarwalYuNaive() {
Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);
// Parameterization
ListParameterization params = new ListParameterization();
params.addParameter(AggarwalYuNaive.K_ID, 2);
params.addParameter(AggarwalYuNaive.PHI_ID, 8);
// setup Algorithm
AggarwalYuNaive<DoubleVector> aggarwalYuNaive = ClassGenericsUtil.parameterizeOrAbort(AggarwalYuNaive.class, params);
testParameterizationOk(params);
// run AggarwalYuNaive on database
OutlierResult result = aggarwalYuNaive.run(db);
testSingleScore(result, 945, -2.3421601750764798);
testAUC(db, "Noise", result, 0.8652037037037);
}
}
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