/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/*
* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
*/
package weka.filters.unsupervised.attribute;
import weka.classifiers.meta.FilteredClassifier;
import weka.clusterers.Clusterer;
import weka.clusterers.EM;
import weka.core.Attribute;
import weka.core.Instances;
import weka.core.TestInstances;
import weka.filters.AbstractFilterTest;
import weka.filters.Filter;
import junit.framework.Test;
import junit.framework.TestSuite;
/**
* Tests AddCluster. Run from the command line with: <p/>
* java weka.filters.unsupervised.attribute.AddClusterTest
*
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 8034 $
*/
public class AddClusterTest
extends AbstractFilterTest {
public AddClusterTest(String name) {
super(name);
}
/** Need to remove attributes that are not nominal/numeric */
protected void setUp() throws Exception {
super.setUp();
// remove attributes that are not nominal/numeric
int i = 0;
while (i < m_Instances.numAttributes()) {
if ( ( !m_Instances.attribute(i).isNominal()
&& !m_Instances.attribute(i).isNumeric() )
|| m_Instances.attribute(i).isDate() )
m_Instances.deleteAttributeAt(i);
else
i++;
}
}
/**
* returns a configured cluster algorithm
*/
protected Clusterer getClusterer() {
EM c = new EM();
try {
c.setOptions(new String[0]);
}
catch (Exception e) {
e.printStackTrace();
}
return c;
}
/** Creates a default AddCluster, with SimpleKMeans as cluster
* @see #getClusterer */
public Filter getFilter() {
AddCluster f = new AddCluster();
f.setClusterer(getClusterer());
return f;
}
/**
* returns the configured FilteredClassifier. Since the base classifier is
* determined heuristically, derived tests might need to adjust it.
*
* @return the configured FilteredClassifier
*/
protected FilteredClassifier getFilteredClassifier() {
FilteredClassifier result;
result = new FilteredClassifier();
result.setFilter(getFilter());
result.setClassifier(new weka.classifiers.trees.J48());
return result;
}
/**
* returns data generated for the FilteredClassifier test
*
* @return the dataset for the FilteredClassifier
* @throws Exception if generation of data fails
*/
protected Instances getFilteredClassifierData() throws Exception{
TestInstances test;
Instances result;
test = TestInstances.forCapabilities(m_FilteredClassifier.getCapabilities());
test.setClassType(Attribute.NOMINAL);
test.setClassIndex(TestInstances.CLASS_IS_LAST);
result = test.generate();
return result;
}
public void testTypical() {
m_Filter = getFilter();
Instances result = useFilter();
assertEquals(m_Instances.numAttributes() + 1, result.numAttributes());
assertEquals(m_Instances.numInstances(), result.numInstances());
}
public static Test suite() {
return new TestSuite(AddClusterTest.class);
}
public static void main(String[] args){
junit.textui.TestRunner.run(suite());
}
}