/*
* 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 2 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, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* Copyright (C) 2002 University of Waikato
*/
package weka.filters;
import weka.core.Instance;
import weka.core.Instances;
import weka.filters.unsupervised.attribute.TimeSeriesTranslate;
import weka.filters.unsupervised.attribute.TimeSeriesTranslateTest;
import junit.framework.Test;
import junit.framework.TestSuite;
/**
* Tests TimeSeriesTranslateFilter. Run from the command line with:<p>
* java weka.filters.TimeSeriesTranslateFilterTest
*
* @author <a href="mailto:len@reeltwo.com">Len Trigg</a>
* @version $Revision: 1.7 $
*/
public abstract class AbstractTimeSeriesFilterTest extends AbstractFilterTest {
/** Tolerance allowed in double comparisons */
protected static final double TOLERANCE = 0.001;
public AbstractTimeSeriesFilterTest(String name) { super(name); }
/** Creates a default TimeSeriesTranslateFilter */
public abstract Filter getFilter();
public void testDefault() {
testInstanceRange_X(((TimeSeriesTranslate)m_Filter).getInstanceRange());
}
public void testInstanceRange() {
testInstanceRange_X(-5);
testInstanceRange_X(-2);
testInstanceRange_X(2);
testInstanceRange_X(5);
}
public void testFillWithMissing() {
((TimeSeriesTranslate)m_Filter).setFillWithMissing(true);
Instances result = useFilter();
// Number of attributes and instances shouldn't change
assertEquals(m_Instances.numAttributes(), result.numAttributes());
assertEquals(m_Instances.numInstances(), result.numInstances());
// Check conversion looks OK
for (int i = 0; i < result.numInstances(); i++) {
Instance in = m_Instances.instance(i);
Instance out = result.instance(i);
for (int j = 0; j < result.numAttributes(); j++) {
if ((j != 1) && (j != 2)) {
if (in.isMissing(j)) {
assertTrue("Nonselected missing values should pass through",
out.isMissing(j));
} else if (result.attribute(j).isString()) {
assertEquals("Nonselected attributes shouldn't change. "
+ in + " --> " + out,
m_Instances.attribute(j).value((int)in.value(j)),
result.attribute(j).value((int)out.value(j)));
} else {
assertEquals("Nonselected attributes shouldn't change. "
+ in + " --> " + out,
in.value(j),
out.value(j), TOLERANCE);
}
}
}
}
}
private void testInstanceRange_X(int range) {
((TimeSeriesTranslate)m_Filter).setInstanceRange(range);
Instances result = useFilter();
// Number of attributes and instances shouldn't change
assertEquals(m_Instances.numAttributes(), result.numAttributes());
assertEquals(m_Instances.numInstances() - Math.abs(range), result.numInstances());
// Check conversion looks OK
for (int i = 0; i < result.numInstances(); i++) {
Instance in = m_Instances.instance(i - ((range > 0) ? 0 : range));
Instance out = result.instance(i);
for (int j = 0; j < result.numAttributes(); j++) {
if ((j != 1) && (j != 2)) {
if (in.isMissing(j)) {
assertTrue("Nonselected missing values should pass through",
out.isMissing(j));
} else if (result.attribute(j).isString()) {
assertEquals("Nonselected attributes shouldn't change. "
+ in + " --> " + out,
m_Instances.attribute(j).value((int)in.value(j)),
result.attribute(j).value((int)out.value(j)));
} else {
assertEquals("Nonselected attributes shouldn't change. "
+ in + " --> " + out,
in.value(j),
out.value(j), TOLERANCE);
}
}
}
}
}
/**
* tests the filter in conjunction with the FilteredClassifier
*/
public void testFilteredClassifier() {
try {
Instances data = getFilteredClassifierData();
for (int i = 0; i < data.numAttributes(); i++) {
if (data.classIndex() == i)
continue;
if (data.attribute(i).isNumeric()) {
((TimeSeriesTranslate) m_FilteredClassifier.getFilter()).setAttributeIndices("" + (i + 1));
((TimeSeriesTranslate) m_FilteredClassifier.getFilter()).setFillWithMissing(true);
break;
}
}
}
catch (Exception e) {
fail("Problem setting up test for FilteredClassifier: " + e.toString());
}
super.testFilteredClassifier();
}
public static Test suite() {
return new TestSuite(TimeSeriesTranslateTest.class);
}
public static void main(String[] args){
junit.textui.TestRunner.run(suite());
}
}