/* * 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()); } }