source: trunk/CrossPare/test/de/ugoe/cs/cpdp/dataselection/TestAsTrainingTest.java

package de.ugoe.cs.cpdp.dataselection;

import static org.junit.Assert.*;

import java.util.ArrayList;
import java.util.LinkedList;

import org.apache.commons.collections4.list.SetUniqueList;
import org.junit.Test;

import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instances;

public class TestAsTrainingTest {

        @Test
        public void testApply() {
                ArrayList<Attribute> attributes = new ArrayList<Attribute>();
                attributes.add(new Attribute("attr1"));
                attributes.add(new Attribute("class"));
                
                Instances testdata = new Instances("test", attributes, 0);
                testdata.setClassIndex(1);
                testdata.add(new DenseInstance(1.0, new double[]{3.0, 0.0}));
                testdata.add(new DenseInstance(1.0, new double[]{6.6, 0.0}));
                testdata.add(new DenseInstance(1.0, new double[]{3.1, 0.0}));
                                
                Instances traindata = new Instances("train", attributes, 0);
                traindata.setClassIndex(1);
                traindata.add(new DenseInstance(1.0, new double[]{2.9, 0.0}));
                traindata.add(new DenseInstance(1.0, new double[]{2.8, 0.0}));
                traindata.add(new DenseInstance(1.0, new double[]{3.2, 0.0}));
                traindata.add(new DenseInstance(1.0, new double[]{3.05, 0.0}));
                traindata.add(new DenseInstance(1.0, new double[]{10.0, 0.0}));
                traindata.add(new DenseInstance(1.0, new double[]{9.0, 0.0}));
                traindata.add(new DenseInstance(1.0, new double[]{8.0, 0.0}));
                traindata.add(new DenseInstance(1.0, new double[]{1.0, 0.0}));
                traindata.add(new DenseInstance(1.0, new double[]{5.0, 0.0}));
                                
                SetUniqueList<Instances> traindataSet = SetUniqueList.setUniqueList(new LinkedList<Instances>());
                traindataSet.add(traindata);
                
                TestAsTraining filter = new TestAsTraining();
                filter.apply(testdata, traindataSet);
                
                assertEquals(1, traindataSet.size());
        
                traindata = traindataSet.get(0);
                assertNotSame(testdata, traindata);
                assertEquals(testdata.numInstances(), traindata.numInstances());
                for( int i=0; i<testdata.numInstances(); i++ ) {
                        assertArrayEquals(testdata.instance(i).toDoubleArray(), traindata.instance(i).toDoubleArray(), 0.000000001);
                }
        }

}