source: trunk/autoquest-core-usageprofiles-test/src/test/java/de/ugoe/cs/autoquest/usageprofiles/HighOrderMarkovModelTest.java @ 1282

//   Copyright 2012 Georg-August-Universität Göttingen, Germany
//
//   Licensed under the Apache License, Version 2.0 (the "License");
//   you may not use this file except in compliance with the License.
//   You may obtain a copy of the License at
//
//       http://www.apache.org/licenses/LICENSE-2.0
//
//   Unless required by applicable law or agreed to in writing, software
//   distributed under the License is distributed on an "AS IS" BASIS,
//   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//   See the License for the specific language governing permissions and
//   limitations under the License.

package de.ugoe.cs.autoquest.usageprofiles;

import java.util.ArrayList;
import java.util.Collection;
import java.util.List;

import de.ugoe.cs.autoquest.eventcore.Event;
import de.ugoe.cs.autoquest.eventcore.StringEventType;
import de.ugoe.cs.autoquest.usageprofiles.HighOrderMarkovModel;

import java.util.Random;
import org.junit.*;

import static org.junit.Assert.*;

/**
 * The class <code>HighOrderMarkovModelTest</code> contains tests for the class
 * <code>{@link HighOrderMarkovModel}</code>.
 * 
 * @author Steffen Herbold
 * @version 1.0
 */
public class HighOrderMarkovModelTest {

        Collection<List<Event>> sequences;

        @Test
        public void testHighOrderMarkovModel_1() throws Exception {
                int maxOrder = 1;
                Random r = new Random();

                HighOrderMarkovModel result = new HighOrderMarkovModel(maxOrder, r);

                assertNotNull(result);
                assertEquals(r, result.r);
                assertEquals(maxOrder + 1, result.trieOrder);
        }

        @Test
        public void testHighOrderMarkovModel_2() throws Exception {
                int maxOrder = 0;
                Random r = new Random();

                HighOrderMarkovModel result = new HighOrderMarkovModel(maxOrder, r);

                assertNotNull(result);
                assertEquals(r, result.r);
                assertEquals(maxOrder + 1, result.trieOrder);
        }

        @Test(expected = java.lang.IllegalArgumentException.class)
        public void testHighOrderMarkovModel_3() throws Exception {
                int maxOrder = 1;
                Random r = null;

                new HighOrderMarkovModel(maxOrder, r);
        }

        @Test(expected = java.lang.IllegalArgumentException.class)
        public void testHighOrderMarkovModel_4() throws Exception {
                int maxOrder = -1;
                Random r = new Random();

                new HighOrderMarkovModel(maxOrder, r);
        }

        @Test
        public void testGetProbability_1() throws Exception {
                int markovOrder = 1;
                HighOrderMarkovModel fixture = new HighOrderMarkovModel(markovOrder,
                                new Random());
                fixture.train(sequences);

                List<Event> context = new ArrayList<Event>();
                context.add(new Event(new StringEventType("a")));

                Event symbol = new Event(new StringEventType("b"));

                double result = fixture.getProbability(context, symbol);

                assertEquals(2.0d / 5.0, result, 0.0001);
        }

        @Test
        public void testGetProbability_2() throws Exception {
                int markovOrder = 1;
                HighOrderMarkovModel fixture = new HighOrderMarkovModel(markovOrder,
                                new Random());
                fixture.train(sequences);

                List<Event> context = new ArrayList<Event>();
                context.add(new Event(new StringEventType("a")));

                Event symbol = new Event(new StringEventType("r"));

                double result = fixture.getProbability(context, symbol);

                assertEquals(0.0d / 5.0, result, 0.0001);
        }

        @Test
        public void testGetProbability_3() throws Exception {
                int markovOrder = 1;
                HighOrderMarkovModel fixture = new HighOrderMarkovModel(markovOrder,
                                new Random());
                fixture.train(sequences);

                List<Event> context = new ArrayList<Event>();
                context.add(new Event(new StringEventType("a")));

                Event symbol = new Event(new StringEventType("c"));

                double result = fixture.getProbability(context, symbol);

                assertEquals(1.0d / 5.0, result, 0.0001);
        }

        @Test
        public void testGetProbability_4() throws Exception {
                int markovOrder = 1;
                HighOrderMarkovModel fixture = new HighOrderMarkovModel(markovOrder,
                                new Random());
                fixture.train(sequences);

                List<Event> context = new ArrayList<Event>();
                context.add(Event.STARTEVENT);
                context.add(new Event(new StringEventType("a")));

                Event symbol = new Event(new StringEventType("b"));

                double result = fixture.getProbability(context, symbol);

                assertEquals(2.0d / 5.0, result, 0.0001);
        }

        @Test
        public void testGetProbability_5() throws Exception {
                int markovOrder = 2;
                HighOrderMarkovModel fixture = new HighOrderMarkovModel(markovOrder,
                                new Random());
                fixture.train(sequences);

                List<Event> context = new ArrayList<Event>();
                context.add(Event.STARTEVENT);
                context.add(new Event(new StringEventType("a")));

                Event symbol = new Event(new StringEventType("b"));

                double result = fixture.getProbability(context, symbol);

                assertEquals(1.0d, result, 0.0001);
        }

        @Test
        public void testGetProbability_6() throws Exception {
                int markovOrder = 2;
                HighOrderMarkovModel fixture = new HighOrderMarkovModel(markovOrder,
                                new Random());
                fixture.train(sequences);

                List<Event> context = new ArrayList<Event>();
                context.add(Event.STARTEVENT);
                context.add(new Event(new StringEventType("b")));

                Event symbol = new Event(new StringEventType("b"));

                double result = fixture.getProbability(context, symbol);

                assertEquals(0.0d, result, 0.0001);
        }

        @Test
        public void testGetProbability_7() throws Exception {
                int markovOrder = 0;
                HighOrderMarkovModel fixture = new HighOrderMarkovModel(markovOrder,
                                new Random());
                fixture.train(sequences);

                List<Event> context = new ArrayList<Event>();
                context.add(Event.STARTEVENT);
                context.add(new Event(new StringEventType("b")));

                Event symbol = new Event(new StringEventType("a"));

                double result = fixture.getProbability(context, symbol);

                assertEquals(5.0d / 13.0, result, 0.0001);
        }

        @Test(expected = java.lang.IllegalArgumentException.class)
        public void testGetProbability_8() throws Exception {
                int markovOrder = 0;
                HighOrderMarkovModel fixture = new HighOrderMarkovModel(markovOrder,
                                new Random());
                fixture.train(sequences);

                List<Event> context = new ArrayList<Event>();
                context.add(Event.STARTEVENT);
                context.add(new Event(new StringEventType("b")));

                Event symbol = null;

                fixture.getProbability(context, symbol);
        }

        @Test(expected = java.lang.IllegalArgumentException.class)
        public void testGetProbability_9() throws Exception {
                int markovOrder = 0;
                HighOrderMarkovModel fixture = new HighOrderMarkovModel(markovOrder,
                                new Random());
                fixture.train(sequences);

                List<Event> context = null;

                Event symbol = new Event(new StringEventType("b"));

                fixture.getProbability(context, symbol);
        }

        @Before
        public void setUp() throws Exception {
                List<Event> sequence = new ArrayList<Event>();
                sequence.add(new Event(new StringEventType("a")));
                sequence.add(new Event(new StringEventType("b")));
                sequence.add(new Event(new StringEventType("r")));
                sequence.add(new Event(new StringEventType("a")));
                sequence.add(new Event(new StringEventType("c")));
                sequence.add(new Event(new StringEventType("a")));
                sequence.add(new Event(new StringEventType("d")));
                sequence.add(new Event(new StringEventType("a")));
                sequence.add(new Event(new StringEventType("b")));
                sequence.add(new Event(new StringEventType("r")));
                sequence.add(new Event(new StringEventType("a")));

                sequences = new ArrayList<List<Event>>();
                sequences.add(sequence);
        }

        public static void main(String[] args) {
                new org.junit.runner.JUnitCore().run(HighOrderMarkovModelTest.class);
        }
}