public void testRejectionSample()
{
BayesNet net = createWetGrassNetwork();
MockRandomizer r = new MockRandomizer(new double[] { 0.1 });
Dictionary<String, bool> evidence = new Dictionary<String, bool>();
evidence.Add("Sprinkler", true);
double[] results = net.rejectionSample("Rain", evidence, 100, r);
Assert.AreEqual(1.0, results[0], 0.001);
Assert.AreEqual(0.0, results[1], 0.001);
}
public void testLikelihoodWeighting()
{
MockRandomizer r = new MockRandomizer(
new double[] { 0.5, 0.5, 0.5, 0.5 });
BayesNet net = createWetGrassNetwork();
Dictionary<String, bool> evidence = new Dictionary<String, bool>();
evidence.Add("Sprinkler", true);
double[] results = net.likelihoodWeighting("Rain", evidence, 1000, r);
Assert.AreEqual(1.0, results[0], 0.001);
Assert.AreEqual(0.0, results[1], 0.001);
}
public void testPriorSample()
{
BayesNet net = createWetGrassNetwork();
MockRandomizer r = new MockRandomizer(
new double[] { 0.5, 0.5, 0.5, 0.5 });
Dictionary<String,bool> table = net.getPriorSample(r);
Assert.AreEqual(4, table.Count);
Assert.AreEqual(true, table["Cloudy"]);
Assert.AreEqual(false, table["Sprinkler"]);
Assert.AreEqual(true, table["Rain"]);
Assert.AreEqual(true, table["WetGrass"]);
}
public void testMCMCask2()
{
BayesNet net = createWetGrassNetwork();
MockRandomizer r = new MockRandomizer(
new double[] { 0.5, 0.5, 0.5, 0.5 });
Dictionary<String, bool> evidence = new Dictionary<String, bool>();
evidence.Add("Sprinkler", true);
double[] results = net.mcmcAsk("Rain", evidence, 1, r);
Assert.AreEqual(0.333, results[0], 0.001);
Assert.AreEqual(0.666, results[1], 0.001);
}
public void testPassiveADPAgent()
{
PassiveADPAgent<CellWorldPosition, String> agent = new PassiveADPAgent<CellWorldPosition, String>(
fourByThree, policy);
// Randomizer r = new JavaRandomizer();
Randomizer r = new MockRandomizer(new double[] { 0.1, 0.9, 0.2, 0.8,
0.3, 0.7, 0.4, 0.6, 0.5 });
MDPUtilityFunction<CellWorldPosition> uf = null;
for (int i = 0; i < 100; i++)
{
agent.executeTrial(r);
uf = agent.getUtilityFunction();
}
Assert.AreEqual(0.676, uf.getUtility(new CellWorldPosition(1, 1)),
0.001);
Assert.AreEqual(0.626, uf.getUtility(new CellWorldPosition(1, 2)),
0.001);
Assert.AreEqual(0.573, uf.getUtility(new CellWorldPosition(1, 3)),
0.001);
Assert.AreEqual(0.519, uf.getUtility(new CellWorldPosition(1, 4)),
0.001);
Assert.AreEqual(0.746, uf.getUtility(new CellWorldPosition(2, 1)),
0.001);
Assert.AreEqual(0.865, uf.getUtility(new CellWorldPosition(2, 3)),
0.001);
// AreEqual(-1.0, uf.getUtility(new
// CellWorldPosition(2,4)),0.001);//the pseudo random genrator never
// gets to this square
Assert.AreEqual(0.796, uf.getUtility(new CellWorldPosition(3, 1)),
0.001);
Assert.AreEqual(0.906, uf.getUtility(new CellWorldPosition(3, 3)),
0.001);
Assert.AreEqual(1.0, uf.getUtility(new CellWorldPosition(3, 4)),
0.001);
}
public void testEnumerationAskinMCMC()
{
BayesNet net = createWetGrassNetwork();
MockRandomizer r = new MockRandomizer(
new double[] { 0.5, 0.5, 0.5, 0.5 });
Dictionary<String, bool> evidence = new Dictionary<String, bool>();
evidence.Add("Rain", true);
evidence.Add("Sprinkler", true);
Query q = new Query("Cloudy", new String[] { "Sprinkler", "Rain" },
new bool[] { true, true });
double[] results = EnumerationAsk.ask(q, net);
double[] results2 = net.mcmcAsk("Cloudy", evidence, 1000);
}
public void testPassiveTDAgent()
{
PassiveTDAgent<CellWorldPosition, String> agent = new PassiveTDAgent<CellWorldPosition, String>(
fourByThree, policy);
// Randomizer r = new JavaRandomizer();
Randomizer r = new MockRandomizer(new double[] { 0.1, 0.9, 0.2, 0.8,
0.3, 0.7, 0.4, 0.6, 0.5 });
MDPUtilityFunction<CellWorldPosition> uf = null;
for (int i = 0; i < 200; i++)
{
agent.executeTrial(r);
uf = agent.getUtilityFunction();
// System.Console.WriteLine(uf);
}
Assert.AreEqual(0.662, uf.getUtility(new CellWorldPosition(1, 1)),
0.001);
Assert.AreEqual(0.610, uf.getUtility(new CellWorldPosition(1, 2)),
0.001);
Assert.AreEqual(0.553, uf.getUtility(new CellWorldPosition(1, 3)),
0.001);
Assert.AreEqual(0.496, uf.getUtility(new CellWorldPosition(1, 4)),
0.001);
Assert.AreEqual(0.735, uf.getUtility(new CellWorldPosition(2, 1)),
0.001);
Assert.AreEqual(0.835, uf.getUtility(new CellWorldPosition(2, 3)),
0.001);
// AreEqual(-1.0, uf.getUtility(new
// CellWorldPosition(2,4)),0.001);//the pseudo random genrator never
// gets to this square
Assert.AreEqual(0.789, uf.getUtility(new CellWorldPosition(3, 1)),
0.001);
Assert.AreEqual(0.889, uf.getUtility(new CellWorldPosition(3, 3)),
0.001);
Assert.AreEqual(1.0, uf.getUtility(new CellWorldPosition(3, 4)),
0.001);
}
public void testFirstStepsOfQLAAgentWhenFirstStepTerminates()
{
QLearningAgent<CellWorldPosition, String> qla = new QLearningAgent<CellWorldPosition, String>(
fourByThree);
CellWorldPosition startingPosition = new CellWorldPosition(1, 4);
String action = qla.decideAction(new MDPPerception<CellWorldPosition>(
startingPosition, -0.04));
Assert.AreEqual(CellWorld.LEFT, action);
Randomizer betweenEightyANdNinetyPercent = new MockRandomizer(
new double[] { 0.85 }); // to force left to become an "up"
qla.execute(action, betweenEightyANdNinetyPercent);
Assert.AreEqual(new CellWorldPosition(2, 4), qla.getCurrentState());
Assert.AreEqual(-1.0, qla.getCurrentReward(), 0.001);
Assert.AreEqual(0.0, qla.getQTable().getQValue(startingPosition,
action), 0.001);
String action2 = qla.decideAction(new MDPPerception<CellWorldPosition>(
new CellWorldPosition(2, 4), -1));
Assert.IsNull(action2);
Assert.AreEqual(-1.0, qla.getQTable().getQValue(startingPosition,
action), 0.001);
}
public void testFirstStepsOfQLAAgentUnderNormalProbability()
{
QLearningAgent<CellWorldPosition, String> qla = new QLearningAgent<CellWorldPosition, String>(
fourByThree);
Randomizer alwaysLessThanEightyPercent = new MockRandomizer(
new double[] { 0.7 });
CellWorldPosition startingPosition = new CellWorldPosition(1, 4);
String action = qla.decideAction(new MDPPerception<CellWorldPosition>(
startingPosition, -0.04));
Assert.AreEqual(CellWorld.LEFT, action);
Assert.AreEqual(0.0, qla.getQTable().getQValue(startingPosition,
action), 0.001);
qla.execute(action, alwaysLessThanEightyPercent);
Assert.AreEqual(new CellWorldPosition(1, 3), qla.getCurrentState());
Assert.AreEqual(-0.04, qla.getCurrentReward(), 0.001);
Assert.AreEqual(0.0, qla.getQTable().getQValue(startingPosition,
action), 0.001);
String action2 = qla.decideAction(new MDPPerception<CellWorldPosition>(
new CellWorldPosition(1, 3), -0.04));
Assert.AreEqual(-0.04, qla.getQTable().getQValue(startingPosition,
action), 0.001);
}
public void testQLearningAgent()
{
QLearningAgent<CellWorldPosition, String> qla = new QLearningAgent<CellWorldPosition,string>(
fourByThree);
Randomizer r = new MockRandomizer(new double[] { 0.1, 0.9, 0.2, 0.8,
0.3, 0.7, 0.4, 0.6, 0.5 });
// Randomizer r = new JavaRandomizer();
Dictionary<Pair<CellWorldPosition, String>, Double> q = null;
QTable<CellWorldPosition, String> qTable = null;
for (int i = 0; i < 100; i++)
{
qla.executeTrial(r);
q = qla.getQ();
qTable = qla.getQTable();
}
// qTable.normalize();
// System.Console.WriteLine(qTable);
// System.Console.WriteLine(qTable.getPolicy());
}