public void testFilteringWithParticleSetsForRobotHmm()
{
Randomizer r = new MockRandomizer(new double[] { 0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9 });
ParticleSet starting = robot.prior().toParticleSet(robot, r, 100);
Assert.AreEqual(56, starting
.numberOfParticlesWithState(HmmConstants.DOOR_OPEN));
Assert.AreEqual(44, starting
.numberOfParticlesWithState(HmmConstants.DOOR_CLOSED));
// step one = robot takes no action but senses open door
ParticleSet afterStepOne = starting.filter(HmmConstants.SEE_DOOR_OPEN,
r);
Assert.AreEqual(66, afterStepOne
.numberOfParticlesWithState(HmmConstants.DOOR_OPEN));
Assert.AreEqual(34, afterStepOne
.numberOfParticlesWithState(HmmConstants.DOOR_CLOSED));
// step two = robot pushes the door and then senses open door
ParticleSet afterStepTwo = starting.filter(HmmConstants.PUSH_DOOR,
HmmConstants.SEE_DOOR_OPEN, r);
Assert.AreEqual(100, afterStepTwo
.numberOfParticlesWithState(HmmConstants.DOOR_OPEN));
Assert.AreEqual(0, afterStepTwo
.numberOfParticlesWithState(HmmConstants.DOOR_CLOSED));
}
public void testFilteringWithParticleSetsWorksForRainmanHmm()
{
Randomizer r = new MockRandomizer(new double[] { 0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9 });
ParticleSet starting = rainman.prior().toParticleSet(rainman, r, 100);
Assert.AreEqual(56, starting
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(44, starting
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
ParticleSet afterSeeingUmbrella = starting.filter(
HmmConstants.SEE_UMBRELLA, r);
Assert.AreEqual(84, afterSeeingUmbrella
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(16, afterSeeingUmbrella
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
// or alternatively
ParticleSet afterNotSeeingUmbrella = starting.filter(
HmmConstants.SEE_NO_UMBRELLA, r);
Assert.AreEqual(12, afterNotSeeingUmbrella
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(88, afterNotSeeingUmbrella
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
}
public void setUp() {
cw = new CellWorld(3, 4, -0.04);
cw.markBlocked(2, 2);
cw.setTerminalState(2, 4);
cw.setReward(2, 4, -1);
cw.setTerminalState(3, 4);
cw.setReward(3, 4, 1);
alwaysLessThanEightyPercent = new MockRandomizer(new double[] { 0.7 });
betweenEightyAndNinetyPercent = new MockRandomizer(
new double[] { 0.85 });
greaterThanNinetyPercent = new MockRandomizer(new double[] { 0.95 });
}
public void testPriorSample_basic()
{
// AIMA3e pg. 530
IBayesianNetwork bn = BayesNetExampleFactory
.constructCloudySprinklerRainWetGrassNetwork();
IRandom r = new MockRandomizer(
new double[] { 0.5, 0.5, 0.5, 0.5 });
PriorSample ps = new PriorSample(r);
IMap <IRandomVariable, object> even = ps.priorSample(bn);
Assert.AreEqual(4, even.GetKeys().Size());
Assert.AreEqual(true, even.Get(ExampleRV.CLOUDY_RV));
Assert.AreEqual(false, even.Get(ExampleRV.SPRINKLER_RV));
Assert.AreEqual(true, even.Get(ExampleRV.RAIN_RV));
Assert.AreEqual(true, even.Get(ExampleRV.WET_GRASS_RV));
}
public void testParticleSetForPredictedStateGeneratedFromOldStateParticleSet()
{
Randomizer r = new MockRandomizer(new double[] { 0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9 });
ParticleSet ps = rainman.prior().toParticleSet(rainman, r, 10);
Assert.AreEqual(6, ps
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(4, ps
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
ParticleSet nps = ps.generateParticleSetForPredictedState(r);
Assert.AreEqual(7, nps
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(3, nps
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
}
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());
}
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 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 List <AssignmentProposition[][]> AskParticleFiltering(object owner, IContextLookup globalVars)
{
var randomVars = DynamicBayesianNetwork.GetRandomVariables(owner, globalVars);
var model = DynamicBayesianNetwork.GetNetwork(randomVars);
Randomizer objRandomizer;
switch (RandomizerType)
{
case RandomizerType.Random:
objRandomizer = new JavaRandomizer();
break;
case RandomizerType.Mock:
objRandomizer = new MockRandomizer(RandomizerValues.ToArray());
break;
default:
throw new ArgumentOutOfRangeException();
}
var objAlgorithm = new ParticleFiltering(SampleNb, (DynamicBayesianNetwork)model, objRandomizer);
var objEvidences = new java.util.ArrayList(Evidences.Count);
var toReturn = new List <AssignmentProposition[][]>();
foreach (List <PropositionInfo> propositions in Evidences)
{
var stepEvidences = new List <AssignmentProposition>(propositions.Count);
foreach (PropositionInfo proposition in propositions)
{
stepEvidences.Add((AssignmentProposition)proposition.GetProposition(owner, globalVars, randomVars));
}
toReturn.Add(objAlgorithm.particleFiltering(stepEvidences.Cast <AssignmentProposition>().ToArray()));
}
return(toReturn);
}
public void testParticleSetForPerceptionUpdatedStateGeneratedFromPredictedStateParticleSetGivenPerception()
{
Randomizer r = new MockRandomizer(new double[] { 0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9 });
ParticleSet starting = rainman.prior().toParticleSet(rainman, r, 10);
ParticleSet predicted = starting
.generateParticleSetForPredictedState(r);
ParticleSet updatedWithPerceptionOfUmbrella = predicted
.perceptionUpdate(HmmConstants.SEE_UMBRELLA, r);
Assert.AreEqual(9, updatedWithPerceptionOfUmbrella
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(1, updatedWithPerceptionOfUmbrella
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
ParticleSet updatedWithPerceptionOfNoUmbrella = predicted
.perceptionUpdate(HmmConstants.SEE_NO_UMBRELLA, r);
Assert.AreEqual(2, updatedWithPerceptionOfNoUmbrella
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(8, updatedWithPerceptionOfNoUmbrella
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
}
public void testParticleSetForPerceptionUpdatedStateGeneratedFromPredictedStateParticleSetGivenPerception()
{
Randomizer r = new MockRandomizer(new double[] { 0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9 });
ParticleSet starting = rainman.prior().toParticleSet(rainman, r, 10);
ParticleSet predicted = starting
.generateParticleSetForPredictedState(r);
ParticleSet updatedWithPerceptionOfUmbrella = predicted
.perceptionUpdate(HmmConstants.SEE_UMBRELLA, r);
Assert.AreEqual(9, updatedWithPerceptionOfUmbrella
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(1, updatedWithPerceptionOfUmbrella
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
ParticleSet updatedWithPerceptionOfNoUmbrella = predicted
.perceptionUpdate(HmmConstants.SEE_NO_UMBRELLA, r);
Assert.AreEqual(2, updatedWithPerceptionOfNoUmbrella
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(8, updatedWithPerceptionOfNoUmbrella
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
}
public void testParticleSetForPredictedStateGeneratedFromOldStateParticleSet()
{
Randomizer r = new MockRandomizer(new double[] { 0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9 });
ParticleSet ps = rainman.prior().toParticleSet(rainman, r, 10);
Assert.AreEqual(6, ps
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(4, ps
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
ParticleSet nps = ps.generateParticleSetForPredictedState(r);
Assert.AreEqual(7, nps
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(3, nps
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
}
public void testRoundTripConversion()
{
RandomVariable rv = particleSet.toRandomVariable();
Randomizer r = new MockRandomizer(new double[] { 0.1, 0.2, 0.3, 0.4,
0.9 });
ParticleSet ps2 = rv.toParticleSet(rainman, r, 10);
Assert.AreEqual(8, ps2
.numberOfParticlesWithState(HmmConstants.RAINING));
Assert.AreEqual(2, ps2
.numberOfParticlesWithState(HmmConstants.NOT_RAINING));
}
public void test_AIMA3e_Fig15_18()
{
IRandom mr = new MockRandomizer(new double[] {
// Prior Sample:
// 8 with Rain_t-1=true from prior distribution
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
// 2 with Rain_t-1=false from prior distribution
0.6, 0.6,
// (a) Propagate 6 samples Rain_t=true
0.7, 0.7, 0.7, 0.7, 0.7, 0.7,
// 4 samples Rain_t=false
0.71, 0.71, 0.31, 0.31,
// (b) Weight should be for first 6 samples:
// Rain_t-1=true, Rain_t=true, Umbrella_t=false = 0.1
// Next 2 samples:
// Rain_t-1=true, Rain_t=false, Umbrealla_t=false= 0.8
// Final 2 samples:
// Rain_t-1=false, Rain_t=false, Umbrella_t=false = 0.8
// gives W[] =
// [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.8, 0.8, 0.8, 0.8]
// normalized =
// [0.026, ...., 0.211, ....] is approx. 0.156 = true
// the remainder is false
// (c) Resample 2 Rain_t=true, 8 Rain_t=false
0.15, 0.15, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
//
// Next Sample:
// (a) Propagate 1 samples Rain_t=true
0.7,
// 9 samples Rain_t=false
0.71, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31,
// (c) resample 1 Rain_t=true, 9 Rain_t=false
0.0001, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2
});
int N = 10;
ParticleFiltering pf = new ParticleFiltering(N, DynamicBayesNetExampleFactory.getUmbrellaWorldNetwork(), mr);
AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(ExampleRV.UMBREALLA_t_RV, false) };
AssignmentProposition[][] S = pf.particleFiltering(e);
Assert.AreEqual(N, S.Length);
for (int i = 0; i < N; ++i)
{
Assert.AreEqual(1, S[i].Length);
AssignmentProposition ap = S[i][0];
Assert.AreEqual(ExampleRV.RAIN_t_RV, ap.getTermVariable());
if (i < 2)
{
Assert.AreEqual(true, ap.getValue());
}
else
{
Assert.AreEqual(false, ap.getValue());
}
}
// Generate next sample to ensure everything roles forward ok
// in this case with prefixed probabilities only expect 1 Rain_t=true
S = pf.particleFiltering(e);
Assert.AreEqual(N, S.Length);
for (int i = 0; i < N; ++i)
{
Assert.AreEqual(1, S[i].Length);
AssignmentProposition ap = S[i][0];
Assert.AreEqual(ExampleRV.RAIN_t_RV, ap.getTermVariable());
if (i < 1)
{
Assert.AreEqual(true, ap.getValue());
}
else
{
Assert.AreEqual(false, ap.getValue());
}
}
}
