static void particleFilterinfDemo()
{
System.Console.WriteLine("DEMO: Particle-Filtering");
System.Console.WriteLine("========================");
System.Console.WriteLine("Figure 15.18");
System.Console.WriteLine("------------");
MockRandomizer 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) };
System.Console.WriteLine("First Sample Set:");
AssignmentProposition[][] S = pf.particleFiltering(e);
for (int i = 0; i < N; ++i)
{
System.Console.WriteLine("Sample " + (i + 1) + " = " + S[i][0]);
}
System.Console.WriteLine("Second Sample Set:");
S = pf.particleFiltering(e);
for (int i = 0; i < N; ++i)
{
System.Console.WriteLine("Sample " + (i + 1) + " = " + S[i][0]);
}
System.Console.WriteLine("========================");
}
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 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());
}
}
}
