protected static void demoToothacheCavityCatchModel(IFiniteProbabilityModel model)
{
System.Console.WriteLine("Toothache, Cavity, and Catch Model");
System.Console.WriteLine("----------------------------------");
AssignmentProposition atoothache = new AssignmentProposition(
ExampleRV.TOOTHACHE_RV, true);
AssignmentProposition acavity = new AssignmentProposition(
ExampleRV.CAVITY_RV, true);
AssignmentProposition anotcavity = new AssignmentProposition(
ExampleRV.CAVITY_RV, false);
AssignmentProposition acatch = new AssignmentProposition(
ExampleRV.CATCH_RV, true);
// AIMA3e pg. 485
System.Console.WriteLine("P(cavity) = " + model.prior(acavity));
System.Console.WriteLine("P(cavity | toothache) = "
+ model.posterior(acavity, atoothache));
// AIMA3e pg. 492
DisjunctiveProposition cavityOrToothache = new DisjunctiveProposition(
acavity, atoothache);
System.Console.WriteLine("P(cavity OR toothache) = "
+ model.prior(cavityOrToothache));
// AIMA3e pg. 493
System.Console.WriteLine("P(~cavity | toothache) = "
+ model.posterior(anotcavity, atoothache));
// AIMA3e pg. 493
// P<>(Cavity | toothache) = <0.6, 0.4>
System.Console.WriteLine("P<>(Cavity | toothache) = "
+ model.posteriorDistribution(ExampleRV.CAVITY_RV, atoothache));
// AIMA3e pg. 497
// P<>(Cavity | toothache AND catch) = <0.871, 0.129>
System.Console.WriteLine("P<>(Cavity | toothache AND catch) = "
+ model.posteriorDistribution(ExampleRV.CAVITY_RV, atoothache,
acatch));
}
public void iterate(IMap <IRandomVariable, object> possibleWorld, double probability)
{
// Assign current values for x<sub>k+1</sub>
foreach (var av in possibleWorld)
{
x_kp1VarAssignMap.Get(av.GetKey()).setValue(av.GetValue());
}
// P(e<sub>k+1</sub> | x<sub>k+1</sub>)
// P(e<sub>k+2:t</sub> | x<sub>k+1</sub>)
double p = sensorModel.posterior(pe_kp1, x_kp1) * probability;
// <b>P</b>(x<sub>k+1</sub> | X<sub>k</sub>)
int i = 0;
foreach (double tp in transitionModel.posteriorDistribution(x_kp1, xk).getValues())
{
b_kp1t.setValue(i, b_kp1t.getValues()[i] + (tp * p));
++i;
}
}
/**
* The particle filtering algorithm implemented as a recursive update
* operation with state (the set of samples).
*
* @param e
* <b>e</b>, the new incoming evidence
* @return a vector of samples of size N, where each sample is a vector of
* assignment propositions for the X_1 state variables, which is
* intended to represent the generated sample for time t.
*/
public AssignmentProposition[][] particleFiltering(AssignmentProposition[] e)
{
// local variables: W, a vector of weights of size N
double[] W = new double[N];
// for i = 1 to N do
for (int i = 0; i < N; ++i)
{
/* step 1 */
// S[i] <- sample from <b>P</b>(<b>X</b><sub>1</sub> |
// <b>X</b><sub>0</sub> = S[i])
sampleFromTransitionModel(i);
/* step 2 */
// W[i] <- <b>P</b>(<b>e</b> | <b>X</b><sub>1</sub> = S[i])
W[i] = sensorModel.posterior(ProbUtil.constructConjunction(e), S_tp1[i]);
}
/* step 3 */
// S <- WEIGHTED-SAMPLE-WITH-REPLACEMENT(N, S, W)
S = weightedSampleWithReplacement(N, S, W);
// return S
return(S);
}
