public List<RandomVariable> forward_backward(List<String> perceptions) {
RandomVariable forwardMessages[] = new RandomVariable[perceptions.size() + 1];
RandomVariable backwardMessage = priorDistribution.createUnitBelief();
RandomVariable smoothedBeliefs[] = new RandomVariable[perceptions.size() + 1];
forwardMessages[0] = priorDistribution;
smoothedBeliefs[0] = null;
// populate forward messages
for (int i = 0; i < perceptions.size(); i++) { // N.B i starts at 1,
// not zero
forwardMessages[i + 1] = forward(forwardMessages[i], perceptions.get(i));
}
for (int i = perceptions.size(); i > 0; i--) {
RandomVariable smoothed = priorDistribution.duplicate();
smoothed.updateFrom(forwardMessages[i].asMatrix().arrayTimes(backwardMessage.asMatrix()));
smoothed.normalize();
smoothedBeliefs[i] = smoothed;
backwardMessage =
calculate_next_backward_message(
forwardMessages[i], backwardMessage, perceptions.get(i - 1));
}
return Arrays.asList(smoothedBeliefs);
}
public RandomVariable perceptionUpdate(RandomVariable aBelief, String perception) {
RandomVariable newBelief = aBelief.duplicate();
// one way - use matrices
Matrix beliefMatrix = aBelief.asMatrix();
Matrix o_matrix = sensorModel.asMatrix(perception);
Matrix updated = o_matrix.times(beliefMatrix);
newBelief.updateFrom(updated);
newBelief.normalize();
return newBelief;
// alternate way of doing this. clearer in intent.
// for (String state : aBelief.states()){
// double probabilityOfPerception= sensorModel.get(state,perception);
// newBelief.setProbabilityOf(state,probabilityOfPerception *
// aBelief.getProbabilityOf(state));
// }
}
public RandomVariable calculate_next_backward_message(
RandomVariable forwardBelief, RandomVariable present_backward_message, String perception) {
RandomVariable result = present_backward_message.duplicate();
// System.out.println("fb :-calculating new backward message");
// System.out.println("fb :-diagonal matrix from sens model = ");
Matrix oMatrix = sensorModel.asMatrix(perception);
// System.out.println(oMatrix);
Matrix transitionMatrix = transitionModel.asMatrix(); // action
// should
// be
// passed
// in
// here?
// System.out.println("fb :-present backward message = "
// +present_backward_message);
Matrix backwardMatrix =
transitionMatrix.times(oMatrix.times(present_backward_message.asMatrix()));
Matrix resultMatrix = backwardMatrix.arrayTimes(forwardBelief.asMatrix());
result.updateFrom(resultMatrix);
result.normalize();
// System.out.println("fb :-normalized new backward message = "
// +result);
return result;
}