/*
* Eliminates all variables defined as evidence. The order of the variables
* that are not eliminated is the same order in the original function.
*/
private ProbabilityFunction check_evidence(ProbabilityFunction pf) {
int i, j, k, v, aux_i;
boolean markers[] = new boolean[bn.number_variables()];
int n = build_evidence_markers(pf, markers);
// Handle special cases
if (n == 0) return (null); // No variable remains
if (n == pf.number_variables()) return (pf); // No relevant evidence
// Calculate necessary quantities in such a
// way that the order of variables in the original
// function is not altered.
int joined_indexes[] = new int[n];
for (i = 0, j = 0, v = 1; i < pf.number_variables(); i++) {
aux_i = pf.get_variable(i).get_index();
if (markers[aux_i] == true) {
joined_indexes[j] = aux_i;
j++;
v *= bn.get_probability_variable(aux_i).number_values();
}
}
// Create new function to be filled with joined variables
ProbabilityFunction new_pf = new ProbabilityFunction(bn, n, v, null);
for (i = 0; i < n; i++) new_pf.set_variable(i, bn.get_probability_variable(joined_indexes[i]));
// Loop through the values
check_evidence_loop(new_pf, pf);
return (new_pf);
}
/*
* Transform an observed ProbabilityVariable into a ProbabilityFunction to
* handle the case where the query involves an observed variable.
*/
private ProbabilityFunction transform_to_probability_function(
BayesNet bn, ProbabilityVariable pv) {
ProbabilityFunction pf = new ProbabilityFunction(bn, 1, pv.number_values(), null);
pf.set_variable(0, pv);
int index_of_value = pv.get_observed_index();
pf.set_value(index_of_value, 1.0);
return (pf);
}