/*
* Obtain the values for the evidence plus function.
*/
private void check_evidence_loop(ProbabilityFunction new_pf, ProbabilityFunction pf) {
int i, j, k, l, m, p, last, current;
int indexes[] = new int[bn.number_variables()];
int value_lengths[] = new int[bn.number_variables()];
for (i = 0; i < bn.number_variables(); i++) {
indexes[i] = 0;
value_lengths[i] = bn.get_probability_variable(i).number_values();
}
for (i = 0; i < bn.number_variables(); i++) {
if (bn.get_probability_variable(i).is_observed()) {
indexes[i] = bn.get_probability_variable(i).get_observed_index();
}
}
last = new_pf.number_variables() - 1;
for (i = 0; i < new_pf.number_values(); i++) {
p = new_pf.get_position_from_indexes(indexes);
new_pf.set_value(p, pf.evaluate(indexes));
indexes[new_pf.get_index(last)]++;
for (j = last; j > 0; j--) {
current = new_pf.get_index(j);
if (indexes[current] >= value_lengths[current]) {
indexes[current] = 0;
indexes[new_pf.get_index(j - 1)]++;
} else break;
}
}
}
/*
* 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);
}
