/* * 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); }