/*
* 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);
}
/**
* Constructor for BucketTree. Does the whole initialization; it should be the only method that
* deals with symbolic names for variables.
*/
public BucketTree(Ordering ord, boolean dpc) {
int i, j, markers[];
ProbabilityFunction pf;
ProbabilityVariable pv;
DiscreteVariable aux_pv;
DiscreteFunction ut;
String order[];
do_produce_clusters = dpc;
ordering = ord;
// Collect information from the Ordering object.
bn = ord.bn;
explanation_status = ord.explanation_status;
order = ord.order;
// Indicate the first bucket to process
active_bucket = 0;
// Check the possibility that the query has an observed variable
i = bn.index_of_variable(order[order.length - 1]);
pv = bn.get_probability_variable(i);
if (pv.is_observed() == true) {
pf = transform_to_probability_function(bn, pv);
bucket_tree = new Bucket[1];
bucket_tree[0] = new Bucket(this, pv, do_produce_clusters);
insert(pf);
} else {
// Initialize the bucket objects
bucket_tree = new Bucket[order.length];
for (i = 0; i < order.length; i++) {
j = bn.index_of_variable(order[i]);
bucket_tree[i] = new Bucket(this, bn.get_probability_variable(j), do_produce_clusters);
}
// Insert the probability functions into the bucket_tree;
// first mark all functions that are actually going
// into the bucket_tree.
markers = new int[bn.number_variables()];
for (i = 0; i < order.length; i++) markers[bn.index_of_variable(order[i])] = 1;
// Now insert functions that are marked and non-null.
for (i = 0; i < bn.number_probability_functions(); i++) {
if (markers[bn.get_probability_function(i).get_index(0)] == 1) {
pf = check_evidence(bn.get_probability_function(i));
if (pf != null) {
aux_pv = (bn.get_probability_function(i)).get_variable(0);
insert(pf, !pf.memberOf(aux_pv.get_index()));
}
}
}
// Insert the utility_function.
ut = bn.get_utility_function();
if (ut != null) insert(ut);
}
}
/*
* 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;
}
}
}
/*
* Build an array of markers. The marker for a variable is true only if the
* variable is present in the input ProbabilityFunction pf and is not
* observed. Even explanatory variables can be observed and taken as
* evidence.
*/
private int build_evidence_markers(ProbabilityFunction pf, boolean markers[]) {
int i, n;
// Initialize the markers
for (i = 0; i < markers.length; i++) markers[i] = false;
// Insert the variables of the ProbabilityFunction
for (i = 0; i < pf.number_variables(); i++) markers[pf.get_index(i)] = true;
// Take the evidence out
for (i = 0; i < bn.number_variables(); i++) {
if (bn.get_probability_variable(i).is_observed()) markers[i] = false;
}
// Count how many variables remain
n = 0;
for (i = 0; i < markers.length; i++) {
if (markers[i] == true) n++;
}
return (n);
}
/** Get the normalized result for the BucketTree. */
public ProbabilityFunction get_normalized_result() {
ProbabilityFunction aux_pf = new ProbabilityFunction(unnormalized_result, bn);
aux_pf.normalize();
return (aux_pf);
}