/*
* Recover the maximizing variables going back through the maximizing
* bucket_tree; the variables are returned as an array of markers
* (non-explanation variables get INVALID_INDEX).
*/
private int[] backward_maximization() {
int i, j;
int bi = bucket_tree.length - 1;
DiscreteFunction back_df;
Bucket b = bucket_tree[bi];
// If there are no explanation variables in the BayesNet, return null
if (b.backward_pointers == null) return (null);
// Initialize the markers for backward pointers with INVALID_INDEX
int backward_markers[] = new int[bn.number_variables()];
for (i = 0; i < backward_markers.length; i++) backward_markers[i] = BayesNet.INVALID_INDEX;
// Initialize the marker for the last bucket
backward_markers[b.variable.get_index()] = (int) (b.backward_pointers.get_value(0) + 0.5);
// Go backwards through the bucket_tree
for (i = (bi - 1); i >= 0; i--) {
if (!bucket_tree[i].is_explanation()) break;
back_df = bucket_tree[i].backward_pointers;
// Skip null pointers (caused by evidence)
if (back_df == null) continue;
// Special treatment for bucket with only one value,
// since it can be a bucket with only the bucket variable left
if (back_df.number_values() == 1) {
backward_markers[bucket_tree[i].variable.get_index()] = (int) (back_df.get_value(0) + 0.5);
continue;
}
// Process the bucket
j = back_df.get_position_from_indexes(bn.get_probability_variables(), backward_markers);
backward_markers[bucket_tree[i].variable.get_index()] = (int) (back_df.get_value(j) + 0.5);
}
return (backward_markers);
}
/*
* 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);
}
/*
* 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);
}
/*
* 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;
}
}
}
/**
* 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);
}
}
/**
* Distribute evidence in the BucketTree.
*
* @return true if successful; false if not.
*/
public boolean distribute() {
int i, j;
boolean mark_non_conditioning[] = new boolean[bn.number_variables()];
// First make sure the BucketTree has been reduced.
if (unnormalized_result == null) reduce();
// Second make sure there is more than one Bucket in the BucketTree.
int last = bucket_tree.length - 1;
if (last < 1) return (true);
// Third, this method is used only if do_produce_clusters is true.
if (do_produce_clusters == false) return (false);
// Fourth, this method is use only if no explanatory variable was max'ed
// out.
if (backward_pointers != null) return (false);
// Go through the Bucket objects, from bottom to top,
// to compute the new separator and cluster for each bucket.
for (i = (last - 1); i >= 0; i--) { // Start from (last-1); last does
// not have child.
// Check whether the Bucket has any valid content.
if (bucket_tree[i].cluster == null) break;
// Take the non-conditioning variables in a boolean array.
for (j = 0; j < mark_non_conditioning.length; j++) mark_non_conditioning[j] = true;
// OBS: The following piece of code will actually be less efficient
// than
// necessary. It will count as "conditioning" any variable in the
// cluster
// except the bucket variable. This will imply that some variables
// in the
// separator will be normalized over without need, and the separator
// will
// be larger than necessary.
// OBS: this code was contributed by Wei Zhou ([email protected]),
// who also detected the problem with the original code.
// if (bucket_tree[i].cluster.number_variables() >
// bucket_tree[i].non_conditioning_variables.size())
for (j = 1; j < bucket_tree[i].cluster.number_variables(); j++) {
mark_non_conditioning[(bucket_tree[i].cluster.get_variables())[j].get_index()] = false;
}
// The following piece of code does the right thing (compared to the
// piece of code above): it selects the
// minimum number of non-conditioning variables. To use this piece
// of code, it will be necessary to create a "normalize" method that
// normalizes with respect to a number of variables at at time.
/*
* for (j=0; j<bucket_tree[i].cluster.number_variables(); j++) {
* mark_non_conditioning[
* (bucket_tree[i].cluster.get_variables())[j].get_index() ] =
* false; } for (Enumeration e =
* bucket_tree[i].non_conditioning_variables.elements();
* e.hasMoreElements(); ) { ProbabilityVariable pv =
* (ProbabilityVariable)(e.nextElement());
* mark_non_conditioning[pv.get_index() ] = true; }
*/
// Update the separator.
bucket_tree[i].separator =
bucket_tree[i].child.cluster.sum_out(
bn.get_probability_variables(), mark_non_conditioning);
// Compute cluster using new separator (note that if separator
// is null, the cluster had all variables already processed).
if (bucket_tree[i].separator != null) {
// OBS: the method here should normalize with respect to more
// than one variable, to allow this algorithm to be more
// efficient!
bucket_tree[i].cluster.normalize_first();
// Now combine the cluster and the separator.
bucket_tree[i].cluster =
bucket_tree[i].cluster.multiply(
bn.get_probability_variables(), bucket_tree[i].separator);
}
// Mark the Bucket as DISTRIBUTED.
bucket_tree[i].bucket_status = Bucket.DISTRIBUTED;
}
// Indicate success.
return (true);
}