/*
* 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);
}
