/** Compute and print a posterior marginal distribution for the InferenceGraph. */
protected void print_marginal(PrintStream pstream, InferenceGraph ig, String queried_variable) {
if (algorithm_type == ALGORITHM_VARIABLE_ELIMINATION)
ig.print_marginal(pstream, queried_variable, false, what_to_show_bucket_tree_state);
else if (algorithm_type == ALGORITHM_BUCKET_TREE)
ig.print_marginal(pstream, queried_variable, true, what_to_show_bucket_tree_state);
else return;
}
/** Save the network. */
public boolean save(String filename) {
InferenceGraph ig = get_inference_graph();
if (filename == null) {
jb.appendText("\n Filename invalid!");
return (false);
}
if (ig == null) {
jb.appendText("\n No Bayesian network to be saved.\n\n");
return (false);
}
try {
FileOutputStream fileout = new FileOutputStream(filename);
PrintStream out = new PrintStream(fileout);
switch (save_format) {
case BIF_FORMAT:
ig.save_bif(out);
break;
case XML_FORMAT:
ig.save_xml(out);
break;
case BUGS_FORMAT:
ig.save_bugs(out);
break;
}
out.close();
fileout.close();
} catch (IOException e) {
jb.appendText("Exception: " + e + "\n");
return (false);
}
return (true);
}
/** Compute and print the metrics for sensitivity analysis of the InferenceGraph. */
protected void print_sensitivity_analysis(PrintStream pstream, InferenceGraph ig) {
ig.print_sensitivity_analysis(pstream);
}
/** Compute and print a full explanation for the InferenceGraph. */
protected void print_full_explanation(PrintStream pstream, InferenceGraph ig) {
ig.print_full_explanation(pstream, what_to_show_bucket_tree_state);
}
/** Print the QuasiBayesNet in the InferenceGraph. */
protected void print_bayes_net(PrintStream pstream, InferenceGraph ig) {
ig.print_bayes_net(pstream);
}
