dlib C++ Library - bayes_net_from_disk_ex.cpp

// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
 This is an example illustrating the use of the Bayesian Network 
 inference utilities found in the dlib C++ library. In this example
 we load a saved Bayesian Network from disk. 
*/
#include <dlib/bayes_utils.h>
#include <dlib/graph_utils.h>
#include <dlib/graph.h>
#include <dlib/directed_graph.h>
#include <iostream>
#include <fstream>
using namespace dlib;
using namespace std;
// ----------------------------------------------------------------------------------------
int main(int argc, char** argv)
{
 try
 {
 // This statement declares a bayesian network called bn. Note that a bayesian network
 // in the dlib world is just a directed_graph object that contains a special kind 
 // of node called a bayes_node.
 directed_graph<bayes_node>::kernel_1a_c bn;
 if (argc != 2)
 {
 cout << "You must supply a file name on the command line. The file should "
 << "contain a serialized Bayesian Network" << endl;
 return 1;
 }
 ifstream fin(argv[1],ios::binary);
 // Note that the saved networks produced by the bayes_net_gui_ex.cpp example can be deserialized
 // into a network. So you can make your networks using that GUI if you like.
 cout << "Loading the network from disk..." << endl;
 deserialize(bn, fin);
 cout << "Number of nodes in the network: " << bn.number_of_nodes() << endl;
 // Let's compute some probability values using the loaded network using the join tree (aka. Junction 
 // Tree) algorithm.
 // First we need to create an undirected graph which contains set objects at each node and
 // edge. This long declaration does the trick.
 typedef graph<dlib::set<unsigned long>::compare_1b_c, dlib::set<unsigned long>::compare_1b_c>::kernel_1a_c join_tree_type;
 join_tree_type join_tree;
 // Now we need to populate the join_tree with data from our bayesian network. The next two 
 // function calls do this. Explaining exactly what they do is outside the scope of this
 // example. Just think of them as filling join_tree with information that is useful 
 // later on for dealing with our bayesian network. 
 create_moral_graph(bn, join_tree);
 create_join_tree(join_tree, join_tree);
 // Now we have a proper join_tree we can use it to obtain a solution to our
 // bayesian network. Doing this is as simple as declaring an instance of
 // the bayesian_network_join_tree object as follows:
 bayesian_network_join_tree solution(bn, join_tree);
 // now print out the probabilities for each node
 cout << "Using the join tree algorithm:\n";
 for (unsigned long i = 0; i < bn.number_of_nodes(); ++i)
 {
 // print out the probability distribution for node i. 
 cout << "p(node " << i <<") = " << solution.probability(i);
 }
 }
 catch (exception& e)
 {
 cout << "exception thrown: " << e.what() << endl;
 return 1;
 }
}