// 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; // ----------------------------------------------------------------------------------------intmain(intargc,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<unsignedlong>::compare_1b_c, dlib::set<unsignedlong>::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 (unsignedlongi = 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;}}