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