Merging upstream version 1.45.3+dfsg.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 83 insertions, 0 deletions

diff --git a/src/ml/dlib/examples/bayes_net_from_disk_ex.cpp b/src/ml/dlib/examples/bayes_net_from_disk_ex.cpp
new file mode 100644
index 000000000..eaab5881a
--- /dev/null
+++ b/src/ml/dlib/examples/bayes_net_from_disk_ex.cpp
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+// 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;
+    }
+}
+
+