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diff --git a/ml/dlib/dlib/test/bayes_nets.cpp b/ml/dlib/dlib/test/bayes_nets.cpp
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+// Copyright (C) 2007 Davis E. King (davis@dlib.net)
+// License: Boost Software License See LICENSE.txt for the full license.
+
+
+#include "dlib/graph_utils.h"
+#include "dlib/graph.h"
+#include "dlib/directed_graph.h"
+#include "dlib/bayes_utils.h"
+#include "dlib/set.h"
+#include <sstream>
+#include <string>
+#include <cstdlib>
+#include <ctime>
+
+#include "tester.h"
+
+namespace
+{
+ using namespace test;
+ using namespace dlib;
+ using namespace std;
+
+ logger dlog("test.bayes_nets");
+ enum nodes
+ {
+ A, T, S, L, O, B, D, X
+ };
+
+ template <typename gtype>
+ void setup_simple_network (
+ gtype& bn
+ )
+ {
+ /*
+ A
+ / \
+ T S
+ */
+
+ using namespace bayes_node_utils;
+
+ bn.set_number_of_nodes(3);
+ bn.add_edge(A, T);
+ bn.add_edge(A, S);
+
+
+ set_node_num_values(bn, A, 2);
+ set_node_num_values(bn, T, 2);
+ set_node_num_values(bn, S, 2);
+
+ assignment parents;
+
+ // set probabilities for node A
+ set_node_probability(bn, A, 1, parents, 0.1);
+ set_node_probability(bn, A, 0, parents, 1-0.1);
+
+ // set probabilities for node T
+ parents.add(A, 1);
+ set_node_probability(bn, T, 1, parents, 0.5);
+ set_node_probability(bn, T, 0, parents, 1-0.5);
+ parents[A] = 0;
+ set_node_probability(bn, T, 1, parents, 0.5);
+ set_node_probability(bn, T, 0, parents, 1-0.5);
+
+ // set probabilities for node S
+ parents[A] = 1;
+ set_node_probability(bn, S, 1, parents, 0.5);
+ set_node_probability(bn, S, 0, parents, 1-0.5);
+ parents[A] = 0;
+ set_node_probability(bn, S, 1, parents, 0.5);
+ set_node_probability(bn, S, 0, parents, 1-0.5);
+
+
+ // test the serialization code here by pushing this network though it
+ ostringstream sout;
+ serialize(bn, sout);
+ bn.clear();
+ DLIB_TEST(bn.number_of_nodes() == 0);
+ istringstream sin(sout.str());
+ deserialize(bn, sin);
+ DLIB_TEST(bn.number_of_nodes() == 3);
+ }
+
+
+ template <typename gtype>
+ void setup_dyspnea_network (
+ gtype& bn,
+ bool deterministic_o_node = true
+ )
+ {
+ /*
+ This is the example network used by David Zaret in his
+ reasoning under uncertainty class at Johns Hopkins
+ */
+
+ using namespace bayes_node_utils;
+
+ bn.set_number_of_nodes(8);
+ bn.add_edge(A, T);
+ bn.add_edge(T, O);
+
+ bn.add_edge(O, D);
+ bn.add_edge(O, X);
+
+ bn.add_edge(S, B);
+ bn.add_edge(S, L);
+
+ bn.add_edge(L, O);
+ bn.add_edge(B, D);
+
+
+ set_node_num_values(bn, A, 2);
+ set_node_num_values(bn, T, 2);
+ set_node_num_values(bn, O, 2);
+ set_node_num_values(bn, X, 2);
+ set_node_num_values(bn, L, 2);
+ set_node_num_values(bn, S, 2);
+ set_node_num_values(bn, B, 2);
+ set_node_num_values(bn, D, 2);
+
+ assignment parents;
+
+ // set probabilities for node A
+ set_node_probability(bn, A, 1, parents, 0.01);
+ set_node_probability(bn, A, 0, parents, 1-0.01);
+
+ // set probabilities for node S
+ set_node_probability(bn, S, 1, parents, 0.5);
+ set_node_probability(bn, S, 0, parents, 1-0.5);
+
+ // set probabilities for node T
+ parents.add(A, 1);
+ set_node_probability(bn, T, 1, parents, 0.05);
+ set_node_probability(bn, T, 0, parents, 1-0.05);
+ parents[A] = 0;
+ set_node_probability(bn, T, 1, parents, 0.01);
+ set_node_probability(bn, T, 0, parents, 1-0.01);
+
+ // set probabilities for node L
+ parents.clear();
+ parents.add(S,1);
+ set_node_probability(bn, L, 1, parents, 0.1);
+ set_node_probability(bn, L, 0, parents, 1-0.1);
+ parents[S] = 0;
+ set_node_probability(bn, L, 1, parents, 0.01);
+ set_node_probability(bn, L, 0, parents, 1-0.01);
+
+
+ // set probabilities for node B
+ parents[S] = 1;
+ set_node_probability(bn, B, 1, parents, 0.6);
+ set_node_probability(bn, B, 0, parents, 1-0.6);
+ parents[S] = 0;
+ set_node_probability(bn, B, 1, parents, 0.3);
+ set_node_probability(bn, B, 0, parents, 1-0.3);
+
+
+ // set probabilities for node O
+ double v;
+ if (deterministic_o_node)
+ v = 1;
+ else
+ v = 0.99;
+
+ parents.clear();
+ parents.add(T,1);
+ parents.add(L,1);
+ set_node_probability(bn, O, 1, parents, v);
+ set_node_probability(bn, O, 0, parents, 1-v);
+ parents[T] = 0; parents[L] = 1;
+ set_node_probability(bn, O, 1, parents, v);
+ set_node_probability(bn, O, 0, parents, 1-v);
+ parents[T] = 1; parents[L] = 0;
+ set_node_probability(bn, O, 1, parents, v);
+ set_node_probability(bn, O, 0, parents, 1-v);
+ parents[T] = 0; parents[L] = 0;
+ set_node_probability(bn, O, 1, parents, 1-v);
+ set_node_probability(bn, O, 0, parents, v);
+
+
+ // set probabilities for node D
+ parents.clear();
+ parents.add(O,1);
+ parents.add(B,1);
+ set_node_probability(bn, D, 1, parents, 0.9);
+ set_node_probability(bn, D, 0, parents, 1-0.9);
+ parents[O] = 1; parents[B] = 0;
+ set_node_probability(bn, D, 1, parents, 0.7);
+ set_node_probability(bn, D, 0, parents, 1-0.7);
+ parents[O] = 0; parents[B] = 1;
+ set_node_probability(bn, D, 1, parents, 0.8);
+ set_node_probability(bn, D, 0, parents, 1-0.8);
+ parents[O] = 0; parents[B] = 0;
+ set_node_probability(bn, D, 1, parents, 0.1);
+ set_node_probability(bn, D, 0, parents, 1-0.1);
+
+
+ // set probabilities for node X
+ parents.clear();
+ parents.add(O,1);
+ set_node_probability(bn, X, 1, parents, 0.98);
+ set_node_probability(bn, X, 0, parents, 1-0.98);
+ parents[O] = 0;
+ set_node_probability(bn, X, 1, parents, 0.05);
+ set_node_probability(bn, X, 0, parents, 1-0.05);
+
+
+ // test the serialization code here by pushing this network though it
+ ostringstream sout;
+ serialize(bn, sout);
+ bn.clear();
+ DLIB_TEST(bn.number_of_nodes() == 0);
+ istringstream sin(sout.str());
+ deserialize(bn, sin);
+ DLIB_TEST(bn.number_of_nodes() == 8);
+ }
+
+
+ void bayes_nets_test (
+ )
+ /*!
+ ensures
+ - runs tests on the bayesian network objects and functions for compliance with the specs
+ !*/
+ {
+
+ print_spinner();
+
+ directed_graph<bayes_node>::kernel_1a_c bn;
+ setup_dyspnea_network(bn);
+
+ using namespace bayes_node_utils;
+
+
+ graph<dlib::set<unsigned long>::compare_1b_c, dlib::set<unsigned long>::compare_1b_c>::kernel_1a_c join_tree;
+
+ create_moral_graph(bn, join_tree);
+ create_join_tree(join_tree, join_tree);
+
+ bayesian_network_join_tree solution(bn, join_tree);
+
+ matrix<double,1,2> dist;
+
+ dist = solution.probability(A);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.01 ) < 1e-5);
+
+ dist = solution.probability(T);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.0104) < 1e-5);
+
+ dist = solution.probability(O);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.064828) < 1e-5);
+
+ dist = solution.probability(X);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.11029004) < 1e-5);
+
+ dist = solution.probability(L);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.055) < 1e-5);
+
+ dist = solution.probability(S);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.5) < 1e-5);
+
+ dist = solution.probability(B);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.4499999) < 1e-5);
+
+ dist = solution.probability(D);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.4359706 ) < 1e-5);
+
+ // now lets modify the probabilities of the bayesian network by making O
+ // not a deterministic node anymore but otherwise leave the network alone
+ setup_dyspnea_network(bn, false);
+
+ set_node_value(bn, A, 1);
+ set_node_value(bn, X, 1);
+ set_node_value(bn, S, 1);
+ // lets also make some of these nodes evidence nodes
+ set_node_as_evidence(bn, A);
+ set_node_as_evidence(bn, X);
+ set_node_as_evidence(bn, S);
+
+ // reload the solution now that we have changed the probabilities of node O
+ bayesian_network_join_tree(bn, join_tree).swap(solution);
+ DLIB_TEST(solution.number_of_nodes() == bn.number_of_nodes());
+
+ dist = solution.probability(A);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 1.0 ) < 1e-5);
+
+ dist = solution.probability(T);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.253508694039 ) < 1e-5);
+
+ dist = solution.probability(O);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.77856184024 ) < 1e-5);
+
+ dist = solution.probability(X);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 1.0 ) < 1e-5);
+
+ dist = solution.probability(L);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.5070173880 ) < 1e-5);
+
+ dist = solution.probability(S);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 1.0 ) < 1e-5);
+
+ dist = solution.probability(B);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.6 ) < 1e-5);
+
+ dist = solution.probability(D);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.7535685520 ) < 1e-5);
+
+
+ // now lets test the bayesian_network_gibbs_sampler
+ set_node_value(bn, A, 1);
+ set_node_value(bn, T, 1);
+ set_node_value(bn, O, 1);
+ set_node_value(bn, X, 1);
+ set_node_value(bn, S, 1);
+ set_node_value(bn, L, 1);
+ set_node_value(bn, B, 1);
+ set_node_value(bn, D, 1);
+
+ bayesian_network_gibbs_sampler sampler;
+ matrix<double,1,8> counts;
+ set_all_elements(counts, 0);
+ const unsigned long rounds = 500000;
+ for (unsigned long i = 0; i < rounds; ++i)
+ {
+ sampler.sample_graph(bn);
+
+ for (long c = 0; c < counts.nc(); ++c)
+ {
+ if (node_value(bn, c) == 1)
+ counts(c) += 1;
+ }
+
+ if ((i&0x3FF) == 0)
+ {
+ print_spinner();
+ }
+ }
+
+ counts /= rounds;
+
+ DLIB_TEST(abs(counts(A) - 1.0 ) < 1e-2);
+ DLIB_TEST(abs(counts(T) - 0.253508694039 ) < 1e-2);
+ DLIB_TEST_MSG(abs(counts(O) - 0.77856184024 ) < 1e-2,abs(counts(O) - 0.77856184024 ) );
+ DLIB_TEST(abs(counts(X) - 1.0 ) < 1e-2);
+ DLIB_TEST(abs(counts(L) - 0.5070173880 ) < 1e-2);
+ DLIB_TEST(abs(counts(S) - 1.0 ) < 1e-2);
+ DLIB_TEST(abs(counts(B) - 0.6 ) < 1e-2);
+ DLIB_TEST(abs(counts(D) - 0.7535685520 ) < 1e-2);
+
+
+ setup_simple_network(bn);
+ create_moral_graph(bn, join_tree);
+ create_join_tree(join_tree, join_tree);
+ bayesian_network_join_tree(bn, join_tree).swap(solution);
+ DLIB_TEST(solution.number_of_nodes() == bn.number_of_nodes());
+
+ dist = solution.probability(A);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.1 ) < 1e-5);
+
+ dist = solution.probability(T);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.5 ) < 1e-5);
+
+ dist = solution.probability(S);
+ DLIB_TEST(abs(sum(dist) - 1.0) < 1e-5);
+ DLIB_TEST(abs(dist(1) - 0.5 ) < 1e-5);
+
+
+ }
+
+
+
+
+ class bayes_nets_tester : public tester
+ {
+ public:
+ bayes_nets_tester (
+ ) :
+ tester ("test_bayes_nets",
+ "Runs tests on the bayes_nets objects and functions.")
+ {}
+
+ void perform_test (
+ )
+ {
+ bayes_nets_test();
+ }
+ } a;
+
+}
+
+
+
+
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