dlib C++ Library - bayes_nets.cpp

// 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;
}