    // Copyright (C) 2007  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_BAYES_UTILs_
    #define DLIB_BAYES_UTILs_

    #include "bayes_utils_abstract.h"

    #include "../string.h"
    #include "../map.h"
    #include "../matrix.h"
    #include "../rand.h"
    #include "../array.h"
    #include "../set.h"
    #include "../algs.h"
    #include "../noncopyable.h"
    #include "../smart_pointers.h"
    #include "../graph.h"
    #include <vector>
    #include <algorithm>
    #include <ctime>

    namespace dlib
    {

    // ----------------------------------------------------------------------------------------

        class assignment
        {
        public:

            assignment()
            {
            }

            assignment(
                const assignment& a
            )
            {
                a.reset();
                while (a.move_next())
                {
                    unsigned long idx = a.element().key();
                    unsigned long value = a.element().value();
                    vals.add(idx,value);
                }
            }

            assignment& operator = (
                const assignment& rhs
            )
            {
                if (this == &rhs)
                    return *this;

                assignment(rhs).swap(*this);
                return *this;
            }

            void clear()
            {
                vals.clear();
            }

            bool operator < (
                const assignment& item
            ) const
            {
                if (size() < item.size())
                    return true;
                else if (size() > item.size())
                    return false;

                reset();
                item.reset();
                while (move_next())
                {
                    item.move_next();
                    if (element().key() < item.element().key())
                        return true;
                    else if (element().key() > item.element().key())
                        return false;
                    else if (element().value() < item.element().value())
                        return true;
                    else if (element().value() > item.element().value())
                        return false;
                }

                return false;
            }

            bool has_index (
                unsigned long idx
            ) const
            {
                return vals.is_in_domain(idx);
            }

            void add (
                unsigned long idx,
                unsigned long value = 0
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( has_index(idx) == false ,
                             "\tvoid assignment::add(idx)"
                             << "\n\tYou can't add the same index to an assignment object more than once"
                             << "\n\tidx:  " << idx
                             << "\n\tthis: " << this
                );

                vals.add(idx, value);
            }

            unsigned long& operator[] (
                const long idx
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( has_index(idx) == true ,
                             "\tunsigned long assignment::operator[](idx)"
                             << "\n\tYou can't access an index value if it isn't already in the object"
                             << "\n\tidx:  " << idx
                             << "\n\tthis: " << this
                );

                return vals[idx];
            }

            const unsigned long& operator[] (
                const long idx
            ) const
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( has_index(idx) == true ,
                             "\tunsigned long assignment::operator[](idx)"
                             << "\n\tYou can't access an index value if it isn't already in the object"
                             << "\n\tidx:  " << idx
                             << "\n\tthis: " << this
                );

                return vals[idx];
            }

            void swap (
                assignment& item
            )
            {
                vals.swap(item.vals);
            }

            void remove (
                unsigned long idx
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( has_index(idx) == true ,
                             "\tunsigned long assignment::remove(idx)"
                             << "\n\tYou can't remove an index value if it isn't already in the object"
                             << "\n\tidx:  " << idx
                             << "\n\tthis: " << this
                );

                vals.destroy(idx);
            }

            unsigned long size() const { return vals.size(); }

            void reset() const { vals.reset(); }

            bool move_next() const { return vals.move_next(); }

            map_pair<unsigned long, unsigned long>& element()
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(current_element_valid() == true,
                            "\tmap_pair<unsigned long,unsigned long>& assignment::element()"
                            << "\n\tyou can't access the current element if it doesn't exist"
                            << "\n\tthis: " << this
                );
                return vals.element();
            }

            const map_pair<unsigned long, unsigned long>& element() const
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(current_element_valid() == true,
                            "\tconst map_pair<unsigned long,unsigned long>& assignment::element() const"
                            << "\n\tyou can't access the current element if it doesn't exist"
                            << "\n\tthis: " << this
                );

                return vals.element();
            }

            bool at_start() const { return vals.at_start(); }

            bool current_element_valid() const { return vals.current_element_valid(); }

            friend inline void serialize (
                const assignment& item,
                std::ostream& out
            )
            {
                serialize(item.vals, out);
            }

            friend inline void deserialize (
                assignment& item,
                std::istream& in
            )
            {
                deserialize(item.vals, in);
            }

        private:
            mutable dlib::map<unsigned long, unsigned long>::kernel_1b_c vals;
        };

        inline std::ostream& operator << (
            std::ostream& out,
            const assignment& a
        )
        {
            a.reset();
            out << "(";
            if (a.move_next())
                out << a.element().key() << ":" << a.element().value();

            while (a.move_next())
            {
                out << ", " << a.element().key() << ":" << a.element().value();
            }

            out << ")";
            return out;
        }

        inline void swap (
            assignment& a,
            assignment& b
        )
        {
            a.swap(b);
        }

    // ------------------------------------------------------------------------

        class joint_probability_table
        {
            /*!
                INITIAL VALUE
                    - table.size() == 0

                CONVENTION
                    - size() == table.size()
                    - probability(a) == table[a]
            !*/
        public:

            joint_probability_table (
                const joint_probability_table& t
            )
            {
                t.reset();
                while (t.move_next())
                {
                    assignment a = t.element().key();
                    double p = t.element().value();
                    set_probability(a,p);
                }
            }

            joint_probability_table() {}

            joint_probability_table& operator= (
                const joint_probability_table& rhs
            )
            {
                if (this == &rhs)
                    return *this;
                joint_probability_table(rhs).swap(*this);
                return *this;
            }

            void set_probability (
                const assignment& a,
                double p
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(0.0 <= p && p <= 1.0,
                            "\tvoid& joint_probability_table::set_probability(a,p)"
                            << "\n\tyou have given an invalid probability value"
                            << "\n\tp:    " << p
                            << "\n\ta:    " << a
                            << "\n\tthis: " << this
                );

                if (table.is_in_domain(a))
                {
                    table[a] = p;
                }
                else
                {
                    assignment temp(a);
                    table.add(temp,p);
                }
            }

            bool has_entry_for (
                const assignment& a
            ) const
            {
                return table.is_in_domain(a);
            }

            void add_probability (
                const assignment& a,
                double p
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(0.0 <= p && p <= 1.0,
                            "\tvoid& joint_probability_table::add_probability(a,p)"
                            << "\n\tyou have given an invalid probability value"
                            << "\n\tp:    " << p
                            << "\n\ta:    " << a
                            << "\n\tthis: " << this
                );

                if (table.is_in_domain(a))
                {
                    table[a] += p;
                    if (table[a] > 1.0)
                        table[a] = 1.0;
                }
                else
                {
                    assignment temp(a);
                    table.add(temp,p);
                }
            }

            double probability (
                const assignment& a
            ) const
            {
                return table[a];
            }

            void clear()
            {
                table.clear();
            }

            unsigned long size () const { return table.size(); }
            bool move_next() const { return table.move_next(); }
            void reset() const { table.reset(); }
            map_pair<assignment,double>& element()
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(current_element_valid() == true,
                            "\tmap_pair<assignment,double>& joint_probability_table::element()"
                            << "\n\tyou can't access the current element if it doesn't exist"
                            << "\n\tthis: " << this
                );

                return table.element();
            }

            const map_pair<assignment,double>& element() const
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(current_element_valid() == true,
                            "\tconst map_pair<assignment,double>& joint_probability_table::element() const"
                            << "\n\tyou can't access the current element if it doesn't exist"
                            << "\n\tthis: " << this
                );

                return table.element();
            }

            bool at_start() const { return table.at_start(); }

            bool current_element_valid() const { return table.current_element_valid(); }

            template <typename T>
            void marginalize (
                const T& vars,
                joint_probability_table& out
            ) const
            {
                out.clear();
                double p;
                reset();
                while (move_next())
                {
                    assignment a;
                    const assignment& asrc = element().key();
                    p = element().value();

                    asrc.reset();
                    while (asrc.move_next())
                    {
                        if (vars.is_member(asrc.element().key()))
                            a.add(asrc.element().key(), asrc.element().value());
                    }

                    out.add_probability(a,p);
                }
            }

            void marginalize (
                const unsigned long var,
                joint_probability_table& out
            ) const
            {
                out.clear();
                double p;
                reset();
                while (move_next())
                {
                    assignment a;
                    const assignment& asrc = element().key();
                    p = element().value();

                    asrc.reset();
                    while (asrc.move_next())
                    {
                        if (var == asrc.element().key())
                            a.add(asrc.element().key(), asrc.element().value());
                    }

                    out.add_probability(a,p);
                }
            }

            void normalize (
            )
            {
                double sum = 0;

                reset();
                while (move_next())
                    sum += element().value();

                reset();
                while (move_next())
                    element().value() /= sum;
            }

            void swap (
                joint_probability_table& item
            )
            {
                table.swap(item.table);
            }

            friend inline void serialize (
                const joint_probability_table& item,
                std::ostream& out
            )
            {
                serialize(item.table, out);
            }

            friend inline void deserialize (
                joint_probability_table& item,
                std::istream& in
            )
            {
                deserialize(item.table, in);
            }

        private:

            dlib::map<assignment, double >::kernel_1b_c table;
        };

        inline void swap (
            joint_probability_table& a,
            joint_probability_table& b
        ) { a.swap(b); }

    // ----------------------------------------------------------------------------------------

        class conditional_probability_table : noncopyable
        {
            /*!
                INITIAL VALUE
                    - table.size() == 0

                CONVENTION
                    - if (table.is_in_domain(ps) && value < num_vals && table[ps](value) >= 0) then
                        - has_entry_for(value,ps) == true
                        - probability(value,ps) == table[ps](value)
                    - else
                        - has_entry_for(value,ps) == false

                    - num_values() == num_vals
            !*/
        public:

            conditional_probability_table()
            {
                clear();
            }

            void set_num_values (
                unsigned long num
            )
            {
                num_vals = num;
                table.clear();
            }

            bool has_entry_for (
                unsigned long value,
                const assignment& ps
            ) const
            {
                if (table.is_in_domain(ps) && value < num_vals && table[ps](value) >= 0)
                    return true;
                else
                    return false;
            }

            unsigned long num_values (
            ) const { return num_vals; }

            void set_probability (
                unsigned long value,
                const assignment& ps,
                double p
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( value < num_values() && 0.0 <= p && p <= 1.0 ,
                             "\tvoid conditional_probability_table::set_probability()"
                             << "\n\tinvalid arguments to set_probability"
                             << "\n\tvalue: " << value
                             << "\n\tnum_values(): " << num_values()
                             << "\n\tp:     " << p
                             << "\n\tps:    " << ps
                             << "\n\tthis:  " << this
                );

                if (table.is_in_domain(ps))
                {
                    table[ps](value) = p;
                }
                else
                {
                    matrix<double,1> dist(num_vals);
                    set_all_elements(dist,-1);
                    dist(value) = p;
                    assignment temp(ps);
                    table.add(temp,dist);
                }
            }

            double probability(
                unsigned long value,
                const assignment& ps
            ) const
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( value < num_values() && has_entry_for(value,ps) ,
                             "\tvoid conditional_probability_table::probability()"
                             << "\n\tinvalid arguments to probability"
                             << "\n\tvalue:        " << value
                             << "\n\tnum_values(): " << num_values()
                             << "\n\tps:           " << ps
                             << "\n\tthis:         " << this
                );

                return table[ps](value);
            }

            void clear()
            {
                table.clear();
                num_vals = 0;
            }

            void empty_table ()
            {
                table.clear();
            }

            void swap (
                conditional_probability_table& item
            )
            {
                exchange(num_vals, item.num_vals);
                table.swap(item.table);
            }

            friend inline void serialize (
                const conditional_probability_table& item,
                std::ostream& out
            )
            {
                serialize(item.table, out);
                serialize(item.num_vals, out);
            }

            friend inline void deserialize (
                conditional_probability_table& item,
                std::istream& in
            )
            {
                deserialize(item.table, in);
                deserialize(item.num_vals, in);
            }

        private:
            dlib::map<assignment, matrix<double,1> >::kernel_1b_c table;
            unsigned long num_vals;
        };

        inline void swap (
            conditional_probability_table& a,
            conditional_probability_table& b
        ) { a.swap(b); }

    // ------------------------------------------------------------------------

        class bayes_node : noncopyable
        {
        public:
            bayes_node ()
            {
                is_instantiated = false;
                value_ = 0;
            }

            unsigned long value (
            ) const { return value_;}

            void set_value (
                unsigned long new_value
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( new_value < table().num_values(),
                             "\tvoid bayes_node::set_value(new_value)"
                             << "\n\tnew_value must be less than the number of possible values for this node"
                             << "\n\tnew_value:            " << new_value
                             << "\n\ttable().num_values(): " << table().num_values()
                             << "\n\tthis:                 " << this
                );

                value_ = new_value;
            }

            conditional_probability_table& table (
            ) { return table_; }

            const conditional_probability_table& table (
            ) const { return table_; }

            bool is_evidence (
            ) const { return is_instantiated; }

            void set_as_nonevidence (
            ) { is_instantiated = false; }

            void set_as_evidence (
            ) { is_instantiated = true; }

            void swap (
                bayes_node& item
            )
            {
                exchange(value_, item.value_);
                exchange(is_instantiated, item.is_instantiated);
                table_.swap(item.table_);
            }

            friend inline void serialize (
                const bayes_node& item,
                std::ostream& out
            )
            {
                serialize(item.value_, out);
                serialize(item.is_instantiated, out);
                serialize(item.table_, out);
            }

            friend inline void deserialize (
                bayes_node& item,
                std::istream& in
            )
            {
                deserialize(item.value_, in);
                deserialize(item.is_instantiated, in);
                deserialize(item.table_, in);
            }

        private:

            unsigned long value_;
            bool is_instantiated;
            conditional_probability_table table_;
        };

        inline void swap (
            bayes_node& a,
            bayes_node& b
        ) { a.swap(b); }

    // ------------------------------------------------------------------------

        namespace bayes_node_utils
        {

            template <typename T>
            unsigned long node_num_values (
                const T& bn,
                unsigned long n
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes(),
                             "\tvoid bayes_node_utils::node_num_values(bn, n)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                             << "\n\tbn.number_of_nodes():  " << bn.number_of_nodes()
                );

                return bn.node(n).data.table().num_values();
            }

        // ----------------------------------------------------------------------------------------

            template <typename T>
            void set_node_value (
                T& bn,
                unsigned long n,
                unsigned long val
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes() && val < node_num_values(bn,n),
                             "\tvoid bayes_node_utils::set_node_value(bn, n, val)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                             << "\n\tval:                   " << val
                             << "\n\tbn.number_of_nodes():  " << bn.number_of_nodes()
                             << "\n\tnode_num_values(bn,n): " << node_num_values(bn,n)
                );

                bn.node(n).data.set_value(val);
            }

        // ----------------------------------------------------------------------------------------
            template <typename T>
            unsigned long node_value (
                const T& bn,
                unsigned long n
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes(),
                             "\tunsigned long bayes_node_utils::node_value(bn, n)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                             << "\n\tbn.number_of_nodes():  " << bn.number_of_nodes()
                );

                return bn.node(n).data.value();
            }
        // ----------------------------------------------------------------------------------------

            template <typename T>
            bool node_is_evidence (
                const T& bn,
                unsigned long n
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes(),
                             "\tbool bayes_node_utils::node_is_evidence(bn, n)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                             << "\n\tbn.number_of_nodes():  " << bn.number_of_nodes()
                );

                return bn.node(n).data.is_evidence();
            }

        // ----------------------------------------------------------------------------------------

            template <typename T>
            void set_node_as_evidence (
                T& bn,
                unsigned long n
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes(),
                             "\tvoid bayes_node_utils::set_node_as_evidence(bn, n)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                             << "\n\tbn.number_of_nodes():  " << bn.number_of_nodes()
                );

                bn.node(n).data.set_as_evidence();
            }

        // ----------------------------------------------------------------------------------------
            template <typename T>
            void set_node_as_nonevidence (
                T& bn,
                unsigned long n
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes(),
                             "\tvoid bayes_node_utils::set_node_as_nonevidence(bn, n)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                             << "\n\tbn.number_of_nodes():  " << bn.number_of_nodes()
                );

                bn.node(n).data.set_as_nonevidence();
            }

        // ----------------------------------------------------------------------------------------

            template <typename T>
            void set_node_num_values (
                T& bn,
                unsigned long n,
                unsigned long num
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes(),
                             "\tvoid bayes_node_utils::set_node_num_values(bn, n, num)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                             << "\n\tbn.number_of_nodes():  " << bn.number_of_nodes()
                );

                bn.node(n).data.table().set_num_values(num);
            }

        // ----------------------------------------------------------------------------------------

            template <typename T>
            double node_probability (
                const T& bn,
                unsigned long n,
                unsigned long value,
                const assignment& parents
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes() && value < node_num_values(bn,n),
                             "\tdouble bayes_node_utils::node_probability(bn, n, value, parents)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                             << "\n\tvalue:                 " << value
                             << "\n\tbn.number_of_nodes():  " << bn.number_of_nodes()
                             << "\n\tnode_num_values(bn,n): " << node_num_values(bn,n)
                );

                DLIB_ASSERT( parents.size() == bn.node(n).number_of_parents(),
                             "\tdouble bayes_node_utils::node_probability(bn, n, value, parents)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                             " << n
                             << "\n\tparents.size():                " << parents.size()
                             << "\n\tb.node(n).number_of_parents(): " << bn.node(n).number_of_parents()
                );

    #ifdef ENABLE_ASSERTS
                parents.reset();
                while (parents.move_next())
                {
                    const unsigned long x = parents.element().key();
                    DLIB_ASSERT( bn.has_edge(x, n),
                                 "\tdouble bayes_node_utils::node_probability(bn, n, value, parents)"
                                 << "\n\tInvalid arguments to this function"
                                 << "\n\tn: " << n
                                 << "\n\tx: " << x
                    );
                    DLIB_ASSERT( parents[x] < node_num_values(bn,x),
                                 "\tdouble bayes_node_utils::node_probability(bn, n, value, parents)"
                                 << "\n\tInvalid arguments to this function"
                                 << "\n\tn:                     " << n
                                 << "\n\tx:                     " << x
                                 << "\n\tparents[x]:            " << parents[x]
                                 << "\n\tnode_num_values(bn,x): " << node_num_values(bn,x)
                    );
                }
    #endif

                return bn.node(n).data.table().probability(value, parents);
            }

        // ----------------------------------------------------------------------------------------

            template <typename T>
            void set_node_probability (
                T& bn,
                unsigned long n,
                unsigned long value,
                const assignment& parents,
                double p
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes() && value < node_num_values(bn,n),
                             "\tvoid bayes_node_utils::set_node_probability(bn, n, value, parents, p)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                             << "\n\tp:                     " << p
                             << "\n\tvalue:                 " << value
                             << "\n\tbn.number_of_nodes():  " << bn.number_of_nodes()
                             << "\n\tnode_num_values(bn,n): " << node_num_values(bn,n)
                );

                DLIB_ASSERT( parents.size() == bn.node(n).number_of_parents(),
                             "\tvoid bayes_node_utils::set_node_probability(bn, n, value, parents, p)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                             " << n
                             << "\n\tp:                             " << p
                             << "\n\tparents.size():                " << parents.size()
                             << "\n\tbn.node(n).number_of_parents(): " << bn.node(n).number_of_parents()
                );

                DLIB_ASSERT( 0.0 <= p && p <= 1.0,
                             "\tvoid bayes_node_utils::set_node_probability(bn, n, value, parents, p)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn: " << n
                             << "\n\tp: " << p
                );

    #ifdef ENABLE_ASSERTS
                parents.reset();
                while (parents.move_next())
                {
                    const unsigned long x = parents.element().key();
                    DLIB_ASSERT( bn.has_edge(x, n),
                                 "\tvoid bayes_node_utils::set_node_probability(bn, n, value, parents, p)"
                                 << "\n\tInvalid arguments to this function"
                                 << "\n\tn: " << n
                                 << "\n\tx: " << x
                    );
                    DLIB_ASSERT( parents[x] < node_num_values(bn,x),
                                 "\tvoid bayes_node_utils::set_node_probability(bn, n, value, parents, p)"
                                 << "\n\tInvalid arguments to this function"
                                 << "\n\tn:                     " << n
                                 << "\n\tx:                     " << x
                                 << "\n\tparents[x]:            " << parents[x]
                                 << "\n\tnode_num_values(bn,x): " << node_num_values(bn,x)
                    );
                }
    #endif

                bn.node(n).data.table().set_probability(value,parents,p);
            }

    // ----------------------------------------------------------------------------------------

            template <typename T>
            const assignment node_first_parent_assignment (
                const T& bn,
                unsigned long n
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes(),
                             "\tconst assignment bayes_node_utils::node_first_parent_assignment(bn, n)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                );

                assignment a;
                const unsigned long num_parents = bn.node(n).number_of_parents();
                for (unsigned long i = 0; i < num_parents; ++i)
                {
                    a.add(bn.node(n).parent(i).index(), 0);
                }
                return a;
            }

    // ----------------------------------------------------------------------------------------

            template <typename T>
            bool node_next_parent_assignment (
                const T& bn,
                unsigned long n,
                assignment& a
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes(),
                             "\tbool bayes_node_utils::node_next_parent_assignment(bn, n, a)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                );

                DLIB_ASSERT( a.size() == bn.node(n).number_of_parents(),
                             "\tbool bayes_node_utils::node_next_parent_assignment(bn, n, a)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                             " << n
                             << "\n\ta.size():                      " << a.size()
                             << "\n\tbn.node(n).number_of_parents(): " << bn.node(n).number_of_parents()
                );

    #ifdef ENABLE_ASSERTS
                a.reset();
                while (a.move_next())
                {
                    const unsigned long x = a.element().key();
                    DLIB_ASSERT( bn.has_edge(x, n),
                                 "\tbool bayes_node_utils::node_next_parent_assignment(bn, n, a)"
                                 << "\n\tInvalid arguments to this function"
                                 << "\n\tn: " << n
                                 << "\n\tx: " << x
                    );
                    DLIB_ASSERT( a[x] < node_num_values(bn,x),
                                 "\tbool bayes_node_utils::node_next_parent_assignment(bn, n, a)"
                                 << "\n\tInvalid arguments to this function"
                                 << "\n\tn:                     " << n
                                 << "\n\tx:                     " << x
                                 << "\n\ta[x]:                  " << a[x]
                                 << "\n\tnode_num_values(bn,x): " << node_num_values(bn,x)
                    );
                }
    #endif

                // basically this loop just adds 1 to the assignment but performs
                // carries if necessary
                for (unsigned long p = 0; p < a.size(); ++p)
                {
                    const unsigned long pindex = bn.node(n).parent(p).index();
                    a[pindex] += 1;

                    // if we need to perform a carry
                    if (a[pindex] >= node_num_values(bn,pindex))
                    {
                        a[pindex] = 0;
                    }
                    else
                    {
                        // no carry necessary so we are done
                        return true;
                    }
                }

                // we got through the entire loop which means a carry propagated all the way out
                // so there must not be any more valid assignments left
                return false;
            }

    // ----------------------------------------------------------------------------------------

            template <typename T>
            bool node_cpt_filled_out (
                const T& bn,
                unsigned long n
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( n < bn.number_of_nodes(),
                             "\tbool bayes_node_utils::node_cpt_filled_out(bn, n)"
                             << "\n\tInvalid arguments to this function"
                             << "\n\tn:                     " << n
                             << "\n\tbn.number_of_nodes():  " << bn.number_of_nodes()
                );

                const unsigned long num_values = node_num_values(bn,n);

                const conditional_probability_table& table = bn.node(n).data.table();

                // now loop over all the possible parent assignments for this node
                assignment a(node_first_parent_assignment(bn,n));
                do
                {
                    double sum = 0;
                    // make sure that this assignment has an entry for all the values this node can take one
                    for (unsigned long value = 0; value < num_values; ++value)
                    {
                        if (table.has_entry_for(value,a) == false)
                            return false;
                        else
                            sum += table.probability(value,a);
                    }

                    // check if the sum of probabilities equals 1 as it should
                    if (std::abs(sum-1.0) > 1e-5)
                        return false;
                } while (node_next_parent_assignment(bn,n,a));

                return true;
            }

        }

    // ----------------------------------------------------------------------------------------

        class bayesian_network_gibbs_sampler : noncopyable
        {
        public:

            bayesian_network_gibbs_sampler ()
            {
                rnd.set_seed(cast_to_string(std::time(0)));
            }

            template <
                typename T
                >
            void sample_graph (
                T& bn
            )
            {
                using namespace bayes_node_utils;
                for (unsigned long n = 0; n < bn.number_of_nodes(); ++n)
                {
                    if (node_is_evidence(bn, n))
                        continue;

                    samples.set_size(node_num_values(bn,n));
                    // obtain the probability distribution for this node
                    for (long i = 0; i < samples.nc(); ++i)
                    {
                        set_node_value(bn, n, i);
                        samples(i) = node_probability(bn, n);

                        for (unsigned long j = 0; j < bn.node(n).number_of_children(); ++j)
                            samples(i) *= node_probability(bn, bn.node(n).child(j).index());
                    }

                    //normalize samples
                    samples /= sum(samples);

                    // select a random point in the probability distribution
                    double prob = rnd.get_random_double();

                    // now find the point in the distribution this probability corresponds to
                    long j;
                    for (j = 0; j < samples.nc()-1; ++j)
                    {
                        if (prob <= samples(j))
                            break;
                        else
                            prob -= samples(j);
                    }

                    set_node_value(bn, n, j);
                }
            }

        private:

            template <
                typename T
                >
            double node_probability (
                const T& bn,
                unsigned long n
            )
            /*!
                requires
                    - n < bn.number_of_nodes()
                ensures
                    - computes the probability of node n having its current value given
                      the current values of its parents in the network bn
            !*/
            {
                v.clear();
                for (unsigned long i = 0; i < bn.node(n).number_of_parents(); ++i)
                {
                    v.add(bn.node(n).parent(i).index(), bn.node(n).parent(i).data.value());
                }
                return bn.node(n).data.table().probability(bn.node(n).data.value(), v);
            }

            assignment v;

            dlib::rand rnd;
            matrix<double,1> samples;
        };

    // ----------------------------------------------------------------------------------------

        namespace bayesian_network_join_tree_helpers
        {
            class bnjt
            {
                /*!
                    this object is the base class used in this pimpl idiom
                !*/
            public:
                virtual ~bnjt() {}

                virtual const matrix<double,1> probability(
                    unsigned long idx
                )  const = 0;
            };

            template <typename T, typename U>
            class bnjt_impl : public bnjt
            {
                /*!
                    This object is the implementation in the pimpl idiom
                !*/

            public:

                bnjt_impl (
                    const T& bn,
                    const U& join_tree
                )
                {
                    create_bayesian_network_join_tree(bn, join_tree, join_tree_values);

                    cliques.resize(bn.number_of_nodes());

                    // figure out which cliques contain each node
                    for (unsigned long i = 0; i < cliques.size(); ++i)
                    {
                        // find the smallest clique that contains node with index i
                        unsigned long smallest_clique = 0;
                        unsigned long size = std::numeric_limits<unsigned long>::max();

                        for (unsigned long n = 0; n < join_tree.number_of_nodes(); ++n)
                        {
                            if (join_tree.node(n).data.is_member(i) && join_tree.node(n).data.size() < size)
                            {
                                size = join_tree.node(n).data.size();
                                smallest_clique = n;
                            }
                        }

                        cliques[i] = smallest_clique;
                    }
                }

                virtual const matrix<double,1> probability(
                    unsigned long idx
                ) const
                {
                    join_tree_values.node(cliques[idx]).data.marginalize(idx, table);
                    table.normalize();
                    var.clear();
                    var.add(idx);
                    dist.set_size(table.size());

                    // read the probabilities out of the table and into the row matrix
                    for (unsigned long i = 0; i < table.size(); ++i)
                    {
                        var[idx] = i;
                        dist(i) = table.probability(var);
                    }

                    return dist;
                }

            private:

                graph< joint_probability_table, joint_probability_table >::kernel_1a_c join_tree_values;
                array<unsigned long> cliques;
                mutable joint_probability_table table;
                mutable assignment var;
                mutable matrix<double,1> dist;

            // ----------------------------------------------------------------------------------------

                template <typename set_type, typename node_type>
                bool set_contains_all_parents_of_node (
                    const set_type& set,
                    const node_type& node
                )
                {
                    for (unsigned long i = 0; i < node.number_of_parents(); ++i)
                    {
                        if (set.is_member(node.parent(i).index()) == false)
                            return false;
                    }
                    return true;
                }

            // ----------------------------------------------------------------------------------------

                template <
                    typename V
                    >
                void pass_join_tree_message (
                    const U& join_tree,
                    V& bn_join_tree ,
                    unsigned long from,
                    unsigned long to
                )
                {
                    using namespace bayes_node_utils;
                    const typename U::edge_type& e = edge(join_tree, from, to);
                    typename V::edge_type& old_s = edge(bn_join_tree, from, to);

                    typedef typename V::edge_type joint_prob_table;

                    joint_prob_table new_s;
                    bn_join_tree.node(from).data.marginalize(e, new_s);

                    joint_probability_table temp(new_s);
                    // divide new_s by old_s and store the result in temp.
                    // if old_s is empty then that is the same as if it was all 1s
                    // so we don't have to do this if that is the case.
                    if (old_s.size() > 0)
                    {
                        temp.reset();
                        old_s.reset();
                        while (temp.move_next())
                        {
                            old_s.move_next();
                            if (old_s.element().value() != 0)
                                temp.element().value()  /= old_s.element().value();
                        }
                    }

                    // now multiply temp by d and store the results in d
                    joint_probability_table& d = bn_join_tree.node(to).data;
                    d.reset();
                    while (d.move_next())
                    {
                        assignment a;
                        const assignment& asrc = d.element().key();
                        asrc.reset();
                        while (asrc.move_next())
                        {
                            if (e.is_member(asrc.element().key()))
                                a.add(asrc.element().key(), asrc.element().value());
                        }

                        d.element().value() *= temp.probability(a);

                    }

                    // store new_s in old_s
                    new_s.swap(old_s);

                }

            // ----------------------------------------------------------------------------------------

                template <
                    typename V
                    >
                void create_bayesian_network_join_tree (
                    const T& bn,
                    const U& join_tree,
                    V& bn_join_tree
                )
                /*!
                    requires
                        - bn is a proper bayesian network
                        - join_tree is the join tree for that bayesian network
                    ensures
                        - bn_join_tree == the output of the join tree algorithm for bayesian network inference.
                          So each node in this graph contains a joint_probability_table for the clique
                          in the corresponding node in the join_tree graph.
                !*/
                {
                    using namespace bayes_node_utils;
                    bn_join_tree.clear();
                    copy_graph_structure(join_tree, bn_join_tree);

                    // we need to keep track of which node is "in" each clique for the purposes of
                    // initializing the tables in each clique.  So this vector will be used to do that
                    // and a value of join_tree.number_of_nodes() means that the node with
                    // that index is unassigned.
                    std::vector<unsigned long> node_assigned_to(bn.number_of_nodes(),join_tree.number_of_nodes());

                    // populate evidence with all the evidence node indices and their values
                    dlib::map<unsigned long, unsigned long>::kernel_1b_c evidence;
                    for (unsigned long i = 0; i < bn.number_of_nodes(); ++i)
                    {
                        if (node_is_evidence(bn, i))
                        {
                            unsigned long idx = i;
                            unsigned long value = node_value(bn, i);
                            evidence.add(idx,value);
                        }
                    }

                    // initialize the bn join tree
                    for (unsigned long i = 0; i < join_tree.number_of_nodes(); ++i)
                    {
                        bool contains_evidence = false;
                        std::vector<unsigned long> indices;
                        assignment value;

                        // loop over all the nodes in this clique in the join tree.  In this loop
                        // we are making an assignment with all the values of the nodes it represents set to 0
                        join_tree.node(i).data.reset();
                        while (join_tree.node(i).data.move_next())
                        {
                            const unsigned long idx = join_tree.node(i).data.element();
                            indices.push_back(idx);
                            value.add(idx);

                            if (evidence.is_in_domain(join_tree.node(i).data.element()))
                                contains_evidence = true;
                        }

                        // now loop over all possible combinations of values that the nodes this
                        // clique in the join tree can take on.  We do this by counting by one through all
                        // legal values
                        bool more_assignments = true;
                        while (more_assignments)
                        {
                            bn_join_tree.node(i).data.set_probability(value,1);

                            // account for any evidence
                            if (contains_evidence)
                            {
                                // loop over all the nodes in this cluster
                                for (unsigned long j = 0; j < indices.size(); ++j)
                                {
                                    // if the current node is an evidence node
                                    if (evidence.is_in_domain(indices[j]))
                                    {
                                        const unsigned long idx = indices[j];
                                        const unsigned long evidence_value = evidence[idx];
                                        if (value[idx] != evidence_value)
                                            bn_join_tree.node(i).data.set_probability(value , 0);
                                    }
                                }
                            }

                            // now check if any of the nodes in this cluster also have their parents in this cluster
                            join_tree.node(i).data.reset();
                            while (join_tree.node(i).data.move_next())
                            {
                                const unsigned long idx = join_tree.node(i).data.element();
                                // if this clique contains all the parents of this node and also hasn't
                                // been assigned to another clique
                                if (set_contains_all_parents_of_node(join_tree.node(i).data,  bn.node(idx)) &&
                                    (i == node_assigned_to[idx] || node_assigned_to[idx] == join_tree.number_of_nodes()) )
                                {
                                    // note that this node is now assigned to this clique
                                    node_assigned_to[idx] = i;
                                    // node idx has all its parents in the cluster
                                    assignment parent_values;
                                    for (unsigned long j = 0; j < bn.node(idx).number_of_parents(); ++j)
                                    {
                                        const unsigned long pidx = bn.node(idx).parent(j).index();
                                        parent_values.add(pidx, value[pidx]);
                                    }

                                    double temp = bn_join_tree.node(i).data.probability(value);
                                    bn_join_tree.node(i).data.set_probability(value, temp * node_probability(bn, idx, value[idx], parent_values));

                                }
                            }

                            // now advance the value variable to its next possible state if there is one
                            more_assignments = false;
                            value.reset();
                            while (value.move_next())
                            {
                                value.element().value() += 1;
                                // if overflow
                                if (value.element().value() == node_num_values(bn, value.element().key()))
                                {
                                    value.element().value() = 0;
                                }
                                else
                                {
                                    more_assignments = true;
                                    break;
                                }
                            }

                        } // end while (more_assignments)
                    }

                    // the tree is now initialized.  Now all we need to do is perform the propagation and
                    // we are done
                    dlib::array<dlib::set<unsigned long>::compare_1b_c> remaining_msg_to_send;
                    dlib::array<dlib::set<unsigned long>::compare_1b_c> remaining_msg_to_receive;
                    remaining_msg_to_receive.resize(join_tree.number_of_nodes());
                    remaining_msg_to_send.resize(join_tree.number_of_nodes());
                    for (unsigned long i = 0; i < remaining_msg_to_receive.size(); ++i)
                    {
                        for (unsigned long j = 0; j < join_tree.node(i).number_of_neighbors(); ++j)
                        {
                            const unsigned long idx = join_tree.node(i).neighbor(j).index();
                            unsigned long temp;
                            temp = idx; remaining_msg_to_receive[i].add(temp);
                            temp = idx; remaining_msg_to_send[i].add(temp);
                        }
                    }

                    // now remaining_msg_to_receive[i] contains all the nodes that node i hasn't yet received
                    // a message from.
                    // we will consider node 0 to be the root node.

                    bool message_sent = true;
                    std::vector<unsigned long>::iterator iter;
                    while (message_sent)
                    {
                        message_sent = false;
                        for (unsigned long i = 1; i < remaining_msg_to_send.size(); ++i)
                        {
                            // if node i hasn't sent any messages but has received all but one then send a message to the one
                            // node who hasn't sent i a message
                            if (remaining_msg_to_send[i].size() == join_tree.node(i).number_of_neighbors() && remaining_msg_to_receive[i].size() == 1)
                            {
                                unsigned long to;
                                // get the last remaining thing from this set
                                remaining_msg_to_receive[i].remove_any(to);

                                // send the message
                                pass_join_tree_message(join_tree, bn_join_tree, i, to);

                                // record that we sent this message
                                remaining_msg_to_send[i].destroy(to);
                                remaining_msg_to_receive[to].destroy(i);

                                // put to back in since we still need to receive it
                                remaining_msg_to_receive[i].add(to);
                                message_sent = true;
                            }
                            else if (remaining_msg_to_receive[i].size() == 0 && remaining_msg_to_send[i].size() > 0)
                            {
                                unsigned long to;
                                remaining_msg_to_send[i].remove_any(to);
                                remaining_msg_to_receive[to].destroy(i);
                                pass_join_tree_message(join_tree, bn_join_tree, i, to);
                                message_sent = true;
                            }
                        }

                        if (remaining_msg_to_receive[0].size() == 0)
                        {
                            // send a message to all of the root nodes neighbors unless we have already sent out he messages
                            while (remaining_msg_to_send[0].size() > 0)
                            {
                                unsigned long to;
                                remaining_msg_to_send[0].remove_any(to);
                                remaining_msg_to_receive[to].destroy(0);
                                pass_join_tree_message(join_tree, bn_join_tree, 0, to);
                                message_sent = true;
                            }
                        }

                    }

                }

            };
        }

        class bayesian_network_join_tree : noncopyable
        {
            /*!
                use the pimpl idiom to push the template arguments from the class level to the
                constructor level
            !*/

        public:

            template <
                typename T,
                typename U
                >
            bayesian_network_join_tree (
                const T& bn,
                const U& join_tree
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( bn.number_of_nodes() > 0 ,
                            "\tbayesian_network_join_tree::bayesian_network_join_tree(bn,join_tree)"
                            << "\n\tYou have given an invalid bayesian network"
                            << "\n\tthis:              " << this
                        );

                DLIB_ASSERT( is_join_tree(bn, join_tree) == true ,
                            "\tbayesian_network_join_tree::bayesian_network_join_tree(bn,join_tree)"
                            << "\n\tYou have given an invalid join tree for the supplied bayesian network"
                            << "\n\tthis:              " << this
                        );
                DLIB_ASSERT( graph_contains_length_one_cycle(bn) == false,
                            "\tbayesian_network_join_tree::bayesian_network_join_tree(bn,join_tree)"
                            << "\n\tYou have given an invalid bayesian network"
                            << "\n\tthis:              " << this
                        );
                DLIB_ASSERT( graph_is_connected(bn) == true,
                            "\tbayesian_network_join_tree::bayesian_network_join_tree(bn,join_tree)"
                            << "\n\tYou have given an invalid bayesian network"
                            << "\n\tthis:              " << this
                        );

    #ifdef ENABLE_ASSERTS
                for (unsigned long i = 0; i < bn.number_of_nodes(); ++i)
                {
                    DLIB_ASSERT(bayes_node_utils::node_cpt_filled_out(bn,i) == true,
                            "\tbayesian_network_join_tree::bayesian_network_join_tree(bn,join_tree)"
                            << "\n\tYou have given an invalid bayesian network. "
                            << "\n\tYou must finish filling out the conditional_probability_table of node " << i
                            << "\n\tthis:              " << this
                        );
                }
    #endif

                impl.reset(new bayesian_network_join_tree_helpers::bnjt_impl<T,U>(bn, join_tree));
                num_nodes = bn.number_of_nodes();
            }

            const matrix<double,1> probability(
                unsigned long idx
            ) const
            {
                // make sure requires clause is not broken
                DLIB_ASSERT( idx < number_of_nodes() ,
                            "\tconst matrix<double,1> bayesian_network_join_tree::probability(idx)"
                            << "\n\tYou have specified an invalid node index"
                            << "\n\tidx:               " << idx
                            << "\n\tnumber_of_nodes(): " << number_of_nodes()
                            << "\n\tthis:              " << this
                        );

                return impl->probability(idx);
            }

            unsigned long number_of_nodes (
            ) const { return num_nodes; }

            void swap (
                bayesian_network_join_tree& item
            )
            {
                exchange(num_nodes, item.num_nodes);
                impl.swap(item.impl);
            }

        private:

            scoped_ptr<bayesian_network_join_tree_helpers::bnjt> impl;
            unsigned long num_nodes;

        };

        inline void swap (
            bayesian_network_join_tree& a,
            bayesian_network_join_tree& b
        ) { a.swap(b); }

    }

    // ----------------------------------------------------------------------------------------

    #endif // DLIB_BAYES_UTILs_