    // Copyright (C) 2011  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #undef DLIB_STRUCTURAL_SVM_DISTRIBUTeD_ABSTRACT_H__
    #ifdef DLIB_STRUCTURAL_SVM_DISTRIBUTeD_ABSTRACT_H__

    #include "structural_svm_problem_abstract.h"
    #include "../optimization/optimization_oca_abstract.h"

    namespace dlib
    {

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

        class svm_struct_processing_node : noncopyable
        {
            /*!
                WHAT THIS OBJECT REPRESENTS
                    This object is a tool for distributing the work involved in solving
                    a dlib::structural_svm_problem across many computers.  It is used in
                    conjunction with the svm_struct_controller_node defined below.
            !*/

        public:

            template <
                typename T,
                typename U
                >
            svm_struct_processing_node (
                const structural_svm_problem<T,U>& problem,
                unsigned short port,
                unsigned short num_threads
            );
            /*!
                requires
                    - port != 0
                    - problem.get_num_samples() != 0
                    - problem.get_num_dimensions() != 0
                ensures
                    - This object will listen on the given port for a TCP connection from a
                      svm_struct_controller_node.  Once connected, the controller node will
                      be able to access the given problem.
                    - Will use num_threads threads at a time to make concurrent calls to the
                      problem.separation_oracle() routine.  You should set this parameter equal
                      to the number of available processing cores.
                    - Note that the following parameters within the given problem are ignored:
                        - problem.get_c()
                        - problem.get_epsilon()
                        - problem.get_cache_based_epsilon()
                        - problem.num_nuclear_norm_regularizers()
                        - weather the problem is verbose or not
                      Instead, they are defined by the svm_struct_controller_node. Note, however,
                      that the problem.get_max_cache_size() parameter is meaningful and controls
                      the size of the separation oracle cache within a svm_struct_processing_node.
            !*/
        };

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

        class svm_struct_controller_node : noncopyable
        {
            /*!
                INITIAL VALUE
                    - get_num_processing_nodes() == 0
                    - get_epsilon() == 0.001
                    - get_c() == 1
                    - This object will not be verbose

                WHAT THIS OBJECT REPRESENTS
                    This object is a tool for distributing the work involved in solving a
                    dlib::structural_svm_problem across many computers.  The best way to understand
                    its use is via example:

                    First, suppose you have defined a structural_svm_problem object by inheriting from
                    it and defining the appropriate virtual functions.  You could solve it by passing
                    an instance to the oca optimizer.  However, if your separation oracle takes a long
                    time to evaluate then the optimization will take a long time to solve.  To speed
                    this up we can distribute the calls to the separation oracle across many computers.

                    To make this concrete, lets imagine you want to distribute the work across three
                    computers. You can accomplish this by creating four programs.  One containing a
                    svm_struct_controller_node and three containing svm_struct_processing_nodes.

                    The programs might look like this:

                    Controller program:
                        int main()
                        {
                            svm_struct_controller_node cont;
                            cont.set_c(100);
                            // Tell cont where the processing nodes are on your network.
                            cont.add_processing_node("192.168.1.10:12345");
                            cont.add_processing_node("192.168.1.11:12345");
                            cont.add_processing_node("192.168.1.12:12345");
                            matrix<double> w;
                            oca solver;
                            cont(solver, w); // Run the optimization.
                            // After this finishes w will contain the solution vector.
                        }

                    Processing programs (they are all the same, except that each loads a different subset
                    of the training data):
                        int main()
                        {
                            // Put one third of your data into this problem object.  How you do this depends on your problem.
                            your_structural_svm_problem problem;
                            svm_struct_processing_node node(problem, 12345, number_of_cores_on_this_computer);
                            cout << "hit enter to terminate this program" << endl;
                            cin.get();
                        }

            !*/

        public:

            svm_struct_controller_node (
            );
            /*!
                ensures
                    - this object is properly initialized
            !*/

            void set_epsilon (
                double eps
            );
            /*!
                requires
                    - eps > 0
                ensures
                    - #get_epsilon() == eps
            !*/

            double get_epsilon (
            ) const;
            /*!
                ensures
                    - returns the error epsilon that determines when training should stop.
                      Smaller values may result in a more accurate solution but take longer
                      to execute.  Specifically, the algorithm stops when the average sample
                      risk (i.e. R(w) as defined by the dlib::structural_svm_problem object) is
                      within epsilon of its optimal value.

                      Also note that sample risk is an upper bound on a sample's loss.  So
                      you can think of this epsilon value as saying "solve the optimization
                      problem until the average loss per sample is within epsilon of it's
                      optimal value".
            !*/

            double get_cache_based_epsilon (
            ) const;
            /*!
                ensures
                    - if (get_max_cache_size() != 0) then
                        - The solver will not stop when the average sample risk is within
                          get_epsilon() of its optimal value.  Instead, it will keep running
                          but will run the optimizer completely on the cache until the average
                          sample risk is within #get_cache_based_epsilon() of its optimal
                          value.  This means that it will perform this additional refinement in
                          the solution accuracy without making any additional calls to the
                          separation_oracle().  This is useful when using a nuclear norm
                          regularization term because it allows you to quickly solve the
                          optimization problem to a high precision, which in the case of a
                          nuclear norm regularized problem means that many of the learned
                          matrices will be low rank or very close to low rank due to the
                          nuclear norm regularizer.  This may not happen without solving the
                          problem to a high accuracy or their ranks may be difficult to
                          determine, so the extra accuracy given by the cache based refinement
                          is very useful.  Finally, note that we include the nuclear norm term
                          as part of the "risk" for the purposes of determining when to stop.
                    - else
                        - The value of #get_cache_based_epsilon() has no effect.
            !*/

            void set_cache_based_epsilon (
                double eps
            );
            /*!
                requires
                    - eps > 0
                ensures
                    - #get_cache_based_epsilon() == eps
            !*/

            void add_nuclear_norm_regularizer (
                long first_dimension,
                long rows,
                long cols,
                double regularization_strength
            );
            /*!
                requires
                    - 0 <= first_dimension < number of dimensions in problem
                    - 0 <= rows
                    - 0 <= cols
                    - first_dimension+rows*cols <= number of dimensions in problem
                    - 0 < regularization_strength
                ensures
                    - Adds a nuclear norm regularization term to the optimization problem
                      solved by this object.  That is, instead of solving:
                        Minimize: h(w) == 0.5*dot(w,w) + C*R(w)
                      this object will solve:
                        Minimize: h(w) == 0.5*dot(w,w) + C*R(w) + regularization_strength*nuclear_norm_of(part of w)
                      where "part of w" is the part of w indicated by the arguments to this
                      function. In particular, the part of w included in the nuclear norm is
                      exactly the matrix reshape(rowm(w, range(first_dimension, first_dimension+rows*cols-1)), rows, cols).
                      Therefore, if you think of the w vector as being the concatenation of a
                      bunch of matrices then you can use multiple calls to add_nuclear_norm_regularizer()
                      to add nuclear norm regularization terms to any of the matrices packed into w.
                    - #num_nuclear_norm_regularizers() == num_nuclear_norm_regularizers() + 1
            !*/

            unsigned long num_nuclear_norm_regularizers (
            ) const;
            /*!
                ensures
                    - returns the number of nuclear norm regularizers that are currently a part
                      of this optimization problem.  That is, returns the number of times
                      add_nuclear_norm_regularizer() has been called since the last call to
                      clear_nuclear_norm_regularizers() or object construction, whichever is
                      most recent.
            !*/

            void clear_nuclear_norm_regularizers (
            );
            /*!
                ensures
                    - #num_nuclear_norm_regularizers() == 0
            !*/

            void be_verbose (
            );
            /*!
                ensures
                    - This object will print status messages to standard out so that a
                      user can observe the progress of the algorithm.
            !*/

            void be_quiet(
            );
            /*!
                ensures
                    - this object will not print anything to standard out
            !*/

            double get_c (
            ) const;
            /*!
                ensures
                    - returns the SVM regularization parameter.  It is the parameter that
                      determines the trade off between trying to fit the training data
                      exactly or allowing more errors but hopefully improving the
                      generalization of the resulting classifier.  Larger values encourage
                      exact fitting while smaller values of C may encourage better
                      generalization.
            !*/

            void set_c (
                double C
            );
            /*!
                requires
                    - C > 0
                ensures
                    - #get_c() == C
            !*/

            void add_processing_node (
                const network_address& addr
            );
            /*!
                requires
                    - addr.port != 0
                ensures
                    - if (this address hasn't already been added) then
                        - #get_num_processing_nodes() == get_num_processing_nodes() + 1
                        - When operator() is invoked to solve the structural svm problem this
                          object will connect to the svm_struct_processing_node located at the
                          given network address and will include it in the distributed
                          optimization.
            !*/

            void add_processing_node (
                const std::string& ip_or_hostname,
                unsigned short port
            );
            /*!
                requires
                    - port != 0
                ensures
                    - invokes: add_processing_node(network_address(ip_or_hostname, port))
            !*/

            unsigned long get_num_processing_nodes (
            ) const;
            /*!
                ensures
                    - returns the number of remote processing nodes that have been
                      registered with this object.
            !*/

            void remove_processing_nodes (
            );
            /*!
                ensures
                    - #get_num_processing_nodes() == 0
            !*/

            class invalid_problem : public error {};

            template <typename matrix_type>
            double operator() (
                const oca& solver,
                matrix_type& w
            ) const;
            /*!
                requires
                    - get_num_processing_nodes() != 0
                    - matrix_type == a dlib::matrix capable of storing column vectors
                ensures
                    - connects to the processing nodes and begins optimizing the structural
                      svm problem using the given oca solver.
                    - stores the solution in #w
                    - returns the objective value at the solution #w
                throws
                    - invalid_problem
                      This exception is thrown if the svm_struct_processing_nodes disagree
                      on the dimensionality of the problem.  That is, if they disagree on
                      the value of structural_svm_problem::get_num_dimensions().
            !*/

        };

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

    }

    #endif // DLIB_STRUCTURAL_SVM_DISTRIBUTeD_ABSTRACT_H__