export_model.m.svn-base

Probabilistic graphical models in matlab.

function [] = export_model(filename, model, method, method_arg1)

fid = fopen(filename, 'w');


unknown_method = true;

if (strcmp (method, 'loopy' ) )

    unknown_method = false;

end

if (strcmp (method, 'exact_bp' ) )

    unknown_method = false;

end

if (strcmp (method, 'gibbs' ) )

    unknown_method = false;

end

if (strcmp (method, 'tree_mcmc' ) )

    unknown_method = false;

end

if (strcmp (method, 'tree_mcmc_no_rb' ) )

    unknown_method = false;

end

if (strcmp (method, 'tree_partition' ) )

    %model.variables_sizes = ones(3);

    unknown_method = false;

end

if (unknown_method)

    error('Error: unknown method type');

    exit;

end



fprintf(fid,'<method> (' );

fprintf(fid, method );

fprintf(fid, ')' );

if (strcmp (method, 'loopy' ) )

    fprintf(fid, '( %d ) ', method_arg1);

end

if (strcmp (method, 'gibbs' ) )

    fprintf(fid, '( %d ) ', method_arg1);

end

if (strcmp (method, 'tree_mcmc' ) )

    fprintf(fid, '( %d ) ', method_arg1);

end

if (strcmp (method, 'tree_mcmc_no_rb' ) )

    fprintf(fid, '( %d ) ', method_arg1);

end

fprintf(fid,'\n' );

% Number of vertices for the graph

fprintf(fid, '<number of vertices> ');

if (strcmp (model.type, 'pairwise-discrete' ) )

    fprintf(fid, '( %d ) ', model.number_vertices);

end

if (strcmp (model.type, 'factorgraph-discrete' ) )

    fprintf(fid, '( %d ) ', model.number_vertices+model.number_potential_tables);

end

fprintf(fid,'\n');

fprintf(fid,'<model type> (' );

fprintf(fid, model.type );

fprintf(fid,') \n' );


% Now comes the RV sizes

if (model.all_variables ~= 1)

    fprintf(fid, '<random variable sizes> ');

    for i = 1:model.number_vertices

        fprintf(fid, '( %d %d ) ', i, model.variables_sizes(1, i));

    end

    fprintf(fid,'\n');

else

    if (strcmp (model.type, 'factorgraph-discrete' ) )

        fprintf(fid, '<all random variable sizes> (');
        fprintf (fid, ' %d %d)\n', model.number_vertices, model.all_variables_size);

    else

        fprintf(fid, '<all random variable sizes> (');
        fprintf (fid, ' %d )\n', model.all_variables_size);

    end

end

% Now comes the links...

if (model.structure)

    fprintf(fid, '%s', model.structure_code);

else

    counted_links = 0;
    fprintf(fid, '<link> ');

    if (strcmp (model.type, 'factorgraph-discrete' ) )

        if (strcmp (model.structure_code, 'qmr' ) )

            fprintf(fid, '(qmr) ');


        else

            fprintf(fid, '(normal) ');

        end


        for i = 1:model.number_potential_tables

            fprintf(fid, '(');

            for j = 1:size(model.link_tables{i},2)

                fprintf(fid, ' %d ', model.link_tables{i} (j) );

            end

            fprintf(fid, ') ');

        end

    end

    if (strcmp (model.type, 'pairwise-discrete' ) )

        for i = 1:model.number_vertices

            for j = 1:model.number_vertices

                if model.link_matrix (i, j) == 1

                    fprintf(fid, '( %d %d) ', i, j);
                    counted_links = counted_links +1 ;

                end

            end

        end

    end

end

fprintf(fid,'\n');

% Now comes the potential tables

if (model.all_potentials ~= 1)

    fprintf(fid, '<potential tables> ');

    if (strcmp (model.type, 'factorgraph-discrete' ) )


        if (strcmp (model.structure_code, 'qmr' ) )

            fprintf(fid, '(qmr) ');


        else

            fprintf(fid, '(normal) ');

        end


        for i = 1:model.number_potential_tables

            fprintf(fid, '(');

            s = prod (size (model.potential_tables{i} ) );

            temp_pot_table = reshape (model.potential_tables{i}, [1 s] );

            for j = 1:size(temp_pot_table, 2)

                fprintf(fid, ' %d ', temp_pot_table (j) );

            end

            fprintf(fid, ') ');

        end

    end


    if (strcmp (model.type, 'pairwise-discrete' ) )

        if ( strcmp (method, 'tree_partition' ) )

        elseif ( counted_links ~= size(model.edges(:,1) ) )

            error('You did not specify the correct number of potential tables');

        else


            for i = 1: size(model.edges)

                fprintf(fid, '( %d %d) ', model.edges(i, 1), model.edges(i, 2) );

                fprintf(fid, '( ');

                [m,n] = size(model.potential_tables{i});

                for j = 1: m

                    for k = 1: n

                        fprintf(fid, ' %d ', model.potential_tables{i}(j,k) );

                    end

                end

                fprintf(fid, ' ) ');

            end


        end

    end

else

    fprintf(fid, '<all potential tables> ');
    %fprintf(fid, '(');
    fprintf (fid, '%s', model.all_potentials_code);

end

fprintf(fid,'\n');

% Observed variables

fprintf(fid,'<observations> ');

for i = 1: size(model.observations,2)
    
    if (model.observations{i} ~= 0)

    fprintf(fid,'(%d %d) ', i, model.observations{i}-1);
    
    end

end

fprintf(fid,'\n');

% Using timer?

if (model.timer)

    fprintf(fid, '<timer>\n');

end

% Using monitor?

if (model.monitor)

    fprintf(fid, '<monitor>\n');
    fprintf(fid, '<ground truth> (external) (');
    fprintf(fid, '%s', model.ground_truth_file);
    fprintf(fid, ')\n');
    
    fprintf(fid, '<monitoring interval> (%d) \n', model.monitoring_interval);

end

% Number of runs?

if (model.number_of_runs ~= 1)

    fprintf(fid, '<number of runs> ( %d )\n', model.number_of_runs);

end

% Internal Potentials (priors)

if (model.all_internal_potentials)

    % Writing special all internal potential code

    fprintf(fid, '%s\n', model.all_internal_potentials_code);

else

    if (model.internal_potentials_bool)

        % Normal case

        fprintf(fid, '<internal potentials> ');

        for i=1:size(model.internal_potentials,2)

            fprintf(fid, '( %d ) ( ', i);

            for j=1:size(model.internal_potentials{i}, 2 )

                fprintf(fid, '%g ', model.internal_potentials{i}(j));


            end

            fprintf(fid, ') ');


        end

    end

end