new_model.m.svn-base

Probabilistic graphical models in matlab.

function [result] = new_model(model_type, argument, first_arg, second_arg )


if nargin < 3

    link_mat = argument;
    argument = 'none';

end

special = false;

result.structure = false;
result.number_potential_tables = 0;
result.all_potentials = false;
result.all_variables = false;
result.all_internal_potentials = false;
result.monitor = false;
result.timer = false;
result.number_of_runs = 1;
result.structure_code = 'none';
result.observations{1} = 0;
result.internal_potentials_bool = false;
result.monitoring_interval = 1.0;

if (strcmp (model_type, 'pairwise' ) )

    result.type = 'pairwise-discrete';

    if (strcmp (argument, 'mrf_all' ) )

        special = true;

        result.structure = true;

        result.number_vertices = first_arg * second_arg;

        result.structure_code = strcat( '<structure> (square-lattice) ( ', strcat( num2str(first_arg), '  ') );

        result.structure_code = [ result.structure_code ' ' num2str(second_arg) ')' ];


        result.link_matrix = zeros(result.number_vertices, result.number_vertices);


    end

    if (strcmp (argument, 'mrf' ) )

        special = true;

        %result.structure = true;

        result.number_vertices = first_arg * second_arg;

        %result.structure_code = strcat( '<structure> (square-lattice) ( ', strcat( num2str(first_arg), '  ') );

        %result.structure_code = [ result.structure_code ' ' num2str(second_arg) ')' ];

        %result.structure_code = strcat( result.structure_code, strcat( num2str(second_arg), ')') );

        result.link_matrix = zeros(result.number_vertices, result.number_vertices);


        for i=1:result.number_vertices

            if ( mod(i,first_arg) ~= 0)



                result.link_matrix(i,i+1) = 1;

            end

            if (i <= first_arg*(second_arg-1) )



                result.link_matrix(i,i+first_arg) = 1;

            end

        end

    end

    if (strcmp (argument, 'fully_connected' ) )

        m = first_arg;
        special = true;

        result.number_vertices = m;

        result.link_matrix = zeros(m,m);

        for i=1:m

            for j=1:m

                if (j > i)

                    result.link_matrix(i,j) = 1;

                end

            end

        end

    end

    if (strcmp (argument, 'random_all' ) )

        result.structure = true;

        special = true;
        result.number_vertices = first_arg;

        result.link_matrix = zeros(result.number_vertices, result.number_vertices);

        model.structure_code = strcat( '<structure> (random) ( ', strcat( num2str(second_arg), ' )') );

    end

    if (strcmp (argument, 'random' ) )


        special = true;
        result.number_vertices = first_arg;

        result.link_matrix = zeros(result.number_vertices, result.number_vertices);

        %model.structure_code = strcat( '<structure> (random) ( ', strcat(
        %num2str(second_arg), ' )') );

        for i=1:result.number_vertices

            for j=i+1:result.number_vertices

                if (rand < second_arg)

                    result.link_matrix(i,j) = 1;

                end



            end

        end


    end

    if (special == false)

        % Normal operation (we are given an adjacency matrix)

        [m,n] = size(link_mat);

        for i=1:m

            for j=1:n

                if link_mat (i,j) == 1

                    if link_mat (j, i) == 1

                        error('Wrong link matrix given');

                    end

                end

            end

        end

        result.number_vertices = m;
        result.link_matrix = link_mat;

    end

    [m,n] = size(result.link_matrix);

    if (m ~= n)

        error ('A problem happened with the link matrix');

    end

    result.link_matrix_table = zeros(m,n);

elseif (strcmp (model_type, 'factor' ) )

    result.type = 'factorgraph-discrete';

    result.number_vertices = link_mat;

    if (strcmp (argument, 'qmr' ) )

        result.structure_code = 'qmr';
        result.all_variables = true;
        result.all_variables_size = 2;

    end

else

    error ('Unknown type of model specified');

end