function [patterns, targets, distribution_parameters] = load_file(filename)

%Load a file with either data points and/or distribution parameters

patterns = []; targets = []; distribution_parameters = [];

if isempty(findstr(filename,'.mat')),
    filename = [filename '.mat'];
end

if (~isempty(dir(filename)))
    load (filename)
    
    if (exist('features'))
        disp('Old data type. Changing the matrix called "features" into a matrix called "patterns". Please save the data once it is transformed');
        patterns = features;
    end
    
    if ((isempty(patterns) | isempty(targets))) & isempty(distribution_parameters) & ~exist('m0')
        error('No patterns, targets, or distribution parameters found in this file')
    end
    
    if (length(unique(targets)) ~= 2) 
        if isempty(distribution_parameters)
            error('The GUI requires two classes of data to operate. Use the text based interface for multiclass or single class data.')
        else
            warning('A distribution definition was found, but no targets found in this file. Please remember to generate a distribution before trying a classifier');
        end
    end
    
    hm = findobj('Tag', 'Messages');
    st= '';
    if (~isempty(patterns)),
        st = ['File loaded. Found ' num2str(size(targets,2)) ' data points'];
        
        if (size(patterns,1) > 2),
            %More than two dimensions in the data
            h = feature_selection;
            waitfor(h, 'UserData',1)
            
            h1		 = findobj(h, 'Tag', 'txtHiddenMethod');
            chosen = get(h1, 'String');
            h1		 = findobj(h, 'Tag', 'txtHiddenParams');
            params = get(h1, 'String');
            
            if (~isempty(str2num(params))),
                params = str2num(params);
            end
            
            [patterns, targets] = feval(chosen, patterns, targets, params);
            
            close(h)
        end
        
        param = max(max(abs(patterns)));
        plot_scatter(patterns, targets) 
        axis([-param,param,-param,param])
    end
    
    if ~isempty(distribution_parameters)
		% try (distribution_parameters.p0)
		%     %Old format
		%     disp('Old parametric structure found. Transforming into the new format. Please save the data once it is transformed');
		%     dp(1).mu    = distribution_parameters.m0;
		%     dp(2).mu    = distribution_parameters.m1;
		%     dp(1).sigma = distribution_parameters.s0;
		%     dp(2).sigma = distribution_parameters.s1;
		%     dp(1).w     = distribution_parameters.w0;
		%     dp(2).w     = distribution_parameters.w1;
		%     dp(1).p     = distribution_parameters.p0;
		%     dp(2).p     = 1 - distribution_parameters.p0;
		%     for i = 1:2,
		%         for j = 1:length(dp(i).w)
		%             dp(i).type(j,:) = cellstr('Gaussian');
		%         end
		%     end
		%     distribution_parameters = dp;
		% catch    
		% end
        
        n0 = size(distribution_parameters(1).sigma,1);
        n1 = size(distribution_parameters(2).sigma,1);
        if (~isempty(st)),
            st = [st ', '];
        else
            st = 'Found ';
        end
        st = [st num2str(n0) ' Gaussians for class 0 and ' num2str(n1) ' Gaussians for class 1.'];         
    end
    
    set(hm,'String',st)  
else
    hm = findobj('Tag', 'Messages');
    set(hm,'String','File not found.')
end 

if (nargout == 1),
    patterns = distribution_parameters;
end