demo_svm.m

很好的matlab模式识别工具箱

    case 1   % SMO
        set(data.tx_cconst,'Enable','on');
        set(data.ed_cconst,'Enable','on');
        set(data.tx_param,'Enable','on');
        set(data.tx_param,'String',SMO_PARAM);
        set(data.ed_param,'Enable','on');
        set(data.ed_param,'String',DEF_SMO_PARAM);
        
%    case 2    % Matlab toolbox
%        set(data.tx_cconst,'Enable','on');
%        set(data.ed_cconst,'Enable','on');
%        set(data.tx_param,'Enable','off');
%        set(data.ed_param,'Enable','off');
     
%    case 3   % Kernel-SK
    case 2   % Kernel-SK
        set(data.tx_cconst,'Enable','on');
        set(data.ed_cconst,'Enable','on');
        set(data.tx_param,'Enable','on');
        set(data.ed_param,'Enable','on');
        set(data.tx_param,'String',KERNELSK_PARAM);
        set(data.ed_param,'String',DEF_KERNELSK_PARAM);
%    case 4    % Matlab toolbox
%        set(data.tx_cconst,'Enable','on');
%        set(data.ed_cconst,'Enable','on');
%        set(data.tx_param,'Enable','off');
%        set(data.ed_param,'Enable','off');
%    case 5    % Kernel Perceptron
    case 3    % Kernel Perceptron
        set(data.tx_cconst,'Enable','off');
        set(data.ed_cconst,'Enable','off');
        set(data.tx_param,'Enable','on');
        set(data.ed_param,'Enable','on');
        set(data.tx_param,'String',KPERCEPTR_PARAM);
        set(data.ed_param,'String',DEF_KPERCEPTR_PARAM);
   end
   
   
%== Handler for C-const edit line ===========================
case 'cconst_handler'
   data=get(hfigure,'UserData');

   C = str2num(get(data.ed_cconst,'String'));
   
   if C <= 0,
      C = 100;
   end
   set( data.ed_cconst,'String',num2str(C));
   
%== Handle for arg edit line ===========================
case 'arg_handler'
   data=get(hfigure,'UserData');

   arg = str2num(get(data.ed_arg,'String'));
   
   if arg < 0,
      arg = 1;
   end
   
   set( data.ed_arg, 'String',num2str( arg));

   
%== Handle for kernel pop up menu ===========================
case 'kernel_handler'
   data=get(hfigure,'UserData');

   ker_inx = get( data.pu_kernel,'Value');
   if ker_inx >= 2,
      set( data.ed_arg,'Enable','on');
      set( data.tx_arg,'Enable','on');
   else
      set( data.ed_arg,'Enable','off');
      set( data.tx_arg,'Enable','off');
   end

%== Calls data creator ==========================================
case 'creatdata'
   createdata('finite',2,'demo_svm','created',hfigure);
   
% == Loads recently created data ================================
case 'created'

   % get handler and make this figure active
   figure(hfigure);
   data = get(hfigure,'UserData');

   % get file name
   path=varargin{1};
   name=varargin{2};
   pathname=strcat(path,name);

   if check2ddata(pathname),                                                    
      file.pathname=pathname;                                                   
      file.path=path;                                                           
      file.name=name;                                                           
      set(data.bt_load,'UserData',file);
      demo_svm('loadsets',hfigure); 
      demo_svm('reset',hfigure); 
   else 
      errordlg('This file does not contain required data.','Bad file','modal'); 
   end 
    

% == Calls standard open file dialog ==========================
case 'getfile'
   
   data=get(hfigure,'UserData');

   [name,path]=uigetfile('*.mat','Open file');
   if name~=0,
      file.pathname=strcat(path,name);
      file.path=path;
      file.name=name;
%      if checkdat(file.pathname,DATA_IDENT,2,[0 0])==1,
      if check2ddata( file.pathname ),
         set(data.bt_load,'UserData',file);
         demo_svm('loadsets',hfigure);
         demo_svm('reset',hfigure);
      else
         errordlg('This file does not contain required data.','Bad file','modal');
      end
   end

case 'loadsets'
   % == Load data from file ==========================================

   data = get( hfigure,'UserData' );

   % Clear axes
   clrchild( data.axes);
   
   % set x and y axes labels
   xlabel('feature x');
   ylabel('feature y');

   % Get file name with sets
   file=get( data.bt_load,'UserData');

   % Load sets
   trn = load(file.pathname );
   trn.I=trn.y;
   trn.N= 2;
   trn.K = [length( find(trn.y==1)),length(find(trn.y==2))];

   % store loaded sets
   set( data.axes,'UserData', trn);

   % focus on axes
   axes( data.axes );

   % plots points
   ppatterns( trn );

   drawnow;
  

% == Reset ==========================================================
case 'reset'

   data = get(hfigure,'UserData');                 
   
   % Clear axes
   clrchild( data.axes);

   % get data set
   trn = get( data.axes, 'UserData');

   % get file
   file = get( data.bt_load,'UserData');
   
   % create comment
   if isempty( trn ) == 0,
     consoletext=sprintf('Data loaded.\nSelect algorithm and press Train SVM button.\n');
     titletext=sprintf('File: %s, # of points K = %d', file.name , size(trn.X,2));
     
     set( data.axes, 'XLimMode','auto', 'YLimMode','auto');
     ppatterns( trn);
     
   else
     consoletext=sprintf(['No data loaded.\n' ...
           'Press Create data button to create your own data.\n'...
           'Press Load data button to load data.\n' ...
           'Load sample data from ../data/binary/']);
     titletext='';

     pos=get( data.axes,'Position');
     fsize=min(pos(3),pos(4))/10;
     setaxis( data.axes,[-1 1 -1 1]);
%     axis([-1 1 -1 1]);
      
     builtin('text',0,0,'Press ''Load data'' button.',...
        'HorizontalAlignment','center',...
        'FontUnits','normalized',...
        'Clipping','on',...
        'FontSize',fsize);
   end

   % show comment
   set( data.console,'String',consoletext );

   % print title
   pos=get( data.axes,'Position');
   fsize=(1-pos(2)-pos(4))*1;
   title(titletext,...
      'Parent', data.axes,...
      'VerticalAlignment','bottom',...
      'HorizontalAlignment','left',...
      'FontUnits','normalized',...
      'Units','normalized',...
      'Position',[0 1 0],...
      'FontSize',fsize);


% == Calls Matlab`s info box ==================================
case 'info'
   helpwin(mfilename);
end

return;

%===============================================================
function [Alpha,bias] = svm_train( data, trn, ker, arg, C )

if strcmpi( ker, 'linear'),
  strarg = '-';
else
  strarg = num2str( arg);
end

param = str2num( get( data.ed_param,'String'));

switch get( data.pu_algo, 'Value' ),
  case 1   % Sequential Minimal Optimizer

%   if length(param) ~= length( str2num(DEF_SMO_PARAM)),
%     param = str2num(DEF_SMO_PARAM);
%   end
   
%   [Alpha,bias,nsv,kercnt,trn_err,margin]=...
%         smo(trn.X,trn.I,ker,arg,C, param(1),param(2) );
     options.ker = ker;
     options.arg = arg;
     options.C = C;
     options.eps = param(1);
     options.tol = param(2);
     
     model = smo( trn, options);
     
     Alpha = zeros(1,size(trn.X,2));
     Alpha(model.sv.inx) = model.Alpha(:)';
     bias = model.b;
     nsv = model.nsv;
     kercnt=model.kercnt;
     trn_err = model.trnerr;
     margin = model.margin;
          
     text = sprintf(...
        ['SVM (L1) by Sequential Minimal Optimizer\n',...
        'Kernel: %s (%s), C: %.4f\n',...
        'Kernel evaluations: %d\n',...
        'Number of Support Vectors: %d\n',...
        'Margin: %.4f\n',...
        'Training error: %.2f%%'],...
        ker, strarg, C, kercnt, nsv, margin, 100*trn_err );

%  case 2   % Matlab Optimization toolbox (L1)
%     model=svmmot(trn, {'ker',ker,'arg',arg,'C',C,'norm',1});
%
%     Alpha = zeros(1,size(trn.X,2));
%     Alpha(model.sv.inx) = model.Alpha;
%     bias = model.b;
%     nsv = model.nsv;
%     kercnt=model.kercnt;
%     trn_err = model.trnerr;
%     margin = model.margin;
%     exitflag=model.exitflag;
%     
%     text = sprintf(...
%        ['SVM (L1) using QUADPROG of the Optimization Toolbox\n',...
%        'Kernel: %s (%s), C: %.4f\n',...
%        'Exitflag: %d\n',...
%        'Kernel evaluations: %d\n',...
%        'Number of Support Vectors: %d\n',...
%        'Margin: %.4f\n',...
%        'Training error: %.2f%%'],...
%        ker, strarg, C, exitflag, kercnt, nsv, margin,100*trn_err);
%
% case 3 % Kernel Schlesinger-Kozinec's algorithm (L2)
  case 2   % Kernel Schlesinger-Kozinec's algorithm (L2)

      model=svmquadprog(trn, {'ker',ker,'arg',arg,'C',C,...
          'tolabs',param(1),'tolrel',0,'tmax',param(2) });

     Alpha = zeros(1,size(trn.X,2));
     Alpha(model.sv.inx) = model.Alpha(:)';
     bias = model.b;
     nsv = model.nsv;
     kercnt=model.kercnt;
     trn_err = model.trnerr;
     margin = model.margin;
     exitflag=model.exitflag;
    
     if( exitflag == 1), exitflag = 'found'; else exitflag = 'not found'; end
     
     text = sprintf(...
        ['SVM (L2) by Kernel Schlesinger-Kozinec`s algorithm\n',...
        'Kernel: %s (%s), C: %.4f\n',...
        'Solution: %s\n', ...
        'Number of Support Vectors: %d\n',...
        'Kernel evaluations: %d\n',...
        'Margin: %.4f\n',...
        'Training error: %.2f%%'],...
        ker, strarg, C, exitflag, nsv, kercnt, margin, trn_err*100 );

%  case 4   % Matlab Optimization toolbox (L2)
%     [Alpha,bias,nsv,exitflag,flps,margin,trn_err]=...
%         svm2mot(trn.X,trn.I,ker,arg,C);
%
%     model=svmmot(trn, {'ker',ker,'arg',arg,'C',C,'norm',2});
%
%     Alpha = zeros(1,size(trn.X,2));
%     Alpha(model.sv.inx) = model.Alpha;
%     bias = model.b;
%     nsv = model.nsv;
%     kercnt=model.kercnt;
%     trn_err = model.trnerr;
%     margin = model.margin;
%     exitflag=model.exitflag;
%
%     text = sprintf(...
%        ['SVM (L2) using QUADPROG of the Optimization Toolbox\n',...
%        'Kernel: %s (%s), C: %.4f\n',...
%        'Exitflag: %d\n',...
%        'Kernel evaluations: %d\n',...
%        'Number of Support Vectors: %d\n',...
%        'Margin: %.4f\n',...
%        'Training error: %.2f%%'],...
%        ker, strarg, C, exitflag, kercnt, nsv, margin,100*trn_err);
  
%  case 5   % Kernel Perceptron
  case 3   % Kernel Perceptron
     model=kperceptr(trn, {'ker',ker,'arg',arg,'tmax',param(1)});

     Alpha = zeros(1,size(trn.X,2));
     Alpha(model.sv.inx) = model.Alpha;
     bias = model.b;
     nsv = model.nsv;
     kercnt=model.kercnt;
     trn_err = model.trnerr;
     exitflag=model.exitflag;

     text = sprintf(...
        ['Kernel Perceptron\n',...
        'Kernel: %s (%s)\n',...
        'Exitflag: %d\n',...
        'Kernel evaluations: %d\n',...
        'Number of Support Vectors: %d\n',...
        'Training error: %.2f%%'],...
        ker, strarg, exitflag, kercnt, nsv, 100*trn_err);
end

% show comment
set( data.console,'String', text );

return;
     
function []=clrchild(handle)
% function []=clraxis(handle)
%
% CLRCHILD clears children of an object with the given handle.

delete(get(handle,'Children'));

return;

function []=setaxis(handle,rect)
% function []=setaxis(handle,rect)
%

set(handle,'XLim',rect(1:2));
set(handle,'YLim',rect(3:4));

if size(rect,2)>=6,
   set(handle,'ZLim',rect(5:6));
end

return;