demo_anderson.m

很好的matlab模式识别工具箱

   h=get(hfigure,'UserData');

   % change path to directory
%%   wres=what('anderson');
%%   cd(wres.path);

   [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 check2dgauss(file.pathname),
         set(h.btload,'UserData',file);
         demo_anderson('loadsets',hfigure);
         demo_anderson('reset',hfigure);
      else
         errordlg('This file does not contain required data.','Bad file','modal');
      end
   end


case 'algohandler'
   % == Handler for check box 'Algorithm' =======================================
   % If new algorithm is selected then prepare data for it.

   h=get(hfigure,'UserData');

   if get(h.pualgo,'UserData') ~= get(h.pualgo,'Value'),
      set(h.pualgo,'UserData',get(h.pualgo,'Value'));

      set(h.edprec,'String',num2str(DEF_PRECISION(get(h.pualgo,'Value'))) );
      set(h.txprec,'String',PREC_TITLE(get(h.pualgo,'Value'),:));

      demo_anderson('loadsets',hfigure);
      demo_anderson('reset',hfigure);
   end


case 'loadsets'
   % == Load sets ==============================================
   % Get given file name and load the data set from him.

   h=get(hfigure,'UserData');                   % uicontrol handlers

   % Get file name
   file=get(h.btload,'UserData');
   if isempty(file)==0,

      % Load sets
      sets=load(file.pathname);

      sets.MI = sets.Mean;
      sets.SIGMA = reshape(sets.Cov,2,2*size(sets.Mean,2));
      sets.I = sets.y;
      sets.K = zeros(1,max(sets.y));
      sets.N = size(sets.Mean,1);
      for i=1:max(sets.y),
        sets.K(i) = length(find(sets.y==i));
      end
      
      % algorithm 2 (Original Anderson`s) solution find solution 
      % for two distributions only
      if get(h.pualgo,'Value')==2,
         % get only one distribution from each class
         class1=0;
         class2=0;
         NI=[];
         NMI=[];
         NSIGMA=[];
         NK=[1 1];
         N = 2;
         i=0;
         while i<sets.K | class1==0 |class2==0,
            i=i+1;
            if sets.I(i)==1 & class1==0,
               class1=1;
               NI=[NI,1];
               NMI=[NMI,sets.MI(:,i)];
               NSIGMA=[NSIGMA,sets.SIGMA(:,(i-1)*2+1:i*2)];
            elseif sets.I(i)==2 & class2==0,
               class2=1;
               NI=[NI,2];
               NMI=[NMI,sets.MI(:,i)];
               NSIGMA=[NSIGMA,sets.SIGMA(:,(i-1)*2+1:i*2)];
            end
         end % while

         % replace old values
         sets.MI=NMI;
         sets.SIGMA=NSIGMA;
         sets.I=NI;
         sets.K=NK;
      end % if get(h.pualgo,....
   else
      % No set is loaded.
      sets=[];
   end

   % store sets
   set(h.axes1,'UserData',sets);


case 'play'
   % == Start up the adaptation process =======================================
   % Perform the adaptation step by step until the solution is found or stop
   % button is pushed down.

   h=get(hfigure,'UserData');                      % get handlers

   % get sets
   sets=get(h.axes1,'UserData');

   % if some set is loaded then perform on step
   if isempty(sets)==0,

      % Disable buttons everything axcept
      set([h.btinfo h.btstep h.btclose h.btplay h.btreset h.btload h.pualgo ...
            h.btcreat h.editer h.edprec h.xbanim],'Enable','off');

      % Only stop button can be pushed down
      set(h.btstop,'Enable','on');

      % Stop button was not pushed down
      set(h.btstop,'UserData',0);
      play=1;

      % get arguments from dialog
      anim=get(h.xbanim,'Value');

      % Play - adaptation process
      while play==1 & get(h.btstop,'UserData')==0,

         % get arguments from dialog
         oldtheta=h.line.theta;
         oldalpha=h.line.alpha;

         % call algorithm
         [h,text,play,solution]=callalgo(h,sets);

         % appear result
         set(h.console,'String',text );
         drawnow;

         if play~=-1,
            % plot result
            if h.line.handler==-1,
               axes(h.axes1);
               h.line.handler=feval(PLOT_FCE,sets.MI,sets.SIGMA,sets.I,...
                  h.line.alpha,h.line.theta );
            else
               feval(PLOT_FCE,sets.MI,sets.SIGMA,sets.I,h.line.alpha,h.line.theta,...
                  h.line.handler,anim,oldalpha,oldtheta);
            end
         end

         % hands on control to MATLAB
         drawnow;
      end % while play == 1 & get(...

      %  store new solution
      set(hfigure,'UserData',h);

      % enable these buttons
      set([h.btinfo h.btstep h.btclose h.btplay h.btreset h.btload ...
         h.btcreat h.pualgo h.editer h.edprec h.xbanim],'Enable','on');

      % disable stop button
      set(h.btstop,'Enable','off');

   else % isempty(sets)==0,
      % write down description
      text=sprintf(['No data loaded.\nPress Load data or Create data button.\n', ...
         'Load sample data from ../toolboxroot/data/anderson_task/' ]  );
      set(h.console,'String',text );
   end   


case 'step'
   % == Perform one adaptation step ================================================
   h=get(hfigure,'UserData');                     % get handlers we will need...

   % get sets
   sets=get(h.axes1,'UserData');

   % if some set is loaded then perform on step
   if isempty(sets)==0,

      % get arguments from dialog
      anim=get(h.xbanim,'Value');
      oldtheta=h.line.theta;
      oldalpha=h.line.alpha;

      % call algorithm
      [h,text,play,solution]=callalgo(h,sets);

      % appear result
      set(h.console,'String',text );
      drawnow;

      if play~=-1,
         % plot result
         if h.line.handler==-1,
            axes(h.axes1);
            h.line.handler=feval(PLOT_FCE,sets.MI,sets.SIGMA,sets.I,...
               h.line.alpha,h.line.theta );
         else
            feval(PLOT_FCE,sets.MI,sets.SIGMA,sets.I,h.line.alpha,...
               h.line.theta,h.line.handler,anim,oldalpha,oldtheta);
         end
      end

      drawnow;

      % store data
      set(hfigure,'UserData',h);

   else % isempty(sets)==0,
      % write down description
      text=sprintf(['No data loaded.\nPress Load data or Create data button.\n', ...
         'Load sample data from ../toolboxroot/data/anderson_task/' ]  );
      set(h.console,'String',text );
   end

case 'reset'
   % == Reset adaptation process ==================================
   % Sets t=0 and redraws axes.

   h=get(hfigure,'UserData');                     % get handlers

   % zeroize all parameters of the solution
   h.line.t=0;
   h.line.theta=0;
   h.line.alpha=[0;0];
   h.line.alpha1=[0;0];
   h.line.alpha2=[0;0];
   h.line.lambda=0;
   set(hfigure,'UserData',h);

   % No line
   h.line.handler=-1;
   set(hfigure,'UserData',h);
   %%%   cla;
   clrchild(h.axes1);
   %%%   win=axis;
   win=getaxis(h.axes1);
   %%%   axis([0 1 0 1]);
   setaxis(h.axes1,[0 1 0 1]);
   %%%   axis(win);
   setaxis(h.axes1,win);

   % Redraw points
   sets=get(h.axes1,'UserData');

   % if some points are loaded than appear it
   if isempty(sets)==0,

      % set axes according to current points MI

      if sum(sets.K) < 3,
         win=cmpwin(min(sets.MI'),max(sets.MI'),BORDER*2,BORDER*2);
      else
         win=cmpwin(min(sets.MI'),max(sets.MI'),BORDER,BORDER);
      end

      %%% axis(win);
      setaxis(h.axes1,win);

%%      pplot(sets.MI,sets.I);
%%      ppoints(sets.MI,sets.I);
      ppatterns(sets.MI,sets.I);

      % comment window text
      consoletext=sprintf('Press Step button or Play button.\n');

      file=get(h.btload,'UserData');
      titletext=sprintf('File: %s, # of distributions K = %d',file.name,sum(sets.K));

   else % if isempty(sets)==0,
      % comment window text
%      consoletext=sprintf(['No data loaded.\n' ...
%             'Press Load button or Create data button.\n']);
%      consoletext=sprintf('No data loaded.\nPress Load data button.\n');
      consoletext=sprintf(['No data loaded.\nPress Load data or Create data button.\n', ...
         'Load sample data from ../toolboxroot/data/anderson_task/' ]  );
      titletext='';

      pos=get(h.axes1,'Position');
      fsize=min(pos(3),pos(4))/10;

      %%%      axis([-1 1 -1 1]);
      setaxis(h.axes1,[-1 1 -1 1]);
      builtin('text',0,0,'Press ''Load data'' button.',...
         'HorizontalAlignment','center',...
         'FontUnits','normalized',...
         'Clipping','on',...
         'FontSize',fsize);
   end

   % print comment
   set(h.console,'String',consoletext );

   % print title
   pos=get(h.axes1,'Position');
   fsize=(1-pos(2)-pos(4))*1;
   title(titletext,...
      'VerticalAlignment','bottom',...
      'HorizontalAlignment','left',...
      'FontUnits','normalized',...
      'Units','normalized',...
      'Position',[0 1 0],...
      'FontSize',fsize);


case 'info'
   % == Call standard Matlab info box =========================================
   helpwin(mfilename);


end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [h,text,play,solution]=callalgo(h,sets)

% get arguments from dialog
precision=str2num(get(h.edprec,'String'));
iter=max([1,fix(str2num(get(h.editer,'String')))]);

% get parameters
t=h.line.t;
alpha=h.line.alpha;
alpha1=h.line.alpha1;
alpha2=h.line.alpha2;
lambda=h.line.lambda;
theta=h.line.theta;

% perform algorithm
switch get(h.pualgo,'Value')
case 4
   % General solution

   distr = sets;
   options.eps = precision;
   options.tmax = t+iter;
   init_model.W=alpha;
   init_model.b=-theta;
   init_model.t = t;
  
%   [nalpha,ntheta,solution,minr,nt,maxerr]=...
%      ganders(sets.MI,sets.SIGMA,sets.I,iter,precision,t,alpha,theta);
   model = ganders( distr, options, init_model);

   nalpha = model.W;
   ntheta = -model.b;
   solution = model.exitflag;
   minr = model.r;
   nt = model.t;
   maxerr = model.err;
   
  text=sprintf(['Interation(s) t=%d\nLinear rule q(x)=sgn([%f , %f]*x %+f)\n'...
                 'Minimal r = %.8f\nClassification error = %.4f%%'],...
      nt,nalpha(1),nalpha(2),-ntheta,minr,maxerr*100);
case 1

   distr = sets;
   options.err = precision/100;
   options.tmax = t+iter;
   init_model.W1=alpha1;
   init_model.W2=alpha2;
   init_model.t = t;

   % e-Optimal solution
   model = eanders( distr, options, init_model );

   nalpha = model.W;
   ntheta = -model.b;
   alpha1 = model.W1;
   alpha2 = model.W2;
   solution = model.exitflag;
   minr = model.r;
   nt = model.t;