function varargout=pboundary(model,options)% PBOUNDARY Plots decision boundary of given classifier in 2D.%% Synopsis:%  h = pboundary(model)%  h = pboundary(model,options)%% Description:%  This function plots decision boundary of given classifier in %  2-dimensional feature space. The classification function%  must be specified in the field model.eval. The decision%  bounary is interpolated from the response of the classifier%    y = feval( model.fun, X, model).%% Input:%  model [struct] Model of classifier.%   .fun [string] Classification function.%  %  options [struct] Controls visualization:%   .gridx [1x1] Sampling density in x-axis (default 200).%   .gridy [1x1] Sampling density in y-axis (default 200).%   .line_style [string] Used line-style to plot decision boundary.%   .fill [1x1] If 1 then the class regions are filled. %  % Output:%  h [1 x nobjects] Handles of used graphics objects.%% Example:%  data = load('riply_trn');%  figure; %  ppatterns(data);%  pboundary( knnrule(data,1) );%% See also %  PPATTERNS, PLINE.%% About: Statistical Pattern Recognition Toolbox% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac% <a href="http://www.cvut.cz">Czech Technical University Prague</a>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>% Modifications:% 1-may-2004, VF% 19-may-2003, VF% process inputsif nargin < 1, error('Not enough input arguments.'); endmodel=c2s(model);if nargin < 2, options=[]; else options=c2s(options); endif ~isfield(options,'fill'), options.fill=0; endif ~isfield(options,'gridx'), options.gridx = 200; endif ~isfield(options,'gridy'), options.gridy = 200; endif ~isfield(options,'line_style'), options.line_style = 'k'; end% get hold optionold_hold = ishold;hold on;%V = axis;dx = (V(2)-V(1))/options.gridx;dy = (V(4)-V(3))/options.gridy;[X,Y] = meshgrid(V(1):dx:V(2),V(3):dy:V(4));% make testing pointstst_data=[reshape(X',1,prod(size(X)));reshape(Y',1,prod(size(Y)))];% classify pointsD = feval( model.fun, tst_data, model );h = plot_boundary( D, V(1):dx:V(2), V(3):dy:V(4), ...    options.fill, options.line_style );if ~old_hold, hold off; end% return handles if requiredif nargout > 0, varargout{1} = h; endreturn;%-------------------------------------------------------function h = plot_boundary( L, X_pos, Y_pos, fill_regions, linestyle )% Plots decision boudary. %dx=X_pos(2)-X_pos(1);dy=Y_pos(2)-Y_pos(1);m = length( X_pos );n = length( Y_pos );Z = NaN*ones( m+2, n+2 );num_classes = max(L(:));% mask=fspecial('gauss',[5 5],1) ; % fspecial is from images toolbox mask = [0.0030    0.0133    0.0219    0.0133    0.0030;        0.0133    0.0596    0.0983    0.0596    0.0133;        0.0219    0.0983    0.1621    0.0983    0.0219;        0.0133    0.0596    0.0983    0.0596    0.0133;        0.0030    0.0133    0.0219    0.0133    0.0030];h = [];      for i = 1:num_classes,    A=L;  A(find(L==i))=1;  A(find(L~=i))=-1;      A = reshape( A', m, n );  A = filter2(mask, A);     Z(2:end-1,2:end-1) = A;      [cc,tmp_h] = contour([X_pos(1)-dx,X_pos,X_pos(end)+dx],...                   [Y_pos(1)-dy,Y_pos,Y_pos(end)+dy],Z',[-0 0],linestyle);    h = [h tmp_h(:)'];    if fill_regions,   while ~isempty(cc)     len = cc(2,1);     tmp_h = fill(cc(1,2:len+1),cc(2,2:len+1),marker_color(i));     h = [h tmp_h(:)'];     cc(:,1:len+1) = [];   end    endendreturn;