function varargout=psvm(model,options)% PSVM Plots decision boundary of binary SVM classifier.%% Synopsis:%  h = psvm(...)%  psvm(model)%  psvm(model,options)%% Description:%  This function samples the Support Vector Machiones (SVM) decision %  function f(x) in 2D feature space and interpolates isoline %  width f(x)=0. The isolines f(x)=+1 and f(x)=-1 are plotted as well. %% Input:%  model [struct] Model of binary SVM classifier:%   .Alpha [1 x nsv] Weights of training data.%   .b [real] Bias of decision function.%   .sv.X [dim x nsv] Support vectors.%   .options.ker [string] Kernel function identifier.%      See 'help kernel' for more info.%   .options.arg [1 x nargs] Kernel argument(s).%% options [struct] Controls apperance:%  .background [1x1] If 1 then backgroud is colored according to %    the value of decision function (default 0).%  .sv [1x1] If 1 then the support vectors are marked (default 1).%  .sv_size [1x1] Marker size of the support vectors.%  .margin [1x1] If 1 then margin is displayed (default 1).%  .gridx [1x1] Sampling in x-axis (default 25).%  .gridy [1x1] Sampling in y-axis (default 25).%  .color [int] Color of decision boundary (default 'k').%% Output:%  h [struct] Handles of used graphical objects.%% Example:%  data = load('riply_trn');  %  model = smo( data, struct('ker','rbf','arg',1,'C',10) );%  figure;  ppatterns(data);  %  psvm( model, struct('background',1) );%% See also %% 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:% 25-may-2004, VF% 10-may-2004, VF% 5-oct-2003, VF, returns handles% 14-Jan-2003, VF% 21-oct-2001, V.Franc% 16-april-2001, V. Franc, created% Process input arguments %------------------------------------------------if nargin < 2, options=[]; else options=c2s(options); endif ~isfield(options,'background'), options.background = 0; endif ~isfield(options,'sv'), options.sv = 1; endif ~isfield(options,'margin'), options.margin = 1; endif ~isfield(options,'gridx'), options.gridx = 25; endif ~isfield(options,'gridy'), options.gridy = 25; endif ~isfield(options,'sv_size'), options.sv_size = 12; endif ~isfield(options,'color'), options.color = 'k'; end% variable for handlesh = [];% get axisa = axis;old_hold = ishold;hold on;% plot Support Vectosif options.sv,  h.sv = ppatterns(model.sv.X,'ok' ,options.sv_size);end% limits of current figurexmin=a(1);xmax=a(2);ymin=a(3);ymax=a(4);  % makes grid [X,Y] = meshgrid(xmin:(xmax-xmin)/options.gridx:xmax,...                 ymin:(ymax-ymin)/options.gridy:ymax);% generate samplestst_data=[reshape(X',1,prod(size(X)));reshape(Y',1,prod(size(Y)))];% classify points[pred_labels, dec_fun] = svmclass(tst_data,model);% compute color limitsl=(-min(dec_fun)+max(dec_fun))/2;% reshape dec_funZ = reshape(dec_fun,size(X,1),size(X,2))';% colors background if options.background,  h.background = pcolor(X,Y,Z);end% smooth shadingshading interp;% plots decision boundary[dummy,h.boundary] = contour(X,Y,Z,[0,0],options.color);% plots marginsif options.margin,   [dummy,h.margin_plus] = contour(X,Y,Z,[1,1],[options.color,'--']);   [dummy,h.margin_minus] = contour(X,Y,Z,[-1,-1],[options.color,'--']);end% set color limits and colormapif options.background,  set(h.background, 'LineStyle','none' );  set(gca,'Clim',[-l l]);    % creates colormap   g=gray(64);  cmp=[g(33:end,:);flipud(g(33:end,:))];  cmp(1:32,1)=cmp(1:32,1)/2;  cmp(1:32,3)=cmp(1:32,3)/2;  cmp(33:end,3)=cmp(33:end,3)/2;  colormap(cmp)endif ~old_hold, hold off; endif nargout >= 1, varargout{1} = h; endreturn;% EOF