showDiscriminantFuncs

%% funcs = showDiscriminantFuncs(cv,bwInvert)
%% Currently only works for windows-based representation

0001 function funcs = showDiscriminantFuncs(cv,bwInvert)
0002 %%% funcs = showDiscriminantFuncs(cv,bwInvert)
0003 %%% Currently only works for windows-based representation
0004 
0005 if nargin < 2
0006   bwInvert = 0;
0007 end
0008 
0009 reps = showTestRep(cv);
0010 
0011 [nTasks,nTrials] = size(cv.classes);
0012 for ttrial = 1:nTrials
0013   w = cv.testResults{ttrial}{2}.weights;
0014   w = w - repmat(mean(w,2),1,nTasks);
0015   b = cv.testResults{ttrial}{2}.bias;
0016   funcs{ttrial} = reps{ttrial}' * w + ...
0017       repmat(b',size(reps{ttrial},2),1);
0018   if bwInvert
0019     f = funcs{ttrial};
0020     funcs{ttrial} = max(f(:)) - f;
0021   end
0022 end
0023 
0024 clf;
0025 for ttrial = 1:nTrials
0026   subplot(nTrials,1,ttrial);
0027   imagesc(funcs{ttrial}');
0028   ylabel('Task Discriminant');
0029   n = size(funcs{ttrial},1);
0030   inc = n / nTasks;
0031   set(gca,'XTick',inc/2:inc:n);
0032   xlab = {};
0033   for i = 1:nTasks
0034     xlab = {xlab{:} ['Task ' num2str(i)]};
0035   end
0036   set(gca,'XTickLabel',xlab);
0037   %plot(funcs{ttrial});
0038   title(['Test Trial ' num2str(ttrial)]);
0039 end