function asdHmmPlot(au, taskOpt)
% hmmEval4audio: HMM evaluation for vibrato detection
%
%	Usage:
%		asdHmmPlot(au, taskOpt)
%
%	Example:
%		auFile='dataSet/abnormal-cue.wav';
%		%auFile='dataSet/normal-cue.wav';
%		asdOpt=asdOptSet;
%		load asdHmmModel.mat	% Obtain asdHmmModel
%		au=hmmEval4audio(auFile, asdOpt, asdHmmModel, 1);

%	Category: HMM plot for vibrato detection
%	Roger Jang, 20130106

if nargin<1, selfdemo; return; end

frameTime=au.other.frameTime;
feaMat=au.feature;
asdFeaPlot(au);		% Plot the features and groundtruths
[feaDim, frameCount]=size(feaMat);
% Plot the predicted output using different colors
markerColor=['k', 'm', 'r'];
for i=1:frameCount
	h=line(frameTime(i), 0, 'marker', 'o', 'color', markerColor(au.cOutput(i)));
end
%% Confusing matrix
confMat=confMatGet(au.tOutput, au.cOutput);
opt=confMatPlot('defaultOpt');
opt.className=taskOpt.outputName;
opt.mode='both';
opt.format='8.2f';
figure; confMatPlot(confMat,opt);
%fprintf('Accuracy=%g%%\n', au.rr*100);
%% Plot groundtruth and prediction
figure;
subplot(211);
plot(frameTime, au.tOutput, frameTime, au.cOutput, 'marker', '.'); grid on
classCount=max(max(au.tOutput), max(au.cOutput));
set(gca, 'ytick', 1:classCount);
set(gca, 'xlim', [min(frameTime), max(frameTime)]);
xlabel('Time (sec)'); ylabel('Classes'); title('Groundtruth and prediction');
legend('Groundtruth', 'Prediction by HMM', 'location', 'northOutside', 'orientation', 'horizontal');
subplot(212);
notEqual=double(au.tOutput~=au.cOutput);
notEqual(notEqual==0)=nan;
plot(frameTime, notEqual, 'marker', 'x'); grid on
set(gca, 'ytick', []);
set(gca, 'xlim', [min(frameTime), max(frameTime)]);
xlabel('Time (sec)'); ylabel('Misclassification indicator'); title('Misclassification positions');
snapnow;
%% ====== Self demo
function selfdemo
mObj=mFileParse(which(mfilename));
strEval(mObj.example);