function out=legTrackerMarkovMat(data,varargin)
%legTrackerMarkovMat uses the vector of KNN classification labels to
%populate a variety of transition Markov Matrices.
%Usage:
%out=legTrackerMarkovMat(data,varargin)
%data = vector of classification labels {0 ... 11}
%varargin = 2 parameter options variable
%varargin{1} = 'pie' or 1 or other/NA
%varargin{2} = 1 or other/NA
% * legTrackerMarkovMat(data, 'pie') returns a 12x1 vector of the percent 
% labels in data that are each behavioral label.
% * legTrackerMarkovMat(data, 1, ?) sets the diagonal of the transition
% Markov Matrix to 0.
% * legTrackerMarkovMat(data, ?, 1) normalizes the sum of the rows of the
% transition Markov Matrix to 1.

%allocate output in memory
out=zeros(12,12);

%for all frame transitions
for i=1:size(data,1)-1
    %increment Markov Matrix entry for particular transition
    out(data(i)+1,data(i+1)+1)=out(data(i)+1,data(i+1)+1)+1;
end

%if there are varargin parameters
if ~isempty(varargin)
    % calculate the percent of labels in the vector of each time i.e. the
    % piechart of time spent in each behavioral mode
    if isequal(varargin{1},'pie')
        out=zeros(12,1);
        for i=0:11
            out(i+1)=sum(data==i);
        end
    else
        %if  varargin{1}==1 set the diagonal of the matrix to 0s.
        if varargin{1}==1
            out(logical(eye(size(out,1))))=0;
        end
        
        %if  varargin{2}==1 normalize so that the sum of each row is 1.
        if varargin{2}==1
            out=out./repmat(sum(out,2),1,12);
            out(isnan(out))=0;
        end
    end
    
end