matthiasm@0: function d = flstViterbiUpdate(obsLik, d, isFinal)
matthiasm@0: 
matthiasm@0: if nargin < 3
matthiasm@0:     isFinal = 0;
matthiasm@0: end
matthiasm@0: 
matthiasm@0: % d.psi(:,1:(d.memory-1)) = d.psi(:,2:d.memory);
matthiasm@0: d.psi = circshift(d.psi, -1, 2);
matthiasm@0: % d.scale(1:(d.memory-1)) = d.scale(2:d.memory);
matthiasm@0: d.scale = circshift(d.scale, -1, 2);
matthiasm@0: 
matthiasm@0: if d.updateCount == 0
matthiasm@0:      % initialise first frame
matthiasm@0:     d.oldDelta = d.init .* obsLik;
matthiasm@0:     deltaSum = sum(d.oldDelta);
matthiasm@0:     d.oldDelta = d.oldDelta / deltaSum;
matthiasm@0:     d.scale(d.memory) = 1.0/deltaSum;
matthiasm@0: else
matthiasm@0:     tempPsi = ones(d.nState, 1);
matthiasm@0: 
matthiasm@0:     % calculate best previous state for every current state
matthiasm@0:     % this would be the "sparse" loop in C++
matthiasm@0: 
matthiasm@0: %     for jState = 1:d.nState
matthiasm@0: %         temp = d.oldDelta(d.from) .* d.prob';
matthiasm@0: %         [d.delta(jState), tempPsi(jState)] = max(temp(d.to==jState));
matthiasm@0: %     end
matthiasm@0: 
matthiasm@0:     for iTrans = 1:d.nTrans
matthiasm@0:         transProb = d.prob(iTrans);
matthiasm@0:         fromState = d.from(iTrans);
matthiasm@0:         toState = d.to(iTrans);
matthiasm@0:         currentValue = d.oldDelta(fromState) * transProb;
matthiasm@0:         if (currentValue > d.delta(toState))
matthiasm@0:             d.delta(toState) = currentValue; % will be multiplied by the right obs later!
matthiasm@0:             tempPsi(toState) = fromState;
matthiasm@0:         end
matthiasm@0:     end
matthiasm@1: %     d.delta = d.delta .* obsLik;
matthiasm@1:     d.delta = d.delta .* obsLik + eps;
matthiasm@0:     deltaSum = sum(d.delta);
matthiasm@0: 
matthiasm@0:     if deltaSum > 0
matthiasm@0:         d.oldDelta = d.delta / deltaSum; % normalise (scale)
matthiasm@0:         d.scale(d.memory) = 1.0/deltaSum;
matthiasm@0:         d.delta = zeros(size(d.delta));
matthiasm@0:     else
matthiasm@0:         warning('Viterbi has been fed some zero probabilities (update %d),\nat least they become zero in combination with the model.', d.updateCount);
matthiasm@0:         d.oldDelta = ones(d.nState,1)/d.nState;
matthiasm@0:         d.scale(d.memory) = 1.0;
matthiasm@0:         d.delta = zeros(size(d.delta));
matthiasm@0:     end
matthiasm@0:     d.psi(:,d.memory) = tempPsi;
matthiasm@0: end
matthiasm@0: d.updateCount = d.updateCount + 1;
matthiasm@0: 
matthiasm@0: if isFinal
matthiasm@0:     temp = flstDecode(d);
matthiasm@0:     d.path = [d.path, temp];
matthiasm@0: elseif d.updateCount > (d.memory-1)
matthiasm@0:     temp = flstDecode(d);
matthiasm@0:     d.path = [d.path, temp(1)];
matthiasm@0: end