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1 function d = flstViterbiUpdate(obsLik, d, isFinal)
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2
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3 if nargin < 3
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4 isFinal = 0;
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5 end
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6
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7 % d.psi(:,1:(d.memory-1)) = d.psi(:,2:d.memory);
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8 d.psi = circshift(d.psi, -1, 2);
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9 % d.scale(1:(d.memory-1)) = d.scale(2:d.memory);
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10 d.scale = circshift(d.scale, -1, 2);
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11
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12 if d.updateCount == 0
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13 % initialise first frame
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14 d.oldDelta = d.init .* obsLik;
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15 deltaSum = sum(d.oldDelta);
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16 d.oldDelta = d.oldDelta / deltaSum;
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17 d.scale(d.memory) = 1.0/deltaSum;
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18 else
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19 tempPsi = ones(d.nState, 1);
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20
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21 % calculate best previous state for every current state
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22 % this would be the "sparse" loop in C++
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23
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24 % for jState = 1:d.nState
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25 % temp = d.oldDelta(d.from) .* d.prob';
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26 % [d.delta(jState), tempPsi(jState)] = max(temp(d.to==jState));
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27 % end
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28
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29 for iTrans = 1:d.nTrans
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30 transProb = d.prob(iTrans);
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31 fromState = d.from(iTrans);
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32 toState = d.to(iTrans);
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33 currentValue = d.oldDelta(fromState) * transProb;
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34 if (currentValue > d.delta(toState))
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35 d.delta(toState) = currentValue; % will be multiplied by the right obs later!
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36 tempPsi(toState) = fromState;
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37 end
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38 end
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matthiasm@1
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39 % d.delta = d.delta .* obsLik;
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40 d.delta = d.delta .* obsLik + eps;
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41 deltaSum = sum(d.delta);
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42
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43 if deltaSum > 0
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44 d.oldDelta = d.delta / deltaSum; % normalise (scale)
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45 d.scale(d.memory) = 1.0/deltaSum;
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46 d.delta = zeros(size(d.delta));
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47 else
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48 warning('Viterbi has been fed some zero probabilities (update %d),\nat least they become zero in combination with the model.', d.updateCount);
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49 d.oldDelta = ones(d.nState,1)/d.nState;
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50 d.scale(d.memory) = 1.0;
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51 d.delta = zeros(size(d.delta));
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52 end
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53 d.psi(:,d.memory) = tempPsi;
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54 end
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55 d.updateCount = d.updateCount + 1;
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56
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57 if isFinal
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58 temp = flstDecode(d);
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59 d.path = [d.path, temp];
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60 elseif d.updateCount > (d.memory-1)
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61 temp = flstDecode(d);
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62 d.path = [d.path, temp(1)];
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63 end
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