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1 function CPD = maximize_params(CPD, temp)
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2 % MAXIMIZE_PARAMS Set the params of a tabular node to their ML/MAP values.
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3 % CPD = maximize_params(CPD, temp)
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4
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5 if ~adjustable_CPD(CPD), return; end
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6
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7 %assert(approxeq(sum(CPD.counts(:)), CPD.nsamples)); % false!
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8 switch CPD.prior_type
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9 case 'none',
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10 counts = reshape(CPD.counts, size(CPD.CPT));
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11 CPD.CPT = mk_stochastic(counts);
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12 case 'dirichlet',
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13 counts = reshape(CPD.counts, size(CPD.CPT));
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14 CPD.CPT = mk_stochastic(counts + CPD.dirichlet);
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15
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16 % case 'entropic',
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17 % % For an HMM,
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18 % % CPT(i,j) = pr(X(t)=j | X(t-1)=i) = transprob(i,j)
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19 % % counts(i,j) = E #(X(t-1)=i, X(t)=j) = exp_num_trans(i,j)
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20 % Z = 1-temp;
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21 % fam_sz = CPD.sizes;
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22 % psz = prod(fam_sz(1:end-1));
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23 % ssz = fam_sz(end);
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24 % counts = reshape(CPD.counts, psz, ssz);
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25 % CPT = zeros(psz, ssz);
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26 % for i=CPD.entropic_pcases(:)'
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27 % [CPT(i,:), logpost] = entropic_map_estimate(counts(i,:), Z);
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28 % end
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29 % non_entropic_pcases = mysetdiff(1:psz, CPD.entropic_pcases);
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30 % for i=non_entropic_pcases(:)'
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31 % CPT(i,:) = mk_stochastic(counts(i,:));
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32 % end
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33 % %for i=1:psz
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34 % % [CPT(i,:), logpost] = entropic_map(counts(i,:), Z);
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35 % %end
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36 % if CPD.trim & (temp < 2) % at high temps, we would trim everything!
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37 % % grad(j) = d log lik / d theta(i ->j)
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38 % % CPT(i,j) = 0 => counts(i,j) = 0
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39 % % so we can safely replace 0s by 1s in the denominator
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40 % denom = CPT(i,:) + (CPT(i,:)==0);
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41 % grad = counts(i,:) ./ denom;
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42 % trim = find(CPT(i,:) <= exp(-(1/Z)*grad)); % eqn 32
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43 % if ~isempty(trim)
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44 % CPT(i,trim) = 0;
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45 % if all(CPD.trimmed_trans(i,trim)==0) % trimming for 1st time
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46 % disp(['trimming CPT(' num2str(i) ',' num2str(trim) ')'])
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47 % end
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48 % CPD.trimmed_trans(i,trim) = 1;
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49 % end
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50 % end
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51 % CPD.CPT = myreshape(CPT, CPD.sizes);
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52 end
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