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1 function CPD = maximize_params(CPD, temp)
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2 % MAXIMIZE_PARAMS Set the params of a CPD to their ML values (dsoftmax) using IRLS
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3 % CPD = maximize_params(CPD, temperature)
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4 % temperature parameter is ignored
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5
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6 % Written by Pierpaolo Brutti
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7
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8 if ~adjustable_CPD(CPD), return; end
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9 options = foptions;
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10
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11 if CPD.verbose
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12 options(1) = 1;
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13 else
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14 options(1) = -1;
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15 end
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16 %options(1) = CPD.verbose;
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17
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18 options(2) = CPD.wthresh;
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19 options(3) = CPD.llthresh;
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20 options(5) = CPD.approx_hess;
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21 options(14) = CPD.max_iter;
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22
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23 dpsize = size(CPD.self_vals,3);
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24 for i=1:dpsize,
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25 mask=find(CPD.eso_weights(:,:,i)>0); % for adapting the parameters we use only positive weighted example
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26 if ~isempty(mask),
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27 if ~isempty(CPD.dps_as_cps.ndx),
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28 puredp_map = find_equiv_posns(CPD.dpndx, union(CPD.dpndx, CPD.dps_as_cps.ndx)); % find the glm structure
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29 subs = ind2subv(CPD.sizes(union(CPD.dpndx, CPD.dps_as_cps.ndx)),i); % that corrisponds to the
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30 active_glm = max([1,subv2ind(CPD.sizes(CPD.dpndx), subs(puredp_map))]); % i-th 'fictitious' example
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31
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32 CPD.glim{active_glm} = netopt_weighted(CPD.glim{active_glm}, options, CPD.parent_vals(mask',:,i),...
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33 CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), 'scg');
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34 else
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35 alfa = 0.4; if CPD.solo, alfa = 1; end % learning step = 1 <=> self is all alone in the net
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36 CPD.glim{i} = glmtrain_weighted(CPD.glim{i}, options, CPD.parent_vals(mask',:),...
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37 CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), alfa);
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38 end
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39 end
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40 mask=[];
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41 end
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