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1 function CPD = set_params(CPD, varargin)
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2 % SET_PARAMS Set the parameters (fields) for a softmax_CPD object
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3 % CPD = set_params(CPD, name/value pairs)
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4 %
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5 % The following optional arguments can be specified in the form of name/value pairs:
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6 % (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y), Q1=ns(dps(1)), Q2=ns(dps(2)), ...
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7 % where dps are the discrete parents; if there are no discrete parents, we set Q1=1.)
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8 %
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9 % weights - (W(:,j,a,b,...) - W(:,j',a,b,...)) is ppn to dec. boundary
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10 % between j,j' given Q1=a,Q2=b,... [ randn(X,Y,Q1,Q2,...) ]
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11 % offset - (offset(j,a,b,...) - offset(j',a,b,...)) is the offset to dec. boundary
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12 % between j,j' given Q1=a,Q2=b,... [ randn(Y,Q1,Q2,...) ]
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13 % clamped - 'yes' means don't adjust params during learning ['no']
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14 % max_iter - the maximum number of steps to take [10]
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15 % verbose - 'yes' means print the LL at each step of IRLS ['no']
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16 % wthresh - convergence threshold for weights [1e-2]
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17 % llthresh - convergence threshold for log likelihood [1e-2]
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18 % approx_hess - 'yes' means approximate the Hessian for speed ['no']
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19 %
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20 % e.g., CPD = set_params(CPD,'offset', zeros(ns(i),1));
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21
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22 args = varargin;
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23 nargs = length(args);
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24 glimsz = prod(CPD.sizes(CPD.dpndx));
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25 for i=1:2:nargs
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26 switch args{i},
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27 case 'discrete', str='nothing to do';
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28 case 'clamped', CPD = set_clamped(CPD, strcmp(args{i+1}, 'yes'));
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29 case 'max_iter', CPD.max_iter = args{i+1};
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30 case 'verbose', CPD.verbose = strcmp(args{i+1}, 'yes');
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31 case 'max_iter', CPD.max_iter = args{i+1};
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32 case 'wthresh', CPD.wthresh = args{i+1};
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33 case 'llthresh', CPD.llthresh = args{i+1};
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34 case 'approx_hess', CPD.approx_hess = strcmp(args{i+1}, 'yes');
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35 case 'weights', for q=1:glimsz, CPD.glim{q}.w1 = args{i+1}(:,:,q); end;
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36 case 'offset',
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37 if glimsz == 1
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38 CPD.glim{1}.b1 = args{i+1};
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39 else
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40 for q=1:glimsz, CPD.glim{q}.b1 = args{i+1}(:,q); end;
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41 end
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42 otherwise,
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43 error(['invalid argument name ' args{i}]);
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44 end
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45 end
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