Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/set_fields.m @ 0:e9a9cd732c1e tip
first hg version after svn
| author | wolffd |
|---|---|
| date | Tue, 10 Feb 2015 15:05:51 +0000 |
| parents | |
| children |
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| -1:000000000000 | 0:e9a9cd732c1e |
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| 1 function CPD = set_params(CPD, varargin) | |
| 2 % SET_PARAMS Set the parameters (fields) for a softmax_CPD object | |
| 3 % CPD = set_params(CPD, name/value pairs) | |
| 4 % | |
| 5 % The following optional arguments can be specified in the form of name/value pairs: | |
| 6 % (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y), Q1=ns(dps(1)), Q2=ns(dps(2)), ... | |
| 7 % where dps are the discrete parents; if there are no discrete parents, we set Q1=1.) | |
| 8 % | |
| 9 % weights - (W(:,j,a,b,...) - W(:,j',a,b,...)) is ppn to dec. boundary | |
| 10 % between j,j' given Q1=a,Q2=b,... [ randn(X,Y,Q1,Q2,...) ] | |
| 11 % offset - (offset(j,a,b,...) - offset(j',a,b,...)) is the offset to dec. boundary | |
| 12 % between j,j' given Q1=a,Q2=b,... [ randn(Y,Q1,Q2,...) ] | |
| 13 % clamped - 'yes' means don't adjust params during learning ['no'] | |
| 14 % max_iter - the maximum number of steps to take [10] | |
| 15 % verbose - 'yes' means print the LL at each step of IRLS ['no'] | |
| 16 % wthresh - convergence threshold for weights [1e-2] | |
| 17 % llthresh - convergence threshold for log likelihood [1e-2] | |
| 18 % approx_hess - 'yes' means approximate the Hessian for speed ['no'] | |
| 19 % | |
| 20 % e.g., CPD = set_params(CPD,'offset', zeros(ns(i),1)); | |
| 21 | |
| 22 args = varargin; | |
| 23 nargs = length(args); | |
| 24 glimsz = prod(CPD.sizes(CPD.dpndx)); | |
| 25 for i=1:2:nargs | |
| 26 switch args{i}, | |
| 27 case 'discrete', str='nothing to do'; | |
| 28 case 'clamped', CPD = set_clamped(CPD, strcmp(args{i+1}, 'yes')); | |
| 29 case 'max_iter', CPD.max_iter = args{i+1}; | |
| 30 case 'verbose', CPD.verbose = strcmp(args{i+1}, 'yes'); | |
| 31 case 'max_iter', CPD.max_iter = args{i+1}; | |
| 32 case 'wthresh', CPD.wthresh = args{i+1}; | |
| 33 case 'llthresh', CPD.llthresh = args{i+1}; | |
| 34 case 'approx_hess', CPD.approx_hess = strcmp(args{i+1}, 'yes'); | |
| 35 case 'weights', for q=1:glimsz, CPD.glim{q}.w1 = args{i+1}(:,:,q); end; | |
| 36 case 'offset', | |
| 37 if glimsz == 1 | |
| 38 CPD.glim{1}.b1 = args{i+1}; | |
| 39 else | |
| 40 for q=1:glimsz, CPD.glim{q}.b1 = args{i+1}(:,q); end; | |
| 41 end | |
| 42 otherwise, | |
| 43 error(['invalid argument name ' args{i}]); | |
| 44 end | |
| 45 end |