comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/set_fields.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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-1:000000000000 0:e9a9cd732c1e
1 function CPD = set_fields(CPD, varargin)
2 % SET_PARAMS Set the parameters (fields) for a gaussian_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 %
7 % mean - mu(:,i) is the mean given Q=i
8 % cov - Sigma(:,:,i) is the covariance given Q=i
9 % weights - W(:,:,i) is the regression matrix given Q=i
10 % cov_type - if 'diag', Sigma(:,:,i) is diagonal
11 % tied_cov - if 1, we constrain Sigma(:,:,i) to be the same for all i
12 % clamp_mean - if 1, we do not adjust mu(:,i) during learning
13 % clamp_cov - if 1, we do not adjust Sigma(:,:,i) during learning
14 % clamp_weights - if 1, we do not adjust W(:,:,i) during learning
15 % clamp - if 1, we do not adjust any params
16 % cov_prior_weight - weight given to I prior for estimating Sigma
17 % cov_prior_entropic - if 1, we also use an entropic prior for Sigma [0]
18 %
19 % e.g., CPD = set_params(CPD, 'mean', [0;0])
20
21 args = varargin;
22 nargs = length(args);
23 for i=1:2:nargs
24 switch args{i},
25 case 'mean', CPD.mean = args{i+1};
26 case 'cov', CPD.cov = args{i+1};
27 case 'weights', CPD.weights = args{i+1};
28 case 'cov_type', CPD.cov_type = args{i+1};
29 %case 'tied_cov', CPD.tied_cov = strcmp(args{i+1}, 'yes');
30 case 'tied_cov', CPD.tied_cov = args{i+1};
31 case 'clamp_mean', CPD.clamped_mean = args{i+1};
32 case 'clamp_cov', CPD.clamped_cov = args{i+1};
33 case 'clamp_weights', CPD.clamped_weights = args{i+1};
34 case 'clamp', clamp = args{i+1};
35 CPD.clamped_mean = clamp;
36 CPD.clamped_cov = clamp;
37 CPD.clamped_weights = clamp;
38 case 'cov_prior_weight', CPD.cov_prior_weight = args{i+1};
39 case 'cov_prior_entropic', CPD.cov_prior_entropic = args{i+1};
40 otherwise,
41 error(['invalid argument name ' args{i}]);
42 end
43 end