Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/bnt/learning/score_dags_old.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/score_dags_old.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +function score = score_dags(data, ns, dags, varargin) +% SCORE_DAGS Compute the score of one or more DAGs +% score = score_dags(data, ns, dags, varargin) +% +% data{i,m} = value of node i in case m (can be a cell array). +% node_sizes(i) is the number of size of node i. +% dags{g} is the g'th dag +% score(g) is the score of the i'th dag +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% +% e.g., score = score_dags(data, ns, mk_all_dags(n), 'scoring_fn', 'bic', 'params', []); +% +% If the DAGs have a lot of families in common, we can cache the sufficient statistics, +% making this potentially more efficient than scoring the DAGs one at a time. +% (Caching is not currently implemented, however.) + +[n ncases] = size(data); + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; +clamped = zeros(n, ncases); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}; + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + end +end + +NG = length(dags); +score = zeros(1, NG); +for g=1:NG + dag = dags{g}; + for j=1:n + u = find(clamped(j,:)==0); + ps = parents(dag, j); + score(g) = score(g) + score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); + end +end