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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;
u = [1:ncases]'; % DWH
isclamped = 0; %DWH
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}, isclamped = 1; %DWH
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
if isclamped %DWH
u = find(clamped(j,:)==0);
end
ps = parents(dag, j);
score(g) = score(g) + score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j});
end
end
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