You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: score_family_wocache.m
    path of file: ./repos/bnt/BNT/learning
    
    the code of the file until where you have to start completion: function score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args)
% SCORE_FAMILY_COMPLETE Compute the score of a node and its parents given completely observed data
% score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args)
%
% data(i,m) is the value of node i in case m (can be a cell array)

    
function score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args)
% SCORE_FAMILY_COMPLETE Compute the score of a node and its parents given completely observed data
% score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args)
%
% data(i,m) is the value of node i in case m (can be a cell array)
% args is a cell array containing optional arguments passed to the constructor,
% or is [] if none
%
% We create a whole Bayes net which only connects parents to node,
% where node has a CPD of the specified type (with default parameters).
% We then evaluate its score ('bic' or 'bayesian')

% We should use a cache to avoid unnecessary computation.
% In particular, log_marginal_prob_node for tabular CPDs calls gammaln
% and compute_counts, both of which are slow.

[n ncases] = size(data);
dag = zeros(n,n);
% SML added to sort ps b/c mk_bnet, learn_params use sorted ps to make
% CPTs
% Kevin had: if ~isempty(ps), dag(ps, j) = 1; end