learn_struct_K2.zip_k2算法_learn_struct_k2_贝叶斯_贝叶斯网络_贝叶斯网络k2

function dag = learn_struct_K2(data, ns, order, varargin) % LEARN_STRUCT_K2 Greedily learn the best structure compatible with a fixed node ordering % best_dag = learn_struct_K2(data, node_sizes, order, ...) % % data(i,m) = value of node i in case m (can be a cell array). % node_sizes(i) is the size of node i. % order(i) is the i'th node in the topological ordering. % % The following optional arguments can be specified in the form of name/value pairs: % [default value in brackets] % % max_fan_in - this the largest number of parents we allow per node [N] % 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) ] % verbose - 'yes' means display output while running [ 'no' ] % % e.g., dag = learn_struct_K2(data, ns, order, 'scoring_fn', 'bic', 'params', []) % % To be backwards compatible with BNT2, you can also specify arguments as follows % dag = learn_struct_K2(data, node_sizes, order, max_fan_in) % % This algorithm is described in % - Cooper and Herskovits, "A Bayesian method for the induction of probabilistic % networks from data", Machine Learning Journal 9:308--347, 1992 [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', 1 }; params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; end scoring_fn = 'bayesian'; discrete = 1:n; clamped = zeros(n, ncases); max_fan_in = n; verbose = 0; args = varargin; nargs = length(args); if length(args) > 0 if isstr(args{1}) for i=1:2:nargs switch args{i}, case 'verbose', verbose = strcmp(args{i+1}, 'yes'); case 'max_fan_in', max_fan_in = args{i+1}; 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 else max_fan_in = args{1}; end end dag = zeros(n,n); for i=1:n ps = []; j = order(i); u = find(clamped(j,:)==0); score = score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); if verbose, fprintf('\nnode %d, empty score %6.4f\n', j, score); end done = 0; while ~done & (length(ps) <= max_fan_in) pps = mysetdiff(order(1:i-1), ps); % potential parents nps = length(pps); pscore = zeros(1, nps); for pi=1:nps p = pps(pi); pscore(pi) = score_family(j, [ps p], type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); if verbose, fprintf('considering adding %d to %d, score %6.4f\n', p, j, pscore(pi)); end end [best_pscore, best_p] = max(pscore); best_p = pps(best_p); if best_pscore > score score = best_pscore; ps = [ps best_p]; if verbose, fprintf('* adding %d to %d, score %6.4f\n', best_p, j, best_pscore); end else done = 1; end end if ~isempty(ps) % need this check for matlab 5.2 dag(ps, j) = 1; end end