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Diffstat (limited to 'sourcecodes/bnt-master/BNT/learning/mcmc_sample_to_hist.m')
| -rw-r--r-- | sourcecodes/bnt-master/BNT/learning/mcmc_sample_to_hist.m | 36 |
1 files changed, 36 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/BNT/learning/mcmc_sample_to_hist.m b/sourcecodes/bnt-master/BNT/learning/mcmc_sample_to_hist.m new file mode 100644 index 00000000..8b277c8a --- /dev/null +++ b/sourcecodes/bnt-master/BNT/learning/mcmc_sample_to_hist.m @@ -0,0 +1,36 @@ +function mcmc_post = mcmc_sample_to_hist(sampled_graphs, dags) +% MCMC_SAMPLE_TO_HIST Convert a set of sampled dags into a histogram over dags +% hist = mcmc_sample_to_hist(sampled_graphs, dags) +% +% sampled_graphs{m} is the m'th sampled dag +% dags{i} is the i'th dag in the hypothesis space +% hist(i) = Pr(model i | data) + +ndags = length(dags); +nsamples = length(sampled_graphs); +nnodes = length(dags{1}); +% sampled_bitv(m, :) is the m'th sampled graph represented as a vector of n^2 bits, computed +% by stacking the columns of the adjacency matrix vertically. +sampled_bitvs = zeros(nsamples, nnodes*nnodes); +for m=1:nsamples + sampled_bitvs(m, :) = sampled_graphs{m}(:)'; +end + +[ugraphs, I, J] = unique(sampled_bitvs, 'rows'); % each row of ugraphs is a unique bit vector +sampled_indices = subv2ind(2*ones(1,nnodes*nnodes), ugraphs+1); +counts = hist(J, 1:size(ugraphs,1)); % counts(i) = number of times graphs(i,:) occurs in the sample + +mcmc_post = zeros(1, ndags); +for i=1:ndags + bitv = dags{i}(:)'; + % Find the samples that corresponds to this graph by converting the graphs to bitvectors and + % then to integers. + ndx = subv2ind(2*ones(1,nnodes*nnodes), bitv+1); + locn = find(ndx == sampled_indices); + if ~isempty(locn) + mcmc_post(i) = counts(locn); + end +end +mcmc_post = normalise(mcmc_post); + + |