Daniel@0: function [data, clamped] = mk_mutilated_samples(bnet, ncases, max_clamp, usecell)
Daniel@0: % GEN_MUTILATED_SAMPLES Do random interventions and then draw random samples
Daniel@0: % [data, clamped] = gen_mutilated_samples(bnet, ncases, max_clamp, usecell)
Daniel@0: %
Daniel@0: % At each step, we pick a random subset of size 0 .. max_clamp, and 
Daniel@0: % clamp these nodes to random values.
Daniel@0: %
Daniel@0: % data(i,m) is the value of node i in case m.
Daniel@0: % clamped(i,m) = 1 if node i in case m was set by intervention.
Daniel@0: 
Daniel@0: if nargin < 4, usecell = 1; end
Daniel@0: 
Daniel@0: ns = bnet.node_sizes;
Daniel@0: n = length(bnet.dag);
Daniel@0: if usecell
Daniel@0:   data = cell(n, ncases);
Daniel@0: else
Daniel@0:   data = zeros(n, ncases);
Daniel@0: end
Daniel@0: clamped = zeros(n, ncases);
Daniel@0: 
Daniel@0: csubsets = subsets(1:n, max_clamp, 0); % includes the empty set
Daniel@0: distrib_cset = normalise(ones(1, length(csubsets)));
Daniel@0: 
Daniel@0: for m=1:ncases
Daniel@0:   cset = csubsets{sample_discrete(distrib_cset)};
Daniel@0:   nvals = prod(ns(cset));
Daniel@0:   distrib_cvals = normalise(ones(1, nvals));
Daniel@0:   cvals = ind2subv(ns(cset), sample_discrete(distrib_cvals));
Daniel@0:   mutilated_bnet = do_intervention(bnet, cset, cvals);
Daniel@0:   ev = sample_bnet(mutilated_bnet);
Daniel@0:   if usecell
Daniel@0:     data(:,m) = ev;
Daniel@0:   else
Daniel@0:     data(:,m) = cell2num(ev);
Daniel@0:   end
Daniel@0:   clamped(cset,m) = 1;
Daniel@0: end