annotate toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/get_slice_dbn.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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children
rev   line source
wolffd@0 1 function slice = get_slice_dbn(bnet, state, i, n, j, m, strides, families, ...
wolffd@0 2 CPT)
wolffd@0 3 % slice = get_slice(bnet, state, i, n, j, m, strides, families, cpt)
wolffd@0 4 %
wolffd@0 5 % GET_SLICE get one-dimensional slice of the CPT for node X_i^n
wolffd@0 6 % that corresponds to the different values of X_j^m, where all
wolffd@0 7 % other nodes have values given by state.
wolffd@0 8 % strides is the result of
wolffd@0 9 % calling compute_strides(bnet)
wolffd@0 10 % families is the result of calling compute_families(bnet)
wolffd@0 11 % cpts is the result of calling get_cpts(bnet)
wolffd@0 12 %
wolffd@0 13 % slice is a 1-d array
wolffd@0 14
wolffd@0 15
wolffd@0 16 if (n == 1)
wolffd@0 17
wolffd@0 18 k = bnet.eclass1(i);
wolffd@0 19 c = CPT{k};
wolffd@0 20
wolffd@0 21 % Figure out evidence on family
wolffd@0 22 fam = families{i, 1};
wolffd@0 23 ev = state(fam, 1);
wolffd@0 24
wolffd@0 25 % Remove evidence on node j
wolffd@0 26 pos = find(fam == j);
wolffd@0 27 ev(pos) = 1;
wolffd@0 28 dim = size(ev, 1);
wolffd@0 29
wolffd@0 30 % Compute initial index and stride
wolffd@0 31 start_ind = 1+strides(k, 1:dim)*(ev-1);
wolffd@0 32 stride = strides(k, pos);
wolffd@0 33
wolffd@0 34 % Compute the slice
wolffd@0 35 slice = c(start_ind:stride:start_ind+(bnet.node_sizes(j, 1)-1)*stride);
wolffd@0 36
wolffd@0 37 else
wolffd@0 38
wolffd@0 39 k = bnet.eclass2(i);
wolffd@0 40 c = CPT{k};
wolffd@0 41
wolffd@0 42 fam = families{i, 2};
wolffd@0 43 ss = length(bnet.intra);
wolffd@0 44
wolffd@0 45 % Divide the family into nodes in this time step and nodes in the
wolffd@0 46 % previous time step
wolffd@0 47 this_time_step = fam(find(fam > ss));
wolffd@0 48 prev_time_step = fam(find(fam <= ss));
wolffd@0 49
wolffd@0 50 % Normalize the node numbers
wolffd@0 51 this_time_step = this_time_step - ss;
wolffd@0 52
wolffd@0 53 % Get the evidence
wolffd@0 54 this_step_ev = state(this_time_step, n);
wolffd@0 55 prev_step_ev = state(prev_time_step, n-1);
wolffd@0 56
wolffd@0 57 % Remove the evidence for X_j^m
wolffd@0 58 if (m == n)
wolffd@0 59 pos = find(this_time_step == j);
wolffd@0 60 this_step_ev(pos) = 1;
wolffd@0 61 pos = pos + size(prev_time_step, 2);
wolffd@0 62 else
wolffd@0 63 assert (m == n-1);
wolffd@0 64 pos = find(prev_time_step == j);
wolffd@0 65 prev_step_ev(pos) = 1;
wolffd@0 66 end
wolffd@0 67
wolffd@0 68 % Combine the two time steps
wolffd@0 69 ev = [prev_step_ev; this_step_ev];
wolffd@0 70 dim = size(ev, 1);
wolffd@0 71
wolffd@0 72
wolffd@0 73 % Compute starting index and stride
wolffd@0 74 start_ind = 1 + strides(k, 1:dim)*(ev-1);
wolffd@0 75 stride = strides(k, pos);
wolffd@0 76
wolffd@0 77 % Compute slice
wolffd@0 78 if (m == 1)
wolffd@0 79 q = 1;
wolffd@0 80 else
wolffd@0 81 q = 2;
wolffd@0 82 end
wolffd@0 83 slice = c(start_ind:stride:start_ind+(bnet.node_sizes(j, q)-1)*stride);
wolffd@0 84 end
wolffd@0 85
wolffd@0 86
wolffd@0 87