wolffd@0: function slice = get_slice_dbn(bnet, state, i, n, j, m, strides, families, ...
wolffd@0: 				 CPT)
wolffd@0: % slice = get_slice(bnet, state, i, n, j, m, strides, families, cpt)
wolffd@0: %
wolffd@0: % GET_SLICE get one-dimensional slice of the CPT for node X_i^n
wolffd@0: % that corresponds to the different values of X_j^m, where all
wolffd@0: % other nodes have values given by state.  
wolffd@0: % strides is the result of
wolffd@0: % calling compute_strides(bnet)
wolffd@0: % families is the result of calling compute_families(bnet)
wolffd@0: % cpts is the result of calling get_cpts(bnet)
wolffd@0: %
wolffd@0: % slice is a 1-d array
wolffd@0: 
wolffd@0: 
wolffd@0: if (n == 1)
wolffd@0: 
wolffd@0:   k = bnet.eclass1(i);
wolffd@0:   c = CPT{k};
wolffd@0:   
wolffd@0:   % Figure out evidence on family
wolffd@0:   fam = families{i, 1};
wolffd@0:   ev = state(fam, 1);
wolffd@0:   
wolffd@0:   % Remove evidence on node j
wolffd@0:   pos = find(fam == j);
wolffd@0:   ev(pos) = 1;
wolffd@0:   dim = size(ev, 1);
wolffd@0:   
wolffd@0:   % Compute initial index and stride
wolffd@0:   start_ind = 1+strides(k, 1:dim)*(ev-1);
wolffd@0:   stride = strides(k, pos);
wolffd@0: 
wolffd@0:   % Compute the slice
wolffd@0:   slice = c(start_ind:stride:start_ind+(bnet.node_sizes(j, 1)-1)*stride);
wolffd@0: 						  
wolffd@0: else
wolffd@0:   
wolffd@0:   k = bnet.eclass2(i);
wolffd@0:   c = CPT{k};
wolffd@0:   
wolffd@0:   fam = families{i, 2};
wolffd@0:   ss = length(bnet.intra);
wolffd@0:   
wolffd@0:   % Divide the family into nodes in this time step and nodes in the
wolffd@0:   % previous time step
wolffd@0:   this_time_step = fam(find(fam > ss));
wolffd@0:   prev_time_step = fam(find(fam <= ss));
wolffd@0: 
wolffd@0:   % Normalize the node numbers
wolffd@0:   this_time_step = this_time_step - ss;
wolffd@0:   
wolffd@0:   % Get the evidence
wolffd@0:   this_step_ev = state(this_time_step, n);
wolffd@0:   prev_step_ev = state(prev_time_step, n-1);
wolffd@0:   
wolffd@0:   % Remove the evidence for X_j^m
wolffd@0:   if (m == n)
wolffd@0:     pos = find(this_time_step == j);
wolffd@0:     this_step_ev(pos) = 1;
wolffd@0:     pos = pos + size(prev_time_step, 2);
wolffd@0:   else
wolffd@0:     assert (m == n-1);
wolffd@0:     pos = find(prev_time_step == j);
wolffd@0:     prev_step_ev(pos) = 1;
wolffd@0:   end
wolffd@0:   
wolffd@0:   % Combine the two time steps
wolffd@0:   ev = [prev_step_ev; this_step_ev];
wolffd@0:   dim = size(ev, 1);
wolffd@0: 
wolffd@0: 
wolffd@0:   % Compute starting index and stride
wolffd@0:   start_ind = 1 + strides(k, 1:dim)*(ev-1);
wolffd@0:   stride = strides(k, pos);
wolffd@0:   
wolffd@0:   % Compute slice 
wolffd@0:   if (m == 1)
wolffd@0:     q = 1;
wolffd@0:   else
wolffd@0:     q = 2;
wolffd@0:   end
wolffd@0:   slice = c(start_ind:stride:start_ind+(bnet.node_sizes(j, q)-1)*stride);
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: