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