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function [ labels, cases, dag, node_sizes, ord_flag ] = checkStructure(labels, cases, dag, node_sizes)
%checkStructure Check to see if nodes are sorted correctly. They must be
% in topological order (i.e., parents before children) before parameter
% learning can take place.
%
%Input and output have the same meaning. The output has just been
%topologically ordered.
% labels = cell array with the names of the nodes.
% cases = cell array with the data.
% dag = matrix with the strucutre of the network.
% node_sizes = vector with the size of each node.
%make connections array
%count how big you need the connections array to be
nnodes = size(dag,1);
narcs = 0;
for i = 1:nnodes
for j = 1:nnodes
if dag(i,j) == 1
narcs = narcs + 1;
end
end
end
%fill connections array with label names
connections = cell(narcs,2);
ncount = 0;
for i = 1:nnodes
for j = 1:nnodes
if dag(i,j) == 1
ncount = ncount + 1;
connections{ncount,1} = labels{i};
connections{ncount,2} = labels{j};
end
end
end
%get topologically sorted dag and labels
[new_dag, new_labels] = mk_adj_mat(connections, labels, 1);
%check to see if order changed
ord_flag = 0;
for i = 1:nnodes
if ~strcmp(new_labels{i},labels{i})
ord_flag = 1;
end
end
if ord_flag
%get new ordering of nodes
order = cell(1,nnodes);
for i = 1:nnodes
for j = 1:nnodes
if strcmp(new_labels{j},labels{i})
order{i} = j;
end
end
end
%reorder cases and node_sizes
new_cases = cell(size(cases));
for i = 1:nnodes
new_cases(order{i},:) = cases(i,:);
end
new_node_sizes = zeros(1,nnodes);
for i = 1:nnodes
new_node_sizes(order{i}) = node_sizes(i);
end
dag = new_dag;
cases = new_cases;
node_sizes = new_node_sizes;
labels = new_labels;
end
end
%end checkStructure.m
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