spm_dcm_sparse_priors.m
8a186cd82567master

spm_dcm_sparse_priors.m
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			function [A,K,k] = spm_dcm_sparse_priors(n)
			% Returns Adjacency matrices for bidirectional coupling
			%
			% INPUT:
			% n - number of nodes
			%
			% OUTPUT:
			% A{:} - adjacency matrices
			% K{1:K}{:} - adjacency matrices (for k - 1 edges)
			% k - row vector of edge numbers (size)
			%__________________________________________________________________________
			% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

			% Karl Friston
			% $Id: spm_dcm_sparse_priors.m 5219 2013-01-29 17:07:07Z spm $


			%--------------------------------------------------------------------------
			if nargin < 2; k = n - 1; end

			% get permutations of connections
			%==========================================================================
			N = (n*n - n)/2; % number of (bidirectional) connections
			np = [1; 0];
			for i = 1:N
			p = np;
			np = [p; p];
			for j = 1:i
			np(:,j) = kron(p(:,j),[1; 1]);
			end
			np(:,(i + 1)) = kron(ones(2^i,1),[1; 0]);
			end


			% get permutations of K connections (in A{:})
			%==========================================================================
			[I,J] = find(triu(ones(n,n),1));
			K = cell(N + 1,1);
			A = {};
			for i = 1:2^N

			% adjacency matrix
			%----------------------------------------------------------------------
			a = full(sparse(I,J,p(i,:),n,n));
			a = a + a' + eye(n,n);

			% save in arrays
			%----------------------------------------------------------------------
			k(i,1) = sum(p(i,:));
			K{1 + k(i)}{end + 1} = a;
			A{i} = a;

			end