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function X = gursoy_atun_layout(A,varargin)
% GURSOY_ATUN_LAYOUT Layout a graph by uniformly distributing vertices
%
% Instead of trying to optimize an objective function of forces or springs
% in the layout, the Gursoy-Atun layout distributes vertices uniformly
% within a topology subject to keeping vertices nearby their neighbors.
%
% X = gursoy_atun_layout(A) compute a Gursoy-Atun layout.
%
% See
% http://www.boost.org/doc/libs/1_36_0/libs/graph/doc/gursoy_atun_layout.html
% for a description of all the parameters.
%
% ... = gursoy_atun_layout(A,...) takes a set of
% key-value pairs or an options structure. See set_matlab_bgl_options
% for the standard options.
% options.topology: the topology spaced used for the layout
% [{'square'} | 'heart' | 'sphere' | 'circle' | 'ballN'* | 'cubeN'*]
% * for the ball and cube topolgy, N can be replaced by any number, so
% ball3 is the same as the sphere, cube2 is the same as the square,
% but cube3 is the true cube and cube4 is a hypercube. At the moment,
% only dimensions between 1 and 10 are implemented.
% options.iterations: the number of iterations [num_vertices(G) | integer]
% options.diameter_range: The inital and final diameters when updating
% [ {[sqrt(num_vertices(G)),1.0]} | [diameter_initial,diameter_final] ]
% where all values are double.
% options.learning_constant_range: The inital and final learning constants
% [ {[0.8,0.2]} | [learning_constant_initial,learning_constant_final] ]
% where all values are double.
%
% Example:
% G1 = cycle_graph(5000,struct('directed',0));
% X1 = gursoy_atun_layout(G1,'topology','heart');
% G2 = grid_graph(50,50);
% X2 = gursoy_atun_layout(G2,'topology','square');
% G3 = grid_graph(50,50);
% X3 = gursoy_atun_layout(G3,'topology','circle');
% subplot(1,3,1); gplot(G1,X1,'k'); subplot(1,3,2); gplot(G2,X2,'k');
% subplot(1,3,3); gplot(G3,X3,'k');
%
% See also KAMADA_KAWAI_SPRING_LAYOUT,
% FRUCHTERMAN_REINGOLD_FORCE_DIRECTED_LAYOUT, LAYOUT
% David F. Gleich
% Copyright, Stanford University, 2008
%% History
% 2008-09-28: Initial coding
%%
[trans check full2sparse] = get_matlab_bgl_options(varargin{:});
if full2sparse && ~issparse(A), A = sparse(A); end
n = num_vertices(A);
options = struct('topology','square','iterations',n,...
'diameter_range',[sqrt(n) 1.0],'learning_constant_range',[0.8 0.2],...
'progressive',0,'edge_weight','matrix');
options = merge_options(options,varargin{:});
% edge_weights is an indicator that is 1 if we are using edge_weights
% passed on the command line or 0 if we are using the matrix, or -1 to use
% nothing
edge_weights = 0;
edge_weight_opt = 'matrix';
if strcmp(options.edge_weight, 'matrix'), % do nothing to use the matrix weights
elseif strcmp(options.edge_weight,'none'), edge_weights = -1; % don't use weights
else edge_weights = 1; edge_weight_opt = options.edge_weight;
end
if check
% make sure the matrix is symmetric
if ~edge_weights==1
check_matlab_bgl(A,struct('values',edge_weights==0));
else
if nnz(A) ~= length(edge_weight_opt)
error('matlab_bgl:invalidParameter', ...
'the vector of edge weights must have length nnz(A)');
end
end
end
progressive_opt = [];
if ~isscalar(options.progressive), progressive_opt = options.progressive; end
X= gursoy_atun_mex(...
A, options.topology, options.iterations, ...
options.diameter_range(1), options.diameter_range(2),...
options.learning_constant_range(1), options.learning_constant_range(2),...
progressive_opt, edge_weights, edge_weight_opt);
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