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iCAPs/packages/gspbox/graphs/gsp_nn_hypergraph.m
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gsp_nn_hypergraph.m
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function [ G ] = gsp_nn_hypergraph( Xin, param )
%GSP_NN_HYPERGRAPH Create a nearest neighbors hypergraph from a point cloud
% Usage : G = gsp_nn_hypergraph( Xin );
% G = gsp_nn_hypergraph( Xin, param );
%
% Input parameters:
% Xin : Input points
% param : Structure of optional parameters
%
% Output parameters:
% G : Resulting graph
%
% Example:
%
% P = rand(100,2);
% G = gsp_nn_hypergraph(P)
%
% Additional parameters
% ---------------------
%
% param.use_flann : [0, 1] use the FLANN library
% param.center : [0, 1] center the data
% param.rescale : [0, 1] rescale the data (in a 1-ball)
% param.sigma : float the variance of the distance kernel
% param.k : int number of neighbors for knn
%
% See also: gsp_nn_graph
%
%
% Url: http://lts2research.epfl.ch/gsp/doc/graphs/gsp_nn_hypergraph.php
% Copyright (C) 2013-2016 Nathanael Perraudin, Johan Paratte, David I Shuman.
% This file is part of GSPbox version 0.7.0
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
% If you use this toolbox please kindly cite
% N. Perraudin, J. Paratte, D. Shuman, V. Kalofolias, P. Vandergheynst,
% and D. K. Hammond. GSPBOX: A toolbox for signal processing on graphs.
% ArXiv e-prints, Aug. 2014.
% http://arxiv.org/abs/1408.5781
% Author: Nathanael Perraudin
% Date: 21 October 2015
% Testing: test_rmse
% * *param.type* : ['knn', 'radius'] the type of graph (default 'knn')
% * *param.epsilon* : float the radius for the range search
% * *param.use_l1* : [0, 1] use the l1 distance
if nargin < 2
% Define parameters
param = {};
end
%Parameters
% if ~isfield(param, 'type'), param.type = 'knn'; end
if ~isfield(param, 'use_flann'), param.use_flann = 0; end
if ~isfield(param, 'center'), param.center = 0; end
if ~isfield(param, 'rescale'), param.rescale = 1; end
if ~isfield(param, 'k'), param.k = 10; end
param.type = 'knn';
% if ~isfield(param, 'epsilon'), param.epsilon = 0.01; end
% if ~isfield(param, 'use_l1'), param.use_l1 = 0; end
% if ~isfield(param, 'target_degree'), param.target_degree = 0; end;
paramnn = param;
% paramnn.k = param.k +1;
[indx, ~, dist] = gsp_nn_distanz(Xin',Xin',paramnn);
% switch param.type
% case 'knn'
% if param.use_l1
% if ~isfield(param, 'sigma'), param.sigma = mean(dist); end
% else
if ~isfield(param, 'sigma'), param.sigma = mean(dist)^2; end
% end
% case 'radius'
% if param.use_l1
% if ~isfield(param, 'sigma'), param.sigma = epsilon/2; end
% else
% if ~isfield(param, 'sigma'), param.sigma = epsilon.^2/2; end
% end
% otherwise
% error('Unknown graph type')
% end
w = exp(-dist.^2/param.sigma);
G.N = size(Xin,1);
G.Ne = G.N;
G.W = sparse(G.N,G.Ne);
G.E = cell(G.Ne,1);
k = param.k;
for ii = 1:G.Ne
edge = indx((1:k)+(ii-1)*k);
G.E{ii} = edge;
% Here we use H for HW...
G.W(edge,ii) = sqrt(sum(w(edge)));
end
G.hypergraph = 1;
G.directed = 0;
%Fill in the graph structure
G.coords = Xin;
G.type = 'Nearest neighboors hypergraph';
G.lap_type = 'normalized';
G.sigma = param.sigma;
G = gsp_graph_default_parameters(G);
end
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