MAP_Gait_Dynamics/mutinfHisPro.m

72 lines
2.8 KiB
Matlab

function mutV=mutinfHisPro(xV,tauV,b,ioxV,ixV)
% mutV=mutinfHisPro(xV,tauV,b,ioxV,ixV)
% mutinfHisPro computes the mutual information on the time series 'xV'
% for given delays in 'tauV'. The estimation of mutual information is
% based on 'b' partitions of equal probability at each dimension.
% The last two input parameters are the ordered time series and the
% corresponding indices that will be used in the equiprobable binning
% (they both have been computed before and therefore they are passed
% here rather than computing it again).
%========================================================================
% <mutinfHisPro.m>, v 1.0 2010/02/11 22:09:14 Kugiumtzis & Tsimpiris
% This is part of the MATS-Toolkit http://eeganalysis.web.auth.gr/
%========================================================================
% Copyright (C) 2010 by Dimitris Kugiumtzis and Alkiviadis Tsimpiris
% <dkugiu@gen.auth.gr>
%========================================================================
% Version: 1.0
% LICENSE:
% 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
% 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/>.
%=========================================================================
% Reference : D. Kugiumtzis and A. Tsimpiris, "Measures of Analysis of Time Series (MATS):
% A Matlab Toolkit for Computation of Multiple Measures on Time Series Data Bases",
% Journal of Statistical Software, in press, 2010
% Link : http://eeganalysis.web.auth.gr/
%=========================================================================
n = length(xV);
ntau = length(tauV);
mutV = NaN*ones(ntau,1);
hM = NaN*ones(b,b);
cumhM = zeros(b,b+1);
cpxV = [1/b:1/b:1]';
for itau=1:ntau
tau = tauV(itau);
ntotal = n-tau;
rxV = [0;round(cpxV*ntotal)];
ix1V = ixV;
ix1V(ioxV(end-tau+1:end)) = [];
x2prV = prctile(xV(ix1V+tau),cpxV*100);
for i = 1:b
for j = 1:b
cumhM(i,j+1) = length(find(xV(ix1V(rxV(i)+1:rxV(i+1))+tau)<=x2prV(j)));
end
hM(i,:) = diff(cumhM(i,:));
end
% The use of formula H(x)=1, when log_b is used.
mutS = 2;
for j=1:b
for i=1:b
if hM(i,j) > 0
mutS=mutS+(hM(i,j)/ntotal)*log(hM(i,j)/ntotal)/log(b);
end
end
end
mutV(itau) = mutS;
end