72 lines
2.8 KiB
Mathematica
72 lines
2.8 KiB
Mathematica
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function mutV=mutinfHisPro(xV,tauV,b,ioxV,ixV)
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% mutV=mutinfHisPro(xV,tauV,b,ioxV,ixV)
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% mutinfHisPro computes the mutual information on the time series 'xV'
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% for given delays in 'tauV'. The estimation of mutual information is
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% based on 'b' partitions of equal probability at each dimension.
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% The last two input parameters are the ordered time series and the
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% corresponding indices that will be used in the equiprobable binning
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% (they both have been computed before and therefore they are passed
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% here rather than computing it again).
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%========================================================================
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% <mutinfHisPro.m>, v 1.0 2010/02/11 22:09:14 Kugiumtzis & Tsimpiris
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% This is part of the MATS-Toolkit http://eeganalysis.web.auth.gr/
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%========================================================================
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% Copyright (C) 2010 by Dimitris Kugiumtzis and Alkiviadis Tsimpiris
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% <dkugiu@gen.auth.gr>
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%========================================================================
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% Version: 1.0
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% LICENSE:
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% This program is free software; you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation; either version 3 of the License, or
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% any later version.
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%
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% This program is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with this program. If not, see http://www.gnu.org/licenses/>.
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%=========================================================================
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% Reference : D. Kugiumtzis and A. Tsimpiris, "Measures of Analysis of Time Series (MATS):
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% A Matlab Toolkit for Computation of Multiple Measures on Time Series Data Bases",
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% Journal of Statistical Software, in press, 2010
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% Link : http://eeganalysis.web.auth.gr/
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%=========================================================================
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n = length(xV);
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ntau = length(tauV);
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mutV = NaN*ones(ntau,1);
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hM = NaN*ones(b,b);
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cumhM = zeros(b,b+1);
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cpxV = [1/b:1/b:1]';
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for itau=1:ntau
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tau = tauV(itau);
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ntotal = n-tau;
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rxV = [0;round(cpxV*ntotal)];
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ix1V = ixV;
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ix1V(ioxV(end-tau+1:end)) = [];
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x2prV = prctile(xV(ix1V+tau),cpxV*100);
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for i = 1:b
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for j = 1:b
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cumhM(i,j+1) = length(find(xV(ix1V(rxV(i)+1:rxV(i+1))+tau)<=x2prV(j)));
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end
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hM(i,:) = diff(cumhM(i,:));
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end
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% The use of formula H(x)=1, when log_b is used.
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mutS = 2;
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for j=1:b
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for i=1:b
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if hM(i,j) > 0
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mutS=mutS+(hM(i,j)/ntotal)*log(hM(i,j)/ntotal)/log(b);
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end
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end
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end
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mutV(itau) = mutS;
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end
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