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55 lines
2.2 KiB
55 lines
2.2 KiB
function minmuttau = findminMutInf(miV,nsam) |
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% minmuttau = findminMutInf(miV,nsam) |
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% findminMutInf finds the lag tau of the first local minimum of mutual |
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% information 'miV' (given from lag 0 and up to a maximum lag 'taumax') |
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% and using a sliding window of length 2*nsam+1. |
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%======================================================================== |
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% <findminMutInf.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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taumax = length(miV)-1; |
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minmuttau = NaN; |
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if taumax>2*nsam |
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i=nsam+1; |
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found = 0; |
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while i<taumax+1-nsam & found==0 |
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winx = miV([i-nsam:i-1 i+1:i+nsam]); |
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check = find(miV(i) < winx); |
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if length(check) == length(winx) |
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found=1; |
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else |
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i=i+1; |
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end |
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end |
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if found |
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minmuttau = i-1; |
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end |
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end
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