147 lines
5.7 KiB
Matlab
147 lines
5.7 KiB
Matlab
function [mutM,cummutM,minmuttauV] = MutualInformationHisPro(xV,tauV,bV,flag)
|
|
% [mutM,cummutM,minmuttauV] = MutualInformationHisPro(xV,tauV,bV,flag)
|
|
% MUTUALINFORMATIONHISPRO 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.
|
|
% A number of different 'b' can be given in the input vector 'bV'.
|
|
% According to a given flag, it can also compute the cumulative mutual
|
|
% information for each given lag, as well as the time of the first minimum
|
|
% of the mutual information.
|
|
% INPUT
|
|
% - xV : a vector for the time series
|
|
% - tauV : a vector of the delays to be evaluated for
|
|
% - bV : a vector of the number of partitions of the histogram-based
|
|
% estimate.
|
|
% - flag : if 0-> compute only mutual information,
|
|
% : if 1-> compute the mutual information, the first minimum of
|
|
% mutual information and the cumulative mutual information.
|
|
% if 2-> compute (also) the cumulative mutual information
|
|
% if 3-> compute (also) the first minimum of mutual information
|
|
% OUTPUT
|
|
% - mutM : the vector of the mutual information values s for the given
|
|
% delays.
|
|
% - cummutM : the vector of the cumulative mutual information values for
|
|
% the given delays
|
|
% - minmuttauV : the time of the first minimum of the mutual information.
|
|
%========================================================================
|
|
% <MutualInformationHisPro.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/
|
|
%=========================================================================
|
|
nsam = 1;
|
|
n = length(xV);
|
|
if nargin==3
|
|
flag = 1;
|
|
elseif nargin==2
|
|
flag = 1;
|
|
bV = round(sqrt(n/5));
|
|
end
|
|
if isempty(bV)
|
|
bV = round(sqrt(n/5));
|
|
end
|
|
bV(bV==0)=round(sqrt(n/5));
|
|
tauV = sort(tauV);
|
|
ntau = length(tauV);
|
|
taumax = tauV(end);
|
|
nb = length(bV);
|
|
[oxV,ixV]=sort(xV);
|
|
[tmpV,ioxV]=sort(ixV);
|
|
switch flag
|
|
case 0
|
|
% Compute only the mutual information for the given lags
|
|
mutM = NaN*ones(ntau,nb);
|
|
for ib=1:nb
|
|
b = bV(ib);
|
|
if n<2*b
|
|
break;
|
|
end
|
|
mutM(:,ib)=mutinfHisPro(xV,tauV,b,ioxV,ixV);
|
|
end % for ib
|
|
cummutM=[];
|
|
minmuttauV=[];
|
|
case 1
|
|
% Compute the mutual information for all lags up to the
|
|
% largest given lag, then compute the lag of the first minimum of
|
|
% mutual information and the cumulative mutual information for the
|
|
% given lags.
|
|
mutM = NaN*ones(ntau,nb);
|
|
cummutM = NaN*ones(ntau,nb);
|
|
minmuttauV = NaN*ones(nb,1);
|
|
miM = NaN*ones(taumax+1,nb);
|
|
for ib=1:nb
|
|
b = bV(ib);
|
|
if n<2*b
|
|
break;
|
|
end
|
|
miM(:,ib)=mutinfHisPro(xV,[0:taumax]',b,ioxV,ixV);
|
|
mutM(:,ib) = miM(tauV+1,ib);
|
|
minmuttauV(ib) = findminMutInf(miM(:,ib),nsam);
|
|
% Compute the cumulative mutual information for the given delays
|
|
for i=1:ntau
|
|
cummutM(i,ib) = sum(miM(1:tauV(i)+1,ib));
|
|
end
|
|
end % for ib
|
|
case 2
|
|
% Compute the mutual information for all lags up to the largest
|
|
% given lag and then sum up to get the cumulative mutual information
|
|
% for the given lags.
|
|
cummutM = NaN*ones(ntau,nb);
|
|
miM = NaN*ones(taumax+1,nb);
|
|
for ib=1:nb
|
|
b = bV(ib);
|
|
if n<2*b
|
|
break;
|
|
end
|
|
miM(:,ib)=mutinfHisPro(xV,[0:taumax]',b,ioxV,ixV);
|
|
% Compute the cumulative mutual information for the given delays
|
|
for i=1:ntau
|
|
cummutM(i,ib) = sum(miM(1:tauV(i)+1,ib));
|
|
end
|
|
end % for ib
|
|
mutM = [];
|
|
minmuttauV=[];
|
|
case 3
|
|
% Compute the mutual information for all lags up to the largest
|
|
% given lag and then compute the lag of the first minimum of the
|
|
% mutual information.
|
|
minmuttauV = NaN*ones(nb,1);
|
|
miM = NaN*ones(taumax+1,nb);
|
|
for ib=1:nb
|
|
b = bV(ib);
|
|
if n<2*b
|
|
break;
|
|
end
|
|
miM(:,ib)=mutinfHisPro(xV,[0:taumax]',b,ioxV,ixV);
|
|
minmuttauV(ib) = findminMutInf(miM(:,ib),nsam);
|
|
end % for ib
|
|
mutM = [];
|
|
cummutM=[];
|
|
end
|