119 lines
3.6 KiB
Matlab
119 lines
3.6 KiB
Matlab
function [L_Estimate,ExtraArgsOut] = CalcMaxLyapConvGait(ThisTimeSeries,FS,ExtraArgsIn)
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if nargin > 2
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if isfield(ExtraArgsIn,'J')
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J=ExtraArgsIn.J;
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end
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if isfield(ExtraArgsIn,'m')
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m=ExtraArgsIn.m;
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end
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if isfield(ExtraArgsIn,'FitWinLen')
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FitWinLen=ExtraArgsIn.FitWinLen;
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end
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end
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%% Initialize output args
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L_Estimate=nan;ExtraArgsOut.Divergence=nan;ExtraArgsOut.J=nan;ExtraArgsOut.m=nan;ExtraArgsOut.FitWinLen=nan;
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%% Some checks
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% predefined J and m should not be NaN or Inf
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if (exist('J','var') && ~isempty(J) && ~isfinite(J)) || (exist('m','var') && ~isempty(m) && ~isfinite(m))
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warning('Predefined J and m cannot be NaN or Inf');
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return;
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end
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% multidimensional time series need predefined J and m
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if size(ThisTimeSeries,2) > 1 && (~exist('J','var') || ~exist('m','var') || isempty(J) || isempty(m))
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warning('Multidimensional time series needs predefined J and m, can''t determine Lyapunov');
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return;
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end
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%Check that there are no NaN or Inf values in the TimeSeries
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if any(~isfinite(ThisTimeSeries(:)))
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warning('Time series contains NaN or Inf, can''t determine Lyapunov');
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return;
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end
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%Check that there is variation in the TimeSeries
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if ~(nanstd(ThisTimeSeries) > 0)
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warning('Time series is constant, can''t determine Lyapunov');
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return;
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end
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%% Determine FitWinLen (=cycle time) of ThisTimeSeries
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if ~exist('FitWinLen','var') || isempty(FitWinLen)
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if size(ThisTimeSeries,2)>1
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for dim=1:size(ThisTimeSeries,2),
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[Pd(:,dim),F] = pwelch(detrend(ThisTimeSeries(:,dim)),[],[],[],FS);
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end
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P = sum(Pd,2);
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else
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[P,F] = pwelch(detrend(ThisTimeSeries),[],[],[],FS);
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end
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MeanF = sum(P.*F)./sum(P);
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CycleTime = 1/MeanF;
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FitWinLen = round(CycleTime*FS);
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else
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CycleTime = FitWinLen/FS;
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end
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ExtraArgsOut.FitWinLen=FitWinLen;
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%% Determine J
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if ~exist('J','var') || isempty(J)
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% Calculate mutual information and take first local minimum Tau as J
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bV = min(40,floor(sqrt(size(ThisTimeSeries,1))));
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tauVmax = FitWinLen;
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[mutMPro,cummutMPro,minmuttauVPro] = MutualInformationHisPro(ThisTimeSeries,(0:tauVmax),bV,1); % (xV,tauV,bV,flag)
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if isnan(minmuttauVPro)
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display(mutMPro);
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warning('minmuttauVPro is NaN. Consider increasing tauVmax.');
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return;
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end
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J=minmuttauVPro;
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end
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ExtraArgsOut.J=J;
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%% Determine m
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if ~exist('m','var') || isempty(m)
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escape = 10;
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max_m = 20;
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max_fnnM = 0.02;
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mV = 0;
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fnnM = 1;
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for mV = 2:max_m % for m=1, FalseNearestNeighbors is slow and lets matlab close if N>500000
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fnnM = FalseNearestNeighborsSR(ThisTimeSeries,J,mV,escape,FS); % (xV,tauV,mV,escape,theiler)
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if fnnM <= max_fnnM || isnan(fnnM)
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break
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end
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end
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if fnnM <= max_fnnM
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m = mV;
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else
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warning('Too many false nearest neighbours');
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return;
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end
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end
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ExtraArgsOut.m=m;
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%% Create state space based upon J and m
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N_ss = size(ThisTimeSeries,1)-(m-1)*J;
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StateSpace=nan(N_ss,m*size(ThisTimeSeries,2));
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for dim=1:size(ThisTimeSeries,2),
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for delay=1:m,
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StateSpace(:,(dim-1)*m+delay)=ThisTimeSeries((1:N_ss)'+(delay-1)*J,dim);
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end
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end
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%% Parameters for Lyapunov
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WindowLen = floor(min(N_ss/5,10*FitWinLen));
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if WindowLen < FitWinLen
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warning('Not enough samples for Lyapunov estimation');
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return;
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end
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WindowLenSec=WindowLen/FS;
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%% Calculate divergence
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Divergence=div_calc(StateSpace,WindowLenSec,FS,CycleTime,0);
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ExtraArgsOut.Divergence=Divergence;
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%% Calculate slope of first FitWinLen samples of divergence curve
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p = polyfit((1:FitWinLen)/FS,Divergence(1:FitWinLen),1);
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L_Estimate = p(1);
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