You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
118 lines
3.6 KiB
118 lines
3.6 KiB
function [L_Estimate,ExtraArgsOut] = CalcMaxLyapConvGait(ThisTimeSeries,FS,ExtraArgsIn)
|
|
if nargin > 2
|
|
if isfield(ExtraArgsIn,'J')
|
|
J=ExtraArgsIn.J;
|
|
end
|
|
if isfield(ExtraArgsIn,'m')
|
|
m=ExtraArgsIn.m;
|
|
end
|
|
if isfield(ExtraArgsIn,'FitWinLen')
|
|
FitWinLen=ExtraArgsIn.FitWinLen;
|
|
end
|
|
end
|
|
|
|
%% Initialize output args
|
|
L_Estimate=nan;ExtraArgsOut.Divergence=nan;ExtraArgsOut.J=nan;ExtraArgsOut.m=nan;ExtraArgsOut.FitWinLen=nan;
|
|
|
|
%% Some checks
|
|
% predefined J and m should not be NaN or Inf
|
|
if (exist('J','var') && ~isempty(J) && ~isfinite(J)) || (exist('m','var') && ~isempty(m) && ~isfinite(m))
|
|
warning('Predefined J and m cannot be NaN or Inf');
|
|
return;
|
|
end
|
|
% multidimensional time series need predefined J and m
|
|
if size(ThisTimeSeries,2) > 1 && (~exist('J','var') || ~exist('m','var') || isempty(J) || isempty(m))
|
|
warning('Multidimensional time series needs predefined J and m, can''t determine Lyapunov');
|
|
return;
|
|
end
|
|
%Check that there are no NaN or Inf values in the TimeSeries
|
|
if any(~isfinite(ThisTimeSeries(:)))
|
|
warning('Time series contains NaN or Inf, can''t determine Lyapunov');
|
|
return;
|
|
end
|
|
%Check that there is variation in the TimeSeries
|
|
if ~(nanstd(ThisTimeSeries) > 0)
|
|
warning('Time series is constant, can''t determine Lyapunov');
|
|
return;
|
|
end
|
|
|
|
%% Determine FitWinLen (=cycle time) of ThisTimeSeries
|
|
if ~exist('FitWinLen','var') || isempty(FitWinLen)
|
|
if size(ThisTimeSeries,2)>1
|
|
for dim=1:size(ThisTimeSeries,2),
|
|
[Pd(:,dim),F] = pwelch(detrend(ThisTimeSeries(:,dim)),[],[],[],FS);
|
|
end
|
|
P = sum(Pd,2);
|
|
else
|
|
[P,F] = pwelch(detrend(ThisTimeSeries),[],[],[],FS);
|
|
end
|
|
MeanF = sum(P.*F)./sum(P);
|
|
CycleTime = 1/MeanF;
|
|
FitWinLen = round(CycleTime*FS);
|
|
else
|
|
CycleTime = FitWinLen/FS;
|
|
end
|
|
ExtraArgsOut.FitWinLen=FitWinLen;
|
|
|
|
%% Determine J
|
|
if ~exist('J','var') || isempty(J)
|
|
% Calculate mutual information and take first local minimum Tau as J
|
|
bV = min(40,floor(sqrt(size(ThisTimeSeries,1))));
|
|
tauVmax = FitWinLen;
|
|
[mutMPro,cummutMPro,minmuttauVPro] = MutualInformationHisPro(ThisTimeSeries,(0:tauVmax),bV,1); % (xV,tauV,bV,flag)
|
|
if isnan(minmuttauVPro)
|
|
display(mutMPro);
|
|
warning('minmuttauVPro is NaN. Consider increasing tauVmax.');
|
|
return;
|
|
end
|
|
J=minmuttauVPro;
|
|
end
|
|
ExtraArgsOut.J=J;
|
|
|
|
%% Determine m
|
|
if ~exist('m','var') || isempty(m)
|
|
escape = 10;
|
|
max_m = 20;
|
|
max_fnnM = 0.02;
|
|
mV = 0;
|
|
fnnM = 1;
|
|
for mV = 2:max_m % for m=1, FalseNearestNeighbors is slow and lets matlab close if N>500000
|
|
fnnM = FalseNearestNeighborsSR(ThisTimeSeries,J,mV,escape,FS); % (xV,tauV,mV,escape,theiler)
|
|
if fnnM <= max_fnnM || isnan(fnnM)
|
|
break
|
|
end
|
|
end
|
|
if fnnM <= max_fnnM
|
|
m = mV;
|
|
else
|
|
warning('Too many false nearest neighbours');
|
|
return;
|
|
end
|
|
end
|
|
ExtraArgsOut.m=m;
|
|
|
|
%% Create state space based upon J and m
|
|
N_ss = size(ThisTimeSeries,1)-(m-1)*J;
|
|
StateSpace=nan(N_ss,m*size(ThisTimeSeries,2));
|
|
for dim=1:size(ThisTimeSeries,2),
|
|
for delay=1:m,
|
|
StateSpace(:,(dim-1)*m+delay)=ThisTimeSeries((1:N_ss)'+(delay-1)*J,dim);
|
|
end
|
|
end
|
|
|
|
%% Parameters for Lyapunov
|
|
WindowLen = floor(min(N_ss/5,10*FitWinLen));
|
|
if WindowLen < FitWinLen
|
|
warning('Not enough samples for Lyapunov estimation');
|
|
return;
|
|
end
|
|
WindowLenSec=WindowLen/FS;
|
|
|
|
%% Calculate divergence
|
|
Divergence=div_calc(StateSpace,WindowLenSec,FS,CycleTime,0);
|
|
ExtraArgsOut.Divergence=Divergence;
|
|
|
|
%% Calculate slope of first FitWinLen samples of divergence curve
|
|
p = polyfit((1:FitWinLen)/FS,Divergence(1:FitWinLen),1);
|
|
L_Estimate = p(1);
|
|
|