Not enough input arguments.
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Hello, i am new in matlab and i am getting error on line 72.
```
runHeartBreathRateKraskov
TE for heart rate <-> breath rate for Kraskov estimation with 1201 samples:
Error using runHeartBreathRateKraskov (line 72)
Not enough input arguments.
```
This is my code
```
%%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% 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
%% (at your option) 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/>.
%%
% function [teHeartToBreath, teBreathToHeart] = runHeartBreathRateKraskov(kHistory, lHistory, knns, numSurrogates)
%
% runHeartBreathRateKraskov
% Version 1.0
% Joseph Lizier
% 22/1/2014
%
% Used to explore information transfer in the heart rate / breath rate example of Schreiber --
% but estimates TE using Kraskov-Stoegbauer-Grassberger estimation.
%
%
% Inputs
% - kHistory - destination embedding length, or "auto" for an auto-embedding (Ragwitz criteria, which takes a minute or two to run)
% - lHistory - source embedding length, or "auto" for an auto-embedding (Ragwitz criteria, which takes a minute or two to run)
% - knns - a scalar specifying a single, or vector specifying multiple, value of K nearest neighbours to evaluate TE (Kraskov) with.
% - numSurrogates - a scalar specifying the number of surrogates to evaluate TE from null distribution (which further multiplies the runtime)
% Outputs
% - teHeartToBreath - TE (heart -> breath) for each value of k nearest neighbours
% - teBreathToHeart - TE (breath -> heart) for each value of k nearest neighbours
function [teHeartToBreath, teBreathToHeart] = runHeartBreathRateKraskov(kHistory, lHistory, knns, numSurrogates)
tic;
% Add utilities to the path
addpath('..');
% Assumes the jar is three levels up - change this if this is not the case
% Octave is happy to have the path added multiple times; I'm unsure if this is true for matlab
javaaddpath('C:/Users/Humair/Desktop/Masters Thesis/Transfer Entropy/jidt-master/demos/python/infodynamics.jar');
if (nargin < 4)
numSurrogates = 0;
end
data = load('C:/Users/Humair/Desktop/Masters Thesis/Transfer Entropy/jidt-master/demos/python/SFI-heartRate_breathVol_bloodOx.txt');
% Restrict to the samples that Schreiber mentions:
data = data(2350:3550,:);
% Separate the data from each column:
heart = data(:,1);
chestVol = data(:,2);
bloodOx = data(:,3);
timeSteps = length(heart);
fprintf('TE for heart rate <-> breath rate for Kraskov estimation with %d samples:\n', timeSteps);
% Using a KSG estimator for TE is the least biased way to run this:
teCalc=javaObject('infodynamics.measures.continuous.kraskov.TransferEntropyCalculatorKraskov');
% Set up for any potential auto-embedding:
if (ischar(kHistory)) % Assume == 'auto'
% we're auto-embedding at least the destination:
if (ischar(lHistory)) % Assume == 'auto'
% we're auto embedding both source and destination
teCalc.setProperty(teCalc.PROP_AUTO_EMBED_METHOD, ...
teCalc.AUTO_EMBED_METHOD_RAGWITZ);
else
% we're auto embedding destination only
teCalc.setProperty(teCalc.PROP_AUTO_EMBED_METHOD, ...
teCalc.AUTO_EMBED_METHOD_RAGWITZ_DEST_ONLY);
end
teCalc.setProperty(teCalc.PROP_K_SEARCH_MAX, '10');
teCalc.setProperty(teCalc.PROP_TAU_SEARCH_MAX, '5');
end
for knnIndex = 1:length(knns)
knn = knns(knnIndex);
% Compute a TE value for knn nearest neighbours
% Perform calculation for heart -> breath (lag 1)
if (ischar(kHistory)) % Assume == 'auto'
% we're auto-embedding at least the destination:
teCalc.initialise();
else
% We're not auto embedding
teCalc.initialise(kHistory,1,lHistory,1,1);
end
teCalc.setProperty('k', sprintf('%d',knn));
teCalc.setObservations(octaveToJavaDoubleArray(heart), ...
octaveToJavaDoubleArray(chestVol));
teHeartToBreath(knnIndex) = teCalc.computeAverageLocalOfObservations();
% Grab the embedding parameters (in case of auto-embedding), converting from Java to native strings
kUsedHB = char(teCalc.getProperty(teCalc.K_PROP_NAME));
kTauUsedHB = char(teCalc.getProperty(teCalc.K_TAU_PROP_NAME));
lUsedHB = char(teCalc.getProperty(teCalc.L_PROP_NAME));
lTauUsedHB = char(teCalc.getProperty(teCalc.L_TAU_PROP_NAME));
% And compare to surrogates if required
if (numSurrogates > 0)
teHeartToBreathNullDist = teCalc.computeSignificance(numSurrogates);
teHeartToBreathNullMean = teHeartToBreathNullDist.getMeanOfDistribution();
teHeartToBreathNullStd = teHeartToBreathNullDist.getStdOfDistribution();
end
% Perform calculation for breath -> heart (lag 1)
if (ischar(kHistory)) % Assume == 'auto'
% we're auto-embedding at least the destination:
teCalc.initialise();
else
% We're not auto embedding
teCalc.initialise(kHistory,1,lHistory,1,1);
end
teCalc.setProperty('k', sprintf('%d',knn));
teCalc.setObservations(octaveToJavaDoubleArray(chestVol), ...
octaveToJavaDoubleArray(heart));
teBreathToHeart(knnIndex) = teCalc.computeAverageLocalOfObservations();
% Grab the embedding parameters (in case of auto-embedding)
kUsedBH = char(teCalc.getProperty(teCalc.K_PROP_NAME));
kTauUsedBH = char(teCalc.getProperty(teCalc.K_TAU_PROP_NAME));
lUsedBH = char(teCalc.getProperty(teCalc.L_PROP_NAME));
lTauUsedBH = char(teCalc.getProperty(teCalc.L_TAU_PROP_NAME));
% And compare to surrogates if required
if (numSurrogates > 0)
teBreathToHeartNullDist = teCalc.computeSignificance(numSurrogates);
teBreathToHeartNullMean = teBreathToHeartNullDist.getMeanOfDistribution();
teBreathToHeartNullStd = teBreathToHeartNullDist.getStdOfDistribution();
end
fprintf('TE(k=%s,kTau=%s,l=%s,lTau=%s,knn=%d): h->b = %.3f', kUsedHB, kTauUsedHB, lUsedHB, lTauUsedHB, knn, teHeartToBreath(knnIndex));
if (numSurrogates > 0)
fprintf(' (null = %.3f +/- %.3f)', teHeartToBreathNullMean, teHeartToBreathNullStd);
end
fprintf('; TE(k=%s,kTau=%s,l=%s,lTau=%s,knn=%d): b->h = %.3f nats', kUsedBH, kTauUsedBH, lUsedBH, lTauUsedBH, knn, teBreathToHeart(knnIndex));
if (numSurrogates > 0)
fprintf('(null = %.3f +/- %.3f)\n', teBreathToHeartNullMean, teBreathToHeartNullStd);
else
fprintf('\n');
end
end
tElapsed = toc;
fprintf('Total runtime was %.1f sec\n', tElapsed);
hold off;
plot(knns, teHeartToBreath, 'rx', 'markersize', 10);
hold on;
plot(knns, teBreathToHeart, 'mo', 'markersize', 10);
hold off;
legend(['TE(heart->breath)'; 'TE(breath->heart)']);
set (gca,'fontsize',26);
xlabel('K nearest neighbours', 'FontSize', 36, 'FontWeight', 'bold');
ylabel('TE', 'FontSize', 36, 'FontWeight', 'bold');
print('heartBreathResults-kraskovTE.eps', '-depsc');
end
```
采纳的回答
Walter Roberson
2020-4-16
You must pass at least 3 parameters when you invoke the function.
% - kHistory - destination embedding length, or "auto" for an auto-embedding (Ragwitz criteria, which takes a minute or two to run)
% - lHistory - source embedding length, or "auto" for an auto-embedding (Ragwitz criteria, which takes a minute or two to run)
% - knns - a scalar specifying a single, or vector specifying multiple, value of K nearest neighbours to evaluate TE (Kraskov) with.
% - numSurrogates - a scalar specifying the number of surrogates to evaluate TE from null distribution (which further multiplies the runtime)
MATLAB never ever looks inside the calling environment to find variables of the same name as named parameters.
2 个评论
Humair Ali
2020-4-17
Walter Roberson
2020-4-17
[H2B, B2H] = runHeartBreathRateKraskov( 'auto', 'auto', 4)
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