Adding loop for a code

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Hatem Akeel 2020-9-26

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/600511-adding-loop-for-a-code

评论： Hatem Akeel 2020-10-5

I'm using the code from the link below

https://www.mathworks.com/matlabcentral/fileexchange/69381-sample-entropy

I need your help in modifing the code in the above link. I want to modify the code to add a loop for it. For example I have 2000 obervation and I need it to calcualte entropy for the first 120 obervation. Then it will take the next 120 obervation by leaving the first observation (1-lag). The output will be a vector of sample entropy.

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Walter Roberson 2020-9-27

Then it will take the next 120 obervation by leaving the first observation (1-lag)

I am not sure that I understand that ? Is lag fixed? Is lag something you would calculate by using a function such as https://www.mathworks.com/help/signal/ref/finddelay.html ?

Do I understand correctly that the first iteration, you would submit the first 120 observations (total = 120), calculate entropy, then each iteration after that, you would add in an artificial first sample that had value (1-lag) together with the next 120 observations, for a total of 121 observations going into the entropy calculation?

Hatem Akeel 2020-9-27

Thanks for your prompt response !

lets say you have the folllowing observatiion ( 10,20,30,40,... 20,000) which are 2000 obsevations. The first iteration or calcuation will be from 10 until 1200. 2nd iteration or calcuation will be from 20 until 1210. 3rd will be from 30 until 1220 and so on and so forth. I need to make this loop of calcuation in the code.

I hope that will explain it.

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Answer 1

Vimal Rathod 2020-9-30

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/600511-adding-loop-for-a-code#answer_503146

在 MATLAB Online 中打开

Hi,

For making a bunch of iterations on the observation array you could directly loop through each element and call the "SampEn" function inside the loop and store the values in an array.

n = 2000; % number of observations
l = 120; % length of single set
observations = rand(1,n); % Consider this as observations
entropy = zeros(1,(n-l+1)); % initializing the entropy vector
for i = 1:(n-l+1)
    % just included temporary values in the function.
    entropy(i) = SampEn(observations(i:i+l-1),1,0.2); 
end

Hope this helps.

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Hatem Akeel 2020-9-30

在 MATLAB Online 中打开

Thank you for your reply. I tried the code as follow:

% ----------------------------------------------------------------------- %
%                           H    Y    D    R    A                         %
% ----------------------------------------------------------------------- %
% Function 'sampen' computes the Sample Entropy of a given signal.        %
%                                                                         %
%   Input parameters:                                                     %
%       - signal:       Signal vector with dims. [1xN]                    %
%       - m:            Embedding dimension (m < N).                      %
%       - r:            Tolerance (percentage applied to the SD).         %
%       - dist_type:    (Optional) Distance type, specified by a string.  %
%                       Default value: 'chebychev' (type help pdist for   %
%                       further information).                             %
%                                                                         %
%   Output variables:                                                     %
%       - value:        SampEn value. Since SampEn is not defined whenever%
%                       B = 0, the output value in that case is NaN.      %
% ----------------------------------------------------------------------- %
%   Versions:                                                             %
%       - 1.0:          (21/09/2018) Original script.                     %
%       - 1.1:          (09/11/2018) Upper bound is added. Now, SampEn is %
%                       not able to return Inf values.                    %
% ----------------------------------------------------------------------- %
%   Script information:                                                   %
%       - Version:      1.0.                                              %
%       - Author:       V. Martínez-Cagigal                               %
%       - Date:         21/09/2018                                        %
% ----------------------------------------------------------------------- %
%   References:                                                           %
%       [1]     Richman, J. S., & Moorman, J. R. (2000). Physiological    %
%               time-series analysis using approximate entropy and sample %
%               entropy. American Journal of Physiology-Heart and         %
%               Circulatory Physiology, 278(6), H2039-H2049.              %
% ----------------------------------------------------------------------- %
function value = sampen(signal, m, r, dist_type)
    % Error detection and defaults
    if nargin < 3, error('Not enough parameters.'); end
    if nargin < 4
        dist_type = 'chebychev';
        fprintf('[WARNING] Using default distance method: chebychev.\n');
    end
    if ~isvector(signal)
        error('The signal parameter must be a vector.');
    end
    if ~ischar(dist_type)
        error('Distance must be a string.');
    end
    if m > length(signal)
        error('Embedding dimension must be smaller than the signal length (m<N).');
    end
    
    % Useful parameters
    signal = signal(:)';
    N = length(signal);     % Signal length
    sigma = std(signal);    % Standard deviation
    
    % Create the matrix of matches
    matches = NaN(m+1,N);
    for i = 1:1:m+1
        matches(i,1:N+1-i) = signal(i:end);
    end
    matches = matches';
    % Check the matches for m
    d_m = pdist(matches(:,1:m), dist_type);
    if isempty(d_m)
        % If B = 0, SampEn is not defined: no regularity detected
        %   Note: Upper bound is returned
        value = Inf;
    else
        % Check the matches for m+1
        d_m1 = pdist(matches(:,1:m+1), dist_type);
        
        % Compute A and B
        %   Note: logical operations over NaN values are always 0
        B = sum(d_m  <= r*sigma);
        A = sum(d_m1 <= r*sigma);
        % Sample entropy value
        %   Note: norm. comes from [nchoosek(N-m+1,2)/nchoosek(N-m,2)]
        value = -log((A/B)*((N-m+1)/(N-m-1))); 
    end
    
    % If A=0 or B=0, SampEn would return an infinite value. However, the
    % lowest non-zero conditional probability that SampEn should
    % report is A/B = 2/[(N-m-1)(N-m)]
    if isinf(value)
        % Note: SampEn has the following limits:
        %       - Lower bound: 0
        %       - Upper bound: log(N-m)+log(N-m-1)-log(2)
        value = -log(2/((N-m-1)*(N-m)));
    end
    n = 2000; % number of observations
    l=120; % length of single set
    observations = rand(1,n); % Consider this as observations
    entropy = zeros(1,(n-l+1)); % initializing the entropy vector
for i = 1:(n-l+1)
    % just included temporary values in the function.
    entropy(i) = SampEn(observations(i:i+l-1),1,0.2); 
end
end

I got the following error:

unrecognized function or variable 'l'.

Walter Roberson 2020-10-1

在 MATLAB Online 中打开

function test_sampen
    n = 2000; % number of observations
    l = 120; % length of single set
    observations = rand(1,n); % Consider this as observations
    entropy = zeros(1,(n-l+1)); % initializing the entropy vector
    for i = 1:(n-l+1)
        % just included temporary values in the function.
        entropy(i) = sampen(observations(i:i+l-1), 1, 0.2, 'chebychev'); 
    end
    
    save('NameOfOutputFile.mat', 'entropy')
end
% ----------------------------------------------------------------------- %
%                           H    Y    D    R    A                         %
% ----------------------------------------------------------------------- %
% Function 'sampen' computes the Sample Entropy of a given signal.        %
%                                                                         %
%   Input parameters:                                                     %
%       - signal:       Signal vector with dims. [1xN]                    %
%       - m:            Embedding dimension (m < N).                      %
%       - r:            Tolerance (percentage applied to the SD).         %
%       - dist_type:    (Optional) Distance type, specified by a string.  %
%                       Default value: 'chebychev' (type help pdist for   %
%                       further information).                             %
%                                                                         %
%   Output variables:                                                     %
%       - value:        SampEn value. Since SampEn is not defined whenever%
%                       B = 0, the output value in that case is NaN.      %
% ----------------------------------------------------------------------- %
%   Versions:                                                             %
%       - 1.0:          (21/09/2018) Original script.                     %
%       - 1.1:          (09/11/2018) Upper bound is added. Now, SampEn is %
%                       not able to return Inf values.                    %
% ----------------------------------------------------------------------- %
%   Script information:                                                   %
%       - Version:      1.0.                                              %
%       - Author:       V. Martínez-Cagigal                               %
%       - Date:         21/09/2018                                        %
% ----------------------------------------------------------------------- %
%   References:                                                           %
%       [1]     Richman, J. S., & Moorman, J. R. (2000). Physiological    %
%               time-series analysis using approximate entropy and sample %
%               entropy. American Journal of Physiology-Heart and         %
%               Circulatory Physiology, 278(6), H2039-H2049.              %
% ----------------------------------------------------------------------- %
function value = sampen(signal, m, r, dist_type)
    % Error detection and defaults
    if nargin < 3, error('Not enough parameters.'); end
    if nargin < 4
        dist_type = 'chebychev';
        fprintf('[WARNING] Using default distance method: chebychev.\n');
    end
    if ~isvector(signal)
        error('The signal parameter must be a vector.');
    end
    if ~ischar(dist_type)
        error('Distance must be a string.');
    end
    if m > length(signal)
        error('Embedding dimension must be smaller than the signal length (m<N).');
    end
    
    % Useful parameters
    signal = signal(:)';
    N = length(signal);     % Signal length
    sigma = std(signal);    % Standard deviation
    
    % Create the matrix of matches
    matches = NaN(m+1,N);
    for i = 1:1:m+1
        matches(i,1:N+1-i) = signal(i:end);
    end
    matches = matches';
    % Check the matches for m
    d_m = pdist(matches(:,1:m), dist_type);
    if isempty(d_m)
        % If B = 0, SampEn is not defined: no regularity detected
        %   Note: Upper bound is returned
        value = Inf;
    else
        % Check the matches for m+1
        d_m1 = pdist(matches(:,1:m+1), dist_type);
        
        % Compute A and B
        %   Note: logical operations over NaN values are always 0
        B = sum(d_m  <= r*sigma);
        A = sum(d_m1 <= r*sigma);
        % Sample entropy value
        %   Note: norm. comes from [nchoosek(N-m+1,2)/nchoosek(N-m,2)]
        value = -log((A/B)*((N-m+1)/(N-m-1))); 
    end
    
    % If A=0 or B=0, SampEn would return an infinite value. However, the
    % lowest non-zero conditional probability that SampEn should
    % report is A/B = 2/[(N-m-1)(N-m)]
    if isinf(value)
        % Note: SampEn has the following limits:
        %       - Lower bound: 0
        %       - Upper bound: log(N-m)+log(N-m-1)-log(2)
        value = -log(2/((N-m-1)*(N-m)));
    end
end

Hatem Akeel 2020-10-1

在 MATLAB Online 中打开

I really appreciate your help. My code right now is as below and it is just for 448 variables not 2000. I have m =2 and r =0.2. I need to incorporate memory mapping or memmapfile so that it will not crash and save the file in the end. How can I do that?

% ----------------------------------------------------------------------- %
%                           H    Y    D    R    A                         %
% ----------------------------------------------------------------------- %
% Function 'sampen' computes the Sample Entropy of a given signal.        %
%                                                                         %
%   Input parameters:                                                     %
%       - signal:       Signal vector with dims. [1xN]                    %
%       - m:            Embedding dimension (m < N).                      %
%       - r:            Tolerance (percentage applied to the SD).         %
%       - dist_type:    (Optional) Distance type, specified by a string.  %
%                       Default value: 'chebychev' (type help pdist for   %
%                       further information).                             %
%                                                                         %
%   Output variables:                                                     %
%       - value:        SampEn value. Since SampEn is not defined whenever%
%                       B = 0, the output value in that case is NaN.      %
% ----------------------------------------------------------------------- %
%   Versions:                                                             %
%       - 1.0:          (21/09/2018) Original script.                     %
%       - 1.1:          (09/11/2018) Upper bound is added. Now, SampEn is %
%                       not able to return Inf values.                    %
% ----------------------------------------------------------------------- %
%   Script information:                                                   %
%       - Version:      1.0.                                              %
%       - Author:       V. MartÃ­nez-Cagigal                               %
%       - Date:         21/09/2018                                        %
% ----------------------------------------------------------------------- %
%   References:                                                           %
%       [1]     Richman, J. S., & Moorman, J. R. (2000). Physiological    %
%               time-series analysis using approximate entropy and sample %
%               entropy. American Journal of Physiology-Heart and         %
%               Circulatory Physiology, 278(6), H2039-H2049.              %
% ----------------------------------------------------------------------- %
function value = sampen(signal, m, r, dist_type)
 
    % Error detection and defaults
    if nargin < 3, error('Not enough parameters.'); end
    if nargin < 4
        dist_type = 'chebychev';
        fprintf('[WARNING] Using default distance method: chebychev.\n');
    end
    if ~isvector(signal)
        error('The signal parameter must be a vector.');
    end
    if ~ischar(dist_type)
        error('Distance must be a string.');
    end
    if m > length(signal)
        error('Embedding dimension must be smaller than the signal length (m<N).');
    end
    
    % Useful parameters
    signal = signal(:)';
    N = length(signal);     % Signal length
    sigma = std(signal);    % Standard deviation
    
    % Create the matrix of matches
    matches = NaN(m+1,N);
    for i = 1:1:m+1
        matches(i,1:N+1-i) = signal(i:end);
    end
    matches = matches';
 
    % Check the matches for m
    d_m = pdist(matches(:,1:m), dist_type);
    if isempty(d_m)
        % If B = 0, SampEn is not defined: no regularity detected
        %   Note: Upper bound is returned
        value = Inf;
    else
        % Check the matches for m+1
        d_m1 = pdist(matches(:,1:m+1), dist_type);
        
        % Compute A and B
        %   Note: logical operations over NaN values are always 0
        B = sum(d_m  <= r*sigma);
        A = sum(d_m1 <= r*sigma);
 
        % Sample entropy value
        %   Note: norm. comes from [nchoosek(N-m+1,2)/nchoosek(N-m,2)]
        value = -log((A/B)*((N-m+1)/(N-m-1))); 
    end
    
    % If A=0 or B=0, SampEn would return an infinite value. However, the
    % lowest non-zero conditional probability that SampEn should
    % report is A/B = 2/[(N-m-1)(N-m)]
    if isinf(value)
        % Note: SampEn has the following limits:
        %       - Lower bound: 0
        %       - Upper bound: log(N-m)+log(N-m-1)-log(2)
        value = -log(2/((N-m-1)*(N-m)));
    end
    n = 448; % number of observations
l = 120; % length of single set
observations = rand(1,n); % Consider this as observations
entropy = zeros(1,(n-l+1)); % initializing the entropy vector
for i = 1:(n-l+1)
    % just included temporary values in the function.
    entropy(i) = sampen(observations(i:i+l-1),1,0.2); 
    save('NameOfOutputFile.mat', 'entropy')
end
end

Hatem Akeel 2020-10-2

编辑：Hatem Akeel 2020-10-2

I got the following error

Error: File: sampen.m Line: 46 Column: 1

Function 'sampen' has already been declared within this scope.

function test_sampen

n = 448; % number of observations

l = 120; % length of single set

observations = rand(1,n); % Consider this as observations

entropy = zeros(1,(n-l+1)); % initializing the entropy vector

for i = 1:(n-l+1)

% just included temporary values in the function.

entropy(i) = sampen(observations(i:i+l-1), 1, 0.2, 'chebychev');

end

save('NameOfOutputFile.mat', 'entropy')

end

% ----------------------------------------------------------------------- %

% H Y D R A %

% ----------------------------------------------------------------------- %

% Function 'sampen' computes the Sample Entropy of a given signal. %

% %

% Input parameters: %

% - signal: Signal vector with dims. [1xN] %

% - m: Embedding dimension (m < N). %

% - r: Tolerance (percentage applied to the SD). %

% - dist_type: (Optional) Distance type, specified by a string. %

% Default value: 'chebychev' (type help pdist for %

% further information). %

% %

% Output variables: %

% - value: SampEn value. Since SampEn is not defined whenever%

% B = 0, the output value in that case is NaN. %

% ----------------------------------------------------------------------- %

% Versions: %

% - 1.0: (21/09/2018) Original script. %

% - 1.1: (09/11/2018) Upper bound is added. Now, SampEn is %

% not able to return Inf values. %

% ----------------------------------------------------------------------- %

% Script information: %

% - Version: 1.0. %

% - Author: V. Martínez-Cagigal %

% - Date: 21/09/2018 %

% ----------------------------------------------------------------------- %

% References: %

% [1] Richman, J. S., & Moorman, J. R. (2000). Physiological %

% time-series analysis using approximate entropy and sample %

% entropy. American Journal of Physiology-Heart and %

% Circulatory Physiology, 278(6), H2039-H2049. %

% ----------------------------------------------------------------------- %

function value = sampen(signal, m, r, dist_type)

% Error detection and defaults

if nargin < 3, error('Not enough parameters.'); end

if nargin < 4

dist_type = 'chebychev';

fprintf('[WARNING] Using default distance method: chebychev.\n');

end

if ~isvector(signal)

error('The signal parameter must be a vector.');

end

if ~ischar(dist_type)

error('Distance must be a string.');

end

if m > length(signal)

error('Embedding dimension must be smaller than the signal length (m<N).');

end

% Useful parameters

signal = signal(:)';

N = length(signal); % Signal length

sigma = std(signal); % Standard deviation

% Create the matrix of matches

matches = NaN(m+1,N);

for i = 1:1:m+1

matches(i,1:N+1-i) = signal(i:end);

end

matches = matches';

% Check the matches for m

d_m = pdist(matches(:,1:m), dist_type);

if isempty(d_m)

% If B = 0, SampEn is not defined: no regularity detected

% Note: Upper bound is returned

value = Inf;

else

% Check the matches for m+1

d_m1 = pdist(matches(:,1:m+1), dist_type);

% Compute A and B

% Note: logical operations over NaN values are always 0

B = sum(d_m <= r*sigma);

A = sum(d_m1 <= r*sigma);

% Sample entropy value

% Note: norm. comes from [nchoosek(N-m+1,2)/nchoosek(N-m,2)]

value = -log((A/B)*((N-m+1)/(N-m-1)));

end

% If A=0 or B=0, SampEn would return an infinite value. However, the

% lowest non-zero conditional probability that SampEn should

% report is A/B = 2/[(N-m-1)(N-m)]

if isinf(value)

% Note: SampEn has the following limits:

% - Lower bound: 0

% - Upper bound: log(N-m)+log(N-m-1)-log(2)

value = -log(2/((N-m-1)*(N-m)));

end

Walter Roberson 2020-10-2

在 MATLAB Online 中打开

Save the following code to sampen.m

% ----------------------------------------------------------------------- %
%                           H    Y    D    R    A                         %
% ----------------------------------------------------------------------- %
% Function 'sampen' computes the Sample Entropy of a given signal.        %
%                                                                         %
%   Input parameters:                                                     %
%       - signal:       Signal vector with dims. [1xN]                    %
%       - m:            Embedding dimension (m < N).                      %
%       - r:            Tolerance (percentage applied to the SD).         %
%       - dist_type:    (Optional) Distance type, specified by a string.  %
%                       Default value: 'chebychev' (type help pdist for   %
%                       further information).                             %
%                                                                         %
%   Output variables:                                                     %
%       - value:        SampEn value. Since SampEn is not defined whenever%
%                       B = 0, the output value in that case is NaN.      %
% ----------------------------------------------------------------------- %
%   Versions:                                                             %
%       - 1.0:          (21/09/2018) Original script.                     %
%       - 1.1:          (09/11/2018) Upper bound is added. Now, SampEn is %
%                       not able to return Inf values.                    %
% ----------------------------------------------------------------------- %
%   Script information:                                                   %
%       - Version:      1.0.                                              %
%       - Author:       V. Martínez-Cagigal                               %
%       - Date:         21/09/2018                                        %
% ----------------------------------------------------------------------- %
%   References:                                                           %
%       [1]     Richman, J. S., & Moorman, J. R. (2000). Physiological    %
%               time-series analysis using approximate entropy and sample %
%               entropy. American Journal of Physiology-Heart and         %
%               Circulatory Physiology, 278(6), H2039-H2049.              %
% ----------------------------------------------------------------------- %
function value = sampen(signal, m, r, dist_type)
    % Error detection and defaults
    if nargin < 3, error('Not enough parameters.'); end
    if nargin < 4
        dist_type = 'chebychev';
        fprintf('[WARNING] Using default distance method: chebychev.\n');
    end
    if ~isvector(signal)
        error('The signal parameter must be a vector.');
    end
    if ~ischar(dist_type)
        error('Distance must be a string.');
    end
    if m > length(signal)
        error('Embedding dimension must be smaller than the signal length (m<N).');
    end
    
    % Useful parameters
    signal = signal(:)';
    N = length(signal);     % Signal length
    sigma = std(signal);    % Standard deviation
    
    % Create the matrix of matches
    matches = NaN(m+1,N);
    for i = 1:1:m+1
        matches(i,1:N+1-i) = signal(i:end);
    end
    matches = matches';
    % Check the matches for m
    d_m = pdist(matches(:,1:m), dist_type);
    if isempty(d_m)
        % If B = 0, SampEn is not defined: no regularity detected
        %   Note: Upper bound is returned
        value = Inf;
    else
        % Check the matches for m+1
        d_m1 = pdist(matches(:,1:m+1), dist_type);
        
        % Compute A and B
        %   Note: logical operations over NaN values are always 0
        B = sum(d_m  <= r*sigma);
        A = sum(d_m1 <= r*sigma);
        % Sample entropy value
        %   Note: norm. comes from [nchoosek(N-m+1,2)/nchoosek(N-m,2)]
        value = -log((A/B)*((N-m+1)/(N-m-1))); 
    end
    
    % If A=0 or B=0, SampEn would return an infinite value. However, the
    % lowest non-zero conditional probability that SampEn should
    % report is A/B = 2/[(N-m-1)(N-m)]
    if isinf(value)
        % Note: SampEn has the following limits:
        %       - Lower bound: 0
        %       - Upper bound: log(N-m)+log(N-m-1)-log(2)
        value = -log(2/((N-m-1)*(N-m)));
    end
end