近似熵/模糊熵/样本熵MATLAB代码

function [apen] = approx_entropy_my(n,r,a) %% Code for computing approximate entropy for a time series: Approximate % Entropy is a measure of complexity. It quantifies the unpredictability of % fluctuations in a time series % To run this function- type: approx_entropy('window length','similarity measure','data set') % i.e approx_entropy(5,0.5,a) % window length= length of the window, which should be considered in each iteration % similarity measure = measure of distance between the elements % data set = data vector % small values of apen (approx entropy) means data is predictable, whereas % higher values mean that data is unpredictable % concept boorowed from http://www.physionet.org/physiotools/ApEn/ % Author: Avinash Parnandi, [email protected], http://robotics.usc.edu/~parnandi/ data =a; for m=n:n+1; % run it twice, with window size differing by 1 set = 0; count = 0; counter = 0; window_correlation = zeros(1,(length(data)-m+1)); for i=1:(length(data))-m+1, current_window = data(i:i+m-1); % current window stores the sequence to be compared with other sequences %% 计算两个之间的差超过多少的个数 for j=1:length(data)-m+1, sliding_window = data(j:j+m-1); % get a window for comparision with the current_window % compare two windows, element by element % can also use some kind of norm measure; that will perform better for k=1:m, if((abs(current_window(k)-sliding_window(k))>r) && set == 0) set = 1; % i.e. the difference between the two sequence is greater than the given value 只要有两个的距离大于r就置为1 end end%k=1:m, if(set==0) count = count+1; % this measures how many sliding_windows are similar to the current_window end set = 0; % reseting 'set' end%j=1:length(data)-m+1, end结束后得到所有落在模板i的r内的个数 counter(i)=log(count/(length(data)-m+1)); % we need the number of similar windows for every cuurent_window count=0; i; end % for i=1:(length(data))-m+1, ends here counter; % this tells how many similar windows are present for each window of length m %total_similar_windows = sum(counter); %window_correlation = counter/(length(data)-m+1); correlation(m-n+1) = ((sum(counter))/(length(data)-m+1)); end % for m=n:n+1; % run it twice correlation(1); correlation(2); apen = correlation(1)-correlation(2); % apen = log(correlation(1)/correlation(2));