This is a "vectorized" version of lsqnonneg to speed-up solving multiple non-negative least square fits of independent data vectors to a common base of model vectors. The function is based on Matlab's lsqnonneg function. I adapted the code to allow for multiple (column) data vectors and vectorized everything I could to speed-up processing.
This was developped in the context of fitting pixel intensitiy (a positive value) of every pixel in a time series of fluorescence images (N voxels, T images) to reference fluorescence time-activity curves.
The speed-up factor will vary depending on the data size, but the syntax is more compact, as shown below.
% Example : 10000 vectors of 100 time points that need to be fitted with non-negative least squares to 5 model curves
nTimePts = 100;
nDataVect = 10000;
nModels = 5;
% Data matrix
d = rand(nTimePts, nDataVect);
% Reference (model) curves
C = rand(nTimePts, nModels);
% Non-negative least-square fit
X = lsqnonnegvect(C,d);
% Instead of :
% X = zeros(nModels,nDataVect);
% for k = 1:nDataVect
% X(:,k) = lsqnonneg(C,d(:,k));
% end
Cita come
David Provencher (2024). lsqnonnegvect.m (https://www.mathworks.com/matlabcentral/fileexchange/47476-lsqnonnegvect-m), MATLAB Central File Exchange. Recuperato .
Compatibilità della release di MATLAB
Compatibilità della piattaforma
Windows macOS LinuxCategorie
Tag
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Scopri Live Editor
Crea script con codice, output e testo formattato in un unico documento eseguibile.