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License: GNU General Public License v3.0
Python library for chemometric data analysis
License: GNU General Public License v3.0
Allows for drop-in replacement if the function syntax and the return arguments of asym_ls
and numpy.linalg.lstsq
are the same. Furthermore, some speedup may be possible by reusing the residuals as often those are interesting with asymmetric least-squares regression.
whittacker
currently relies on the cojugate-gradient method for solving the linear system. scipy.sparse.linalg
provides methods for prefactorizing the relevant sparse matrix (LU-factorization) which can further speed up the calculations if whittacker is applied to many samples simultanously.
base.fit_base
is a function for selecting the optimal number of latent variables in a latent variable model based on a . Currently, base.fit_base
is however only used during the call to regression.fit_pls
. To reduce code dublication, decomposition.fit_pca
should be adjusted to use base.fit_base
.
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