metbit.base

Other module in metbit 6.0.1.

import metbit.base

Functions

nipals(x: np.ndarray, y: np.ndarray, tol: float=1e-10, max_iter: int=1000, dot=np.dot)

Non-linear Iterative Partial Least Squares

Parameters

xnp.ndarray

Variable matrix with size n by p, where n number of samples, p number of variables.

ynp.ndarray

Dependent variable with size n by 1.

tolfloat

Tolerance for the convergence.

max_iterint

Maximal number of iterations.

Returns

wnp.ndarray

Weights with size p by 1.

unp.ndarray

Y-scores with size n by 1.

cfloat

Y-weight

tnp.ndarray

Scores with size n by 1

References

[1] Wold S, et al. PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab Sys 2001, 58, 109–130. [2] Bylesjo M, et al. Model Based Preprocessing and Background

EliminationOSC, OPLS, and O2PLS. in Comprehensive Chemometrics.

Source

metbit/base.py at v6.0.1