BRINE
This package is a Python implementation of BRINE (Blind Resolvent-based Iterative Noise Equalizer), a data-driven procedure that iteratively rescales the rows and columns of the observed matrix to equalize the average noise variance across both dimensions.
The method builds on the Dyson Equalizer, described in detail in the article The Dyson Equalizer: Adaptive Noise Stabilization for Low-Rank Signal Detection and Recovery .
The documentation is available at https://klugerlab.github.io/BRINE.
Installation
The main version of the package can be installed as
pip install brine-equalizer
The development version of the package can be installed as
pip install git+https://github.com/Klugerlab/BRINE.git
Getting started
To import the package and apply BRINE to a test matrix
from brine.examples import generate_Y_with_heteroskedastic_noise
from brine.brine import BRINE
Y = generate_Y_with_heteroskedastic_noise()
brine = BRINE(Y).run()
The BRINE result class will contain the following attributes
Y: The original data matrixx_scale: The row scaling factorsy_scale: The column scaling factorsY_hat: The normalized data matrix so that the variance of the error is 1X_bar: The estimated signal matrix. It has rankr_hatr_hat: The estimated rank of the signal matrixS: The principal values of the data matrixYS_hat: The principal values of the data matrixY_hat
The original, non-iterative DysonEqualizer is still available (brine.dyson_equalizer.DysonEqualizer)
for one-shot use or comparison against BRINE.
Detailed examples are available on the Examples page.
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