dyson-equalizer 0.1.5

Computes the Dyson Equalizer and related low rank approximation of the input data

Dyson Equalizer

This package is a Python implementation of the Dyson Equalizer. The method is 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/DysonEqualizer.

Installation

The main version of the package can be installed as

pip install dyson-equalizer

The development version of the package can be installed as

pip install git+https://github.com/Klugerlab/DysonEqualizer.git

Getting started

To import the package and apply the Dyson Equalizer to a test matrix

from dyson_equalizer.examples import generate_Y_with_heteroskedastic_noise
from dyson_equalizer.dyson_equalizer import DysonEqualizer

Y = generate_Y_with_heteroskedastic_noise()
de = DysonEqualizer(Y).compute()

The DysonEqualizer result class will contain the following attributes

• Y: The original data matrix
• x_hat: The normalizing factors for the rows
• y_hat: The normalizing factors for the columns
• Y_hat: The normalized data matrix so that the variance of the error is 1
• X_bar: The estimated signal matrix. It has rank r_hat
• r_hat: The estimated rank of the signal matrix
• S: The principal values of the data matrix Y
• S_hat: The principal values of the data matrix Y_hat

Detailed examples are available on the Examples page.