dyson-equalizer

Name	dyson-equalizer JSON
Version	0.1.6 JSON
	download
home_page	None
Summary	Computes the Dyson Equalizer and related low rank approximation of the input data
upload_time	2024-10-25 11:35:11
maintainer	None
docs_url	None
author	None
requires_python	>=3.10
license	None
keywords	dyson equalizer low rank approximation
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # 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
](https://doi.org/10.48550/arXiv.2306.11263)

The documentation is available at [https://klugerlab.github.io/DysonEqualizer](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

```python
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](https://klugerlab.github.io/DysonEqualizer/examples.html) 
page.

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    "description": "# Dyson Equalizer #\n\nThis package is a Python implementation of the Dyson Equalizer. \nThe method is described in detail in the article [The Dyson Equalizer: Adaptive Noise Stabilization for Low-Rank Signal Detection and Recovery\n](https://doi.org/10.48550/arXiv.2306.11263)\n\nThe documentation is available at [https://klugerlab.github.io/DysonEqualizer](https://klugerlab.github.io/DysonEqualizer).\n\n## Installation ##\nThe main version of the package can be installed as \n```\npip install dyson-equalizer\n```\n\nThe development version of the package can be installed as \n```\npip install git+https://github.com/Klugerlab/DysonEqualizer.git\n```\n\n## Getting started ##\n\nTo import the package and apply the Dyson Equalizer to a test matrix\n\n```python\nfrom dyson_equalizer.examples import generate_Y_with_heteroskedastic_noise\nfrom dyson_equalizer.dyson_equalizer import DysonEqualizer\n\nY = generate_Y_with_heteroskedastic_noise()\nde = DysonEqualizer(Y).compute()\n\n```\n\nThe `DysonEqualizer` result class will contain the following attributes\n- `Y`: The original data matrix\n- `x_hat`: The normalizing factors for the rows\n- `y_hat`: The normalizing factors for the columns\n- `Y_hat`: The normalized data matrix so that the variance of the error is 1\n- `X_bar`: The estimated signal matrix. It has rank `r_hat`\n- `r_hat`:  The estimated rank of the signal matrix\n- `S`: The principal values of the data matrix `Y`\n- `S_hat`:  The principal values of the data matrix `Y_hat`\n\nDetailed examples are available on the [Examples](https://klugerlab.github.io/DysonEqualizer/examples.html) \npage.\n\n",
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