decompy 0.1.0

A python package containing several robust algorithms for matrix decomposition and analysis.

decompy

decompy is a Python package containing several robust algorithms for matrix decomposition and analysis. The types of algorithms includes

• Robust PCA or SVD based methods
• Matrix completion methods
• Robust matrix or tensor factorization methods.

Features

• Data decomposition using various methods
• Support for sparse decomposition, low-rank approximation, and more
• User-friendly API for easy integration into your projects
• Extensive documentation and examples

Installation

You can install decompy using pip:

pip install decompy

Usage

Here's a simple example demonstrating how to use decompy for data decomposition:

import numpy as np
import decompy

# Load your data
data = np.arange(100).reshape(20,5)

# Perform data decomposition
algo = decompy.robust_svd.DensityPowerDivergence(alpha = 0.5)
result = algo.decompose(data, method='sparse')

# Access the decomposed components
U, V = result.get_singular_vectors(type = "both")
S = result.get_singular_values()
low_rank_component = U @ S @ V.T
sparse_component = data - low_rank_component

You can find more example notebooks in examples folder. For more detailed usage instructions, please refer to the documentation.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please create an issue or submit a pull request on the GitHub repository. For contributing developers, please refer to Contributing.md file.

License

This project is licensed under the BSD 3-Clause License.