Metadata-Version: 2.1
Name: mprod-package
Version: 0.0.2a1
Summary: Software implementation for tensor-tensor m-product framework
Home-page: https://github.com/UriaMorP/mprod_package
Author: Uria Mor, Rafael Valdes Mas, Yotam Cohen, Haim Avron
Author-email: uriamo@gmail.com
License: BSD
Description: # mprod_package
        
        [![Build and test [Python 3.6, 3.7, 3.8, 3.9]](https://github.com/UriaMorP/mprod_package/actions/workflows/build.yaml/badge.svg)](https://github.com/UriaMorP/mprod_package/actions/workflows/build.yaml)
        [![Documentation Status](https://readthedocs.org/projects/mprod-package/badge/?version=latest)](https://mprod-package.readthedocs.io/en/latest/?badge=latest)
        
        Software implementation for tensor-tensor m-product framework [[1]](#1).
        The library currently contains tubal QR and tSVDM decompositions, and the TCAM method for dimensionality reduction.
        
        
        <p align="center">
          <img width="80%",height="80%",  src="https://user-images.githubusercontent.com/16097812/143407367-36c30aa4-da1f-4a8b-93db-470114486064.png" />
        </p>
        
        ## Installation 
        
        ### using pip
        
        The package is available at pypi and can be installed via the command
        ```
        pip install mprod-package 
        ```
        
        
        ### from source 
        Make sure that all dependencies listed below are installed in a newly created conda environment, preferably - using the conda-forge channel.
        
        We stated the exact versions used to locally test the code, more recent versions of these packages should work as well.
        
        Dependencies:
        * python                    3.6.8
        * scipy                     1.5.3
        * scikit-learn              0.24.1
        * numpy                     1.19.2
        * dataclasses               0.7   (Only for python version < 3.7)
        * pip                       21.0.1
        
        
        Clone the repository, then from the package directory, run
        ```
        pip install -e .
        ```
        
        
        
        ## References
        <a id="1">[1]</a> 
        Misha E. Kilmer, Lior Horesh, Haim Avron, and Elizabeth Newman.  Tensor-tensor algebra for optimal representation and compression of multiway data. Proceedings of the National Academy of Sciences, 118(28):e2015851118, jul 2021.
        
Keywords: Tensor,multi way,omics,longitudinal,factorization,analysis,TCA,TCAM,PCA,M product,tensor tensor product,tSVD,tSVDM,tensor decomposition
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.6.8
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: docs
