Metadata-Version: 1.2
Name: scikit-tensor-py3
Version: 0.2.1
Summary: Python module for multilinear algebra and tensor factorizations
Home-page: http://github.com/evertrol/scikit-tensor-py3
Maintainer: Evert Rol
Maintainer-email: evert.rol@gmail.com
License: GPLv3
Download-URL: http://github.com/evertrol/scikit-tensor-py3
Description: # scikit-tensor
        ![Travis CI](https://travis-ci.org/evertrol/scikit-tensor-py3.svg?branch=master)
        
        scikit-tensor is a Python module for multilinear algebra and tensor
        factorizations. Currently, scikit-tensor supports basic tensor operations
        such as folding/unfolding, tensor-matrix and tensor-vector products as
        well as the following tensor factorizations:
        
        * Canonical / Parafac Decomposition
        * Tucker Decomposition
        * RESCAL
        * DEDICOM
        * INDSCAL
        
        Moreover, all operations support dense and tensors.
        
        ## Note
        
        This is a Python 3 only compatible maintenance release. It appears the
        development for scikit-tensor has stalled, and the project has been
        abondoned. This fork only supports Python 3.4 and later, and is
        available on PyPI as `scikit-tensor-py3`, for easier installation.
        
        Issues and pull requests are welcomed, but issues relating algorithms and requests for additional algorithms may be postponed or ignored altogether. Technical (code) issues are welcomed.
        
        ## Dependencies
        The required dependencies to build the software are `Numpy` and `SciPy`.
        
        ## Usage
        Example script to decompose sensory bread data (available from http://www.models.life.ku.dk/datasets) using CP-ALS
        
        ```python
        import logging
        from scipy.io.matlab import loadmat
        from sktensor import dtensor, cp_als
        
        # Set logging to DEBUG to see CP-ALS information
        logging.basicConfig(level=logging.DEBUG)
        
        # Load Matlab data and convert it to dense tensor format
        mat = loadmat('../data/sensory-bread/brod.mat')
        T = dtensor(mat['X'])
        
        # Decompose tensor using CP-ALS
        P, fit, itr, exectimes = cp_als(T, 3, init='random')
        ```
        
        ## Installation
        
        This package uses distutils, which is the default way of installing python modules. The use of virtual environments is recommended.
        
            pip install scikit-tensor-py3
        
        To install in development mode
        
            git clone https://github.com/evertrol/scikit-tensor-py3.git
            pip install -e scikit-tensor
        
        ## Contributing & Development
        
        scikit-tensor is still an extremely young project, and I'm happy for any contributions (patches, code, bugfixes, *documentation*, whatever) to get it to a stable and useful point. Feel free to get in touch with me via email (mnick at AT mit DOT edu) or directly via github. See also the note above.
        
        Development is synchronized via git. Feel free to fork this project and make pull requests from that fork.
        
        ## Authors
        
        - Maximilian Nickel: [Web](http://web.mit.edu/~mnick/www), [Email](mailto://mnick AT mit DOT edu), [Twitter](http://twitter.com/mnick)
        - Evert Rol (maintenance for Python 3 version): [Email](mailto:evert.rol@gmail.com)
        
        ## License
        
        scikit-tensor-py3 is licensed under the [GPLv3](http://www.gnu.org/licenses/gpl-3.0.txt)
        
        ## Related Projects
        
        * [Matlab Tensor Toolbox](http://www.sandia.gov/~tgkolda/TensorToolbox/index-2.5.html):
          A Matlab toolbox for tensor factorizations and tensor operations freely available for research and evaluation.
        * [Matlab Tensorlab](http://www.tensorlab.net/)
          A Matlab toolbox for tensor factorizations, complex optimization, and tensor optimization freely available for
          non-commercial academic research.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.4
