scikit-tensor 0.1

Description:

scikittensor 0.1

scikit-tensor is a Python module for multilinear algebra and tensor
factorizations.

Dependencies
The required dependencies to build the software are Numpy >= 1.3,
SciPy >= 0.7.


Usage
Example script to decompose sensory bread data (available from
http://www.models.life.ku.dk/datasets) using CP-ALS
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')


References
If you use scikit-tensor in your research, please cite
Maximilian Nickel. scikit-tensor Library (Version 0.1). Available Online, November 2013.


Install
This package uses distutils, which is the default way of installing
python modules. To install in your home directory, use
python setup.py install --user
To install for all users on Unix/Linux
python setup.py build
sudo python setup.py install
To install in development mode
python setup.py develop


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.
Development is synchronized via git. To clone this repository, run
git clone git://github.com/scikit-learn/scikit-learn.git


Authors
Maximilian Nickel

http://twitter.com/mnick
http://github.com/mnick



License
scikit-tensor is licensed under the GPLv3
http://www.gnu.org/licenses/gpl-3.0.txt