scikit-tensor 0.1

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