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.
The required dependencies to build the software are Numpy >= 1.3, SciPy >= 0.7.
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')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
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/mnick/scikit-tensor.git
Maximilian Nickel: Web, [Email](mailto://mnick AT mit DOT edu), Twitter
scikit-tensor is licensed under the GPLv3
- Matlab Tensor Toolbox: A Matlab toolbox for tensor factorizations and tensor operations freely available for research and evaluation.
- Matlab Tensorlab A Matlab toolbox for tensor factorizations, complex optimization, and tensor optimization freely available for non-commercial academic research.