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https://github.com/sandialabs/pyttb

Python Tensor Toolbox
https://github.com/sandialabs/pyttb

cp-decomposition data-science python scr-2671 snl-data-analysis tensors tucker-decomposition

Last synced: 4 days ago
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Python Tensor Toolbox

Host: GitHub
URL: https://github.com/sandialabs/pyttb
Owner: sandialabs
License: bsd-2-clause
Created: 2022-05-10T03:21:22.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-11-11T21:54:15.000Z (5 days ago)
Last Synced: 2024-11-11T22:11:38.803Z (5 days ago)
Topics: cp-decomposition, data-science, python, scr-2671, snl-data-analysis, tensors, tucker-decomposition
Language: Python
Homepage: https://pyttb.readthedocs.io
Size: 875 KB
Stars: 25
Watchers: 3
Forks: 13
Open Issues: 17
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.bib

Awesome Lists containing this project

README

        ```

Copyright 2024 National Technology & Engineering Solutions of Sandia,

LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the

U.S. Government retains certain rights in this software.

```

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# pyttb: Python Tensor Toolbox

Welcome to `pyttb`, a refactor of the 

[Tensor Toolbox for MATLAB](https://www.tensortoolbox.org) in Python.

This package contains data classes and methods for manipulating dense, 

sparse, and structured tensors, along with algorithms for computing 

low-rank tensor decompositions:

- Data Classes: 

[`tensor`](https://pyttb.readthedocs.io/en/stable/tensor.html "dense tensors"), 

[`sptensor`](https://pyttb.readthedocs.io/en/stable/sptensor.html "sparse tensors"), 

[`ktensor`](https://pyttb.readthedocs.io/en/stable/ktensor.html "Kruskal tensors"), 

[`ttensor`](https://pyttb.readthedocs.io/en/stable/ttensor.html "Tucker tensors"), 

[`tenmat`](https://pyttb.readthedocs.io/en/stable/tenmat.html "matricized dense tensors"), 

[`sptenmat`](https://pyttb.readthedocs.io/en/stable/sptenmat.html "matricized sparse tensors"), 

[`sumtensor`](https://pyttb.readthedocs.io/en/stable/sumtensor.html "implicit sum of tensors")

- Algorithms:

[`cp_als`](https://pyttb.readthedocs.io/en/stable/cpals.html "CP decomposition via Alternating Least Squares"),

[`cp_apr`](https://pyttb.readthedocs.io/en/stable/cpapr.html "CP decomposition via Alternating Poisson Regression"), 

[`gcp_opt`](https://pyttb.readthedocs.io/en/stable/gcpopt.html "Generalized CP decomposition"), 

[`hosvd`](https://pyttb.readthedocs.io/en/stable/hosvd.html "Tucker decomposition via Higher Order Singular Value Decomposition"),

[`tucker_als`](https://pyttb.readthedocs.io/en/stable/tuckerals.html "Tucker decompostion via Alternating Least Squares")

## Quick Start

### Installation

```commandline

python3 -m pip install pyttb

```

### Example

```python

>>> import pyttb as ttb

>>> X = ttb.tenrand((2,2,2))

>>> type(X)

>>> M = ttb.cp_als(X, rank=1)

CP_ALS:

 Iter 0: f = 7.367245e-01 f-delta = 7.4e-01

 Iter 1: f = 7.503069e-01 f-delta = 1.4e-02

 Iter 2: f = 7.508240e-01 f-delta = 5.2e-04

 Iter 3: f = 7.508253e-01 f-delta = 1.3e-06

 Final f = 7.508253e-01

 ```

### Getting Help

- [Documentation](https://pyttb.readthedocs.io)

- [Tutorials](https://pyttb.readthedocs.io/en/stable/tutorials.html)

- [Info for users coming from MATLAB](https://pyttb.readthedocs.io/en/stable/for_matlab_users.html)

- Learn about tensor decompositions: 

[tensor paper](https://doi.org/10.1137/07070111X "Tensor Decompositions and Applications by Tamara G. Kolda, Brett W. Bader"), 

[tensor book](https://www.mathsci.ai/post/tensor-textbook/ "Tensor Decompositions for Data Science by Grey Balard and Tamara G. Kolda") 

### Contributing

- [Report a bug](https://github.com/sandialabs/pyttb/issues/new)

- [Guide for contributors](CONTRIBUTING.md)

- [List of contributors](CONTRIBUTORS.md)

### Citing pyttb in your work 

If you use pyttb in your work, please cite it using the citation info [here](CITATION.bib).