Metadata-Version: 2.1
Name: tensorlearn
Version: 1.0.65
Summary: A Python Package for Advanced Tensor Learning Methods
Home-page: https://github.com/rmsolgi/tensorlearn.git
Author: Ryan (Mohammad) Solgi
Author-email: ryan.solgi@gmail.com
Maintainer: Ryan (Mohammad) Solgi
Keywords: tensor,decomposition,tensor-train,rank,auto-rank
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy


# tensorlearn

tensorlearn is a Python library distributed on [Pypi](https://pypi.org) for implementing 
tensor learning 

This is a package under development. Yet, the available methods are final and functional. The backend is [Numpy](https://numpy.org).

    
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install tensorlearn in Python.

```python
pip install tensorlearn
```

## methods
### Decomposition Methods
[Tensor-Train Decomposition with Auto Rank Determination](#1111-id)


## <a name="1111-id"></a>Tensor-Train Decomposition with Auto Rank Determination

Tensorlearn.decomposition.tensor_train.auto_rank_tt(tensor,epsilon)

This implementation of tensor-train decomposition determines rank automatically based on a given error bound written according to [Oseledets (2011)](https://epubs.siam.org/doi/10.1137/090752286). Therefore the user does not need to specify a rank. Instead the user specifies an upper error bound (epsilon) which is the frobenius norm of the error divided by the frobenius norm of the given tensor to be decomposed.

### Arguments 
@tensor <numpy array> - dimension must be at least 3.

@epsilon <float> - Error bound = frobenius norm of the error / frobenius norm of the given tensor. 
### Outputs
@factors <list> - The list includes 2D numpy arrays of factors according to TT decomposition. Length of the list equals the dimension of the given tensor to be decomposed.





