Metadata-Version: 2.1
Name: tensor-maximum-entropy
Version: 0.0.2
Summary: Python implementation of the Tensor Maximum Entropy (TME)
Home-page: https://github.com/ArdeleanRichard/TME
Author: Eugen-Richard Ardelean
Author-email: ardeleaneugenrichard@gmail.com
License: MIT
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: scikit-learn
Provides-Extra: dev
Requires-Dist: twine >=4.0.2 ; extra == 'dev'

# TME
Python implementation of Tensor Maximum Entropy (TME)

Install:
```
pip install tensor-maximum-entropy
```

Usage example:
```
import numpy as np
import scipy.io
from tensor_maximum_entropy import TME

model_dim = 10

data = scipy.io.loadmat('./exampleData.mat')
dataTensor = data['dataTensor']

print(dataTensor.shape)
t = data['t']

mask = np.logical_and(t > - 50, t < 350)
TME(dataTensor, mask, model_dim)
```

The algorithm description can be found in the following article:
```
Elsayed, G.F.; Cunningham, J.P. Structure in Neural Population Recordings: An Expected Byproduct of Simpler Phenomena? Nat Neurosci 2017, 20, 1310â€“1318, doi:10.1038/nn.4617.
```

A matlab implementation can be found at the following link:
https://github.com/gamaleldin/TME
