approximating mutual information in high dimensions
Project description
latentmi
Latent MI (LMI) approximation is a method for estimating mutual information high dimensions. It is built on the idea that real world high-dimensional data has underlying low-dimensional structure. For more details, see our manuscript. latentmi is our Python implementation of LMI approximation, built with the hope that practicioners can use it with minimal pain and suffering :)
Installation
$ pip install latentmi
Usage
from latentmi import lmi
Xs = # some samples of a high dimensional variable
Ys = # some samples of a high dimensional variable
pmis, embedding, model = lmi.lmi(Xs, Ys)
MI_estimate = np.nanmean(pmis) # voila !
More detailed instructions can be found in the example usage notebook.
License
latentmi was created by Gokul Gowri. It is licensed under the terms of the MIT license.
Credits
latentmi was created with cookiecutter and the py-pkgs-cookiecutter template.
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