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The component initializers behave differently based on the type of the `distribution` argument. If it is a Python [list](https://docs.python.org/3/tutorial/datastructures.html), it is assumed that there are as many entries in this list as there are classes, and the number at each location of this list describes the number of prototypes to be used for that particular class. So, `[1, 1, 1]` implies that we have three classes with one prototype per class. If it is a Python [tuple](https://docs.python.org/3/tutorial/datastructures.html), it a shorthand of `(num_classes, prototypes_per_class)` is assumed. If it is a Python [dictionary](https://docs.python.org/3/tutorial/datastructures.html), the key-value pairs describe the class label and the number of prototypes for that class respectively. So, `{0: 2, 1: 2, 2: 2}` implies that we have three classes with labels `{1, 2, 3}`, each equipped with two prototypes.
ProtoTorch: Prototype Learning in PyTorch
Tensorflow users, see: ProtoFlow
Description
This is a Python toolbox brewed at the Mittweida University of Applied Sciences in Germany for bleeding-edge research in Prototype-based Machine Learning methods and other interpretable models. The focus of ProtoTorch is ease-of-use, extensibility and speed.
Installation
ProtoTorch can be installed using pip.
pip install -U prototorch
To also install the extras, use
pip install -U prototorch[all]
Note: If you're using ZSH (which is also the default
shell on MacOS now), the square brackets [ ] have to be escaped like so:
\[\], making the install command pip install -U prototorch\[all\].
To install the bleeding-edge features and improvements:
git clone https://github.com/si-cim/prototorch.git
cd prototorch
git checkout dev
pip install -e .[all]
Documentation
The documentation is available at https://www.prototorch.ml/en/latest/. Should that link not work try https://prototorch.readthedocs.io/en/latest/.
Bibtex
If you would like to cite the package, please use this:
@misc{Ravichandran2020b,
author = {Ravichandran, J},
title = {ProtoTorch},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/si-cim/prototorch}}
}