Metadata-Version: 2.1
Name: pycle
Version: 1.0
Summary: Python toolbox for Compressive Learning (machine learning from a sketch)
Home-page: https://github.com/schellekensv/pycle
Author: Vincent Schellekens
Author-email: vincent.schellekens@uclouvain.be
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# The pycle toolbox
Pycle stands for PYthon Compressive LEarning; it is a toolbox of methods to perform compressive learning (also known as sketched learning), where the training set is not used directly but first heavily compressed; this allows to learn from large-scale datasets with drastically reduced computational resources.

Pycle is now stable (version `1.0`), but *still under development*, so don't hesitate to report bugs or other difficulties encountered with the toolbox.


## Contains:
For documentation:
* A "Guide" folder with semi-detailed introductory guide to this toolbox.
* A series of "DEMO_i" jupyter notebooks, that illustrate some core concepts of the toolbox in practice.

If you're new here, I suggest you start by opening either of those items first to get a hang of what this is all about.


The code itself, located in the "pycle" folder, structured into 3 main files:
* `sketching.py` contains everything related to building a feature map and sketching a dataset with it;
* `compressive_learning.py` contains the actual learning algorithms from that sketch, for k-means and GMM fitting for example;
* `utils.py` contains a diverse set of functions that can be useful, e.g., for generating synthetic datasets, or evaluating the learned models through different metrics and visualization utilities.

## Citing this toolbox:
If you publish research using this toolbox, please follow this link for to get citation references (e.g., to generate BibTeX export files)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3855114.svg)](https://doi.org/10.5281/zenodo.3855114)



