Metadata-Version: 2.1
Name: vhash
Version: 0.0.16
Summary: hash tables for vectorizing text-based documents
Home-page: UNKNOWN
Author: Mike Powell PhD
Author-email: mike@lakeslegendaries.com
License: MIT License
        
        Copyright (c) 2021 Lake's Legendaries LLC
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Platform: UNKNOWN
Requires-Python: >=3.7
License-File: LICENSE

##############################
VHash: Vectorizing Hash Tables
##############################

If you have documents with class labels, and you want to create numeric
representations of those documents that maximize inter-class differences, then
this package is for you. This package provides vectorizing hash tables that
quickly transform your text, optimizing for the maximum distance between
document-class vectors.

This project has a C++ backend with a python interface, allowing for maximum
speed and maximum interopability.

To get started, check out the `docs <https://lakes-legendaries.github.io/vhash/>`_!

If you will be contributing to this repo, checkout the
`developer guide <https://lakes-legendaries.github.io/vhash/dev.html>`_.


