semvec/pypi-packages huggingface dataset:
keyserver-ng 0.1.0
PGP keyserver with email and key validation.

Keyserver NG
************

Key server with email and key validation.

Why?
====

All available keyservers allow to upload any public key without
any validation/confirmaion.

This keyserver sends encrypted email with confirmation link
to public key owner. Public key will be added to database only
if owner retrieve this email, decrypt it, and open confirmaion
link in the browser.

How?
====

Installation
------------

To install in debian based GNU/Linux::

    $ sudo apt-get install python3-pip libgpgme11-dev
    $ sudo pip3 install keyserver-ng

Configuration
-------------

Self documented configuration file can be found in /etc/keyserver-ng.ini

Running
-------

Upstart script::

    $ sudo cp /etc/keyserver-ng/upstart.conf /etc/init/keyserver-ng.conf
    $ sudo service keyserver-ng start

Run in foreground::

    $ sudo keyserver-ng /etc/keyserver-ng/config.ini

Current status
==============

Implemented
-----------

* Upload key/Confirm key
* Fetch key

Not implemented yet
-------------------

* Search key
* Update key
* Sendmail mailer
* Dump DB/Load DB
featureforge 0.1.6
A library to build and test machine learning features
Keywords: machine learning,scikit,scikit-learn,sklearn,features,testing,vectorization,preprocessing

Feature Forge
=============

This library provides a set of tools that can be useful in many machine
learning applications (classification, clustering, regression, etc.), and
particularly helpful if you use scikit-learn (although this can work if
you have a different algorithm).

Most machine learning problems involve an step of feature definition and
preprocessing. Feature Forge helps you with:

 * Defining and documenting features
 * Testing your features against specified cases and against randomly generated
   cases (stress-testing). This helps you making your application more robust
   against invalid/misformatted input data. This also helps you checking that
   low-relevance results when doing feature analysis is actually because the
   feature is bad, and not because there's a slight bug in your feature code.
 * Evaluating your features on a data set, producing a feature evaluation
   matrix. The evaluator has a robust mode that allows you some tolerance both
   for invalid data and buggy features.
 * Experimentation: running, registering, classifying and reproducing
   experiments for determining best settings for your problems.

Installation
------------

Just `pip install featureforge`.

Documentation
-------------

Documentation is available at http://feature-forge.readthedocs.org/en/latest/

Contact information
-------------------

Feature Forge is © 2014 Machinalis (http://www.machinalis.com/). Its primary
authors are:

 * Javier Mansilla <jmansilla@machinalis.com> (jmansilla at github)
 * Daniel Moisset <dmoisset@machinalis.com> (dmoisset at github)
 * Rafael Carrascosa <rcarrascosa@machinalis.com> (rafacarrascosa at github)

Any contributions or suggestions are welcome, the official channel for this is
submitting github pull requests or issues.

Changelog
---------
0.1.6:
    - Bug fixes related to sparse matrices.
    - Small documentation improvements.
    - Reduced default logging verbosity.

0.1.5:
    - Using sparse numpy matrices by default.

0.1.4:
    - Discarded the need of using forked version of Schema library.

0.1.3:
    - Added support for running and generating stats for experiments

0.1.2:
    - Fixing installer dependencies

0.1.1:
    - Added support for python 3
    - Added support for bag-of-words features

0.1:
    - Initial release