semvec/pypi-packages huggingface dataset:
robotframework-efarkas-usefulutils 1.0.0
A sample Python project
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

# A sample Python project

![Python Logo](https://www.python.org/static/community_logos/python-logo.png "Sample inline image")

A sample project that exists as an aid to the [Python Packaging User
Guide][packaging guide]'s [Tutorial on Packaging and Distributing
Projects][distribution tutorial].

This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.

[The source for this project is available here][src].

Most of the configuration for a Python project is done in the `setup.py` file,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.

----

This is the README file for the project.

The file should use UTF-8 encoding and can be written using
[reStructuredText][rst] or [markdown][md use] with the appropriate [key set][md
use]. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.

Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.

[packaging guide]: https://packaging.python.org
[distribution tutorial]: https://packaging.python.org/tutorials/packaging-projects/
[src]: https://github.com/pypa/sampleproject
[rst]: http://docutils.sourceforge.net/rst.html
[md]: https://tools.ietf.org/html/rfc7764#section-3.5 "CommonMark variant"
[md use]: https://packaging.python.org/specifications/core-metadata/#description-content-type-optional
ubuntu-release-info 21.1
Ubuntu distribution release information
Requires Python: >=3.6
Keywords: ubuntu,distro,release,info

# Ubuntu Release Info

[![Build Status](https://travis-ci.com/powersj/ubuntu-release-info.svg?branch=master)](https://travis-ci.com/powersj/ubuntu-release-info) [![Snap Status](https://build.snapcraft.io/badge/powersj/ubuntu-release-info.svg)](https://build.snapcraft.io/user/powersj/ubuntu-release-info)

[![Get it from the Snap Store](https://snapcraft.io/static/images/badges/en/snap-store-black.svg)](https://snapcraft.io/ubuntu-release-info)

Ubuntu distribution release information

Similar to the 'distro-info' package, this class parses and contains
the Ubuntu release information. Instead of a static data file, this
instead downloads the the meta-release information from
changelogs.ubuntu.com, and parses it to determine the most recent
release information.

## Install

Users can obtain ubuntu-release-download as a snap:

```shell
snap install ubuntu-release-info
```

Or via PyPI:

```shell
pip3 install ubuntu-release-info
```

## Usage

Run the command with the flag to see the codenames matching that flag:

```shell
ubuntu-release-info
```

Options:

* all:
* lts:
* devel:
* supported:
* unsupported:
bpm-tools 0.2.5
Shared functions for BPM data pipelines.
Requires Python: >=3.6

# README #

This README would normally document whatever steps are necessary to get your application up and running.

### What is this repository for? ###

* Quick summary
* Version
* [Learn Markdown](https://bitbucket.org/tutorials/markdowndemo)

### How do I get set up? ###

* Summary of set up
* Configuration
* Dependencies
* Database configuration
* How to run tests
* Deployment instructions

### Contribution guidelines ###

* Writing tests
* Code review
* Other guidelines

### Who do I talk to? ###

* Repo owner or admin
* Other community or team contact
hlda 0.3.1
Gibbs sampler for the Hierarchical Latent Dirichlet Allocation topic model. This is based on the hLDA implementation from Mallet, having a fixed depth on the nCRP tree.
Requires Python: >=3

Hierarchical Latent Dirichlet Allocation
----------------------------------------

Hierarchical Latent Dirichlet Allocation (hLDA) addresses the problem of learning topic hierarchies from data. The model relies on a non-parametric prior called the nested Chinese restaurant process, which allows for arbitrarily large branching factors and readily accommodates growing
data collections. The hLDA model combines this prior with a likelihood that is based on a hierarchical variant of latent Dirichlet allocation.

[Hierarchical Topic Models and the Nested Chinese Restaurant Process](http://www.cs.columbia.edu/~blei/papers/BleiGriffithsJordanTenenbaum2003.pdf)

[The Nested Chinese Restaurant Process and Bayesian Nonparametric Inference of Topic Hierarchies](http://cocosci.berkeley.edu/tom/papers/ncrp.pdf)

Implementation
--------------

- [hlda/sampler.py](hlda/sampler.py) is the Gibbs sampler for hLDA inference, based on the implementation from [Mallet](http://mallet.cs.umass.edu/topics.php) having a fixed depth on the nCRP tree.


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

- Simply use `pip install hlda` to install the package.
- An example notebook that infers the hierarchical topics on the BBC Insight corpus can be found in [notebooks/bbc_test.ipynb](notebooks/bbc_test.ipynb).