semvec/pypi-packages huggingface dataset:
tuotuo 0.0.4
LDA & Neura based topic modelling library
License: MIT
Keywords: Generative Topic Modelling,Latent Dirichlet Allocation

Implemented the Smoothed Varational Inference version of Latent Dirichlet Allocation, following David M. Blei et al. 2003

1. Complete the Varitaional Inference version for batch & online LDA [80%]
    - Following the original LDA Paper: [https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf]
    - Folloing the Online LDA Papaer: [https://papers.nips.cc/paper_files/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf]
2. Extend the library to support neural varaiaitonal inference 
    - Following this ICML paper [https://arxiv.org/pdf/1511.06038.pdf]
3. Extend the training to support Reinforcement Learning 
    - Follwoing this ACL paper [https://aclanthology.org/D19-1350.pdf]
klocmod 0.3.1
A simple module providing facilities to localize small programs via textual files.
License: MIT
Requires Python: >=3.8,<4.0
Keywords: localization library

klocmod -- Kozalo's Localization Module
======================================

*Screw you, gettext! I don't wanna bother of compiling strings into binary files!*

[![Build Status](https://github.com/kozalosev/klocmod/actions/workflows/ci-build.yml/badge.svg)](https://github.com/kozalosev/klocmod/actions/workflows/ci-build.yml)
[![Documentation Status](https://readthedocs.org/projects/klocmod/badge/?version=latest)](https://klocmod.readthedocs.io/en/latest/?badge=latest)

This module provides a very simple, suboptimal way for localizing your scripts, bots or applications. The advantage is
its simplicity: to supply some sets of different string literals for different languages, you just need a simple JSON,
YAML or INI file (or even a dict) fed to the library. After that, the only thing you should take care of is to get an
instance of the dictionary for a specific language and extract messages from it by key values.

All you mostly want is the `LocalizationsContainer` class. In particular, its static method 
`LocalizationsContainer.from_file()` that reads a localization file and returns an instance of the factory. The factory
is supposed to produce instances of the `LanguageDictionary` class. Most likely, you will encounter instances of its
subclass -- the `SpecificLanguageDictionary` class (the base class is only used as a fallback that returns passed key
values back).


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

```bash
# basic installation
pip install klocmod
# or with YAML files support enabled
pip install klocmod[YAML]
```


Examples of localization files
------------------------------

### JSON (language first)

```json
{
  "en": {
    "yes": "yes",
    "no": "no"
  },
  "ru-RU": {
    "yes": "да",
    "no": "нет"
  }
}
```

### JSON (phrase first)

```json
{
  "yes": {
    "en": "yes",
    "ru-RU": "да"
  },
  "no": {
    "en": "no",
    "ru-RU": "нет"
  }
}
```

### INI

```ini
[DEFAULT]
yes = yes
no = no

[ru-RU]
yes = да
no = нет
```

### YAML

Requires an extra dependency: *PyYAML*.

```yaml
# language first
en:
  yes: yes
  no: no
ru-RU:
  yes: да
  no: нет
---
# phrase first
yes:
  en: yes
  ru-RU: да
no:
  en: no
  ru-RU: нет
```


Code example
------------

```python
from klocmod import LocalizationsContainer

localizations = LocalizationsContainer.from_file("localization.json")
ru = localizations.get_lang("ru")
# or
en = localizations.get_lang()    # get default language
# then
print(ru['yes'])    # output: да
# alternative ways to get a specific phrase:
localizations.get_phrase("ru-RU", "no")
localizations['ru-RU']['no']
```
pkcs1 0.9.7
Python implementation of the RFC3447 or PKCS #1 version 2.0
License: MIT

python-pkcs1
------------

.. image:: https://travis-ci.org/bdauvergne/python-pkcs1.svg?branch=master
    :target: https://travis-ci.org/bdauvergne/python-pkcs1

This package implements the PKCS #1 v2.0 standard from RSA Laboratories. It
aims at a full and tested coverage of the standard, maximum portability by
using only pure python and external dependencies only for performance gains.

Performance are improved by using the gmpy package to compute modular
exponentiation if it is present.

The package contains the following modules:

  - pkcs1.primitives - integer<->byte string conversion
  - pkcs1.keys - RSA keys classes, naive RSA encryption
  - pkcs1.primes - prime number generation
  - pkcs1.eme_pkcs1_v15 - PKCS#1 v1.5 encoding for encryption
  - pkcs1.emsa_pkcs1_v15 - PKCS#1 v1.5 encoding for signature
  - pkcs1.mgf - mask generation function number one
  - pkcs1.exceptions - specialized exceptions
  - pkcs1.rsaes_pkcs1_v15 - RSA encryption using PKCS#1 v1.5 padding
  - pkcs1.rsaes_oaep - RSA encryption using non-deterministic OAEP padding
  - pkcs1.rsassa_pkcs1_v15 - RSA signature using PKCS#1 v1.5 encoding
  - pkcs1.rsassa_pss - RSA signature using non-deterministic encoding