semvec/pypi-packages huggingface dataset:
chia-bech32m 1.0.0
A BIP350 compatible Bech32m encoding/decoding library for chia.
Requires Python: >=3.0

# chia_bech32m for python

A BIP350 compatible Bech32m encoding/decoding library for chia.

## install

```bash
pip install chia-bech32m
```

## Use

### import

```python
from chia_bech32m import bech32m
```

### Example
```python
print(bech32m.encode("7382e99e0679b68b6e8211c48224c99fc941a5f035bd85f9f32c4a8f48d3bb29","xch"))
print(bech32m.encode("0x7382e99e0679b68b6e8211c48224c99fc941a5f035bd85f9f32c4a8f48d3bb29","xch"))
print(bech32m.encode("7382e99e0679b68b6e8211c48224c99fc941a5f035bd85f9f32c4a8f48d3bb29",""))
print(bech32m.encode("7382e99e0679b68b6e8211c48224c99fc941a5f035bd85f9f32c4a8f48d3bb29","nft"))
# xch1wwpwn8sx0xmgkm5zz8zgyfxfnly5rf0sxk7ct70n939g7jxnhv5smryxmv
# xch1wwpwn8sx0xmgkm5zz8zgyfxfnly5rf0sxk7ct70n939g7jxnhv5smryxmv
# xch1wwpwn8sx0xmgkm5zz8zgyfxfnly5rf0sxk7ct70n939g7jxnhv5smryxmv
# nft1wwpwn8sx0xmgkm5zz8zgyfxfnly5rf0sxk7ct70n939g7jxnhv5sam5p80
```

```python
print(bech32m.decode("xch1wwpwn8sx0xmgkm5zz8zgyfxfnly5rf0sxk7ct70n939g7jxnhv5smryxmv"))
# 7382e99e0679b68b6e8211c48224c99fc941a5f035bd85f9f32c4a8f48d3bb29
```

## Support
XCH: `xch1crwg0ryz5rf90a6xmmspza2jmhc437sllnen8m7nu2dlx7ugjs9qaxgyjw`

pawket: `hezuoshe.xch`
siamphishtest 2.1.8
SMS Bomber Project.
Requires Python: >=3.6

<!-- CIRCLE_TOOLS -->
<!-- CODED BY ARU -->

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</p>

<p align="center">
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</p>

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  <h3>  If You need any Help please connect with us  </h3>
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`` 📡 Get in Touch `` 
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sanstylex-demo 0.0.1
Dive into Python.
Requires Python: >=3.10

# sanstylex/sanstylex-demo

![repo size](https://img.shields.io/github/repo-size/sanstylex/sanstylex-demo.svg)
[![PyPI][pypi-badge]][pypi-link]
[![GitHub issues][issue-badge]][issue-link]
[![GitHub forks][fork-badge]][fork-link]
[![GitHub stars][star-badge]][star-link]
[![GitHub license][license-badge]][license-link]
[![contributors][contributor-badge]][contributor-link]
[![watcher][watcher-badge]][watcher-link]
[![Binder][binder-badge]][binder-link]
[![Downloads][download-badge]][download-link]
[![Documentation Status][status-badge]][status-link]
[![PyPI - Downloads][install-badge]][install-link]

学无止境！

[pypi-badge]: https://img.shields.io/pypi/v/sanstylex-demo.svg
[pypi-link]: https://pypi.org/project/sanstylex-demo/
[issue-badge]: https://img.shields.io/github/issues/sanstylex/sanstylex-demo
[issue-link]: https://github.com/sanstylex/sanstylex-demo/issues
[fork-badge]: https://img.shields.io/github/forks/sanstylex/sanstylex-demo
[fork-link]: https://github.com/sanstylex/sanstylex-demo/network
[star-badge]: https://img.shields.io/github/stars/sanstylex/sanstylex-demo
[star-link]: https://github.com/sanstylex/sanstylex-demo/stargazers
[license-badge]: https://img.shields.io/github/license/sanstylex/sanstylex-demo
[license-link]: https://github.com/sanstylex/sanstylex-demo/LICENSE
[contributor-badge]: https://img.shields.io/github/contributors/sanstylex/sanstylex-demo
[contributor-link]: https://github.com/sanstylex/sanstylex-demo/contributors
[watcher-badge]: https://img.shields.io/github/watchers/sanstylex/sanstylex-demo
[watcher-link]: https://github.com/sanstylex/sanstylex-demo/watchers
[binder-badge]: https://mybinder.org/badge_logo.svg
[binder-link]: https://mybinder.org/v2/gh/sanstylex/sanstylex-demo/main
[install-badge]: https://img.shields.io/pypi/dw/sanstylex-demo?label=pypi%20installs
[install-link]: https://pypistats.org/packages/sanstylex-demo
[status-badge]: https://readthedocs.org/projects/sanstylex-demo/badge/?version=latest
[status-link]: https://sanstylex-demo.readthedocs.io/zh/latest/?badge=latest
[download-badge]: https://pepy.tech/badge/sanstylex-demo
[download-link]: https://pepy.tech/project/sanstylex-demo

## PyPI

支持 PyPI：

```sh
pip install sanstylex/sanstylex-demo
```
gbnnetwork 1.3.0
Implementation of Bayesian network.
Keywords: python,probability,bayes


# BNNetwork Library
Esta librería permite crear redes Bayesianas a partir de la probabilidad condicional de cada nodo.

 ## Instalar
`pip install gbnnetwork`

## Uso
Para crear una Red de Inferencia, se deben de seguir los siguientes pasos:
1. Crear una instancia de la clase BNNetwork `network = BNetwork()`
2. Agregar variables a la red `network.add_variable("A")`
3. Asignar los padres de cada variable (en caso la variable no tenga padres este paso se puede omitir) `network.set_parents_to_variable("A", ["B", "E"])`
4. Agregar las probabilidades a las variables para definir la red totalmente. **Toda la red debe de estar definida si se desea responder cualquier consulta** `network.add_probability("A|BE", 0.001)`
5. Hacer una query `p = network.inference({'B': False}, {'A': False})` (Si se desea hacer una query sin valores observados, el diccionario se debe enviar vacío)

## Funciones De Utilidad
1. Obtener la representación compacta `network.compact_string()` (string)
2. Obtener los factores de la red `network.factor_string()` (string)
3. Saber si la red esta totalmente definida `network.validate_defined_state()` (boolean)

## Ejemplo
Tomando como referencia el siguiente documento: https://people.cs.pitt.edu/~milos/courses/cs2740/Lectures/class19.pdf

La red que se propone tiene la siguiente topología:
![Example Network](example_network.png)

Se procede de la siguiente manera:

```python
from gbnnetwork import BNetwork
network = BNetwork()    # Paso 1

# Paso 2
network.add_variable("B")
network.add_variable("E")
network.add_variable("A")
network.add_variable("J")
network.add_variable("M")

# Paso 3
network.set_parents_to_variable("A", ["B", "E"])
network.set_parents_to_variable("J", ["A"])
network.set_parents_to_variable("M", ["A"])

# Paso 4
network.add_probability("B", 0.001)
network.add_probability("E", 0.002)
network.add_probability("A|BE", 0.95)
network.add_probability("A|-BE", 0.29)
network.add_probability("A|B-E", 0.94)
network.add_probability("A|-B-E", 0.001)
network.add_probability("M|A", 0.7)
network.add_probability("M|-A", 0.01)
network.add_probability("J|A", 0.9)
network.add_probability("J|-A", 0.05)

# Hacer la query P(B=True|J=True,M=True)
p = network.inference({'B': True}, {'J': True, 'M': True})  # 0.2841718353643929 = 28%

```
sanstylex 0.0.1
Dive into Python.
Requires Python: >=3.10

# 动手学习 Python

![repo size](https://img.shields.io/github/repo-size/sanstylex.github.io.svg)
[![PyPI][pypi-badge]][pypi-link]
[![GitHub issues][issue-badge]][issue-link]
[![GitHub forks][fork-badge]][fork-link]
[![GitHub stars][star-badge]][star-link]
[![GitHub license][license-badge]][license-link]
[![contributors][contributor-badge]][contributor-link]
[![watcher][watcher-badge]][watcher-link]
[![Binder][binder-badge]][binder-link]
[![Downloads][download-badge]][download-link]
[![Documentation Status][status-badge]][status-link]
[![PyPI - Downloads][install-badge]][install-link]

学无止境！

[pypi-badge]: https://img.shields.io/pypi/v/d2py.svg
[pypi-link]: https://pypi.org/project/d2py/
[issue-badge]: https://img.shields.io/github/issues/sanstylex.github.io
[issue-link]: https://github.com/sanstylex.github.io/issues
[fork-badge]: https://img.shields.io/github/forks/sanstylex.github.io
[fork-link]: https://github.com/sanstylex.github.io/network
[star-badge]: https://img.shields.io/github/stars/sanstylex.github.io
[star-link]: https://github.com/sanstylex.github.io/stargazers
[license-badge]: https://img.shields.io/github/license/sanstylex.github.io
[license-link]: https://github.com/sanstylex.github.io/LICENSE
[contributor-badge]: https://img.shields.io/github/contributors/sanstylex.github.io
[contributor-link]: https://github.com/sanstylex.github.io/contributors
[watcher-badge]: https://img.shields.io/github/watchers/sanstylex.github.io
[watcher-link]: https://github.com/sanstylex.github.io/watchers
[binder-badge]: https://mybinder.org/badge_logo.svg
[binder-link]: https://mybinder.org/v2/gh/sanstylex.github.io/main
[install-badge]: https://img.shields.io/pypi/dw/d2py?label=pypi%20installs
[install-link]: https://pypistats.org/packages/d2py
[status-badge]: https://readthedocs.org/projects/d2py/badge/?version=latest
[status-link]: https://d2py.readthedocs.io/zh/latest/?badge=latest
[download-badge]: https://pepy.tech/badge/d2py
[download-link]: https://pepy.tech/project/d2py

## PyPI

支持 PyPI：

```sh
pip install d2py
```

## 文档模板

优质文档模板：

- [xbook](https://xinetzone.github.io/xbook/index.html)