semvec/pypi-packages huggingface dataset:
wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt3 2023.2.15.0
吾爱破解移动安全区 2019.12~2020.6 PT3
Requires Python: >=3.6

# 吾爱破解移动安全区 2019.12~2020.6 PT3

## 下载

### Docker

```
docker pull apachecn0/wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt3
docker run -tid -p <port>:80 apachecn0/wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt3
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt3
wuai-pojie-yidong-anquanqu-2019-12-2020-6-pt3 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g wuai-pojie-yidong-anquanqu-erlingyijiu-yier-erlingerling-6-pt3
wuai-pojie-yidong-anquanqu-erlingyijiu-yier-erlingerling-6-pt3 <port>
# 访问 http://localhost:{port} 查看文档
```
Lab2-IA 0.0.13
R bayesianas
Keywords: python,bayesian,statistics


# UVG_IA_Lab2
Laboratorio 2 de Inteligencia Artificial

PyPI: https://pypi.org/project/Lab2-IA/
PR Aprobada (0.5 pts extras!): https://github.com/SantiagoTaracena01/cool-bayesian-networks/pull/2

## Instrucciones de empaquetamiento:
python setup.py sdist bdist_wheel

## Instrucciones de instalación:
pip install Lab2-IA==0.0.11

## Instrucciones de uso:
Para poder utilizar este paquete, se debe de hacer lo siguiente. Se debe de tener un archivo de texto de la siguiente manera
###### P(A) = 0.30
###### P(B) = 0.23
###### P(C|!A, !B) = 0.5
###### P(C|!A, B) = 0.10
###### P(C|A, !B) = 0.77
###### P(C|A, B) = 0.20

La estrucutra de debe der P(A) o P(C|A, B), donde después del "|", debe de estar la probabilidad, y para cada condicion añadida, se añade una coma y un espacio después para que se pueda leer. 

La estructura de las probabilidades condicionales, debe de ser que todas esten negadas al principio, y después empezar a negarlas de derecha a izquierda, hasta que todas sean las positivas.
midgy 2023.2.14
run markdown as python code
Requires Python: >=3.7

# `midgy`

`midgy` transforms markdown to python modules and scripts.

### command line interface

```bash
midgy README.md     # run a readme file as python
midgy run README.md     # run a readme file as python
midgy -m README     # run a readme file as python
```   
   
      >>> from midgy.run import Markdown
      >>> with Markdown():
      ...   import README
      >>> print(README)
      <module 'README' from '...README.md'>
      

`midgy` is one half of `pidgy`, together they bring literate programming and computing afforandances to [python] and [`IPython`]. `midgy` is only concerned with the half of literate programming that translates a document to compiled code. `midgy` has a small api:

* `md_to_python` - a function that converts a markdown document to python
* `Python` - a class that parses a markdown document and renders python
* `midgy.run.Markdown` - is an `importnb` context manager that includes markdown documents when importing python modules.


## tangling literate programs

 literate programming is paradigm that treats code as literate, and vice versa; documents are evaluated on their literary and computational qualities. there are two actions defined in the framework of literate programming:

1. render
   : the act of translating the document into a programming language
   : `midgy` renders markdown to python
2. weave
   : the act of translating the document to a rendered format
   : `pidgy` weaves markdown to html, pdf, or md through the `jinja2` template system.

`midgy` focuses only on the `render` actions, and is extended in `pidgy` which implements the weave action.

### extending the lexical analysis of CommonMark markdown

`midgy` extends the commonmark spec to reflect some common conventions.

1. shebang
   : `midgy` documents are programs and may begin with a shebang.
2. front matter
   : can be included at the beginning of a document or after a shebang.
   : either toml, json or yaml can be used
3. doctest
   : a literate programming convention for including tests in python strings
4. code
   : our code blocks are modified to be aware of doctests and include lexical diagnostics of the content

### rendering tokenized CommonMark as Python

`midgy` translates markdown to python relative to indented code blocks. content between indented code blocks are wrapped a python block strings, and non-indented code blocks can be included in python programs. the translation from markdown to python is meant to require the fewest changes to have valid python code.

`midgy` goes to great lengths to generate a line-for-line translation of the markdown to python.
the line-for-line translation improves the error handling experience when using non-python documents as modules.

      from sys import argv
      print(argv)