semvec/pypi-packages huggingface dataset:
qiyue-zaixian-shiyanshi-201805-201901 2023.2.16.0
七月在线实验室 201805-201901
Requires Python: >=3.6

# 七月在线实验室 201805-201901

## 下载

### Docker

```
docker pull apachecn0/qiyue-zaixian-shiyanshi-201805-201901
docker run -tid -p <port>:80 apachecn0/qiyue-zaixian-shiyanshi-201805-201901
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install qiyue-zaixian-shiyanshi-201805-201901
qiyue-zaixian-shiyanshi-201805-201901 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g qiyue-zaixian-shiyanshi-erlingyibalingwu-erlingyijiulingyi
qiyue-zaixian-shiyanshi-erlingyibalingwu-erlingyijiulingyi <port>
# 访问 http://localhost:{port} 查看文档
```
qiyue-zaixian-shiyanshi-201711-201805 2023.2.16.0
七月在线实验室 201711-201805
Requires Python: >=3.6

# 七月在线实验室 201711-201805

## 下载

### Docker

```
docker pull apachecn0/qiyue-zaixian-shiyanshi-201711-201805
docker run -tid -p <port>:80 apachecn0/qiyue-zaixian-shiyanshi-201711-201805
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install qiyue-zaixian-shiyanshi-201711-201805
qiyue-zaixian-shiyanshi-201711-201805 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g qiyue-zaixian-shiyanshi-erlingyiqiyiyi-erlingyibalingwu
qiyue-zaixian-shiyanshi-erlingyiqiyiyi-erlingyibalingwu <port>
# 访问 http://localhost:{port} 查看文档
```
qiyue-zaixian-shiyanshi-201611-201711 2023.2.16.0
七月在线实验室 201611-201711
Requires Python: >=3.6

# 七月在线实验室 201611-201711

## 下载

### Docker

```
docker pull apachecn0/qiyue-zaixian-shiyanshi-201611-201711
docker run -tid -p <port>:80 apachecn0/qiyue-zaixian-shiyanshi-201611-201711
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install qiyue-zaixian-shiyanshi-201611-201711
qiyue-zaixian-shiyanshi-201611-201711 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g qiyue-zaixian-shiyanshi-erlingyiliuyiyi-erlingyiqiyiyi
qiyue-zaixian-shiyanshi-erlingyiliuyiyi-erlingyiqiyiyi <port>
# 访问 http://localhost:{port} 查看文档
```
python-aihaozhe-shequ-202209-202302 2023.2.16.0
Python爱好者社区 202209-202302
Requires Python: >=3.6

# Python爱好者社区 202209-202302

## 下载

### Docker

```
docker pull apachecn0/python-aihaozhe-shequ-202209-202302
docker run -tid -p <port>:80 apachecn0/python-aihaozhe-shequ-202209-202302
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install python-aihaozhe-shequ-202209-202302
python-aihaozhe-shequ-202209-202302 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g python-aihaozhe-shequ-erlingererlingjiu-erlingersanlinger
python-aihaozhe-shequ-erlingererlingjiu-erlingersanlinger <port>
# 访问 http://localhost:{port} 查看文档
```
python-aihaozhe-shequ-202204-202209 2023.2.16.0
Python爱好者社区 202204-202209
Requires Python: >=3.6

# Python爱好者社区 202204-202209

## 下载

### Docker

```
docker pull apachecn0/python-aihaozhe-shequ-202204-202209
docker run -tid -p <port>:80 apachecn0/python-aihaozhe-shequ-202204-202209
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install python-aihaozhe-shequ-202204-202209
python-aihaozhe-shequ-202204-202209 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g python-aihaozhe-shequ-erlingererlingsi-erlingererlingjiu
python-aihaozhe-shequ-erlingererlingsi-erlingererlingjiu <port>
# 访问 http://localhost:{port} 查看文档
```
RedBayes 0.0.0
Implementación del algoritmo para una red Bayesiana.
Requires Python: >=3.7
Keywords: python,bayes

# Explicación de la funcionalidad de la librería.

La presente librería permite crear y analizar redes bayesianas. Para lo mismo, se necsita crear lo siguiente: 

1. Una red que contenga los nodos y las aristas que se desean analizar de la siguiente forma: 

red = {
    "A": ["B", "C"],
    "B": [],
    "C": ["D"],
    "D": []
}

2. Una red de probabildades que contenga las probabilidades de cada nodo de la siguiente forma:

probabilidades = {
    "A": {"distribucion": [0.1, 0.9], "padres": []},
    "B": {"distribucion": [0.8, 0.2], "padres": [("A", 0), ("A", 1)]}, # 0.8 = P(B=0|A=0, C=0), 0.2 = P(B=0|A=0, C=1)
    "C": {"distribucion": [0.7, 0.3], "padres": [("A", 0), ("A", 1)]}, # 0.7 = P(C=0|A=0, C=0), 0.3 = P(C=0|A=0, C=1)
    "D": {"distribucion": [0.6, 0.4], "padres": [("C", 0), ("C", 1)]} # 0.6 = P(D=0|C=0), 0.4 = P(D=0|C=1)
}

3. Estas dos redes se deben pasar a la librería de RedBayes de la siguiente forma: 

bayes = RedBayes(red, probabilidades)

Esto devolverá un texto que indica si la red está completamente descrita o no.

4. Para calcular la probabilidad de una variable, se debe llamar a la función de la siguiente forma:

Crear un query de la siguiente forma:
query = ("A", ("B", 0), ("C", 1), ("D", 0))

Luego, llamar a la función de inferencia de la siguiente forma:
bayes.inferencia(red, probabilidades, query)

Con lo anterior se obtiene la probabilidad de la variable A, dada la evidencia de que B=0, C=1 y D=0.
it-ebooks-archive 2023.2.16.0
计算机电子书
Requires Python: >=3.6

# 计算机电子书

> 不装逼的人，我们都不想认识。

* [在线阅读](https://it-ebooks.apachecn.org)
* [在线阅读（Gitee）](https://apachecn.gitee.io/doc-template/)
* [ApacheCN 学习资源](http://docs.apachecn.org/)

## 联系方式

### 负责人

* [飞龙](https://github.com/wizardforcel): 562826179

### 其他

*   在我们的 [apachecn/it-ebooks-archive](https://github.com/apachecn/it-ebooks-archive) github 上提 issue.
*   发邮件到 Email: `apachecn@163.com`.
*   在我们的 [组织学习交流群](http://www.apachecn.org/organization/348.html) 中联系群主/管理员即可.

## 下载

### Docker

```
docker pull apachecn0/it-ebooks-archive
docker run -tid -p <port>:80 apachecn0/it-ebooks-archive
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install it-ebooks-archive
it-ebooks-archive <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g it-ebooks-archive
it-ebooks-archive <port>
# 访问 http://localhost:{port} 查看文档
```

## 赞助我们

![](http://data.apachecn.org/img/about/donate.jpg)
python-aihaozhe-shequ-202112-202204 2023.2.16.0
Python爱好者社区 202112-202204
Requires Python: >=3.6

# Python爱好者社区 202112-202204

## 下载

### Docker

```
docker pull apachecn0/python-aihaozhe-shequ-202112-202204
docker run -tid -p <port>:80 apachecn0/python-aihaozhe-shequ-202112-202204
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install python-aihaozhe-shequ-202112-202204
python-aihaozhe-shequ-202112-202204 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g python-aihaozhe-shequ-erlingeryiyier-erlingererlingsi
python-aihaozhe-shequ-erlingeryiyier-erlingererlingsi <port>
# 访问 http://localhost:{port} 查看文档
```
python-aihaozhe-shequ-202108-202112 2023.2.16.0
Python爱好者社区 202108-202112
Requires Python: >=3.6

# Python爱好者社区 202108-202112

## 下载

### Docker

```
docker pull apachecn0/python-aihaozhe-shequ-202108-202112
docker run -tid -p <port>:80 apachecn0/python-aihaozhe-shequ-202108-202112
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install python-aihaozhe-shequ-202108-202112
python-aihaozhe-shequ-202108-202112 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g python-aihaozhe-shequ-erlingeryilingba-erlingeryiyier
python-aihaozhe-shequ-erlingeryilingba-erlingeryiyier <port>
# 访问 http://localhost:{port} 查看文档
```
jbnetwork 0.0.3
Implementación del algoritmo para una red Bayesiana.
Requires Python: >=3.7
Keywords: python,bayes

# Explicación de la funcionalidad de la librería.

La presente librería permite crear y analizar redes bayesianas. Para lo mismo, se necsita crear lo siguiente: 

1. Una red que contenga los nodos y las aristas que se desean analizar de la siguiente forma: 

red = {
    "A": ["B", "C"],
    "B": [],
    "C": ["D"],
    "D": []
}

2. Una red de probabildades que contenga las probabilidades de cada nodo de la siguiente forma:

probabilidades = {
    "A": {"distribucion": [0.1, 0.9], "padres": []},
    "B": {"distribucion": [0.8, 0.2], "padres": [("A", 0), ("A", 1)]}, # 0.8 = P(B=0|A=0, C=0), 0.2 = P(B=0|A=0, C=1)
    "C": {"distribucion": [0.7, 0.3], "padres": [("A", 0), ("A", 1)]}, # 0.7 = P(C=0|A=0, C=0), 0.3 = P(C=0|A=0, C=1)
    "D": {"distribucion": [0.6, 0.4], "padres": [("C", 0), ("C", 1)]} # 0.6 = P(D=0|C=0), 0.4 = P(D=0|C=1)
}

3. Estas dos redes se deben pasar a la librería de Bayes de la siguiente forma: 

bayes = Bayes(red, probabilidades)

Esto devolverá un texto que indica si la red está completamente descrita o no.

4. Para calcular la probabilidad de una variable, se debe llamar a la función de la siguiente forma:

Crear un query de la siguiente forma:
query = ("A", ("B", 0), ("C", 1), ("D", 0))

Luego, llamar a la función de inferencia de la siguiente forma:
bayes.inferencia(red, probabilidades, query)

Con lo anterior se obtiene la probabilidad de la variable A, dada la evidencia de que B=0, C=1 y D=0.
BayesNetCreacion 1.2.0
Library capable of creating Bayesian Networks and making probabilistic inference over them, as well additional functions
Keywords: python,bayes,bayesian network,zero dependencies,bayesian inference,python3,open source


# BayesNetCreacion
This library has the objective of building Bayesian networks and making probabilistic inference over them. Also, adding some other additional features that could serve the developers that make use of this library.  
This library has zero dependencies to assure it is futureproof, easier to debug, to contribute to and use.  
For the most part this library works over classes like BayesNetCreacion and Node, this was chosen so that in a way it could facilitate the usage of the OOP paradigm.


For further inquiries you about the usage of the library you can consult [Usage](#usage) section of this repo or the [testing](/BayesNetCreacion/testing.py) python file to see the Burglar Alarm System example being used.

### Features

- Created without using any dependencies/libraries.
- Can create Bayesian Networks, represented as a dictionary.
- Calculation of probabilistic inference given the created Bayesian Network.
- Can check if network is fully defined or not.
- Has the capability of returning a compact representation of the network as a string.
- Can return the network's factors as a dictionary.
- Is able to return the numeration of the network given evidence and a query.

### Prerequisites
- Python 3.10.^

### Install
```bash
pip install BayesNetCreacion
```

### Usage
```python
from BayesNetCreacion import BayesNetCreacion, Node #Import the library

examplenode = Node('E') #Create a Node object
example2node = Node('E2') #Create another Node object
example2node.set_parents([str(examplenode)]) #Set node as parent

bnc = BayesNetCreacion() #Instantiate the Bayes Network class

#Add both nodes into the network
bnc.add_node(examplenode)
bnc.add_node(example2node) 

#Add the probability values of each node 
bnc.add_prob({
    ('E', True): 0.001,
    ('E', False): 0.999,
    ('E2', True, 'E', True): 0.25,
    ('E2', True, 'E', False): 0.9,
    ('E2', False, 'E', True): 0.75,
    ('E2', False, 'E', False): 0.1
})

#Features:

#Get dictionary with network
bn = bnc.get_network()

#Probabilistic inference
evidence = {'E': True}
query = 'E2'

result = bnc.probabilistic_inference(query, evidence) #Calculate the inference
print(result) #Profit?

#Compact
WIP

#Factors
print(bnc.get_factors())

#Definition Check
WIP

#Enumeration
evidence = {'E': False}
query = 'E2'
result = bnc.pre_enum(query, evidence) #Calculate numeration
print(result)

```

## Author
👤 Andrés de la Roca
 - <a href = "https://www.linkedin.com/in/andr%C3%A8s-de-la-roca-pineda-10a40319b/">Linkedin</a> 
 - <a href="https://github.com/andresdlRoca">Github</a>
python-aihaozhe-shequ-202105-202108 2023.2.16.0
Python爱好者社区 202105-202108
Requires Python: >=3.6

# Python爱好者社区 202105-202108

## 下载

### Docker

```
docker pull apachecn0/python-aihaozhe-shequ-202105-202108
docker run -tid -p <port>:80 apachecn0/python-aihaozhe-shequ-202105-202108
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install python-aihaozhe-shequ-202105-202108
python-aihaozhe-shequ-202105-202108 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g python-aihaozhe-shequ-erlingeryilingwu-erlingeryilingba
python-aihaozhe-shequ-erlingeryilingwu-erlingeryilingba <port>
# 访问 http://localhost:{port} 查看文档
```
python-aihaozhe-shequ-202102-202105 2023.2.16.0
Python爱好者社区 202102-202105
Requires Python: >=3.6

# Python爱好者社区 202102-202105

## 下载

### Docker

```
docker pull apachecn0/python-aihaozhe-shequ-202102-202105
docker run -tid -p <port>:80 apachecn0/python-aihaozhe-shequ-202102-202105
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install python-aihaozhe-shequ-202102-202105
python-aihaozhe-shequ-202102-202105 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g python-aihaozhe-shequ-erlingeryilinger-erlingeryilingwu
python-aihaozhe-shequ-erlingeryilinger-erlingeryilingwu <port>
# 访问 http://localhost:{port} 查看文档
```
python-aihaozhe-shequ-202011-202102 2023.2.16.0
Python爱好者社区 202011-202102
Requires Python: >=3.6

# Python爱好者社区 202011-202102

## 下载

### Docker

```
docker pull apachecn0/python-aihaozhe-shequ-202011-202102
docker run -tid -p <port>:80 apachecn0/python-aihaozhe-shequ-202011-202102
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install python-aihaozhe-shequ-202011-202102
python-aihaozhe-shequ-202011-202102 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g python-aihaozhe-shequ-erlingerlingyiyi-erlingeryilinger
python-aihaozhe-shequ-erlingerlingyiyi-erlingeryilinger <port>
# 访问 http://localhost:{port} 查看文档
```
python-aihaozhe-shequ-202008-202011 2023.2.16.0
Python爱好者社区 202008-202011
Requires Python: >=3.6

# Python爱好者社区 202008-202011

## 下载

### Docker

```
docker pull apachecn0/python-aihaozhe-shequ-202008-202011
docker run -tid -p <port>:80 apachecn0/python-aihaozhe-shequ-202008-202011
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install python-aihaozhe-shequ-202008-202011
python-aihaozhe-shequ-202008-202011 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g python-aihaozhe-shequ-erlingerlinglingba-erlingerlingyiyi
python-aihaozhe-shequ-erlingerlinglingba-erlingerlingyiyi <port>
# 访问 http://localhost:{port} 查看文档
```
jbnetwork-javiervallebs 0.0.1
Implementación del algoritmo para una red Bayesiana.
Requires Python: >=3.7
Keywords: python,bayes

# Explicación de la funcionalidad de la librería.

La presente librería permite crear y analizar redes bayesianas. Para lo mismo, se necsita crear lo siguiente: 

1. Una red que contenga los nodos y las aristas que se desean analizar de la siguiente forma: 

red = {
    "A": ["B", "C"],
    "B": [],
    "C": ["D"],
    "D": []
}

2. Una red de probabildades que contenga las probabilidades de cada nodo de la siguiente forma:

probabilidades = {
    "A": {"distribucion": [0.1, 0.9], "padres": []},
    "B": {"distribucion": [0.8, 0.2], "padres": [("A", 0), ("A", 1)]}, # 0.8 = P(B=0|A=0, C=0), 0.2 = P(B=0|A=0, C=1)
    "C": {"distribucion": [0.7, 0.3], "padres": [("A", 0), ("A", 1)]}, # 0.7 = P(C=0|A=0, C=0), 0.3 = P(C=0|A=0, C=1)
    "D": {"distribucion": [0.6, 0.4], "padres": [("C", 0), ("C", 1)]} # 0.6 = P(D=0|C=0), 0.4 = P(D=0|C=1)
}

3. Estas dos redes se deben pasar a la librería de Bayes de la siguiente forma: 

bayes = Bayes(red, probabilidades)

Esto devolverá un texto que indica si la red está completamente descrita o no.

4. Para calcular la probabilidad de una variable, se debe llamar a la función de la siguiente forma:

Crear un query de la siguiente forma:
query = ("A", ("B", 0), ("C", 1), ("D", 0))

Luego, llamar a la función de inferencia de la siguiente forma:
bayes.inferencia(red, probabilidades, query)

Con lo anterior se obtiene la probabilidad de la variable A, dada la evidencia de que B=0, C=1 y D=0.
python-aihaozhe-shequ-202005-202008 2023.2.16.0
Python爱好者社区 202005-202008
Requires Python: >=3.6

# Python爱好者社区 202005-202008

## 下载

### Docker

```
docker pull apachecn0/python-aihaozhe-shequ-202005-202008
docker run -tid -p <port>:80 apachecn0/python-aihaozhe-shequ-202005-202008
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install python-aihaozhe-shequ-202005-202008
python-aihaozhe-shequ-202005-202008 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g python-aihaozhe-shequ-erlingerlinglingwu-erlingerlinglingba
python-aihaozhe-shequ-erlingerlinglingwu-erlingerlinglingba <port>
# 访问 http://localhost:{port} 查看文档
```
python-aihaozhe-shequ-202002-202005 2023.2.16.0
Python爱好者社区 202002-202005
Requires Python: >=3.6

# Python爱好者社区 202002-202005

## 下载

### Docker

```
docker pull apachecn0/python-aihaozhe-shequ-202002-202005
docker run -tid -p <port>:80 apachecn0/python-aihaozhe-shequ-202002-202005
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install python-aihaozhe-shequ-202002-202005
python-aihaozhe-shequ-202002-202005 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g python-aihaozhe-shequ-erlingerlinglinger-erlingerlinglingwu
python-aihaozhe-shequ-erlingerlinglinger-erlingerlinglingwu <port>
# 访问 http://localhost:{port} 查看文档
```