semvec/pypi-packages huggingface dataset:
test-upload-lartweib 0.1.0
Una biblioteca para consultar cursos de hack4u

#Hack4U Academy Courses Library

Una biblioteca Python para consultar cursos de la academia Hack4U.

## Cursos disponibles:

- Introducción a Linux [15 horas]
- Personalización de Linux [3 horas]
- Introducción al Hacking [53 horas]

## Instalación

Instala el paquete usando pip3:

```python3
pip3 install hack4u
```

## Uso básico

### Listar todos los cursos

```python3
from hack4u import list_courses

for course in list_courses():
    print(course)
```

### Obtener un curso por nombre

```python3 
from hack4u import get_course_by_name

course = get_course_by_name("Introducción a Linux")
print(course)
```

### Calcular duración total de los cursos

```python3
from hack4u.utils import total_duration

print(f"Duración total: {total_duration()} horas")
```
basecfg 2.1.1
typed 12-factor app configuration helper
Requires Python: >=3.10

# basecfg

`basecfg` is a Python library designed to simplify and standardize the process of configuring a Python application, particularly a Dockerized application.

Users of this library create a class which inherits from `basecfg.BaseCfg`. For each configuration option an app should support, a type-annotated class attribute is defined using a call to `opt`.

Once a config class is instantiated it automatically populates the configuration from these sources:

1. default values declared in the configuration class
2. a JSON config file (such as a [Kubernetes ConfigMap](https://kubernetes.io/docs/concepts/configuration/configmap/))
3. a `.env` file (in `docker run` envfile format)
4. environment variables
5. docker secrets
6. command-line arguments

## Example

This file is an excerpt from [a more extensive example](docs/example.md) implementation of the library:

```python
#!/usr/bin/env python3
from typing import Optional

from basecfg import BaseCfg, opt


class ExampleAppConf(BaseCfg):
    server_username: str = opt(
        default="demoperson",
        doc="The username to use on the server",
    )
    server_password: Optional[str] = opt(
        default=None,
        doc="The password to use on the server",
        redact=True,
    )
    verbose: bool = opt(
        default=False,
        doc="whether to log verbosely",
    )
    batch_size: Optional[int] = opt(
        default=None,
        doc="how many objects to transfer at a time",
    )
```
customllib 0.0.3
A custom machine learning library
Requires Python: >=3.11

# Customllib

A custom machine learning library.

## Install

```shell
pip install customllib
```

## Usage

See the [full example](examples%2Fhouse_prices_prediction.py) at the examples directory

```python
import numpy as np
from customllib.supervised.regression.linear_regression import LinearRegression

# Load our data set
X_train = np.array([1.0, 2.0])
y_train = np.array([300.0, 500.0])

# Train the model
linear_regression = LinearRegression()
final_w, final_b, _, _ = linear_regression.train(
    x=X_train,
    y=y_train,
    w_init=0,
    b_init=0,
    alpha=1.0e-1,
    iterations=1000,
    lambda_=0,
)

# Predict
yhat = linear_regression.predict(x=X_train, w=final_w, b=final_b)

# Result
print(f"yhat: {yhat}")
print(f"ytrain: {y_train}")
```

## Examples

See [README.md](examples%2FREADME.md) of the examples' directory.

## License

[MIT License](LICENSE)