semvec/pypi-packages huggingface dataset:
reddit-2-album 0.0.10
Return photo list and caption (markdown format) from reddit.
Requires Python: >=3.0

# reddit_2_album

Return photo list and caption (markdown format) from reddit.

## usage

```
import reddit_2_album
result = reddit_2_album.get(url)
result.imgs
result.video
result.cap

```

## how to install

`pip3 install reddit_2_album`
vanilla-pkg 0.1
A small example package
Requires Python: >=3.7

# Vanilla package
[![Build Status](https://travis-ci.org/Feralo/vanilla_pkg.svg?branch=master)](https://travis-ci.org/Feralo/vanilla_pkg)
[![Coverage Status](https://coveralls.io/repos/github/Feralo/vanilla_pkg/badge.svg?branch=master)](https://coveralls.io/github/Feralo/vanilla_pkg?branch=master)

## PyPI
This is a simple class which takes borrows heavily from the examples in [the docs](https://docs.python.org/3/tutorial/classes.html) package, built process outlined [python's](https://python.org) official [packaging project tutorial](https://packaging.python.org/tutorials/packaging-projects/).

## Testing
Unit tests reside in the **test** subdirectory of the vanilla_pkg directory. 

Tests can be run with:
     `pytest -v --cov=vanilla_pkg`
     
## Anaconda package
After [building a conda package](https://github.com/conda/conda-build#building-your-own-packages), it can be published by following the [user guide for uploading packages](http://docs.anaconda.com/anaconda-cloud/user-guide/tasks/work-with-packages/#uploading-packages). 
The user guide refers to a meta.yaml file but provides no examples. Fortunately, there is an [example package in the conda demo](https://github.com/Anaconda-Platform/anaconda-client).
irl-maxent 0.1.0
A small package for Maximum Entropy Inverse Reinforcement Learning on simple MDPs
Requires Python: >=3.6


# Maximum Entropy Inverse Reinforcement Learning

This is a python implementation of the Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) algorithm based on the similarly named paper by Ziebart et al. and the Maximum Causal Entropy Inverse Reinforcement Learning (MaxCausalEnt IRL) algorithm based on his PhD thesis.

You may also want to have a look at the accompanying [presentation][presentation].

For an example demonstrating how the Maximum (non-causal) Entropy IRL algorithm works, see the corresponding Jupyter notebook ([`notebooks/maxent.ipynb`][nb-viewer]).
Note that this python package contains a slightly more optimized implementation of the algorithms.

For an example on how to use this framework, have a look at the [`example.py`][example] file.
The framework contains:
- Two GridWorld implementations for demonstration (`irl_maxent.gridworld`)
- The algorithm implementations (`irl_maxent.maxent`)
- A gradient based optimizer framework (`irl_maxent.optimizer`)
- Plotting helper functions (`irl_maxent.plot`)
- A MDP solver framework, i.e. value iteration and corresponding utilities (`irl_maxent.solver`)
- A trajectory/trajectory generation framework (`irl_maxent.trajectory`)

[nb-viewer]: https://nbviewer.jupyter.org/github/qzed/irl-maxent/blob/master/notebooks/maxent.ipynb
[presentation]: https://nbviewer.jupyter.org/github/qzed/irl-maxent/blob/master/Presentation.pdf
[example]: https://github.com/qzed/irl-maxent/blob/master/src/example.py
green-screen-picture 0.0.1
simple package to add background images to green screen
Requires Python: >=3.8

# green_screen_picture

The package green_screen_picture is used to merge background to green screen picture

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install green_screen_picture

```bash
pip install green_screen_picture
```

## Usage

```python
from green_screen_picture.combination import combine
combine(picture, background)
```

## Author

mrcs22

## License

[MIT](https://choosealicense.com/licenses/mit/)
package-dio-test 0.0.1
test publication python package
Requires Python: >=3.8

# package_name

Description.
The package package_name is used to: - test publication -

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install package_name
```

## Usage

```python
from package_name import file1_name
file1_name.my_function()
```

## Author

Raila

## License

[MIT](https://choosealicense.com/licenses/mit/)