semvec/pypi-packages huggingface dataset:
pyController 1.0
Event Callback system for Bluetooth Controller
Keywords: playstation,ps4,controller,robot controls

# pyController
[![PyPI version shields.io](https://img.shields.io/pypi/v/pyPS4Controller.svg)](https://pypi.python.org/pypi/pyPS4Controller/) 
[![PyPI pyversions](https://img.shields.io/pypi/pyversions/pyPS4Controller.svg)](https://pypi.python.org/pypi/pyPS4Controller/)
##
<img src="https://github.com/jbiams77/pyController/blob/master/documents/AcE2c.png?raw=true" width="50%">

## Architecture
<img src="https://github.com/jbiams77/pyController/blob/master/documents/SoftwareArchitecture.JPG?raw=true" width="60%">


## Installation
`INSTALLATION INSTRUCTIONS TO COME`

## Usage

1. USAGE INSTRUCTIONS TO COME
aiostp-beta-manu 0.0.9.dev5
asyncio client library for stpmex.com
Requires Python: >=3.7

asyncio client library for stpmex.com
# STP – Async python client library for stpmex.com

[![test](https://github.com/cuenca-mx/aiostp/workflows/test/badge.svg)](https://github.com/cuenca-mx/aiostp/actions?query=workflow%3Atest)
[![codecov](https://codecov.io/gh/cuenca-mx/aiostp/branch/main/graph/badge.svg)](https://codecov.io/gh/cuenca-mx/aiostp)


# Installation

`pip install ...`

# Authentication

The preferred way to configure the credentials for the client is to set the
`PRIV_KEY` and `PRIV_KEY_PASSPHRASE` environment variables. The client
library will automatically configure based on the values of those variables.
`PRIV_KEY` can be the route to the file, or the private key itself.

To configure manually:
```python
import aiostp

aiostp.configure(priv_key='PKxxxx', priv_key_passphrase='yyyyyy')
```
cygraph 0.2.1
A graph theory library written in Cython

# Cygraph

*A graph theory library implemented in Cython*

## Installation

First, try:

```bash
pip install cygraph
```

if that doesn't work, try:

```bash
pip install git+https://github.com/lol-cubes/cygraph
```

## Usage

### Python

Here is an example Python script that utilizes some of the features of cygraph.

```python
import cygraph as cg
import cygraph.algorithms as alg

G = cg.graph(vertices=list(range(4)))
G.add_edges({(1, 2), (2, 3), (3, 1)})
children = G.get_children(1)
print(f'{children=}')
components = alg.get_number_connected_components(G)
print(f'{components=}')
```

It should output:

```
children={2, 3}
components=2
```

### Cython

Here is the above script but with static typing in Cython.

```
#!python
#cython: language_level=3
import cygraph as cg  # cg.graph function.
cimport cygraph.graph_ as gp  # StaticGraph and DynamicGraph types.
cimport cygraph.algorithms as alg

cdef gp.DynamicGraph G = cg.graph(vertices=list(range(4)))
G.add_edges({(1, 2), (2, 3), (3, 1)})
cdef set children = G.get_children(1)
print(f'children={str(children)}')
cdef int components = alg.get_number_connected_components(G)
print(f'components={components}')
```

For more information on the Python and Cython APIs, see [the documentation](https://lol-cubes.github.io/cygraph)

---
**NOTE**

The current documentation is not up-to-date.

---

## Contribution

I am currently not accepting any pull requests. This project is still in its very early stages and I have many things in mind to do with it. Once I get those things done, I will add contribution guidelines.
denemefilz 1.0.2
Queueing Recurrent Neural Network

# Queueing Recurrent Neural Network (Q-RNN) [![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg)](https://www.python.org/)

**Queueing Recurrent Neural Network (Q-RNN)** is a new kind of Artificial Neural Network that has been designed to use in time-series forecasting applications. According to experiments that have been run, QRNN mostly outperforms the LSTM, Simple RNN and GRU, in the cases where the dataset has highly non-linear characteristics.

# Table of contents

- [What is Q-RNN?](#QRNN)
- [Comparison](#comparison)
- [Installation](#installation)
- [Usage](#usage)
- [License](#license)

## What is Q-RNN?

It is a compose of Simple RNN and Random Neural Network.

## Comparison

![Overall Comparison](test_results/overall_comparison.png)

## Installation

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

```bash
pip install queueing-rnn
```

## Usage

```python
import queueing-rnn
```

## License

[![GitHub license](https://img.shields.io/github/license/Naereen/StrapDown.js.svg)](https://github.com/Naereen/StrapDown.js/blob/master/LICENSE)