semvec/pypi-packages huggingface dataset:
sota-dqn 0.0.7
State of the art opinionanted DQN training and inference
License: MIT

# State of the Art Deep Q Networks

sota_dqn is an opinionated deep Q network library focused on productive implementation.

sota_dqn makes state of the art Reinforcement Learning easy and productive to use.
Methods such as [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952) and fixed q target training are implemented by default.

Other methods are under consideration for implementation such as:
- https://cs.brown.edu/people/kasadiat/pdf/deep_mellow.pdf
- https://www.hindawi.com/journals/mpe/2018/2129393/abs/
- [Hindsight Experience Replay](http://papers.nips.cc/paper/7090-hindsight-experience-replay) are exposed through simple to use interfaces.


# Overview
## Goals
- Provide a sound api to interact with state of the art deep Q learning
- Abstract where necessary without over-abstracting.  This is not a library for those without knowledge of DQN architecture

## Non-Goals
- Provide a complete deep Q learning api. 
- one liners for dqn models (i.e. `rainbow = RainbowModel(env=env)`)

## Examples
- [basic DQN](examples/basic/)
- [CNN on Image Inputs](examples/cnn/)

# Installation
sota_dqn is published on pypi by a CI/CD workflow
```
pip install sota_dqn
```

sota_dqn can also be installed as a git repo:
```
git clone https://github.com/LukeWood/sota-dqn.git &&
cd sota-dqn && 
pip install -r requirements.txt .
```

# Quickstart
Batteries included examples held in `examples/`.

## Cartpole
The quickest example to get started with is the [basic](examples/basic) which uses the `CartPole-v1` environment.

![random sample video](media/random-cartpole.gif)

_Cartpole-V1 before training_

You can train a model for yourself quickly.

The model has the following architecture:

![model architecture](media/basic_model.png)

This is created by the [training script](examples/basic/train.py).

```
python examples/basic/dqn_train.py
```

After the first checkpoint is saved you can test the model.

```
python examples/basic/dqn_inference.py
```

![trained sample video](media/trained-cartpole.gif)

_Cartpole-V1 after training_

## Ms Pacman
This example builds on on the foundations set in the basic example.
In the Ms Pacman example the raw image data is returned from the environment at each step.
Preprocessing converts the images to gray scale to reduce the dimensionality of the input space.

On top of this a convolutional model with the following architecture is used:
![cnn architecture](media/cnn_model.png)

_A GPU is highly recommended for training this model_.
pysftptail 0.0.1
pySFTPtail is a Python3 implementaion of the tail command over a sftp connection
License: MIT
Requires Python: >=3.7
Keywords: pysftp,sfpt,tail,logs

___________ ___________ _        _ _ 
                /  ___|  ___|_   _| ___ \ |      (_) |
     _ __  _   _\ `--.| |_    | | | |_/ / |_ __ _ _| |
    | '_ \| | | |`--. \  _|   | | |  __/| __/ _` | | |
    | |_) | |_| /\__/ / |     | | | |   | || (_| | | |
    | .__/ \__, \____/\_|     \_/ \_|    \__\__,_|_|_|
    | |     __/ |                                     
    |_|    |___/                                      

[![Build Status](https://travis-ci.com/tcvieira/pysftptail.svg?branch=master)](https://travis-ci.com/tcvieira/pysftptail)
[![codecov](https://codecov.io/gh/tcvieira/pysftptail/branch/master/graph/badge.svg)](https://codecov.io/gh/tcvieira/pysftptail)

[![MIT Licence](https://badges.frapsoft.com/os/mit/mit.svg?v=103)](https://opensource.org/licenses/mit-license.php)


# pysftp-tail
pySFTPtail is a Python3 implementaion of the tail command over a sftp connection

# Usage

```
# pysftptail script

python3 pysftptail.py -h host -u username -p password -n 5

Usage: pysftptail.py [OPTIONS] FILE_PATH

Options:
  -h, --host TEXT      SFTP host server, prompted if omitted.
  -u, --username TEXT  SFTP username, prompted if omitted.
  -p, --password TEXT  SFTP password, prompted if omitted.
  -n, --lines INTEGER  Output the last n lines.  [default: 5]
  -f, --follow         Output data as the file grows.
  --version            Show the version and exit.
  --help               Show this message and exit.

# Execute tests
pytest

```
mercs-mixed 0.0.45
MERCS: Multi-Directional Ensembles of Regression and Classification treeS
License: MIT
Requires Python: >=3.8.0,<4.0.0

# MERCS

MERCS stands for **multi-directional ensembles of classification and regression trees**. It is a novel ML-paradigm under active development at the [DTAI-lab at KU Leuven](https://dtai.cs.kuleuven.be/).

## Installation

Easy via pip:

```
pip install mercs-mixed
```

## Website

Cf. [https://systemallica.github.io/mercs/](https://systemallica.github.io/mercs/)

## Tutorials

Cf. the quickstart section of the website, [https://systemallica.github.io/mercs/quickstart](https://systemallica.github.io/mercs/quickstart).

## Code

MERCS is fully open-source cf. our [github-repository](https://github.com/systemallica/mercs/)

## Publications

MERCS is an active research project, hence we periodically publish our findings;

### MERCS: Multi-Directional Ensembles of Regression and Classification Trees

**Abstract**
*Learning a function f(X) that predicts Y from X is the archetypal Machine Learning (ML) problem. Typically, both sets of attributes (i.e., X,Y) have to be known before a model can be trained. When this is not the case, or when functions f(X) that predict Y from X are needed for varying X and Y, this may introduce significant overhead (separate learning runs for each function). In this paper, we explore the possibility of omitting the specification of X and Y at training time altogether, by learning a multi-directional, or versatile model, which will allow prediction of any Y from any X. Specifically, we introduce a decision tree-based paradigm that generalizes the well-known Random Forests approach to allow for multi-directionality. The result of these efforts is a novel method called MERCS: Multi-directional Ensembles of Regression and Classification treeS. Experiments show the viability of the approach.*

**Authors**
Elia Van Wolputte, Evgeniya Korneva, Hendrik Blockeel

**Open Access**
A pdf version can be found at [AAAI-publications](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16875/16735)

## People

People involved in this project:

* [Elia Van Wolputte](https://eliavw.github.io/)
* Evgeniya Korneva
* [Prof. Hendrik Blockeel](https://people.cs.kuleuven.be/~hendrik.blockeel/)
* [Andrés Reverón Molina](https://andres.reveronmolina.me)
predictit-client 0.2
Client API wrapper for PredictIt
License: MIT

# PredictIt API Client

[![python-unittest Actions Status](https://github.com/evbarnett/predictit-client/workflows/python-unittest/badge.svg?branch=master)](https://github.com/evbarnett/predictit-client/actions)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

PredictIt API Client for Python. Python 3+ required.

## Installation

`pip install predictit-client`

## Usage

```python
from predictit_client import Client
```

**Get all markets**

```python
market_id = 2721
client = Client()
markets = client.get_all_markets() # -> List[Market]
```

**Get single market**

```python
market_id = 2721
client = Client()
market = client.get_market_with_id(market_id) # -> Optional[Market]
```

**Market Fields**

```python
market_id: int  
name: str  
short_name: str  
image_url: str  
url: str  
contracts: List[Contract]  
time_stamp: str  
status: str
```

**Contract Fields**

```python
contract_id: int  
date_end: str  
image_url: str  
name: str   
short_name: str  
status: str  
last_trade_price: float  
best_buy_yes_cost: float  
best_buy_no_cost: float  
best_sell_yes_cost: float  
best_sell_no_cost: float  
display_order: int  
```
python-papermc 1.0.0
Python wrapper for the PaperMC Downloads API (https://papermc.io)
License: MIT
Requires Python: >=3.5

# python-papermc
![License](https://img.shields.io/github/license/hoot-w00t/python-papermc?style=flat-square) ![Python Version](https://img.shields.io/pypi/pyversions/python-papermc?style=flat-square) [![PyPI](https://img.shields.io/pypi/v/python-papermc?style=flat-square)](https://pypi.org/project/python-papermc/)

Python wrapper for the PaperMC Downloads API (https://papermc.io)

## Installing
Using PyPI:
```
python3 -m pip install python-papermc
```

## Example usage
```py
import papermc

with papermc.Paper() as paper:
    version = paper.get_latest_version()
    paper.download_build_to_file("paper-{}.jar".format(version), version, "latest")
```

## How to use
- `Paper()`, `Waterfall()` and `Travertine()` provide the following functions:
    - `get_versions()`
      - Returns a `list` of available versions
    - `get_latest_version()`
      - Returns the latest version
    - `get_builds(version)`
      - Returns a `list` of builds for `version`
    - `get_latest_build(version)`
      - Returns the latest build for `version`
    - `download_build(version, build)`
      - Both `version` and `build` can be set to `latest`
      - Returns the corresponding JAR file's `bytes`
    - `download_build_to_file(filepath, version, build)`
      - Same as above but writes the JAR file's bytes to `filepath` instead of returning them

- `get_from_name(project_name)`
  - Returns the corresponding class from the project name (e.g. `paper` will return a `Paper()` object)
  - Raises a `ProjectNotFoundError` if the project name is invalid
forismatic 0.1.3
An unofficial API wrapper for the forismatic.com API.
License: MIT
Requires Python: >=3.6
Keywords: Forismatic,ForismaticPy,Forismatic.py

An unofficial API wrapper for the forismatic.com API.

# Installation
 
## Install with Pip (Recommended)

```bash
pip install forismatic
```

## Install from source 

```bash
git clone https://github.com/ihumanbeing/forismatic.git
cd forismatic
python setup.py install
```

# Usage

Example getting an english quote:

```python
from forismatic import *
f = forismatic.ForismaticPy()
f.get_Quote('en') # English, for russian, use 'ru'
```

Output:
```bash
('Victory belongs to the most persevering.  ', 'Napoleon Bonaparte ')
```