semvec/pypi-packages huggingface dataset:
LPDT 0.0.2
Locally differentially Private Decision Tree
Requires Python: >=3

# Locally differentially Private Decision Tree (LPDT)

This repository implements **LPDT**, the Locally differentially Private Decision Tree described in the paper Decision Tree for Locally Private Estimation with Public Data accepted for NeurIPS 2023.
The implementation is based on pure Python with the following required packages:

- Scikit-learn
- NumPy
- Numba
- Scipy

## Contents

- [Installation](#Installation)
- [Demo](#Demo)
- [References](#References)


## Installation

### Via PyPI

```bash
pip install LPDT
```

### Via GitHub

```bash
pip install git+https://github.com/Karlmyh/LPDT.git
```


### Manual Install
  > 
```bash
git clone git@github.com:Karlmyh/LPDT.git
cd LPDT 
python setup.py install
```

## Demo
See simulation.py for a demo of the use of the class.



## References

- *Decision Tree for Locally Private Estimation with Public Data* (NeurIPS 2023)
calculator-advanced 1.0.0
A simple Python package for calculation.
Requires Python: >=3.8

#### calculator package

- This package provides basic calculation of two numbers, for e.g, (add, subtract, divide, multiply)

MIT License

Copyright (c) 2023 Aman Mourya

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
TannhauserGate 0.3.2.dev0
P2P Cross Chain Atomic Swap
Requires Python: >=3.7, <4
Keywords: tannhausergate,tannhauser,atomicswap,atomic,swap,bitcoin,litecoin,cryptocurrencies

# Tannhauser Gate Atomic Swap

## Video Showcase (YouTube)

[![Video](https://img.youtube.com/vi/qfZ3Hac58Pw/0.jpg)](https://youtu.be/watch?v=qfZ3Hac58Pw "TannhauserGate")

## Installation

```
python3 -m venv venv
. venv/bin/activate
pip install TannhauserGate
tannhauser
```

## Depends

**Tannhauser Gate depends at [Tor](https://github.com/torproject/tor), [BitcoinCore](https://github.com/bitcoin/bitcoin) and [LitecoinCore](https://github.com/litecoin-project/litecoin). Please install it first. You will find sample config files [here](https://github.com/TannhauserGate420/tannhauser/tree/main/atomicswap/contrib).**

## Transactions:

|           Action | TXID                                                         |
| ---------------: | ------------------------------------------------------------ |
|      Client Bond | https://chain.so/tx/BTC/1242b63342d6222a55cfb7c339142acb3e23937b255aeb108a6783ddad56b07c |
|       TG Funding | https://chain.so/tx/LTC/b9bf6e608636560084f0abdb2a552feaaa18452439c90c0c38abc77c0976fddc |
|   Client Funding | https://chain.so/tx/BTC/756c491c236a1ff87feb10f72001fc2450f1815333b38995f23daf034e17fdb0 |
| TG Bond withdraw | https://chain.so/tx/BTC/503ba2a313de754ee850340dd8aac601df11c3586d115abd8c647ce34bf3e46c |
| TG Swap withdraw | https://chain.so/tx/BTC/24872849fef2f7ee6a284b675eb28c9d2a16bf8b550f586cd185004588dcdf14 |
|  Client withdraw | https://chain.so/tx/LTC/9349b37068f8cffb04d7327c2d92930f3d7771999e31d2e8362c8bb03d7a23f0 |

## Notes

**Tannhauser Gate  is a simple POC for an automatic atomic swap service. Tannhauser  is still in development mode - so use it at your own risk. For the Litecoin connection Tannhauser uses a customized version of python-bitcoinlib. There is a simple GUI as wrapper for easy handling. The GUI is a little bit guerrilla - but it does the job. So if you are a QT wizard - feel free to make a PR. The GUI uses a different (additional) library for RPC in contrast to the CLI. For some reason the GUI produces a broken pipe error at irregular intervals and I don't have time to look into it at the moment. The main goal of the development is a simple p2p client, where the user can be both maker and taker. Currently I take a closer look at [GNUnet](https://www.gnunet.org/en/) to realize this in a safe way. Until then you can swap smaller amounts with Tannhauser. For Tannhauser there is a general refund window of 2 hours. Refunds for the user are possible after 1 hour or after 2 hours (Bond).**

## Donations

**I do not accept donations. If you have some sats left donate them to the [Torproject](https://donate.torproject.org/cryptocurrency/) or to the [EFF](https://supporters.eff.org/donate/join-eff-4).**
TwitAnalysis 1.1
Package for analyzing Twitter data
Requires Python: >=3.10

[![Generic badge](https://img.shields.io/badge/Licence-MIT-blue.svg)](https://shields.io/)
[![Generic badge](https://img.shields.io/badge/Maintained-yes-green.svg)](https://shields.io/)
[![Generic badge](https://img.shields.io/badge/Python-3.10.6-yellow.svg)](https://shields.io/)
[![Generic badge](https://img.shields.io/badge/TwitAnalysis-1.1-red.svg)](https://pypi.org/project/TwitAnalysis/)

## Purpose
Every individual user on Twitter has a customized, personal experience and only views a tiny portion of the actual conversations, content and opinions that are put out on the platform. The purpose of this project is to provide tools to allow people to use Twitter data to obtain a more holistic perspective of the social network landscape. The TwitAnalysis modules allow for the live processing of Tweet streams as well as processing mass amounts of posted content related to certain topics, trends or users. This allows for analysis of a much larger sample size of Twitter data, allowing us to estimate the impact/reach of Twitter content. Basically, this means that we can go beyond just seeing what our friends are thinking/saying on the platform, and see the opinions of Twitter at large.

## Scope
The scope of the project is limited by a number of different factors which we will attempt to document to allow for transparency. While not necessarily all inclusive, hopefully this can serve as a foundation for Twitter analysis, and a starting point for more targeted projects in the future.

## Functionality
Currently the project is split into two main modules. The `TwitLive` module is used for streaming/processing live Twitter data. The `TwitProcess` module is used for processing bulk Twitter data.


**TwitLive** Example
```python
from TwitAnalysis import *
from time import sleep

live = TwitLive()

# Process and display trend analysis
live.TopTrendAnalysis("United States",2,False, 30)
live.trends_summary()

# Stream tweets based on search
stream = live.SearchAnalysis("healthcare",False)
sleep(10)
stream.disconnect()

live.search_summary(stream)

```

![tweets](https://user-images.githubusercontent.com/38412172/210646662-c83fcbfc-68e6-422e-a47e-a81fa1227d3a.png)

**TwitProcess** Example
```python
from TwitAnalysis import *

# Initialize new process object with a specific query
p = TwitProcess("Python Programming")
# Search/Process Tweets
p.analyze()

# Display stats for search results
print("Stats for query: 'Python Programming'")
print(f"Sentiment: {p.overall_sentiment()}")
print(f"Retweets: {p.retweets}")
print(f"Tweets: {p.reg_tweets}")
print(f"Impact: {p.impact}")

```
```
#==== OUTPUT ====#
Stats for query: 'Python Programming'
Sentiment: -0.366
Retweets: 485
Tweets: 282
Impact: 6,228,425
```


-----

**Twitter Documentation**

https://developer.twitter.com/en/docs/tutorials/building-high-quality-filters

**Research**

https://www.sciencedirect.com/science/article/pii/S0268401218306005 \
https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0178-0