semvec/pypi-packages huggingface dataset:
priorityY 0.0.2
Priority Based Connected Components
License: MIT
Keywords: Connected Components

# Priority Based Connected Components
In many scenarios we see there is a need to run connected components but first connect entities with higher priority edges and then move to lower priority ones. Also apart from it, every group has a size-limit.
To tackle such analytical challenges we have come up with an algorithm to adjust the priority of edges and limit the size of the components.

## Installation
```pip install priorityY```

## How to use it?
```
from priorityY import *
G = [(1,4,1),(1,5,3),(2,3,2),(2,6,2),(6,7,3),(4,8,2),(5,9,1)]
threshold = 3
components = priority_based_linkage(G,threshold)
print(components)
```
## Output
```
[[1, 4, 8], [2, 3, 6], [5, 9], [7]]
```
Let us visualise the above example for better understanding how the library works.
The threshold is set to 3, limiting the size of components. Dotted edges denote the link and priority associated.

![](https://github.com/walmartlabs/priorityY/blob/main/img/1.png?raw=true)


At the first iteration, all nodes having edges with Priority-1 get connected.

![](https://github.com/walmartlabs/priorityY/blob/main/img/2.png?raw=true)
1–4 and 5–9 gets connected.

In the next iteration, the algorithm connects nodes with edges between them of Priority-2.

![](https://github.com/walmartlabs/priorityY/blob/main/img/3.png?raw=true)
1–4–8 gets connected, 2–3–6 gets connected and both reach the threshold

The algorithm stops as the threshold is reached for components and no more edges left. Above are the final connected components with the set threshold and provided edges priority.

## License

© 2021 Abhishek Mungoli, Pravesh Garg, Somedip Karmakar

This repository is licensed under the MIT license. See LICENSE for details.


Change Log
============


0.0.1 (23/08/2021)
---------------------------
- First Release

0.0.2 (27/01/2022)
---------------------------
- Second Release
pycounts-tat 0.1.5
Calculate word counts in a text file!
License: MIT
Requires Python: >=3.8,<4.0

# pycounts_tat

Author: Tiffany Timbers

[![ci-cd](https://github.com/ttimbers/pycounts_tat/actions/workflows/ci-cd.yml/badge.svg)](https://github.com/ttimbers/pycounts_tat/actions/workflows/ci-cd.yml)

Calculate word counts in a text file!

## Installation

```bash
$ pip install pycounts_tat
```

## Usage

To import the package and check the version:

```
import pycounts_tat

print(pycounts_tat.__version__)
```

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`pycounts_tat` was created by Monty Python. It is licensed under the terms of the MIT license.

## Credits

`pycounts_tat` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
udgram 1.2.1
Compute probabilities in N-grams model for tags in UD format
License: MIT

Compute probabilities in N-grams model for tags in UD format

Usage
===========

  - bigram  : function for bigrams with specify tag
  - trigram : function for trigrams with specify tag
  - ngram   : general function for ngram with specify tag

  - bigram_matrizs	: matriz with bigram probabilities for tags
  - ngram_matriz	: matriz with ngram probabilities for tags