semvec/pypi-packages huggingface dataset:
hamwcs 0.0.3
homeassistant friendly wrapper for J River MCWS
License: MIT
Requires Python: >=3.11,<3.12

# hamcws

A python based async wrapper around J River [MCWS](https://wiki.jriver.com/index.php/Web_Service_Interface) designed for use
by a [Home Assistant](https://www.home-assistant.io/) integration.
audioaddictapilib 0.1.2
Library to interact with the Audioaddict API
License: MIT
Keywords: audioaddictapilib

=================
audioaddictapilib
=================

Library to interact with the Audioaddict API


* Documentation: https://audioaddictapilib.readthedocs.org/en/latest


Development Workflow
====================

The workflow supports the following steps

 * lint
 * test
 * build
 * document
 * upload
 * graph

These actions are supported out of the box by the corresponding scripts under _CI/scripts directory with sane defaults based on best practices.
Sourcing setup_aliases.ps1 for windows powershell or setup_aliases.sh in bash on Mac or Linux will provide with handy aliases for the shell of all those commands prepended with an underscore.

The bootstrap script creates a .venv directory inside the project directory hosting the virtual environment. It uses pipenv for that.
It is called by all other scripts before they do anything. So one could simple start by calling _lint and that would set up everything before it tried to actually lint the project

Once the code is ready to be delivered the _tag script should be called accepting one of three arguments, patch, minor, major following the semantic versioning scheme.
So for the initial delivery one would call

    $ _tag --minor

which would bump the version of the project to 0.1.0 tag it in git and do a push and also ask for the change and automagically update HISTORY.rst with the version and the change provided.


So the full workflow after git is initialized is:

 * repeat as necessary (of course it could be test - code - lint :) )

   * code
   * lint
   * test
 * commit and push
 * develop more through the code-lint-test cycle
 * tag (with the appropriate argument)
 * build
 * upload (if you want to host your package in pypi)
 * document (of course this could be run at any point)


Important Information
=====================

This template is based on pipenv. In order to be compatible with requirements.txt so the actual created package can be used by any part of the existing python ecosystem some hacks were needed.
So when building a package out of this **do not** simple call

    $ python setup.py sdist bdist_egg

**as this will produce an unusable artifact with files missing.**
Instead use the provided build and upload scripts that create all the necessary files in the artifact.



Project Features
================

* TODO




History
-------

0.0.1 (22-12-2023)
---------------------

* First code creation


0.1.0 (28-12-2023)
------------------

* Initial release


0.1.1 (31-12-2023)
------------------

* UserAgent now gets set before the first request.


0.1.2 (31-12-2023)
------------------

* Linting fixes
KNearestNeighborSampling 0.0.2
Dataset size reduction using KNN Sampling algorithm
License: MIT
Keywords: python,K-NN,Sampling,Size reduction,Optimization,knn sampler


# KNNSampler
KNNSampler is an implementation of the [Research paper](https://ieeexplore.ieee.org/document/8990391). It is created to help developers reduce the size of their Datasets by sampling the "Representatives" from the same. NN_SCORES and MNN_SCORES, as discussed in the referred paper, were used to find these "Representatives". KNNSampler works in both dynamic and static way, as discussed by the author in the paper.
### Setup
- Python 3.10
- Requirenments : numpy, pandas, sklearn
### Enhancements
- MNN_SCORES are calculated after every iteration for the entire dataset in the algorithm suggested in the research paperwhich. This leads to redundant calculations. Hence, in this package we only calculate MNN_SCORES for the shortlisted rows using NN_SCORES, producing the same result as the original algorithm but in an optimal way.
- Error was found in the line : train sample = train sample ∪ X[index] in the algorithm given in the research paper, we replace X[index] with X[train_index] for correct outcome.
- Error was found in the Until loop logic of algorithm in the research paper : (NN − score(X) = 0) ∨ (| train sample |≤ k); The second condition must be |X| <= k, changes were done.
- Values of t, m, s for (t,m,s)-nets were not provided in the paper, We give users the freedom to choose the t, m, and s values or use the default values provided.
### Important
- The dataset passed to the sample() function must **NOT CONTAIN COLUMN NAMED "idx"**.
- Warnings produced by "drop()" function in pandas.DataFrame must be **IGNORED**, since they have been added for debug purposes.
### Navigate
- [Package](https://github.com/snimale/KNNSampler/tree/dev/KNearestNeighborSampling)
- [Example Usecase](https://github.com/snimale/KNNSampler/tree/dev/others/example-usecase)
- [Example Results](https://github.com/snimale/KNNSampler/tree/dev/others/sampled_data_plotted_results)
- [Sratch Code](https://github.com/snimale/KNNSampler/tree/dev/others/knn-sampling-scratch-code)

### Acknowledgement
I have "implemented" and "added optimizations" to the original research work done by : Bheekya Dharamsotu, K. Swarupa Rani, Salman Abdul Moiz, and C. Raghavendra Rao in the research paper : </br> </br>
B. Dharamsotu, K. S. Rani, S. Abdul Moiz and C. R. Rao, "k-NN Sampling for Visualization of Dynamic Data Using LION-tSNE," 2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC), Hyderabad, India, 2019, pp. 63-72, doi: 10.1109/HiPC.2019.00019.