semvec/pypi-packages huggingface dataset:
EduKit 0.0.3
Library for teaching Python to students
Requires Python: >=3.6

# EduKit

## Library for teaching Python to students

---

[![PyPI Latest Release](https://img.shields.io/pypi/v/EduKit)](https://pypi.org/project/EduKit/)
![License](https://img.shields.io/github/license/lill74/EduKit)
![Last Commit](https://img.shields.io/github/last-commit/lill74/Edukit)
![Dependency](https://img.shields.io/requires/github/lill74/EduKit)

```
pip install EduKit
```

PPT Files
[Link](https://github.com/lill74/EduKit/tree/master/readyforlecture/%EA%B0%95%EC%9D%98%EC%9E%90%EB%A3%8C)

Documentation
[Link](https://chekly.co/do/edukit)

Examples
[Link](https://github.com/lill74/EduKit/tree/master/examples)

    - main.py 성경 데이터를 불러와 명사들의 빈도수 출력한다
    - example_multiple_crawling.py 여러 웹사이트에서 데이터를 불러와서 명사의 빈도수를 출력한다
    - example_extract_nouns.py 텍스트에서 명사들을 추출한다
    - example_draw_word_frequency.py 텍스트에서 추출한 명사들의 빈도수를 분석한다
mlworks 0.0.6
Python Package for Unlimited Machine Learning Works
Requires Python: >=3.6
Keywords: mlworks,Python,projects,machine-learning,feature-engineering,modelling,data-science

# mlworks
Python Package for Unlimited Machine Learning Works

[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/adelmofilho/mlworks/issues)
[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
[![pypi](https://img.shields.io/pypi/v/mlworks)](https://pypi.org/project/mlworks/)
[![python](https://img.shields.io/pypi/pyversions/mlworks)](https://pypi.org/project/mlworks/)
[![license](https://img.shields.io/github/license/adelmofilho/mlworks?color=blue)](https://scrutinizer-ci.com/g/adelmofilho/mlworks/badges/quality-score.png?b=master)
[![Code Quality](https://img.shields.io/scrutinizer/g/adelmofilho/mlworks.svg)](https://scrutinizer-ci.com/g/adelmofilho/mlworks/badges/quality-score.png?b=master)
[![Build Status](https://api.travis-ci.org/adelmofilho/mlworks.svg?branch=master&status=unknown)](https://travis-ci.org/github/adelmofilho/mlworks/)
[![codecov](https://codecov.io/gh/adelmofilho/mlworks/branch/master/graphs/badge.svg?branch=master)](https://codecov.io/github/adelmofilho/mlworks?branch=master)
[![docs](https://readthedocs.org/projects/mlworks/badge/?version=latest)](https://readthedocs.org/projects/mlworks/?badge=latest)
[![Upload PyPI](https://github.com/adelmofilho/mlworks/workflows/Upload%20Python%20Package/badge.svg)](https://github.com/adelmofilho/mlworks/actions)
![Python package](https://github.com/adelmofilho/mlworks/workflows/Python%20package/badge.svg)
distance-map 1.0.1
Distance Map library
Requires Python: >=3.6

# DistanceMap
Python Distance Map library

For a N-dimension (2 ≤ N ≤ 3) binary matrix, return the corresponding distance map.
For each pixel, the value is equal to the minimum distance to a "positive" pixel.
Due to the way I plan to use this library, the implementation is in reality articulate
over a list of positive points positions and not a binary matrix.

```
Δ = distance to nearest positive point
α = a user defined function (ℝ+ → ℝ+) (default : x ↦ x)
Ω = the maximum value

P_result(x, y) = min(α(Δ), Ω)
```

### Examples
![Example 1](https://raw.githubusercontent.com/Cyril-Meyer/DistanceMap/master/media/example_01.png)
![Example 2](https://raw.githubusercontent.com/Cyril-Meyer/DistanceMap/master/media/example_02.png)

### Usage
Input is a list of positive points

```
distance_map(input.shape, points)
```

Input is binary (boolean) matrix

```
distance_map_from_binary_matrix(input)
```

Input is a list of positive points and you want to use custom arguments

```
distance_map(input.shape, points, distance="manhattan", beta=15.0, alpha="square")
```


Parameters
* Distance
  * euclidean
  * manhattan
* Alpha
  * identity (x ↦ x)
  * square (x ↦ x*x)
  * linear (x ↦ A*x + B)
    * use set_a(a) and def set_b(b) to change A and B values

### Benchmark
The following tests has been made on a laptop with an Intel Core i7-7700HQ and 24Go of RAM.

| Test | Time distance_map | Time distance_map_from_binary_matrix |
|---------------------------------------------------|------|------|
| 2D 1024 x 1024 image with 10 positive points      |  8ms | 11ms |
| 2D 1024 x 1024 image with  5 positive points      |  5ms |  9ms |
| 3D 128 x 128 x 128 image with 10 positive points  | 61ms |148ms |

### Acknowledgement
This library is inspired for use as in the following paper.
```
N. Xu, B. Price, S. Cohen, J. Yang and T. Huang
"Deep Interactive Object Selection"
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Las Vegas, NV, 2016, pp. 373-381
doi: 10.1109/CVPR.2016.47
```