semvec/pypi-packages huggingface dataset:
evo-spotis 0.0.9
Package for Multi-Criteria Decision Analysis with Preference Identification
Requires Python: >=3.4

# EVO-SPOTIS

This is Python 3 library for multi-criteria decision analysis with decision-maker preference identification based on historical datasets.

## Installation

Downloading and installation of `evo_spotis` package can be done using pip

```
pip install evo-spotis
```

## Methods provided

`mcda_methods` module includes:
- `spotis` with SPOTIS method (the Stable Preference Ordering Towards Ideal Solution method)

`stochastic_algorithms` module includes:
- DE algorithm `DE_algorithm` (the Differential Evolution algorithm)

The DE algorithm is applied for the identification of criteria weights (decision-maker preferences) based on a training dataset with evaluated alternatives,
including alternatives performances (training features) and their ranking (target variable). The goal (fitness) function uses the correlation coefficient
of predicted ranking with real ranking. The predicted ranking is generated using the SPOTIS method and weights calculated by the DE algorithm in each DE iteration.
 It is a profit function. Therefore, higher values denote better results. Examples of use of `evo_spotis` are included on [GitHub](https://github.com/energyinpython/EVO-SPOTIS) in [examples](https://github.com/energyinpython/EVO-SPOTIS/tree/main/examples)

Other modules:

- `additions` including `rank_preference` method for ranking alternatives according to MCDA score.

- `correlations` containing: 

	- Spearman rank correlation coefficient `spearman`, 
	- Weighted Spearman rank correlation coefficient `weighted_spearman`,
	- Pearson correlation coefficient `pearson_coeff`.
	
- `normalizations` with methods for decision matrix normalization:

	- `linear_normalization` - Linear normalization,
	- `minmax_normalization` - Minimum- Maximum normalization,
	- `max_normalization` - Maximum normalization,
	- `sum_normalization` - Sum normalization,
	- `vector_normalization` - Vector normalization.
	
- `weighting_methods` containing:

	- `entropy_weighting` - Entropy objective weighting method.

## License

The evo-spotis library is licensed under the terms of the MIT license.

## Documentation

Documentation of this library with instruction for installation and usage is provided
[here](https://evo-spotis.readthedocs.io/en/latest/)
mapaction 0.1.9
CLI for managing some MapAction tasks
Requires Python: >=3.7,<4.0

<p align="center">
  <a href="https://mapaction.org/">
      <img alt="MapAction" src="https://qb19onvfjt-flywheel.netdna-ssl.com/wp-content/themes/mapaction/images/logo.svg" width="210" />
  </a>
</p>
<h1 align="center">
MapAction CLI
</h1>

CLI tool for managing MapAction tasks.

## Getting Started

Requirements:

- [Poetry](https://python-poetry.org/)

### Development

```bash
poetry shell
poetry install
pre-commit install
python -m mapaction -v
```

### Build

```bash
poetry shell
poetry build
```

### Generate requirements.txt

```bash
poetry shell
poetry export -f requirements.txt --output requirements.txt
```

### Creating a release

```bash
poetry shell
poetry version <patch, minor, major>
git commit -m "Release v<Version>"
// After Merge
git tag -a v<Version> -m "release v<Version>"
```
LwdCoreUtil 0.0.6
a collection of utils
Requires Python: >=3.6

This project is an util package.
It contains many tools which can finish part function.
Function list:
```python
生成矩阵
删除文本中的特殊字符
遍历文件夹
word 文本翻译
神经网络经过卷积层，最大池化层之后数据的大小
去除文件夹中所有的PDF文件的权限并保存的新文件夹中（需要qpdf支持）
显示矩阵
快速排序
```
There is an example:

```python
from LwdCoreUtil.CoreUtils import *


if __name__ == '__main__':
    matrixShow(generateMatrix(4, 4, []))
    matrixShow(generateMatrix(4, 4, [1, 2]))
    matrixShow(generateMatrix(4, 4, [], is_integer=1, is_random=0))
    matrixShow(generateMatrix(4, 4, [], is_integer=0, is_random=0))
    matrixShow(generateMatrix(4, 4, [], is_integer=0, is_random=1))
    getFilenameFromAllFolder("E:\安全委员工作\反恐知识问答", recursion=1, suffix='jpg')
    deleteWordFromText(text="",
                       begin_word="[", span_number=100, end_word="]")
    print(afterConvPara(32, 32, (1, 1), (3, 3), (1, 1)))
    print(afterMaxPoolPara(32, 32, (0, 0), (2, 2), (2, 2)))
    print(afterConvPara(16, 16, (1, 1), (3, 3), (1, 1)))
    print(afterMaxPoolPara(16, 16, (0, 0), (2, 2), (2, 2)))
    print(afterConvPara(8, 8, (1, 1), (3, 3), (1, 1)))
    print(afterMaxPoolPara(8, 8, (0, 0), (2, 2), (2, 2)))

```