semvec/pypi-packages huggingface dataset:
bavard-nlu 0.0.27
A library and CLI for NLP tasks
Requires Python: >=3.6

# bavard-nlu

## Releasing The Package

Releasing the package is automatically handled by CI, but three steps must be taken to trigger a successful release:

1. Increment the `VERSION` variable in `setup.py` to the new desired version (e.g. `VERSION="1.1.1"`)
2. Commit and tag the repo with the **exact same** value you populated the `VERSION` variable with (e.g. `git tag 1.1.1`)
3. Push the commit and tag to remote. These can be done together using: `git push --atomic origin <branch name> <tag>`

CI will then release the package to pypi with that version once the commit and tag are pushed.

## Local Development

### Install Dependencies

```
pip3 install -e .
pip3 install -r requirements-test.txt
```

### NLUModel CLI

There is a convenience CLI for training, evaluating, predicting, tuning, and interacting with NLU models. To see the CLI documentation:

```
python3 -m bavard_nlu.cli --help
```

You can also view the documentation for a sub-command for example:

```
python3 -m bavard_nlu.cli train --help
```

## Testing Locally

The tests for this repo consist of functional tests for catching bugs/code regressions, and validation tests for catching model performance regressions. The validation tests take much longer to execute. Both are run in CI. To run just the functional tests:

```
python3 -m unittest discover test/functional
```
cwitools 0.8.7
Analysis and Reduction Tools for PCWI/KCWI Data

CWITools
--------
A Python3 package for analysis of integral field spectroscopy data, specifically designed for the Palomar and Keck Cosmic Web Imagers!

Documentation on ReadTheDocs
-------------------------------------
https://cwitools.readthedocs.io/en/latest/


Downloadable Examples (with sample data)
----------------------------------------
https://github.com/dbosul/cwitools-examples
TOPSIS-Yash-101803457 1.0.0
Topsis Calculator

# TOPSIS-Python


Submitted By: **YASH MITTAL 101803457**

***
pypi: <https://pypi.org/project/TOPSIS-Yash--101803457>
<br>

***

## What is TOPSIS

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
**S**olution (TOPSIS) originated in the 1980s as a multi-criteria decision
making method. TOPSIS chooses the alternative of shortest Euclidean distance
from the ideal solution, and greatest distance from the negative-ideal
solution. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-YASH-101803457  can be run as in the following example:



### In Command Prompt
```
>> pip install TOPSIS-Yash--101803457==1.0.0
```
>> python
>>>from topsis_gen.topsis_cal import topsis
>>>topsis("data.csv","1,1,1,2","+,+,-,+")

<br>


## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Model | Correlation | R<sup>2</sup> | RMSE | Accuracy
------------ | ------------- | ------------ | ------------- | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89
M2 |  0.66 | 0.44	| 2.89 | 63.07
M3 |	0.56 | 0.31	| 1.57 | 62.87
M4 |	0.82 | 0.67	| 2.68 | 70.19
M5 |	0.75 | 0.56	| 1.3	 | 80.39

Weights (`w`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `I`.

<br>

## Output

```
Model   Score    Rank
-----  --------  ----
  1    0.639133    2
  2    0.212592    5
  3    0.407846    4
  4    0.519153    3
  5    0.828267    1
```
<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
pro-video-ferramentas 1.0
Este pacote irá fornecer ferramentas de processamento de vídeo
Keywords: camera,video,processamento

# Pro Video Ferramentas

DescriÃ§Ã£o de como seu pacote funciona...

* Passos para instalaÃ§aÃµ
* Outras info. relevantes(autores, empresa,contato)