semvec/pypi-packages huggingface dataset:
django-cpss-facebook 1.2.0
This is the Django component that helps to integrate aspects of facebook into your application.
License: MIT

> ## �� Status: In Development
> django-cpss-facebook currently in development.

# Python Facebook API - Django  [<img src="https://github.com/xgdfalcon/django-cpss-facebook/blob/master/cpss_facebook/static/cpss/logo.png?raw=true" alt="CPSS by XGDFalcon®" height="20px" />](https://controlpointsw.com) 

[![Build Status](https://travis-ci.org/xgdfalcon/django-cpss-facebook.svg?branch=master)](https://travis-ci.org/xgdfalcon/django-cpss-facebook)
[![PyPI version](https://badge.fury.io/py/django-cpss-facebook.svg)](https://badge.fury.io/py/django-cpss-facebook)

## Description

This is the [Django](https://www.djangoproject.com/) component that helps to integrate aspects of facebook into your application

## Django version

This project will focus on the currently supported Django releases as
stated on the [Django Project Supported Versions table](https://www.djangoproject.com/download/#supported-versions).

Backward compatibility with unsupported versions won't be enforced.

## Documentation

Project documentation is available at TBP.

## Setup

1. Add "django-cpss-facebook" to your INSTALLED_APPS setting like this::
```
    INSTALLED_APPS = [
        ...
        'cpss_facebook.CPSSFacebookConfig',
    ]
```
2. Run `python manage.py migrate` to create the django-cpss-facebook models.


## Contributing
See the [CONTRIBUTING.md](CONTRIBUTING.md) document for details.

## Versioning
This project follows [Semantic Versioning 2.0.0](http://semver.org/spec/v2.0.0.html).

## License
This project follows the Apache license. See the [LICENSE](LICENSE) for details.
topsis-Anurag 1.0.0
A Python package to choose the best alternative from a finite set of decision alternatives.
License: MIT

#TOPSIS Package

TOPSIS stands for Technique for Oder Preference by Similarity to Ideal Solution.
It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. In this Python package Vector Normalization has been implemented.

This package has been created based on Project 1 of course UCS633. 
Anurag Aggarwal COE-4 101703088

#In Command Prompt
>>topsis data.csv "1,1,1,1" "+,+,-,+"
topsis33 1.0.0
A Python package to implement Topsis
License: MIT

# topsis-python
Topsis analysis of a csv file in python


## About Topsis

It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. Normalisation is usually required as the parameters or criteria are often of incongruous dimensions in multi-criteria problems

## Installation


```bash
pip install topsis33
```

## Usage

Query on terminal will provide you the topsis analysis for input csv file.

```
topsis33 -n "dataset-name.csv" -w "w1,w2,w3,w4,..." -i "i1,i2,i3,i4,..."

```

w1,w2,w3,w4 represent weights, and i1,i2,i3,i4 represent impacts where 1 is used for maximize and 0 for minimize. 
Size of w and i is equal to number of features. 

Note that the first row and first column of dataset is dropped

Rank 1 signifies best decision


## License
[MIT](https://choosealicense.com/licenses/mit/)