semvec/pypi-packages huggingface dataset:
Topsis-Samarth-101903793 1.0.1
Calculate TOPSIS Ranks
License: MIT

# TOPSIS
## Assignment for UCS654
---
Samarth Varshney

101903793

3CO17
### What is TOPSIS?

TOPSIS stands for "The Technique for Order of Preference by Similarity to Ideal Solution" is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 with further developments by Yoon in 1987, and Hwang, Lai and Liu in 1993. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and farthest distance from the negative-ideal solution. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS). The TOPSIS algorithm is succinctly explained in this [paper comparing TOPSIS and VIKOR methods]https://www.sciencedirect.com/science/article/abs/pii/S0377221703000201)
### How do I use TOPSIS?

First, install the TOPSIS module using pip.
```pip install Topsis-Samarth-101903793```

Then, run the TOPSIS file with the arguements - 
```python Topsis-Samarth-101903793 101903793-data.csv 1,2,2,1 +,-,+,- 101903793-result.csv```

Here, the first arguement after the python program is your subject dataframe is stored in csv format in data.csv.
The second arguement is the weights you are assigning to each feature of the dataset. The default weights are 1.
The third arguement are the assigned impacts. The default impact is '+'.
The fourth argument is the filename in which the output table is to be stored. If no file name is given then '101903793-data.csv' file is chosen.

Output is the csv file of the model with TOPSIS Score and TOPSIS Rank.


### Example
Running command -

```python Topsis-Samarth-101903793 101903793-data.csv 2,2,3,3,4 -,+,-,+,- 101903793-result.csv```

Input Dataset - (101903793-data.csv)

|Fund Name|P1|P2|P3|P4|P5|
|--------------|--------------|--------------|--------------|--------------|--------------|
|M1|0.68|0.46|5|38.5|11.16|
|M2|0.78|0.61|5|30.6|9.25|
|M3|0.79|0.62|6.9|34.9|10.8|
|M4|0.82|0.67|6.2|36.1|10.95|
|M5|0.64|0.41|6.7|46.8|13.64|
|M6|0.63|0.4|7|59.5|16.88|
|M7|0.77|0.59|4|45.3|12.67|
|M8|0.79|0.62|3.9|33.6|9.73|

Weights -> 2,2,3,3,4
Impacts -> -,+,-,+,-

Output Dataset - (stored in 101903793-result.csv)

|Fund Name|P1|P2|P3|P4|P5|Topsis Score|Rank|
|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|
|M1|0.68|0.46|5|38.5|11.16|0.545126218|4|
|M2|0.78|0.61|5|30.6|9.25|0.561913424|3|
|M3|0.79|0.62|6.9|34.9|10.8|0.469000051|6|
|M4|0.82|0.67|6.2|36.1|10.95|0.50574866|5|
|M5|0.64|0.41|6.7|46.8|13.64|0.405123307|7|
|M6|0.63|0.4|7|59.5|16.88|0.404922284|8|
|M7|0.77|0.59|4|45.3|12.67|0.60568518|2|
|M8|0.79|0.62|3.9|33.6|9.73|0.607093772|1|
rfc-cli 1.0.0
A command line viewer for RFCs
License: MIT
Requires Python: >=3.6
Keywords: rfc,rfc-cli

# rfc-cli

 A command line viewer for RFCs

## Installation

Install with pip using any of these commands

```sh
pip install rfc # General
pip3 install rfc # Mostly MacOS/Linux
python -m pip install rfc # General
python3 -m pip install rfc # Mostly MacOS/Linux
py -m pip install rfc # Windows
```

## Usage

```sh
rfc show 2822 # Read the RFC 2822
rfc show draft-ietf-core-coap-18 # Read IETF draft core-coap version 18
```