semvec/pypi-packages huggingface dataset:
Topsis-Shrey-102183040 1.0.4
Compute Topsis scores and ranks for a given csv file using topsis method for multiple-criteria decision making(MCDM)
License: MIT

# Topsis-Shrey-102183040
## What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (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.

## Installation
```pip install Topsis-Shrey-102183040```

## Input csv format
Input file contain three or more columns
First column is the object/variable name
From 2nd to last columns contain numeric values only

## How to use it
Command line arguement
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
Example:
topsis inputfile.csv “1,1,1,2” “+,+,-,+” result.csv

## Note: The weights and impacts should be ',' seperated, input file should be in pwd.

## Sample input data

| Model | Corr | Rseq | 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|

## Sample output data

| Model | Corr | Rseq | RMSE | Accuracy |Topsis score|Rank|
|-------|------|------|------|----------|----------|-------|
|M1|	0.79|	0.62|	1.25|	60.89|	0.7731301458119156|	2|
|M2|	0.66|	0.44|	2.89|	63.07|	0.22667595732024362|	5|
|M3|	0.56|	0.31|	1.57|	62.87|	0.4389494866695491|	4|
|M4	|0.82	|0.67	|2.68	|70.19|	0.5237626971836845|	3|
|M5|	0.75|	0.56|	1.3|	    80.39|	0.8128626132980138|	1|
## License
© 2023 Shrey Saxena

This repository is licensed under the MIT license. See LICENSE for details.