semvec/pypi-packages huggingface dataset:
Topsis-Kriti-102003628 1.0.1
This is a Python library for handling problems related to Multiple Criteria Decision Making(MCDM)
License: MIT

# TOPSIS

Title: *TOPSIS method for multiple-criteria decision making (MCDM)*.

Version: *1.0.1*.

Author: *Kriti Bhatnagar*.

Description: *Evaluation of alternatives based on multiple criteria using TOPSIS method.*

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## How to install this package:


>> pip install Topsis__Kriti_102003628


### In Command Prompt


>> topsis data.csv "1,2,1,2,1" "+,+,-,-,+" output_file.csv


<br>

The output file will contain columns of input file along with two additional columns having Topsis_score and Rank
Topsis-Jitesh-Garg-102017180 1.0.0
A Python package for handling problems of Multiple Criteria Decision Making(MCDM) for a given dataset.
License: MIT

# Topsis-Jitesh-102017180
Topsis-Jitesh-102017180 is a Python package for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).
Topsis is a method of compensatory aggregation that compares a set of alternatives, 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.

#### Installation
Use the package manager pip to install Topsis-Jitesh-102017180

#### Syntax
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
Example:
topsis inputfile.csv 1,2,1,2,1 +,+,-,+,- result.csv

#### Example
Sample Input Data
| Name | P1 | P2 | P3 | P4 | P5 |
| --- | --- | --- | --- | --- | --- |
| M1 | 0.71 |0.5|3.8|40.8|11.5|
| M2 | 0.94 | 0.88| 5.3|56.2 |15.83 |
| M3 |0.85 |0.72|4 |30.5 |9.02 |
| M4 |0.61  |0.37|5.4 |56.9 |15.82 |
| M5 |0.91 |0.83|3.4 |53.4 |14.64 |

Weights: 1,1,1,1,1
Impacts: +,+,+,+,+

Sample Output Data
| Name | P1 | P2 | P3 | P4 | P5 |Score|Rank|
| --- | --- | --- | --- | --- | --- |---|---|
| M1 | 0.71 |0.5|3.8|40.8|11.5|0.3015751942839768|5|
| M2 | 0.94 | 0.88| 5.3|56.2 |15.83 |0.97815026808521971|1
| M3 |0.85 |0.72|4 |30.5 |9.02 |0.4172925776259159|4
| M4 |0.61  |0.37|5.4 |56.9 |15.82 |0.5053936295885693|3
| M5 |0.91 |0.83|3.4 |53.4 |14.64 |0.6774035368116197|2

#### Note
1. Enter the path of your input csv file.
2. Enter the weights and impacts vector with each entry separated by commas.
3. Enter the name of output file in .csv format.
4. The Output file will be created in the current working directory

### License
MIT