semvec/pypi-packages huggingface dataset:
Topsis-Chirag-102103554 1.0.0
A python package for Multiple Criteria Decision Making (MCDM) using Topsis
License: MIT
Keywords: TOPSIS,102103554,Chirag Mohan Gupta

# 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.

### Installation

```sh
>> pip install TOPSIS-Chirag-102103554
```
### How to run in command prompt

```sh
>> from Topsis_Chirag_102103554.topsis_102103554 import topsis
>> topsis("data.csv","1,1,1,2","+,+,-,+","result.csv")
```

### Input File (data.csv)
1) Input file contain three or more columns
2) First column is the object/variable name (e.g. M1, M2, M3, M4â€¦...)
3) From 2nd to last columns contain numeric values only

### Output File (result.csv)
Result file contains all the columns of input file and two additional columns having
TOPSIS SCORE and RANK

License
----

MIT
Topsis-Sakshams-102103521 1.1.0
Topsis-Sakshams-102103521
License: MIT

# Topsis
Topsis( Technique for order for preference by similarity to Ideal solution ) for MCDM (Multiple criteria decision making) in Python compiled by Saksham Sikri, 102103521, TIET, Patiala. 

## Installation
Use the package manager pip to install Topsis.
pip install Topsis_saksham_102103521

## Usage
Enter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-) and enter the output file name followed by .csv extension.

python [InputDataFile as .csv] [Weights as a string] [Impacts as a string] [ResultFileName as .csv]

### Example
python sample.csv "1,1,1,1" "-,+,+,+" output.csv

## Please Note That"

The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So the csv MUST follow the format as shown in sample.csv shown in the Example section.
The input data file MUST contain three or more columns.
The second to last columns of the data file MUST contain NUMERIC values.
The number of weights, impacts and columns (second to last) MUST be SAME.
Impacts MUST either be '+' or '-'.
Impacts and Weights MUST be separated by , (comma).

## License

©️ 2024 Saksham Sikri

This reopsitory is licensed under MIT License. See LICENSE for details.
TOPSIS-102103268 0.0.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: topsis,MCDM,TIET

Topsis (MCDM)

CalcTopsis is a Python package implementing Topsis method sed for multi-criteria decision analysis.
Topsis stands for Technique for Order of Preference by Similarity to Ideal Solution

Just provide your input attributes and it will give you the results


## Installation

$ pip install TOPSIS-102103268 == 0.0.1
In the commandline, you can write as -
    $ python <package_name> <path to input_data_file_name> <weights as strings> <impacts as strings> <result_file_name>

E.g for input data file as data.csv, command will be like
    $ python topsis.py data.csv "1,1,1,1" "+,+,-,+" output.csv

This will print all the output attribute values along with the Rank column, in a tabular format

License -> MIT

Change Log
==========

0.0.1 (12/11/2020)
------------------
- First Release
topsis-102103411 1.1.0
Topsis
License: MIT

# Topsis
Topsis( Technique for order for preference by similarity to Ideal solution ) for MCDM (Multiple criteria decision making) in Python compiled by Harshit Goyal, 102103411, TIET, Patiala. 

## Installation
Use the package manager pip to install Topsis
pip install Topsis_harshit_102103411

## Usage
Enter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-) and enter the output file name followed by .csv extension.

python [InputDataFile as .csv] [Weights as a string] [Impacts as a string] [ResultFileName as .csv]

### Example
python sample.csv "1,1,1,1" "-,+,+,+" output.csv

## Please Note That"

The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So the csv MUST follow the format as shown in sample.csv shown in the Example section.
The input data file MUST contain three or more columns.
The second to last columns of the data file MUST contain NUMERIC values.
The number of weights, impacts and columns (second to last) MUST be SAME.
Impacts MUST either be '+' or '-'.
Impacts and Weights MUST be separated by , (comma).

## License

Â© 2024 Harshit Goyal

This reopsitory is licensed under MIT License. See LICENSE for details.