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102203092-topsis

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MCDM solved using TOPSIS

Versions collected1
Direct dependencies2From release 1.1.2
Last observed20 Oct, 15:08 UTC

README

Version 1.1.2 documentation

Project description

MCDM solution using TOPSIS using CLI

A Python package made by me to solve the problem of MCDM (Multi Criteria Decision Making) implemented using TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) algorithm. This package supports both CSV and Excel files.

Installation

Follow the steps below to install and use the package:

Install the Package from PyPI (recommended):

pip install 102203092_topsis

Repo Clone (optional):

git clone

cd TOPSIS-pypi

(Optional) Install Locally: If you have the source code and want to install the package locally:

pip install setuptools wheel

python setup.py sdist bdist_wheel

pip install .

ArgumentDescription
input_file"CSV/Excel" file path
weights`Comma-separated numbers
impactsComma-separated '+' or '-'
output_fileOutput CSV/Excel file path

Usage

  1. After installation, you can use the package directly from the command line using topsis-cli.
  2. The CLI supports both .csv and .xlsx formats for input and output files.
  3. Command-Line Interface (CLI)
  4. Package installed from PyPi can be globally used irrespective of directory

topsis-cli <input_file> <weights> <impacts> <output_file>

Args:

  1. input_file: Path to the input file (must be .csv or .xlsx).
  2. weights: Comma-separated list of weights for the criteria (e.g., 0.4,0.3,0.3).
  3. impacts: Comma-separated list of impacts for the criteria (+ for positive, - for negative).
  4. output_file: Path to save the output file (can be .csv or .xlsx).

Input File Format

Col 1Col 2Col 3
Alternte IDCriterion 1 ValueCriterion 2 Value

Output File Format

Col 1Col 2Col 3Col 4Col 5
Alternate IDCriterion 1 ValueV2Topsis ScoreRank

Uday Sharma

102203092

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Versions 1

Collected releases
VersionPublishedDirect dependenciesOpen version
1.1.219 Jan 20252

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Observed since 20 Oct, 15:08 UTC

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Record identity

package:fe0ecfc9e2b361153d76e761f065777021ee3e9e31b15fbe8cd85eb52259cbae