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A Python package implementing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) for multi-criteria decision making.

Project description

Topsis-Sameer-102316089

A Python package that implements TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) — a multi-criteria decision-making method.

Installation

pip install Topsis-Sameer-102316089

Usage

Run from the command line:

topsis <InputDataFile> <Weights> <Impacts> <OutputResultFileName>

Example

topsis data.csv "1,1,1,2" "+,+,-,+" output.csv

Parameters

Parameter Description
InputDataFile Path to input CSV/Excel file
Weights Comma-separated weights (e.g., "1,1,1,2")
Impacts Comma-separated impacts, + or - (e.g., "+,+,-,+")
OutputResultFileName Path for the output CSV file

Input File Format

  • The input file must contain three or more columns.
  • The first column is the name/label of each alternative (e.g., M1, M2, …).
  • Column 2 onwards must contain numeric values only.

Sample Input (data.csv)

Fund Name P1 P2 P3 P4 P5
M1 0.67 0.45 6.5 42.6 12.56
M2 0.60 0.36 3.6 53.3 14.47
M3 0.82 0.67 3.8 63.1 17.10

Output File Format

The output file contains all original columns plus two new columns:

Column Description
Topsis Score The computed TOPSIS score (0 to 1)
Rank Rank based on score (1 = best)

Sample Output (output.csv)

Fund Name P1 P2 P3 P4 P5 Topsis Score Rank
M1 0.67 0.45 6.5 42.6 12.56 20.58 2
M2 0.60 0.36 3.6 53.3 14.47 40.83 4
M3 0.82 0.67 3.8 63.1 17.10 30.07 3

Input Validations

The program checks for the following:

  1. Correct number of parameters — exactly 4 arguments required
  2. File not found — shows an appropriate error message
  3. Minimum columns — input file must have ≥ 3 columns
  4. Numeric values — columns 2 onwards must be numeric
  5. Count match — number of weights, impacts, and criteria columns must be equal
  6. Valid impacts — impacts must be + or - only
  7. Comma-separated — weights and impacts must be separated by commas

License

MIT © Sameer

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