Metadata-Version: 2.1
Name: Topsis-Jitesh-Garg-102017180
Version: 1.0.0
Summary: A Python package for handling problems of Multiple Criteria Decision Making(MCDM) for a given dataset.
Author: Jitesh Garg
Author-email: jgarg_be20@thapar.edu
License: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas

# 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
