Metadata-Version: 2.1
Name: TOPSIS-Abhiroop-101803109
Version: 1.0.1
Summary: A Python package implementing TOPSIS technique.
Home-page: https://coe1316.ml/
Author: Abhiroop Agarwal
Author-email: aagarwal_be18@thapar.edu
License: MIT
Description: # TOPSIS
        
        
        Submitted By: **Abhiroop 101803109**
        
        ***
        
        ## What is TOPSIS?
        
        **T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution 
        (TOPSIS) originated in the 1980s as a multi-criteria decision making method.
        TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, 
        and greatest distance from the negative-ideal solution. 
        
        <br>
        
        ## How to install this package:
        ```
        >> pip install TOPSIS-Abhiroop-101803109
        ```
        
        
        ### In Command Prompt
        ```
        >> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
        ```
        
        ## Input file (data.csv)
        
        The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.
        
        Model | Correlation | R<sup>2</sup> | RMSE | Accuracy
        ------------ | ------------- | ------------ | ------------- | ------------
        M1 |	0.79 | 0.62	| 1.25 | 60.89
        M2 |  0.66 | 0.44	| 2.89 | 63.07
        M3 |	0.56 | 0.31	| 1.57 | 62.87
        M4 |	0.82 | 0.67	| 2.68 | 70.19
        M5 |	0.75 | 0.56	| 1.3	 | 80.39
        
        Weights (`weights`) is not already normalised will be normalised later in the code.
        
        Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.
        
        <br>
        
        ## Output file (result.csv)
        
        
        Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank
        ------------ | ------------- | ------------ | ------------- | ------------ | ------------- | ------------- 
        M1 |	0.79 | 0.62	| 1.25 | 60.89 | 0.7722 | 2
        M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.2255 | 5
        M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.4388 | 4
        M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.5238 | 3
        M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.8113 | 1
        
        
        <br>
        The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank** 
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
