Metadata-Version: 2.1
Name: topsis_101803128
Version: 1.4
Summary: A Python package implementing TOPSIS technique.
Home-page: https://github.com/Divyamdj/Topsis-Pypi-Package
Author: Divyam Jain
Author-email: divyamvswild@gmail.com
License: MIT
Download-URL: https://github.com/Divyamdj/Topsis-Pypi-Package/archive/v1.4.tar.gz
Description: # TOPSIS-Python
        
        **Assignment 6: UCS538**
        
        
        Submitted By: **DIVYAM JAIN-101803128**
        
        ***
        
        ## 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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).
        
        <br>
        
        ## How to use this package:
        
        TOPSIS-Divyam-101803128 can be run as in the following example:
        
        
        
        ### Python Script
        ```
        from topsis_101803128.topsis import CalcTopsisScore
        filename = "input.csv"
        weight = "1,1,1,2"
        impact = "+,+,-,+"
        CalcTopsisScore(filename, weight, impact )
        ```
        <br>
        
        
        ## Sample dataset
        
        The decision matrix (`a`) 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 (`w`) is not already normalised will be normalised later in the code.
        
        Information of benefit positive(+) or negative(-) impact criteria should be provided in `I`.
        
        <br>
        
        ## Output
        
        Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Score | Rank
        ------------ | ------------- | ------------ | ------------- | ------------ | ------------ | ------------
        M1 |	0.79 | 0.62	| 1.25 | 60.89 | 0.77221 | 2
        M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.225599 | 5
        M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.438897 | 4
        M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.523878 | 3
        M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.811389 | 1
        
        
        <br>
        The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
        
        ## License
        
        Â© 2020 Divyam Jain
        
        This repository is licensed under the MIT license. See LICENSE for details.
Keywords: Topsis,Topsis Ranking
Platform: UNKNOWN
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
