Metadata-Version: 1.0
Name: TOPSIS-Paras-101983048
Version: 1.0.2
Summary: UNKNOWN
Home-page: UNKNOWN
Author: UNKNOWN
Author-email: UNKNOWN
License: UNKNOWN
Description: Metadata-Version:1.0
        Name: TOPSIS-Paras-101983048
        Version: 1.0.2
        Summary: A Python package implementing TOPSIS technique.
        Home-page: UNKNOWN
        Author: Paras
        Author-email: pparas_be18@thapar.edu
        License: MIT
        Platform: UNKNOWN
        Classifier: License :: OSI Approved :: MIT License
        Classifier: Programming Language :: Python :: 3
        Classifier: Programming Language :: Python :: 3.7
        Description-Content-Type: text/markdown
        Requires-Dist: scipy
        Requires-Dist: tabulate
        Requires-Dist: numpy
        Requires-Dist: pandas
        Description: # TOPSIS-Python
                
                
                Submitted By: **Paras 101983048**
                
                
                pypi: <https://pypi.org/project/TOPSIS-Paras-101983048/1.0.2/>
                <br>
                
                
                
                ## What is TOPSIS
                
                Technique for Order Preference by Similarity to Ideal
                Solution (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-Paras-101983048  can be run as in the following example:
                
                
                
                ### In Command Prompt
                ```
                >> pip install TOPSIS-Paras-101983048==1.0.2
                ```
                >> python
                >>>from topsis_create.topsis_cal import topsis
                >>>topsis("data.csv","1,1,1,2","+,+,-,+")
                
                <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   Score    Rank
                -----  --------  ----
                1    0.639133    2
                2    0.212592    5
                3    0.407846    4
                4    0.519153    3
                5    0.828267    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.
        
Platform: UNKNOWN
