Metadata-Version: 2.1
Name: TOPSIS-Paras-101983048
Version: 1.0.2
Summary: UNKNOWN
Home-page: UNKNOWN
Author: UNKNOWN
Author-email: UNKNOWN
License: UNKNOWN
Platform: UNKNOWN
Requires-Dist: pandas
Requires-Dist: numpy

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.