Metadata-Version: 2.1
Name: TOPSIS_Shivam_101803158
Version: 0.0.2
Summary: TOPSIS Implementation
Home-page: https://github.com/shivamPUNDIR/TOPSIS-ShivamPundir-101803158
Author: Shivam Pundir
Author-email: shivampundir009@gmail.com
License: UNKNOWN
Description: 
        # TOPSIS-ShivamPundir-101803158
        Submitted By: Shivam Pundir(101803158)
        
        ## 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.
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install **TOPSIS**.
        Dependencies and devDependencies will be installed automatically.
        
        ```bash
        pip install TOPSIS-Shivam-101803158
        ```
        
        ## Usage
        ##### 1) As a Library:
        Import in your python File:
        ```python
        from TOPSIS import topsis
        topsis()
        ```
        Run the python file by typing in terminal/cmd:
        ```sh
        python nameOfFile.py nameOfDataFile.csv "weights" "impacts" nameOfOutputFile.csv
        ```
        ##### 2) Using Command Promt:
        
        Command line args:
        - name of input File(csv format)
        - weights(as a string)
        - impacts(as a string)
        - name of output file(csv format)
        Eg. 
        ```bash
        topsis data.csv "1,1,1,1" "+,+,-,+" output.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, R2, Root Mean Squared Error, Correlation, and many more.
        
        |Model|Corr|Rseq|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.
        # Output file (output.csv)
        
        |Model|Corr|Rseq|RMSE|Accuracy|Topsis_score       |Rank|
        |---|----|----|----|--------|-------------------|----|
        |M1   |0.79|0.62|1.25|60.89   |0.7722097345612788 |2   |
        |M2   |0.66|0.44|2.89|63.07   |0.22559875426413367|5   |
        |M3   |0.56|0.31|1.57|62.87   |0.43889731728018605|4   |
        |M4   |0.82|0.67|2.68|70.19   |0.5238778712729114 |3   |
        |M5   |0.75|0.56|1.3 |80.39   |0.8113887082429979 |1   |
        
        The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
        ## License
        [MIT](https://choosealicense.com/licenses/mit/)
        
        
          
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: dev
