Metadata-Version: 2.1
Name: kt-toposis
Version: 1.0.7
Summary: A Python package to get toposis rankings for any table.
Home-page: https://github.com/kartikeytiwari37/Toposis.git
Author: Kartikey Tiwari
Author-email: kartikeytiwari37@gmail.com
License: MIT
Description: ### UCS633 Project Submission
        * **Name** - *Kartikey Tiwari* 
        * **Roll no.** - *101703282* 
        
        # kt-toposis
        
        kt-toposis is a Python package for displaying ranking of all criteria using Topsis technique to get good computational efficiency and ability to measure the relative performance for each alternative in a simple mathematical form. 
        
        ## Topsis Description
        
        Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is one of the multi-criteria models in making decision which is known for its  simplicity, rationality, comprehensibility and good computational efficiency. Multi-criteria decision making (MCDM) refers to making choice of the best alternative from among a finite set of decision alternatives in terms of multiple, usually conflicting criteria.
        
        ## Getting Started
        
        These instructions will help you to install and use this package for general use. 
        
        ## Prerequisites
        
        Your csv file should not have categorical data
        
        
        ##Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.
        
        ```bash
        pip install kt-toposis
        ```
        
        ## Usage
        You can import it either in Python IDLE or run directly through command prompt
        
        ### For Command Prompt
        
        If you want to use this package on "data.csv" file with 4 columns. You need to change the directory where "data.csv" is stored then. Here -w represents weights which signifies weight of each feature or column in our dataset and -i represents impacts which signifies impact of each column or feature in our data. If a feature is good we will use + to denote else we will use -
        
        ```bash
        kt-toposis data.csv -w 1 1 1 1 -i + + - +
        ```
        You can use the following command for help
        
        ```bash
        kt-toposis -h
        ```
        
        ### For Python IDLE
        
        ```python
        from kt_toposis.topsis import top
        top(X,weights,impacts)
        
        #X should be a matrix
        #impacts should be a list of string + for positive impact - for negative impact
        #weights should be a list of int or float
        ```
        ### Sample dataset
        
        
        |Singer ID	   |Sur     |Taal	|Laaye	|Pitch	|Pace|
        | ------------ |:------:|:-----:| -----:|------:|:---:       
        |S1	0.79	   | 0.79   | 0.62	|1.25	|60.89	|11  |
        |S2	0.66	   | 0.66   | 0.44	|2.89	|3.07	|20  | 
        |S3	0.56	   | 0.56   |0.31	|1.57	|62.87	|16  | 
        |S4	0.82	   | 0.82   |0.67	|2.68	|70.19	|16  |
        |S5	0.75	   | 0.75   |0.56	|1.3	|80.39	|20  |
        
        ```python
        kt-toposis Book1.csv -w 1 1 1 1 1 -i + + + + +
        ```
        
        ### Result
        
        ```python
          Topsis Selection
        Models     | Rank
        -----------------------
        1          | 3
        2          | 5
        3          | 4
        4          | 1
        5          | 2
        Successfully executed
        ```
        
        ## Contributing
        Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
        
        Please make sure to update tests as appropriate.
        
        ## License
        [MIT](https://choosealicense.com/licenses/mit/)
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
