Metadata-Version: 2.1
Name: TOPSIS-Ishika-101803017
Version: 1.1.0
Summary: Implementing TOPSIS
Home-page: UNKNOWN
Author: Ishika
Author-email: ishikasofat@gmail.com
License: MIT
Download-URL: https://github.com/ishikasofat/TOPSIS-Ishika-101803017/archive/1.1.0.tar.gz
Description: This package is implementation of topsis technique of multi-criteria decision analysis.
        
        You can install this package using following command
        pip install TOPSIS-Ishika-101803017
        
        This packages will work on command line interface
        > - import TOPSIS-Ishika-101803017 as t
        > - t.topsis(InputDataFile, Weights, Impacts, resultfile)
        ### Input specifications
        > - InputDataFile is path to your input csv file(eg inputfile.csv)
        > - weights is  string in which each digit represent weight of corresponding column(eg "1,1,1,1")
        > - impacts are impacts of column (eg "-,+,+,+" ) 
        > - result is name of output file(eg result.csv)
        
        ## PRECAUTIONS
        > No of weights and no of impacts should be equal to no. of columns in dataset excluding the first column
        > Impacts must be either +ve or -ve
        > Input file must contain three or more columns
        > 2nd to last columns contain numeric values only
        
        Result file contains all the columns of input file and two additional columns having TOPSIS SCORE and RANK
        
        ### EXAMPLE
        
        >IF INPUT FILE IS AS FOLLOWS:
        
        | Model | Correlation | 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
        
        > OUTPUT FILE:
        
        | Model | Correlation | RSeq | RMSE | Accuracy | Topsis Score | Rank
        | ------ | ------ | ----- | ----- | -------- | -------- | --------
        | M1 | 0.79 | 0.62 | 1.25 | 60.89 | 0.639133 | 2
        | M2 | 0.66 | 0.44 | 2.89 | 63.07 | 0.212592 | 5
        | M3 | 0.56 | 0.31 | 1.57 | 62.87 | 0.407846 | 4
        | M4 | 0.82 | 0.67 | 2.68 | 70.19 | 0.519153 | 3
        | M5 | 0.75 | 0.56 | 1.3 | 80.39 | 0.828267 | 1
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
