Metadata-Version: 2.1
Name: TOPSIS_Vipul_101803491
Version: 1.2
Summary: TOPSIS implementation
Home-page: https://github.com/Vipul767/TOPSIS_Vipul_101803491
Author: Vipul Goel
Author-email: vgoel_be18@thapar.edu
License: MIT
Download-URL: https://github.com/Vipul767/TOPSIS_Vipul_101803491/archive/v_12.tar.gz
Description: # TOPSIS_Vipul_101803491 #
        TOPSIS-Vipul-101803491 is an implementation of Technique of Order Preference Similarity to the Ideal Solution(TOPSIS).
        It will help you to place your data acc to preference that is by looking at the data and performing some Maths it will tell you the Preference in which you will be most benefitted. 
        If you want to run the code from command line use the following format: 
        python topsis.py <InputDataFile> <Weights> <Impacts> <ResultFileName>
        Example: python topsis.py inputfile.csv “1,1,1,2” “+,+,-,+” result.csv
         
        ## Remember!!!! ##
        ### When you create the .py file (topsis.py in this case) ###
        ### Just Import the package and call the function just_import_this_fxn_for_whole_topsis_result() by writing :- ###
        
        ### import TOPSIS_Vipul_101803491 ###
        ### TOPSIS_Vipul_101803491.just_import_this_fxn_for_whole_topsis_result() ###
        
        And you will see the desired result!!!
        
        Now let's see the whole thing with the help of an example
        
        Sample dataset
        The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, Rseq, 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 (`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`.
        
        ## Result of our input dataset (or example) ##
        Model | Corr | Rseq | RMSE | Accuracy | TOPSIS SCORE | RANK
        ------------ | ------------- | ------------ | ------------- | ------------ | ------------ | ------------
        M1 |	0.79 | 0.62	| 1.25 | 60.89 | 0.639133
        M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.212592
        M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.407846
        M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.519153
        M5 |	0.75 | 0.56	| 1.3  | 80.39 | 0.828267
        
        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.
Keywords: TOSPSIS,Normalised_Matrix,Performance_score,Rank,Weighted_Normalised_matrix
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
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: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
