Metadata-Version: 2.1
Name: TOPSIS-Siddharth-101803604
Version: 1.0.0
Summary: A package that makes it easy to create Pypi packages
Home-page: UNKNOWN
Author: Siddharth Agarwal
Author-email: siddharthagarwal112000@gmail.com
License: UNKNOWN
Description: # TOPSIS Package in Python
        
        Submitted by: Siddharth Agarwal
        
        Roll no: 101803604
        
        UCS538
        
        * * *
        ## Concept of TOPSIS
        
        TOPSIS is an acronym that stands for â€˜Technique of Order Preference Similarity to the Ideal Solutionâ€™ and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those.
        
        * * *
        
        ## How to use
        
        The package TOPSIS-Siddharth-101803604 can be run though the command line as follows:
        ```
        >> pip install TOPSIS-Siddharth-101803604==0.3.0
        ```
        ```
        >> python
        >>>from topsis_analysis.topsispackage import topsis
        >>>topsis("data.csv","1,1,1,2","+,+,-,+","output.csv")
        ```
        > **Note:**
        > 
        > *   Usages: 
        >     
        >       topsis( <**InputDataFile**> <**Weights**> <**Impacts**> <**ResultFileName**>)
        > *   **Input File**:
        >     
        >     > *   Input file contain three or more columns.
        >     > *   First column is the object/variable name (e.g. M1, M2, M3, M4â€¦â€¦) .
        >     > *   From 2nd to last columns contain numeric values only.
        > *   **Output File**:
        >     
        >     > *   Result file contains all the columns of input file and two additional columns having TOPSIS SCORE and RANK
        > *   The output is created in the form of csv file and stored and also it is displayed.
        > *   The impacts given in the command line should be either â€˜+â€™ or â€˜â€“â€™ depending if you want to maximise the column parameter or minimise it.
        
        ## Sample Input
        
        Here is a sample set of data which can be used for the following package:
        
        <table><thead><tr><th>Model</th><th>Correlation</th><th>R2</th><th>RMSE</th><th>Accuracy</th></tr></thead><tbody><tr><td>M1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>
        
        ## Output of this sample input
        
        The output that will be generated from the following input data will be:
        
        <table><thead><tr><th>Model</th><th align="right">Correlation</th><th align="center">R2</th><th>RMSE</th><th>Accuracy</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">0.79</td><td align="center">0.62</td><td>1.25</td><td>60.89</td><td>0.6391330141342590</td><td>2.0</td></tr><tr><td>M2</td><td align="right">0.66</td><td align="center">0.44</td><td>2.89</td><td>63.07</td><td>0.21259182969277900</td><td>5.0</td></tr><tr><td>M3</td><td align="right">0.56</td><td align="center">0.31</td><td>1.57</td><td>62.87</td><td>0.4078456776130520</td><td>4.0</td></tr><tr><td>M4</td><td align="right">0.82</td><td align="center">0.67</td><td>2.68</td><td>70.19</td><td>0.5191532395007470</td><td>3.0</td></tr><tr><td>M5</td><td align="right">0.75</td><td align="center">0.56</td><td>1.3</td><td>80.39</td><td>0.8282665851935810</td><td>1.0</td></tr></tbody></table>
        
        Here the ranks are given as rank 1 is the best solution according to the weights and impacts given and rank 5 is the worst solution.
        
        * * *
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
