Metadata-Version: 2.1
Name: TOPSIS-DILREET-101803048
Version: 1.0.2
Summary: TOPSIS is an acronym that stands for â€˜Technique of Order Preference Similarity to the Ideal Solutionâ€™ and is a pretty straightforward MCDA method
Home-page: https://github.com/coderprodigy/TOPSIS-DILREET-101803048
Author: DILREET SINGH
Author-email: coderprodigy@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: numpy (==1.19.3)

# What is TOPSIS

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is one of the numerical methods of the multi-criteria decision making. This is a broadly applicable method with a simple mathematical model.It chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.

# To Install the package use

```
pip install TOPSIS-DILREET-101803048==1.0.2
```

# To Run the package use :

```
cd TOPSIS-DILREET-101803048
python topsis.py data.csv 1,1,1,2 +,+,-,+ result.csv
```

# The format is

topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

## Steps used in TOPSIS

1)Normalize the given decision data
2)Find weighted normalized
3)Determine positive ideal and negative ideal solution
4)Calculate separation measures
5)Find relative closesness to ideal solution
6)Rank the preference order

## Sample Data:

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

## Output of This Data

1,2,3,4,performance score,rank as per topsis
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


