semvec/pypi-packages huggingface dataset:
Topsis-Paryagdeep-101903573 0.0.3
TOPSIS is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.
Requires Python: >=3.10
Keywords: TOPSIS,PANDAS,AGGREGATION

# Topsis-Paryagdeep-101903573
TOPSIS is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. Normalisation is usually required as the parameters or criteria are often of incongruous dimensions in multi-criteria problems

## Installation Instructions 
Supported version - Python 3.10
```
pip install Topsis-Paryagdeep-101903573
```

Running the package requires 4 arguments to be passed to the package-
- Input data file in ```.xlsx``` format
- Weights of each parameter (equal to the number of parameters)
- Impact of each parameter (equal to the number of output parameters)
- Output file name in ```.csv``` format

### Example 

Example command line argument -

```
Topsis-Paryagdeep-101903573 data.xlsx 1,1,2,1,1 +,+,-,-,+ output.csv
```

**Input File (data.xlsx) :**

![img.png](Images/img.png)

The resultant file will be saved as per the ``output file name`` that is passed in the command line. Must be ```csv``` file type.

**Output file (output.csv) :**

![img.png](Images/img2.png)

The output file will contain the **Topsis score** and **Rank** appended to the original dataset.
pyreader 0.0.1
Allows you to alter text files very easily
Requires Python: >=3.6

FUNCTIONS
==================
Use pyreader.write(file, text) to write new text in the file without clearing existing text

Use pyreader.clear(file) to completly clear the file 

Use pyreader.replace(file, text) to completly re-write the file with new text.

Use pyreader.read(file) to read the entire file and print it out automatically
Topsis-Jayant-401903026 0.0.2
TOPSIS ASSIGNMENT BY PS RANA
Requires Python: >=3.6

THIS IS A WORK OF ART BY JAYANT NARAYAN FOR P.S. RANA AS TOPSIS ASSIGNMENT 4 
TO INSTALL THIS INTO PC:
pip install Topsis-Jayant-401903026==0.0.2

TO RUN IN CMD:

python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>
Topsis-Gurkawalbir-101917084 1.0.0
A package to find the topsis score
Requires Python: >=3.6

# 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-GURKAWALBIR-101917084==1.0.2
```

# To Run the package use :

```
cd TOPSIS-GURKAWALBIR-101917084
python topsis.py data.csv result.csv 1,1,1,1,1 +,-,+,-,+
```

# The format is

topsis <InputDataFile> <ResultFileName> <Weights> <Impacts> 

## 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