semvec/pypi-packages huggingface dataset:
Topsis-Khushi-102183044 0.0.7
Package for calculating topsis score for multiple criteria decision making problems
License: MIT

# TOPSIS

### What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) came in the 1980s as a multi-criteria-based decision-making (MCDM) method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution. 

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## How to Install this Package?
``` pip install Topsis-Khushi-102183044 ```

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## How to Run this Package?
``` topsis <inputFileName> <weights> <impacts> <resultFileName> ```

Eg. ``` topsis /Users/khushi/Desktop/102183044-data.csv "1,1,1,1,1" "+,+,-,+,+" /Users/khushi/Desktop/result.csv ```

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## Constraints Applied
1. Number of parameters should be correct i.e. 5.
2. Print error message if input file doesn't exist.
3. The impacts and weights should be comma separated.
4. Impacts should only have +ve or -ve symbols.
5. Number of columns in the input csv file should be more or equal to 3.
6. The 2nd to last columns should be in numeric data type.
7. Number of weights, impacts and columns should be equal.

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## Input File
| Fund Name  | P1 | P2 | P3 | P4 | P5  |
| ---------- | -- | -- | -- | -- | --  |
| M1         |0.75|0.56|6.3 |51.1|14.68|
| M2         |0.82|0.67|4.2 |41.2|11.72|
| M3         |0.89|0.79|6.5 |40.2|12.1 |
| M4         |0.92|0.85|5.8 |49.7|14.32|
| M6         |0.72|0.52|5.3 |61.1|16.91|
| M7         |0.69|0.48|3.6 |57.9|15.67|
| M8         |0.92|0.85|5.7 |31.2|9.67 |

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## Output File
| Fund Name  | P1 | P2 | P3 | P4 | P5  | TOPSIS Score | Rank |
| ---------- | -- | -- | -- | -- | --- | ------------ | ---- |
| M1         |0.32|0.29|0.39|0.36|0.37 | 0.3655       | 8    |
| M2         |0.35|0.34|0.26|0.29|0.29 | 0.55         | 2    |
| M3         |0.38|0.41|0.41|0.28|0.30 | 0.48         | 5    |
| M4         |0.39|0.44|0.36|0.35|0.36 | 0.57         | 1    |
| M5         |0.33|0.30|0.41|0.39|0.39 | 0.39         | 7    |
| M6         |0.31|0.27|0.33|0.43|0.42 | 0.44         | 6    |
| M7         |0.29|0.25|0.22|0.41|0.39 | 0.50         | 4    |
| M8         |0.39|0.44|0.36|0.22|0.24 | 0.53         | 3    |

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## License
MIT

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## Written By
Name : Khushi Prasad
  
Roll No. : 102183044
Topsis-Tanishq-102003205 1.1.1
A Python package to implement Topsis
License: MIT

# Topsis-Tanishq-102003205
Topsis analysis of a csv file in python


## About Topsis

It 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


```bash
pip install Topsis-Tanishq-102003205
```

## Usage

Enter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-).

```
topsis 102003205-data.csv "1,1,1,1,1" "+,+,+,+,+" output.csv

```

Rank 1 signifies best decision.


## Other
The csv file (from 2nd column to last column) should not contain non-numeric values.
The first column and first row are dropped in processing the data.


## License
Â©  2023 Tanishq Singla
[MIT](https://choosealicense.com/licenses/mit/)