TOPSIS-Python
Project 1 : UCS654
Submitted By: Dhruv Singla 102003697
What is TOPSIS
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution. More details at wikipedia.
How to use this package:
TOPSIS-DHRUV-102003697 can be run as in the following example:
In Command Prompt
>> topsis 102003697-data.csv "1,1,1,1,1" "+,+,-,+,+" 102003697-result.csv
Sample dataset
The decision matrix (a) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.
| Model | Correlation | R2 | 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.
Output
Model Score Rank
----- -------- ----
1 0.77221 2
2 0.225599 5
3 0.438897 4
4 0.523878 3
5 0.811389 1
Release files for topsis-dhruv-102003697 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| topsis-dhruv-102003697-1.0.0.tar.gz | 4.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| topsis_dhruv_102003697-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 9.2 kB
Release files / topsis-dhruv-102003697-1.0.0.tar.gz
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Release files / topsis_dhruv_102003697-1.0.0-py3-none-any.whl
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