A Python package to find TOPSIS for multi-criteria decision analysis method
Project description
Project description TOPSIS-ANALYSIS By: Sarvagy Jain
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.
Installation
pip install topsis-sarvagyjain-102003553==1.0.1
Usage
Arguments Required: (Assumne we have 5 attributes in dataset.)
You have to required one .csv file. (102003553-data.csv) Pass weights to each attribute. (e.g.: [1,1,1,1,1]) Pass impacts to each attribute. (e.g.: [+,-,+,-,+]) Pass the name of the file with you want to put on .csv file. (102003553-result-1.csv)
Enter csv filename followed by .csv extension, then enter the weights string with values separated by commas, followed by the impacts string with comma separated signs (+,-) and name of file followed by -.csv- extension in which the user wants the output file
Example
sample.csv
Fund Name P1 P2 P3 P4 P5
M1 0.84 0.71 6.7 42.1 12.59
M2 0.91 0.83 7 31.7 10.11
M3 0.79 0.62 4.8 46.7 13.23
M4 0.78 0.61 6.4 42.4 12.55
M5 0.94 0.88 3.6 62.2 16.91
M6 0.88 0.77 6.5 51.5 14.91
M7 0.66 0.44 5.3 48.9 13.83
M8 0.93 0.86 3.4 37 10.55
INPUT
topsis 102003553-data.csv 1,1,1,1,1 +,-,+,-,+ 102003553-result-1.csv
OUTPUT
Fund Name P1 P2 P3 P4 P5 Topsis Score Rank
M1 0.351077437 0.344400588 0.421433661 0.322539084 0.335992288 0.594551725 2
M2 0.380333891 0.402609138 0.440303825 0.24286197 0.269807945 0.566246179 3
M3 0.330179971 0.300744175 0.301922623 0.357780884 0.353072118 0.485394123 6
M4 0.326000478 0.295893463 0.402563497 0.324837462 0.334924798 0.612775882 1
M5 0.39287237 0.4268627 0.226441967 0.476530428 0.451281142 0.361550918 8
M6 0.367795411 0.373504863 0.408853551 0.394554936 0.397906673 0.538764066 5
M7 0.275846558 0.21343135 0.333372896 0.374635658 0.369084459 0.560458621 4
M8 0.388692877 0.417161275 0.213861858 0.283466653 0.281550328 0.38966293 7
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