semvec/pypi-packages huggingface dataset:
Topsis-Prerit-102217030 1.0.8
A Python package for performing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis.
License: MIT
Requires Python: >=3.7
Keywords: topsis mcdm multi-criteria decision-making ranking analysis

# TOPSIS Implementation

This package provides a Python implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for multi-criteria decision analysis.

---

## Features
- **Ease of Use**: Simple and clear implementation of the TOPSIS algorithm.
- **Weighted Decision Making**: Allows users to define weights for each criterion.
- **Impact Analysis**: Accounts for both positive and negative impacts of criteria.
- **Command-Line Interface**: Execute TOPSIS directly from the terminal with input and output files.

---

## Installation

To install the package, use:

pip install TOPSIS_Prerit_102217030


## Usage
Run the TOPSIS analysis using the command-line interface:

topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

Example
Suppose you have a CSV file data.csv containing a decision matrix where:

The first column is the identifier for alternatives.
The subsequent columns contain numeric data for each criterion.

If you want to apply TOPSIS with weights [1, 1, 1, 2] and impacts [+, +, -, +], use:
python topsis data.csv "1,1,1,2" "+,+,-,+" result.csv

This will generate a result file result.csv with the calculated TOPSIS scores and rankings.
<!-- ## Functions -->
<!-- ### Funtion(parameter) -->
unet3d 0.1.0
Single-file PyTorch implementations of 3D UNets
License: MIT
Requires Python: <4.0,>=3.9

# unet3d
`pip install unet3d` to get single-file implementations of 3D UNet architectures for 3D image segmentation!

## Usage 💡
Run a `UNet3d`...
```python
import torch
from unet3d import UNet3d, LinkNet3d

unet = UNet3d(c_in=1, c_out=2, c_start=8, layers=4)
x = torch.rand(1, 1, 64, 64, 64)
output = unet(x)
print(output.shape)  # (1, 2, 64, 64, 64)
```
...or a `LinkNet3d`—a more compute efficient UNet variant—on `torch.rand`om data 🤓
```python
linknet = LinkNet3d(c_in=1, c_out=2, c_start=8, layers=4)
x = torch.rand(1, 1, 64, 64, 64)
output = linknet(x)
print(output.shape)  # (1, 2, 64, 64, 64)
```