semvec/pypi-packages huggingface dataset:
Topsis-Chahat-102203637 0.1.0
A Python package to implement the TOPSIS method for multi-criteria decision analysis.
License: MIT

# Topsis Package

A Python package to calculate **TOPSIS** (Technique for Order Preference by Similarity to Ideal Solution) scores for decision-making.

## Installation

Install the package from PyPI using `pip`:

```bash
pip install Topsis-Chahat-102203637
```
## Usage
Run the TOPSIS algorithm via the command line using the topsis-cli tool.
```bash
topsis-cli <input_file.csv> <weights> <impacts> <output_file.csv>
```

### Arguments
- **<input_file.csv>**: Path to the input CSV file with alternatives and criteria.
- **<weights>**: Comma-separated list of weights for each criterion (e.g., 0.3, 0.4, 0.3).
- **<impacts>**: Comma-separated list of impacts for each criterion (+ for positive, - for negative).
- **<output_file.csv>**: Path for saving the output with TOPSIS scores.

### Example
```bash
topsis-cli input.csv 0.3,0.4,0.3 +,+,- output.csv
```



#### Example Input File (`input.csv`):
| Model   | P1  | P2  | P3  |
|---------|------|------|------|
| Model1  | 25   | 35   | 30   |
| Model2  | 30   | 40   | 35   |
| Model3  | 20   | 30   | 25   |






### Sample Output:

#### Output File (`output.csv`):
| Model   | P1  | P2  | P3  | Topsis Score | Rank |
|---------|------|------|------|--------------|------|
| Model1  | 25   | 35   | 30   | 0.72         | 1    |
| Model2  | 30   | 40   | 35   | 0.85         | 2    |
| Model3  | 20   | 30   | 25   | 0.60         | 3    |

---

### How It Works:

1. **Normalization**:  
   The input data is normalized using the root sum of squares method for each column.

2. **Weight Normalization**:  
   The normalized data is then weighted according to the provided weights.

3. **Best and Worst Values**:  
   The best and worst values for each criterion are determined based on the given impacts.

4. **Performance Calculation**:  
   The Euclidean distance to the best and worst values is computed, and a performance score is generated.

5. **Ranking**:  
   The alternatives are ranked based on their performance scores.
codexp 0.1.0
A CLI tool to analyze Python packages and visualize their structure and dependencies
License: MIT
Requires Python: >=3.8

# CodeXP (Code Explorer) 

[![Python](https://img.shields.io/badge/python-3.8%2B-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI version](https://badge.fury.io/py/codexp.svg)](https://badge.fury.io/py/codexp)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

A CLI tool to analyze Python packages and visualize their structure and dependencies in the terminal.

## Features

- **Package Structure Analysis** 📦
  - Identifies entry points
  - Lists all modules in the package
  - Shows top-level symbols and their definitions

- **Dependency Analysis** 🔍
  - Maps internal imports between package modules
  - Tracks external package dependencies
  - Shows import locations with source code context

- **Symbol Usage Tracking** 🎯
  - Traces both internal and external symbol usage
  - Shows where symbols are defined and used
  - Provides source code context for each usage

- **Flexible Output** 📊
  - Rich terminal visualization with syntax highlighting
  - JSON output for programmatic analysis

## Installation

```bash
pip install codexp
```

## Usage

```bash
codexp <source_directory> [options]

Arguments:
  source_directory    Directory containing source code to analyze

Options:
  --no-line-info, -n    Hide file and line information
  --json, -j           Output results in JSON format
  --help               Show help message and exit
```

## Examples

```bash
# Analyze current directory with visual output
codexp .

# Analyze without line information
codexp ./my_package --no-line-info

# Get JSON output for programmatic use
codexp ./my_package --json > analysis.json
```

The default output provides a rich terminal visualization with syntax highlighting and tree-style formatting. For programmatic analysis, use the `--json` flag to get structured data output.

## License

MIT License - see the [LICENSE](LICENSE) file for details.
fashion-recommenders 0.1.1
An easy-to-use PyTorch-based package for fashion recommendation models
License: MIT
Requires Python: >=3.10

<div style="width: 100%;">
  <img src="title.svg" style="width: 100%;" alt="Click to see the source">
</div>

<br>

<div align="center">

![Python Versions](https://img.shields.io/badge/python-3.7%20|%203.8%20|%203.9%20|%203.10-blue)
[![License](https://img.shields.io/github/license/owj0421/DeepFashion.svg)](https://github.com/owj0421/DeepFashion/blob/master/LICENSE)

</div>

# 🚧 This project is under construction. 🚧

## 🤗 Introduction

Fashion Recommenders is a research-oriented platform designed to facilitate the development and deployment of advanced fashion recommendation systems. Built on PyTorch, it provides researchers and practitioners with the tools needed to explore and implement cutting-edge techniques in fashion-recommendation modeling.

Although numerous advanced recommendation methods have been proposed in the literature since 2018, practical implementations remain scarce. This repository bridges the gap by offering a robust foundation, complete with a growing collection of pre-implemented models inspired by recent research. While we strive to faithfully reproduce methods from the literature, some customizations reflect the experimental nature of this project. Contributions from the community are highly encouraged to further enrich this platform.

### Key Features

- **Pre-Implemented Models**: A diverse collection of recommendation models ready for use and experimentation, saving you the effort of starting from scratch.
- **Streamlined Input Processing**: Standardized tools for structuring item data into formats optimized for model input.
- **Modular Design**: Flexible components for data preprocessing, model design, training, and evaluation, all seamlessly integrating with PyTorch.
- **Multimodal Support**: Easily incorporate images, text, and metadata to enhance recommendation performance.

### Get Involved

We welcome community contributions! From adding new models and features to optimizing existing implementations or exploring innovative ideas, your input is invaluable to the growth of Fashion Recommenders.
<br><br>

## 📥 Installation
```
pip install fashion_recommenders==0.1.1
```


## 📚 Supported Models

<div align="center">

|Model|Paper|FITB<br>Acc.<br>(Ours)|FITB<br>Acc.<br>(Original)|
|:-:|:-|:-:|:-:|
|siamese-net|Baseline|**50.7**<br>32, ResNet18 <br>Image|**54.0**<br>64, ResNet18 <br>Image|
|type-aware-net|[ECCV 2018] [Learning Type-Aware Embeddings for Fashion Compatibility](https://arxiv.org/abs/1803.09196)|**52.6**<br>32, ResNet18 <br>Image|**54.5**<br>64, ResNet18 <br>Image + Text|
|csa-net|[CVPR 2020] [Category-based Subspace Attention Network (CSA-Net)](https://arxiv.org/abs/1912.08967?ref=dl-staging-website.ghost.io)|**55.8**<br>32, ResNet18 <br>Image|**59.3**<br>64, ResNet18 <br>Image|
|fashion-swin|[IEEE 2023] [Fashion Compatibility Learning Via Triplet-Swin Transformer](https://ieeexplore.ieee.org/abstract/document/10105392)|?<br>32, Swin-t <br>Image|**60.7**<br>64, Swin-t <br>Image + Text|

</div>