semvec/pypi-packages huggingface dataset:
topsis-vani-102303078 1.0.1
A Python package for TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)
Requires Python: >=3.7
Keywords: topsis mcdm decision-making multi-criteria optimization ranking

# Topsis-Vani-102303078

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A Python package implementing TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) for multi-criteria decision making.

**Author:** Vani Goyal  
**Roll Number:** 102303078  
**Email:** vgoyal_be23@thapar.edu

## What is TOPSIS?

TOPSIS is a multi-criteria decision analysis method that identifies the best alternative by calculating the geometric distance from the ideal solution and the negative-ideal solution.

## Installation

```bash
pip install Topsis-Vani-102303078
```

## Usage

### Command Line Interface

```bash
topsis <InputDataFile> <Weights> <Impacts> <OutputFileName>
```

### Example

```bash
topsis test_data/data.csv "1,1,1,2" "+,+,-,+" test_data/result.csv
```

### Python Module

```python
from topsis_vani_102303078 import topsis

topsis('input.csv', '1,1,1,2', '+,+,-,+', 'output.csv')
```

## Input Format

CSV file with:
- **Column 1:** Alternative names/IDs
- **Columns 2-N:** Numeric criteria values

Example:
```csv
Fund Name,P1,P2,P3,P4,P5
M1,0.84,0.71,6.7,42.1,12.59
M2,0.91,0.83,7.0,31.7,10.11
```

## Parameters

### Weights
Comma-separated positive numbers (e.g., `"1,1,1,2"`)

### Impacts
Comma-separated `+` or `-` signs:
- `+` : Higher values are better (benefit criterion)
- `-` : Lower values are better (cost criterion)

Example: `"+,+,-,+"` means maximize P1, P2, P4 and minimize P3

## Output

CSV file with original data plus:
- **Topsis Score:** Performance score (0-100, higher is better)
- **Rank:** Final ranking (1 is best)

## Algorithm Steps

1. **Normalization:** Vector normalization of decision matrix
2. **Weighted Matrix:** Apply criterion weights  
3. **Ideal Solutions:** Identify best and worst solutions
4. **Distance Calculation:** Euclidean distances from ideals
5. **TOPSIS Score:** Closeness coefficient calculation
6. **Ranking:** Sort by TOPSIS score

## Requirements

- Python >= 3.7
- pandas >= 1.0.0
- numpy >= 1.18.0

## License

MIT License - see LICENSE file

## Links

- **GitHub:** https://github.com/vgalpha/Topsis-Vani-102303078
- **PyPI:** https://pypi.org/project/Topsis-Vani-102303078/

## Author

Vani Goyal  
Roll Number: 102303078  
Thapar Institute of Engineering & Technology  
Email: vgoyal_be23@thapar.edu
suphia 0.1.1
A deterministic, cross-platform agent skill publisher.
Requires Python: >=3.10

# Suphia

**Suphia** is a compatibility layer for AI agent ecosystems.

It provides a **set of tools** that allow developers to work with
**multiple, fast-evolving AI agents** using a **single source of truth**
for context and configuration — without duplication.

Suphia is designed to keep context **close to your code**, while adapting
that context to many different agents and formats.

---

## The Problem Suphia Solves

AI development today rarely involves just one agent.

Developers often experiment with:
- Claude
- Gemini
- Copilot
- OpenCode
- Codex
- new or experimental agents
- changing formats and conventions

Each agent introduces:
- different discovery rules
- different configuration formats
- different expectations about layout

Without Suphia, developers end up:
- copying context files again and again
- restructuring repos for each tool
- losing alignment between code and context

Suphia exists so you **adapt once**, not agent-by-agent.

---

## Suphia’s Core Idea

> Keep **one canonical source of context**, close to your code,
> and project it outward to any agent that needs it.

Suphia does not enforce a single format.
It provides **adapters and tooling** so agents can coexist.

---

## What Suphia Is

- A **CLI toolset**
- A **Python module library**
- An **adapter layer** between agent ecosystems

Suphia can be:
- used directly via CLI
- integrated into other frameworks
- embedded in automation or CI

---

## What Suphia Is Not

- An agent runtime
- A prompt framework
- A vendor-specific solution

Suphia complements agents — it does not replace them.

---

## Initial Focus: Skills

Suphia v1 starts with **skills** as a practical, high-impact use case.

It allows you to:

- Place skills anywhere in your repo
- Keep them near relevant code
- Discover them recursively
- Publish them into agent-specific discovery roots
- Avoid copying or rewriting skill files

This enables skill reuse across:
- Claude-style agents
- Copilot-style agents
- OpenCode and others

---

## Installation

Suphia uses **`uv`** for Python tooling.

Install:

```bash
uv pip install suphia