semvec/pypi-packages huggingface dataset:
rogvibe 0.5.0
Textual-based terminal lottery wheel.
Requires Python: >=3.11

# Rogvibe

Terminal lottery wheel built with [Textual](https://github.com/Textualize/textual): press Space to spin the wheel and randomly pick a participant.

<img width="1280" height="762" alt="image" src="https://github.com/user-attachments/assets/4ef4e440-a6aa-4dd0-9d92-861c979bfdde" />

https://github.com/user-attachments/assets/f819f175-acd0-48c4-af22-c9d1a7134d73

## Quick start

```cli
uvx rogvibe
# or
pip install rogvibe
rogvibe
# or
uv tool install --python 3.13 rogvibe
rogvibe
```

777 slot mode

```cli
uvx rogvibe --slot
```

https://github.com/user-attachments/assets/7ba0ad45-fa7b-4007-b59c-dbb547bfb2fd

flip mode

```cli
uvx rogvibe --flip
```
coo-algorithm 1.1.1
COO Algorithm — Canine Olfactory Optimization (COO). Surrogate-assisted, pack-based black-box optimizer.
Requires Python: >=3.8
Keywords: metaheuristic, optimization, surrogate, hyperparameter-tuning, black-box, evolutionary

# coo — Canine Olfactory Optimization (COO)

COO (Canine Olfactory Optimization) is an adaptive, surrogate-assisted, multi-pack black-box optimizer inspired by olfactory search behaviors. It is intended for expensive, noisy, multimodal continuous optimization and hyperparameter tuning.

This repository contains:

- `coo.py` — the COO optimizer implementation
- `examples/` — example scripts (ANN tuning)
- `tests/` — smoke tests
- `docs/` — mathematical explanation and pseudocode

## Features

- Multi-pack population (clusters) for structured exploration
- Lightweight surrogate ensemble (RF/GBM/GP/LGBM) with uncertainty-based selection
- Olfactory map: coarse grid memory guiding attraction between promising regions
- Gradient refinement (finite-difference) on top candidates
- Caching of evaluations to avoid repeated expensive function calls

## Quick usage

```python
from coo import COO
# define bounds as list of (low, high)
bounds = [(-5, -1), (1, 3), (8, 64), (-6, -1), (16, 64), (0, 2)]
opt = COO(bounds=bounds, n_packs=4, init_pack_size=12, max_iterations=100, surrogate_enabled=True)
best_x, best_f, hist, diag, best_pos_hist = opt.optimize(lambda x: -objective(x))
# note: this implementation treats objectives as maximization; to minimize, pass -objective(x)
```