Metadata-Version: 2.4
Name: matplotlab
Version: 0.1.16
Summary: Laboratory Intelligence Toolkit - Swarm Intelligence Module
Home-page: https://github.com/yourusername/matplotlab
Author: Lab Work
License: MIT
Project-URL: Homepage, https://github.com/yourusername/matplotlab
Keywords: swarm-intelligence,pso,aco,optimization
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.19.0
Requires-Dist: matplotlib>=3.3.0
Dynamic: home-page
Dynamic: requires-python

# matplotlab - Laboratory Intelligence Toolkit

A lightweight Python library implementing Swarm Intelligence algorithms from lab coursework.

## Features

**Swarm Intelligence (SI)**
- Particle Swarm Optimization (PSO) - 3 implementations (basic, adaptive, modular)
- Ant Colony Optimization (ACO) for Traveling Salesman Problem
- 6 benchmark fitness functions (sphere, rastrigin, quadratic, oscillating, multi_modal, lab6_custom)
- 5 boundary handling strategies (clamping, reflecting, wraparound, random reset, velocity damping)
- 2 search methods (random search, brute force)
- 7 lab implementation codes

## Installation

### Local Installation (Development)
```bash
pip install -e .
```

### From PyPI (After Publishing)
```bash
pip install matplotlab
```

## Quick Start

### SI Module

```python
from matplotlab.si import pso_adaptive, rastrigin
import numpy as np

best_x, best_val, history = pso_adaptive(
    rastrigin,
    dim=10,
    bounds=(-5.12, 5.12),
    num_particles=30,
    iterations=100
)
print(f"Best solution: {best_x}, Value: {best_val}")
```

### Available Functions

```python
from matplotlab.si import show_all_params

# View all 25 available functions and their parameters
show_all_params()
```

### Get Help on Any Function

```python
from matplotlab.si import pso_basic

# View function documentation and parameters
pso_basic.help()

# View quick parameter reference
pso_basic.params()

# View source code
pso_basic.show()
```

```python
from matplotlab.si import run_aco

best_path, best_distance = run_aco(
    distances=distance_matrix,
    n_ants=20,
    n_iterations=50,
    alpha=1,
    beta=2,
    evaporation=0.5
)
print(f"Best tour: {best_path}, Distance: {best_distance}")
```

## Requirements

- Python 3.8+
- numpy >= 1.19.0
- matplotlib >= 3.3.0

## Modules

### SI (Swarm Intelligence)
- `algorithms.pso` - PSO implementations
- `algorithms.aco` - Ant Colony Optimization
- `algorithms.search` - Random and Brute Force Search
- `optimization.fitness` - Fitness functions
- `optimization.boundaries` - Boundary handling strategies

### CV (Computer Vision)
- To be implemented

### HCI (Human-Computer Interaction)
- To be implemented

## License

MIT License
