Metadata-Version: 2.4
Name: metanion
Version: 0.1.0
Summary: A zero-weight symbolic tensor engine
Home-page: https://github.com/rohitpatraoutlook-dotcom/metanion
Author: Rohit Patra
Author-email: rohitpatra@outlook.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.19.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# 🧠 Metanion - Zero-Weight Symbolic Tensor Engine

[![PyPI version](https://badge.fury.io/py/metanion.svg)](https://badge.fury.io/py/metanion)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**Metanion** is a revolutionary tensor engine where **weights are symbolic expressions, not numbers**. It learns mathematical relationships using Genetic Programming and JIT compilation.

## 🎯 Why Metanion?

- ✅ **No Numerical Weights** - Only operation sequences stored
- ✅ **Explainable** - Outputs human-readable equations
- ✅ **Fast** - JIT compiled to Python bytecode
- ✅ **Differentiable** - Full symbolic differentiation
- ✅ **Lightweight** - Minimal memory footprint

## 🚀 Quick Start

```python
from metanion import create_model, train, predict
import numpy as np

# Create data: y = 2*x + 1 + noise
X = np.random.randn(200, 1)
y = 2 * X[:, 0] + 1 + 0.1 * np.random.randn(200)

# Create and train model
model = create_model([1, 10, 1])
train(X, y, epochs=30)

# Make predictions
predictions = predict(X)

# Get the learned equation
print(model._best_individual.get_expression())
