NeuralFabric

A from-scratch machine learning and deep learning framework, from tensors to transformers.

Features

• Tensor engine built on NumPy
• Automatic differentiation (Autograd)
• Linear Regression
• Logistic Regression
• Train-test split utility
• Type-safe codebase with MyPy
• Comprehensive test suite with Pytest
• Ruff and Black for code quality
• GitHub Actions CI/CD workflows
• Modern Python packaging and PyPI distribution

Installation

pip install neuralfabric

For development:

pip install -e ".[dev]"

Quick Start

from neuralfabric.core.tensor import Tensor
from neuralfabric.linear_model import LinearRegression
from neuralfabric.model_selection import train_test_split

X = Tensor([
    [800, 2],
    [1000, 2],
    [1200, 3],
    [1500, 3],
    [1800, 4],
    [2000, 4],
    [2200, 5],
    [2500, 5],
])

y = Tensor([
    [120000],
    [150000],
    [180000],
    [220000],
    [260000],
    [290000],
    [320000],
    [370000],
])

X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.25,
    random_state=42,
)

model = LinearRegression(
    lr=1e-8,
    epochs=10000,
)

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print("Predictions:")
print(predictions.data)

print("\nR² Score:")
print(model.score(X_test, y_test))

Available Models

Project Structure

src/neuralfabric/
├── core/
│   └── Tensor and Autograd Engine
├── linear_model/
│   ├── LinearRegression
│   └── LogisticRegression
└── model_selection/
    └── train_test_split

Roadmap

Completed

• Tensor implementation
• Automatic differentiation engine
• Linear Regression
• Logistic Regression
• Train-test split utility
• Unit testing
• CI/CD workflows
• PyPI publishing

Planned

• Decision Trees
• Random Forests
• Support Vector Machines
• K-Means Clustering
• Principal Component Analysis (PCA)
• Neural Network API
• Optimizers (SGD, Adam)
• Transformer Architecture
• Dataset utilities
• Documentation website

Development

make dev
make test
make lint
make format
make build
make publish

Contributing

Contributions, bug reports, feature requests, and discussions are welcome.

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Open a pull request

License

Distributed under the MIT License. See the LICENSE file for details.