neuroflex 0.0.3

An advanced neural network framework with interpretability, generalization, robustness, and fairness features

NeuroFlex: Advanced Neural Network Framework

NeuroFlex is a cutting-edge neural network framework built on JAX and Flax, designed to address key challenges in modern machine learning: interpretability, generalization, robustness, and fairness. This project showcases state-of-the-art techniques and methodologies for creating more transparent, reliable, and ethical AI systems.

Latest Updates

• Integration of AlphaFold for advanced protein structure prediction
• Enhanced capabilities for neural protein modeling and drug discovery
• Quantum Neural Network module for quantum computing integration
• Improved Brain-Computer Interface (BCI) functionality
• Advanced cognitive architecture with consciousness simulation
• Support for multiple Python versions (3.9, 3.10, 3.11, 3.12)

Features

• Advanced neural network architectures (CNN, RNN, LSTM, GAN)
• Integration of JAX, TensorFlow, and PyTorch modules
• Quantum Neural Network integration
• Reinforcement learning capabilities
• Brain-Computer Interface (BCI) integration
• Fairness constraints and bias mitigation
• Adversarial training for improved robustness
• Interpretability tools (SHAP)
• 2D and 3D convolution support
• Data augmentation techniques
• Cognitive architecture with consciousness simulation
• AlphaFold integration for protein structure prediction
• Neural protein modeling for neuroscience applications
• Drug discovery support through protein structure analysis
• Synthetic biology insights from protein folding predictions
• Compatibility with numpy < 2 and torch 1.11.0
• Resolved dependency issues for improved stability
• Successful test runs for neural network components

Installation

pip install neuroflex

Environment Setup

NeuroFlex supports multiple operating systems and Python versions:

• Ubuntu
• Windows
• macOS
• Python 3.9, 3.10, 3.11, 3.12

To set up your environment:

1. Clone the repository: git clone https://github.com/neuroflex/neuroflex.git
2. Create a virtual environment: python -m venv neuroflex-env
3. Activate the environment:
   • Ubuntu/macOS: source neuroflex-env/bin/activate
   • Windows: neuroflex-env\Scripts\activate
4. Install dependencies: pip install -r requirements.txt

Quick Start

from neuroflex import NeuroFlexNN, train_model, AlphaFoldIntegration

Define your model

model = NeuroFlexNN(
    features=[64, 32, 10],
    use_cnn=True,
    use_rnn=True,
    fairness_constraint=0.1,
    use_quantum=True,  # Enable quantum neural network
    use_alphafold=True  # Enable AlphaFold integration
)

Initialize AlphaFold integration

alphafold = AlphaFoldIntegration()
alphafold.setup_model(model_params={'max_recycling': 3})

Predict protein structure

predicted_structure = alphafold.predict_structure()

Train your model with AlphaFold integration

trained_state, trained_model = train_model(
    model, train_data, val_data,
    num_epochs=10, batch_size=32, learning_rate=1e-3,
    alphafold_structure=predicted_structure
)

Get pLDDT scores and predicted aligned error

plddt_scores = alphafold.get_plddt_scores()
predicted_aligned_error = alphafold.get_predicted_aligned_error()

print(f"Average pLDDT score: {plddt_scores.mean()}")
print(f"Average predicted aligned error: {predicted_aligned_error.mean()}")

Testing

To run tests for different Python versions and operating systems:

pytest tests/

Documentation

For detailed documentation, please visit our official documentation.

Contributing

We welcome contributions! Please see our contributing guidelines for more information.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use NeuroFlex in your research, please cite:

@software{neuroflex2024,
  author = {kasinadhsarma},
  title = {NeuroFlex: Advanced Neural Network Framework},
  year = {2024},
  url = {https://github.com/VishwamAI/NeuroFlex}
}

Contact

For any questions or feedback, please open an issue on our GitHub repository or contact us at [e-mail].