jacobmcasey / bayesian_net

Bayesian_Net: Neural Naive Bayes Classifier

Bayesian_Net employs a fully connected, 3-layer neural network implemented in NumPy for predicting priors which are input into Bernoulli Naive Bayes 🌐

Features

• 🤖Hybrid Model: Combines neural networks and Naive Bayes for intelligent probabilies.
• 🔢NumPy Based: Efficient matrix operations and computations.
• ⚙️Customisable: Easily adjust layers, learning rate, and iterations.
• 📊One-hot Encoding Utility: Convert integer lists to one-hot encoded numpy arrays.

How To Use

Import the Classifier.

from hybrid_classifier import Classifier

Initialize the Classifier with desired hyper-parameters.

clf = Classifier(layers=[25,8,4], learning_rate=0.005, iterations=100)

Train the model using data.

clf.fit(good_moves_data, target)

Predict using a given state of Pacman.

best_move = clf.predict(state_data, legal)

📖 Documentation

For detailed documentation on each class and function, please refer to the project documentation's DOCSTRINGS using PEP8.

💾 Installation & Requirements

• Python 3.x
• NumPy

Clone the repository and install using pip:

git clone https://github.com/your_username/bayesian_net.git cd bayesian_net pip install numpy

🤝 Contributing

Feel free to submit pull requests, enhancements, or report bugs. My email is [e-mail] for any questions!

📜 License

This project is licensed under the MIT License.