- 🤖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.
from hybrid_classifier import Classifier
clf = Classifier(layers=[25,8,4], learning_rate=0.005, iterations=100)
clf.fit(good_moves_data, target)
best_move = clf.predict(state_data, legal)
For detailed documentation on each class and function, please refer to the project documentation's DOCSTRINGS using PEP8.
- Python 3.x
- NumPy
git clone https://github.com/your_username/bayesian_net.git cd bayesian_net pip install numpy
Feel free to submit pull requests, enhancements, or report bugs. My email is jacobcasey.999@gmail.com for any questions!
This project is licensed under the MIT License.