probnet 0.2.0

ProbNet: A Unified Probabilistic Neural Network Framework for Classification and Regression Tasks

ProbNet: A Unified Probabilistic Neural Network Framework for Classification and Regression Tasks

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🌟 Overview

ProbNet is a lightweight and extensible Python library that provides a unified implementation of Probabilistic Neural Network (PNN) and its key variant, the General Regression Neural Network (GRNN). It supports both classification and regression tasks, making it suitable for a wide range of supervised learning applications.

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🔧 Features

• 🧠 Full implementation of PNN for classification
• 📈 GRNN for regression modeling
• 🔍 Scikit-learn compatible interface (fit, predict, score)
• 🔄 Built-in support for many kernels and distance metrics
• 🧪 Fast prototyping and evaluation
• 🧩 Easily extendable and readable codebase
• 📚 Auto-generated documentation with Sphinx
• Probabilistic models: PnnClassifier, GrnnRegressor

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📖 Citation Request

Please include these citations if you plan to use this library:

@software{thieu20250503,
  author       = {Nguyen Van Thieu},
  title        = {ProbNet: A Unified Probabilistic Neural Network Framework for Classification and Regression Tasks},
  month        = may,
  year         = 2025,
  doi         = {10.6084/m9.figshare.28802435},
  url          = {https://github.com/thieu1995/ProbNet}
}

📦 Installation

Install the latest version using pip:

pip install probnet

After installation, check the version to ensure successful installation:

$ python
>>> import probnet
>>> probnet.__version__

🚀 Quick Start

For Classification using PNN:

from probnet import PnnClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = PnnClassifier(sigma=0.1)
model.fit(X_train, y_train)
print("Accuracy:", model.score(X_test, y_test))

For Regression using GRNN:

from probnet import GrnnRegressor
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = GrnnRegressor(sigma=0.5)
model.fit(X_train, y_train)
print("R2 Score:", model.score(X_test, y_test))

📚 Documentation

Documentation is available at: 👉 https://probnet.readthedocs.io

You can build the documentation locally:

cd docs
make html

🧪 Testing

You can run unit tests using:

pytest tests/

🤝 Contributing

We welcome contributions to ProbNet! If you have suggestions, improvements, or bug fixes, feel free to fork the repository, create a pull request, or open an issue.

📄 License

This project is licensed under the GPLv3 License. See the LICENSE file for more details.

📎 Official channels

• 🔗 Official source code repository
• 📘 Official document
• 📦 Download releases
• 🐞 Issue tracker
• 📝 Notable changes log
• 💬 Official discussion group

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Developed by: Thieu @ 2025