🧠 Neurogenesis

Python Version: 3.11.9
Status: ⚠️ Pre-Alpha (Under Active Development)

Neurogenesis is a lightweight, educational machine learning library built from scratch in Python. Its primary aim is to focus on simplicity to aid in learning and to provide a platform for experimentation of unique ideas without having to face complex production-grade codebases of popular libraries which are highly optimized for performance.

🚧 Disclaimer:
Neurogenesis is currently in its early development stages. Expect incomplete features, bugs, and minimal optimization. Restructuring, refactoring, and formal documentation are planned for future releases.

✅ Algorithms Implemented (v0.0.1)

• K-Nearest Neighbors (KNN)
• Linear Regression
• Logistic Regression
• Naive Bayes
• Support Vector Machine (SVM) – Binary Classification
• Decision Tree
• Random Forest

📌 Planned for v1.0.0

• KNN
• Linear Regression
• Logistic Regression
• Naive Bayes
• SVM
• Decision Tree
• Random Forest
• K-Means Clustering
• AdaBoost
• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)

🚀 Installation

Install Neurogenesis via pip:

pip install neurogenesis

⚙️ How to use

Here's a basic example using the RandomForest classifier:

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from neurogenesis.models import RandomForest

data = datasets.load_breast_cancer()
X = data.data
y = data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

clf = RandomForest(n_trees=3)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
acc = accuracy(y_test, y_pred)

print("Accuracy:", np.sum(y_true == y_pred) / len(y_true))

🧪 Make sure your data (X_train, y_train, X_test, y_test) is preprocessed and in the correct format (e.g., NumPy arrays).

📄 License

This project is licensed under the GNU General Public License v3.0.
You may redistribute, modify, and/or use the code under the terms of this license.

Please see the full license text online:
🔗 https://www.gnu.org/licenses/gpl-3.0.en.html

⚠️ Note: Any derivative works must also be licensed under GPLv3 or a compatible license.

🙋‍♂️ Contributing

Contributions, bug reports, and feature requests are welcome!
If you'd like to improve the library, fix a bug, or suggest a new algorithm, feel free to open an issue or submit a pull request.

📧 mzainkhalidofficial@gmail.com
🔗 GitHub Issues

Thanks to the open-source community for continuous inspiration and learning!