🧠 Random-ML: A Randomized Machine Learning Framework

Random-ML is a high-performance machine learning library that implements randomized neural networks and functional link architectures.
It provides fast, accurate, and scalable models like Random Vector Functional Link (RVFL), Extreme Learning Machines (ELM), and Stochastic Configuration Networks (SCN).

🚀 Key Features

• Randomized Neural Networks → Avoids backpropagation, leading to faster training.
• Supports Ensemble Learning → Includes Boosting & Bagging.
• Multiple Activation Functions → relu, sigmoid, tanh, leaky_relu, sin.
• Works with Scikit-Learn → Seamlessly integrates into the existing ML ecosystem.
• Customizable Models → Allows tuning hidden units, weight initialization, and regularization.

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⚡ Installation

You can install Random-ML via pip:

pip install random-ml

or install from source:

git clone https://github.com/yourusername/random-ml.git
cd random-ml
pip install -e .

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🛠 Usage Examples

🚀 1. Regression with RVFL

from random_ml.regressor import RVFLRegressor
import numpy as np

# Generate sample data
X = np.random.rand(100, 10)
y = np.sin(X[:, 0])  # Regression target

# Train RVFL model
rvfl_reg = RVFLRegressor(in_dim=10, n_hidden_units=50, activation="relu")
rvfl_reg.fit(X, y)
y_pred = rvfl_reg.predict(X)

print("Predictions:", y_pred[:5])

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🔥 2. Classification with RVFL

from random_ml.classifier import RVFLClassifier
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split

# Generate classification data
X, y = make_moons(n_samples=500, noise=0.2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train RVFL classifier
rvfl_cls = RVFLClassifier(in_dim=2, n_hidden_units=50, activation="relu")
rvfl_cls.fit(X_train, y_train)
y_pred = rvfl_cls.predict(X_test)

# Accuracy
from sklearn.metrics import accuracy_score
print(f"RVFL Accuracy: {accuracy_score(y_test, y_pred) * 100:.2f}%")

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🤖 3. Using Boosting & Bagging for Classification

from random_ml.ensemble import RVFLBoostingClassifier, RVFLBaggingClassifier

rvfl_kwargs = {"n_hidden_units": 50, "activation": "relu"}

# AdaBoost with RVFL
boosting_cls = RVFLBoostingClassifier(in_dim=2, rvfl_kwargs=rvfl_kwargs, n_estimators=20, random_state=42)
boosting_cls.fit(X_train, y_train)
y_pred_boosting = boosting_cls.predict(X_test)

# Bagging with RVFL
bagging_cls = RVFLBaggingClassifier(in_dim=2, rvfl_kwargs=rvfl_kwargs, n_estimators=20, random_state=42)
bagging_cls.fit(X_train, y_train)
y_pred_bagging = bagging_cls.predict(X_test)

print(f"Boosting Accuracy: {accuracy_score(y_test, y_pred_boosting) * 100:.2f}%")
print(f"Bagging Accuracy: {accuracy_score(y_test, y_pred_bagging) * 100:.2f}%")

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📖 Documentation & API Reference

Full documentation is available at ReadTheDocs.

To build the documentation locally:

cd docs
make html

Then open docs/build/html/index.html in your browser.

📄 Available Modules

Module	Description
random_ml.rvfl	Implements RVFL-based models
random_ml.ensemble	Includes Boosting & Bagging implementations
randomml.classifier	Provides RVFL-based classifiers
random_ml.regressor	Contains RVFL-based regressors
randomml.mlpedrvfl	Implements the MLPedRVFL model

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🤝 Contributing

We welcome contributions to improve Random-ML! To contribute:

1. Fork the repository.
2. Clone your fork:

   git clone https://github.com/yourusername/random-ml.git
   cd random-ml
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Create a new branch:

   git checkout -b feature_branch
   

5. Make changes & commit:

   git add .
   git commit -m "Describe your changes"
   

6. Push to GitHub & submit a pull request.

For details, see Contributing Guide.

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🏆 Citing random-ml

The preprint is Submitted to Pattern Recognition Letter, please cite it:

Vinay Kumar Giri, Rahul Goswami, Vimlesh Kumar, Synergistic Regression through MLP and edRVFL Fusion: The MLPedRVFL Model for Enhanced Performance and Efficiency, Preprint Submitted to Pattern Recognition Letter.

BibTeX Citation

@misc{randomml2024,
  A placeholder for bibTeX citation
}

Coming Soon - The paper is under review. Yaay!

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📝 License

Random-ML is released under the MIT License. See LICENSE for details.

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🎯 What's Next?

• ✅ Implement more activation functions
• ✅ Improve training speed
• ✅ Support GPU acceleration
• ✅ Add new ensemble methods
• ✅ Open to community contributions!

🔥 Now you're ready to use Random-ML for fast and scalable machine learning! 🚀
Let us know if you need help or have feature requests! 😊✨