JASMINE - JAX Accelerated Statistical Models and Integrated Neural Engine

JASMINE is a high-performance machine learning library built on JAX, designed to leverage GPU and TPU acceleration for statistical modeling and neural computation.

📖 Read the Documentation | 🚀 Quick Start | 📚 API Reference

Features

• JIT-compiled models: Linear/Logistic Regression with GPU/TPU acceleration
• Multiple optimizers: SGD, Momentum, Adam with adaptive learning rates
• Advanced regularization: L1, L2, Elastic Net penalties
• Data preprocessing: StandardScaler with JIT acceleration
• Sklearn-compatible API: Familiar interface with JAX performance
• Automatic differentiation: Powered by JAX's grad transformations

Quick Start

from jasmine import LinearRegression, LogisticRegression
from jasmine.datasets import generate_classification

# Generate data and train model
X, y = generate_classification(n_samples=1000, n_features=20)
model = LogisticRegression(learning_rate=0.1, n_epochs=1000)
model.train(X, y)

# Make predictions
predictions = model.inference(X)
accuracy = model.evaluate(X, y)
print(f"Accuracy: {accuracy:.3f}")

Installation

git clone https://github.com/jaseempaloth/JASMINE.git
cd JASMINE
pip install -r requirements.txt
pip install -e .

Documentation

📖 Complete documentation is available at jasmine.readthedocs.io

Quick Links:

• 🚀 Quick Start Guide - Get up and running in 5 minutes
• 📚 API Reference - Complete function and class documentation
• 📝 Examples & Tutorials - Detailed use cases and best practices
• 💾 Installation Guide - Platform-specific setup instructions

Requirements

• Python 3.8+
• JAX >= 0.4.0
• NumPy >= 1.21.0

License

MIT License - see LICENSE file for details.- JAX Accelerated Statistical Models and Integrated Neural Engine

JASMINE is a lightweight machine learning library built on top of JAX, designed to leverage GPU and TPU acceleration for high-performance computing. The project aims to provide an accessible interface to JAX’s powerful capabilities while continuously updating with new features and models.

Features

• Modular and extensible framework supporting diverse machine learning algorithms
• Core components for model development, training, and evaluation
• Data preprocessing utilities
• Automatic differentiation powered by JAX