sampen-gpu 0.1.0

GPU-accelerated Sample Entropy in Python

GPU-Accelerated Sample Entropy in Python

This repository provides a fast, GPU-accelerated implementation of Sample Entropy (SampEn) for time series analysis using Python, Numba, and CUDA.

Sample Entropy is a widely-used statistical measure that quantifies the complexity or regularity of a time series. This implementation leverages GPU parallelism to efficiently compute SampEn for large datasets. It has been benchmarked as 100x faster than CPU-based implementations.

Features

• Efficient GPU-based kernel using Numba CUDA
• Chebyshev distance for sequence similarity
• Dynamic chunk processing to handle large time series
• Graceful handling of edge cases (zero matches, infinite entropy)

Requirements

• Python 3.8+
• NumPy
• Numba
• CUDA-enabled GPU and NVIDIA drivers

Install dependencies via:

git clone https://github.com/4d30/sampen.git
cd sampen
pip install -e .

Usage

from sampen import sampen
import numpy as np

data = np.random.rand(10000)  # your time series data
m = 2                         # template length
r = 0.2                       # similarity threshold

entropy = sampen_gpu(data, m, r)
print("Sample Entropy:", entropy)