Power-series based computation of Signature Kernels
Using ADM-derived Neumann series to compute signature kernels.
Installation
pip install git+https://github.com/geekbeast/powersig.git
Requires Python 3.12+
Requires PyTorch 2.5+, JAX 0.6.0+, or cupy 13.4.1+ depending on which implementation you prefer.
Getting Started
import jax.numpy as jnp
from powersig.jax.utils import fractional_brownian_motion
from powersig.jax.algorithm import PowerSigJax
def main():
# Generate fBM paths
n_steps = 1000
n_paths = 2
hurst = 0.7
# Generate fBM using the jax wrapper
fbm_paths, dt = fractional_brownian_motion(
n_steps=n_steps,
n_paths=n_paths,
hurst=hurst,
dim=1
)
# Initialize PowerSigJax with polynomial order 8
powersig = PowerSigJax(order=8)
# Compute the signature kernel
kernel_matrix = powersig(fbm_paths)
print("Shape of fBM paths:", fbm_paths.shape)
print("Shape of kernel matrix:", kernel_matrix.shape)
print("\nKernel matrix:")
print(kernel_matrix)
if __name__ == "__main__":
main()
This example is also available in the repo under examples.
1 maintainer
Metadata
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