Skip to main content

Kernels in Jax.

Project description


This project has now been incorporated into GPJax.

JaxKern's logo

Kernels in Jax.

codecov CircleCI Documentation Status PyPI version Downloads Slack Invite

Introduction

JaxKern is Python library for working with kernel functions in JAX. We currently support the following kernels:

  • Stationary
    • Radial basis function (Squared exponential)
    • Matérn
    • Powered exponential
    • Rational quadratic
    • White noise
    • Periodic
  • Non-stationary
    • Linear
    • Polynomial
  • Non-Euclidean
    • Graph kernels

In addition to this, we implement kernel approximations using the Random Fourier feature approach.

Example

The following code snippet demonstrates how the first order Matérn kernel can be computed and, subsequently, approximated using random Fourier features.

import jaxkern as jk
import jax.numpy as jnp
import jax.random as jr
key = jr.PRNGKey(123)

# Define the points on which we'll evaluate the kernel
X = jr.uniform(key, shape = (10, 1), minval=-3., maxval=3.)
Y = jr.uniform(key, shape = (20, 1), minval=-3., maxval=3.)

# Instantiate the kernel and its parameters
kernel = jk.Matern32()
params = kernel.init_params(key)

# Compute the 10x10 Gram matrix
Kxx = kernel.gram(params, X)

# Compute the 10x20 cross-covariance matrix
Kxy = kernel.cross_covariance(params, X, Y)

# Build a RFF approximation
approx = RFF(kernel, num_basis_fns = 5)
rff_params = approx.init_params(key)

# Build an approximation to the Gram matrix
Qff = approx.gram(rff_params, X)

Code Structure

All kernels are supplied with a gram and cross_covariance method. When computing a Gram matrix, there is often some structure in the data (e.g., Markov) that can be exploited to yield a sparse matrix. To instruct JAX how to operate on this, the return type of gram is a Linear Operator from JaxLinOp.

Within GPJax, all kernel computations are handled using JaxKern.

Documentation

A full set of documentation is a work in progress. However, many of the details in JaxKern can be found in the GPJax documentation.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

jaxkern-nightly-0.0.5.dev20241021.tar.gz (33.8 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file jaxkern-nightly-0.0.5.dev20241021.tar.gz.

File metadata

File hashes

Hashes for jaxkern-nightly-0.0.5.dev20241021.tar.gz
Algorithm Hash digest
SHA256 7249cb7b688ae2aeda301a6c5497fe67c043896da9811412859643d5004efd21
MD5 3627349282b221458fa79c20ae1a60b5
BLAKE2b-256 3febd06d8bfbcf05668f42b62c08ae7f0b079b9fba65b66679d76fa2540e0b30

See more details on using hashes here.

File details

Details for the file jaxkern_nightly-0.0.5.dev20241021-py3-none-any.whl.

File metadata

File hashes

Hashes for jaxkern_nightly-0.0.5.dev20241021-py3-none-any.whl
Algorithm Hash digest
SHA256 6aef1a4d8bd61d9ad8d718c33426bf7f9d66f11bc31f0e153811735fca6424f3
MD5 12e3e3a7de8ed7d43c50a928647b40a8
BLAKE2b-256 fdb2a90f611e942fb594b466fe2f21721b62bab7999bef0e0fb806c6ada6d2df

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page