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KernelFunctions.jl

CIcodecovDocumentation (stable)Documentation (latest)ColPrac: Contributor's Guide on Collaborative Practices for Community PackagesCode Style: BlueDOI

Kernel functions for machine learning

KernelFunctions.jl is a general purpose kernel package. It provides a flexible framework for creating kernel functions and manipulating them, and an extensive collection of implementations. The main goals of this package are:

  • Flexibility: operations between kernels should be fluid and easy without breaking, with a user-friendly API.
  • Plug-and-play: being model-agnostic; including the kernels before/after other steps should be straightforward. To interoperate well with generic packages for handling parameters like ParameterHandling.jl and FluxML's Functors.jl.
  • Automatic Differentiation compatibility: all kernel functions which ought to be differentiable using AD packages like ForwardDiff.jl or Zygote.jlshould be.

Examples

x =range(-3.0, 3.0; length=100)
# A simple standardised squared-exponential / exponentiated-quadratic kernel.
k₁ =SqExponentialKernel()
K₁ =kernelmatrix(k₁, x)
# Set a function transformation on the data
k₂ =Matern32Kernel() ∘FunctionTransform(sin)
K₂ =kernelmatrix(k₂, x)
# Set a matrix premultiplication on the data
k₃ =PolynomialKernel(; c=2.0, degree=2) ∘LinearTransform(randn(4, 1))
K₃ =kernelmatrix(k₃, x)
# Add and sum kernels
k₄ =0.5*SqExponentialKernel() *LinearKernel(; c=0.5) +0.4* k₂
K₄ =kernelmatrix(k₄, x)
plot(
heatmap.([K₁, K₂, K₃, K₄]; yflip=true, colorbar=false)...;
layout=(2, 2), title=["K₁""K₂""K₃""K₄"],
)

Related Work

This package replaces the now-defunct MLKernels.jl. It incorporates lots of excellent existing work from packages such as GaussianProcesses.jl, and is used in downstream packages such as AbstractGPs.jl, ApproximateGPs.jl, Stheno.jl, and AugmentedGaussianProcesses.jl.

See the JuliaGaussianProcesses Github organisation and website for more information.

Issues/Contributing

If you notice a problem or would like to contribute by adding more kernel functions or features please submit an issue, or open a PR (please see the ColPrac contribution guidelines).

About

Julia package for kernel functions for machine learning

juliagaussianprocesses.github.io/KernelFunctions.jl/stable/

Topics

gaussian-processeskernel-functionskernelsmachine-learning

Resources

Readme
MIT license

Contributing

Contributing
Activity
Custom properties

Stars

274 stars

Watchers

6 watching

Forks

41 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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KernelFunctions.jl
CIcodecovDocumentation (stable)Documentation (latest)ColPrac: Contributor's Guide on Collaborative Practices for Community PackagesCode Style: BlueDOI
Kernel functions for machine learning
KernelFunctions.jl is a general purpose kernel package.
It provides a flexible framework for creating kernel functions and manipulating them, and an extensive collection of implementations.
The main goals of this package are:
  • Flexibility: operations between kernels should be fluid and easy without breaking, with a user-friendly API.
  • Plug-and-play: being model-agnostic; including the kernels before/after other steps should be straightforward. To interoperate well with generic packages for handling parameters like ParameterHandling.jl and FluxML's Functors.jl.
  • Automatic Differentiation compatibility: all kernel functions which ought to be differentiable using AD packages like ForwardDiff.jl or Zygote.jlshould be.
Examples
x =range(-3.0, 3.0; length=100)
# A simple standardised squared-exponential / exponentiated-quadratic kernel.
k₁ =SqExponentialKernel()
K₁ =kernelmatrix(k₁, x)
# Set a function transformation on the data
k₂ =Matern32Kernel() ∘FunctionTransform(sin)
K₂ =kernelmatrix(k₂, x)
# Set a matrix premultiplication on the data
k₃ =PolynomialKernel(; c=2.0, degree=2) ∘LinearTransform(randn(4, 1))
K₃ =kernelmatrix(k₃, x)
# Add and sum kernels
k₄ =0.5*SqExponentialKernel() *LinearKernel(; c=0.5) +0.4* k₂
K₄ =kernelmatrix(k₄, x)
plot(
heatmap.([K₁, K₂, K₃, K₄]; yflip=true, colorbar=false)...;
layout=(2, 2), title=["K₁""K₂""K₃""K₄"],
)
Related Work
This package replaces the now-defunct MLKernels.jl. It incorporates lots of excellent existing work from packages such as GaussianProcesses.jl, and is used in downstream packages such as AbstractGPs.jl, ApproximateGPs.jl, Stheno.jl, and AugmentedGaussianProcesses.jl.
See the JuliaGaussianProcesses Github organisation and website for more information.
Issues/Contributing
If you notice a problem or would like to contribute by adding more kernel functions or features please submit an issue, or open a PR (please see the ColPrac contribution guidelines).
About
Julia package for kernel functions for machine learning
juliagaussianprocesses.github.io/KernelFunctions.jl/stable/
Topics
gaussian-processeskernel-functionskernelsmachine-learning
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
274 stars
Watchers
6 watching
Forks
41 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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KernelFunctions.jl
CIcodecovDocumentation (stable)Documentation (latest)ColPrac: Contributor's Guide on Collaborative Practices for Community PackagesCode Style: BlueDOI
Kernel functions for machine learning
KernelFunctions.jl is a general purpose kernel package.
It provides a flexible framework for creating kernel functions and manipulating them, and an extensive collection of implementations.
The main goals of this package are:
  • Flexibility: operations between kernels should be fluid and easy without breaking, with a user-friendly API.
  • Plug-and-play: being model-agnostic; including the kernels before/after other steps should be straightforward. To interoperate well with generic packages for handling parameters like ParameterHandling.jl and FluxML's Functors.jl.
  • Automatic Differentiation compatibility: all kernel functions which ought to be differentiable using AD packages like ForwardDiff.jl or Zygote.jlshould be.
Examples
x =range(-3.0, 3.0; length=100)
# A simple standardised squared-exponential / exponentiated-quadratic kernel.
k₁ =SqExponentialKernel()
K₁ =kernelmatrix(k₁, x)
# Set a function transformation on the data
k₂ =Matern32Kernel() ∘FunctionTransform(sin)
K₂ =kernelmatrix(k₂, x)
# Set a matrix premultiplication on the data
k₃ =PolynomialKernel(; c=2.0, degree=2) ∘LinearTransform(randn(4, 1))
K₃ =kernelmatrix(k₃, x)
# Add and sum kernels
k₄ =0.5*SqExponentialKernel() *LinearKernel(; c=0.5) +0.4* k₂
K₄ =kernelmatrix(k₄, x)
plot(
heatmap.([K₁, K₂, K₃, K₄]; yflip=true, colorbar=false)...;
layout=(2, 2), title=["K₁""K₂""K₃""K₄"],
)
Related Work
This package replaces the now-defunct MLKernels.jl. It incorporates lots of excellent existing work from packages such as GaussianProcesses.jl, and is used in downstream packages such as AbstractGPs.jl, ApproximateGPs.jl, Stheno.jl, and AugmentedGaussianProcesses.jl.
See the JuliaGaussianProcesses Github organisation and website for more information.
Issues/Contributing
If you notice a problem or would like to contribute by adding more kernel functions or features please submit an issue, or open a PR (please see the ColPrac contribution guidelines).
About
Julia package for kernel functions for machine learning
juliagaussianprocesses.github.io/KernelFunctions.jl/stable/
Topics
gaussian-processeskernel-functionskernelsmachine-learning
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
274 stars
Watchers
6 watching
Forks
41 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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KernelFunctions.jl
CIcodecovDocumentation (stable)Documentation (latest)ColPrac: Contributor's Guide on Collaborative Practices for Community PackagesCode Style: BlueDOI
Kernel functions for machine learning
KernelFunctions.jl is a general purpose kernel package.
It provides a flexible framework for creating kernel functions and manipulating them, and an extensive collection of implementations.
The main goals of this package are:
  • Flexibility: operations between kernels should be fluid and easy without breaking, with a user-friendly API.
  • Plug-and-play: being model-agnostic; including the kernels before/after other steps should be straightforward. To interoperate well with generic packages for handling parameters like ParameterHandling.jl and FluxML's Functors.jl.
  • Automatic Differentiation compatibility: all kernel functions which ought to be differentiable using AD packages like ForwardDiff.jl or Zygote.jlshould be.
Examples
x =range(-3.0, 3.0; length=100)
# A simple standardised squared-exponential / exponentiated-quadratic kernel.
k₁ =SqExponentialKernel()
K₁ =kernelmatrix(k₁, x)
# Set a function transformation on the data
k₂ =Matern32Kernel() ∘FunctionTransform(sin)
K₂ =kernelmatrix(k₂, x)
# Set a matrix premultiplication on the data
k₃ =PolynomialKernel(; c=2.0, degree=2) ∘LinearTransform(randn(4, 1))
K₃ =kernelmatrix(k₃, x)
# Add and sum kernels
k₄ =0.5*SqExponentialKernel() *LinearKernel(; c=0.5) +0.4* k₂
K₄ =kernelmatrix(k₄, x)
plot(
heatmap.([K₁, K₂, K₃, K₄]; yflip=true, colorbar=false)...;
layout=(2, 2), title=["K₁""K₂""K₃""K₄"],
)
Related Work
This package replaces the now-defunct MLKernels.jl. It incorporates lots of excellent existing work from packages such as GaussianProcesses.jl, and is used in downstream packages such as AbstractGPs.jl, ApproximateGPs.jl, Stheno.jl, and AugmentedGaussianProcesses.jl.
See the JuliaGaussianProcesses Github organisation and website for more information.
Issues/Contributing
If you notice a problem or would like to contribute by adding more kernel functions or features please submit an issue, or open a PR (please see the ColPrac contribution guidelines).
About
Julia package for kernel functions for machine learning
juliagaussianprocesses.github.io/KernelFunctions.jl/stable/
Topics
gaussian-processeskernel-functionskernelsmachine-learning
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
274 stars
Watchers
6 watching
Forks
41 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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KernelFunctions.jl
CIcodecovDocumentation (stable)Documentation (latest)ColPrac: Contributor's Guide on Collaborative Practices for Community PackagesCode Style: BlueDOI
Kernel functions for machine learning
KernelFunctions.jl is a general purpose kernel package.
It provides a flexible framework for creating kernel functions and manipulating them, and an extensive collection of implementations.
The main goals of this package are:
  • Flexibility: operations between kernels should be fluid and easy without breaking, with a user-friendly API.
  • Plug-and-play: being model-agnostic; including the kernels before/after other steps should be straightforward. To interoperate well with generic packages for handling parameters like ParameterHandling.jl and FluxML's Functors.jl.
  • Automatic Differentiation compatibility: all kernel functions which ought to be differentiable using AD packages like ForwardDiff.jl or Zygote.jlshould be.
Examples
x =range(-3.0, 3.0; length=100)
# A simple standardised squared-exponential / exponentiated-quadratic kernel.
k₁ =SqExponentialKernel()
K₁ =kernelmatrix(k₁, x)
# Set a function transformation on the data
k₂ =Matern32Kernel() ∘FunctionTransform(sin)
K₂ =kernelmatrix(k₂, x)
# Set a matrix premultiplication on the data
k₃ =PolynomialKernel(; c=2.0, degree=2) ∘LinearTransform(randn(4, 1))
K₃ =kernelmatrix(k₃, x)
# Add and sum kernels
k₄ =0.5*SqExponentialKernel() *LinearKernel(; c=0.5) +0.4* k₂
K₄ =kernelmatrix(k₄, x)
plot(
heatmap.([K₁, K₂, K₃, K₄]; yflip=true, colorbar=false)...;
layout=(2, 2), title=["K₁""K₂""K₃""K₄"],
)
Related Work
This package replaces the now-defunct MLKernels.jl. It incorporates lots of excellent existing work from packages such as GaussianProcesses.jl, and is used in downstream packages such as AbstractGPs.jl, ApproximateGPs.jl, Stheno.jl, and AugmentedGaussianProcesses.jl.
See the JuliaGaussianProcesses Github organisation and website for more information.
Issues/Contributing
If you notice a problem or would like to contribute by adding more kernel functions or features please submit an issue, or open a PR (please see the ColPrac contribution guidelines).
About
Julia package for kernel functions for machine learning
juliagaussianprocesses.github.io/KernelFunctions.jl/stable/
Topics
gaussian-processeskernel-functionskernelsmachine-learning
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
274 stars
Watchers
6 watching
Forks
41 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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KernelFunctions.jl
CIcodecovDocumentation (stable)Documentation (latest)ColPrac: Contributor's Guide on Collaborative Practices for Community PackagesCode Style: BlueDOI
Kernel functions for machine learning
KernelFunctions.jl is a general purpose kernel package.
It provides a flexible framework for creating kernel functions and manipulating them, and an extensive collection of implementations.
The main goals of this package are:
  • Flexibility: operations between kernels should be fluid and easy without breaking, with a user-friendly API.
  • Plug-and-play: being model-agnostic; including the kernels before/after other steps should be straightforward. To interoperate well with generic packages for handling parameters like ParameterHandling.jl and FluxML's Functors.jl.
  • Automatic Differentiation compatibility: all kernel functions which ought to be differentiable using AD packages like ForwardDiff.jl or Zygote.jlshould be.
Examples
x =range(-3.0, 3.0; length=100)
# A simple standardised squared-exponential / exponentiated-quadratic kernel.
k₁ =SqExponentialKernel()
K₁ =kernelmatrix(k₁, x)
# Set a function transformation on the data
k₂ =Matern32Kernel() ∘FunctionTransform(sin)
K₂ =kernelmatrix(k₂, x)
# Set a matrix premultiplication on the data
k₃ =PolynomialKernel(; c=2.0, degree=2) ∘LinearTransform(randn(4, 1))
K₃ =kernelmatrix(k₃, x)
# Add and sum kernels
k₄ =0.5*SqExponentialKernel() *LinearKernel(; c=0.5) +0.4* k₂
K₄ =kernelmatrix(k₄, x)
plot(
heatmap.([K₁, K₂, K₃, K₄]; yflip=true, colorbar=false)...;
layout=(2, 2), title=["K₁""K₂""K₃""K₄"],
)
Related Work
This package replaces the now-defunct MLKernels.jl. It incorporates lots of excellent existing work from packages such as GaussianProcesses.jl, and is used in downstream packages such as AbstractGPs.jl, ApproximateGPs.jl, Stheno.jl, and AugmentedGaussianProcesses.jl.
See the JuliaGaussianProcesses Github organisation and website for more information.
Issues/Contributing
If you notice a problem or would like to contribute by adding more kernel functions or features please submit an issue, or open a PR (please see the ColPrac contribution guidelines).
About
Julia package for kernel functions for machine learning
juliagaussianprocesses.github.io/KernelFunctions.jl/stable/
Topics
gaussian-processeskernel-functionskernelsmachine-learning
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
274 stars
Watchers
6 watching
Forks
41 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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KernelFunctions.jl
CIcodecovDocumentation (stable)Documentation (latest)ColPrac: Contributor's Guide on Collaborative Practices for Community PackagesCode Style: BlueDOI
Kernel functions for machine learning
KernelFunctions.jl is a general purpose kernel package.
It provides a flexible framework for creating kernel functions and manipulating them, and an extensive collection of implementations.
The main goals of this package are:
  • Flexibility: operations between kernels should be fluid and easy without breaking, with a user-friendly API.
  • Plug-and-play: being model-agnostic; including the kernels before/after other steps should be straightforward. To interoperate well with generic packages for handling parameters like ParameterHandling.jl and FluxML's Functors.jl.
  • Automatic Differentiation compatibility: all kernel functions which ought to be differentiable using AD packages like ForwardDiff.jl or Zygote.jlshould be.
Examples
x =range(-3.0, 3.0; length=100)
# A simple standardised squared-exponential / exponentiated-quadratic kernel.
k₁ =SqExponentialKernel()
K₁ =kernelmatrix(k₁, x)
# Set a function transformation on the data
k₂ =Matern32Kernel() ∘FunctionTransform(sin)
K₂ =kernelmatrix(k₂, x)
# Set a matrix premultiplication on the data
k₃ =PolynomialKernel(; c=2.0, degree=2) ∘LinearTransform(randn(4, 1))
K₃ =kernelmatrix(k₃, x)
# Add and sum kernels
k₄ =0.5*SqExponentialKernel() *LinearKernel(; c=0.5) +0.4* k₂
K₄ =kernelmatrix(k₄, x)
plot(
heatmap.([K₁, K₂, K₃, K₄]; yflip=true, colorbar=false)...;
layout=(2, 2), title=["K₁""K₂""K₃""K₄"],
)
Related Work
This package replaces the now-defunct MLKernels.jl. It incorporates lots of excellent existing work from packages such as GaussianProcesses.jl, and is used in downstream packages such as AbstractGPs.jl, ApproximateGPs.jl, Stheno.jl, and AugmentedGaussianProcesses.jl.
See the JuliaGaussianProcesses Github organisation and website for more information.
Issues/Contributing
If you notice a problem or would like to contribute by adding more kernel functions or features please submit an issue, or open a PR (please see the ColPrac contribution guidelines).
About
Julia package for kernel functions for machine learning
juliagaussianprocesses.github.io/KernelFunctions.jl/stable/
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KernelFunctions.jl
CIcodecovDocumentation (stable)Documentation (latest)ColPrac: Contributor's Guide on Collaborative Practices for Community PackagesCode Style: BlueDOI
Kernel functions for machine learning
KernelFunctions.jl is a general purpose kernel package.
It provides a flexible framework for creating kernel functions and manipulating them, and an extensive collection of implementations.
The main goals of this package are:
  • Flexibility: operations between kernels should be fluid and easy without breaking, with a user-friendly API.
  • Plug-and-play: being model-agnostic; including the kernels before/after other steps should be straightforward. To interoperate well with generic packages for handling parameters like ParameterHandling.jl and FluxML's Functors.jl.
  • Automatic Differentiation compatibility: all kernel functions which ought to be differentiable using AD packages like ForwardDiff.jl or Zygote.jlshould be.
Examples
x =range(-3.0, 3.0; length=100)
# A simple standardised squared-exponential / exponentiated-quadratic kernel.
k₁ =SqExponentialKernel()
K₁ =kernelmatrix(k₁, x)
# Set a function transformation on the data
k₂ =Matern32Kernel() ∘FunctionTransform(sin)
K₂ =kernelmatrix(k₂, x)
# Set a matrix premultiplication on the data
k₃ =PolynomialKernel(; c=2.0, degree=2) ∘LinearTransform(randn(4, 1))
K₃ =kernelmatrix(k₃, x)
# Add and sum kernels
k₄ =0.5*SqExponentialKernel() *LinearKernel(; c=0.5) +0.4* k₂
K₄ =kernelmatrix(k₄, x)
plot(
heatmap.([K₁, K₂, K₃, K₄]; yflip=true, colorbar=false)...;
layout=(2, 2), title=["K₁""K₂""K₃""K₄"],
)
Related Work
This package replaces the now-defunct MLKernels.jl. It incorporates lots of excellent existing work from packages such as GaussianProcesses.jl, and is used in downstream packages such as AbstractGPs.jl, ApproximateGPs.jl, Stheno.jl, and AugmentedGaussianProcesses.jl.
See the JuliaGaussianProcesses Github organisation and website for more information.
Issues/Contributing
If you notice a problem or would like to contribute by adding more kernel functions or features please submit an issue, or open a PR (please see the ColPrac contribution guidelines).
About
Julia package for kernel functions for machine learning
juliagaussianprocesses.github.io/KernelFunctions.jl/stable/
Topics
gaussian-processeskernel-functionskernelsmachine-learning
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
274 stars
Watchers
6 watching
Forks
41 forks
Report repository
Releases
Packages
Used by
Contributors
Languages