From 21a0e5116631d9833bbaf4f0c4675556e3f62e34 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Tue, 6 Feb 2024 19:58:10 +0000 Subject: [PATCH] build based on e6b42a9 --- previews/PR530/api/index.html | 22 +- previews/PR530/create_kernel/index.html | 2 +- previews/PR530/design/index.html | 2 +- .../gaussian-process-priors/Manifest.toml | 266 +- .../gaussian-process-priors/index.html | 5229 ++++---- .../gaussian-process-priors/notebook.ipynb | 10446 ++++++++-------- .../kernel-ridge-regression/Manifest.toml | 266 +- .../kernel-ridge-regression/index.html | 1682 +-- .../kernel-ridge-regression/notebook.ipynb | 3352 ++--- .../support-vector-machine/Manifest.toml | 272 +- .../support-vector-machine/index.html | 300 +- .../support-vector-machine/notebook.ipynb | 584 +- .../train-kernel-parameters/Manifest.toml | 373 +- .../train-kernel-parameters/index.html | 456 +- .../train-kernel-parameters/notebook.ipynb | 786 +- previews/PR530/index.html | 2 +- previews/PR530/kernels/index.html | 32 +- previews/PR530/metrics/index.html | 2 +- previews/PR530/search/index.html | 2 +- previews/PR530/search_index.js | 2 +- previews/PR530/transform/index.html | 22 +- previews/PR530/userguide/index.html | 2 +- 22 files changed, 11884 insertions(+), 12218 deletions(-) diff --git a/previews/PR530/api/index.html b/previews/PR530/api/index.html index 38c23cd36..ac971ae12 100644 --- a/previews/PR530/api/index.html +++ b/previews/PR530/api/index.html @@ -1,6 +1,6 @@ API · KernelFunctions.jl
Edit on GitHub

API Library

Functions

The KernelFunctions API comprises the following four functions.

KernelFunctions.kernelmatrix — Function
kernelmatrix(κ::Kernel, x::AbstractVector)

Compute the kernel κ for each pair of inputs in x. Returns a matrix of size (length(x), length(x)) satisfying kernelmatrix(κ, x)[p, q] == κ(x[p], x[q]).

kernelmatrix(κ::Kernel, x::AbstractVector, y::AbstractVector)

Compute the kernel κ for each pair of inputs in x and y. Returns a matrix of size (length(x), length(y)) satisfying kernelmatrix(κ, x, y)[p, q] == κ(x[p], y[q]).

kernelmatrix(κ::Kernel, X::AbstractMatrix; obsdim)
-kernelmatrix(κ::Kernel, X::AbstractMatrix, Y::AbstractMatrix; obsdim)

If obsdim=1, equivalent to kernelmatrix(κ, RowVecs(X)) and kernelmatrix(κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix(κ, ColVecs(X)) and kernelmatrix(κ, ColVecs(X), ColVecs(Y)), respectively.

See also: ColVecs, RowVecs

source
KernelFunctions.kernelmatrix! — Function
kernelmatrix!(K::AbstractMatrix, κ::Kernel, x::AbstractVector)
+kernelmatrix(κ::Kernel, X::AbstractMatrix, Y::AbstractMatrix; obsdim)

If obsdim=1, equivalent to kernelmatrix(κ, RowVecs(X)) and kernelmatrix(κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix(κ, ColVecs(X)) and kernelmatrix(κ, ColVecs(X), ColVecs(Y)), respectively.

See also: ColVecs, RowVecs

source
KernelFunctions.kernelmatrix! — Function
kernelmatrix!(K::AbstractMatrix, κ::Kernel, x::AbstractVector)
 kernelmatrix!(K::AbstractMatrix, κ::Kernel, x::AbstractVector, y::AbstractVector)

In-place version of kernelmatrix where pre-allocated matrix K will be overwritten with the kernel matrix.

kernelmatrix!(K::AbstractMatrix, κ::Kernel, X::AbstractMatrix; obsdim)
 kernelmatrix!(
     K::AbstractMatrix,
@@ -8,8 +8,8 @@
     X::AbstractMatrix,
     Y::AbstractMatrix;
     obsdim,
-)

If obsdim=1, equivalent to kernelmatrix!(K, κ, RowVecs(X)) and kernelmatrix(K, κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix!(K, κ, ColVecs(X)) and kernelmatrix(K, κ, ColVecs(X), ColVecs(Y)), respectively.

See also: ColVecs, RowVecs

source
KernelFunctions.kernelmatrix_diag — Function
kernelmatrix_diag(κ::Kernel, x::AbstractVector)

Compute the diagonal of kernelmatrix(κ, x) efficiently.

kernelmatrix_diag(κ::Kernel, x::AbstractVector, y::AbstractVector)

Compute the diagonal of kernelmatrix(κ, x, y) efficiently. Requires that x and y are the same length.

kernelmatrix_diag(κ::Kernel, X::AbstractMatrix; obsdim)
-kernelmatrix_diag(κ::Kernel, X::AbstractMatrix, Y::AbstractMatrix; obsdim)

If obsdim=1, equivalent to kernelmatrix_diag(κ, RowVecs(X)) and kernelmatrix_diag(κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix_diag(κ, ColVecs(X)) and kernelmatrix_diag(κ, ColVecs(X), ColVecs(Y)), respectively.

See also: ColVecs, RowVecs

source
KernelFunctions.kernelmatrix_diag! — Function
kernelmatrix_diag!(K::AbstractVector, κ::Kernel, x::AbstractVector)
+)

If obsdim=1, equivalent to kernelmatrix!(K, κ, RowVecs(X)) and kernelmatrix(K, κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix!(K, κ, ColVecs(X)) and kernelmatrix(K, κ, ColVecs(X), ColVecs(Y)), respectively.

See also: ColVecs, RowVecs

source
KernelFunctions.kernelmatrix_diag — Function
kernelmatrix_diag(κ::Kernel, x::AbstractVector)

Compute the diagonal of kernelmatrix(κ, x) efficiently.

kernelmatrix_diag(κ::Kernel, x::AbstractVector, y::AbstractVector)

Compute the diagonal of kernelmatrix(κ, x, y) efficiently. Requires that x and y are the same length.

kernelmatrix_diag(κ::Kernel, X::AbstractMatrix; obsdim)
+kernelmatrix_diag(κ::Kernel, X::AbstractMatrix, Y::AbstractMatrix; obsdim)

If obsdim=1, equivalent to kernelmatrix_diag(κ, RowVecs(X)) and kernelmatrix_diag(κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix_diag(κ, ColVecs(X)) and kernelmatrix_diag(κ, ColVecs(X), ColVecs(Y)), respectively.

See also: ColVecs, RowVecs

source
KernelFunctions.kernelmatrix_diag! — Function
kernelmatrix_diag!(K::AbstractVector, κ::Kernel, x::AbstractVector)
 kernelmatrix_diag!(K::AbstractVector, κ::Kernel, x::AbstractVector, y::AbstractVector)

In place version of kernelmatrix_diag.

kernelmatrix_diag!(K::AbstractVector, κ::Kernel, X::AbstractMatrix; obsdim)
 kernelmatrix_diag!(
     K::AbstractVector,
@@ -17,7 +17,7 @@
     X::AbstractMatrix,
     Y::AbstractMatrix;
     obsdim
-)

If obsdim=1, equivalent to kernelmatrix_diag!(K, κ, RowVecs(X)) and kernelmatrix_diag!(K, κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix_diag!(K, κ, ColVecs(X)) and kernelmatrix_diag!(K, κ, ColVecs(X), ColVecs(Y)), respectively.

See also: ColVecs, RowVecs

source

Input Types

The above API operates on collections of inputs. All collections of inputs in KernelFunctions.jl are represented as AbstractVectors. To understand this choice, please see the design notes on collections of inputs. The length of any such AbstractVector is equal to the number of inputs in the collection. For example, this means that

size(kernelmatrix(k, x)) == (length(x), length(x))

is always true, for some Kernel k, and AbstractVector x.

Univariate Inputs

If each input to your kernel is Real-valued, then any AbstractVector{<:Real} is a valid representation for a collection of inputs. More generally, it's completely fine to represent a collection of inputs of type T as, for example, a Vector{T}. However, this may not be the most efficient way to represent collection of inputs. See Vector-Valued Inputs for an example.

Vector-Valued Inputs

We recommend that collections of vector-valued inputs are stored in an AbstractMatrix{<:Real} when possible, and wrapped inside a ColVecs or RowVecs to make their interpretation clear:

KernelFunctions.ColVecs — Type
ColVecs(X::AbstractMatrix)

A lightweight wrapper for an AbstractMatrix which interprets it as a vector-of-vectors, in which each column of X represents a single vector.

That is, by writing x = ColVecs(X), you are saying "x is a vector-of-vectors, each of which has length size(X, 1). The total number of vectors is size(X, 2)."

Phrased differently, ColVecs(X) says that X should be interpreted as a vector of horizontally-concatenated column-vectors, hence the name ColVecs.

julia> X = randn(2, 5);
+)

If obsdim=1, equivalent to kernelmatrix_diag!(K, κ, RowVecs(X)) and kernelmatrix_diag!(K, κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix_diag!(K, κ, ColVecs(X)) and kernelmatrix_diag!(K, κ, ColVecs(X), ColVecs(Y)), respectively.

See also: ColVecs, RowVecs

source

Input Types

The above API operates on collections of inputs. All collections of inputs in KernelFunctions.jl are represented as AbstractVectors. To understand this choice, please see the design notes on collections of inputs. The length of any such AbstractVector is equal to the number of inputs in the collection. For example, this means that

size(kernelmatrix(k, x)) == (length(x), length(x))

is always true, for some Kernel k, and AbstractVector x.

Univariate Inputs

If each input to your kernel is Real-valued, then any AbstractVector{<:Real} is a valid representation for a collection of inputs. More generally, it's completely fine to represent a collection of inputs of type T as, for example, a Vector{T}. However, this may not be the most efficient way to represent collection of inputs. See Vector-Valued Inputs for an example.

Vector-Valued Inputs

We recommend that collections of vector-valued inputs are stored in an AbstractMatrix{<:Real} when possible, and wrapped inside a ColVecs or RowVecs to make their interpretation clear:

KernelFunctions.ColVecs — Type
ColVecs(X::AbstractMatrix)

A lightweight wrapper for an AbstractMatrix which interprets it as a vector-of-vectors, in which each column of X represents a single vector.

That is, by writing x = ColVecs(X), you are saying "x is a vector-of-vectors, each of which has length size(X, 1). The total number of vectors is size(X, 2)."

Phrased differently, ColVecs(X) says that X should be interpreted as a vector of horizontally-concatenated column-vectors, hence the name ColVecs.

julia> X = randn(2, 5);
 
 julia> x = ColVecs(X);
 
@@ -28,7 +28,7 @@
 true

ColVecs is related to RowVecs via transposition:

julia> X = randn(2, 5);
 
 julia> ColVecs(X) == RowVecs(X')
-true
source
KernelFunctions.RowVecs — Type
RowVecs(X::AbstractMatrix)

A lightweight wrapper for an AbstractMatrix which interprets it as a vector-of-vectors, in which each row of X represents a single vector.

That is, by writing x = RowVecs(X), you are saying "x is a vector-of-vectors, each of which has length size(X, 2). The total number of vectors is size(X, 1)."

Phrased differently, RowVecs(X) says that X should be interpreted as a vector of vertically-concatenated row-vectors, hence the name RowVecs.

Internally, the data continues to be represented as an AbstractMatrix, so using this type does not introduce any kind of performance penalty.

julia> X = randn(5, 2);
+true
source
KernelFunctions.RowVecs — Type
RowVecs(X::AbstractMatrix)

A lightweight wrapper for an AbstractMatrix which interprets it as a vector-of-vectors, in which each row of X represents a single vector.

That is, by writing x = RowVecs(X), you are saying "x is a vector-of-vectors, each of which has length size(X, 2). The total number of vectors is size(X, 1)."

Phrased differently, RowVecs(X) says that X should be interpreted as a vector of vertically-concatenated row-vectors, hence the name RowVecs.

Internally, the data continues to be represented as an AbstractMatrix, so using this type does not introduce any kind of performance penalty.

julia> X = randn(5, 2);
 
 julia> x = RowVecs(X);
 
@@ -39,7 +39,7 @@
 true

RowVecs is related to ColVecs via transposition:

julia> X = randn(5, 2);
 
 julia> RowVecs(X) == ColVecs(X')
-true
source

These types are specialised upon to ensure good performance e.g. when computing Euclidean distances between pairs of elements. The benefit of using this representation, rather than using a Vector{Vector{<:Real}}, is that optimised matrix-matrix multiplication functionality can be utilised when computing pairwise distances between inputs, which are needed for kernelmatrix computation.

Inputs for Multiple Outputs

KernelFunctions.jl views multi-output GPs as GPs on an extended input domain. For an explanation of this design choice, see the design notes on multi-output GPs.

An input to a multi-output Kernel should be a Tuple{T, Int}, whose first element specifies a location in the domain of the multi-output GP, and whose second element specifies which output the inputs corresponds to. The type of collections of inputs for multi-output GPs is therefore AbstractVector{<:Tuple{T, Int}}.

KernelFunctions.jl provides the following helper functions to reduce the cognitive load associated with working with multi-output kernels by dealing with transforming data from the formats in which it is commonly found into the format required by KernelFunctions. The intention is that users can pass their data to these functions, and use the returned values throughout their code, without having to worry further about correctly formatting their data for KernelFunctions' sake:

KernelFunctions.prepare_isotopic_multi_output_data — Method
prepare_isotopic_multi_output_data(x::AbstractVector, y::ColVecs)

Utility functionality to convert a collection of N = length(x) inputs x, and a vector-of-vectors y (efficiently represented by a ColVecs) into a format suitable for use with multi-output kernels.

y[n] is the vector-valued output corresponding to the input x[n]. Consequently, it is necessary that length(x) == length(y).

For example, if outputs are initially stored in a num_outputs × N matrix:

julia> x = [1.0, 2.0, 3.0];
+true
source

These types are specialised upon to ensure good performance e.g. when computing Euclidean distances between pairs of elements. The benefit of using this representation, rather than using a Vector{Vector{<:Real}}, is that optimised matrix-matrix multiplication functionality can be utilised when computing pairwise distances between inputs, which are needed for kernelmatrix computation.

Inputs for Multiple Outputs

KernelFunctions.jl views multi-output GPs as GPs on an extended input domain. For an explanation of this design choice, see the design notes on multi-output GPs.

An input to a multi-output Kernel should be a Tuple{T, Int}, whose first element specifies a location in the domain of the multi-output GP, and whose second element specifies which output the inputs corresponds to. The type of collections of inputs for multi-output GPs is therefore AbstractVector{<:Tuple{T, Int}}.

KernelFunctions.jl provides the following helper functions to reduce the cognitive load associated with working with multi-output kernels by dealing with transforming data from the formats in which it is commonly found into the format required by KernelFunctions. The intention is that users can pass their data to these functions, and use the returned values throughout their code, without having to worry further about correctly formatting their data for KernelFunctions' sake:

KernelFunctions.prepare_isotopic_multi_output_data — Method
prepare_isotopic_multi_output_data(x::AbstractVector, y::ColVecs)

Utility functionality to convert a collection of N = length(x) inputs x, and a vector-of-vectors y (efficiently represented by a ColVecs) into a format suitable for use with multi-output kernels.

y[n] is the vector-valued output corresponding to the input x[n]. Consequently, it is necessary that length(x) == length(y).

For example, if outputs are initially stored in a num_outputs × N matrix:

julia> x = [1.0, 2.0, 3.0];
 
 julia> Y = [1.1 2.1 3.1; 1.2 2.2 3.2]
 2×3 Matrix{Float64}:
@@ -64,7 +64,7 @@
  2.1
  2.2
  3.1
- 3.2

See also prepare_heterotopic_multi_output_data.

source
KernelFunctions.prepare_isotopic_multi_output_data — Method
prepare_isotopic_multi_output_data(x::AbstractVector, y::RowVecs)

Utility functionality to convert a collection of N = length(x) inputs x and output vectors y (efficiently represented by a RowVecs) into a format suitable for use with multi-output kernels.

y[n] is the vector-valued output corresponding to the input x[n]. Consequently, it is necessary that length(x) == length(y).

For example, if outputs are initial stored in an N × num_outputs matrix:

julia> x = [1.0, 2.0, 3.0];
+ 3.2

See also prepare_heterotopic_multi_output_data.

source
KernelFunctions.prepare_isotopic_multi_output_data — Method
prepare_isotopic_multi_output_data(x::AbstractVector, y::RowVecs)

Utility functionality to convert a collection of N = length(x) inputs x and output vectors y (efficiently represented by a RowVecs) into a format suitable for use with multi-output kernels.

y[n] is the vector-valued output corresponding to the input x[n]. Consequently, it is necessary that length(x) == length(y).

For example, if outputs are initial stored in an N × num_outputs matrix:

julia> x = [1.0, 2.0, 3.0];
 
 julia> Y = [1.1 1.2; 2.1 2.2; 3.1 3.2]
 3×2 Matrix{Float64}:
@@ -90,7 +90,7 @@
  3.1
  1.2
  2.2
- 3.2

See also prepare_heterotopic_multi_output_data.

source
KernelFunctions.prepare_heterotopic_multi_output_data — Function
prepare_heterotopic_multi_output_data(
     x::AbstractVector, y::AbstractVector{<:Real}, output_indices::AbstractVector{Int},
 )

Utility functionality to convert a collection of inputs x, observations y, and output_indices into a format suitable for use with multi-output kernels. Handles the situation in which only one (or a subset) of outputs are observed at each feature. Ensures that all arguments are compatible with one another, and returns a vector of inputs and a vector of outputs.

y[n] should be the observed value associated with output output_indices[n] at feature x[n].

julia> x = [1.0, 2.0, 3.0];
 
@@ -110,7 +110,7 @@
 3-element Vector{Float64}:
  -1.0
   0.0
-  1.0

See also prepare_isotopic_multi_output_data.

source

The input types returned by prepare_isotopic_multi_output_data can also be constructed manually:

The input types returned by prepare_isotopic_multi_output_data can also be constructed manually:

KernelFunctions.MOInput — Type
MOInput(x::AbstractVector, out_dim::Integer)

A data type to accommodate modelling multi-dimensional output data. MOInput(x, out_dim) has length length(x) * out_dim.

julia> x = [1, 2, 3];
 
 julia> MOInput(x, 2)
 6-element KernelFunctions.MOInputIsotopicByOutputs{Int64, Vector{Int64}, Int64}:
@@ -119,6 +119,6 @@
  (3, 1)
  (1, 2)
  (2, 2)
- (3, 2)

As shown above, an MOInput represents a vector of tuples. The first length(x) elements represent the inputs for the first output, the second length(x) elements represent the inputs for the second output, etc. See Inputs for Multiple Outputs in the docs for more info.

MOInput will be deprecated in version 0.11 in favour of MOInputIsotopicByOutputs, and removed in version 0.12.

source

As with ColVecs and RowVecs for vector-valued input spaces, this type enables specialised implementations of e.g. kernelmatrix for MOInputs in some situations.

To find out more about the background, read this review of kernels for vector-valued functions.

Generic Utilities

KernelFunctions also provides miscellaneous utility functions.

KernelFunctions.nystrom — Function
nystrom(k::Kernel, X::AbstractVector, S::AbstractVector{<:Integer})

Compute a factorization of a Nystrom approximation of the square kernel matrix of data vector X with respect to kernel k, using indices S. Returns a NystromFact struct which stores a Nystrom factorization satisfying:

\[\mathbf{K} \approx \mathbf{C}^{\intercal}\mathbf{W}\mathbf{C}\]

source
nystrom(k::Kernel, X::AbstractVector, r::Real)

Compute a factorization of a Nystrom approximation of the square kernel matrix of data vector X with respect to kernel k using a sample ratio of r. Returns a NystromFact struct which stores a Nystrom factorization satisfying:

\[\mathbf{K} \approx \mathbf{C}^{\intercal}\mathbf{W}\mathbf{C}\]

source
nystrom(k::Kernel, X::AbstractMatrix, S::AbstractVector{<:Integer}; obsdim)

If obsdim=1, equivalent to nystrom(k, RowVecs(X), S). If obsdim=2, equivalent to nystrom(k, ColVecs(X), S).

See also: ColVecs, RowVecs

source
nystrom(k::Kernel, X::AbstractMatrix, r::Real; obsdim)

If obsdim=1, equivalent to nystrom(k, RowVecs(X), r). If obsdim=2, equivalent to nystrom(k, ColVecs(X), r).

See also: ColVecs, RowVecs

source
KernelFunctions.NystromFact — Type
NystromFact

Type for storing a Nystrom factorization. The factorization contains two fields: W and C, two matrices satisfying:

\[\mathbf{K} \approx \mathbf{C}^{\intercal}\mathbf{W}\mathbf{C}\]

source

Conditional Utilities

To keep the dependencies of KernelFunctions lean, some functionality is only available if specific other packages are explicitly loaded (using).

Kronecker.jl

https://github.com/MichielStock/Kronecker.jl

KernelFunctions.kronecker_kernelmatrix — Function
kronecker_kernelmatrix(
+ (3, 2)

As shown above, an MOInput represents a vector of tuples. The first length(x) elements represent the inputs for the first output, the second length(x) elements represent the inputs for the second output, etc. See Inputs for Multiple Outputs in the docs for more info.

MOInput will be deprecated in version 0.11 in favour of MOInputIsotopicByOutputs, and removed in version 0.12.

source

As with ColVecs and RowVecs for vector-valued input spaces, this type enables specialised implementations of e.g. kernelmatrix for MOInputs in some situations.

To find out more about the background, read this review of kernels for vector-valued functions.

Generic Utilities

KernelFunctions also provides miscellaneous utility functions.

KernelFunctions.nystrom — Function
nystrom(k::Kernel, X::AbstractVector, S::AbstractVector{<:Integer})

Compute a factorization of a Nystrom approximation of the square kernel matrix of data vector X with respect to kernel k, using indices S. Returns a NystromFact struct which stores a Nystrom factorization satisfying:

\[\mathbf{K} \approx \mathbf{C}^{\intercal}\mathbf{W}\mathbf{C}\]

source
nystrom(k::Kernel, X::AbstractVector, r::Real)

Compute a factorization of a Nystrom approximation of the square kernel matrix of data vector X with respect to kernel k using a sample ratio of r. Returns a NystromFact struct which stores a Nystrom factorization satisfying:

\[\mathbf{K} \approx \mathbf{C}^{\intercal}\mathbf{W}\mathbf{C}\]

source
nystrom(k::Kernel, X::AbstractMatrix, S::AbstractVector{<:Integer}; obsdim)

If obsdim=1, equivalent to nystrom(k, RowVecs(X), S). If obsdim=2, equivalent to nystrom(k, ColVecs(X), S).

See also: ColVecs, RowVecs

source
nystrom(k::Kernel, X::AbstractMatrix, r::Real; obsdim)

If obsdim=1, equivalent to nystrom(k, RowVecs(X), r). If obsdim=2, equivalent to nystrom(k, ColVecs(X), r).

See also: ColVecs, RowVecs

source
KernelFunctions.NystromFact — Type
NystromFact

Type for storing a Nystrom factorization. The factorization contains two fields: W and C, two matrices satisfying:

\[\mathbf{K} \approx \mathbf{C}^{\intercal}\mathbf{W}\mathbf{C}\]

source

Conditional Utilities

To keep the dependencies of KernelFunctions lean, some functionality is only available if specific other packages are explicitly loaded (using).

Kronecker.jl

https://github.com/MichielStock/Kronecker.jl

KernelFunctions.kronecker_kernelmatrix — Function
kronecker_kernelmatrix(
     k::Union{IndependentMOKernel,IntrinsicCoregionMOKernel}, x::MOI, y::MOI
-) where {MOI<:IsotopicMOInputsUnion}

Requires Kronecker.jl: Computes the kernelmatrix for the IndependentMOKernel and the IntrinsicCoregionMOKernel, but returns a lazy kronecker product. This object can be very efficiently inverted or decomposed. See also kernelmatrix.

source
KernelFunctions.kernelkronmat — Function
kernelkronmat(κ::Kernel, X::AbstractVector{<:Real}, dims::Int) -> KroneckerPower

Return a KroneckerPower matrix on the D-dimensional input grid constructed by $\otimes_{i=1}^D X$, where D is given by dims.

Warning

Requires Kronecker.jl and for iskroncompatible(κ) to return true.

source
kernelkronmat(κ::Kernel, X::AbstractVector{<:AbstractVector}) -> KroneckerProduct

Returns a KroneckerProduct matrix on the grid built with the collection of vectors $\{X_i\}_{i=1}^D$: $\otimes_{i=1}^D X_i$.

Warning

Requires Kronecker.jl and for iskroncompatible(κ) to return true.

source

PDMats.jl

https://github.com/JuliaStats/PDMats.jl

KernelFunctions.kernelpdmat — Function
kernelpdmat(k::Kernel, X::AbstractVector)

Compute a positive-definite matrix in the form of a PDMat matrix (see PDMats.jl), with the Cholesky decomposition precomputed. The algorithm adds a diagonal "nugget" term to the kernel matrix which is increased until positive definiteness is achieved. The algorithm gives up with an error if the nugget becomes larger than 1% of the largest value in the kernel matrix.

source
kernelpdmat(k::Kernel, X::AbstractMatrix; obsdim)

If obsdim=1, equivalent to kernelpdmat(k, RowVecs(X)). If obsdim=2, equivalent to kernelpdmat(k, ColVecs(X)).

See also: ColVecs, RowVecs

source
+) where {MOI<:IsotopicMOInputsUnion}

Requires Kronecker.jl: Computes the kernelmatrix for the IndependentMOKernel and the IntrinsicCoregionMOKernel, but returns a lazy kronecker product. This object can be very efficiently inverted or decomposed. See also kernelmatrix.

source
KernelFunctions.kernelkronmat — Function
kernelkronmat(κ::Kernel, X::AbstractVector{<:Real}, dims::Int) -> KroneckerPower

Return a KroneckerPower matrix on the D-dimensional input grid constructed by $\otimes_{i=1}^D X$, where D is given by dims.

Warning

Requires Kronecker.jl and for iskroncompatible(κ) to return true.

source
kernelkronmat(κ::Kernel, X::AbstractVector{<:AbstractVector}) -> KroneckerProduct

Returns a KroneckerProduct matrix on the grid built with the collection of vectors $\{X_i\}_{i=1}^D$: $\otimes_{i=1}^D X_i$.

Warning

Requires Kronecker.jl and for iskroncompatible(κ) to return true.

source

PDMats.jl

https://github.com/JuliaStats/PDMats.jl

KernelFunctions.kernelpdmat — Function
kernelpdmat(k::Kernel, X::AbstractVector)

Compute a positive-definite matrix in the form of a PDMat matrix (see PDMats.jl), with the Cholesky decomposition precomputed. The algorithm adds a diagonal "nugget" term to the kernel matrix which is increased until positive definiteness is achieved. The algorithm gives up with an error if the nugget becomes larger than 1% of the largest value in the kernel matrix.

source
kernelpdmat(k::Kernel, X::AbstractMatrix; obsdim)

If obsdim=1, equivalent to kernelpdmat(k, RowVecs(X)). If obsdim=2, equivalent to kernelpdmat(k, ColVecs(X)).

See also: ColVecs, RowVecs

source
diff --git a/previews/PR530/create_kernel/index.html b/previews/PR530/create_kernel/index.html index ae1a7697c..66f87f182 100644 --- a/previews/PR530/create_kernel/index.html +++ b/previews/PR530/create_kernel/index.html @@ -23,4 +23,4 @@ return MyKernel(x.n, xs.a) end return (a = x.a,), reconstruct_mykernel -end +end diff --git a/previews/PR530/design/index.html b/previews/PR530/design/index.html index 6ac4dfcc9..a739ca75f 100644 --- a/previews/PR530/design/index.html +++ b/previews/PR530/design/index.html @@ -4,4 +4,4 @@ kernelmatrix(k.kernels[2], x; obsdim=obsdim) end

While this prevents this package from having to pre-specify a convention, it doesn't resolve the length issue, or the issue of representing collections of inputs which aren't immediately represented as vectors. Moreover, it complicates the internals; in contrast, consider what this function looks like with an AbstractVector:

function kernelmatrix(k::KernelSum, x::AbstractVector)
     return kernelmatrix(k.kernels[1], x) + kernelmatrix(k.kernels[2], x)
-end

This code is clearer (less visual noise), and has removed a possible bug – if the implementer of kernelmatrix forgets to pass the obsdim kwarg into each subsequent kernelmatrix call, it's possible to get the wrong answer.

This being said, we do support matrix-valued inputs – see Why We Have Support for Both.

AbstractVectors

Requiring all collections of inputs to be AbstractVectors resolves all of these problems, and ensures that the data is self-describing to the extent that KernelFunctions.jl requires.

Firstly, the question of how to interpret the columns and rows of a matrix of inputs is resolved. Users must wrap matrices which represent collections of inputs in either a ColVecs or RowVecs, both of which have clearly defined semantics which are hard to confuse.

By design, there is also no discrepancy between the number of inputs in the collection, and the length function – the length of a ColVecs, RowVecs, or Vector{<:Real} is equal to the number of inputs.

There is no loss of performance.

A collection of N Real-valued inputs can be represented by an AbstractVector{<:Real} of length N, rather than needing to use an AbstractMatrix{<:Real} of size either N x 1 or 1 x N. The same can be said for any other input type T, and new subtypes of AbstractVector can be added if particularly efficient ways exist to store collections of inputs of type T. A good example of this in practice is using Tuple{S, Int}, for some input type S, as the Inputs for Multiple Outputs.

This approach can also lead to clearer user code. A user need only wrap their inputs in a ColVecs or RowVecs once in their code, and this specification is automatically re-used everywhere in their code. In this sense, it is straightforward to write code in such a way that there is one unique source of "truth" about the way in which a particular data set should be interpreted. Conversely, the obsdim resolution requires that the obsdim keyword argument is passed around with the data every single time that you use it.

The benefits of the AbstractVector approach are likely most strongly felt when writing a substantial amount of code on top of KernelFunctions.jl – in the same way that using AbstractVectors inside KernelFunctions.jl removes the need for large amounts of keyword argument propagation, the same will be true of other code.

Why We Have Support for Both

In short: many people like matrices, and are familiar with obsdim-style keyword arguments.

All internals are implemented using AbstractVectors though, and the obsdim interface is just a thin layer of utility functionality which sits on top of this. To avoid confusion and silent errors, we do not favour a specific convention (rows or columns) but instead it is necessary to specify the obsdim keyword argument explicitly.

Kernels for Multiple-Outputs

There are two equally-valid perspectives on multi-output kernels: they can either be treated as matrix-valued kernels, or standard kernels on an extended input domain. Each of these perspectives are convenient in different circumstances, but the latter greatly simplifies the incorporation of multi-output kernels in KernelFunctions.

More concretely, let k_mat be a matrix-valued kernel, mapping pairs of inputs of type T to matrices of size P x P to describe the covariance between P outputs. Given inputs x and y of type T, and integers p and q, we can always find an equivalent standard kernel k mapping from pairs of inputs of type Tuple{T, Int} to the Reals as follows:

k((x, p), (y, q)) = k_mat(x, y)[p, q]

This ability to treat multi-output kernels as single-output kernels is very helpful, as it means that there is no need to introduce additional concepts into the API of KernelFunctions.jl, just additional kernels! This in turn simplifies downstream code as they don't need to "know" about the existence of multi-output kernels in addition to standard kernels. For example, GP libraries built on top of KernelFunctions.jl just need to know about Kernels, and they get multi-output kernels, and hence multi-output GPs, for free.

Where there is the need to specialise implementations for multi-output kernels, this is done in an encapsulated manner – parts of KernelFunctions that have nothing to do with multi-output kernels know nothing about the existence of multi-output kernels.

+end

This code is clearer (less visual noise), and has removed a possible bug – if the implementer of kernelmatrix forgets to pass the obsdim kwarg into each subsequent kernelmatrix call, it's possible to get the wrong answer.

This being said, we do support matrix-valued inputs – see Why We Have Support for Both.

AbstractVectors

Requiring all collections of inputs to be AbstractVectors resolves all of these problems, and ensures that the data is self-describing to the extent that KernelFunctions.jl requires.

Firstly, the question of how to interpret the columns and rows of a matrix of inputs is resolved. Users must wrap matrices which represent collections of inputs in either a ColVecs or RowVecs, both of which have clearly defined semantics which are hard to confuse.

By design, there is also no discrepancy between the number of inputs in the collection, and the length function – the length of a ColVecs, RowVecs, or Vector{<:Real} is equal to the number of inputs.

There is no loss of performance.

A collection of N Real-valued inputs can be represented by an AbstractVector{<:Real} of length N, rather than needing to use an AbstractMatrix{<:Real} of size either N x 1 or 1 x N. The same can be said for any other input type T, and new subtypes of AbstractVector can be added if particularly efficient ways exist to store collections of inputs of type T. A good example of this in practice is using Tuple{S, Int}, for some input type S, as the Inputs for Multiple Outputs.

This approach can also lead to clearer user code. A user need only wrap their inputs in a ColVecs or RowVecs once in their code, and this specification is automatically re-used everywhere in their code. In this sense, it is straightforward to write code in such a way that there is one unique source of "truth" about the way in which a particular data set should be interpreted. Conversely, the obsdim resolution requires that the obsdim keyword argument is passed around with the data every single time that you use it.

The benefits of the AbstractVector approach are likely most strongly felt when writing a substantial amount of code on top of KernelFunctions.jl – in the same way that using AbstractVectors inside KernelFunctions.jl removes the need for large amounts of keyword argument propagation, the same will be true of other code.

Why We Have Support for Both

In short: many people like matrices, and are familiar with obsdim-style keyword arguments.

All internals are implemented using AbstractVectors though, and the obsdim interface is just a thin layer of utility functionality which sits on top of this. To avoid confusion and silent errors, we do not favour a specific convention (rows or columns) but instead it is necessary to specify the obsdim keyword argument explicitly.

Kernels for Multiple-Outputs

There are two equally-valid perspectives on multi-output kernels: they can either be treated as matrix-valued kernels, or standard kernels on an extended input domain. Each of these perspectives are convenient in different circumstances, but the latter greatly simplifies the incorporation of multi-output kernels in KernelFunctions.

More concretely, let k_mat be a matrix-valued kernel, mapping pairs of inputs of type T to matrices of size P x P to describe the covariance between P outputs. Given inputs x and y of type T, and integers p and q, we can always find an equivalent standard kernel k mapping from pairs of inputs of type Tuple{T, Int} to the Reals as follows:

k((x, p), (y, q)) = k_mat(x, y)[p, q]

This ability to treat multi-output kernels as single-output kernels is very helpful, as it means that there is no need to introduce additional concepts into the API of KernelFunctions.jl, just additional kernels! This in turn simplifies downstream code as they don't need to "know" about the existence of multi-output kernels in addition to standard kernels. For example, GP libraries built on top of KernelFunctions.jl just need to know about Kernels, and they get multi-output kernels, and hence multi-output GPs, for free.

Where there is the need to specialise implementations for multi-output kernels, this is done in an encapsulated manner – parts of KernelFunctions that have nothing to do with multi-output kernels know nothing about the existence of multi-output kernels.

diff --git a/previews/PR530/examples/gaussian-process-priors/Manifest.toml b/previews/PR530/examples/gaussian-process-priors/Manifest.toml index 43a39efbc..322aba491 100644 --- a/previews/PR530/examples/gaussian-process-priors/Manifest.toml +++ b/previews/PR530/examples/gaussian-process-priors/Manifest.toml @@ -1,6 +1,6 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.9.3" +julia_version = "1.10.0" manifest_format = "2.0" project_hash = "3f5817959c36abf3cab0a72cc306a1c0e4f6e332" @@ -15,15 +15,15 @@ uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" [[deps.BitFlags]] -git-tree-sha1 = "43b1a4a8f797c1cddadf60499a8a077d4af2cd2d" +git-tree-sha1 = "2dc09997850d68179b69dafb58ae806167a32b1b" uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35" -version = "0.1.7" +version = "0.1.8" [[deps.Bzip2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2" +git-tree-sha1 = "9e2a6b69137e6969bab0152632dcb3bc108c8bdd" uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0" -version = "1.0.8+0" +version = "1.0.8+1" [[deps.Cairo_jll]] deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"] @@ -38,16 +38,20 @@ uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9" version = "0.5.1" [[deps.ChainRulesCore]] -deps = ["Compat", "LinearAlgebra", "SparseArrays"] -git-tree-sha1 = "e30f2f4e20f7f186dc36529910beaedc60cfa644" +deps = ["Compat", "LinearAlgebra"] +git-tree-sha1 = "1287e3872d646eed95198457873249bd9f0caed2" uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" -version = "1.16.0" +version = "1.20.1" +weakdeps = ["SparseArrays"] + + [deps.ChainRulesCore.extensions] + ChainRulesCoreSparseArraysExt = "SparseArrays" [[deps.CodecZlib]] deps = ["TranscodingStreams", "Zlib_jll"] -git-tree-sha1 = "02aa26a4cf76381be7f66e020a3eddeb27b0a092" +git-tree-sha1 = "59939d8a997469ee05c4b4944560a820f9ba0d73" uuid = "944b1d66-785c-5afd-91f1-9de20f533193" -version = "0.7.2" +version = "0.7.4" [[deps.ColorSchemes]] deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"] @@ -78,10 +82,10 @@ uuid = "5ae59095-9a9b-59fe-a467-6f913c188581" version = "0.12.10" [[deps.Compat]] -deps = ["UUIDs"] -git-tree-sha1 = "8a62af3e248a8c4bad6b32cbbe663ae02275e32c" +deps = ["TOML", "UUIDs"] +git-tree-sha1 = "75bd5b6fc5089df449b5d35fa501c846c9b6549b" uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" -version = "4.10.0" +version = "4.12.0" weakdeps = ["Dates", "LinearAlgebra"] [deps.Compat.extensions] @@ -90,7 +94,7 @@ weakdeps = ["Dates", "LinearAlgebra"] [[deps.CompilerSupportLibraries_jll]] deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" -version = "1.0.5+0" +version = "1.0.5+1" [[deps.CompositionsBase]] git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad" @@ -105,9 +109,9 @@ version = "0.1.2" [[deps.ConcurrentUtilities]] deps = ["Serialization", "Sockets"] -git-tree-sha1 = "5372dbbf8f0bdb8c700db5367132925c0771ef7e" +git-tree-sha1 = "8cfa272e8bdedfa88b6aefbbca7c19f1befac519" uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" -version = "2.2.1" +version = "2.3.0" [[deps.Contour]] git-tree-sha1 = "d05d9e7b7aedff4e5b51a029dced05cfb6125781" @@ -115,15 +119,15 @@ uuid = "d38c429a-6771-53c6-b99e-75d170b6e991" version = "0.6.2" [[deps.DataAPI]] -git-tree-sha1 = "8da84edb865b0b5b0100c0666a9bc9a0b71c553c" +git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe" uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a" -version = "1.15.0" +version = "1.16.0" [[deps.DataStructures]] deps = ["Compat", "InteractiveUtils", "OrderedCollections"] -git-tree-sha1 = "3dbd312d370723b6bb43ba9d02fc36abade4518d" +git-tree-sha1 = "ac67408d9ddf207de5cfa9a97e114352430f01ed" uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" -version = "0.18.15" +version = "0.18.16" [[deps.Dates]] deps = ["Printf"] @@ -137,9 +141,9 @@ version = "1.9.1" [[deps.Distances]] deps = ["LinearAlgebra", "Statistics", "StatsAPI"] -git-tree-sha1 = "5225c965635d8c21168e32a12954675e7bea1151" +git-tree-sha1 = "66c4c81f259586e8f002eacebc177e1fb06363b0" uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7" -version = "0.10.10" +version = "0.10.11" weakdeps = ["ChainRulesCore", "SparseArrays"] [deps.Distances.extensions] @@ -147,18 +151,20 @@ weakdeps = ["ChainRulesCore", "SparseArrays"] DistancesSparseArraysExt = "SparseArrays" [[deps.Distributions]] -deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Test"] -git-tree-sha1 = "3d5873f811f582873bb9871fc9c451784d5dc8c7" +deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"] +git-tree-sha1 = "7c302d7a5fec5214eb8a5a4c466dcf7a51fcf169" uuid = "31c24e10-a181-5473-b8eb-7969acd0382f" -version = "0.25.102" +version = "0.25.107" [deps.Distributions.extensions] DistributionsChainRulesCoreExt = "ChainRulesCore" DistributionsDensityInterfaceExt = "DensityInterface" + DistributionsTestExt = "Test" [deps.Distributions.weakdeps] ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d" + Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [[deps.DocStringExtensions]] deps = ["LibGit2"] @@ -185,9 +191,9 @@ version = "0.0.20230411+0" [[deps.ExceptionUnwrapping]] deps = ["Test"] -git-tree-sha1 = "e90caa41f5a86296e014e148ee061bd6c3edec96" +git-tree-sha1 = "dcb08a0d93ec0b1cdc4af184b26b591e9695423a" uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4" -version = "0.1.9" +version = "0.1.10" [[deps.Expat_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] @@ -202,22 +208,23 @@ uuid = "c87230d0-a227-11e9-1b43-d7ebe4e7570a" version = "0.4.1" [[deps.FFMPEG_jll]] -deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Pkg", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"] -git-tree-sha1 = "74faea50c1d007c85837327f6775bea60b5492dd" +deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"] +git-tree-sha1 = "466d45dc38e15794ec7d5d63ec03d776a9aff36e" uuid = "b22a6f82-2f65-5046-a5b2-351ab43fb4e5" -version = "4.4.2+2" +version = "4.4.4+1" [[deps.FileWatching]] uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee" [[deps.FillArrays]] deps = ["LinearAlgebra", "Random"] -git-tree-sha1 = "a20eaa3ad64254c61eeb5f230d9306e937405434" +git-tree-sha1 = "5b93957f6dcd33fc343044af3d48c215be2562f1" uuid = "1a297f60-69ca-5386-bcde-b61e274b549b" -version = "1.6.1" -weakdeps = ["SparseArrays", "Statistics"] +version = "1.9.3" +weakdeps = ["PDMats", "SparseArrays", "Statistics"] [deps.FillArrays.extensions] + FillArraysPDMatsExt = "PDMats" FillArraysSparseArraysExt = "SparseArrays" FillArraysStatisticsExt = "Statistics" @@ -258,22 +265,22 @@ uuid = "d9f16b24-f501-4c13-a1f2-28368ffc5196" version = "0.4.5" [[deps.GLFW_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pkg", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"] -git-tree-sha1 = "d972031d28c8c8d9d7b41a536ad7bb0c2579caca" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"] +git-tree-sha1 = "ff38ba61beff76b8f4acad8ab0c97ef73bb670cb" uuid = "0656b61e-2033-5cc2-a64a-77c0f6c09b89" -version = "3.3.8+0" +version = "3.3.9+0" [[deps.GR]] deps = ["Artifacts", "Base64", "DelimitedFiles", "Downloads", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Pkg", "Preferences", "Printf", "Random", "Serialization", "Sockets", "TOML", "Tar", "Test", "UUIDs", "p7zip_jll"] -git-tree-sha1 = "27442171f28c952804dede8ff72828a96f2bfc1f" +git-tree-sha1 = "3458564589be207fa6a77dbbf8b97674c9836aab" uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" -version = "0.72.10" +version = "0.73.2" [[deps.GR_jll]] deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "FreeType2_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Qt6Base_jll", "Zlib_jll", "libpng_jll"] -git-tree-sha1 = "025d171a2847f616becc0f84c8dc62fe18f0f6dd" +git-tree-sha1 = "77f81da2964cc9fa7c0127f941e8bce37f7f1d70" uuid = "d2c73de3-f751-5644-a686-071e5b155ba9" -version = "0.72.10+0" +version = "0.73.2+0" [[deps.Gettext_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "XML2_jll"] @@ -300,9 +307,9 @@ version = "1.0.2" [[deps.HTTP]] deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] -git-tree-sha1 = "5eab648309e2e060198b45820af1a37182de3cce" +git-tree-sha1 = "abbbb9ec3afd783a7cbd82ef01dcd088ea051398" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "1.10.0" +version = "1.10.1" [[deps.HarfBuzz_jll]] deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "Graphite2_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg"] @@ -318,9 +325,9 @@ version = "0.3.23" [[deps.IOCapture]] deps = ["Logging", "Random"] -git-tree-sha1 = "d75853a0bdbfb1ac815478bacd89cd27b550ace6" +git-tree-sha1 = "8b72179abc660bfab5e28472e019392b97d0985c" uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89" -version = "0.2.3" +version = "0.2.4" [[deps.InteractiveUtils]] deps = ["Markdown"] @@ -333,9 +340,9 @@ version = "0.2.2" [[deps.JLFzf]] deps = ["Pipe", "REPL", "Random", "fzf_jll"] -git-tree-sha1 = "f377670cda23b6b7c1c0b3893e37451c5c1a2185" +git-tree-sha1 = "a53ebe394b71470c7f97c2e7e170d51df21b17af" uuid = "1019f520-868f-41f5-a6de-eb00f4b6a39c" -version = "0.1.5" +version = "0.1.7" [[deps.JLLWrappers]] deps = ["Artifacts", "Preferences"] @@ -351,17 +358,17 @@ version = "0.21.4" [[deps.JpegTurbo_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "6f2675ef130a300a112286de91973805fcc5ffbc" +git-tree-sha1 = "60b1194df0a3298f460063de985eae7b01bc011a" uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8" -version = "2.1.91+0" +version = "3.0.1+0" [[deps.KernelFunctions]] deps = ["ChainRulesCore", "Compat", "CompositionsBase", "Distances", "FillArrays", "Functors", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "Random", "Requires", "SpecialFunctions", "Statistics", "StatsBase", "TensorCore", "Test", "ZygoteRules"] -git-tree-sha1 = "2aa6c20d4a9d162ccfd43b45893e1ee73828481b" -repo-rev = "dff053f25e3cf29d9bc23b922ff0643b0904d2f8" +git-tree-sha1 = "296720f2cbd7938cfcb367ff25e910c90aa18ada" +repo-rev = "e6b42a9bdcfbaac6b6c2431f64d16ee03d9851c3" repo-url = "/home/runner/work/KernelFunctions.jl/KernelFunctions.jl" uuid = "ec8451be-7e33-11e9-00cf-bbf324bd1392" -version = "0.10.57" +version = "0.10.60" [[deps.LAME_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -376,10 +383,10 @@ uuid = "88015f11-f218-50d7-93a8-a6af411a945d" version = "3.0.0+1" [[deps.LLVMOpenMP_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "f689897ccbe049adb19a065c495e75f372ecd42b" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "d986ce2d884d49126836ea94ed5bfb0f12679713" uuid = "1d63c593-3942-5779-bab2-d838dc0a180e" -version = "15.0.4+0" +version = "15.0.7+0" [[deps.LZO_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -388,9 +395,9 @@ uuid = "dd4b983a-f0e5-5f8d-a1b7-129d4a5fb1ac" version = "2.10.1+0" [[deps.LaTeXStrings]] -git-tree-sha1 = "f2355693d6778a178ade15952b7ac47a4ff97996" +git-tree-sha1 = "50901ebc375ed41dbf8058da26f9de442febbbec" uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f" -version = "1.3.0" +version = "1.3.1" [[deps.Latexify]] deps = ["Formatting", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Printf", "Requires"] @@ -409,21 +416,26 @@ version = "0.16.1" [[deps.LibCURL]] deps = ["LibCURL_jll", "MozillaCACerts_jll"] uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21" -version = "0.6.3" +version = "0.6.4" [[deps.LibCURL_jll]] deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"] uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0" -version = "7.84.0+0" +version = "8.4.0+0" [[deps.LibGit2]] -deps = ["Base64", "NetworkOptions", "Printf", "SHA"] +deps = ["Base64", "LibGit2_jll", "NetworkOptions", "Printf", "SHA"] uuid = "76f85450-5226-5b5a-8eaa-529ad045b433" +[[deps.LibGit2_jll]] +deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll"] +uuid = "e37daf67-58a4-590a-8e99-b0245dd2ffc5" +version = "1.6.4+0" + [[deps.LibSSH2_jll]] deps = ["Artifacts", "Libdl", "MbedTLS_jll"] uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8" -version = "1.10.2+0" +version = "1.11.0+1" [[deps.Libdl]] uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" @@ -482,9 +494,9 @@ uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" [[deps.Literate]] deps = ["Base64", "IOCapture", "JSON", "REPL"] -git-tree-sha1 = "ae5703dde29228490f03cbd64c47be8131819485" +git-tree-sha1 = "bad26f1ccd99c553886ec0725e99a509589dcd11" uuid = "98b081ad-f1c9-55d3-8b20-4c87d4299306" -version = "2.15.0" +version = "2.16.1" [[deps.LogExpFunctions]] deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] @@ -513,24 +525,24 @@ version = "1.0.3" [[deps.MacroTools]] deps = ["Markdown", "Random"] -git-tree-sha1 = "9ee1618cbf5240e6d4e0371d6f24065083f60c48" +git-tree-sha1 = "2fa9ee3e63fd3a4f7a9a4f4744a52f4856de82df" uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" -version = "0.5.11" +version = "0.5.13" [[deps.Markdown]] deps = ["Base64"] uuid = "d6f4376e-aef5-505a-96c1-9c027394607a" [[deps.MbedTLS]] -deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "Random", "Sockets"] -git-tree-sha1 = "03a9b9718f5682ecb107ac9f7308991db4ce395b" +deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "NetworkOptions", "Random", "Sockets"] +git-tree-sha1 = "c067a280ddc25f196b5e7df3877c6b226d390aaf" uuid = "739be429-bea8-5141-9913-cc70e7f3736d" -version = "1.1.7" +version = "1.1.9" [[deps.MbedTLS_jll]] deps = ["Artifacts", "Libdl"] uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1" -version = "2.28.2+0" +version = "2.28.2+1" [[deps.Measures]] git-tree-sha1 = "c13304c81eec1ed3af7fc20e75fb6b26092a1102" @@ -548,7 +560,7 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804" [[deps.MozillaCACerts_jll]] uuid = "14a3606d-f60d-562e-9121-12d972cd8159" -version = "2022.10.11" +version = "2023.1.10" [[deps.NaNMath]] deps = ["OpenLibm_jll"] @@ -569,12 +581,12 @@ version = "1.3.5+1" [[deps.OpenBLAS_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" -version = "0.3.21+4" +version = "0.3.23+2" [[deps.OpenLibm_jll]] deps = ["Artifacts", "Libdl"] uuid = "05823500-19ac-5b8b-9628-191a04bc5112" -version = "0.8.1+0" +version = "0.8.1+2" [[deps.OpenSSL]] deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"] @@ -584,9 +596,9 @@ version = "1.4.1" [[deps.OpenSSL_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "a12e56c72edee3ce6b96667745e6cbbe5498f200" +git-tree-sha1 = "60e3045590bd104a16fefb12836c00c0ef8c7f8c" uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" -version = "1.1.23+0" +version = "3.0.13+0" [[deps.OpenSpecFun_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"] @@ -601,26 +613,26 @@ uuid = "91d4177d-7536-5919-b921-800302f37372" version = "1.3.2+0" [[deps.OrderedCollections]] -git-tree-sha1 = "2e73fe17cac3c62ad1aebe70d44c963c3cfdc3e3" +git-tree-sha1 = "dfdf5519f235516220579f949664f1bf44e741c5" uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" -version = "1.6.2" +version = "1.6.3" [[deps.PCRE2_jll]] deps = ["Artifacts", "Libdl"] uuid = "efcefdf7-47ab-520b-bdef-62a2eaa19f15" -version = "10.42.0+0" +version = "10.42.0+1" [[deps.PDMats]] deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] -git-tree-sha1 = "fcf8fd477bd7f33cb8dbb1243653fb0d415c256c" +git-tree-sha1 = "949347156c25054de2db3b166c52ac4728cbad65" uuid = "90014a1f-27ba-587c-ab20-58faa44d9150" -version = "0.11.25" +version = "0.11.31" [[deps.Parsers]] deps = ["Dates", "PrecompileTools", "UUIDs"] -git-tree-sha1 = "716e24b21538abc91f6205fd1d8363f39b442851" +git-tree-sha1 = "8489905bcdbcfac64d1daa51ca07c0d8f0283821" uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" -version = "2.7.2" +version = "2.8.1" [[deps.Pipe]] git-tree-sha1 = "6842804e7867b115ca9de748a0cf6b364523c16d" @@ -636,7 +648,7 @@ version = "0.42.2+0" [[deps.Pkg]] deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" -version = "1.9.2" +version = "1.10.0" [[deps.PlotThemes]] deps = ["PlotUtils", "Statistics"] @@ -646,15 +658,15 @@ version = "3.1.0" [[deps.PlotUtils]] deps = ["ColorSchemes", "Colors", "Dates", "PrecompileTools", "Printf", "Random", "Reexport", "Statistics"] -git-tree-sha1 = "f92e1315dadf8c46561fb9396e525f7200cdc227" +git-tree-sha1 = "862942baf5663da528f66d24996eb6da85218e76" uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043" -version = "1.3.5" +version = "1.4.0" [[deps.Plots]] -deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Preferences", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"] -git-tree-sha1 = "ccee59c6e48e6f2edf8a5b64dc817b6729f99eb5" +deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"] +git-tree-sha1 = "c4fa93d7d66acad8f6f4ff439576da9d2e890ee0" uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -version = "1.39.0" +version = "1.40.1" [deps.Plots.extensions] FileIOExt = "FileIO" @@ -688,22 +700,22 @@ uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7" [[deps.Qt6Base_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Fontconfig_jll", "Glib_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "OpenSSL_jll", "Vulkan_Loader_jll", "Xorg_libSM_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Xorg_libxcb_jll", "Xorg_xcb_util_cursor_jll", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_keysyms_jll", "Xorg_xcb_util_renderutil_jll", "Xorg_xcb_util_wm_jll", "Zlib_jll", "libinput_jll", "xkbcommon_jll"] -git-tree-sha1 = "7c29f0e8c575428bd84dc3c72ece5178caa67336" +git-tree-sha1 = "37b7bb7aabf9a085e0044307e1717436117f2b3b" uuid = "c0090381-4147-56d7-9ebc-da0b1113ec56" -version = "6.5.2+2" +version = "6.5.3+1" [[deps.QuadGK]] deps = ["DataStructures", "LinearAlgebra"] -git-tree-sha1 = "9ebcd48c498668c7fa0e97a9cae873fbee7bfee1" +git-tree-sha1 = "9b23c31e76e333e6fb4c1595ae6afa74966a729e" uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc" -version = "2.9.1" +version = "2.9.4" [[deps.REPL]] deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" [[deps.Random]] -deps = ["SHA", "Serialization"] +deps = ["SHA"] uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" [[deps.RecipesBase]] @@ -725,9 +737,9 @@ version = "1.2.2" [[deps.RelocatableFolders]] deps = ["SHA", "Scratch"] -git-tree-sha1 = "90bc7a7c96410424509e4263e277e43250c05691" +git-tree-sha1 = "ffdaf70d81cf6ff22c2b6e733c900c3321cab864" uuid = "05181044-ff0b-4ac5-8273-598c1e38db00" -version = "1.0.0" +version = "1.0.1" [[deps.Requires]] deps = ["UUIDs"] @@ -753,9 +765,9 @@ version = "0.7.0" [[deps.Scratch]] deps = ["Dates"] -git-tree-sha1 = "30449ee12237627992a99d5e30ae63e4d78cd24a" +git-tree-sha1 = "3bac05bc7e74a75fd9cba4295cde4045d9fe2386" uuid = "6c6a2e73-6563-6170-7368-637461726353" -version = "1.2.0" +version = "1.2.1" [[deps.Serialization]] uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" @@ -776,13 +788,14 @@ uuid = "6462fe0b-24de-5631-8697-dd941f90decc" [[deps.SortingAlgorithms]] deps = ["DataStructures"] -git-tree-sha1 = "c60ec5c62180f27efea3ba2908480f8055e17cee" +git-tree-sha1 = "66e0a8e672a0bdfca2c3f5937efb8538b9ddc085" uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c" -version = "1.1.1" +version = "1.2.1" [[deps.SparseArrays]] deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"] uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" +version = "1.10.0" [[deps.SpecialFunctions]] deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] @@ -797,7 +810,7 @@ weakdeps = ["ChainRulesCore"] [[deps.Statistics]] deps = ["LinearAlgebra", "SparseArrays"] uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" -version = "1.9.0" +version = "1.10.0" [[deps.StatsAPI]] deps = ["LinearAlgebra"] @@ -830,9 +843,9 @@ deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"] uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" [[deps.SuiteSparse_jll]] -deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"] +deps = ["Artifacts", "Libdl", "libblastrampoline_jll"] uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c" -version = "5.10.1+6" +version = "7.2.1+1" [[deps.TOML]] deps = ["Dates"] @@ -855,15 +868,18 @@ deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [[deps.TranscodingStreams]] -deps = ["Random", "Test"] -git-tree-sha1 = "9a6ae7ed916312b41236fcef7e0af564ef934769" +git-tree-sha1 = "54194d92959d8ebaa8e26227dbe3cdefcdcd594f" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.9.13" +version = "0.10.3" +weakdeps = ["Random", "Test"] + + [deps.TranscodingStreams.extensions] + TestExt = ["Test", "Random"] [[deps.URIs]] -git-tree-sha1 = "b7a5e99f24892b6824a954199a45e9ffcc1c70f0" +git-tree-sha1 = "67db6cc7b3821e19ebe75791a9dd19c9b1188f2b" uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4" -version = "1.5.0" +version = "1.5.1" [[deps.UUIDs]] deps = ["Random", "SHA"] @@ -880,9 +896,9 @@ version = "0.4.1" [[deps.Unitful]] deps = ["Dates", "LinearAlgebra", "Random"] -git-tree-sha1 = "a72d22c7e13fe2de562feda8645aa134712a87ee" +git-tree-sha1 = "3c793be6df9dd77a0cf49d80984ef9ff996948fa" uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" -version = "1.17.0" +version = "1.19.0" [deps.Unitful.extensions] ConstructionBaseUnitfulExt = "ConstructionBase" @@ -917,15 +933,15 @@ version = "1.21.0+1" [[deps.Wayland_protocols_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "4528479aa01ee1b3b4cd0e6faef0e04cf16466da" +git-tree-sha1 = "93f43ab61b16ddfb2fd3bb13b3ce241cafb0e6c9" uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91" -version = "1.25.0+0" +version = "1.31.0+0" [[deps.XML2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"] -git-tree-sha1 = "24b81b59bd35b3c42ab84fa589086e19be919916" +git-tree-sha1 = "801cbe47eae69adc50f36c3caec4758d2650741b" uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a" -version = "2.11.5+0" +version = "2.12.2+0" [[deps.XSLT_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"] @@ -935,9 +951,9 @@ version = "1.1.34+0" [[deps.XZ_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] -git-tree-sha1 = "cf2c7de82431ca6f39250d2fc4aacd0daa1675c0" +git-tree-sha1 = "522b8414d40c4cbbab8dee346ac3a09f9768f25d" uuid = "ffd25f8a-64ca-5728-b0f7-c24cf3aae800" -version = "5.4.4+0" +version = "5.4.5+0" [[deps.Xorg_libICE_jll]] deps = ["Libdl", "Pkg"] @@ -1086,7 +1102,7 @@ version = "1.5.0+0" [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" -version = "1.2.13+0" +version = "1.2.13+1" [[deps.Zstd_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] @@ -1096,9 +1112,9 @@ version = "1.5.5+0" [[deps.ZygoteRules]] deps = ["ChainRulesCore", "MacroTools"] -git-tree-sha1 = "977aed5d006b840e2e40c0b48984f7463109046d" +git-tree-sha1 = "27798139afc0a2afa7b1824c206d5e87ea587a00" uuid = "700de1a5-db45-46bc-99cf-38207098b444" -version = "0.2.3" +version = "0.2.5" [[deps.eudev_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "gperf_jll"] @@ -1107,10 +1123,10 @@ uuid = "35ca27e7-8b34-5b7f-bca9-bdc33f59eb06" version = "3.2.9+0" [[deps.fzf_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "868e669ccb12ba16eaf50cb2957ee2ff61261c56" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "a68c9655fbe6dfcab3d972808f1aafec151ce3f8" uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09" -version = "0.29.0+0" +version = "0.43.0+0" [[deps.gperf_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -1133,7 +1149,7 @@ version = "0.15.1+0" [[deps.libblastrampoline_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" -version = "5.8.0+0" +version = "5.8.0+1" [[deps.libevdev_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -1154,10 +1170,10 @@ uuid = "36db933b-70db-51c0-b978-0f229ee0e533" version = "1.18.0+0" [[deps.libpng_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] -git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"] +git-tree-sha1 = 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We can now visualize a kernel and show samples from a Gaussian process with a given kernel:

plot(visualize(SqExponentialKernel()); size=(800, 210), bottommargin=5mm, topmargin=5mm)
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Kernel comparison

This also allows us to compare different kernels:

kernels = [
     Matern12Kernel(),
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Package and system information

@@ -2970,10 +2835,10 @@ 
Package and system information

 Package information (click to expand)
 
 Status `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/gaussian-process-priors/Project.toml`
-  [31c24e10] Distributions v0.25.102
-  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`
-  [98b081ad] Literate v2.15.0
-  [91a5bcdd] Plots v1.39.0
+  [31c24e10] Distributions v0.25.107
+  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`
+  [98b081ad] Literate v2.16.1
+  [91a5bcdd] Plots v1.40.1
   [37e2e46d] LinearAlgebra
   [9a3f8284] Random
 

@@ -2984,19 +2849,19 @@ 
Package and system information

 

 System information (click to expand)
 
-Julia Version 1.9.3
-Commit bed2cd540a1 (2023-08-24 14:43 UTC)
+Julia Version 1.10.0
+Commit 3120989f39b (2023-12-25 18:01 UTC)
 Build Info:
   Official https://julialang.org/ release
 Platform Info:
   OS: Linux (x86_64-linux-gnu)
-  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz
+  CPU: 4 × AMD EPYC 7763 64-Core Processor
   WORD_SIZE: 64
   LIBM: libopenlibm
-  LLVM: libLLVM-14.0.6 (ORCJIT, skylake-avx512)
-  Threads: 1 on 2 virtual cores
+  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
+  Threads: 1 on 4 virtual cores
 Environment:
   JULIA_DEBUG = Documenter
   JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src
 

-
This page was generated using Literate.jl.

+
This page was generated using Literate.jl.

diff --git a/previews/PR530/examples/gaussian-process-priors/notebook.ipynb b/previews/PR530/examples/gaussian-process-priors/notebook.ipynb
index d007763b5..f9c98e4aa 100644
--- a/previews/PR530/examples/gaussian-process-priors/notebook.ipynb
+++ b/previews/PR530/examples/gaussian-process-priors/notebook.ipynb
@@ -202,479 +202,403 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": "Plot{Plots.GRBackend() n=10}\nCaptured extra kwargs:\n  Series{1}:\n    vlim: (0, 1)\n",
-      "image/png": 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",
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@@ -703,1111 +627,1039 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": "Plot{Plots.GRBackend() n=100}\nCaptured extra kwargs:\n  Series{1}:\n    vlim: (0, 1)\n  Series{11}:\n    vlim: (0, 1)\n  Series{21}:\n    vlim: (0, 1)\n  Series{31}:\n    vlim: (0, 1)\n  Series{41}:\n    vlim: (0, 1)\n  Series{51}:\n    vlim: (0, 1)\n  Series{61}:\n    vlim: (0, 1)\n  Series{71}:\n    vlim: (0, 1)\n  Series{81}:\n    vlim: (0, 1)\n  Series{91}:\n    vlim: (0, 1)\n",
-      "image/png": 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",
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        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
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-       "\n",
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-       "\n",
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-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
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+       "\n",
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
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+       "\n",
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
-       "  \n",
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        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
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-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
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-       "\n",
-       "\n",
-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n"
       ]
      },
@@ -6063,10 +5793,10 @@
     "Package information (click to expand)\n",
     "
\n",
     "Status `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/gaussian-process-priors/Project.toml`\n",
-    "  [31c24e10] Distributions v0.25.102\n",
-    "  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`\n",
-    "  [98b081ad] Literate v2.15.0\n",
-    "  [91a5bcdd] Plots v1.39.0\n",
+    "  [31c24e10] Distributions v0.25.107\n",
+    "  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`\n",
+    "  [98b081ad] Literate v2.16.1\n",
+    "  [91a5bcdd] Plots v1.40.1\n",
     "  [37e2e46d] LinearAlgebra\n",
     "  [9a3f8284] Random\n",
     "
\n",
@@ -6077,17 +5807,17 @@
     "
\n",
     "System information (click to expand)\n",
     "
\n",
-    "Julia Version 1.9.3\n",
-    "Commit bed2cd540a1 (2023-08-24 14:43 UTC)\n",
+    "Julia Version 1.10.0\n",
+    "Commit 3120989f39b (2023-12-25 18:01 UTC)\n",
     "Build Info:\n",
     "  Official https://julialang.org/ release\n",
     "Platform Info:\n",
     "  OS: Linux (x86_64-linux-gnu)\n",
-    "  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz\n",
+    "  CPU: 4 × AMD EPYC 7763 64-Core Processor\n",
     "  WORD_SIZE: 64\n",
     "  LIBM: libopenlibm\n",
-    "  LLVM: libLLVM-14.0.6 (ORCJIT, skylake-avx512)\n",
-    "  Threads: 1 on 2 virtual cores\n",
+    "  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\n",
+    "  Threads: 1 on 4 virtual cores\n",
     "Environment:\n",
     "  JULIA_DEBUG = Documenter\n",
     "  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n",
@@ -6112,11 +5842,11 @@
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.9.3"
+   "version": "1.10.0"
   },
   "kernelspec": {
-   "name": "julia-1.9",
-   "display_name": "Julia 1.9.3",
+   "name": "julia-1.10",
+   "display_name": "Julia 1.10.0",
    "language": "julia"
   }
  },
diff --git a/previews/PR530/examples/kernel-ridge-regression/Manifest.toml b/previews/PR530/examples/kernel-ridge-regression/Manifest.toml
index 8a365cc43..4c80e995a 100644
--- a/previews/PR530/examples/kernel-ridge-regression/Manifest.toml
+++ b/previews/PR530/examples/kernel-ridge-regression/Manifest.toml
@@ -1,6 +1,6 @@
 # This file is machine-generated - editing it directly is not advised
 
-julia_version = "1.9.3"
+julia_version = "1.10.0"
 manifest_format = "2.0"
 project_hash = "871a60b57cfc97ea19ecb86f8d3c3aac749bf4ef"
 
@@ -15,15 +15,15 @@ uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 
 [[deps.BitFlags]]
-git-tree-sha1 = "43b1a4a8f797c1cddadf60499a8a077d4af2cd2d"
+git-tree-sha1 = "2dc09997850d68179b69dafb58ae806167a32b1b"
 uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35"
-version = "0.1.7"
+version = "0.1.8"
 
 [[deps.Bzip2_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2"
+git-tree-sha1 = "9e2a6b69137e6969bab0152632dcb3bc108c8bdd"
 uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
-version = "1.0.8+0"
+version = "1.0.8+1"
 
 [[deps.Cairo_jll]]
 deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"]
@@ -38,16 +38,20 @@ uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
 version = "0.5.1"
 
 [[deps.ChainRulesCore]]
-deps = ["Compat", "LinearAlgebra", "SparseArrays"]
-git-tree-sha1 = "e30f2f4e20f7f186dc36529910beaedc60cfa644"
+deps = ["Compat", "LinearAlgebra"]
+git-tree-sha1 = "1287e3872d646eed95198457873249bd9f0caed2"
 uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
-version = "1.16.0"
+version = "1.20.1"
+weakdeps = ["SparseArrays"]
+
+    [deps.ChainRulesCore.extensions]
+    ChainRulesCoreSparseArraysExt = "SparseArrays"
 
 [[deps.CodecZlib]]
 deps = ["TranscodingStreams", "Zlib_jll"]
-git-tree-sha1 = "02aa26a4cf76381be7f66e020a3eddeb27b0a092"
+git-tree-sha1 = "59939d8a997469ee05c4b4944560a820f9ba0d73"
 uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
-version = "0.7.2"
+version = "0.7.4"
 
 [[deps.ColorSchemes]]
 deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"]
@@ -78,10 +82,10 @@ uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
 version = "0.12.10"
 
 [[deps.Compat]]
-deps = ["UUIDs"]
-git-tree-sha1 = "8a62af3e248a8c4bad6b32cbbe663ae02275e32c"
+deps = ["TOML", "UUIDs"]
+git-tree-sha1 = "75bd5b6fc5089df449b5d35fa501c846c9b6549b"
 uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
-version = "4.10.0"
+version = "4.12.0"
 weakdeps = ["Dates", "LinearAlgebra"]
 
     [deps.Compat.extensions]
@@ -90,7 +94,7 @@ weakdeps = ["Dates", "LinearAlgebra"]
 [[deps.CompilerSupportLibraries_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "1.0.5+0"
+version = "1.0.5+1"
 
 [[deps.CompositionsBase]]
 git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad"
@@ -105,9 +109,9 @@ version = "0.1.2"
 
 [[deps.ConcurrentUtilities]]
 deps = ["Serialization", "Sockets"]
-git-tree-sha1 = "5372dbbf8f0bdb8c700db5367132925c0771ef7e"
+git-tree-sha1 = "8cfa272e8bdedfa88b6aefbbca7c19f1befac519"
 uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb"
-version = "2.2.1"
+version = "2.3.0"
 
 [[deps.Contour]]
 git-tree-sha1 = "d05d9e7b7aedff4e5b51a029dced05cfb6125781"
@@ -115,15 +119,15 @@ uuid = "d38c429a-6771-53c6-b99e-75d170b6e991"
 version = "0.6.2"
 
 [[deps.DataAPI]]
-git-tree-sha1 = "8da84edb865b0b5b0100c0666a9bc9a0b71c553c"
+git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe"
 uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
-version = "1.15.0"
+version = "1.16.0"
 
 [[deps.DataStructures]]
 deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
-git-tree-sha1 = "3dbd312d370723b6bb43ba9d02fc36abade4518d"
+git-tree-sha1 = "ac67408d9ddf207de5cfa9a97e114352430f01ed"
 uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-version = "0.18.15"
+version = "0.18.16"
 
 [[deps.Dates]]
 deps = ["Printf"]
@@ -137,9 +141,9 @@ version = "1.9.1"
 
 [[deps.Distances]]
 deps = ["LinearAlgebra", "Statistics", "StatsAPI"]
-git-tree-sha1 = "5225c965635d8c21168e32a12954675e7bea1151"
+git-tree-sha1 = "66c4c81f259586e8f002eacebc177e1fb06363b0"
 uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-version = "0.10.10"
+version = "0.10.11"
 weakdeps = ["ChainRulesCore", "SparseArrays"]
 
     [deps.Distances.extensions]
@@ -147,18 +151,20 @@ weakdeps = ["ChainRulesCore", "SparseArrays"]
     DistancesSparseArraysExt = "SparseArrays"
 
 [[deps.Distributions]]
-deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Test"]
-git-tree-sha1 = "3d5873f811f582873bb9871fc9c451784d5dc8c7"
+deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"]
+git-tree-sha1 = "7c302d7a5fec5214eb8a5a4c466dcf7a51fcf169"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.25.102"
+version = "0.25.107"
 
     [deps.Distributions.extensions]
     DistributionsChainRulesCoreExt = "ChainRulesCore"
     DistributionsDensityInterfaceExt = "DensityInterface"
+    DistributionsTestExt = "Test"
 
     [deps.Distributions.weakdeps]
     ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
     DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
+    Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [[deps.DocStringExtensions]]
 deps = ["LibGit2"]
@@ -185,9 +191,9 @@ version = "0.0.20230411+0"
 
 [[deps.ExceptionUnwrapping]]
 deps = ["Test"]
-git-tree-sha1 = "e90caa41f5a86296e014e148ee061bd6c3edec96"
+git-tree-sha1 = "dcb08a0d93ec0b1cdc4af184b26b591e9695423a"
 uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4"
-version = "0.1.9"
+version = "0.1.10"
 
 [[deps.Expat_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
@@ -202,22 +208,23 @@ uuid = "c87230d0-a227-11e9-1b43-d7ebe4e7570a"
 version = "0.4.1"
 
 [[deps.FFMPEG_jll]]
-deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Pkg", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"]
-git-tree-sha1 = "74faea50c1d007c85837327f6775bea60b5492dd"
+deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"]
+git-tree-sha1 = "466d45dc38e15794ec7d5d63ec03d776a9aff36e"
 uuid = "b22a6f82-2f65-5046-a5b2-351ab43fb4e5"
-version = "4.4.2+2"
+version = "4.4.4+1"
 
 [[deps.FileWatching]]
 uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"
 
 [[deps.FillArrays]]
 deps = ["LinearAlgebra", "Random"]
-git-tree-sha1 = "a20eaa3ad64254c61eeb5f230d9306e937405434"
+git-tree-sha1 = "5b93957f6dcd33fc343044af3d48c215be2562f1"
 uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
-version = "1.6.1"
-weakdeps = ["SparseArrays", "Statistics"]
+version = "1.9.3"
+weakdeps = ["PDMats", "SparseArrays", "Statistics"]
 
     [deps.FillArrays.extensions]
+    FillArraysPDMatsExt = "PDMats"
     FillArraysSparseArraysExt = "SparseArrays"
     FillArraysStatisticsExt = "Statistics"
 
@@ -258,22 +265,22 @@ uuid = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 version = "0.4.5"
 
 [[deps.GLFW_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pkg", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"]
-git-tree-sha1 = "d972031d28c8c8d9d7b41a536ad7bb0c2579caca"
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"]
+git-tree-sha1 = "ff38ba61beff76b8f4acad8ab0c97ef73bb670cb"
 uuid = "0656b61e-2033-5cc2-a64a-77c0f6c09b89"
-version = "3.3.8+0"
+version = "3.3.9+0"
 
 [[deps.GR]]
 deps = ["Artifacts", "Base64", "DelimitedFiles", "Downloads", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Pkg", "Preferences", "Printf", "Random", "Serialization", "Sockets", "TOML", "Tar", "Test", "UUIDs", "p7zip_jll"]
-git-tree-sha1 = "27442171f28c952804dede8ff72828a96f2bfc1f"
+git-tree-sha1 = "3458564589be207fa6a77dbbf8b97674c9836aab"
 uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
-version = "0.72.10"
+version = "0.73.2"
 
 [[deps.GR_jll]]
 deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "FreeType2_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Qt6Base_jll", "Zlib_jll", "libpng_jll"]
-git-tree-sha1 = "025d171a2847f616becc0f84c8dc62fe18f0f6dd"
+git-tree-sha1 = "77f81da2964cc9fa7c0127f941e8bce37f7f1d70"
 uuid = "d2c73de3-f751-5644-a686-071e5b155ba9"
-version = "0.72.10+0"
+version = "0.73.2+0"
 
 [[deps.Gettext_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "XML2_jll"]
@@ -300,9 +307,9 @@ version = "1.0.2"
 
 [[deps.HTTP]]
 deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"]
-git-tree-sha1 = "5eab648309e2e060198b45820af1a37182de3cce"
+git-tree-sha1 = "abbbb9ec3afd783a7cbd82ef01dcd088ea051398"
 uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3"
-version = "1.10.0"
+version = "1.10.1"
 
 [[deps.HarfBuzz_jll]]
 deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "Graphite2_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg"]
@@ -318,9 +325,9 @@ version = "0.3.23"
 
 [[deps.IOCapture]]
 deps = ["Logging", "Random"]
-git-tree-sha1 = "d75853a0bdbfb1ac815478bacd89cd27b550ace6"
+git-tree-sha1 = "8b72179abc660bfab5e28472e019392b97d0985c"
 uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89"
-version = "0.2.3"
+version = "0.2.4"
 
 [[deps.InteractiveUtils]]
 deps = ["Markdown"]
@@ -333,9 +340,9 @@ version = "0.2.2"
 
 [[deps.JLFzf]]
 deps = ["Pipe", "REPL", "Random", "fzf_jll"]
-git-tree-sha1 = "f377670cda23b6b7c1c0b3893e37451c5c1a2185"
+git-tree-sha1 = "a53ebe394b71470c7f97c2e7e170d51df21b17af"
 uuid = "1019f520-868f-41f5-a6de-eb00f4b6a39c"
-version = "0.1.5"
+version = "0.1.7"
 
 [[deps.JLLWrappers]]
 deps = ["Artifacts", "Preferences"]
@@ -351,17 +358,17 @@ version = "0.21.4"
 
 [[deps.JpegTurbo_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "6f2675ef130a300a112286de91973805fcc5ffbc"
+git-tree-sha1 = "60b1194df0a3298f460063de985eae7b01bc011a"
 uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8"
-version = "2.1.91+0"
+version = "3.0.1+0"
 
 [[deps.KernelFunctions]]
 deps = ["ChainRulesCore", "Compat", "CompositionsBase", "Distances", "FillArrays", "Functors", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "Random", "Requires", "SpecialFunctions", "Statistics", "StatsBase", "TensorCore", "Test", "ZygoteRules"]
-git-tree-sha1 = "2aa6c20d4a9d162ccfd43b45893e1ee73828481b"
-repo-rev = "dff053f25e3cf29d9bc23b922ff0643b0904d2f8"
+git-tree-sha1 = "296720f2cbd7938cfcb367ff25e910c90aa18ada"
+repo-rev = "e6b42a9bdcfbaac6b6c2431f64d16ee03d9851c3"
 repo-url = "/home/runner/work/KernelFunctions.jl/KernelFunctions.jl"
 uuid = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
-version = "0.10.57"
+version = "0.10.60"
 
 [[deps.LAME_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -376,10 +383,10 @@ uuid = "88015f11-f218-50d7-93a8-a6af411a945d"
 version = "3.0.0+1"
 
 [[deps.LLVMOpenMP_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "f689897ccbe049adb19a065c495e75f372ecd42b"
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "d986ce2d884d49126836ea94ed5bfb0f12679713"
 uuid = "1d63c593-3942-5779-bab2-d838dc0a180e"
-version = "15.0.4+0"
+version = "15.0.7+0"
 
 [[deps.LZO_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -388,9 +395,9 @@ uuid = "dd4b983a-f0e5-5f8d-a1b7-129d4a5fb1ac"
 version = "2.10.1+0"
 
 [[deps.LaTeXStrings]]
-git-tree-sha1 = "f2355693d6778a178ade15952b7ac47a4ff97996"
+git-tree-sha1 = "50901ebc375ed41dbf8058da26f9de442febbbec"
 uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
-version = "1.3.0"
+version = "1.3.1"
 
 [[deps.Latexify]]
 deps = ["Formatting", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Printf", "Requires"]
@@ -409,21 +416,26 @@ version = "0.16.1"
 [[deps.LibCURL]]
 deps = ["LibCURL_jll", "MozillaCACerts_jll"]
 uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"
-version = "0.6.3"
+version = "0.6.4"
 
 [[deps.LibCURL_jll]]
 deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"]
 uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0"
-version = "7.84.0+0"
+version = "8.4.0+0"
 
 [[deps.LibGit2]]
-deps = ["Base64", "NetworkOptions", "Printf", "SHA"]
+deps = ["Base64", "LibGit2_jll", "NetworkOptions", "Printf", "SHA"]
 uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
 
+[[deps.LibGit2_jll]]
+deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll"]
+uuid = "e37daf67-58a4-590a-8e99-b0245dd2ffc5"
+version = "1.6.4+0"
+
 [[deps.LibSSH2_jll]]
 deps = ["Artifacts", "Libdl", "MbedTLS_jll"]
 uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8"
-version = "1.10.2+0"
+version = "1.11.0+1"
 
 [[deps.Libdl]]
 uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
@@ -482,9 +494,9 @@ uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 
 [[deps.Literate]]
 deps = ["Base64", "IOCapture", "JSON", "REPL"]
-git-tree-sha1 = "ae5703dde29228490f03cbd64c47be8131819485"
+git-tree-sha1 = "bad26f1ccd99c553886ec0725e99a509589dcd11"
 uuid = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
-version = "2.15.0"
+version = "2.16.1"
 
 [[deps.LogExpFunctions]]
 deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"]
@@ -513,24 +525,24 @@ version = "1.0.3"
 
 [[deps.MacroTools]]
 deps = ["Markdown", "Random"]
-git-tree-sha1 = "9ee1618cbf5240e6d4e0371d6f24065083f60c48"
+git-tree-sha1 = "2fa9ee3e63fd3a4f7a9a4f4744a52f4856de82df"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
-version = "0.5.11"
+version = "0.5.13"
 
 [[deps.Markdown]]
 deps = ["Base64"]
 uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
 
 [[deps.MbedTLS]]
-deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "Random", "Sockets"]
-git-tree-sha1 = "03a9b9718f5682ecb107ac9f7308991db4ce395b"
+deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "NetworkOptions", "Random", "Sockets"]
+git-tree-sha1 = "c067a280ddc25f196b5e7df3877c6b226d390aaf"
 uuid = "739be429-bea8-5141-9913-cc70e7f3736d"
-version = "1.1.7"
+version = "1.1.9"
 
 [[deps.MbedTLS_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1"
-version = "2.28.2+0"
+version = "2.28.2+1"
 
 [[deps.Measures]]
 git-tree-sha1 = "c13304c81eec1ed3af7fc20e75fb6b26092a1102"
@@ -548,7 +560,7 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804"
 
 [[deps.MozillaCACerts_jll]]
 uuid = "14a3606d-f60d-562e-9121-12d972cd8159"
-version = "2022.10.11"
+version = "2023.1.10"
 
 [[deps.NaNMath]]
 deps = ["OpenLibm_jll"]
@@ -569,12 +581,12 @@ version = "1.3.5+1"
 [[deps.OpenBLAS_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"]
 uuid = "4536629a-c528-5b80-bd46-f80d51c5b363"
-version = "0.3.21+4"
+version = "0.3.23+2"
 
 [[deps.OpenLibm_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "05823500-19ac-5b8b-9628-191a04bc5112"
-version = "0.8.1+0"
+version = "0.8.1+2"
 
 [[deps.OpenSSL]]
 deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"]
@@ -584,9 +596,9 @@ version = "1.4.1"
 
 [[deps.OpenSSL_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "a12e56c72edee3ce6b96667745e6cbbe5498f200"
+git-tree-sha1 = "60e3045590bd104a16fefb12836c00c0ef8c7f8c"
 uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95"
-version = "1.1.23+0"
+version = "3.0.13+0"
 
 [[deps.OpenSpecFun_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
@@ -601,26 +613,26 @@ uuid = "91d4177d-7536-5919-b921-800302f37372"
 version = "1.3.2+0"
 
 [[deps.OrderedCollections]]
-git-tree-sha1 = "2e73fe17cac3c62ad1aebe70d44c963c3cfdc3e3"
+git-tree-sha1 = "dfdf5519f235516220579f949664f1bf44e741c5"
 uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
-version = "1.6.2"
+version = "1.6.3"
 
 [[deps.PCRE2_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "efcefdf7-47ab-520b-bdef-62a2eaa19f15"
-version = "10.42.0+0"
+version = "10.42.0+1"
 
 [[deps.PDMats]]
 deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"]
-git-tree-sha1 = "fcf8fd477bd7f33cb8dbb1243653fb0d415c256c"
+git-tree-sha1 = "949347156c25054de2db3b166c52ac4728cbad65"
 uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
-version = "0.11.25"
+version = "0.11.31"
 
 [[deps.Parsers]]
 deps = ["Dates", "PrecompileTools", "UUIDs"]
-git-tree-sha1 = "716e24b21538abc91f6205fd1d8363f39b442851"
+git-tree-sha1 = "8489905bcdbcfac64d1daa51ca07c0d8f0283821"
 uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
-version = "2.7.2"
+version = "2.8.1"
 
 [[deps.Pipe]]
 git-tree-sha1 = "6842804e7867b115ca9de748a0cf6b364523c16d"
@@ -636,7 +648,7 @@ version = "0.42.2+0"
 [[deps.Pkg]]
 deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"]
 uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
-version = "1.9.2"
+version = "1.10.0"
 
 [[deps.PlotThemes]]
 deps = ["PlotUtils", "Statistics"]
@@ -646,15 +658,15 @@ version = "3.1.0"
 
 [[deps.PlotUtils]]
 deps = ["ColorSchemes", "Colors", "Dates", "PrecompileTools", "Printf", "Random", "Reexport", "Statistics"]
-git-tree-sha1 = "f92e1315dadf8c46561fb9396e525f7200cdc227"
+git-tree-sha1 = "862942baf5663da528f66d24996eb6da85218e76"
 uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043"
-version = "1.3.5"
+version = "1.4.0"
 
 [[deps.Plots]]
-deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Preferences", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"]
-git-tree-sha1 = "ccee59c6e48e6f2edf8a5b64dc817b6729f99eb5"
+deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"]
+git-tree-sha1 = "c4fa93d7d66acad8f6f4ff439576da9d2e890ee0"
 uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
-version = "1.39.0"
+version = "1.40.1"
 
     [deps.Plots.extensions]
     FileIOExt = "FileIO"
@@ -688,22 +700,22 @@ uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 
 [[deps.Qt6Base_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "Fontconfig_jll", "Glib_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "OpenSSL_jll", "Vulkan_Loader_jll", "Xorg_libSM_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Xorg_libxcb_jll", "Xorg_xcb_util_cursor_jll", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_keysyms_jll", "Xorg_xcb_util_renderutil_jll", "Xorg_xcb_util_wm_jll", "Zlib_jll", "libinput_jll", "xkbcommon_jll"]
-git-tree-sha1 = "7c29f0e8c575428bd84dc3c72ece5178caa67336"
+git-tree-sha1 = "37b7bb7aabf9a085e0044307e1717436117f2b3b"
 uuid = "c0090381-4147-56d7-9ebc-da0b1113ec56"
-version = "6.5.2+2"
+version = "6.5.3+1"
 
 [[deps.QuadGK]]
 deps = ["DataStructures", "LinearAlgebra"]
-git-tree-sha1 = "9ebcd48c498668c7fa0e97a9cae873fbee7bfee1"
+git-tree-sha1 = "9b23c31e76e333e6fb4c1595ae6afa74966a729e"
 uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
-version = "2.9.1"
+version = "2.9.4"
 
 [[deps.REPL]]
 deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
 uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
 
 [[deps.Random]]
-deps = ["SHA", "Serialization"]
+deps = ["SHA"]
 uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [[deps.RecipesBase]]
@@ -725,9 +737,9 @@ version = "1.2.2"
 
 [[deps.RelocatableFolders]]
 deps = ["SHA", "Scratch"]
-git-tree-sha1 = "90bc7a7c96410424509e4263e277e43250c05691"
+git-tree-sha1 = "ffdaf70d81cf6ff22c2b6e733c900c3321cab864"
 uuid = "05181044-ff0b-4ac5-8273-598c1e38db00"
-version = "1.0.0"
+version = "1.0.1"
 
 [[deps.Requires]]
 deps = ["UUIDs"]
@@ -753,9 +765,9 @@ version = "0.7.0"
 
 [[deps.Scratch]]
 deps = ["Dates"]
-git-tree-sha1 = "30449ee12237627992a99d5e30ae63e4d78cd24a"
+git-tree-sha1 = "3bac05bc7e74a75fd9cba4295cde4045d9fe2386"
 uuid = "6c6a2e73-6563-6170-7368-637461726353"
-version = "1.2.0"
+version = "1.2.1"
 
 [[deps.Serialization]]
 uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
@@ -776,13 +788,14 @@ uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
 
 [[deps.SortingAlgorithms]]
 deps = ["DataStructures"]
-git-tree-sha1 = "c60ec5c62180f27efea3ba2908480f8055e17cee"
+git-tree-sha1 = "66e0a8e672a0bdfca2c3f5937efb8538b9ddc085"
 uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
-version = "1.1.1"
+version = "1.2.1"
 
 [[deps.SparseArrays]]
 deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"]
 uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+version = "1.10.0"
 
 [[deps.SpecialFunctions]]
 deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
@@ -797,7 +810,7 @@ weakdeps = ["ChainRulesCore"]
 [[deps.Statistics]]
 deps = ["LinearAlgebra", "SparseArrays"]
 uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-version = "1.9.0"
+version = "1.10.0"
 
 [[deps.StatsAPI]]
 deps = ["LinearAlgebra"]
@@ -830,9 +843,9 @@ deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
 uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
 
 [[deps.SuiteSparse_jll]]
-deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"]
+deps = ["Artifacts", "Libdl", "libblastrampoline_jll"]
 uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c"
-version = "5.10.1+6"
+version = "7.2.1+1"
 
 [[deps.TOML]]
 deps = ["Dates"]
@@ -855,15 +868,18 @@ deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [[deps.TranscodingStreams]]
-deps = ["Random", "Test"]
-git-tree-sha1 = "9a6ae7ed916312b41236fcef7e0af564ef934769"
+git-tree-sha1 = "54194d92959d8ebaa8e26227dbe3cdefcdcd594f"
 uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
-version = "0.9.13"
+version = "0.10.3"
+weakdeps = ["Random", "Test"]
+
+    [deps.TranscodingStreams.extensions]
+    TestExt = ["Test", "Random"]
 
 [[deps.URIs]]
-git-tree-sha1 = "b7a5e99f24892b6824a954199a45e9ffcc1c70f0"
+git-tree-sha1 = "67db6cc7b3821e19ebe75791a9dd19c9b1188f2b"
 uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
-version = "1.5.0"
+version = "1.5.1"
 
 [[deps.UUIDs]]
 deps = ["Random", "SHA"]
@@ -880,9 +896,9 @@ version = "0.4.1"
 
 [[deps.Unitful]]
 deps = ["Dates", "LinearAlgebra", "Random"]
-git-tree-sha1 = "a72d22c7e13fe2de562feda8645aa134712a87ee"
+git-tree-sha1 = "3c793be6df9dd77a0cf49d80984ef9ff996948fa"
 uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
-version = "1.17.0"
+version = "1.19.0"
 
     [deps.Unitful.extensions]
     ConstructionBaseUnitfulExt = "ConstructionBase"
@@ -917,15 +933,15 @@ version = "1.21.0+1"
 
 [[deps.Wayland_protocols_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
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diff --git a/previews/PR530/examples/kernel-ridge-regression/index.html b/previews/PR530/examples/kernel-ridge-regression/index.html
index 552a2db07..92d8e93aa 100644
--- a/previews/PR530/examples/kernel-ridge-regression/index.html
+++ b/previews/PR530/examples/kernel-ridge-regression/index.html
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 scatter!(x_train, y_train; seriescolor=1, label="observations")

 
 
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Linear regression
For training inputs $\mathrm{X}=(\mathbf{x}_n)_{n=1}^N$ and observations $\mathbf{y}=(y_n)_{n=1}^N$, the linear regression weights $\mathbf{w}$ using the least-squares estimator are given by
\[\mathbf{w} = (\mathrm{X}^\top \mathrm{X})^{-1} \mathrm{X}^\top \mathbf{y}\]
We predict at test inputs $\mathbf{x}_*$ using
\[\hat{y}_* = \mathbf{x}_*^\top \mathbf{w}\]
This is implemented by linear_regression:
function linear_regression(X, y, Xstar)
     weights = (X' * X) \ (X' * y)
     return Xstar * weights
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Featurization
We can improve the fit by including additional features, i.e. generalizing to $\tilde{\mathrm{X}} = (\phi(x_n))_{n=1}^N$, where $\phi(x)$ constructs a feature vector for each input $x$. Here we include powers of the input, $\phi(x) = (1, x, x^2, \dots, x^d)$:
function featurize_poly(x; degree=1)
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 end
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Feat
 plot((featurized_fit_and_plot(degree) for degree in 1:4)...)

 
 
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Note that the fit becomes perfect when we include exactly as many orders in the features as we have in the underlying polynomial (4).
However, when increasing the number of features, we can quickly overfit to noise in the data set:
featurized_fit_and_plot(20)

 
 
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Ridge regression
To counteract this unwanted behaviour, we can introduce regularization. This leads to ridge regression with $L_2$ regularization of the weights (Tikhonov regularization). Instead of the weights in linear regression,
\[\mathbf{w} = (\mathrm{X}^\top \mathrm{X})^{-1} \mathrm{X}^\top \mathbf{y}\]
we introduce the ridge parameter $\lambda$:
\[\mathbf{w} = (\mathrm{X}^\top \mathrm{X} + \lambda \mathbb{1})^{-1} \mathrm{X}^\top \mathbf{y}\]
As before, we predict at test inputs $\mathbf{x}_*$ using
\[\hat{y}_* = \mathbf{x}_*^\top \mathbf{w}\]
This is implemented by ridge_regression:
function ridge_regression(X, y, Xstar, lambda)
     weights = (X' * X + lambda * I) \ (X' * y)
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Kernel ridge regression
Instead of constructing the feature matrix explicitly, we can use kernels to replace inner products of feature vectors with a kernel evaluation: $\langle \phi(x), \phi(x') \rangle = k(x, x')$ or $\tilde{\mathrm{X}} \tilde{\mathrm{X}}^\top = \mathrm{K}$, where $\mathrm{K}_{ij} = k(x_i, x_j)$.
To apply this "kernel trick" to ridge regression, we can rewrite the ridge estimate for the weights
\[\mathbf{w} = (\mathrm{X}^\top \mathrm{X} + \lambda \mathbb{1})^{-1} \mathrm{X}^\top \mathbf{y}\]
using the matrix inversion lemma as
\[\mathbf{w} = \mathrm{X}^\top (\mathrm{X} \mathrm{X}^\top + \lambda \mathbb{1})^{-1} \mathbf{y}\]
where we can now replace the inner product with the kernel matrix,
\[\mathbf{w} = \mathrm{X}^\top (\mathrm{K} + \lambda \mathbb{1})^{-1} \mathbf{y}\]
And the prediction yields another inner product,
\[\hat{y}_* = \mathbf{x}_*^\top \mathbf{w} = \langle \mathbf{x}_*, \mathbf{w} \rangle = \mathbf{k}_* (\mathrm{K} + \lambda \mathbb{1})^{-1} \mathbf{y}\]
where $(\mathbf{k}_*)_n = k(x_*, x_n)$.
This is implemented by kernel_ridge_regression:
function kernel_ridge_regression(k, X, y, Xstar, lambda)
     K = kernelmatrix(k, X)
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However, we can now also use kernels that would have an infinite-dimensional feature expansion, such as the squared exponential kernel:
kernelized_fit_and_plot(SqExponentialKernel())

 
 
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Package and system information

@@ -1040,10 +1040,10 @@ 
Package and system information

 Package information (click to expand)
 
 Status `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/kernel-ridge-regression/Project.toml`
-  [31c24e10] Distributions v0.25.102
-  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`
-  [98b081ad] Literate v2.15.0
-  [91a5bcdd] Plots v1.39.0
+  [31c24e10] Distributions v0.25.107
+  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`
+  [98b081ad] Literate v2.16.1
+  [91a5bcdd] Plots v1.40.1
   [37e2e46d] LinearAlgebra
 

 To reproduce this notebook's package environment, you can
@@ -1053,19 +1053,19 @@ 
Package and system information

 

 System information (click to expand)
 
-Julia Version 1.9.3
-Commit bed2cd540a1 (2023-08-24 14:43 UTC)
+Julia Version 1.10.0
+Commit 3120989f39b (2023-12-25 18:01 UTC)
 Build Info:
   Official https://julialang.org/ release
 Platform Info:
   OS: Linux (x86_64-linux-gnu)
-  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz
+  CPU: 4 × AMD EPYC 7763 64-Core Processor
   WORD_SIZE: 64
   LIBM: libopenlibm
-  LLVM: libLLVM-14.0.6 (ORCJIT, skylake-avx512)
-  Threads: 1 on 2 virtual cores
+  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
+  Threads: 1 on 4 virtual cores
 Environment:
   JULIA_DEBUG = Documenter
   JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src
 

-
This page was generated using Literate.jl.

+
This page was generated using Literate.jl.

diff --git a/previews/PR530/examples/kernel-ridge-regression/notebook.ipynb b/previews/PR530/examples/kernel-ridge-regression/notebook.ipynb
index 383477089..8aae67e02 100644
--- a/previews/PR530/examples/kernel-ridge-regression/notebook.ipynb
+++ b/previews/PR530/examples/kernel-ridge-regression/notebook.ipynb
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n"
       ]
      },
@@ -946,145 +946,145 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": "Plot{Plots.GRBackend() n=2}",
-      "image/png": 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",
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      "data": {
       "text/plain": "Plot{Plots.GRBackend() n=8}",
-      "image/png": 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",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
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-       "\n",
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-       "\n",
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-       "\n",
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-       "\n",
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-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n"
       ]
      },
@@ -2177,140 +2177,140 @@
        "\n",
        "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
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-       "\n",
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-       "\n",
-       "\n",
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-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
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+       "\n",
+       "\n",
+       "\n",
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+       "\n",
+       "\n",
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n"
       ],
       "image/svg+xml": [
        "\n",
        "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
+       "\n",
        "\n",
-       "  \n",
+       "  \n",
        "    \n",
        "  \n",
        "\n",
-       "\n",
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-       "\n",
-       "\n",
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-       "\n",
-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n"
       ]
      },
@@ -2334,10 +2334,10 @@
     "Package information (click to expand)\n",
     "
\n",
     "Status `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/kernel-ridge-regression/Project.toml`\n",
-    "  [31c24e10] Distributions v0.25.102\n",
-    "  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`\n",
-    "  [98b081ad] Literate v2.15.0\n",
-    "  [91a5bcdd] Plots v1.39.0\n",
+    "  [31c24e10] Distributions v0.25.107\n",
+    "  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`\n",
+    "  [98b081ad] Literate v2.16.1\n",
+    "  [91a5bcdd] Plots v1.40.1\n",
     "  [37e2e46d] LinearAlgebra\n",
     "
\n",
     "To reproduce this notebook's package environment, you can\n",
@@ -2347,17 +2347,17 @@
     "
\n",
     "System information (click to expand)\n",
     "
\n",
-    "Julia Version 1.9.3\n",
-    "Commit bed2cd540a1 (2023-08-24 14:43 UTC)\n",
+    "Julia Version 1.10.0\n",
+    "Commit 3120989f39b (2023-12-25 18:01 UTC)\n",
     "Build Info:\n",
     "  Official https://julialang.org/ release\n",
     "Platform Info:\n",
     "  OS: Linux (x86_64-linux-gnu)\n",
-    "  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz\n",
+    "  CPU: 4 × AMD EPYC 7763 64-Core Processor\n",
     "  WORD_SIZE: 64\n",
     "  LIBM: libopenlibm\n",
-    "  LLVM: libLLVM-14.0.6 (ORCJIT, skylake-avx512)\n",
-    "  Threads: 1 on 2 virtual cores\n",
+    "  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\n",
+    "  Threads: 1 on 4 virtual cores\n",
     "Environment:\n",
     "  JULIA_DEBUG = Documenter\n",
     "  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n",
@@ -2382,11 +2382,11 @@
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.9.3"
+   "version": "1.10.0"
   },
   "kernelspec": {
-   "name": "julia-1.9",
-   "display_name": "Julia 1.9.3",
+   "name": "julia-1.10",
+   "display_name": "Julia 1.10.0",
    "language": "julia"
   }
  },
diff --git a/previews/PR530/examples/support-vector-machine/Manifest.toml b/previews/PR530/examples/support-vector-machine/Manifest.toml
index 3b0ffb7a9..2b37a0a8e 100644
--- a/previews/PR530/examples/support-vector-machine/Manifest.toml
+++ b/previews/PR530/examples/support-vector-machine/Manifest.toml
@@ -1,6 +1,6 @@
 # This file is machine-generated - editing it directly is not advised
 
-julia_version = "1.9.3"
+julia_version = "1.10.0"
 manifest_format = "2.0"
 project_hash = "d0b26683c92b747389c4e23a24f873fcb30a1126"
 
@@ -15,15 +15,15 @@ uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 
 [[deps.BitFlags]]
-git-tree-sha1 = "43b1a4a8f797c1cddadf60499a8a077d4af2cd2d"
+git-tree-sha1 = "2dc09997850d68179b69dafb58ae806167a32b1b"
 uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35"
-version = "0.1.7"
+version = "0.1.8"
 
 [[deps.Bzip2_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2"
+git-tree-sha1 = "9e2a6b69137e6969bab0152632dcb3bc108c8bdd"
 uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
-version = "1.0.8+0"
+version = "1.0.8+1"
 
 [[deps.Cairo_jll]]
 deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"]
@@ -38,16 +38,20 @@ uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
 version = "0.5.1"
 
 [[deps.ChainRulesCore]]
-deps = ["Compat", "LinearAlgebra", "SparseArrays"]
-git-tree-sha1 = "e30f2f4e20f7f186dc36529910beaedc60cfa644"
+deps = ["Compat", "LinearAlgebra"]
+git-tree-sha1 = "1287e3872d646eed95198457873249bd9f0caed2"
 uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
-version = "1.16.0"
+version = "1.20.1"
+weakdeps = ["SparseArrays"]
+
+    [deps.ChainRulesCore.extensions]
+    ChainRulesCoreSparseArraysExt = "SparseArrays"
 
 [[deps.CodecZlib]]
 deps = ["TranscodingStreams", "Zlib_jll"]
-git-tree-sha1 = "02aa26a4cf76381be7f66e020a3eddeb27b0a092"
+git-tree-sha1 = "59939d8a997469ee05c4b4944560a820f9ba0d73"
 uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
-version = "0.7.2"
+version = "0.7.4"
 
 [[deps.ColorSchemes]]
 deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"]
@@ -78,10 +82,10 @@ uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
 version = "0.12.10"
 
 [[deps.Compat]]
-deps = ["UUIDs"]
-git-tree-sha1 = "8a62af3e248a8c4bad6b32cbbe663ae02275e32c"
+deps = ["TOML", "UUIDs"]
+git-tree-sha1 = "75bd5b6fc5089df449b5d35fa501c846c9b6549b"
 uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
-version = "4.10.0"
+version = "4.12.0"
 weakdeps = ["Dates", "LinearAlgebra"]
 
     [deps.Compat.extensions]
@@ -90,7 +94,7 @@ weakdeps = ["Dates", "LinearAlgebra"]
 [[deps.CompilerSupportLibraries_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "1.0.5+0"
+version = "1.0.5+1"
 
 [[deps.CompositionsBase]]
 git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad"
@@ -105,9 +109,9 @@ version = "0.1.2"
 
 [[deps.ConcurrentUtilities]]
 deps = ["Serialization", "Sockets"]
-git-tree-sha1 = "5372dbbf8f0bdb8c700db5367132925c0771ef7e"
+git-tree-sha1 = "8cfa272e8bdedfa88b6aefbbca7c19f1befac519"
 uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb"
-version = "2.2.1"
+version = "2.3.0"
 
 [[deps.Contour]]
 git-tree-sha1 = "d05d9e7b7aedff4e5b51a029dced05cfb6125781"
@@ -115,15 +119,15 @@ uuid = "d38c429a-6771-53c6-b99e-75d170b6e991"
 version = "0.6.2"
 
 [[deps.DataAPI]]
-git-tree-sha1 = "8da84edb865b0b5b0100c0666a9bc9a0b71c553c"
+git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe"
 uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
-version = "1.15.0"
+version = "1.16.0"
 
 [[deps.DataStructures]]
 deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
-git-tree-sha1 = "3dbd312d370723b6bb43ba9d02fc36abade4518d"
+git-tree-sha1 = "ac67408d9ddf207de5cfa9a97e114352430f01ed"
 uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-version = "0.18.15"
+version = "0.18.16"
 
 [[deps.Dates]]
 deps = ["Printf"]
@@ -137,9 +141,9 @@ version = "1.9.1"
 
 [[deps.Distances]]
 deps = ["LinearAlgebra", "Statistics", "StatsAPI"]
-git-tree-sha1 = "5225c965635d8c21168e32a12954675e7bea1151"
+git-tree-sha1 = "66c4c81f259586e8f002eacebc177e1fb06363b0"
 uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-version = "0.10.10"
+version = "0.10.11"
 weakdeps = ["ChainRulesCore", "SparseArrays"]
 
     [deps.Distances.extensions]
@@ -147,18 +151,20 @@ weakdeps = ["ChainRulesCore", "SparseArrays"]
     DistancesSparseArraysExt = "SparseArrays"
 
 [[deps.Distributions]]
-deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Test"]
-git-tree-sha1 = "3d5873f811f582873bb9871fc9c451784d5dc8c7"
+deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"]
+git-tree-sha1 = "7c302d7a5fec5214eb8a5a4c466dcf7a51fcf169"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.25.102"
+version = "0.25.107"
 
     [deps.Distributions.extensions]
     DistributionsChainRulesCoreExt = "ChainRulesCore"
     DistributionsDensityInterfaceExt = "DensityInterface"
+    DistributionsTestExt = "Test"
 
     [deps.Distributions.weakdeps]
     ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
     DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
+    Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [[deps.DocStringExtensions]]
 deps = ["LibGit2"]
@@ -185,9 +191,9 @@ version = "0.0.20230411+0"
 
 [[deps.ExceptionUnwrapping]]
 deps = ["Test"]
-git-tree-sha1 = "e90caa41f5a86296e014e148ee061bd6c3edec96"
+git-tree-sha1 = "dcb08a0d93ec0b1cdc4af184b26b591e9695423a"
 uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4"
-version = "0.1.9"
+version = "0.1.10"
 
 [[deps.Expat_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
@@ -202,22 +208,23 @@ uuid = "c87230d0-a227-11e9-1b43-d7ebe4e7570a"
 version = "0.4.1"
 
 [[deps.FFMPEG_jll]]
-deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Pkg", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"]
-git-tree-sha1 = "74faea50c1d007c85837327f6775bea60b5492dd"
+deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"]
+git-tree-sha1 = "466d45dc38e15794ec7d5d63ec03d776a9aff36e"
 uuid = "b22a6f82-2f65-5046-a5b2-351ab43fb4e5"
-version = "4.4.2+2"
+version = "4.4.4+1"
 
 [[deps.FileWatching]]
 uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"
 
 [[deps.FillArrays]]
 deps = ["LinearAlgebra", "Random"]
-git-tree-sha1 = "a20eaa3ad64254c61eeb5f230d9306e937405434"
+git-tree-sha1 = "5b93957f6dcd33fc343044af3d48c215be2562f1"
 uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
-version = "1.6.1"
-weakdeps = ["SparseArrays", "Statistics"]
+version = "1.9.3"
+weakdeps = ["PDMats", "SparseArrays", "Statistics"]
 
     [deps.FillArrays.extensions]
+    FillArraysPDMatsExt = "PDMats"
     FillArraysSparseArraysExt = "SparseArrays"
     FillArraysStatisticsExt = "Statistics"
 
@@ -258,22 +265,22 @@ uuid = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 version = "0.4.5"
 
 [[deps.GLFW_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pkg", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"]
-git-tree-sha1 = "d972031d28c8c8d9d7b41a536ad7bb0c2579caca"
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"]
+git-tree-sha1 = "ff38ba61beff76b8f4acad8ab0c97ef73bb670cb"
 uuid = "0656b61e-2033-5cc2-a64a-77c0f6c09b89"
-version = "3.3.8+0"
+version = "3.3.9+0"
 
 [[deps.GR]]
 deps = ["Artifacts", "Base64", "DelimitedFiles", "Downloads", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Pkg", "Preferences", "Printf", "Random", "Serialization", "Sockets", "TOML", "Tar", "Test", "UUIDs", "p7zip_jll"]
-git-tree-sha1 = "27442171f28c952804dede8ff72828a96f2bfc1f"
+git-tree-sha1 = "3458564589be207fa6a77dbbf8b97674c9836aab"
 uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
-version = "0.72.10"
+version = "0.73.2"
 
 [[deps.GR_jll]]
 deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "FreeType2_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Qt6Base_jll", "Zlib_jll", "libpng_jll"]
-git-tree-sha1 = "025d171a2847f616becc0f84c8dc62fe18f0f6dd"
+git-tree-sha1 = "77f81da2964cc9fa7c0127f941e8bce37f7f1d70"
 uuid = "d2c73de3-f751-5644-a686-071e5b155ba9"
-version = "0.72.10+0"
+version = "0.73.2+0"
 
 [[deps.Gettext_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "XML2_jll"]
@@ -300,9 +307,9 @@ version = "1.0.2"
 
 [[deps.HTTP]]
 deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"]
-git-tree-sha1 = "5eab648309e2e060198b45820af1a37182de3cce"
+git-tree-sha1 = "abbbb9ec3afd783a7cbd82ef01dcd088ea051398"
 uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3"
-version = "1.10.0"
+version = "1.10.1"
 
 [[deps.HarfBuzz_jll]]
 deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "Graphite2_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg"]
@@ -318,9 +325,9 @@ version = "0.3.23"
 
 [[deps.IOCapture]]
 deps = ["Logging", "Random"]
-git-tree-sha1 = "d75853a0bdbfb1ac815478bacd89cd27b550ace6"
+git-tree-sha1 = "8b72179abc660bfab5e28472e019392b97d0985c"
 uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89"
-version = "0.2.3"
+version = "0.2.4"
 
 [[deps.InteractiveUtils]]
 deps = ["Markdown"]
@@ -333,9 +340,9 @@ version = "0.2.2"
 
 [[deps.JLFzf]]
 deps = ["Pipe", "REPL", "Random", "fzf_jll"]
-git-tree-sha1 = "f377670cda23b6b7c1c0b3893e37451c5c1a2185"
+git-tree-sha1 = "a53ebe394b71470c7f97c2e7e170d51df21b17af"
 uuid = "1019f520-868f-41f5-a6de-eb00f4b6a39c"
-version = "0.1.5"
+version = "0.1.7"
 
 [[deps.JLLWrappers]]
 deps = ["Artifacts", "Preferences"]
@@ -351,17 +358,17 @@ version = "0.21.4"
 
 [[deps.JpegTurbo_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "6f2675ef130a300a112286de91973805fcc5ffbc"
+git-tree-sha1 = "60b1194df0a3298f460063de985eae7b01bc011a"
 uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8"
-version = "2.1.91+0"
+version = "3.0.1+0"
 
 [[deps.KernelFunctions]]
 deps = ["ChainRulesCore", "Compat", "CompositionsBase", "Distances", "FillArrays", "Functors", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "Random", "Requires", "SpecialFunctions", "Statistics", "StatsBase", "TensorCore", "Test", "ZygoteRules"]
-git-tree-sha1 = "2aa6c20d4a9d162ccfd43b45893e1ee73828481b"
-repo-rev = "dff053f25e3cf29d9bc23b922ff0643b0904d2f8"
+git-tree-sha1 = "296720f2cbd7938cfcb367ff25e910c90aa18ada"
+repo-rev = "e6b42a9bdcfbaac6b6c2431f64d16ee03d9851c3"
 repo-url = "/home/runner/work/KernelFunctions.jl/KernelFunctions.jl"
 uuid = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
-version = "0.10.57"
+version = "0.10.60"
 
 [[deps.LAME_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -388,10 +395,10 @@ uuid = "b1bec4e5-fd48-53fe-b0cb-9723c09d164b"
 version = "0.8.0"
 
 [[deps.LLVMOpenMP_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "f689897ccbe049adb19a065c495e75f372ecd42b"
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "d986ce2d884d49126836ea94ed5bfb0f12679713"
 uuid = "1d63c593-3942-5779-bab2-d838dc0a180e"
-version = "15.0.4+0"
+version = "15.0.7+0"
 
 [[deps.LZO_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -400,9 +407,9 @@ uuid = "dd4b983a-f0e5-5f8d-a1b7-129d4a5fb1ac"
 version = "2.10.1+0"
 
 [[deps.LaTeXStrings]]
-git-tree-sha1 = "f2355693d6778a178ade15952b7ac47a4ff97996"
+git-tree-sha1 = "50901ebc375ed41dbf8058da26f9de442febbbec"
 uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
-version = "1.3.0"
+version = "1.3.1"
 
 [[deps.Latexify]]
 deps = ["Formatting", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Printf", "Requires"]
@@ -421,21 +428,26 @@ version = "0.16.1"
 [[deps.LibCURL]]
 deps = ["LibCURL_jll", "MozillaCACerts_jll"]
 uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"
-version = "0.6.3"
+version = "0.6.4"
 
 [[deps.LibCURL_jll]]
 deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"]
 uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0"
-version = "7.84.0+0"
+version = "8.4.0+0"
 
 [[deps.LibGit2]]
-deps = ["Base64", "NetworkOptions", "Printf", "SHA"]
+deps = ["Base64", "LibGit2_jll", "NetworkOptions", "Printf", "SHA"]
 uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
 
+[[deps.LibGit2_jll]]
+deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll"]
+uuid = "e37daf67-58a4-590a-8e99-b0245dd2ffc5"
+version = "1.6.4+0"
+
 [[deps.LibSSH2_jll]]
 deps = ["Artifacts", "Libdl", "MbedTLS_jll"]
 uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8"
-version = "1.10.2+0"
+version = "1.11.0+1"
 
 [[deps.Libdl]]
 uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
@@ -494,9 +506,9 @@ uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 
 [[deps.Literate]]
 deps = ["Base64", "IOCapture", "JSON", "REPL"]
-git-tree-sha1 = "ae5703dde29228490f03cbd64c47be8131819485"
+git-tree-sha1 = "bad26f1ccd99c553886ec0725e99a509589dcd11"
 uuid = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
-version = "2.15.0"
+version = "2.16.1"
 
 [[deps.LogExpFunctions]]
 deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"]
@@ -525,24 +537,24 @@ version = "1.0.3"
 
 [[deps.MacroTools]]
 deps = ["Markdown", "Random"]
-git-tree-sha1 = "9ee1618cbf5240e6d4e0371d6f24065083f60c48"
+git-tree-sha1 = "2fa9ee3e63fd3a4f7a9a4f4744a52f4856de82df"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
-version = "0.5.11"
+version = "0.5.13"
 
 [[deps.Markdown]]
 deps = ["Base64"]
 uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
 
 [[deps.MbedTLS]]
-deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "Random", "Sockets"]
-git-tree-sha1 = "03a9b9718f5682ecb107ac9f7308991db4ce395b"
+deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "NetworkOptions", "Random", "Sockets"]
+git-tree-sha1 = "c067a280ddc25f196b5e7df3877c6b226d390aaf"
 uuid = "739be429-bea8-5141-9913-cc70e7f3736d"
-version = "1.1.7"
+version = "1.1.9"
 
 [[deps.MbedTLS_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1"
-version = "2.28.2+0"
+version = "2.28.2+1"
 
 [[deps.Measures]]
 git-tree-sha1 = "c13304c81eec1ed3af7fc20e75fb6b26092a1102"
@@ -560,7 +572,7 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804"
 
 [[deps.MozillaCACerts_jll]]
 uuid = "14a3606d-f60d-562e-9121-12d972cd8159"
-version = "2022.10.11"
+version = "2023.1.10"
 
 [[deps.NaNMath]]
 deps = ["OpenLibm_jll"]
@@ -581,12 +593,12 @@ version = "1.3.5+1"
 [[deps.OpenBLAS_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"]
 uuid = "4536629a-c528-5b80-bd46-f80d51c5b363"
-version = "0.3.21+4"
+version = "0.3.23+2"
 
 [[deps.OpenLibm_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "05823500-19ac-5b8b-9628-191a04bc5112"
-version = "0.8.1+0"
+version = "0.8.1+2"
 
 [[deps.OpenSSL]]
 deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"]
@@ -596,9 +608,9 @@ version = "1.4.1"
 
 [[deps.OpenSSL_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "a12e56c72edee3ce6b96667745e6cbbe5498f200"
+git-tree-sha1 = "60e3045590bd104a16fefb12836c00c0ef8c7f8c"
 uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95"
-version = "1.1.23+0"
+version = "3.0.13+0"
 
 [[deps.OpenSpecFun_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
@@ -613,26 +625,26 @@ uuid = "91d4177d-7536-5919-b921-800302f37372"
 version = "1.3.2+0"
 
 [[deps.OrderedCollections]]
-git-tree-sha1 = "2e73fe17cac3c62ad1aebe70d44c963c3cfdc3e3"
+git-tree-sha1 = "dfdf5519f235516220579f949664f1bf44e741c5"
 uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
-version = "1.6.2"
+version = "1.6.3"
 
 [[deps.PCRE2_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "efcefdf7-47ab-520b-bdef-62a2eaa19f15"
-version = "10.42.0+0"
+version = "10.42.0+1"
 
 [[deps.PDMats]]
 deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"]
-git-tree-sha1 = "fcf8fd477bd7f33cb8dbb1243653fb0d415c256c"
+git-tree-sha1 = "949347156c25054de2db3b166c52ac4728cbad65"
 uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
-version = "0.11.25"
+version = "0.11.31"
 
 [[deps.Parsers]]
 deps = ["Dates", "PrecompileTools", "UUIDs"]
-git-tree-sha1 = "716e24b21538abc91f6205fd1d8363f39b442851"
+git-tree-sha1 = "8489905bcdbcfac64d1daa51ca07c0d8f0283821"
 uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
-version = "2.7.2"
+version = "2.8.1"
 
 [[deps.Pipe]]
 git-tree-sha1 = "6842804e7867b115ca9de748a0cf6b364523c16d"
@@ -648,7 +660,7 @@ version = "0.42.2+0"
 [[deps.Pkg]]
 deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"]
 uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
-version = "1.9.2"
+version = "1.10.0"
 
 [[deps.PlotThemes]]
 deps = ["PlotUtils", "Statistics"]
@@ -658,15 +670,15 @@ version = "3.1.0"
 
 [[deps.PlotUtils]]
 deps = ["ColorSchemes", "Colors", "Dates", "PrecompileTools", "Printf", "Random", "Reexport", "Statistics"]
-git-tree-sha1 = "f92e1315dadf8c46561fb9396e525f7200cdc227"
+git-tree-sha1 = "862942baf5663da528f66d24996eb6da85218e76"
 uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043"
-version = "1.3.5"
+version = "1.4.0"
 
 [[deps.Plots]]
-deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Preferences", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"]
-git-tree-sha1 = "ccee59c6e48e6f2edf8a5b64dc817b6729f99eb5"
+deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"]
+git-tree-sha1 = "c4fa93d7d66acad8f6f4ff439576da9d2e890ee0"
 uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
-version = "1.39.0"
+version = "1.40.1"
 
     [deps.Plots.extensions]
     FileIOExt = "FileIO"
@@ -700,22 +712,22 @@ uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 
 [[deps.Qt6Base_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "Fontconfig_jll", "Glib_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "OpenSSL_jll", "Vulkan_Loader_jll", "Xorg_libSM_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Xorg_libxcb_jll", "Xorg_xcb_util_cursor_jll", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_keysyms_jll", "Xorg_xcb_util_renderutil_jll", "Xorg_xcb_util_wm_jll", "Zlib_jll", "libinput_jll", "xkbcommon_jll"]
-git-tree-sha1 = "7c29f0e8c575428bd84dc3c72ece5178caa67336"
+git-tree-sha1 = "37b7bb7aabf9a085e0044307e1717436117f2b3b"
 uuid = "c0090381-4147-56d7-9ebc-da0b1113ec56"
-version = "6.5.2+2"
+version = "6.5.3+1"
 
 [[deps.QuadGK]]
 deps = ["DataStructures", "LinearAlgebra"]
-git-tree-sha1 = "9ebcd48c498668c7fa0e97a9cae873fbee7bfee1"
+git-tree-sha1 = "9b23c31e76e333e6fb4c1595ae6afa74966a729e"
 uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
-version = "2.9.1"
+version = "2.9.4"
 
 [[deps.REPL]]
 deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
 uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
 
 [[deps.Random]]
-deps = ["SHA", "Serialization"]
+deps = ["SHA"]
 uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [[deps.RecipesBase]]
@@ -737,9 +749,9 @@ version = "1.2.2"
 
 [[deps.RelocatableFolders]]
 deps = ["SHA", "Scratch"]
-git-tree-sha1 = "90bc7a7c96410424509e4263e277e43250c05691"
+git-tree-sha1 = "ffdaf70d81cf6ff22c2b6e733c900c3321cab864"
 uuid = "05181044-ff0b-4ac5-8273-598c1e38db00"
-version = "1.0.0"
+version = "1.0.1"
 
 [[deps.Requires]]
 deps = ["UUIDs"]
@@ -771,9 +783,9 @@ version = "0.5.0"
 
 [[deps.Scratch]]
 deps = ["Dates"]
-git-tree-sha1 = "30449ee12237627992a99d5e30ae63e4d78cd24a"
+git-tree-sha1 = "3bac05bc7e74a75fd9cba4295cde4045d9fe2386"
 uuid = "6c6a2e73-6563-6170-7368-637461726353"
-version = "1.2.0"
+version = "1.2.1"
 
 [[deps.Serialization]]
 uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
@@ -794,13 +806,14 @@ uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
 
 [[deps.SortingAlgorithms]]
 deps = ["DataStructures"]
-git-tree-sha1 = "c60ec5c62180f27efea3ba2908480f8055e17cee"
+git-tree-sha1 = "66e0a8e672a0bdfca2c3f5937efb8538b9ddc085"
 uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
-version = "1.1.1"
+version = "1.2.1"
 
 [[deps.SparseArrays]]
 deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"]
 uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+version = "1.10.0"
 
 [[deps.SpecialFunctions]]
 deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
@@ -815,7 +828,7 @@ weakdeps = ["ChainRulesCore"]
 [[deps.Statistics]]
 deps = ["LinearAlgebra", "SparseArrays"]
 uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-version = "1.9.0"
+version = "1.10.0"
 
 [[deps.StatsAPI]]
 deps = ["LinearAlgebra"]
@@ -848,9 +861,9 @@ deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
 uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
 
 [[deps.SuiteSparse_jll]]
-deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"]
+deps = ["Artifacts", "Libdl", "libblastrampoline_jll"]
 uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c"
-version = "5.10.1+6"
+version = "7.2.1+1"
 
 [[deps.TOML]]
 deps = ["Dates"]
@@ -873,15 +886,18 @@ deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [[deps.TranscodingStreams]]
-deps = ["Random", "Test"]
-git-tree-sha1 = "9a6ae7ed916312b41236fcef7e0af564ef934769"
+git-tree-sha1 = "54194d92959d8ebaa8e26227dbe3cdefcdcd594f"
 uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
-version = "0.9.13"
+version = "0.10.3"
+weakdeps = ["Random", "Test"]
+
+    [deps.TranscodingStreams.extensions]
+    TestExt = ["Test", "Random"]
 
 [[deps.URIs]]
-git-tree-sha1 = "b7a5e99f24892b6824a954199a45e9ffcc1c70f0"
+git-tree-sha1 = "67db6cc7b3821e19ebe75791a9dd19c9b1188f2b"
 uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
-version = "1.5.0"
+version = "1.5.1"
 
 [[deps.UUIDs]]
 deps = ["Random", "SHA"]
@@ -898,9 +914,9 @@ version = "0.4.1"
 
 [[deps.Unitful]]
 deps = ["Dates", "LinearAlgebra", "Random"]
-git-tree-sha1 = "a72d22c7e13fe2de562feda8645aa134712a87ee"
+git-tree-sha1 = "3c793be6df9dd77a0cf49d80984ef9ff996948fa"
 uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
-version = "1.17.0"
+version = "1.19.0"
 
     [deps.Unitful.extensions]
     ConstructionBaseUnitfulExt = "ConstructionBase"
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diff --git a/previews/PR530/examples/support-vector-machine/index.html b/previews/PR530/examples/support-vector-machine/index.html
index 23f38f823..877a9ce85 100644
--- a/previews/PR530/examples/support-vector-machine/index.html
+++ b/previews/PR530/examples/support-vector-machine/index.html
@@ -29,172 +29,172 @@
 scatter!(X2[:, 1], X2[:, 2]; color=:blue, label="training data: class 1")

 
 
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Package and system information

 

 Package information (click to expand)
 
 Status `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/support-vector-machine/Project.toml`
-  [31c24e10] Distributions v0.25.102
-  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`
+  [31c24e10] Distributions v0.25.107
+  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`
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+  [98b081ad] Literate v2.16.1
+  [91a5bcdd] Plots v1.40.1
   [37e2e46d] LinearAlgebra
 

 To reproduce this notebook's package environment, you can
@@ -204,19 +204,19 @@ 
Package and system information

 

 System information (click to expand)
 
-Julia Version 1.9.3
-Commit bed2cd540a1 (2023-08-24 14:43 UTC)
+Julia Version 1.10.0
+Commit 3120989f39b (2023-12-25 18:01 UTC)
 Build Info:
   Official https://julialang.org/ release
 Platform Info:
   OS: Linux (x86_64-linux-gnu)
-  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz
+  CPU: 4 × AMD EPYC 7763 64-Core Processor
   WORD_SIZE: 64
   LIBM: libopenlibm
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+  Threads: 1 on 4 virtual cores
 Environment:
   JULIA_DEBUG = Documenter
   JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src
 

-
This page was generated using Literate.jl.

+
This page was generated using Literate.jl.

diff --git a/previews/PR530/examples/support-vector-machine/notebook.ipynb b/previews/PR530/examples/support-vector-machine/notebook.ipynb
index c81c5c8f0..bfd29e5dc 100644
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+++ b/previews/PR530/examples/support-vector-machine/notebook.ipynb
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
       ]
      },
      "metadata": {},
@@ -538,11 +538,11 @@
     "Package information (click to expand)\n",
     "
\n",
     "Status `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/support-vector-machine/Project.toml`\n",
-    "  [31c24e10] Distributions v0.25.102\n",
-    "  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`\n",
+    "  [31c24e10] Distributions v0.25.107\n",
+    "  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`\n",
     "  [b1bec4e5] LIBSVM v0.8.0\n",
-    "  [98b081ad] Literate v2.15.0\n",
-    "  [91a5bcdd] Plots v1.39.0\n",
+    "  [98b081ad] Literate v2.16.1\n",
+    "  [91a5bcdd] Plots v1.40.1\n",
     "  [37e2e46d] LinearAlgebra\n",
     "
\n",
     "To reproduce this notebook's package environment, you can\n",
@@ -552,17 +552,17 @@
     "
\n",
     "System information (click to expand)\n",
     "
\n",
-    "Julia Version 1.9.3\n",
-    "Commit bed2cd540a1 (2023-08-24 14:43 UTC)\n",
+    "Julia Version 1.10.0\n",
+    "Commit 3120989f39b (2023-12-25 18:01 UTC)\n",
     "Build Info:\n",
     "  Official https://julialang.org/ release\n",
     "Platform Info:\n",
     "  OS: Linux (x86_64-linux-gnu)\n",
-    "  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz\n",
+    "  CPU: 4 × AMD EPYC 7763 64-Core Processor\n",
     "  WORD_SIZE: 64\n",
     "  LIBM: libopenlibm\n",
-    "  LLVM: libLLVM-14.0.6 (ORCJIT, skylake-avx512)\n",
-    "  Threads: 1 on 2 virtual cores\n",
+    "  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\n",
+    "  Threads: 1 on 4 virtual cores\n",
     "Environment:\n",
     "  JULIA_DEBUG = Documenter\n",
     "  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n",
@@ -587,11 +587,11 @@
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.9.3"
+   "version": "1.10.0"
   },
   "kernelspec": {
-   "name": "julia-1.9",
-   "display_name": "Julia 1.9.3",
+   "name": "julia-1.10",
+   "display_name": "Julia 1.10.0",
    "language": "julia"
   }
  },
diff --git a/previews/PR530/examples/train-kernel-parameters/Manifest.toml b/previews/PR530/examples/train-kernel-parameters/Manifest.toml
index 81fa326ff..3ec062108 100644
--- a/previews/PR530/examples/train-kernel-parameters/Manifest.toml
+++ b/previews/PR530/examples/train-kernel-parameters/Manifest.toml
@@ -1,6 +1,6 @@
 # This file is machine-generated - editing it directly is not advised
 
-julia_version = "1.9.3"
+julia_version = "1.10.0"
 manifest_format = "2.0"
 project_hash = "f3af2d5178fe96f25b295696ea2c040e29f49bbf"
 
@@ -17,9 +17,9 @@ weakdeps = ["ChainRulesCore", "Test"]
 
 [[deps.Adapt]]
 deps = ["LinearAlgebra", "Requires"]
-git-tree-sha1 = "76289dc51920fdc6e0013c872ba9551d54961c24"
+git-tree-sha1 = "0fb305e0253fd4e833d486914367a2ee2c2e78d0"
 uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-version = "3.6.2"
+version = "4.0.1"
 weakdeps = ["StaticArrays"]
 
     [deps.Adapt.extensions]
@@ -45,9 +45,9 @@ version = "0.1.0"
 
 [[deps.BangBang]]
 deps = ["Compat", "ConstructionBase", "InitialValues", "LinearAlgebra", "Requires", "Setfield", "Tables"]
-git-tree-sha1 = "e28912ce94077686443433c2800104b061a827ed"
+git-tree-sha1 = "7aa7ad1682f3d5754e3491bb59b8103cae28e3a3"
 uuid = "198e06fe-97b7-11e9-32a5-e1d131e6ad66"
-version = "0.3.39"
+version = "0.3.40"
 
     [deps.BangBang.extensions]
     BangBangChainRulesCoreExt = "ChainRulesCore"
@@ -73,25 +73,25 @@ version = "0.1.1"
 
 [[deps.BenchmarkTools]]
 deps = ["JSON", "Logging", "Printf", "Profile", "Statistics", "UUIDs"]
-git-tree-sha1 = "d9a9701b899b30332bbcb3e1679c41cce81fb0e8"
+git-tree-sha1 = "f1f03a9fa24271160ed7e73051fba3c1a759b53f"
 uuid = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
-version = "1.3.2"
+version = "1.4.0"
 
 [[deps.BitFlags]]
-git-tree-sha1 = "43b1a4a8f797c1cddadf60499a8a077d4af2cd2d"
+git-tree-sha1 = "2dc09997850d68179b69dafb58ae806167a32b1b"
 uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35"
-version = "0.1.7"
+version = "0.1.8"
 
 [[deps.Bzip2_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2"
+git-tree-sha1 = "9e2a6b69137e6969bab0152632dcb3bc108c8bdd"
 uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
-version = "1.0.8+0"
+version = "1.0.8+1"
 
 [[deps.CEnum]]
-git-tree-sha1 = "eb4cb44a499229b3b8426dcfb5dd85333951ff90"
+git-tree-sha1 = "389ad5c84de1ae7cf0e28e381131c98ea87d54fc"
 uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
-version = "0.4.2"
+version = "0.5.0"
 
 [[deps.Cairo_jll]]
 deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"]
@@ -107,21 +107,25 @@ version = "0.5.1"
 
 [[deps.ChainRules]]
 deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "SparseInverseSubset", "Statistics", "StructArrays", "SuiteSparse"]
-git-tree-sha1 = "01b0594d8907485ed894bc59adfc0a24a9cde7a3"
+git-tree-sha1 = "213f001d1233fd3b8ef007f50c8cab29061917d8"
 uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2"
-version = "1.55.0"
+version = "1.61.0"
 
 [[deps.ChainRulesCore]]
-deps = ["Compat", "LinearAlgebra", "SparseArrays"]
-git-tree-sha1 = "e30f2f4e20f7f186dc36529910beaedc60cfa644"
+deps = ["Compat", "LinearAlgebra"]
+git-tree-sha1 = "1287e3872d646eed95198457873249bd9f0caed2"
 uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
-version = "1.16.0"
+version = "1.20.1"
+weakdeps = ["SparseArrays"]
+
+    [deps.ChainRulesCore.extensions]
+    ChainRulesCoreSparseArraysExt = "SparseArrays"
 
 [[deps.CodecZlib]]
 deps = ["TranscodingStreams", "Zlib_jll"]
-git-tree-sha1 = "02aa26a4cf76381be7f66e020a3eddeb27b0a092"
+git-tree-sha1 = "59939d8a997469ee05c4b4944560a820f9ba0d73"
 uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
-version = "0.7.2"
+version = "0.7.4"
 
 [[deps.ColorSchemes]]
 deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"]
@@ -158,10 +162,10 @@ uuid = "bbf7d656-a473-5ed7-a52c-81e309532950"
 version = "0.3.0"
 
 [[deps.Compat]]
-deps = ["UUIDs"]
-git-tree-sha1 = "8a62af3e248a8c4bad6b32cbbe663ae02275e32c"
+deps = ["TOML", "UUIDs"]
+git-tree-sha1 = "75bd5b6fc5089df449b5d35fa501c846c9b6549b"
 uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
-version = "4.10.0"
+version = "4.12.0"
 weakdeps = ["Dates", "LinearAlgebra"]
 
     [deps.Compat.extensions]
@@ -170,7 +174,7 @@ weakdeps = ["Dates", "LinearAlgebra"]
 [[deps.CompilerSupportLibraries_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "1.0.5+0"
+version = "1.0.5+1"
 
 [[deps.CompositionsBase]]
 git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad"
@@ -183,9 +187,9 @@ weakdeps = ["InverseFunctions"]
 
 [[deps.ConcurrentUtilities]]
 deps = ["Serialization", "Sockets"]
-git-tree-sha1 = "5372dbbf8f0bdb8c700db5367132925c0771ef7e"
+git-tree-sha1 = "8cfa272e8bdedfa88b6aefbbca7c19f1befac519"
 uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb"
-version = "2.2.1"
+version = "2.3.0"
 
 [[deps.ConstructionBase]]
 deps = ["LinearAlgebra"]
@@ -213,15 +217,15 @@ uuid = "d38c429a-6771-53c6-b99e-75d170b6e991"
 version = "0.6.2"
 
 [[deps.DataAPI]]
-git-tree-sha1 = "8da84edb865b0b5b0100c0666a9bc9a0b71c553c"
+git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe"
 uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
-version = "1.15.0"
+version = "1.16.0"
 
 [[deps.DataStructures]]
 deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
-git-tree-sha1 = "3dbd312d370723b6bb43ba9d02fc36abade4518d"
+git-tree-sha1 = "ac67408d9ddf207de5cfa9a97e114352430f01ed"
 uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-version = "0.18.15"
+version = "0.18.16"
 
 [[deps.DataValueInterfaces]]
 git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6"
@@ -257,9 +261,9 @@ version = "1.15.1"
 
 [[deps.Distances]]
 deps = ["LinearAlgebra", "Statistics", "StatsAPI"]
-git-tree-sha1 = "5225c965635d8c21168e32a12954675e7bea1151"
+git-tree-sha1 = "66c4c81f259586e8f002eacebc177e1fb06363b0"
 uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-version = "0.10.10"
+version = "0.10.11"
 weakdeps = ["ChainRulesCore", "SparseArrays"]
 
     [deps.Distances.extensions]
@@ -271,18 +275,20 @@ deps = ["Random", "Serialization", "Sockets"]
 uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 
 [[deps.Distributions]]
-deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "Test"]
-git-tree-sha1 = "3d5873f811f582873bb9871fc9c451784d5dc8c7"
+deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"]
+git-tree-sha1 = "7c302d7a5fec5214eb8a5a4c466dcf7a51fcf169"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.25.102"
+version = "0.25.107"
 
     [deps.Distributions.extensions]
     DistributionsChainRulesCoreExt = "ChainRulesCore"
     DistributionsDensityInterfaceExt = "DensityInterface"
+    DistributionsTestExt = "Test"
 
     [deps.Distributions.weakdeps]
     ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
     DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
+    Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [[deps.DocStringExtensions]]
 deps = ["LibGit2"]
@@ -309,9 +315,9 @@ version = "0.0.20230411+0"
 
 [[deps.ExceptionUnwrapping]]
 deps = ["Test"]
-git-tree-sha1 = "e90caa41f5a86296e014e148ee061bd6c3edec96"
+git-tree-sha1 = "dcb08a0d93ec0b1cdc4af184b26b591e9695423a"
 uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4"
-version = "0.1.9"
+version = "0.1.10"
 
 [[deps.Expat_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
@@ -326,10 +332,10 @@ uuid = "c87230d0-a227-11e9-1b43-d7ebe4e7570a"
 version = "0.4.1"
 
 [[deps.FFMPEG_jll]]
-deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Pkg", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"]
-git-tree-sha1 = "74faea50c1d007c85837327f6775bea60b5492dd"
+deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"]
+git-tree-sha1 = "466d45dc38e15794ec7d5d63ec03d776a9aff36e"
 uuid = "b22a6f82-2f65-5046-a5b2-351ab43fb4e5"
-version = "4.4.2+2"
+version = "4.4.4+1"
 
 [[deps.FLoops]]
 deps = ["BangBang", "Compat", "FLoopsBase", "InitialValues", "JuliaVariables", "MLStyle", "Serialization", "Setfield", "Transducers"]
@@ -348,12 +354,13 @@ uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"
 
 [[deps.FillArrays]]
 deps = ["LinearAlgebra", "Random"]
-git-tree-sha1 = "a20eaa3ad64254c61eeb5f230d9306e937405434"
+git-tree-sha1 = "5b93957f6dcd33fc343044af3d48c215be2562f1"
 uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
-version = "1.6.1"
-weakdeps = ["SparseArrays", "Statistics"]
+version = "1.9.3"
+weakdeps = ["PDMats", "SparseArrays", "Statistics"]
 
     [deps.FillArrays.extensions]
+    FillArraysPDMatsExt = "PDMats"
     FillArraysSparseArraysExt = "SparseArrays"
     FillArraysStatisticsExt = "Statistics"
 
@@ -364,10 +371,10 @@ uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93"
 version = "0.8.4"
 
 [[deps.Flux]]
-deps = ["Adapt", "ChainRulesCore", "Functors", "LinearAlgebra", "MLUtils", "MacroTools", "NNlib", "OneHotArrays", "Optimisers", "Preferences", "ProgressLogging", "Random", "Reexport", "SparseArrays", "SpecialFunctions", "Statistics", "Zygote"]
-git-tree-sha1 = "b97c3fc4f3628b8835d83789b09382961a254da4"
+deps = ["Adapt", "ChainRulesCore", "Compat", "Functors", "LinearAlgebra", "MLUtils", "MacroTools", "NNlib", "OneHotArrays", "Optimisers", "Preferences", "ProgressLogging", "Random", "Reexport", "SparseArrays", "SpecialFunctions", "Statistics", "Zygote"]
+git-tree-sha1 = "39a9e46b4e92d5b56c0712adeb507555a2327240"
 uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-version = "0.14.6"
+version = "0.14.11"
 
     [deps.Flux.extensions]
     FluxAMDGPUExt = "AMDGPU"
@@ -426,34 +433,34 @@ deps = ["Random"]
 uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
 
 [[deps.GLFW_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pkg", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"]
-git-tree-sha1 = "d972031d28c8c8d9d7b41a536ad7bb0c2579caca"
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"]
+git-tree-sha1 = "ff38ba61beff76b8f4acad8ab0c97ef73bb670cb"
 uuid = "0656b61e-2033-5cc2-a64a-77c0f6c09b89"
-version = "3.3.8+0"
+version = "3.3.9+0"
 
 [[deps.GPUArrays]]
 deps = ["Adapt", "GPUArraysCore", "LLVM", "LinearAlgebra", "Printf", "Random", "Reexport", "Serialization", "Statistics"]
-git-tree-sha1 = "8ad8f375ae365aa1eb2f42e2565a40b55a4b69a8"
+git-tree-sha1 = "47e4686ec18a9620850bad110b79966132f14283"
 uuid = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
-version = "9.0.0"
+version = "10.0.2"
 
 [[deps.GPUArraysCore]]
 deps = ["Adapt"]
-git-tree-sha1 = "2d6ca471a6c7b536127afccfa7564b5b39227fe0"
+git-tree-sha1 = "ec632f177c0d990e64d955ccc1b8c04c485a0950"
 uuid = "46192b85-c4d5-4398-a991-12ede77f4527"
-version = "0.1.5"
+version = "0.1.6"
 
 [[deps.GR]]
 deps = ["Artifacts", "Base64", "DelimitedFiles", "Downloads", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Pkg", "Preferences", "Printf", "Random", "Serialization", "Sockets", "TOML", "Tar", "Test", "UUIDs", "p7zip_jll"]
-git-tree-sha1 = "27442171f28c952804dede8ff72828a96f2bfc1f"
+git-tree-sha1 = "3458564589be207fa6a77dbbf8b97674c9836aab"
 uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
-version = "0.72.10"
+version = "0.73.2"
 
 [[deps.GR_jll]]
 deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "FreeType2_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Qt6Base_jll", "Zlib_jll", "libpng_jll"]
-git-tree-sha1 = "025d171a2847f616becc0f84c8dc62fe18f0f6dd"
+git-tree-sha1 = "77f81da2964cc9fa7c0127f941e8bce37f7f1d70"
 uuid = "d2c73de3-f751-5644-a686-071e5b155ba9"
-version = "0.72.10+0"
+version = "0.73.2+0"
 
 [[deps.Gettext_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "XML2_jll"]
@@ -480,9 +487,9 @@ version = "1.0.2"
 
 [[deps.HTTP]]
 deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"]
-git-tree-sha1 = "5eab648309e2e060198b45820af1a37182de3cce"
+git-tree-sha1 = "abbbb9ec3afd783a7cbd82ef01dcd088ea051398"
 uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3"
-version = "1.10.0"
+version = "1.10.1"
 
 [[deps.HarfBuzz_jll]]
 deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "Graphite2_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg"]
@@ -498,15 +505,15 @@ version = "0.3.23"
 
 [[deps.IOCapture]]
 deps = ["Logging", "Random"]
-git-tree-sha1 = "d75853a0bdbfb1ac815478bacd89cd27b550ace6"
+git-tree-sha1 = "8b72179abc660bfab5e28472e019392b97d0985c"
 uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89"
-version = "0.2.3"
+version = "0.2.4"
 
 [[deps.IRTools]]
 deps = ["InteractiveUtils", "MacroTools", "Test"]
-git-tree-sha1 = "eac00994ce3229a464c2847e956d77a2c64ad3a5"
+git-tree-sha1 = "5d8c5713f38f7bc029e26627b687710ba406d0dd"
 uuid = "7869d1d1-7146-5819-86e3-90919afe41df"
-version = "0.4.10"
+version = "0.4.12"
 
 [[deps.InitialValues]]
 git-tree-sha1 = "4da0f88e9a39111c2fa3add390ab15f3a44f3ca3"
@@ -529,9 +536,9 @@ uuid = "92d709cd-6900-40b7-9082-c6be49f344b6"
 version = "0.2.2"
 
 [[deps.IterTools]]
-git-tree-sha1 = "4ced6667f9974fc5c5943fa5e2ef1ca43ea9e450"
+git-tree-sha1 = "42d5f897009e7ff2cf88db414a389e5ed1bdd023"
 uuid = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
-version = "1.8.0"
+version = "1.10.0"
 
 [[deps.IteratorInterfaceExtensions]]
 git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856"
@@ -540,9 +547,9 @@ version = "1.0.0"
 
 [[deps.JLFzf]]
 deps = ["Pipe", "REPL", "Random", "fzf_jll"]
-git-tree-sha1 = "f377670cda23b6b7c1c0b3893e37451c5c1a2185"
+git-tree-sha1 = "a53ebe394b71470c7f97c2e7e170d51df21b17af"
 uuid = "1019f520-868f-41f5-a6de-eb00f4b6a39c"
-version = "0.1.5"
+version = "0.1.7"
 
 [[deps.JLLWrappers]]
 deps = ["Artifacts", "Preferences"]
@@ -558,9 +565,9 @@ version = "0.21.4"
 
 [[deps.JpegTurbo_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "6f2675ef130a300a112286de91973805fcc5ffbc"
+git-tree-sha1 = "60b1194df0a3298f460063de985eae7b01bc011a"
 uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8"
-version = "2.1.91+0"
+version = "3.0.1+0"
 
 [[deps.JuliaVariables]]
 deps = ["MLStyle", "NameResolution"]
@@ -570,9 +577,9 @@ version = "0.2.4"
 
 [[deps.KernelAbstractions]]
 deps = ["Adapt", "Atomix", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "Requires", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"]
-git-tree-sha1 = "4c5875e4c228247e1c2b087669846941fb6e0118"
+git-tree-sha1 = "4e0cb2f5aad44dcfdc91088e85dee4ecb22c791c"
 uuid = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
-version = "0.9.8"
+version = "0.9.16"
 
     [deps.KernelAbstractions.extensions]
     EnzymeExt = "EnzymeCore"
@@ -582,11 +589,11 @@ version = "0.9.8"
 
 [[deps.KernelFunctions]]
 deps = ["ChainRulesCore", "Compat", "CompositionsBase", "Distances", "FillArrays", "Functors", "IrrationalConstants", "LinearAlgebra", "LogExpFunctions", "Random", "Requires", "SpecialFunctions", "Statistics", "StatsBase", "TensorCore", "Test", "ZygoteRules"]
-git-tree-sha1 = "2aa6c20d4a9d162ccfd43b45893e1ee73828481b"
-repo-rev = "dff053f25e3cf29d9bc23b922ff0643b0904d2f8"
+git-tree-sha1 = "296720f2cbd7938cfcb367ff25e910c90aa18ada"
+repo-rev = "e6b42a9bdcfbaac6b6c2431f64d16ee03d9851c3"
 repo-url = "/home/runner/work/KernelFunctions.jl/KernelFunctions.jl"
 uuid = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
-version = "0.10.57"
+version = "0.10.60"
 
 [[deps.LAME_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -601,22 +608,28 @@ uuid = "88015f11-f218-50d7-93a8-a6af411a945d"
 version = "3.0.0+1"
 
 [[deps.LLVM]]
-deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Printf", "Unicode"]
-git-tree-sha1 = "4ea2928a96acfcf8589e6cd1429eff2a3a82c366"
+deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Preferences", "Printf", "Requires", "Unicode"]
+git-tree-sha1 = "cb4619f7353fc62a1a22ffa3d7ed9791cfb47ad8"
 uuid = "929cbde3-209d-540e-8aea-75f648917ca0"
-version = "6.3.0"
+version = "6.4.2"
+
+    [deps.LLVM.extensions]
+    BFloat16sExt = "BFloat16s"
+
+    [deps.LLVM.weakdeps]
+    BFloat16s = "ab4f0b2a-ad5b-11e8-123f-65d77653426b"
 
 [[deps.LLVMExtra_jll]]
 deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"]
-git-tree-sha1 = "e7c01b69bcbcb93fd4cbc3d0fea7d229541e18d2"
+git-tree-sha1 = "98eaee04d96d973e79c25d49167668c5c8fb50e2"
 uuid = "dad2f222-ce93-54a1-a47d-0025e8a3acab"
-version = "0.0.26+0"
+version = "0.0.27+1"
 
 [[deps.LLVMOpenMP_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "f689897ccbe049adb19a065c495e75f372ecd42b"
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "d986ce2d884d49126836ea94ed5bfb0f12679713"
 uuid = "1d63c593-3942-5779-bab2-d838dc0a180e"
-version = "15.0.4+0"
+version = "15.0.7+0"
 
 [[deps.LZO_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -625,9 +638,9 @@ uuid = "dd4b983a-f0e5-5f8d-a1b7-129d4a5fb1ac"
 version = "2.10.1+0"
 
 [[deps.LaTeXStrings]]
-git-tree-sha1 = "f2355693d6778a178ade15952b7ac47a4ff97996"
+git-tree-sha1 = "50901ebc375ed41dbf8058da26f9de442febbbec"
 uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
-version = "1.3.0"
+version = "1.3.1"
 
 [[deps.Latexify]]
 deps = ["Formatting", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Printf", "Requires"]
@@ -650,21 +663,26 @@ uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3"
 [[deps.LibCURL]]
 deps = ["LibCURL_jll", "MozillaCACerts_jll"]
 uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"
-version = "0.6.3"
+version = "0.6.4"
 
 [[deps.LibCURL_jll]]
 deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"]
 uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0"
-version = "7.84.0+0"
+version = "8.4.0+0"
 
 [[deps.LibGit2]]
-deps = ["Base64", "NetworkOptions", "Printf", "SHA"]
+deps = ["Base64", "LibGit2_jll", "NetworkOptions", "Printf", "SHA"]
 uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
 
+[[deps.LibGit2_jll]]
+deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll"]
+uuid = "e37daf67-58a4-590a-8e99-b0245dd2ffc5"
+version = "1.6.4+0"
+
 [[deps.LibSSH2_jll]]
 deps = ["Artifacts", "Libdl", "MbedTLS_jll"]
 uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8"
-version = "1.10.2+0"
+version = "1.11.0+1"
 
 [[deps.Libdl]]
 uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
@@ -723,9 +741,9 @@ uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 
 [[deps.Literate]]
 deps = ["Base64", "IOCapture", "JSON", "REPL"]
-git-tree-sha1 = "ae5703dde29228490f03cbd64c47be8131819485"
+git-tree-sha1 = "bad26f1ccd99c553886ec0725e99a509589dcd11"
 uuid = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
-version = "2.15.0"
+version = "2.16.1"
 
 [[deps.LogExpFunctions]]
 deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"]
@@ -759,30 +777,30 @@ version = "0.4.17"
 
 [[deps.MLUtils]]
 deps = ["ChainRulesCore", "Compat", "DataAPI", "DelimitedFiles", "FLoops", "NNlib", "Random", "ShowCases", "SimpleTraits", "Statistics", "StatsBase", "Tables", "Transducers"]
-git-tree-sha1 = "3504cdb8c2bc05bde4d4b09a81b01df88fcbbba0"
+git-tree-sha1 = "b45738c2e3d0d402dffa32b2c1654759a2ac35a4"
 uuid = "f1d291b0-491e-4a28-83b9-f70985020b54"
-version = "0.4.3"
+version = "0.4.4"
 
 [[deps.MacroTools]]
 deps = ["Markdown", "Random"]
-git-tree-sha1 = "9ee1618cbf5240e6d4e0371d6f24065083f60c48"
+git-tree-sha1 = "2fa9ee3e63fd3a4f7a9a4f4744a52f4856de82df"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
-version = "0.5.11"
+version = "0.5.13"
 
 [[deps.Markdown]]
 deps = ["Base64"]
 uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
 
 [[deps.MbedTLS]]
-deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "Random", "Sockets"]
-git-tree-sha1 = "03a9b9718f5682ecb107ac9f7308991db4ce395b"
+deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "NetworkOptions", "Random", "Sockets"]
+git-tree-sha1 = "c067a280ddc25f196b5e7df3877c6b226d390aaf"
 uuid = "739be429-bea8-5141-9913-cc70e7f3736d"
-version = "1.1.7"
+version = "1.1.9"
 
 [[deps.MbedTLS_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1"
-version = "2.28.2+0"
+version = "2.28.2+1"
 
 [[deps.Measures]]
 git-tree-sha1 = "c13304c81eec1ed3af7fc20e75fb6b26092a1102"
@@ -806,13 +824,13 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804"
 
 [[deps.MozillaCACerts_jll]]
 uuid = "14a3606d-f60d-562e-9121-12d972cd8159"
-version = "2022.10.11"
+version = "2023.1.10"
 
 [[deps.NNlib]]
 deps = ["Adapt", "Atomix", "ChainRulesCore", "GPUArraysCore", "KernelAbstractions", "LinearAlgebra", "Pkg", "Random", "Requires", "Statistics"]
-git-tree-sha1 = "3bc568de99214f72a76c7773ade218819afcc36e"
+git-tree-sha1 = "d2811b435d2f571bdfdfa644bb806a66b458e186"
 uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-version = "0.9.7"
+version = "0.9.11"
 
     [deps.NNlib.extensions]
     NNlibAMDGPUExt = "AMDGPU"
@@ -850,19 +868,19 @@ version = "1.3.5+1"
 
 [[deps.OneHotArrays]]
 deps = ["Adapt", "ChainRulesCore", "Compat", "GPUArraysCore", "LinearAlgebra", "NNlib"]
-git-tree-sha1 = "5e4029759e8699ec12ebdf8721e51a659443403c"
+git-tree-sha1 = "963a3f28a2e65bb87a68033ea4a616002406037d"
 uuid = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f"
-version = "0.2.4"
+version = "0.2.5"
 
 [[deps.OpenBLAS_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"]
 uuid = "4536629a-c528-5b80-bd46-f80d51c5b363"
-version = "0.3.21+4"
+version = "0.3.23+2"
 
 [[deps.OpenLibm_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "05823500-19ac-5b8b-9628-191a04bc5112"
-version = "0.8.1+0"
+version = "0.8.1+2"
 
 [[deps.OpenSSL]]
 deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"]
@@ -872,9 +890,9 @@ version = "1.4.1"
 
 [[deps.OpenSSL_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "a12e56c72edee3ce6b96667745e6cbbe5498f200"
+git-tree-sha1 = "60e3045590bd104a16fefb12836c00c0ef8c7f8c"
 uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95"
-version = "1.1.23+0"
+version = "3.0.13+0"
 
 [[deps.OpenSpecFun_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
@@ -895,32 +913,32 @@ uuid = "91d4177d-7536-5919-b921-800302f37372"
 version = "1.3.2+0"
 
 [[deps.OrderedCollections]]
-git-tree-sha1 = "2e73fe17cac3c62ad1aebe70d44c963c3cfdc3e3"
+git-tree-sha1 = "dfdf5519f235516220579f949664f1bf44e741c5"
 uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
-version = "1.6.2"
+version = "1.6.3"
 
 [[deps.PCRE2_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "efcefdf7-47ab-520b-bdef-62a2eaa19f15"
-version = "10.42.0+0"
+version = "10.42.0+1"
 
 [[deps.PDMats]]
 deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"]
-git-tree-sha1 = "fcf8fd477bd7f33cb8dbb1243653fb0d415c256c"
+git-tree-sha1 = "949347156c25054de2db3b166c52ac4728cbad65"
 uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
-version = "0.11.25"
+version = "0.11.31"
 
 [[deps.ParameterHandling]]
 deps = ["ChainRulesCore", "Compat", "InverseFunctions", "IterTools", "LinearAlgebra", "LogExpFunctions", "SparseArrays", "Test"]
-git-tree-sha1 = "d2a5316cb09c254a51faf96dc60c5a5f7a23ef53"
+git-tree-sha1 = "11bb9d2aaa7113031456cfe8f100e7a587e18ebf"
 uuid = "2412ca09-6db7-441c-8e3a-88d5709968c5"
-version = "0.4.7"
+version = "0.4.10"
 
 [[deps.Parsers]]
 deps = ["Dates", "PrecompileTools", "UUIDs"]
-git-tree-sha1 = "716e24b21538abc91f6205fd1d8363f39b442851"
+git-tree-sha1 = "8489905bcdbcfac64d1daa51ca07c0d8f0283821"
 uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
-version = "2.7.2"
+version = "2.8.1"
 
 [[deps.Pipe]]
 git-tree-sha1 = "6842804e7867b115ca9de748a0cf6b364523c16d"
@@ -936,7 +954,7 @@ version = "0.42.2+0"
 [[deps.Pkg]]
 deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"]
 uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
-version = "1.9.2"
+version = "1.10.0"
 
 [[deps.PlotThemes]]
 deps = ["PlotUtils", "Statistics"]
@@ -946,15 +964,15 @@ version = "3.1.0"
 
 [[deps.PlotUtils]]
 deps = ["ColorSchemes", "Colors", "Dates", "PrecompileTools", "Printf", "Random", "Reexport", "Statistics"]
-git-tree-sha1 = "f92e1315dadf8c46561fb9396e525f7200cdc227"
+git-tree-sha1 = "862942baf5663da528f66d24996eb6da85218e76"
 uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043"
-version = "1.3.5"
+version = "1.4.0"
 
 [[deps.Plots]]
-deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Preferences", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"]
-git-tree-sha1 = "ccee59c6e48e6f2edf8a5b64dc817b6729f99eb5"
+deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"]
+git-tree-sha1 = "c4fa93d7d66acad8f6f4ff439576da9d2e890ee0"
 uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
-version = "1.39.0"
+version = "1.40.1"
 
     [deps.Plots.extensions]
     FileIOExt = "FileIO"
@@ -1003,22 +1021,22 @@ version = "0.1.4"
 
 [[deps.Qt6Base_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "Fontconfig_jll", "Glib_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "OpenSSL_jll", "Vulkan_Loader_jll", "Xorg_libSM_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Xorg_libxcb_jll", "Xorg_xcb_util_cursor_jll", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_keysyms_jll", "Xorg_xcb_util_renderutil_jll", "Xorg_xcb_util_wm_jll", "Zlib_jll", "libinput_jll", "xkbcommon_jll"]
-git-tree-sha1 = "7c29f0e8c575428bd84dc3c72ece5178caa67336"
+git-tree-sha1 = "37b7bb7aabf9a085e0044307e1717436117f2b3b"
 uuid = "c0090381-4147-56d7-9ebc-da0b1113ec56"
-version = "6.5.2+2"
+version = "6.5.3+1"
 
 [[deps.QuadGK]]
 deps = ["DataStructures", "LinearAlgebra"]
-git-tree-sha1 = "9ebcd48c498668c7fa0e97a9cae873fbee7bfee1"
+git-tree-sha1 = "9b23c31e76e333e6fb4c1595ae6afa74966a729e"
 uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
-version = "2.9.1"
+version = "2.9.4"
 
 [[deps.REPL]]
 deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
 uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
 
 [[deps.Random]]
-deps = ["SHA", "Serialization"]
+deps = ["SHA"]
 uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [[deps.RealDot]]
@@ -1046,9 +1064,9 @@ version = "1.2.2"
 
 [[deps.RelocatableFolders]]
 deps = ["SHA", "Scratch"]
-git-tree-sha1 = "90bc7a7c96410424509e4263e277e43250c05691"
+git-tree-sha1 = "ffdaf70d81cf6ff22c2b6e733c900c3321cab864"
 uuid = "05181044-ff0b-4ac5-8273-598c1e38db00"
-version = "1.0.0"
+version = "1.0.1"
 
 [[deps.Requires]]
 deps = ["UUIDs"]
@@ -1074,9 +1092,9 @@ version = "0.7.0"
 
 [[deps.Scratch]]
 deps = ["Dates"]
-git-tree-sha1 = "30449ee12237627992a99d5e30ae63e4d78cd24a"
+git-tree-sha1 = "3bac05bc7e74a75fd9cba4295cde4045d9fe2386"
 uuid = "6c6a2e73-6563-6170-7368-637461726353"
-version = "1.2.0"
+version = "1.2.1"
 
 [[deps.Serialization]]
 uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
@@ -1114,19 +1132,20 @@ uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
 
 [[deps.SortingAlgorithms]]
 deps = ["DataStructures"]
-git-tree-sha1 = "c60ec5c62180f27efea3ba2908480f8055e17cee"
+git-tree-sha1 = "66e0a8e672a0bdfca2c3f5937efb8538b9ddc085"
 uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
-version = "1.1.1"
+version = "1.2.1"
 
 [[deps.SparseArrays]]
 deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"]
 uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+version = "1.10.0"
 
 [[deps.SparseInverseSubset]]
 deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"]
-git-tree-sha1 = "91402087fd5d13b2d97e3ef29bbdf9d7859e678a"
+git-tree-sha1 = "52962839426b75b3021296f7df242e40ecfc0852"
 uuid = "dc90abb0-5640-4711-901d-7e5b23a2fada"
-version = "0.1.1"
+version = "0.1.2"
 
 [[deps.SpecialFunctions]]
 deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
@@ -1145,13 +1164,14 @@ uuid = "171d559e-b47b-412a-8079-5efa626c420e"
 version = "0.1.15"
 
 [[deps.StaticArrays]]
-deps = ["LinearAlgebra", "Random", "StaticArraysCore"]
-git-tree-sha1 = "0adf069a2a490c47273727e029371b31d44b72b2"
+deps = ["LinearAlgebra", "PrecompileTools", "Random", "StaticArraysCore"]
+git-tree-sha1 = "7b0e9c14c624e435076d19aea1e5cbdec2b9ca37"
 uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "1.6.5"
-weakdeps = ["Statistics"]
+version = "1.9.2"
+weakdeps = ["ChainRulesCore", "Statistics"]
 
     [deps.StaticArrays.extensions]
+    StaticArraysChainRulesCoreExt = "ChainRulesCore"
     StaticArraysStatisticsExt = "Statistics"
 
 [[deps.StaticArraysCore]]
@@ -1162,7 +1182,7 @@ version = "1.4.2"
 [[deps.Statistics]]
 deps = ["LinearAlgebra", "SparseArrays"]
 uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-version = "1.9.0"
+version = "1.10.0"
 
 [[deps.StatsAPI]]
 deps = ["LinearAlgebra"]
@@ -1189,18 +1209,18 @@ weakdeps = ["ChainRulesCore", "InverseFunctions"]
 
 [[deps.StructArrays]]
 deps = ["Adapt", "ConstructionBase", "DataAPI", "GPUArraysCore", "StaticArraysCore", "Tables"]
-git-tree-sha1 = "0a3db38e4cce3c54fe7a71f831cd7b6194a54213"
+git-tree-sha1 = "1b0b1205a56dc288b71b1961d48e351520702e24"
 uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
-version = "0.6.16"
+version = "0.6.17"
 
 [[deps.SuiteSparse]]
 deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
 uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
 
 [[deps.SuiteSparse_jll]]
-deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"]
+deps = ["Artifacts", "Libdl", "libblastrampoline_jll"]
 uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c"
-version = "5.10.1+6"
+version = "7.2.1+1"
 
 [[deps.TOML]]
 deps = ["Dates"]
@@ -1215,9 +1235,9 @@ version = "1.0.1"
 
 [[deps.Tables]]
 deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits"]
-git-tree-sha1 = "a1f34829d5ac0ef499f6d84428bd6b4c71f02ead"
+git-tree-sha1 = "cb76cf677714c095e535e3501ac7954732aeea2d"
 uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
-version = "1.11.0"
+version = "1.11.1"
 
 [[deps.Tar]]
 deps = ["ArgTools", "SHA"]
@@ -1235,16 +1255,19 @@ deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [[deps.TranscodingStreams]]
-deps = ["Random", "Test"]
-git-tree-sha1 = "9a6ae7ed916312b41236fcef7e0af564ef934769"
+git-tree-sha1 = "54194d92959d8ebaa8e26227dbe3cdefcdcd594f"
 uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
-version = "0.9.13"
+version = "0.10.3"
+weakdeps = ["Random", "Test"]
+
+    [deps.TranscodingStreams.extensions]
+    TestExt = ["Test", "Random"]
 
 [[deps.Transducers]]
 deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "ConstructionBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"]
-git-tree-sha1 = "53bd5978b182fa7c57577bdb452c35e5b4fb73a5"
+git-tree-sha1 = "3064e780dbb8a9296ebb3af8f440f787bb5332af"
 uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999"
-version = "0.4.78"
+version = "0.4.80"
 
     [deps.Transducers.extensions]
     TransducersBlockArraysExt = "BlockArrays"
@@ -1261,9 +1284,9 @@ version = "0.4.78"
     Referenceables = "42d2dcc6-99eb-4e98-b66c-637b7d73030e"
 
 [[deps.URIs]]
-git-tree-sha1 = "b7a5e99f24892b6824a954199a45e9ffcc1c70f0"
+git-tree-sha1 = "67db6cc7b3821e19ebe75791a9dd19c9b1188f2b"
 uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
-version = "1.5.0"
+version = "1.5.1"
 
 [[deps.UUIDs]]
 deps = ["Random", "SHA"]
@@ -1280,9 +1303,9 @@ version = "0.4.1"
 
 [[deps.Unitful]]
 deps = ["Dates", "LinearAlgebra", "Random"]
-git-tree-sha1 = "a72d22c7e13fe2de562feda8645aa134712a87ee"
+git-tree-sha1 = "3c793be6df9dd77a0cf49d80984ef9ff996948fa"
 uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
-version = "1.17.0"
+version = "1.19.0"
 weakdeps = ["ConstructionBase", "InverseFunctions"]
 
     [deps.Unitful.extensions]
@@ -1325,15 +1348,15 @@ version = "1.21.0+1"
 
 [[deps.Wayland_protocols_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "4528479aa01ee1b3b4cd0e6faef0e04cf16466da"
+git-tree-sha1 = "93f43ab61b16ddfb2fd3bb13b3ce241cafb0e6c9"
 uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91"
-version = "1.25.0+0"
+version = "1.31.0+0"
 
 [[deps.XML2_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"]
-git-tree-sha1 = "24b81b59bd35b3c42ab84fa589086e19be919916"
+git-tree-sha1 = "801cbe47eae69adc50f36c3caec4758d2650741b"
 uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
-version = "2.11.5+0"
+version = "2.12.2+0"
 
 [[deps.XSLT_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
@@ -1343,9 +1366,9 @@ version = "1.1.34+0"
 
 [[deps.XZ_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "cf2c7de82431ca6f39250d2fc4aacd0daa1675c0"
+git-tree-sha1 = "522b8414d40c4cbbab8dee346ac3a09f9768f25d"
 uuid = "ffd25f8a-64ca-5728-b0f7-c24cf3aae800"
-version = "5.4.4+0"
+version = "5.4.5+0"
 
 [[deps.Xorg_libICE_jll]]
 deps = ["Libdl", "Pkg"]
@@ -1494,7 +1517,7 @@ version = "1.5.0+0"
 [[deps.Zlib_jll]]
 deps = ["Libdl"]
 uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
-version = "1.2.13+0"
+version = "1.2.13+1"
 
 [[deps.Zstd_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
@@ -1504,9 +1527,9 @@ version = "1.5.5+0"
 
 [[deps.Zygote]]
 deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "PrecompileTools", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"]
-git-tree-sha1 = "16848c23e7961e099a4152ba0d10db887f412ee9"
+git-tree-sha1 = "4ddb4470e47b0094c93055a3bcae799165cc68f1"
 uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
-version = "0.6.65"
+version = "0.6.69"
 
     [deps.Zygote.extensions]
     ZygoteColorsExt = "Colors"
@@ -1520,9 +1543,9 @@ version = "0.6.65"
 
 [[deps.ZygoteRules]]
 deps = ["ChainRulesCore", "MacroTools"]
-git-tree-sha1 = "977aed5d006b840e2e40c0b48984f7463109046d"
+git-tree-sha1 = "27798139afc0a2afa7b1824c206d5e87ea587a00"
 uuid = "700de1a5-db45-46bc-99cf-38207098b444"
-version = "0.2.3"
+version = "0.2.5"
 
 [[deps.eudev_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "gperf_jll"]
@@ -1531,10 +1554,10 @@ uuid = "35ca27e7-8b34-5b7f-bca9-bdc33f59eb06"
 version = "3.2.9+0"
 
 [[deps.fzf_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "868e669ccb12ba16eaf50cb2957ee2ff61261c56"
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "a68c9655fbe6dfcab3d972808f1aafec151ce3f8"
 uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09"
-version = "0.29.0+0"
+version = "0.43.0+0"
 
 [[deps.gperf_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -1557,7 +1580,7 @@ version = "0.15.1+0"
 [[deps.libblastrampoline_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
-version = "5.8.0+0"
+version = "5.8.0+1"
 
 [[deps.libevdev_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -1578,10 +1601,10 @@ uuid = "36db933b-70db-51c0-b978-0f229ee0e533"
 version = "1.18.0+0"
 
 [[deps.libpng_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
-git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"]
+git-tree-sha1 = "93284c28274d9e75218a416c65ec49d0e0fcdf3d"
 uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
-version = "1.6.38+0"
+version = "1.6.40+0"
 
 [[deps.libvorbis_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
@@ -1598,12 +1621,12 @@ version = "1.1.6+0"
 [[deps.nghttp2_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
-version = "1.48.0+0"
+version = "1.52.0+1"
 
 [[deps.p7zip_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
-version = "17.4.0+0"
+version = "17.4.0+2"
 
 [[deps.x264_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
diff --git a/previews/PR530/examples/train-kernel-parameters/index.html b/previews/PR530/examples/train-kernel-parameters/index.html
index 6a6d77d98..33564b3e1 100644
--- a/previews/PR530/examples/train-kernel-parameters/index.html
+++ b/previews/PR530/examples/train-kernel-parameters/index.html
@@ -17,108 +17,108 @@
 plot!(x_test, sinc; label="true function")

 
 
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Manual Approach
The first option is to rebuild the parametrized kernel from a vector of parameters in each evaluation of the cost function. This is similar to the approach taken in Stheno.jl.
To train the kernel parameters via Zygote.jl, we need to create a function creating a kernel from an array. A simple way to ensure that the kernel parameters are positive is to optimize over the logarithm of the parameters.
function kernel_creator(θ)
     return (exp(θ[1]) * SqExponentialKernel() + exp(θ[2]) * Matern32Kernel()) ∘
            ScaleTransform(exp(θ[3]))
@@ -134,128 +134,128 @@ 

 plot!(x_test, ŷ; label="prediction")

 
 
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We define the following loss:
function loss(θ)
     ŷ = f(x_train, x_train, y_train, θ)
     return norm(y_train - ŷ) + exp(θ[4]) * norm(ŷ)
-end
The loss with our starting point:
loss(θ)
2.613933959118425
Computational cost for one step:
@benchmark let
+end
The loss with our starting point:
loss(θ)
2.613933959118708
Computational cost for one step:
@benchmark let
     θ = log.(p0)
     opt = Optimise.ADAGrad(0.5)
     grads = only((Zygote.gradient(loss, θ)))
     Optimise.update!(opt, θ, grads)
-end
BenchmarkTools.Trial: 3548 samples with 1 evaluation.
- Range (min … max):  1.099 ms …   4.998 ms  ┊ GC (min … max):  0.00% … 44.69%
- Time  (median):     1.235 ms               ┊ GC (median):     0.00%
- Time  (mean ± σ):   1.402 ms ± 626.261 μs  ┊ GC (mean ± σ):  11.18% ± 15.65%
+end
BenchmarkTools.Trial: 5900 samples with 1 evaluation.
+ Range (min … max):  722.880 μs …   4.704 ms  ┊ GC (min … max): 0.00% … 18.77%
+ Time  (median):     782.641 μs               ┊ GC (median):    0.00%
+ Time  (mean ± σ):   844.350 μs ± 226.566 μs  ┊ GC (mean ± σ):  5.48% ± 11.02%
 
-   ▄██▃                                                   ▂▂▁ ▁
-  ▇████▇█▄▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▄▇████ █
-  1.1 ms       Histogram: log(frequency) by time      3.78 ms <
+   ▃██▇▆▅▃▂                                          ▁▁▁▁▁      ▂
+  ▄█████████▇▇▅▅▅▁▃▃▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▃▁▁▃▁▃▁▁▁▁▁▁▁▃▄▆▇████████▇▇▆ █
+  723 μs        Histogram: log(frequency) by time       1.75 ms <
 
- Memory estimate: 2.98 MiB, allocs estimate: 1535.
Training the model
Setting an initial value and initializing the optimizer:
θ = log.(p0) # Initial vector
+ Memory estimate: 2.98 MiB, allocs estimate: 1563.
Training the model
Setting an initial value and initializing the optimizer:
θ = log.(p0) # Initial vector
 opt = Optimise.ADAGrad(0.5)
Optimize
anim = Animation()
 for i in 1:15
     grads = only((Zygote.gradient(loss, θ)))
@@ -267,7 +267,7 @@ 

     plot!(x_test, f(x_test, x_train, y_train, θ); lab="Prediction", lw=3.0)
     frame(anim)
 end
-gif(anim, "train-kernel-param.gif"; show_msg=false, fps=15);
Final loss
loss(θ)
0.524111822806358
Using ParameterHandling.jl
Alternatively, we can use the ParameterHandling.jl package to handle the requirement that all kernel parameters should be positive. The package also allows arbitrarily nesting named tuples that make the parameters more human readable, without having to remember their position in a flat vector.
using ParameterHandling
+gif(anim, "train-kernel-param.gif"; show_msg=false, fps=15);
Final loss
loss(θ)
0.5241118228076058
Using ParameterHandling.jl
Alternatively, we can use the ParameterHandling.jl package to handle the requirement that all kernel parameters should be positive. The package also allows arbitrarily nesting named tuples that make the parameters more human readable, without having to remember their position in a flat vector.
using ParameterHandling
 
 raw_initial_θ = (
     k1=positive(1.1), k2=positive(0.1), k3=positive(0.01), noise_var=positive(0.001)
@@ -292,25 +292,25 @@ 

     return norm(y_train - ŷ) + θ.noise_var * norm(ŷ)
 end
 
-initial_θ = ParameterHandling.value(raw_initial_θ)
The loss at the initial parameter values:
(loss ∘ unflatten)(flat_θ)
2.613933959118425
Cost per step
@benchmark let
+initial_θ = ParameterHandling.value(raw_initial_θ)
The loss at the initial parameter values:
(loss ∘ unflatten)(flat_θ)
2.613933959118708
Cost per step
@benchmark let
     θ = flat_θ[:]
     opt = Optimise.ADAGrad(0.5)
     grads = (Zygote.gradient(loss ∘ unflatten, θ))[1]
     Optimise.update!(opt, θ, grads)
-end
BenchmarkTools.Trial: 3042 samples with 1 evaluation.
- Range (min … max):  1.338 ms …   6.105 ms  ┊ GC (min … max):  0.00% … 47.05%
- Time  (median):     1.424 ms               ┊ GC (median):     0.00%
- Time  (mean ± σ):   1.637 ms ± 787.405 μs  ┊ GC (mean ± σ):  12.40% ± 16.47%
+end
BenchmarkTools.Trial: 4901 samples with 1 evaluation.
+ Range (min … max):  882.109 μs …   4.975 ms  ┊ GC (min … max): 0.00% … 21.56%
+ Time  (median):     963.350 μs               ┊ GC (median):    0.00%
+ Time  (mean ± σ):     1.017 ms ± 247.795 μs  ┊ GC (mean ± σ):  4.88% ± 10.50%
 
-  ▄█▅                                                     ▁▂▁  
-  ███▇▁▃▁▃▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▆███ █
-  1.34 ms      Histogram: log(frequency) by time      4.56 ms <
+  ▄▇▆██▇▅▃▁                                               ▁▁▁▁  ▂
+  ██████████▇▆▅▄▅▄▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▄▅▆███████ █
+  882 μs        Histogram: log(frequency) by time        2.1 ms <
 
- Memory estimate: 3.06 MiB, allocs estimate: 2215.
Training the model
Optimize
opt = Optimise.ADAGrad(0.5)
+ Memory estimate: 3.08 MiB, allocs estimate: 2228.
Training the model
Optimize
opt = Optimise.ADAGrad(0.5)
 for i in 1:15
     grads = (Zygote.gradient(loss ∘ unflatten, flat_θ))[1]
     Optimise.update!(opt, flat_θ, grads)
-end
Final loss
(loss ∘ unflatten)(flat_θ)
0.5241176241222036
Flux.destructure
If we don't want to write an explicit function to construct the kernel, we can alternatively use the Flux.destructure function. Again, we need to ensure that the parameters are positive. Note that the exp function is now part of the loss function, instead of part of the kernel construction.
We could also use ParameterHandling.jl here. To do so, one would remove the exps from the loss function below and call loss ∘ unflatten as above.
θ = [1.1, 0.1, 0.01, 0.001]
+end
Final loss
(loss ∘ unflatten)(flat_θ)
0.524117624126251
Flux.destructure
If we don't want to write an explicit function to construct the kernel, we can alternatively use the Flux.destructure function. Again, we need to ensure that the parameters are positive. Note that the exp function is now part of the loss function, instead of part of the kernel construction.
We could also use ParameterHandling.jl here. To do so, one would remove the exps from the loss function below and call loss ∘ unflatten as above.
θ = [1.1, 0.1, 0.01, 0.001]
 
 kernel = (θ[1] * SqExponentialKernel() + θ[2] * Matern32Kernel()) ∘ ScaleTransform(θ[3])
 
@@ -330,34 +330,34 @@ 

 end
Cost for one step
@benchmark let θt = θ[:], optt = Optimise.ADAGrad(0.5)
     grads = only((Zygote.gradient(loss, θt)))
     Optimise.update!(optt, θt, grads)
-end
BenchmarkTools.Trial: 3415 samples with 1 evaluation.
- Range (min … max):  1.155 ms …   6.451 ms  ┊ GC (min … max):  0.00% … 45.45%
- Time  (median):     1.240 ms               ┊ GC (median):     0.00%
- Time  (mean ± σ):   1.457 ms ± 807.545 μs  ┊ GC (mean ± σ):  14.23% ± 17.31%
+end
BenchmarkTools.Trial: 6044 samples with 1 evaluation.
+ Range (min … max):  709.496 μs …   3.264 ms  ┊ GC (min … max): 0.00% … 33.73%
+ Time  (median):     779.306 μs               ┊ GC (median):    0.00%
+ Time  (mean ± σ):   824.141 μs ± 220.509 μs  ┊ GC (mean ± σ):  4.92% ± 10.45%
 
-  ▅█▄                                                     ▁▂▁  
-  ████▅▁▄▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▇███ █
-  1.15 ms      Histogram: log(frequency) by time       4.5 ms <
+  ▄▆▆█▇▅▄▂                                                ▁ ▁   ▂
+  ████████▇▆▆▆▆▅▃▃▁▅▆▄▄▄▃▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▄▅▇██████ █
+  709 μs        Histogram: log(frequency) by time       1.88 ms <
 
- Memory estimate: 2.98 MiB, allocs estimate: 1556.
Training the model
The loss at our initial parameter values:
θ = log.([1.1, 0.1, 0.01, 0.001]) # Initial vector
-loss(θ)
2.613933959118425
Initialize optimizer
opt = Optimise.ADAGrad(0.5)
Optimize
for i in 1:15
+ Memory estimate: 2.98 MiB, allocs estimate: 1558.
Training the model
The loss at our initial parameter values:
θ = log.([1.1, 0.1, 0.01, 0.001]) # Initial vector
+loss(θ)
2.613933959118708
Initialize optimizer
opt = Optimise.ADAGrad(0.5)
Optimize
for i in 1:15
     grads = only((Zygote.gradient(loss, θ)))
     Optimise.update!(opt, θ, grads)
-end
Final loss
loss(θ)
0.524111822806358

+end

Final loss

loss(θ)
0.5241118228076058
Package and system information
Package information (click to expand) 
 Status `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/train-kernel-parameters/Project.toml`
-  [6e4b80f9] BenchmarkTools v1.3.2
-  [31c24e10] Distributions v0.25.102
-  [587475ba] Flux v0.14.6
+  [6e4b80f9] BenchmarkTools v1.4.0
+  [31c24e10] Distributions v0.25.107
+  [587475ba] Flux v0.14.11
   [f6369f11] ForwardDiff v0.10.36
-  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`
-  [98b081ad] Literate v2.15.0
-  [2412ca09] ParameterHandling v0.4.7
-  [91a5bcdd] Plots v1.39.0
-  [e88e6eb3] Zygote v0.6.65
+  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`
+  [98b081ad] Literate v2.16.1
+  [2412ca09] ParameterHandling v0.4.10
+  [91a5bcdd] Plots v1.40.1
+  [e88e6eb3] Zygote v0.6.69
   [37e2e46d] LinearAlgebra
To reproduce this notebook's package environment, you can @@ -367,19 +367,19 @@
Package and system information
System information (click to expand) 
-Julia Version 1.9.3
-Commit bed2cd540a1 (2023-08-24 14:43 UTC)
+Julia Version 1.10.0
+Commit 3120989f39b (2023-12-25 18:01 UTC)
 Build Info:
   Official https://julialang.org/ release
 Platform Info:
   OS: Linux (x86_64-linux-gnu)
-  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz
+  CPU: 4 × AMD EPYC 7763 64-Core Processor
   WORD_SIZE: 64
   LIBM: libopenlibm
-  LLVM: libLLVM-14.0.6 (ORCJIT, skylake-avx512)
-  Threads: 1 on 2 virtual cores
+  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
+  Threads: 1 on 4 virtual cores
 Environment:
   JULIA_DEBUG = Documenter
   JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src
-

This page was generated using Literate.jl.

+

This page was generated using Literate.jl.

diff --git a/previews/PR530/examples/train-kernel-parameters/notebook.ipynb b/previews/PR530/examples/train-kernel-parameters/notebook.ipynb index c2429685b..8b628861b 100644 --- a/previews/PR530/examples/train-kernel-parameters/notebook.ipynb +++ b/previews/PR530/examples/train-kernel-parameters/notebook.ipynb @@ -87,215 +87,215 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ], "image/svg+xml": [ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, "metadata": {}, @@ -385,219 +385,219 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ], "image/svg+xml": [ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", + "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, "metadata": {}, @@ -648,7 +648,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "2.613933959118425" + "text/plain": "2.613933959118708" }, "metadata": {}, "execution_count": 8 @@ -673,7 +673,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "BenchmarkTools.Trial: 3471 samples with 1 evaluation.\n Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m1.108 ms\u001b[22m\u001b[39m … \u001b[35m 5.741 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m 0.00% … 52.33%\n Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m1.221 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m 0.00%\n Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m1.434 ms\u001b[22m\u001b[39m ± \u001b[32m803.051 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m14.37% ± 17.36%\n\n \u001b[39m▃\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m▁\u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m 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MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m1531\u001b[39m." + "text/plain": "BenchmarkTools.Trial: 6048 samples with 1 evaluation.\n Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m716.709 μs\u001b[22m\u001b[39m … \u001b[35m 5.133 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 18.71%\n Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m783.639 μs \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m823.759 μs\u001b[22m\u001b[39m ± \u001b[32m221.399 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m4.47% ± 10.06%\n\n 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\u001b[39m\u001b[33m2.98 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m1559\u001b[39m." }, "metadata": {}, "execution_count": 9 @@ -763,7 +763,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "0.524111822806358" + "text/plain": "0.5241118228076058" }, "metadata": {}, "execution_count": 12 @@ -859,7 +859,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "2.613933959118425" + "text/plain": "2.613933959118708" }, "metadata": {}, "execution_count": 15 @@ -884,7 +884,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "BenchmarkTools.Trial: 2981 samples with 1 evaluation.\n Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m1.314 ms\u001b[22m\u001b[39m … \u001b[35m 6.267 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m 0.00% … 51.85%\n Time 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\u001b[39m\u001b[33m3.08 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m2228\u001b[39m." }, "metadata": {}, "execution_count": 16 @@ -942,7 +942,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "0.5241176241222036" + "text/plain": "0.524117624126251" }, "metadata": {}, "execution_count": 18 @@ -1049,7 +1049,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "BenchmarkTools.Trial: 3398 samples with 1 evaluation.\n Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m1.094 ms\u001b[22m\u001b[39m … \u001b[35m 6.320 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m 0.00% … 50.88%\n Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m1.236 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m 0.00%\n Time 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MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m1556\u001b[39m." + "text/plain": "BenchmarkTools.Trial: 6236 samples with 1 evaluation.\n Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m701.350 μs\u001b[22m\u001b[39m … \u001b[35m 2.647 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 54.18%\n Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m760.515 μs \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m798.808 μs\u001b[22m\u001b[39m ± \u001b[32m211.152 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m4.20% ± 9.62%\n\n 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\u001b[39m\u001b[33m2.98 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m1558\u001b[39m." }, "metadata": {}, "execution_count": 22 @@ -1084,7 +1084,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "2.613933959118425" + "text/plain": "2.613933959118708" }, "metadata": {}, "execution_count": 23 @@ -1147,7 +1147,7 @@ { "output_type": "execute_result", "data": { - "text/plain": "0.524111822806358" + "text/plain": "0.5241118228076058" }, "metadata": {}, "execution_count": 26 @@ -1169,15 +1169,15 @@ "Package information (click to expand)\n", "
\n",
     "Status `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/train-kernel-parameters/Project.toml`\n",
-    "  [6e4b80f9] BenchmarkTools v1.3.2\n",
-    "  [31c24e10] Distributions v0.25.102\n",
-    "  [587475ba] Flux v0.14.6\n",
+    "  [6e4b80f9] BenchmarkTools v1.4.0\n",
+    "  [31c24e10] Distributions v0.25.107\n",
+    "  [587475ba] Flux v0.14.11\n",
     "  [f6369f11] ForwardDiff v0.10.36\n",
-    "  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`\n",
-    "  [98b081ad] Literate v2.15.0\n",
-    "  [2412ca09] ParameterHandling v0.4.7\n",
-    "  [91a5bcdd] Plots v1.39.0\n",
-    "  [e88e6eb3] Zygote v0.6.65\n",
+    "  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`\n",
+    "  [98b081ad] Literate v2.16.1\n",
+    "  [2412ca09] ParameterHandling v0.4.10\n",
+    "  [91a5bcdd] Plots v1.40.1\n",
+    "  [e88e6eb3] Zygote v0.6.69\n",
     "  [37e2e46d] LinearAlgebra\n",
     "
\n", "To reproduce this notebook's package environment, you can\n", @@ -1187,17 +1187,17 @@ "
\n", "System information (click to expand)\n", "
\n",
-    "Julia Version 1.9.3\n",
-    "Commit bed2cd540a1 (2023-08-24 14:43 UTC)\n",
+    "Julia Version 1.10.0\n",
+    "Commit 3120989f39b (2023-12-25 18:01 UTC)\n",
     "Build Info:\n",
     "  Official https://julialang.org/ release\n",
     "Platform Info:\n",
     "  OS: Linux (x86_64-linux-gnu)\n",
-    "  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz\n",
+    "  CPU: 4 × AMD EPYC 7763 64-Core Processor\n",
     "  WORD_SIZE: 64\n",
     "  LIBM: libopenlibm\n",
-    "  LLVM: libLLVM-14.0.6 (ORCJIT, skylake-avx512)\n",
-    "  Threads: 1 on 2 virtual cores\n",
+    "  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\n",
+    "  Threads: 1 on 4 virtual cores\n",
     "Environment:\n",
     "  JULIA_DEBUG = Documenter\n",
     "  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n",
@@ -1222,11 +1222,11 @@
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.9.3"
+   "version": "1.10.0"
   },
   "kernelspec": {
-   "name": "julia-1.9",
-   "display_name": "Julia 1.9.3",
+   "name": "julia-1.10",
+   "display_name": "Julia 1.10.0",
    "language": "julia"
   }
  },
diff --git a/previews/PR530/index.html b/previews/PR530/index.html
index e7e585b30..2fae043fe 100644
--- a/previews/PR530/index.html
+++ b/previews/PR530/index.html
@@ -1,2 +1,2 @@
 
-Home · KernelFunctions.jl
Edit on GitHub
KernelFunctions.jl
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.jl should be.
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 User guide for a brief introduction.

+Home · KernelFunctions.jl
Edit on GitHub
KernelFunctions.jl
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.jl should be.
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 User guide for a brief introduction.

diff --git a/previews/PR530/kernels/index.html b/previews/PR530/kernels/index.html
index 44fc5b253..d64a01e9e 100644
--- a/previews/PR530/kernels/index.html
+++ b/previews/PR530/kernels/index.html
@@ -1,20 +1,20 @@
 
-Kernel Functions · KernelFunctions.jl
Edit on GitHub
Kernel Functions
Base Kernels
These are the basic kernels without any transformation of the data. They are the building blocks of KernelFunctions.
Constant Kernels
KernelFunctions.WhiteKernel — Type
WhiteKernel()
White noise kernel.
Definition
For inputs $x, x'$, the white noise kernel is defined as
\[k(x, x') = \delta(x, x').\]
source
Cosine Kernel
KernelFunctions.CosineKernel — Type
CosineKernel(; metric=Euclidean())
Cosine kernel with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the cosine kernel is defined as
\[k(x, x') = \cos(\pi d(x, x')).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
source
Exponential Kernels
KernelFunctions.ExponentialKernel — Type
ExponentialKernel(; metric=Euclidean())
Exponential kernel with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the exponential kernel is defined as
\[k(x, x') = \exp\big(- d(x, x')\big).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
See also: GammaExponentialKernel
source
KernelFunctions.GibbsKernel — Type
GibbsKernel(; lengthscale)
Gibbs Kernel with lengthscale function lengthscale.
The Gibbs kernel is a non-stationary generalisation of the squared exponential kernel. The lengthscale parameter $l$ becomes a function of position $l(x)$.
Definition
For inputs $x, x'$, the Gibbs kernel with lengthscale function $l(\cdot)$ is defined as
\[k(x, x'; l) = \sqrt{\left(\frac{2 l(x) l(x')}{l(x)^2 + l(x')^2}\right)}
-\quad \exp{\left(-\frac{(x - x')^2}{l(x)^2 + l(x')^2}\right)}.\]
For a constant function $l \equiv c$, one recovers the SqExponentialKernel with lengthscale c.
References
Mark N. Gibbs. "Bayesian Gaussian Processes for Regression and Classication." PhD thesis, 1997
Christopher J. Paciorek and Mark J. Schervish. "Nonstationary Covariance Functions for Gaussian Process Regression". NeurIPS, 2003
Sami Remes, Markus Heinonen, Samuel Kaski. "Non-Stationary Spectral Kernels". arXiV:1705.08736, 2017
Sami Remes, Markus Heinonen, Samuel Kaski. "Neural Non-Stationary Spectral Kernel". arXiv:1811.10978, 2018
source
KernelFunctions.SqExponentialKernel — Type
SqExponentialKernel(; metric=Euclidean())
Squared exponential kernel with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the squared exponential kernel is defined as
\[k(x, x') = \exp\bigg(- \frac{d(x, x')^2}{2}\bigg).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
See also: GammaExponentialKernel
source
KernelFunctions.GammaExponentialKernel — Type
GammaExponentialKernel(; γ::Real=1.0, metric=Euclidean())
γ-exponential kernel with respect to the metric and with parameter γ.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the γ-exponential kernel[RW] with parameter $\gamma \in (0, 2]$ is defined as
\[k(x, x'; \gamma) = \exp\big(- d(x, x')^{\gamma}\big).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
See also: ExponentialKernel, SqExponentialKernel
source
Exponentiated Kernel
KernelFunctions.ExponentiatedKernel — Type
ExponentiatedKernel()
Exponentiated kernel.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the exponentiated kernel is defined as
\[k(x, x') = \exp(x^\top x').\]
source
Fractional Brownian Motion Kernel
KernelFunctions.FBMKernel — Type
FBMKernel(; h::Real=0.5)
Fractional Brownian motion kernel with Hurst index h.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the fractional Brownian motion kernel with Hurst index $h \in [0,1]$ is defined as
\[k(x, x'; h) =  \frac{\|x\|_2^{2h} + \|x'\|_2^{2h} - \|x - x'\|^{2h}}{2}.\]
source
Gabor Kernel
KernelFunctions.gaborkernel — Function
gaborkernel(;
+Kernel Functions · KernelFunctions.jl
Edit on GitHub
Kernel Functions
Base Kernels
These are the basic kernels without any transformation of the data. They are the building blocks of KernelFunctions.
Constant Kernels
KernelFunctions.WhiteKernel — Type
WhiteKernel()
White noise kernel.
Definition
For inputs $x, x'$, the white noise kernel is defined as
\[k(x, x') = \delta(x, x').\]
source
Cosine Kernel
KernelFunctions.CosineKernel — Type
CosineKernel(; metric=Euclidean())
Cosine kernel with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the cosine kernel is defined as
\[k(x, x') = \cos(\pi d(x, x')).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
source
Exponential Kernels
KernelFunctions.ExponentialKernel — Type
ExponentialKernel(; metric=Euclidean())
Exponential kernel with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the exponential kernel is defined as
\[k(x, x') = \exp\big(- d(x, x')\big).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
See also: GammaExponentialKernel
source
KernelFunctions.GibbsKernel — Type
GibbsKernel(; lengthscale)
Gibbs Kernel with lengthscale function lengthscale.
The Gibbs kernel is a non-stationary generalisation of the squared exponential kernel. The lengthscale parameter $l$ becomes a function of position $l(x)$.
Definition
For inputs $x, x'$, the Gibbs kernel with lengthscale function $l(\cdot)$ is defined as
\[k(x, x'; l) = \sqrt{\left(\frac{2 l(x) l(x')}{l(x)^2 + l(x')^2}\right)}
+\quad \exp{\left(-\frac{(x - x')^2}{l(x)^2 + l(x')^2}\right)}.\]
For a constant function $l \equiv c$, one recovers the SqExponentialKernel with lengthscale c.
References
Mark N. Gibbs. "Bayesian Gaussian Processes for Regression and Classication." PhD thesis, 1997
Christopher J. Paciorek and Mark J. Schervish. "Nonstationary Covariance Functions for Gaussian Process Regression". NeurIPS, 2003
Sami Remes, Markus Heinonen, Samuel Kaski. "Non-Stationary Spectral Kernels". arXiV:1705.08736, 2017
Sami Remes, Markus Heinonen, Samuel Kaski. "Neural Non-Stationary Spectral Kernel". arXiv:1811.10978, 2018
source
KernelFunctions.SqExponentialKernel — Type
SqExponentialKernel(; metric=Euclidean())
Squared exponential kernel with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the squared exponential kernel is defined as
\[k(x, x') = \exp\bigg(- \frac{d(x, x')^2}{2}\bigg).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
See also: GammaExponentialKernel
source
KernelFunctions.GammaExponentialKernel — Type
GammaExponentialKernel(; γ::Real=1.0, metric=Euclidean())
γ-exponential kernel with respect to the metric and with parameter γ.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the γ-exponential kernel[RW] with parameter $\gamma \in (0, 2]$ is defined as
\[k(x, x'; \gamma) = \exp\big(- d(x, x')^{\gamma}\big).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
See also: ExponentialKernel, SqExponentialKernel
source
Exponentiated Kernel
KernelFunctions.ExponentiatedKernel — Type
ExponentiatedKernel()
Exponentiated kernel.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the exponentiated kernel is defined as
\[k(x, x') = \exp(x^\top x').\]
source
Fractional Brownian Motion Kernel
KernelFunctions.FBMKernel — Type
FBMKernel(; h::Real=0.5)
Fractional Brownian motion kernel with Hurst index h.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the fractional Brownian motion kernel with Hurst index $h \in [0,1]$ is defined as
\[k(x, x'; h) =  \frac{\|x\|_2^{2h} + \|x'\|_2^{2h} - \|x - x'\|^{2h}}{2}.\]
source
Gabor Kernel
KernelFunctions.gaborkernel — Function
gaborkernel(;
     sqexponential_transform=IdentityTransform(), cosine_tranform=IdentityTransform()
 )
Construct a Gabor kernel with transformations sqexponential_transform and cosine_transform of the inputs of the underlying squared exponential and cosine kernel, respectively.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the Gabor kernel with transformations $f$ and $g$ of the inputs to the squared exponential and cosine kernel, respectively, is defined as
\[k(x, x'; f, g) = \exp\bigg(- \frac{\| f(x) - f(x')\|_2^2}{2}\bigg)
-                 \cos\big(\pi \|g(x) - g(x')\|_2 \big).\]
source
Matérn Kernels
KernelFunctions.MaternKernel — Type
MaternKernel(; ν::Real=1.5, metric=Euclidean())
Matérn kernel of order ν with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the Matérn kernel of order $\nu > 0$ is defined as
\[k(x,x';\nu) = \frac{2^{1-\nu}}{\Gamma(\nu)}\big(\sqrt{2\nu} d(x, x')\big) K_\nu\big(\sqrt{2\nu} d(x, x')\big),\]
where $\Gamma$ is the Gamma function and $K_{\nu}$ is the modified Bessel function of the second kind of order $\nu$. By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
A Gaussian process with a Matérn kernel is $\lceil \nu \rceil - 1$-times differentiable in the mean-square sense.
Note
Differentiation with respect to the order ν is not currently supported.
See also: Matern12Kernel, Matern32Kernel, Matern52Kernel
source
KernelFunctions.Matern32Kernel — Type
Matern32Kernel(; metric=Euclidean())
Matérn kernel of order $3/2$ with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the Matérn kernel of order $3/2$ is  given by
\[k(x, x') = \big(1 + \sqrt{3} d(x, x') \big) \exp\big(- \sqrt{3} d(x, x') \big).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
See also: MaternKernel
source
KernelFunctions.Matern52Kernel — Type
Matern52Kernel(; metric=Euclidean())
Matérn kernel of order $5/2$ with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the Matérn kernel of order $5/2$ is given by
\[k(x, x') = \bigg(1 + \sqrt{5} d(x, x') + \frac{5}{3} d(x, x')^2\bigg)
-           \exp\big(- \sqrt{5} d(x, x') \big).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
See also: MaternKernel
source
Neural Network Kernel
KernelFunctions.NeuralNetworkKernel — Type
NeuralNetworkKernel()
Kernel of a Gaussian process obtained as the limit of a Bayesian neural network with a single hidden layer as the number of units goes to infinity.
Definition
Consider the single-layer Bayesian neural network $f \colon \mathbb{R}^d \to \mathbb{R}$ with $h$ hidden units defined by
\[f(x; b, v, u) = b + \sqrt{\frac{\pi}{2}} \sum_{i=1}^{h} v_i \mathrm{erf}\big(u_i^\top x\big),\]
where $\mathrm{erf}$ is the error function, and with prior distributions
\[\begin{aligned}
+                 \cos\big(\pi \|g(x) - g(x')\|_2 \big).\]
source
Matérn Kernels
KernelFunctions.MaternKernel — Type
MaternKernel(; ν::Real=1.5, metric=Euclidean())
Matérn kernel of order ν with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the Matérn kernel of order $\nu > 0$ is defined as
\[k(x,x';\nu) = \frac{2^{1-\nu}}{\Gamma(\nu)}\big(\sqrt{2\nu} d(x, x')\big) K_\nu\big(\sqrt{2\nu} d(x, x')\big),\]
where $\Gamma$ is the Gamma function and $K_{\nu}$ is the modified Bessel function of the second kind of order $\nu$. By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
A Gaussian process with a Matérn kernel is $\lceil \nu \rceil - 1$-times differentiable in the mean-square sense.
Note
Differentiation with respect to the order ν is not currently supported.
See also: Matern12Kernel, Matern32Kernel, Matern52Kernel
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KernelFunctions.Matern32Kernel — Type
Matern32Kernel(; metric=Euclidean())
Matérn kernel of order $3/2$ with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the Matérn kernel of order $3/2$ is  given by
\[k(x, x') = \big(1 + \sqrt{3} d(x, x') \big) \exp\big(- \sqrt{3} d(x, x') \big).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
See also: MaternKernel
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KernelFunctions.Matern52Kernel — Type
Matern52Kernel(; metric=Euclidean())
Matérn kernel of order $5/2$ with respect to the metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the Matérn kernel of order $5/2$ is given by
\[k(x, x') = \bigg(1 + \sqrt{5} d(x, x') + \frac{5}{3} d(x, x')^2\bigg)
+           \exp\big(- \sqrt{5} d(x, x') \big).\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
See also: MaternKernel
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Neural Network Kernel
KernelFunctions.NeuralNetworkKernel — Type
NeuralNetworkKernel()
Kernel of a Gaussian process obtained as the limit of a Bayesian neural network with a single hidden layer as the number of units goes to infinity.
Definition
Consider the single-layer Bayesian neural network $f \colon \mathbb{R}^d \to \mathbb{R}$ with $h$ hidden units defined by
\[f(x; b, v, u) = b + \sqrt{\frac{\pi}{2}} \sum_{i=1}^{h} v_i \mathrm{erf}\big(u_i^\top x\big),\]
where $\mathrm{erf}$ is the error function, and with prior distributions
\[\begin{aligned}
 b &\sim \mathcal{N}(0, \sigma_b^2),\\
 v &\sim \mathcal{N}(0, \sigma_v^2 \mathrm{I}_{h}/h),\\
 u_i &\sim \mathcal{N}(0, \mathrm{I}_{d}/2) \qquad (i = 1,\ldots,h).
-\end{aligned}\]
As $h \to \infty$, the neural network converges to the Gaussian process
\[g(\cdot) \sim \mathcal{GP}\big(0, \sigma_b^2 + \sigma_v^2 k(\cdot, \cdot)\big),\]
where the neural network kernel $k$ is given by
\[k(x, x') = \arcsin\left(\frac{x^\top x'}{\sqrt{\big(1 + \|x\|^2_2\big) \big(1 + \|x'\|_2^2\big)}}\right)\]
for inputs $x, x' \in \mathbb{R}^d$.[CW]
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Periodic Kernel
KernelFunctions.PeriodicKernel — Type
PeriodicKernel(; r::AbstractVector=ones(Float64, 1))
Periodic kernel with parameter r.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the periodic kernel with parameter $r_i > 0$ is defined[DM] as
\[k(x, x'; r) = \exp\bigg(- \frac{1}{2} \sum_{i=1}^d \bigg(\frac{\sin\big(\pi(x_i - x'_i)\big)}{r_i}\bigg)^2\bigg).\]
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Piecewise Polynomial Kernel
KernelFunctions.PiecewisePolynomialKernel — Type
PiecewisePolynomialKernel(; dim::Int, degree::Int=0, metric=Euclidean())
+\end{aligned}\]
As $h \to \infty$, the neural network converges to the Gaussian process
\[g(\cdot) \sim \mathcal{GP}\big(0, \sigma_b^2 + \sigma_v^2 k(\cdot, \cdot)\big),\]
where the neural network kernel $k$ is given by
\[k(x, x') = \arcsin\left(\frac{x^\top x'}{\sqrt{\big(1 + \|x\|^2_2\big) \big(1 + \|x'\|_2^2\big)}}\right)\]
for inputs $x, x' \in \mathbb{R}^d$.[CW]
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Periodic Kernel
KernelFunctions.PeriodicKernel — Type
PeriodicKernel(; r::AbstractVector=ones(Float64, 1))
Periodic kernel with parameter r.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the periodic kernel with parameter $r_i > 0$ is defined[DM] as
\[k(x, x'; r) = \exp\bigg(- \frac{1}{2} \sum_{i=1}^d \bigg(\frac{\sin\big(\pi(x_i - x'_i)\big)}{r_i}\bigg)^2\bigg).\]
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Piecewise Polynomial Kernel
KernelFunctions.PiecewisePolynomialKernel — Type
PiecewisePolynomialKernel(; dim::Int, degree::Int=0, metric=Euclidean())
 PiecewisePolynomialKernel{degree}(; dim::Int, metric=Euclidean())
Piecewise polynomial kernel of degree degree for inputs of dimension dim with support in the unit ball with respect to the metric.
Definition
For inputs $x, x'$ of dimension $m$ and metric $d(\cdot, \cdot)$, the piecewise polynomial kernel of degree $v \in \{0,1,2,3\}$ is defined as
\[k(x, x'; v) = \max(1 - d(x, x'), 0)^{\alpha(v,m)} f_{v,m}(d(x, x')),\]
where $\alpha(v, m) = \lfloor \frac{m}{2}\rfloor + 2v + 1$ and $f_{v,m}$ are polynomials of degree $v$ given by
\[\begin{aligned}
 f_{0,m}(r) &= 1, \\
 f_{1,m}(r) &= 1 + (j + 1) r, \\
 f_{2,m}(r) &= 1 + (j + 2) r + \big((j^2 + 4j + 3) / 3\big) r^2, \\
 f_{3,m}(r) &= 1 + (j + 3) r + \big((6 j^2 + 36j + 45) / 15\big) r^2 + \big((j^3 + 9 j^2 + 23j + 15) / 15\big) r^3,
-\end{aligned}\]
where $j = \lfloor \frac{m}{2}\rfloor + v + 1$. By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
The kernel is $2v$ times continuously differentiable and the corresponding Gaussian process is hence $v$ times mean-square differentiable.
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Polynomial Kernels
KernelFunctions.LinearKernel — Type
LinearKernel(; c::Real=0.0)
Linear kernel with constant offset c.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the linear kernel with constant offset $c \geq 0$ is defined as
\[k(x, x'; c) = x^\top x' + c.\]
See also: PolynomialKernel
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KernelFunctions.PolynomialKernel — Type
PolynomialKernel(; degree::Int=2, c::Real=0.0)
Polynomial kernel of degree degree with constant offset c.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the polynomial kernel of degree $\nu \in \mathbb{N}$ with constant offset $c \geq 0$ is defined as
\[k(x, x'; c, \nu) = (x^\top x' + c)^\nu.\]
See also: LinearKernel
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Rational Kernels
KernelFunctions.RationalKernel — Type
RationalKernel(; α::Real=2.0, metric=Euclidean())
Rational kernel with shape parameter α and given metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the rational kernel with shape parameter $\alpha > 0$ is defined as
\[k(x, x'; \alpha) = \bigg(1 + \frac{d(x, x')}{\alpha}\bigg)^{-\alpha}.\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
The ExponentialKernel is recovered in the limit as $\alpha \to \infty$.
See also: GammaRationalKernel
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KernelFunctions.RationalQuadraticKernel — Type
RationalQuadraticKernel(; α::Real=2.0, metric=Euclidean())
Rational-quadratic kernel with respect to the metric and with shape parameter α.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the rational-quadratic kernel with shape parameter $\alpha > 0$ is defined as
\[k(x, x'; \alpha) = \bigg(1 + \frac{d(x, x')^2}{2\alpha}\bigg)^{-\alpha}.\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
The SqExponentialKernel is recovered in the limit as $\alpha \to \infty$.
See also: GammaRationalKernel
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KernelFunctions.GammaRationalKernel — Type
GammaRationalKernel(; α::Real=2.0, γ::Real=1.0, metric=Euclidean())
γ-rational kernel with respect to the metric with shape parameters α and γ.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the γ-rational kernel with shape parameters $\alpha > 0$ and $\gamma \in (0, 2]$ is defined as
\[k(x, x'; \alpha, \gamma) = \bigg(1 + \frac{d(x, x')^{\gamma}}{\alpha}\bigg)^{-\alpha}.\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
The GammaExponentialKernel is recovered in the limit as $\alpha \to \infty$.
See also: RationalKernel, RationalQuadraticKernel
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Spectral Mixture Kernels
KernelFunctions.spectral_mixture_kernel — Function
spectral_mixture_kernel(
+\end{aligned}\]
where $j = \lfloor \frac{m}{2}\rfloor + v + 1$. By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
The kernel is $2v$ times continuously differentiable and the corresponding Gaussian process is hence $v$ times mean-square differentiable.
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Polynomial Kernels
KernelFunctions.LinearKernel — Type
LinearKernel(; c::Real=0.0)
Linear kernel with constant offset c.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the linear kernel with constant offset $c \geq 0$ is defined as
\[k(x, x'; c) = x^\top x' + c.\]
See also: PolynomialKernel
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KernelFunctions.PolynomialKernel — Type
PolynomialKernel(; degree::Int=2, c::Real=0.0)
Polynomial kernel of degree degree with constant offset c.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the polynomial kernel of degree $\nu \in \mathbb{N}$ with constant offset $c \geq 0$ is defined as
\[k(x, x'; c, \nu) = (x^\top x' + c)^\nu.\]
See also: LinearKernel
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Rational Kernels
KernelFunctions.RationalKernel — Type
RationalKernel(; α::Real=2.0, metric=Euclidean())
Rational kernel with shape parameter α and given metric.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the rational kernel with shape parameter $\alpha > 0$ is defined as
\[k(x, x'; \alpha) = \bigg(1 + \frac{d(x, x')}{\alpha}\bigg)^{-\alpha}.\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
The ExponentialKernel is recovered in the limit as $\alpha \to \infty$.
See also: GammaRationalKernel
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KernelFunctions.RationalQuadraticKernel — Type
RationalQuadraticKernel(; α::Real=2.0, metric=Euclidean())
Rational-quadratic kernel with respect to the metric and with shape parameter α.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the rational-quadratic kernel with shape parameter $\alpha > 0$ is defined as
\[k(x, x'; \alpha) = \bigg(1 + \frac{d(x, x')^2}{2\alpha}\bigg)^{-\alpha}.\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
The SqExponentialKernel is recovered in the limit as $\alpha \to \infty$.
See also: GammaRationalKernel
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KernelFunctions.GammaRationalKernel — Type
GammaRationalKernel(; α::Real=2.0, γ::Real=1.0, metric=Euclidean())
γ-rational kernel with respect to the metric with shape parameters α and γ.
Definition
For inputs $x, x'$ and metric $d(\cdot, \cdot)$, the γ-rational kernel with shape parameters $\alpha > 0$ and $\gamma \in (0, 2]$ is defined as
\[k(x, x'; \alpha, \gamma) = \bigg(1 + \frac{d(x, x')^{\gamma}}{\alpha}\bigg)^{-\alpha}.\]
By default, $d$ is the Euclidean metric $d(x, x') = \|x - x'\|_2$.
The GammaExponentialKernel is recovered in the limit as $\alpha \to \infty$.
See also: RationalKernel, RationalQuadraticKernel
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Spectral Mixture Kernels
KernelFunctions.spectral_mixture_kernel — Function
spectral_mixture_kernel(
     h::Kernel=SqExponentialKernel(),
     αs::AbstractVector{<:Real},
     γs::AbstractMatrix{<:Real},
@@ -25,14 +25,14 @@
 [3] Covariance kernels for fast automatic pattern discovery and extrapolation
     with Gaussian processes, Andrew Gordon Wilson, PhD Thesis, January 2014.
     http://www.cs.cmu.edu/~andrewgw/andrewgwthesis.pdf
-[4] http://www.cs.cmu.edu/~andrewgw/pattern/.
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KernelFunctions.spectral_mixture_product_kernel — Function
spectral_mixture_product_kernel(
     h::Kernel=SqExponentialKernel(),
     αs::AbstractMatrix{<:Real},
     γs::AbstractMatrix{<:Real},
     ωs::AbstractMatrix{<:Real},
 )
where αs are the weights of dimension (D, A), γs is the covariance matrix of dimension (D, A) and ωs are the mean vectors and is of dimension (D, A). Here, D is input dimension and A is the number of spectral components.
Spectral Mixture Product Kernel. With enough components A, the SMP kernel can model any product kernel to arbitrary precision, and is flexible even with a small number of components [1]
h is the kernel, which defaults to SqExponentialKernel if not specified.
\[   κ(x, y) = Πᵢ₌₁ᴷ Σ(αsᵢᵀ .* (h(-(γsᵢᵀ * tᵢ)²) .* cos(ωsᵢᵀ * tᵢ))), tᵢ = xᵢ - yᵢ\]
References:
[1] GPatt: Fast Multidimensional Pattern Extrapolation with GPs,
     arXiv 1310.5288, 2013, by Andrew Gordon Wilson, Elad Gilboa,
-    Arye Nehorai and John P. Cunningham
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Wiener Kernel
Wiener Kernel
KernelFunctions.WienerKernel — Type
WienerKernel(; i::Int=0)
 WienerKernel{i}()
The i-times integrated Wiener process kernel function.
Definition
For inputs $x, x' \in \mathbb{R}^d$, the $i$-times integrated Wiener process kernel with $i \in \{-1, 0, 1, 2, 3\}$ is defined[SDH] as
\[k_i(x, x') = \begin{cases}
     \delta(x, x') & \text{if } i=-1,\\
     \min\big(\|x\|_2, \|x'\|_2\big) & \text{if } i=0,\\
@@ -47,13 +47,13 @@
 r_1(t, t') &= 1,\\
 r_2(t, t') &= t + t' - \frac{\min(t, t')}{2},\\
 r_3(t, t') &= 5 \max(t, t')^2 + 2 tt' + 3 \min(t, t')^2.
-\end{aligned}\]
The WhiteKernel is recovered for $i = -1$.
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Composite Kernels
The modular design of KernelFunctions uses base kernels as building blocks for more complex kernels. There are a variety of composite kernels implemented, including those which transform the inputs to a wrapped kernel to implement length scales, scale the variance of a kernel, and sum or multiply collections of kernels together.
KernelFunctions.TransformedKernel — Type
TransformedKernel(k::Kernel, t::Transform)
Kernel derived from k for which inputs are transformed via a Transform t.
The preferred way to create kernels with input transformations is to use the composition operator ∘ or its alias compose instead of TransformedKernel directly since this allows optimized implementations for specific kernels and transformations.
See also: ∘
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Composite Kernels
The modular design of KernelFunctions uses base kernels as building blocks for more complex kernels. There are a variety of composite kernels implemented, including those which transform the inputs to a wrapped kernel to implement length scales, scale the variance of a kernel, and sum or multiply collections of kernels together.
KernelFunctions.TransformedKernel — Type
TransformedKernel(k::Kernel, t::Transform)
Kernel derived from k for which inputs are transformed via a Transform t.
The preferred way to create kernels with input transformations is to use the composition operator ∘ or its alias compose instead of TransformedKernel directly since this allows optimized implementations for specific kernels and transformations.
See also: ∘
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Base.:∘ — Method
kernel ∘ transform
 ∘(kernel, transform)
 compose(kernel, transform)
Compose a kernel with a transformation transform of its inputs.
The prefix forms support chains of multiple transformations: ∘(kernel, transform1, transform2) = kernel ∘ transform1 ∘ transform2.
Definition
For inputs $x, x'$, the transformed kernel $\widetilde{k}$ derived from kernel $k$ by input transformation $t$ is defined as
\[\widetilde{k}(x, x'; k, t) = k\big(t(x), t(x')\big).\]
Examples
julia> (SqExponentialKernel() ∘ ScaleTransform(0.5))(0, 2) == exp(-0.5)
 true
 
 julia> ∘(ExponentialKernel(), ScaleTransform(2), ScaleTransform(0.5))(1, 2) == exp(-1)
-true
See also: TransformedKernel
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KernelFunctions.ScaledKernel — Type
ScaledKernel(k::Kernel, σ²::Real=1.0)
Scaled kernel derived from k by multiplication with variance σ².
Definition
For inputs $x, x'$, the scaled kernel $\widetilde{k}$ derived from kernel $k$ by multiplication with variance $\sigma^2 > 0$ is defined as
\[\widetilde{k}(x, x'; k, \sigma^2) = \sigma^2 k(x, x').\]
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KernelFunctions.KernelSum — Type
KernelSum <: Kernel
Create a sum of kernels. One can also use the operator +.
There are various ways in which you create a KernelSum:
The simplest way to specify a KernelSum would be to use the overloaded + operator. This is  equivalent to creating a KernelSum by specifying the kernels as the arguments to the constructor.  
julia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5);
+true
See also: TransformedKernel
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KernelFunctions.ScaledKernel — Type
ScaledKernel(k::Kernel, σ²::Real=1.0)
Scaled kernel derived from k by multiplication with variance σ².
Definition
For inputs $x, x'$, the scaled kernel $\widetilde{k}$ derived from kernel $k$ by multiplication with variance $\sigma^2 > 0$ is defined as
\[\widetilde{k}(x, x'; k, \sigma^2) = \sigma^2 k(x, x').\]
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KernelFunctions.KernelSum — Type
KernelSum <: Kernel
Create a sum of kernels. One can also use the operator +.
There are various ways in which you create a KernelSum:
The simplest way to specify a KernelSum would be to use the overloaded + operator. This is  equivalent to creating a KernelSum by specifying the kernels as the arguments to the constructor.  
julia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5);
 
 julia> (k = k1 + k2) == KernelSum(k1, k2)
 true
@@ -66,7 +66,7 @@
 true
 
 julia> KernelSum([k1, k2]) == KernelSum((k1, k2)) == k1 + k2
-true
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KernelFunctions.KernelProduct — Type
KernelProduct <: Kernel
Create a product of kernels. One can also use the overloaded operator *.
There are various ways in which you create a KernelProduct:
The simplest way to specify a KernelProduct would be to use the overloaded * operator. This is  equivalent to creating a KernelProduct by specifying the kernels as the arguments to the constructor.  
julia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5);
+true
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KernelFunctions.KernelProduct — Type
KernelProduct <: Kernel
Create a product of kernels. One can also use the overloaded operator *.
There are various ways in which you create a KernelProduct:
The simplest way to specify a KernelProduct would be to use the overloaded * operator. This is  equivalent to creating a KernelProduct by specifying the kernels as the arguments to the constructor.  
julia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5);
 
 julia> (k = k1 * k2) == KernelProduct(k1, k2)
 true
@@ -79,7 +79,7 @@
 true
 
 julia> KernelProduct([k1, k2]) == KernelProduct((k1, k2)) == k1 * k2
-true
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KernelFunctions.KernelTensorProduct — Type
KernelTensorProduct
Tensor product of kernels.
Definition
For inputs $x = (x_1, \ldots, x_n)$ and $x' = (x'_1, \ldots, x'_n)$, the tensor product of kernels $k_1, \ldots, k_n$ is defined as
\[k(x, x'; k_1, \ldots, k_n) = \Big(\bigotimes_{i=1}^n k_i\Big)(x, x') = \prod_{i=1}^n k_i(x_i, x'_i).\]
Construction
The simplest way to specify a KernelTensorProduct is to use the overloaded tensor operator or its alias ⊗ (can be typed by \otimes<tab>).
julia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5, 2);
+true
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KernelFunctions.KernelTensorProduct — Type
KernelTensorProduct
Tensor product of kernels.
Definition
For inputs $x = (x_1, \ldots, x_n)$ and $x' = (x'_1, \ldots, x'_n)$, the tensor product of kernels $k_1, \ldots, k_n$ is defined as
\[k(x, x'; k_1, \ldots, k_n) = \Big(\bigotimes_{i=1}^n k_i\Big)(x, x') = \prod_{i=1}^n k_i(x_i, x'_i).\]
Construction
The simplest way to specify a KernelTensorProduct is to use the overloaded tensor operator or its alias ⊗ (can be typed by \otimes<tab>).
julia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5, 2);
 
 julia> kernelmatrix(k1 ⊗ k2, RowVecs(X)) == kernelmatrix(k1, X[:, 1]) .* kernelmatrix(k2, X[:, 2])
 true
You can also specify a KernelTensorProduct by providing kernels as individual arguments or as an iterable data structure such as a Tuple or a Vector. Using a tuple or individual arguments guarantees that KernelTensorProduct is concretely typed but might lead to large compilation times if the number of kernels is large.
julia> KernelTensorProduct(k1, k2) == k1 ⊗ k2
@@ -89,9 +89,9 @@
 true
 
 julia> KernelTensorProduct([k1, k2]) == k1 ⊗ k2
-true
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KernelFunctions.NormalizedKernel — Type
NormalizedKernel(k::Kernel)
A normalized kernel derived from k.
Definition
For inputs $x, x'$, the normalized kernel $\widetilde{k}$ derived from kernel $k$ is defined as
\[\widetilde{k}(x, x'; k) = \frac{k(x, x')}{\sqrt{k(x, x) k(x', x')}}.\]
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Multi-output Kernels
Kernelfunctions implements multi-output kernels as scalar kernels on an extended output domain. For more details on this read the section on inputs for multi-output GPs.
For a function $f(x) \rightarrow y$ denote the inputs as $x, x'$, such that we compute the covariance between output components $y_{p}$ and $y_{p'}$. The total number of outputs is $m$.
KernelFunctions.IndependentMOKernel — Type
IndependentMOKernel(k::Kernel)
Kernel for multiple independent outputs with kernel k each.
Definition
For inputs $x, x'$ and output dimensions $p, p'$, the kernel $\widetilde{k}$ for independent outputs with kernel $k$ each is defined as
\[\widetilde{k}\big((x, p), (x', p')\big) = \begin{cases}
+true
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KernelFunctions.NormalizedKernel — Type
NormalizedKernel(k::Kernel)
A normalized kernel derived from k.
Definition
For inputs $x, x'$, the normalized kernel $\widetilde{k}$ derived from kernel $k$ is defined as
\[\widetilde{k}(x, x'; k) = \frac{k(x, x')}{\sqrt{k(x, x) k(x', x')}}.\]
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Multi-output Kernels
Kernelfunctions implements multi-output kernels as scalar kernels on an extended output domain. For more details on this read the section on inputs for multi-output GPs.
For a function $f(x) \rightarrow y$ denote the inputs as $x, x'$, such that we compute the covariance between output components $y_{p}$ and $y_{p'}$. The total number of outputs is $m$.
KernelFunctions.IndependentMOKernel — Type
IndependentMOKernel(k::Kernel)
Kernel for multiple independent outputs with kernel k each.
Definition
For inputs $x, x'$ and output dimensions $p, p'$, the kernel $\widetilde{k}$ for independent outputs with kernel $k$ each is defined as
\[\widetilde{k}\big((x, p), (x', p')\big) = \begin{cases}
     k(x, x') & \text{if } p = p', \\
     0 & \text{otherwise}.
-\end{cases}\]
Mathematically, it is equivalent to a matrix-valued kernel defined as
\[\widetilde{K}(x, x') = \mathrm{diag}\big(k(x, x'), \ldots, k(x, x')\big) \in \mathbb{R}^{m \times m},\]
where $m$ is the number of outputs.
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KernelFunctions.LatentFactorMOKernel — Type
LatentFactorMOKernel(g::AbstractVector{<:Kernel}, e::MOKernel, A::AbstractMatrix)
Kernel associated with the semiparametric latent factor model.
Definition
For inputs $x, x'$ and output dimensions $p_x, p_{x'}'$, the kernel is defined as[STJ]
\[k\big((x, p_x), (x, p_{x'})\big) = \sum^{Q}_{q=1} A_{p_xq}g_q(x, x')A_{p_{x'}q}
-                                   + e\big((x, p_x), (x', p_{x'})\big),\]
where $g_1, \ldots, g_Q$ are $Q$ kernels, one for each latent process, $e$ is a multi-output kernel for $m$ outputs, and $A$ is a matrix of weights for the kernels of size $m \times Q$.
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KernelFunctions.IntrinsicCoregionMOKernel — Type
IntrinsicCoregionMOKernel(; kernel::Kernel, B::AbstractMatrix)
Kernel associated with the intrinsic coregionalization model.
Definition
For inputs $x, x'$ and output dimensions $p, p'$, the kernel is defined as[ARL]
\[k\big((x, p), (x', p'); B, \tilde{k}\big) = B_{p, p'} \tilde{k}\big(x, x'\big),\]
where $B$ is a positive semidefinite matrix of size $m \times m$, with $m$ being the number of outputs, and $\tilde{k}$ is a scalar-valued kernel shared by the latent processes.
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KernelFunctions.LinearMixingModelKernel — Type
LinearMixingModelKernel(k::Kernel, H::AbstractMatrix)
-LinearMixingModelKernel(Tk::AbstractVector{<:Kernel},Th::AbstractMatrix)
Kernel associated with the linear mixing model, taking a vector of Q kernels and a Q × m mixing matrix H for a function with m outputs. Also accepts a single kernel k for use across all Q basis vectors. 
Definition
For inputs $x, x'$ and output dimensions $p, p'$, the kernel is defined as[BPTHST]
\[k\big((x, p), (x, p')\big) = H_{:,p}K(x, x')H_{:,p'}\]
where $K(x, x') = Diag(k_1(x, x'), ..., k_Q(x, x'))$ with zero off-diagonal entries. $H_{:,p}$ is the $p$-th column (p-th output) of $H \in \mathbb{R}^{Q \times m}$ representing $Q$ basis vectors for the $m$ dimensional output space of $f$. $k_1, \ldots, k_Q$ are $Q$ kernels, one for each latent process, $H$ is a mixing matrix of $Q$ basis vectors spanning the output space.
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+\end{cases}\]
Mathematically, it is equivalent to a matrix-valued kernel defined as
\[\widetilde{K}(x, x') = \mathrm{diag}\big(k(x, x'), \ldots, k(x, x')\big) \in \mathbb{R}^{m \times m},\]
where $m$ is the number of outputs.
source
KernelFunctions.LatentFactorMOKernel — Type
LatentFactorMOKernel(g::AbstractVector{<:Kernel}, e::MOKernel, A::AbstractMatrix)
Kernel associated with the semiparametric latent factor model.
Definition
For inputs $x, x'$ and output dimensions $p_x, p_{x'}'$, the kernel is defined as[STJ]
\[k\big((x, p_x), (x, p_{x'})\big) = \sum^{Q}_{q=1} A_{p_xq}g_q(x, x')A_{p_{x'}q}
+                                   + e\big((x, p_x), (x', p_{x'})\big),\]
where $g_1, \ldots, g_Q$ are $Q$ kernels, one for each latent process, $e$ is a multi-output kernel for $m$ outputs, and $A$ is a matrix of weights for the kernels of size $m \times Q$.
source
KernelFunctions.IntrinsicCoregionMOKernel — Type
IntrinsicCoregionMOKernel(; kernel::Kernel, B::AbstractMatrix)
Kernel associated with the intrinsic coregionalization model.
Definition
For inputs $x, x'$ and output dimensions $p, p'$, the kernel is defined as[ARL]
\[k\big((x, p), (x', p'); B, \tilde{k}\big) = B_{p, p'} \tilde{k}\big(x, x'\big),\]
where $B$ is a positive semidefinite matrix of size $m \times m$, with $m$ being the number of outputs, and $\tilde{k}$ is a scalar-valued kernel shared by the latent processes.
source
KernelFunctions.LinearMixingModelKernel — Type
LinearMixingModelKernel(k::Kernel, H::AbstractMatrix)
+LinearMixingModelKernel(Tk::AbstractVector{<:Kernel},Th::AbstractMatrix)
Kernel associated with the linear mixing model, taking a vector of Q kernels and a Q × m mixing matrix H for a function with m outputs. Also accepts a single kernel k for use across all Q basis vectors. 
Definition
For inputs $x, x'$ and output dimensions $p, p'$, the kernel is defined as[BPTHST]
\[k\big((x, p), (x, p')\big) = H_{:,p}K(x, x')H_{:,p'}\]
where $K(x, x') = Diag(k_1(x, x'), ..., k_Q(x, x'))$ with zero off-diagonal entries. $H_{:,p}$ is the $p$-th column (p-th output) of $H \in \mathbb{R}^{Q \times m}$ representing $Q$ basis vectors for the $m$ dimensional output space of $f$. $k_1, \ldots, k_Q$ are $Q$ kernels, one for each latent process, $H$ is a mixing matrix of $Q$ basis vectors spanning the output space.
source

diff --git a/previews/PR530/metrics/index.html b/previews/PR530/metrics/index.html
index 36e01ca8a..6cb50cf76 100644
--- a/previews/PR530/metrics/index.html
+++ b/previews/PR530/metrics/index.html
@@ -10,4 +10,4 @@
 end
 
 @inline (dist::Delta)(a::AbstractArray,b::AbstractArray) = Distances._evaluate(dist,a,b)
-@inline (dist::Delta)(a::Number,b::Number) = a==b
+@inline (dist::Delta)(a::Number,b::Number) = a==b diff --git a/previews/PR530/search/index.html b/previews/PR530/search/index.html index 9a5a7112f..9d41fe241 100644 --- a/previews/PR530/search/index.html +++ b/previews/PR530/search/index.html @@ -1,2 +1,2 @@ -Search · KernelFunctions.jl

Loading search...

    +Search · KernelFunctions.jl

    Loading search...

      diff --git a/previews/PR530/search_index.js b/previews/PR530/search_index.js index 81a62a485..4a784ee31 100644 --- a/previews/PR530/search_index.js +++ b/previews/PR530/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"create_kernel/#Custom-Kernels","page":"Custom Kernels","title":"Custom Kernels","text":"","category":"section"},{"location":"create_kernel/#Creating-your-own-kernel","page":"Custom Kernels","title":"Creating your own kernel","text":"","category":"section"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"KernelFunctions.jl contains the most popular kernels already but you might want to make your own!","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"Here are a few ways depending on how complicated your kernel is:","category":"page"},{"location":"create_kernel/#SimpleKernel-for-kernel-functions-depending-on-a-metric","page":"Custom Kernels","title":"SimpleKernel for kernel functions depending on a metric","text":"","category":"section"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"If your kernel function is of the form k(x, y) = f(d(x, y)) where d(x, y) is a PreMetric, you can construct your custom kernel by defining kappa and metric for your kernel. Here is for example how one can define the SqExponentialKernel again:","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"struct MyKernel <: KernelFunctions.SimpleKernel end\n\nKernelFunctions.kappa(::MyKernel, d2::Real) = exp(-d2)\nKernelFunctions.metric(::MyKernel) = SqEuclidean()","category":"page"},{"location":"create_kernel/#Kernel-for-more-complex-kernels","page":"Custom Kernels","title":"Kernel for more complex kernels","text":"","category":"section"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"If your kernel does not satisfy such a representation, all you need to do is define (k::MyKernel)(x, y) and inherit from Kernel. For example, we recreate here the NeuralNetworkKernel:","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"struct MyKernel <: KernelFunctions.Kernel end\n\n(::MyKernel)(x, y) = asin(dot(x, y) / sqrt((1 + sum(abs2, x)) * (1 + sum(abs2, y))))","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"Note that the fallback implementation of the base Kernel evaluation does not use Distances.jl and can therefore be a bit slower.","category":"page"},{"location":"create_kernel/#Additional-Options","page":"Custom Kernels","title":"Additional Options","text":"","category":"section"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"Finally there are additional functions you can define to bring in more features:","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"KernelFunctions.iskroncompatible(k::MyKernel): if your kernel factorizes in dimensions, you can declare your kernel as iskroncompatible(k) = true to use Kronecker methods.\nKernelFunctions.dim(x::MyDataType): by default the dimension of the inputs will only be checked for vectors of type AbstractVector{<:Real}. If you want to check the dimensionality of your inputs, dispatch the dim function on your datatype. Note that 0 is the default.\ndim is called within KernelFunctions.validate_inputs(x::MyDataType, y::MyDataType), which can instead be directly overloaded if you want to run special checks for your input types.\nkernelmatrix(k::MyKernel, ...): you can redefine the diverse kernelmatrix functions to eventually optimize the computations.\nBase.print(io::IO, k::MyKernel): if you want to specialize the printing of your kernel.","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"KernelFunctions uses Functors.jl for specifying trainable kernel parameters in a way that is compatible with the Flux ML framework. You can use Functors.@functor if all fields of your kernel struct are trainable. Note that optimization algorithms in Flux are not compatible with scalar parameters (yet), and hence vector-valued parameters should be preferred.","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"import Functors\n\nstruct MyKernel{T} <: KernelFunctions.Kernel\n a::Vector{T}\nend\n\nFunctors.@functor MyKernel","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"If only a subset of the fields are trainable, you have to specify explicitly how to (re)construct the kernel with modified parameter values by implementing Functors.functor(::Type{<:MyKernel}, x) for your kernel struct:","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"import Functors\n\nstruct MyKernel{T} <: KernelFunctions.Kernel\n n::Int\n a::Vector{T}\nend\n\nfunction Functors.functor(::Type{<:MyKernel}, x::MyKernel)\n function reconstruct_mykernel(xs)\n # keep field `n` of the original kernel and set `a` to (possibly different) `xs.a`\n return MyKernel(x.n, xs.a)\n end\n return (a = x.a,), reconstruct_mykernel\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"EditURL = \"../../../../examples/train-kernel-parameters/script.jl\"","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/master/examples/train-kernel-parameters/script.jl\"","category":"page"},{"location":"examples/train-kernel-parameters/#Train-Kernel-Parameters","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"(Image: )","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Here we show a few ways to train (optimize) the kernel (hyper)parameters at the example of kernel-based regression using KernelFunctions.jl. All options are functionally identical, but differ a little in readability, dependencies, and computational cost.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"We load KernelFunctions and some other packages. Note that while we use Zygote for automatic differentiation and Flux.optimise for optimization, you should be able to replace them with your favourite autodiff framework or optimizer.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"using KernelFunctions\nusing LinearAlgebra\nusing Distributions\nusing Plots\nusing BenchmarkTools\nusing Flux\nusing Flux: Optimise\nusing Zygote\nusing Random: seed!\nseed!(42);","category":"page"},{"location":"examples/train-kernel-parameters/#Data-Generation","page":"Train Kernel Parameters","title":"Data Generation","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"We generate a toy dataset in 1 dimension:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"xmin, xmax = -3, 3 # Bounds of the data\nN = 50 # Number of samples\nx_train = rand(Uniform(xmin, xmax), N) # sample the inputs\nσ = 0.1\ny_train = sinc.(x_train) + randn(N) * σ # evaluate a function and add some noise\nx_test = range(xmin - 0.1, xmax + 0.1; length=300)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Plot the data","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"scatter(x_train, y_train; label=\"data\")\nplot!(x_test, sinc; label=\"true function\")","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/train-kernel-parameters/#Manual-Approach","page":"Train Kernel Parameters","title":"Manual Approach","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"The first option is to rebuild the parametrized kernel from a vector of parameters in each evaluation of the cost function. This is similar to the approach taken in Stheno.jl.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"To train the kernel parameters via Zygote.jl, we need to create a function creating a kernel from an array. A simple way to ensure that the kernel parameters are positive is to optimize over the logarithm of the parameters.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"function kernel_creator(θ)\n return (exp(θ[1]) * SqExponentialKernel() + exp(θ[2]) * Matern32Kernel()) ∘\n ScaleTransform(exp(θ[3]))\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"From theory we know the prediction for a test set x given the kernel parameters and normalization constant:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"function f(x, x_train, y_train, θ)\n k = kernel_creator(θ[1:3])\n return kernelmatrix(k, x, x_train) *\n ((kernelmatrix(k, x_train) + exp(θ[4]) * I) \\ y_train)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Let's look at our prediction. With starting parameters p0 (picked so we get the right local minimum for demonstration) we get:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"p0 = [1.1, 0.1, 0.01, 0.001]\nθ = log.(p0)\nŷ = f(x_test, x_train, y_train, θ)\nscatter(x_train, y_train; label=\"data\")\nplot!(x_test, sinc; label=\"true function\")\nplot!(x_test, ŷ; label=\"prediction\")","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"We define the following loss:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"function loss(θ)\n ŷ = f(x_train, x_train, y_train, θ)\n return norm(y_train - ŷ) + exp(θ[4]) * norm(ŷ)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"The loss with our starting point:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"loss(θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"2.613933959118425","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Computational cost for one step:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"@benchmark let\n θ = log.(p0)\n opt = Optimise.ADAGrad(0.5)\n grads = only((Zygote.gradient(loss, θ)))\n Optimise.update!(opt, θ, grads)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"BenchmarkTools.Trial: 3548 samples with 1 evaluation.\n Range (min … max): 1.099 ms … 4.998 ms ┊ GC (min … max): 0.00% … 44.69%\n Time (median): 1.235 ms ┊ GC (median): 0.00%\n Time (mean ± σ): 1.402 ms ± 626.261 μs ┊ GC (mean ± σ): 11.18% ± 15.65%\n\n ▄██▃ ▂▂▁ ▁\n ▇████▇█▄▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▄▇████ █\n 1.1 ms Histogram: log(frequency) by time 3.78 ms <\n\n Memory estimate: 2.98 MiB, allocs estimate: 1535.","category":"page"},{"location":"examples/train-kernel-parameters/#Training-the-model","page":"Train Kernel Parameters","title":"Training the model","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Setting an initial value and initializing the optimizer:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"θ = log.(p0) # Initial vector\nopt = Optimise.ADAGrad(0.5)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Optimize","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"anim = Animation()\nfor i in 1:15\n grads = only((Zygote.gradient(loss, θ)))\n Optimise.update!(opt, θ, grads)\n scatter(\n x_train, y_train; lab=\"data\", title=\"i = $(i), Loss = $(round(loss(θ), digits = 4))\"\n )\n plot!(x_test, sinc; lab=\"true function\")\n plot!(x_test, f(x_test, x_train, y_train, θ); lab=\"Prediction\", lw=3.0)\n frame(anim)\nend\ngif(anim, \"train-kernel-param.gif\"; show_msg=false, fps=15);","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"(Image: )","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Final loss","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"loss(θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"0.524111822806358","category":"page"},{"location":"examples/train-kernel-parameters/#Using-ParameterHandling.jl","page":"Train Kernel Parameters","title":"Using ParameterHandling.jl","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Alternatively, we can use the ParameterHandling.jl package to handle the requirement that all kernel parameters should be positive. The package also allows arbitrarily nesting named tuples that make the parameters more human readable, without having to remember their position in a flat vector.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"using ParameterHandling\n\nraw_initial_θ = (\n k1=positive(1.1), k2=positive(0.1), k3=positive(0.01), noise_var=positive(0.001)\n)\n\nflat_θ, unflatten = ParameterHandling.value_flatten(raw_initial_θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"4-element Vector{Float64}:\n 0.09531016625781467\n -2.3025852420056685\n -4.6051716761053205\n -6.907770180254354","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"We define a few relevant functions and note that compared to the previous kernel_creator function, we do not need explicit exps.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"function kernel_creator(θ)\n return (θ.k1 * SqExponentialKernel() + θ.k2 * Matern32Kernel()) ∘ ScaleTransform(θ.k3)\nend\n\nfunction f(x, x_train, y_train, θ)\n k = kernel_creator(θ)\n return kernelmatrix(k, x, x_train) *\n ((kernelmatrix(k, x_train) + θ.noise_var * I) \\ y_train)\nend\n\nfunction loss(θ)\n ŷ = f(x_train, x_train, y_train, θ)\n return norm(y_train - ŷ) + θ.noise_var * norm(ŷ)\nend\n\ninitial_θ = ParameterHandling.value(raw_initial_θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"The loss at the initial parameter values:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"(loss ∘ unflatten)(flat_θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"2.613933959118425","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Cost per step","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"@benchmark let\n θ = flat_θ[:]\n opt = Optimise.ADAGrad(0.5)\n grads = (Zygote.gradient(loss ∘ unflatten, θ))[1]\n Optimise.update!(opt, θ, grads)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"BenchmarkTools.Trial: 3042 samples with 1 evaluation.\n Range (min … max): 1.338 ms … 6.105 ms ┊ GC (min … max): 0.00% … 47.05%\n Time (median): 1.424 ms ┊ GC (median): 0.00%\n Time (mean ± σ): 1.637 ms ± 787.405 μs ┊ GC (mean ± σ): 12.40% ± 16.47%\n\n ▄█▅ ▁▂▁ \n ███▇▁▃▁▃▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▆███ █\n 1.34 ms Histogram: log(frequency) by time 4.56 ms <\n\n Memory estimate: 3.06 MiB, allocs estimate: 2215.","category":"page"},{"location":"examples/train-kernel-parameters/#Training-the-model-2","page":"Train Kernel Parameters","title":"Training the model","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Optimize","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"opt = Optimise.ADAGrad(0.5)\nfor i in 1:15\n grads = (Zygote.gradient(loss ∘ unflatten, flat_θ))[1]\n Optimise.update!(opt, flat_θ, grads)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Final loss","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"(loss ∘ unflatten)(flat_θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"0.5241176241222036","category":"page"},{"location":"examples/train-kernel-parameters/#Flux.destructure","page":"Train Kernel Parameters","title":"Flux.destructure","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"If we don't want to write an explicit function to construct the kernel, we can alternatively use the Flux.destructure function. Again, we need to ensure that the parameters are positive. Note that the exp function is now part of the loss function, instead of part of the kernel construction.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"We could also use ParameterHandling.jl here. To do so, one would remove the exps from the loss function below and call loss ∘ unflatten as above.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"θ = [1.1, 0.1, 0.01, 0.001]\n\nkernel = (θ[1] * SqExponentialKernel() + θ[2] * Matern32Kernel()) ∘ ScaleTransform(θ[3])\n\nparams, kernelc = Flux.destructure(kernel);","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"This returns the trainable params of the kernel and a function to reconstruct the kernel.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"kernelc(params)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Sum of 2 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- σ² = 1.1\n\tMatern 3/2 Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- σ² = 0.1\n\t- Scale Transform (s = 0.01)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"From theory we know the prediction for a test set x given the kernel parameters and normalization constant","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"function f(x, x_train, y_train, θ)\n k = kernelc(θ[1:3])\n return kernelmatrix(k, x, x_train) * ((kernelmatrix(k, x_train) + (θ[4]) * I) \\ y_train)\nend\n\nfunction loss(θ)\n ŷ = f(x_train, x_train, y_train, exp.(θ))\n return norm(y_train - ŷ) + exp(θ[4]) * norm(ŷ)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Cost for one step","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"@benchmark let θt = θ[:], optt = Optimise.ADAGrad(0.5)\n grads = only((Zygote.gradient(loss, θt)))\n Optimise.update!(optt, θt, grads)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"BenchmarkTools.Trial: 3415 samples with 1 evaluation.\n Range (min … max): 1.155 ms … 6.451 ms ┊ GC (min … max): 0.00% … 45.45%\n Time (median): 1.240 ms ┊ GC (median): 0.00%\n Time (mean ± σ): 1.457 ms ± 807.545 μs ┊ GC (mean ± σ): 14.23% ± 17.31%\n\n ▅█▄ ▁▂▁ \n ████▅▁▄▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▇███ █\n 1.15 ms Histogram: log(frequency) by time 4.5 ms <\n\n Memory estimate: 2.98 MiB, allocs estimate: 1556.","category":"page"},{"location":"examples/train-kernel-parameters/#Training-the-model-3","page":"Train Kernel Parameters","title":"Training the model","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"The loss at our initial parameter values:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"θ = log.([1.1, 0.1, 0.01, 0.001]) # Initial vector\nloss(θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"2.613933959118425","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Initialize optimizer","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"opt = Optimise.ADAGrad(0.5)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Optimize","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"for i in 1:15\n grads = only((Zygote.gradient(loss, θ)))\n Optimise.update!(opt, θ, grads)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Final loss","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"loss(θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"0.524111822806358","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"
      \n
      Package and system information
      \n
      \nPackage information (click to expand)\n
      \nStatus `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/train-kernel-parameters/Project.toml`\n  [6e4b80f9] BenchmarkTools v1.3.2\n  [31c24e10] Distributions v0.25.102\n  [587475ba] Flux v0.14.6\n  [f6369f11] ForwardDiff v0.10.36\n  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`\n  [98b081ad] Literate v2.15.0\n  [2412ca09] ParameterHandling v0.4.7\n  [91a5bcdd] Plots v1.39.0\n  [e88e6eb3] Zygote v0.6.65\n  [37e2e46d] LinearAlgebra\n
      \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
      \n
      \nSystem information (click to expand)\n
      \nJulia Version 1.9.3\nCommit bed2cd540a1 (2023-08-24 14:43 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-14.0.6 (ORCJIT, skylake-avx512)\n  Threads: 1 on 2 virtual cores\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
      \n
      ","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"This page was generated using Literate.jl.","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"EditURL = \"../../../../examples/kernel-ridge-regression/script.jl\"","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/master/examples/kernel-ridge-regression/script.jl\"","category":"page"},{"location":"examples/kernel-ridge-regression/#Kernel-Ridge-Regression","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"(Image: )","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"Building on linear regression, we can fit non-linear data sets by introducing a feature space. In a higher-dimensional feature space, we can overfit the data; ridge regression introduces regularization to avoid this. In this notebook we show how we can use KernelFunctions.jl for kernel ridge regression.","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"# Loading and setup of required packages\nusing KernelFunctions\nusing LinearAlgebra\nusing Distributions\n\n# Plotting\nusing Plots;\ndefault(; lw=2.0, legendfontsize=11.0, ylims=(-150, 500));\n\nusing Random: seed!\nseed!(42);","category":"page"},{"location":"examples/kernel-ridge-regression/#Toy-data","page":"Kernel Ridge Regression","title":"Toy data","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"Here we use a one-dimensional toy problem. We generate data using the fourth-order polynomial f(x) = (x+4)(x+1)(x-1)(x-3):","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"f_truth(x) = (x + 4) * (x + 1) * (x - 1) * (x - 3)\n\nx_train = -5:0.5:5\nx_test = -7:0.1:7\n\nnoise = rand(Uniform(-20, 20), length(x_train))\ny_train = f_truth.(x_train) + noise\ny_test = f_truth.(x_test)\n\nplot(x_test, y_test; label=raw\"$f(x)$\")\nscatter!(x_train, y_train; seriescolor=1, label=\"observations\")","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/#Linear-regression","page":"Kernel Ridge Regression","title":"Linear regression","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"For training inputs mathrmX=(mathbfx_n)_n=1^N and observations mathbfy=(y_n)_n=1^N, the linear regression weights mathbfw using the least-squares estimator are given by","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = (mathrmX^top mathrmX)^-1 mathrmX^top mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"We predict at test inputs mathbfx_* using","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"haty_* = mathbfx_*^top mathbfw","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"This is implemented by linear_regression:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"function linear_regression(X, y, Xstar)\n weights = (X' * X) \\ (X' * y)\n return Xstar * weights\nend;","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"A linear regression fit to the above data set:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"y_pred = linear_regression(x_train, y_train, x_test)\nscatter(x_train, y_train; label=\"observations\")\nplot!(x_test, y_pred; label=\"linear fit\")","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/#Featurization","page":"Kernel Ridge Regression","title":"Featurization","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"We can improve the fit by including additional features, i.e. generalizing to tildemathrmX = (phi(x_n))_n=1^N, where phi(x) constructs a feature vector for each input x. Here we include powers of the input, phi(x) = (1 x x^2 dots x^d):","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"function featurize_poly(x; degree=1)\n return repeat(x, 1, degree + 1) .^ (0:degree)'\nend\n\nfunction featurized_fit_and_plot(degree)\n X = featurize_poly(x_train; degree=degree)\n Xstar = featurize_poly(x_test; degree=degree)\n y_pred = linear_regression(X, y_train, Xstar)\n scatter(x_train, y_train; legend=false, title=\"fit of order $degree\")\n return plot!(x_test, y_pred)\nend\n\nplot((featurized_fit_and_plot(degree) for degree in 1:4)...)","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"Note that the fit becomes perfect when we include exactly as many orders in the features as we have in the underlying polynomial (4).","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"However, when increasing the number of features, we can quickly overfit to noise in the data set:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"featurized_fit_and_plot(20)","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/#Ridge-regression","page":"Kernel Ridge Regression","title":"Ridge regression","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"To counteract this unwanted behaviour, we can introduce regularization. This leads to ridge regression with L_2 regularization of the weights (Tikhonov regularization). Instead of the weights in linear regression,","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = (mathrmX^top mathrmX)^-1 mathrmX^top mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"we introduce the ridge parameter lambda:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = (mathrmX^top mathrmX + lambda mathbb1)^-1 mathrmX^top mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"As before, we predict at test inputs mathbfx_* using","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"haty_* = mathbfx_*^top mathbfw","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"This is implemented by ridge_regression:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"function ridge_regression(X, y, Xstar, lambda)\n weights = (X' * X + lambda * I) \\ (X' * y)\n return Xstar * weights\nend\n\nfunction regularized_fit_and_plot(degree, lambda)\n X = featurize_poly(x_train; degree=degree)\n Xstar = featurize_poly(x_test; degree=degree)\n y_pred = ridge_regression(X, y_train, Xstar, lambda)\n scatter(x_train, y_train; legend=false, title=\"\\$\\\\lambda=$lambda\\$\")\n return plot!(x_test, y_pred)\nend\n\nplot((regularized_fit_and_plot(20, lambda) for lambda in (1e-3, 1e-2, 1e-1, 1))...)","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/#Kernel-ridge-regression","page":"Kernel Ridge Regression","title":"Kernel ridge regression","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"Instead of constructing the feature matrix explicitly, we can use kernels to replace inner products of feature vectors with a kernel evaluation: langle phi(x) phi(x) rangle = k(x x) or tildemathrmX tildemathrmX^top = mathrmK, where mathrmK_ij = k(x_i x_j).","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"To apply this \"kernel trick\" to ridge regression, we can rewrite the ridge estimate for the weights","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = (mathrmX^top mathrmX + lambda mathbb1)^-1 mathrmX^top mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"using the matrix inversion lemma as","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = mathrmX^top (mathrmX mathrmX^top + lambda mathbb1)^-1 mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"where we can now replace the inner product with the kernel matrix,","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = mathrmX^top (mathrmK + lambda mathbb1)^-1 mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"And the prediction yields another inner product,","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"haty_* = mathbfx_*^top mathbfw = langle mathbfx_* mathbfw rangle = mathbfk_* (mathrmK + lambda mathbb1)^-1 mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"where (mathbfk_*)_n = k(x_* x_n).","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"This is implemented by kernel_ridge_regression:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"function kernel_ridge_regression(k, X, y, Xstar, lambda)\n K = kernelmatrix(k, X)\n kstar = kernelmatrix(k, Xstar, X)\n return kstar * ((K + lambda * I) \\ y)\nend;","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"Now, instead of explicitly constructing features, we can simply pass in a PolynomialKernel object:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"function kernelized_fit_and_plot(kernel, lambda=1e-4)\n y_pred = kernel_ridge_regression(kernel, x_train, y_train, x_test, lambda)\n if kernel isa PolynomialKernel\n title = string(\"order \", kernel.degree)\n else\n title = string(nameof(typeof(kernel)))\n end\n scatter(x_train, y_train; label=nothing)\n return plot!(x_test, y_pred; label=nothing, title=title)\nend\n\nplot((kernelized_fit_and_plot(PolynomialKernel(; degree=degree, c=1)) for degree in 1:4)...)","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"However, we can now also use kernels that would have an infinite-dimensional feature expansion, such as the squared exponential kernel:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"kernelized_fit_and_plot(SqExponentialKernel())","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"
      \n
      Package and system information
      \n
      \nPackage information (click to expand)\n
      \nStatus `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/kernel-ridge-regression/Project.toml`\n  [31c24e10] Distributions v0.25.102\n  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`\n  [98b081ad] Literate v2.15.0\n  [91a5bcdd] Plots v1.39.0\n  [37e2e46d] LinearAlgebra\n
      \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
      \n
      \nSystem information (click to expand)\n
      \nJulia Version 1.9.3\nCommit bed2cd540a1 (2023-08-24 14:43 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-14.0.6 (ORCJIT, skylake-avx512)\n  Threads: 1 on 2 virtual cores\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
      \n
      ","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"This page was generated using Literate.jl.","category":"page"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":" CurrentModule = KernelFunctions","category":"page"},{"location":"kernels/#Kernel-Functions","page":"Kernel Functions","title":"Kernel Functions","text":"","category":"section"},{"location":"kernels/#base_kernels","page":"Kernel Functions","title":"Base Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"These are the basic kernels without any transformation of the data. They are the building blocks of KernelFunctions.","category":"page"},{"location":"kernels/#Constant-Kernels","page":"Kernel Functions","title":"Constant Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"ZeroKernel\nConstantKernel\nWhiteKernel\nEyeKernel","category":"page"},{"location":"kernels/#KernelFunctions.ZeroKernel","page":"Kernel Functions","title":"KernelFunctions.ZeroKernel","text":"ZeroKernel()\n\nZero kernel.\n\nDefinition\n\nFor inputs x x, the zero kernel is defined as\n\nk(x x) = 0\n\nThe output type depends on x and x.\n\nSee also: ConstantKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.ConstantKernel","page":"Kernel Functions","title":"KernelFunctions.ConstantKernel","text":"ConstantKernel(; c::Real=1.0)\n\nKernel of constant value c.\n\nDefinition\n\nFor inputs x x, the kernel of constant value c geq 0 is defined as\n\nk(x x) = c\n\nSee also: ZeroKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.WhiteKernel","page":"Kernel Functions","title":"KernelFunctions.WhiteKernel","text":"WhiteKernel()\n\nWhite noise kernel.\n\nDefinition\n\nFor inputs x x, the white noise kernel is defined as\n\nk(x x) = delta(x x)\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.EyeKernel","page":"Kernel Functions","title":"KernelFunctions.EyeKernel","text":"EyeKernel()\n\nAlias of WhiteKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Cosine-Kernel","page":"Kernel Functions","title":"Cosine Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"CosineKernel","category":"page"},{"location":"kernels/#KernelFunctions.CosineKernel","page":"Kernel Functions","title":"KernelFunctions.CosineKernel","text":"CosineKernel(; metric=Euclidean())\n\nCosine kernel with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the cosine kernel is defined as\n\nk(x x) = cos(pi d(x x))\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Exponential-Kernels","page":"Kernel Functions","title":"Exponential Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"ExponentialKernel\nGibbsKernel\nLaplacianKernel\nSqExponentialKernel\nSEKernel\nGaussianKernel\nRBFKernel\nGammaExponentialKernel","category":"page"},{"location":"kernels/#KernelFunctions.ExponentialKernel","page":"Kernel Functions","title":"KernelFunctions.ExponentialKernel","text":"ExponentialKernel(; metric=Euclidean())\n\nExponential kernel with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the exponential kernel is defined as\n\nk(x x) = expbig(- d(x x)big)\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nSee also: GammaExponentialKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.GibbsKernel","page":"Kernel Functions","title":"KernelFunctions.GibbsKernel","text":"GibbsKernel(; lengthscale)\n\nGibbs Kernel with lengthscale function lengthscale.\n\nThe Gibbs kernel is a non-stationary generalisation of the squared exponential kernel. The lengthscale parameter l becomes a function of position l(x).\n\nDefinition\n\nFor inputs x x, the Gibbs kernel with lengthscale function l(cdot) is defined as\n\nk(x x l) = sqrtleft(frac2 l(x) l(x)l(x)^2 + l(x)^2right)\nquad expleft(-frac(x - x)^2l(x)^2 + l(x)^2right)\n\nFor a constant function l equiv c, one recovers the SqExponentialKernel with lengthscale c.\n\nReferences\n\nMark N. Gibbs. \"Bayesian Gaussian Processes for Regression and Classication.\" PhD thesis, 1997\n\nChristopher J. Paciorek and Mark J. Schervish. \"Nonstationary Covariance Functions for Gaussian Process Regression\". NeurIPS, 2003\n\nSami Remes, Markus Heinonen, Samuel Kaski. \"Non-Stationary Spectral Kernels\". arXiV:1705.08736, 2017\n\nSami Remes, Markus Heinonen, Samuel Kaski. \"Neural Non-Stationary Spectral Kernel\". arXiv:1811.10978, 2018\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.LaplacianKernel","page":"Kernel Functions","title":"KernelFunctions.LaplacianKernel","text":"LaplacianKernel()\n\nAlias of ExponentialKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.SqExponentialKernel","page":"Kernel Functions","title":"KernelFunctions.SqExponentialKernel","text":"SqExponentialKernel(; metric=Euclidean())\n\nSquared exponential kernel with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the squared exponential kernel is defined as\n\nk(x x) = expbigg(- fracd(x x)^22bigg)\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nSee also: GammaExponentialKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.SEKernel","page":"Kernel Functions","title":"KernelFunctions.SEKernel","text":"SEKernel()\n\nAlias of SqExponentialKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.GaussianKernel","page":"Kernel Functions","title":"KernelFunctions.GaussianKernel","text":"GaussianKernel()\n\nAlias of SqExponentialKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.RBFKernel","page":"Kernel Functions","title":"KernelFunctions.RBFKernel","text":"RBFKernel()\n\nAlias of SqExponentialKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.GammaExponentialKernel","page":"Kernel Functions","title":"KernelFunctions.GammaExponentialKernel","text":"GammaExponentialKernel(; γ::Real=1.0, metric=Euclidean())\n\nγ-exponential kernel with respect to the metric and with parameter γ.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the γ-exponential kernel[RW] with parameter gamma in (0 2 is defined as\n\nk(x x gamma) = expbig(- d(x x)^gammabig)\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nSee also: ExponentialKernel, SqExponentialKernel\n\n[RW]: C. E. Rasmussen & C. K. I. Williams (2006). Gaussian Processes for Machine Learning.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Exponentiated-Kernel","page":"Kernel Functions","title":"Exponentiated Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"ExponentiatedKernel","category":"page"},{"location":"kernels/#KernelFunctions.ExponentiatedKernel","page":"Kernel Functions","title":"KernelFunctions.ExponentiatedKernel","text":"ExponentiatedKernel()\n\nExponentiated kernel.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the exponentiated kernel is defined as\n\nk(x x) = exp(x^top x)\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Fractional-Brownian-Motion-Kernel","page":"Kernel Functions","title":"Fractional Brownian Motion Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"FBMKernel","category":"page"},{"location":"kernels/#KernelFunctions.FBMKernel","page":"Kernel Functions","title":"KernelFunctions.FBMKernel","text":"FBMKernel(; h::Real=0.5)\n\nFractional Brownian motion kernel with Hurst index h.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the fractional Brownian motion kernel with Hurst index h in 01 is defined as\n\nk(x x h) = fracx_2^2h + x_2^2h - x - x^2h2\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Gabor-Kernel","page":"Kernel Functions","title":"Gabor Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"gaborkernel","category":"page"},{"location":"kernels/#KernelFunctions.gaborkernel","page":"Kernel Functions","title":"KernelFunctions.gaborkernel","text":"gaborkernel(;\n sqexponential_transform=IdentityTransform(), cosine_tranform=IdentityTransform()\n)\n\nConstruct a Gabor kernel with transformations sqexponential_transform and cosine_transform of the inputs of the underlying squared exponential and cosine kernel, respectively.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the Gabor kernel with transformations f and g of the inputs to the squared exponential and cosine kernel, respectively, is defined as\n\nk(x x f g) = expbigg(- frac f(x) - f(x)_2^22bigg)\n cosbig(pi g(x) - g(x)_2 big)\n\n\n\n\n\n","category":"function"},{"location":"kernels/#Matérn-Kernels","page":"Kernel Functions","title":"Matérn Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"MaternKernel\nMatern12Kernel\nMatern32Kernel\nMatern52Kernel","category":"page"},{"location":"kernels/#KernelFunctions.MaternKernel","page":"Kernel Functions","title":"KernelFunctions.MaternKernel","text":"MaternKernel(; ν::Real=1.5, metric=Euclidean())\n\nMatérn kernel of order ν with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the Matérn kernel of order nu 0 is defined as\n\nk(xxnu) = frac2^1-nuGamma(nu)big(sqrt2nu d(x x)big) K_nubig(sqrt2nu d(x x)big)\n\nwhere Gamma is the Gamma function and K_nu is the modified Bessel function of the second kind of order nu. By default, d is the Euclidean metric d(x x) = x - x_2.\n\nA Gaussian process with a Matérn kernel is lceil nu rceil - 1-times differentiable in the mean-square sense.\n\nnote: Note\nDifferentiation with respect to the order ν is not currently supported.\n\nSee also: Matern12Kernel, Matern32Kernel, Matern52Kernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.Matern12Kernel","page":"Kernel Functions","title":"KernelFunctions.Matern12Kernel","text":"Matern12Kernel()\n\nAlias of ExponentialKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.Matern32Kernel","page":"Kernel Functions","title":"KernelFunctions.Matern32Kernel","text":"Matern32Kernel(; metric=Euclidean())\n\nMatérn kernel of order 32 with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the Matérn kernel of order 32 is given by\n\nk(x x) = big(1 + sqrt3 d(x x) big) expbig(- sqrt3 d(x x) big)\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nSee also: MaternKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.Matern52Kernel","page":"Kernel Functions","title":"KernelFunctions.Matern52Kernel","text":"Matern52Kernel(; metric=Euclidean())\n\nMatérn kernel of order 52 with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the Matérn kernel of order 52 is given by\n\nk(x x) = bigg(1 + sqrt5 d(x x) + frac53 d(x x)^2bigg)\n expbig(- sqrt5 d(x x) big)\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nSee also: MaternKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Neural-Network-Kernel","page":"Kernel Functions","title":"Neural Network Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"NeuralNetworkKernel","category":"page"},{"location":"kernels/#KernelFunctions.NeuralNetworkKernel","page":"Kernel Functions","title":"KernelFunctions.NeuralNetworkKernel","text":"NeuralNetworkKernel()\n\nKernel of a Gaussian process obtained as the limit of a Bayesian neural network with a single hidden layer as the number of units goes to infinity.\n\nDefinition\n\nConsider the single-layer Bayesian neural network f colon mathbbR^d to mathbbR with h hidden units defined by\n\nf(x b v u) = b + sqrtfracpi2 sum_i=1^h v_i mathrmerfbig(u_i^top xbig)\n\nwhere mathrmerf is the error function, and with prior distributions\n\nbeginaligned\nb sim mathcalN(0 sigma_b^2)\nv sim mathcalN(0 sigma_v^2 mathrmI_hh)\nu_i sim mathcalN(0 mathrmI_d2) qquad (i = 1ldotsh)\nendaligned\n\nAs h to infty, the neural network converges to the Gaussian process\n\ng(cdot) sim mathcalGPbig(0 sigma_b^2 + sigma_v^2 k(cdot cdot)big)\n\nwhere the neural network kernel k is given by\n\nk(x x) = arcsinleft(fracx^top xsqrtbig(1 + x^2_2big) big(1 + x_2^2big)right)\n\nfor inputs x x in mathbbR^d.[CW]\n\n[CW]: C. K. I. Williams (1998). Computation with infinite neural networks.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Periodic-Kernel","page":"Kernel Functions","title":"Periodic Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"PeriodicKernel\nPeriodicKernel(::DataType, ::Int)","category":"page"},{"location":"kernels/#KernelFunctions.PeriodicKernel","page":"Kernel Functions","title":"KernelFunctions.PeriodicKernel","text":"PeriodicKernel(; r::AbstractVector=ones(Float64, 1))\n\nPeriodic kernel with parameter r.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the periodic kernel with parameter r_i 0 is defined[DM] as\n\nk(x x r) = expbigg(- frac12 sum_i=1^d bigg(fracsinbig(pi(x_i - x_i)big)r_ibigg)^2bigg)\n\n[DM]: D. J. C. MacKay (1998). Introduction to Gaussian Processes.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.PeriodicKernel-Tuple{DataType, Int64}","page":"Kernel Functions","title":"KernelFunctions.PeriodicKernel","text":"PeriodicKernel([T=Float64, dims::Int=1])\n\nCreate a PeriodicKernel with parameter r=ones(T, dims).\n\n\n\n\n\n","category":"method"},{"location":"kernels/#Piecewise-Polynomial-Kernel","page":"Kernel Functions","title":"Piecewise Polynomial Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"PiecewisePolynomialKernel","category":"page"},{"location":"kernels/#KernelFunctions.PiecewisePolynomialKernel","page":"Kernel Functions","title":"KernelFunctions.PiecewisePolynomialKernel","text":"PiecewisePolynomialKernel(; dim::Int, degree::Int=0, metric=Euclidean())\nPiecewisePolynomialKernel{degree}(; dim::Int, metric=Euclidean())\n\nPiecewise polynomial kernel of degree degree for inputs of dimension dim with support in the unit ball with respect to the metric.\n\nDefinition\n\nFor inputs x x of dimension m and metric d(cdot cdot), the piecewise polynomial kernel of degree v in 0123 is defined as\n\nk(x x v) = max(1 - d(x x) 0)^alpha(vm) f_vm(d(x x))\n\nwhere alpha(v m) = lfloor fracm2rfloor + 2v + 1 and f_vm are polynomials of degree v given by\n\nbeginaligned\nf_0m(r) = 1 \nf_1m(r) = 1 + (j + 1) r \nf_2m(r) = 1 + (j + 2) r + big((j^2 + 4j + 3) 3big) r^2 \nf_3m(r) = 1 + (j + 3) r + big((6 j^2 + 36j + 45) 15big) r^2 + big((j^3 + 9 j^2 + 23j + 15) 15big) r^3\nendaligned\n\nwhere j = lfloor fracm2rfloor + v + 1. By default, d is the Euclidean metric d(x x) = x - x_2.\n\nThe kernel is 2v times continuously differentiable and the corresponding Gaussian process is hence v times mean-square differentiable.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Polynomial-Kernels","page":"Kernel Functions","title":"Polynomial Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"LinearKernel\nPolynomialKernel","category":"page"},{"location":"kernels/#KernelFunctions.LinearKernel","page":"Kernel Functions","title":"KernelFunctions.LinearKernel","text":"LinearKernel(; c::Real=0.0)\n\nLinear kernel with constant offset c.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the linear kernel with constant offset c geq 0 is defined as\n\nk(x x c) = x^top x + c\n\nSee also: PolynomialKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.PolynomialKernel","page":"Kernel Functions","title":"KernelFunctions.PolynomialKernel","text":"PolynomialKernel(; degree::Int=2, c::Real=0.0)\n\nPolynomial kernel of degree degree with constant offset c.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the polynomial kernel of degree nu in mathbbN with constant offset c geq 0 is defined as\n\nk(x x c nu) = (x^top x + c)^nu\n\nSee also: LinearKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Rational-Kernels","page":"Kernel Functions","title":"Rational Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"RationalKernel\nRationalQuadraticKernel\nGammaRationalKernel","category":"page"},{"location":"kernels/#KernelFunctions.RationalKernel","page":"Kernel Functions","title":"KernelFunctions.RationalKernel","text":"RationalKernel(; α::Real=2.0, metric=Euclidean())\n\nRational kernel with shape parameter α and given metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the rational kernel with shape parameter alpha 0 is defined as\n\nk(x x alpha) = bigg(1 + fracd(x x)alphabigg)^-alpha\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nThe ExponentialKernel is recovered in the limit as alpha to infty.\n\nSee also: GammaRationalKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.RationalQuadraticKernel","page":"Kernel Functions","title":"KernelFunctions.RationalQuadraticKernel","text":"RationalQuadraticKernel(; α::Real=2.0, metric=Euclidean())\n\nRational-quadratic kernel with respect to the metric and with shape parameter α.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the rational-quadratic kernel with shape parameter alpha 0 is defined as\n\nk(x x alpha) = bigg(1 + fracd(x x)^22alphabigg)^-alpha\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nThe SqExponentialKernel is recovered in the limit as alpha to infty.\n\nSee also: GammaRationalKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.GammaRationalKernel","page":"Kernel Functions","title":"KernelFunctions.GammaRationalKernel","text":"GammaRationalKernel(; α::Real=2.0, γ::Real=1.0, metric=Euclidean())\n\nγ-rational kernel with respect to the metric with shape parameters α and γ.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the γ-rational kernel with shape parameters alpha 0 and gamma in (0 2 is defined as\n\nk(x x alpha gamma) = bigg(1 + fracd(x x)^gammaalphabigg)^-alpha\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nThe GammaExponentialKernel is recovered in the limit as alpha to infty.\n\nSee also: RationalKernel, RationalQuadraticKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Spectral-Mixture-Kernels","page":"Kernel Functions","title":"Spectral Mixture Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"spectral_mixture_kernel\nspectral_mixture_product_kernel","category":"page"},{"location":"kernels/#KernelFunctions.spectral_mixture_kernel","page":"Kernel Functions","title":"KernelFunctions.spectral_mixture_kernel","text":"spectral_mixture_kernel(\n h::Kernel=SqExponentialKernel(),\n αs::AbstractVector{<:Real},\n γs::AbstractMatrix{<:Real},\n ωs::AbstractMatrix{<:Real},\n)\n\nwhere αs are the weights of dimension (A, ), γs is the covariance matrix of dimension (D, A) and ωs are the mean vectors and is of dimension (D, A). Here, D is input dimension and A is the number of spectral components.\n\nh is the kernel, which defaults to SqExponentialKernel if not specified.\n\nwarning: Warning\nIf you want to make sure that the constructor is type-stable, you should provide StaticArrays arguments: αs as a StaticVector, γs and ωs as StaticMatrix.\n\nGeneralised Spectral Mixture kernel function. This family of functions is dense in the family of stationary real-valued kernels with respect to the pointwise convergence.[1]\n\n κ(x y) = αs (h(-(γs * t)^2) * cos(π * ωs * t) t = x - y\n\nReferences:\n\n[1] Generalized Spectral Kernels, by Yves-Laurent Kom Samo and Stephen J. Roberts\n[2] SM: Gaussian Process Kernels for Pattern Discovery and Extrapolation,\n ICML, 2013, by Andrew Gordon Wilson and Ryan Prescott Adams,\n[3] Covariance kernels for fast automatic pattern discovery and extrapolation\n with Gaussian processes, Andrew Gordon Wilson, PhD Thesis, January 2014.\n http://www.cs.cmu.edu/~andrewgw/andrewgwthesis.pdf\n[4] http://www.cs.cmu.edu/~andrewgw/pattern/.\n\n\n\n\n\n","category":"function"},{"location":"kernels/#KernelFunctions.spectral_mixture_product_kernel","page":"Kernel Functions","title":"KernelFunctions.spectral_mixture_product_kernel","text":"spectral_mixture_product_kernel(\n h::Kernel=SqExponentialKernel(),\n αs::AbstractMatrix{<:Real},\n γs::AbstractMatrix{<:Real},\n ωs::AbstractMatrix{<:Real},\n)\n\nwhere αs are the weights of dimension (D, A), γs is the covariance matrix of dimension (D, A) and ωs are the mean vectors and is of dimension (D, A). Here, D is input dimension and A is the number of spectral components.\n\nSpectral Mixture Product Kernel. With enough components A, the SMP kernel can model any product kernel to arbitrary precision, and is flexible even with a small number of components [1]\n\nh is the kernel, which defaults to SqExponentialKernel if not specified.\n\n κ(x y) = Πᵢ₁ᴷ Σ(αsᵢᵀ * (h(-(γsᵢᵀ * tᵢ)²) * cos(ωsᵢᵀ * tᵢ))) tᵢ = xᵢ - yᵢ\n\nReferences:\n\n[1] GPatt: Fast Multidimensional Pattern Extrapolation with GPs,\n arXiv 1310.5288, 2013, by Andrew Gordon Wilson, Elad Gilboa,\n Arye Nehorai and John P. Cunningham\n\n\n\n\n\n","category":"function"},{"location":"kernels/#Wiener-Kernel","page":"Kernel Functions","title":"Wiener Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"WienerKernel","category":"page"},{"location":"kernels/#KernelFunctions.WienerKernel","page":"Kernel Functions","title":"KernelFunctions.WienerKernel","text":"WienerKernel(; i::Int=0)\nWienerKernel{i}()\n\nThe i-times integrated Wiener process kernel function.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the i-times integrated Wiener process kernel with i in -1 0 1 2 3 is defined[SDH] as\n\nk_i(x x) = begincases\n delta(x x) textif i=-1\n minbig(x_2 x_2big) textif i=0\n a_i1^-1 minbig(x_2 x_2big)^2i + 1\n + a_i2^-1 x - x_2 r_ibig(x_2 x_2big) minbig(x_2 x_2big)^i + 1\n textotherwise\nendcases\n\nwhere the coefficients a are given by\n\na = beginbmatrix\n3 2 \n20 12 \n252 720\nendbmatrix\n\nand the functions r_i are defined as\n\nbeginaligned\nr_1(t t) = 1\nr_2(t t) = t + t - fracmin(t t)2\nr_3(t t) = 5 max(t t)^2 + 2 tt + 3 min(t t)^2\nendaligned\n\nThe WhiteKernel is recovered for i = -1.\n\n[SDH]: Schober, Duvenaud & Hennig (2014). Probabilistic ODE Solvers with Runge-Kutta Means.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Composite-Kernels","page":"Kernel Functions","title":"Composite Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"The modular design of KernelFunctions uses base kernels as building blocks for more complex kernels. There are a variety of composite kernels implemented, including those which transform the inputs to a wrapped kernel to implement length scales, scale the variance of a kernel, and sum or multiply collections of kernels together.","category":"page"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"TransformedKernel\n∘(::Kernel, ::Transform)\nScaledKernel\nKernelSum\nKernelProduct\nKernelTensorProduct\nNormalizedKernel","category":"page"},{"location":"kernels/#KernelFunctions.TransformedKernel","page":"Kernel Functions","title":"KernelFunctions.TransformedKernel","text":"TransformedKernel(k::Kernel, t::Transform)\n\nKernel derived from k for which inputs are transformed via a Transform t.\n\nThe preferred way to create kernels with input transformations is to use the composition operator ∘ or its alias compose instead of TransformedKernel directly since this allows optimized implementations for specific kernels and transformations.\n\nSee also: ∘\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Base.:∘-Tuple{Kernel, Transform}","page":"Kernel Functions","title":"Base.:∘","text":"kernel ∘ transform\n∘(kernel, transform)\ncompose(kernel, transform)\n\nCompose a kernel with a transformation transform of its inputs.\n\nThe prefix forms support chains of multiple transformations: ∘(kernel, transform1, transform2) = kernel ∘ transform1 ∘ transform2.\n\nDefinition\n\nFor inputs x x, the transformed kernel widetildek derived from kernel k by input transformation t is defined as\n\nwidetildek(x x k t) = kbig(t(x) t(x)big)\n\nExamples\n\njulia> (SqExponentialKernel() ∘ ScaleTransform(0.5))(0, 2) == exp(-0.5)\ntrue\n\njulia> ∘(ExponentialKernel(), ScaleTransform(2), ScaleTransform(0.5))(1, 2) == exp(-1)\ntrue\n\nSee also: TransformedKernel\n\n\n\n\n\n","category":"method"},{"location":"kernels/#KernelFunctions.ScaledKernel","page":"Kernel Functions","title":"KernelFunctions.ScaledKernel","text":"ScaledKernel(k::Kernel, σ²::Real=1.0)\n\nScaled kernel derived from k by multiplication with variance σ².\n\nDefinition\n\nFor inputs x x, the scaled kernel widetildek derived from kernel k by multiplication with variance sigma^2 0 is defined as\n\nwidetildek(x x k sigma^2) = sigma^2 k(x x)\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.KernelSum","page":"Kernel Functions","title":"KernelFunctions.KernelSum","text":"KernelSum <: Kernel\n\nCreate a sum of kernels. One can also use the operator +.\n\nThere are various ways in which you create a KernelSum:\n\nThe simplest way to specify a KernelSum would be to use the overloaded + operator. This is equivalent to creating a KernelSum by specifying the kernels as the arguments to the constructor. \n\njulia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5);\n\njulia> (k = k1 + k2) == KernelSum(k1, k2)\ntrue\n\njulia> kernelmatrix(k1 + k2, X) == kernelmatrix(k1, X) .+ kernelmatrix(k2, X)\ntrue\n\njulia> kernelmatrix(k, X) == kernelmatrix(k1 + k2, X)\ntrue\n\nYou could also specify a KernelSum by providing a Tuple or a Vector of the kernels to be summed. We suggest you to use a Tuple when you have fewer components and a Vector when dealing with a large number of components.\n\njulia> KernelSum((k1, k2)) == k1 + k2\ntrue\n\njulia> KernelSum([k1, k2]) == KernelSum((k1, k2)) == k1 + k2\ntrue\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.KernelProduct","page":"Kernel Functions","title":"KernelFunctions.KernelProduct","text":"KernelProduct <: Kernel\n\nCreate a product of kernels. One can also use the overloaded operator *.\n\nThere are various ways in which you create a KernelProduct:\n\nThe simplest way to specify a KernelProduct would be to use the overloaded * operator. This is equivalent to creating a KernelProduct by specifying the kernels as the arguments to the constructor. \n\njulia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5);\n\njulia> (k = k1 * k2) == KernelProduct(k1, k2)\ntrue\n\njulia> kernelmatrix(k1 * k2, X) == kernelmatrix(k1, X) .* kernelmatrix(k2, X)\ntrue\n\njulia> kernelmatrix(k, X) == kernelmatrix(k1 * k2, X)\ntrue\n\nYou could also specify a KernelProduct by providing a Tuple or a Vector of the kernels to be multiplied. We suggest you to use a Tuple when you have fewer components and a Vector when dealing with a large number of components.\n\njulia> KernelProduct((k1, k2)) == k1 * k2\ntrue\n\njulia> KernelProduct([k1, k2]) == KernelProduct((k1, k2)) == k1 * k2\ntrue\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.KernelTensorProduct","page":"Kernel Functions","title":"KernelFunctions.KernelTensorProduct","text":"KernelTensorProduct\n\nTensor product of kernels.\n\nDefinition\n\nFor inputs x = (x_1 ldots x_n) and x = (x_1 ldots x_n), the tensor product of kernels k_1 ldots k_n is defined as\n\nk(x x k_1 ldots k_n) = Big(bigotimes_i=1^n k_iBig)(x x) = prod_i=1^n k_i(x_i x_i)\n\nConstruction\n\nThe simplest way to specify a KernelTensorProduct is to use the overloaded tensor operator or its alias ⊗ (can be typed by \\otimes).\n\njulia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5, 2);\n\njulia> kernelmatrix(k1 ⊗ k2, RowVecs(X)) == kernelmatrix(k1, X[:, 1]) .* kernelmatrix(k2, X[:, 2])\ntrue\n\nYou can also specify a KernelTensorProduct by providing kernels as individual arguments or as an iterable data structure such as a Tuple or a Vector. Using a tuple or individual arguments guarantees that KernelTensorProduct is concretely typed but might lead to large compilation times if the number of kernels is large.\n\njulia> KernelTensorProduct(k1, k2) == k1 ⊗ k2\ntrue\n\njulia> KernelTensorProduct((k1, k2)) == k1 ⊗ k2\ntrue\n\njulia> KernelTensorProduct([k1, k2]) == k1 ⊗ k2\ntrue\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.NormalizedKernel","page":"Kernel Functions","title":"KernelFunctions.NormalizedKernel","text":"NormalizedKernel(k::Kernel)\n\nA normalized kernel derived from k.\n\nDefinition\n\nFor inputs x x, the normalized kernel widetildek derived from kernel k is defined as\n\nwidetildek(x x k) = frack(x x)sqrtk(x x) k(x x)\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Multi-output-Kernels","page":"Kernel Functions","title":"Multi-output Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"Kernelfunctions implements multi-output kernels as scalar kernels on an extended output domain. For more details on this read the section on inputs for multi-output GPs.","category":"page"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"For a function f(x) rightarrow y denote the inputs as x x, such that we compute the covariance between output components y_p and y_p. The total number of outputs is m.","category":"page"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"MOKernel\nIndependentMOKernel\nLatentFactorMOKernel\nIntrinsicCoregionMOKernel\nLinearMixingModelKernel","category":"page"},{"location":"kernels/#KernelFunctions.MOKernel","page":"Kernel Functions","title":"KernelFunctions.MOKernel","text":"MOKernel\n\nAbstract type for kernels with multiple outpus.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.IndependentMOKernel","page":"Kernel Functions","title":"KernelFunctions.IndependentMOKernel","text":"IndependentMOKernel(k::Kernel)\n\nKernel for multiple independent outputs with kernel k each.\n\nDefinition\n\nFor inputs x x and output dimensions p p, the kernel widetildek for independent outputs with kernel k each is defined as\n\nwidetildekbig((x p) (x p)big) = begincases\n k(x x) textif p = p \n 0 textotherwise\nendcases\n\nMathematically, it is equivalent to a matrix-valued kernel defined as\n\nwidetildeK(x x) = mathrmdiagbig(k(x x) ldots k(x x)big) in mathbbR^m times m\n\nwhere m is the number of outputs.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.LatentFactorMOKernel","page":"Kernel Functions","title":"KernelFunctions.LatentFactorMOKernel","text":"LatentFactorMOKernel(g::AbstractVector{<:Kernel}, e::MOKernel, A::AbstractMatrix)\n\nKernel associated with the semiparametric latent factor model.\n\nDefinition\n\nFor inputs x x and output dimensions p_x p_x, the kernel is defined as[STJ]\n\nkbig((x p_x) (x p_x)big) = sum^Q_q=1 A_p_xqg_q(x x)A_p_xq\n + ebig((x p_x) (x p_x)big)\n\nwhere g_1 ldots g_Q are Q kernels, one for each latent process, e is a multi-output kernel for m outputs, and A is a matrix of weights for the kernels of size m times Q.\n\n[STJ]: M. Seeger, Y. Teh, & M. I. Jordan (2005). Semiparametric Latent Factor Models.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.IntrinsicCoregionMOKernel","page":"Kernel Functions","title":"KernelFunctions.IntrinsicCoregionMOKernel","text":"IntrinsicCoregionMOKernel(; kernel::Kernel, B::AbstractMatrix)\n\nKernel associated with the intrinsic coregionalization model.\n\nDefinition\n\nFor inputs x x and output dimensions p p, the kernel is defined as[ARL]\n\nkbig((x p) (x p) B tildekbig) = B_p p tildekbig(x xbig)\n\nwhere B is a positive semidefinite matrix of size m times m, with m being the number of outputs, and tildek is a scalar-valued kernel shared by the latent processes.\n\n[ARL]: M. Álvarez, L. Rosasco, & N. Lawrence (2012). Kernels for Vector-Valued Functions: a Review.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.LinearMixingModelKernel","page":"Kernel Functions","title":"KernelFunctions.LinearMixingModelKernel","text":"LinearMixingModelKernel(k::Kernel, H::AbstractMatrix)\nLinearMixingModelKernel(Tk::AbstractVector{<:Kernel},Th::AbstractMatrix)\n\nKernel associated with the linear mixing model, taking a vector of Q kernels and a Q × m mixing matrix H for a function with m outputs. Also accepts a single kernel k for use across all Q basis vectors. \n\nDefinition\n\nFor inputs x x and output dimensions p p, the kernel is defined as[BPTHST]\n\nkbig((x p) (x p)big) = H_pK(x x)H_p\n\nwhere K(x x) = Diag(k_1(x x) k_Q(x x)) with zero off-diagonal entries. H_p is the p-th column (p-th output) of H in mathbbR^Q times m representing Q basis vectors for the m dimensional output space of f. k_1 ldots k_Q are Q kernels, one for each latent process, H is a mixing matrix of Q basis vectors spanning the output space.\n\n[BPTHST]: Wessel P. Bruinsma, Eric Perim, Will Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner (2020). Scalable Exact Inference in Multi-Output Gaussian Processes.\n\n\n\n\n\n","category":"type"},{"location":"api/#API-Library","page":"API","title":"API Library","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"CurrentModule = KernelFunctions","category":"page"},{"location":"api/#Functions","page":"API","title":"Functions","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"The KernelFunctions API comprises the following four functions.","category":"page"},{"location":"api/","page":"API","title":"API","text":"kernelmatrix\nkernelmatrix!\nkernelmatrix_diag\nkernelmatrix_diag!","category":"page"},{"location":"api/#KernelFunctions.kernelmatrix","page":"API","title":"KernelFunctions.kernelmatrix","text":"kernelmatrix(κ::Kernel, x::AbstractVector)\n\nCompute the kernel κ for each pair of inputs in x. Returns a matrix of size (length(x), length(x)) satisfying kernelmatrix(κ, x)[p, q] == κ(x[p], x[q]).\n\nkernelmatrix(κ::Kernel, x::AbstractVector, y::AbstractVector)\n\nCompute the kernel κ for each pair of inputs in x and y. Returns a matrix of size (length(x), length(y)) satisfying kernelmatrix(κ, x, y)[p, q] == κ(x[p], y[q]).\n\nkernelmatrix(κ::Kernel, X::AbstractMatrix; obsdim)\nkernelmatrix(κ::Kernel, X::AbstractMatrix, Y::AbstractMatrix; obsdim)\n\nIf obsdim=1, equivalent to kernelmatrix(κ, RowVecs(X)) and kernelmatrix(κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix(κ, ColVecs(X)) and kernelmatrix(κ, ColVecs(X), ColVecs(Y)), respectively.\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"api/#KernelFunctions.kernelmatrix!","page":"API","title":"KernelFunctions.kernelmatrix!","text":"kernelmatrix!(K::AbstractMatrix, κ::Kernel, x::AbstractVector)\nkernelmatrix!(K::AbstractMatrix, κ::Kernel, x::AbstractVector, y::AbstractVector)\n\nIn-place version of kernelmatrix where pre-allocated matrix K will be overwritten with the kernel matrix.\n\nkernelmatrix!(K::AbstractMatrix, κ::Kernel, X::AbstractMatrix; obsdim)\nkernelmatrix!(\n K::AbstractMatrix,\n κ::Kernel,\n X::AbstractMatrix,\n Y::AbstractMatrix;\n obsdim,\n)\n\nIf obsdim=1, equivalent to kernelmatrix!(K, κ, RowVecs(X)) and kernelmatrix(K, κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix!(K, κ, ColVecs(X)) and kernelmatrix(K, κ, ColVecs(X), ColVecs(Y)), respectively.\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"api/#KernelFunctions.kernelmatrix_diag","page":"API","title":"KernelFunctions.kernelmatrix_diag","text":"kernelmatrix_diag(κ::Kernel, x::AbstractVector)\n\nCompute the diagonal of kernelmatrix(κ, x) efficiently.\n\nkernelmatrix_diag(κ::Kernel, x::AbstractVector, y::AbstractVector)\n\nCompute the diagonal of kernelmatrix(κ, x, y) efficiently. Requires that x and y are the same length.\n\nkernelmatrix_diag(κ::Kernel, X::AbstractMatrix; obsdim)\nkernelmatrix_diag(κ::Kernel, X::AbstractMatrix, Y::AbstractMatrix; obsdim)\n\nIf obsdim=1, equivalent to kernelmatrix_diag(κ, RowVecs(X)) and kernelmatrix_diag(κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix_diag(κ, ColVecs(X)) and kernelmatrix_diag(κ, ColVecs(X), ColVecs(Y)), respectively.\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"api/#KernelFunctions.kernelmatrix_diag!","page":"API","title":"KernelFunctions.kernelmatrix_diag!","text":"kernelmatrix_diag!(K::AbstractVector, κ::Kernel, x::AbstractVector)\nkernelmatrix_diag!(K::AbstractVector, κ::Kernel, x::AbstractVector, y::AbstractVector)\n\nIn place version of kernelmatrix_diag.\n\nkernelmatrix_diag!(K::AbstractVector, κ::Kernel, X::AbstractMatrix; obsdim)\nkernelmatrix_diag!(\n K::AbstractVector,\n κ::Kernel,\n X::AbstractMatrix,\n Y::AbstractMatrix;\n obsdim\n)\n\nIf obsdim=1, equivalent to kernelmatrix_diag!(K, κ, RowVecs(X)) and kernelmatrix_diag!(K, κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix_diag!(K, κ, ColVecs(X)) and kernelmatrix_diag!(K, κ, ColVecs(X), ColVecs(Y)), respectively.\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"api/#Input-Types","page":"API","title":"Input Types","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"The above API operates on collections of inputs. All collections of inputs in KernelFunctions.jl are represented as AbstractVectors. To understand this choice, please see the design notes on collections of inputs. The length of any such AbstractVector is equal to the number of inputs in the collection. For example, this means that","category":"page"},{"location":"api/","page":"API","title":"API","text":"size(kernelmatrix(k, x)) == (length(x), length(x))","category":"page"},{"location":"api/","page":"API","title":"API","text":"is always true, for some Kernel k, and AbstractVector x.","category":"page"},{"location":"api/#Univariate-Inputs","page":"API","title":"Univariate Inputs","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"If each input to your kernel is Real-valued, then any AbstractVector{<:Real} is a valid representation for a collection of inputs. More generally, it's completely fine to represent a collection of inputs of type T as, for example, a Vector{T}. However, this may not be the most efficient way to represent collection of inputs. See Vector-Valued Inputs for an example.","category":"page"},{"location":"api/#Vector-Valued-Inputs","page":"API","title":"Vector-Valued Inputs","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"We recommend that collections of vector-valued inputs are stored in an AbstractMatrix{<:Real} when possible, and wrapped inside a ColVecs or RowVecs to make their interpretation clear:","category":"page"},{"location":"api/","page":"API","title":"API","text":"ColVecs\nRowVecs","category":"page"},{"location":"api/#KernelFunctions.ColVecs","page":"API","title":"KernelFunctions.ColVecs","text":"ColVecs(X::AbstractMatrix)\n\nA lightweight wrapper for an AbstractMatrix which interprets it as a vector-of-vectors, in which each column of X represents a single vector.\n\nThat is, by writing x = ColVecs(X), you are saying \"x is a vector-of-vectors, each of which has length size(X, 1). The total number of vectors is size(X, 2).\"\n\nPhrased differently, ColVecs(X) says that X should be interpreted as a vector of horizontally-concatenated column-vectors, hence the name ColVecs.\n\njulia> X = randn(2, 5);\n\njulia> x = ColVecs(X);\n\njulia> length(x) == 5\ntrue\n\njulia> X[:, 3] == x[3]\ntrue\n\nColVecs is related to RowVecs via transposition:\n\njulia> X = randn(2, 5);\n\njulia> ColVecs(X) == RowVecs(X')\ntrue\n\n\n\n\n\n","category":"type"},{"location":"api/#KernelFunctions.RowVecs","page":"API","title":"KernelFunctions.RowVecs","text":"RowVecs(X::AbstractMatrix)\n\nA lightweight wrapper for an AbstractMatrix which interprets it as a vector-of-vectors, in which each row of X represents a single vector.\n\nThat is, by writing x = RowVecs(X), you are saying \"x is a vector-of-vectors, each of which has length size(X, 2). The total number of vectors is size(X, 1).\"\n\nPhrased differently, RowVecs(X) says that X should be interpreted as a vector of vertically-concatenated row-vectors, hence the name RowVecs.\n\nInternally, the data continues to be represented as an AbstractMatrix, so using this type does not introduce any kind of performance penalty.\n\njulia> X = randn(5, 2);\n\njulia> x = RowVecs(X);\n\njulia> length(x) == 5\ntrue\n\njulia> X[3, :] == x[3]\ntrue\n\nRowVecs is related to ColVecs via transposition:\n\njulia> X = randn(5, 2);\n\njulia> RowVecs(X) == ColVecs(X')\ntrue\n\n\n\n\n\n","category":"type"},{"location":"api/","page":"API","title":"API","text":"These types are specialised upon to ensure good performance e.g. when computing Euclidean distances between pairs of elements. The benefit of using this representation, rather than using a Vector{Vector{<:Real}}, is that optimised matrix-matrix multiplication functionality can be utilised when computing pairwise distances between inputs, which are needed for kernelmatrix computation.","category":"page"},{"location":"api/#Inputs-for-Multiple-Outputs","page":"API","title":"Inputs for Multiple Outputs","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"KernelFunctions.jl views multi-output GPs as GPs on an extended input domain. For an explanation of this design choice, see the design notes on multi-output GPs.","category":"page"},{"location":"api/","page":"API","title":"API","text":"An input to a multi-output Kernel should be a Tuple{T, Int}, whose first element specifies a location in the domain of the multi-output GP, and whose second element specifies which output the inputs corresponds to. The type of collections of inputs for multi-output GPs is therefore AbstractVector{<:Tuple{T, Int}}.","category":"page"},{"location":"api/","page":"API","title":"API","text":"KernelFunctions.jl provides the following helper functions to reduce the cognitive load associated with working with multi-output kernels by dealing with transforming data from the formats in which it is commonly found into the format required by KernelFunctions. The intention is that users can pass their data to these functions, and use the returned values throughout their code, without having to worry further about correctly formatting their data for KernelFunctions' sake:","category":"page"},{"location":"api/","page":"API","title":"API","text":"prepare_isotopic_multi_output_data(x::AbstractVector, y::ColVecs)\nprepare_isotopic_multi_output_data(x::AbstractVector, y::RowVecs)\nprepare_heterotopic_multi_output_data","category":"page"},{"location":"api/#KernelFunctions.prepare_isotopic_multi_output_data-Tuple{AbstractVector, ColVecs}","page":"API","title":"KernelFunctions.prepare_isotopic_multi_output_data","text":"prepare_isotopic_multi_output_data(x::AbstractVector, y::ColVecs)\n\nUtility functionality to convert a collection of N = length(x) inputs x, and a vector-of-vectors y (efficiently represented by a ColVecs) into a format suitable for use with multi-output kernels.\n\ny[n] is the vector-valued output corresponding to the input x[n]. Consequently, it is necessary that length(x) == length(y).\n\nFor example, if outputs are initially stored in a num_outputs × N matrix:\n\njulia> x = [1.0, 2.0, 3.0];\n\njulia> Y = [1.1 2.1 3.1; 1.2 2.2 3.2]\n2×3 Matrix{Float64}:\n 1.1 2.1 3.1\n 1.2 2.2 3.2\n\njulia> inputs, outputs = prepare_isotopic_multi_output_data(x, ColVecs(Y));\n\njulia> inputs\n6-element KernelFunctions.MOInputIsotopicByFeatures{Float64, Vector{Float64}, Int64}:\n (1.0, 1)\n (1.0, 2)\n (2.0, 1)\n (2.0, 2)\n (3.0, 1)\n (3.0, 2)\n\njulia> outputs\n6-element Vector{Float64}:\n 1.1\n 1.2\n 2.1\n 2.2\n 3.1\n 3.2\n\nSee also prepare_heterotopic_multi_output_data.\n\n\n\n\n\n","category":"method"},{"location":"api/#KernelFunctions.prepare_isotopic_multi_output_data-Tuple{AbstractVector, RowVecs}","page":"API","title":"KernelFunctions.prepare_isotopic_multi_output_data","text":"prepare_isotopic_multi_output_data(x::AbstractVector, y::RowVecs)\n\nUtility functionality to convert a collection of N = length(x) inputs x and output vectors y (efficiently represented by a RowVecs) into a format suitable for use with multi-output kernels.\n\ny[n] is the vector-valued output corresponding to the input x[n]. Consequently, it is necessary that length(x) == length(y).\n\nFor example, if outputs are initial stored in an N × num_outputs matrix:\n\njulia> x = [1.0, 2.0, 3.0];\n\njulia> Y = [1.1 1.2; 2.1 2.2; 3.1 3.2]\n3×2 Matrix{Float64}:\n 1.1 1.2\n 2.1 2.2\n 3.1 3.2\n\njulia> inputs, outputs = prepare_isotopic_multi_output_data(x, RowVecs(Y));\n\njulia> inputs\n6-element KernelFunctions.MOInputIsotopicByOutputs{Float64, Vector{Float64}, Int64}:\n (1.0, 1)\n (2.0, 1)\n (3.0, 1)\n (1.0, 2)\n (2.0, 2)\n (3.0, 2)\n\njulia> outputs\n6-element Vector{Float64}:\n 1.1\n 2.1\n 3.1\n 1.2\n 2.2\n 3.2\n\nSee also prepare_heterotopic_multi_output_data.\n\n\n\n\n\n","category":"method"},{"location":"api/#KernelFunctions.prepare_heterotopic_multi_output_data","page":"API","title":"KernelFunctions.prepare_heterotopic_multi_output_data","text":"prepare_heterotopic_multi_output_data(\n x::AbstractVector, y::AbstractVector{<:Real}, output_indices::AbstractVector{Int},\n)\n\nUtility functionality to convert a collection of inputs x, observations y, and output_indices into a format suitable for use with multi-output kernels. Handles the situation in which only one (or a subset) of outputs are observed at each feature. Ensures that all arguments are compatible with one another, and returns a vector of inputs and a vector of outputs.\n\ny[n] should be the observed value associated with output output_indices[n] at feature x[n].\n\njulia> x = [1.0, 2.0, 3.0];\n\njulia> y = [-1.0, 0.0, 1.0];\n\njulia> output_indices = [3, 2, 1];\n\njulia> inputs, outputs = prepare_heterotopic_multi_output_data(x, y, output_indices);\n\njulia> inputs\n3-element Vector{Tuple{Float64, Int64}}:\n (1.0, 3)\n (2.0, 2)\n (3.0, 1)\n\njulia> outputs\n3-element Vector{Float64}:\n -1.0\n 0.0\n 1.0\n\nSee also prepare_isotopic_multi_output_data.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"The input types returned by prepare_isotopic_multi_output_data can also be constructed manually:","category":"page"},{"location":"api/","page":"API","title":"API","text":"MOInput","category":"page"},{"location":"api/#KernelFunctions.MOInput","page":"API","title":"KernelFunctions.MOInput","text":"MOInput(x::AbstractVector, out_dim::Integer)\n\nA data type to accommodate modelling multi-dimensional output data. MOInput(x, out_dim) has length length(x) * out_dim.\n\njulia> x = [1, 2, 3];\n\njulia> MOInput(x, 2)\n6-element KernelFunctions.MOInputIsotopicByOutputs{Int64, Vector{Int64}, Int64}:\n (1, 1)\n (2, 1)\n (3, 1)\n (1, 2)\n (2, 2)\n (3, 2)\n\nAs shown above, an MOInput represents a vector of tuples. The first length(x) elements represent the inputs for the first output, the second length(x) elements represent the inputs for the second output, etc. See Inputs for Multiple Outputs in the docs for more info.\n\nMOInput will be deprecated in version 0.11 in favour of MOInputIsotopicByOutputs, and removed in version 0.12.\n\n\n\n\n\n","category":"type"},{"location":"api/","page":"API","title":"API","text":"As with ColVecs and RowVecs for vector-valued input spaces, this type enables specialised implementations of e.g. kernelmatrix for MOInputs in some situations.","category":"page"},{"location":"api/","page":"API","title":"API","text":"To find out more about the background, read this review of kernels for vector-valued functions.","category":"page"},{"location":"api/#Generic-Utilities","page":"API","title":"Generic Utilities","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"KernelFunctions also provides miscellaneous utility functions.","category":"page"},{"location":"api/","page":"API","title":"API","text":"nystrom\nNystromFact","category":"page"},{"location":"api/#KernelFunctions.nystrom","page":"API","title":"KernelFunctions.nystrom","text":"nystrom(k::Kernel, X::AbstractVector, S::AbstractVector{<:Integer})\n\nCompute a factorization of a Nystrom approximation of the square kernel matrix of data vector X with respect to kernel k, using indices S. Returns a NystromFact struct which stores a Nystrom factorization satisfying:\n\nmathbfK approx mathbfC^intercalmathbfWmathbfC\n\n\n\n\n\nnystrom(k::Kernel, X::AbstractVector, r::Real)\n\nCompute a factorization of a Nystrom approximation of the square kernel matrix of data vector X with respect to kernel k using a sample ratio of r. Returns a NystromFact struct which stores a Nystrom factorization satisfying:\n\nmathbfK approx mathbfC^intercalmathbfWmathbfC\n\n\n\n\n\nnystrom(k::Kernel, X::AbstractMatrix, S::AbstractVector{<:Integer}; obsdim)\n\nIf obsdim=1, equivalent to nystrom(k, RowVecs(X), S). If obsdim=2, equivalent to nystrom(k, ColVecs(X), S).\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\nnystrom(k::Kernel, X::AbstractMatrix, r::Real; obsdim)\n\nIf obsdim=1, equivalent to nystrom(k, RowVecs(X), r). If obsdim=2, equivalent to nystrom(k, ColVecs(X), r).\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"api/#KernelFunctions.NystromFact","page":"API","title":"KernelFunctions.NystromFact","text":"NystromFact\n\nType for storing a Nystrom factorization. The factorization contains two fields: W and C, two matrices satisfying:\n\nmathbfK approx mathbfC^intercalmathbfWmathbfC\n\n\n\n\n\n","category":"type"},{"location":"api/#Conditional-Utilities","page":"API","title":"Conditional Utilities","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"To keep the dependencies of KernelFunctions lean, some functionality is only available if specific other packages are explicitly loaded (using).","category":"page"},{"location":"api/#Kronecker.jl","page":"API","title":"Kronecker.jl","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"https://github.com/MichielStock/Kronecker.jl","category":"page"},{"location":"api/","page":"API","title":"API","text":"kronecker_kernelmatrix\nkernelkronmat","category":"page"},{"location":"api/#KernelFunctions.kronecker_kernelmatrix","page":"API","title":"KernelFunctions.kronecker_kernelmatrix","text":"kronecker_kernelmatrix(\n k::Union{IndependentMOKernel,IntrinsicCoregionMOKernel}, x::MOI, y::MOI\n) where {MOI<:IsotopicMOInputsUnion}\n\nRequires Kronecker.jl: Computes the kernelmatrix for the IndependentMOKernel and the IntrinsicCoregionMOKernel, but returns a lazy kronecker product. This object can be very efficiently inverted or decomposed. See also kernelmatrix.\n\n\n\n\n\n","category":"function"},{"location":"api/#KernelFunctions.kernelkronmat","page":"API","title":"KernelFunctions.kernelkronmat","text":"kernelkronmat(κ::Kernel, X::AbstractVector{<:Real}, dims::Int) -> KroneckerPower\n\nReturn a KroneckerPower matrix on the D-dimensional input grid constructed by otimes_i=1^D X, where D is given by dims.\n\nwarning: Warning\nRequires Kronecker.jl and for iskroncompatible(κ) to return true.\n\n\n\n\n\nkernelkronmat(κ::Kernel, X::AbstractVector{<:AbstractVector}) -> KroneckerProduct\n\nReturns a KroneckerProduct matrix on the grid built with the collection of vectors X_i_i=1^D: otimes_i=1^D X_i.\n\nwarning: Warning\nRequires Kronecker.jl and for iskroncompatible(κ) to return true.\n\n\n\n\n\n","category":"function"},{"location":"api/#PDMats.jl","page":"API","title":"PDMats.jl","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"https://github.com/JuliaStats/PDMats.jl","category":"page"},{"location":"api/","page":"API","title":"API","text":"kernelpdmat","category":"page"},{"location":"api/#KernelFunctions.kernelpdmat","page":"API","title":"KernelFunctions.kernelpdmat","text":"kernelpdmat(k::Kernel, X::AbstractVector)\n\nCompute a positive-definite matrix in the form of a PDMat matrix (see PDMats.jl), with the Cholesky decomposition precomputed. The algorithm adds a diagonal \"nugget\" term to the kernel matrix which is increased until positive definiteness is achieved. The algorithm gives up with an error if the nugget becomes larger than 1% of the largest value in the kernel matrix.\n\n\n\n\n\nkernelpdmat(k::Kernel, X::AbstractMatrix; obsdim)\n\nIf obsdim=1, equivalent to kernelpdmat(k, RowVecs(X)). If obsdim=2, equivalent to kernelpdmat(k, ColVecs(X)).\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"design/#Design","page":"Design","title":"Design","text":"","category":"section"},{"location":"design/#why_abstract_vectors","page":"Design","title":"Why AbstractVectors Everywhere?","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"To understand the advantages of using AbstractVectors everywhere to represent collections of inputs, first consider the following properties that it is desirable for a collection of inputs to satisfy.","category":"page"},{"location":"design/#Unique-Ordering","page":"Design","title":"Unique Ordering","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"There must be a clearly-defined first, second, etc element of an input collection. If this were not the case, it would not be possible to determine a unique mapping between a collection of inputs and the output of kernelmatrix, as it would not be clear what order the rows and columns of the output should appear in.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"Moreover, ordering guarantees that if you permute the collection of inputs, the ordering of the rows and columns of the kernelmatrix are correspondingly permuted.","category":"page"},{"location":"design/#Generality","page":"Design","title":"Generality","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"There must be no restriction on the domain of the input. Collections of Reals, vectors, graphs, finite-dimensional domains, or really anything else that you fancy should be straightforwardly representable. Moreover, whichever input class is chosen should not prevent optimal performance from being obtained.","category":"page"},{"location":"design/#Unambiguously-Defined-Length","page":"Design","title":"Unambiguously-Defined Length","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"Knowing the length of a collection of inputs is important. For example, a well-defined length guarantees that the size of the output of kernelmatrix, and related functions, are predictable. It also makes it possible to perform internal error-checking that ensures that e.g. there are the same number of inputs in two collections of inputs.","category":"page"},{"location":"design/#AbstractMatrices-Do-Not-Cut-It","page":"Design","title":"AbstractMatrices Do Not Cut It","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"Notably, while AbstractMatrix objects are often used to represent collections of vector-valued inputs, they do not immediately satisfy these properties as it is unclear whether a matrix of size P x Q represents a collection of P Q-dimensional inputs (each row is an input), or Q P-dimensional inputs (each column is an input).","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"Moreover, they occasionally add some aesthetic inconvenience. For example, a collection of Real-valued inputs, which might be straightforwardly represented as an AbstractVector{<:Real}, must be reshaped into a matrix.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"There are two commonly used ways to partly resolve these shortcomings:","category":"page"},{"location":"design/#Resolution-1:-Specify-a-Convention","page":"Design","title":"Resolution 1: Specify a Convention","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"One way that these shortcomings can be partly resolved is by specifying a convention that everyone adheres to regarding the interpretation of rows vs columns. However, opinions about the choice of convention are often surprisingly strongly held, and users regularly have to remind themselves which convention has been chosen. While this resolves the ordering problem, and in principle defines the \"length\" of a collection of inputs, AbstractMatrixs already have a length defined in Julia, which would generally disagree with our internal notion of length. This isn't a show-stopper, but it isn't an especially clean situation.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"There is also the opportunity for some kinds of silent bugs. For example, if an input matrix happens to be square because the number of input dimensions is the same as the number of inputs, it would be hard to know whether the correct kernelmatrix has been computed. This kind of bug seems unlikely, but it exists regardless.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"Finally, suppose that your inputs are some type T that is not simply a vector of real numbers, say a graph. In this situation, how should a collection of inputs be represented? A N x 1 or 1 x N matrix is the only obvious candidate, but the additional singular dimension seems somewhat redundant.","category":"page"},{"location":"design/#Resolution-2:-Always-Specify-An-obsdim-Argument","page":"Design","title":"Resolution 2: Always Specify An obsdim Argument","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"Another way to partly resolve these problems is to not commit to a convention, and instead to propagate some additional information through the codebase that specifies how the input data is to be interpreted. For example, a kernel k that represents the sum of two other kernels might implement kernelmatrix as follows:","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"function kernelmatrix(k::KernelSum, x::AbstractMatrix; obsdim=1)\n return kernelmatrix(k.kernels[1], x; obsdim=obsdim) +\n kernelmatrix(k.kernels[2], x; obsdim=obsdim)\nend","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"While this prevents this package from having to pre-specify a convention, it doesn't resolve the length issue, or the issue of representing collections of inputs which aren't immediately represented as vectors. Moreover, it complicates the internals; in contrast, consider what this function looks like with an AbstractVector:","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"function kernelmatrix(k::KernelSum, x::AbstractVector)\n return kernelmatrix(k.kernels[1], x) + kernelmatrix(k.kernels[2], x)\nend","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"This code is clearer (less visual noise), and has removed a possible bug – if the implementer of kernelmatrix forgets to pass the obsdim kwarg into each subsequent kernelmatrix call, it's possible to get the wrong answer.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"This being said, we do support matrix-valued inputs – see Why We Have Support for Both.","category":"page"},{"location":"design/#AbstractVectors","page":"Design","title":"AbstractVectors","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"Requiring all collections of inputs to be AbstractVectors resolves all of these problems, and ensures that the data is self-describing to the extent that KernelFunctions.jl requires.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"Firstly, the question of how to interpret the columns and rows of a matrix of inputs is resolved. Users must wrap matrices which represent collections of inputs in either a ColVecs or RowVecs, both of which have clearly defined semantics which are hard to confuse.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"By design, there is also no discrepancy between the number of inputs in the collection, and the length function – the length of a ColVecs, RowVecs, or Vector{<:Real} is equal to the number of inputs.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"There is no loss of performance.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"A collection of N Real-valued inputs can be represented by an AbstractVector{<:Real} of length N, rather than needing to use an AbstractMatrix{<:Real} of size either N x 1 or 1 x N. The same can be said for any other input type T, and new subtypes of AbstractVector can be added if particularly efficient ways exist to store collections of inputs of type T. A good example of this in practice is using Tuple{S, Int}, for some input type S, as the Inputs for Multiple Outputs.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"This approach can also lead to clearer user code. A user need only wrap their inputs in a ColVecs or RowVecs once in their code, and this specification is automatically re-used everywhere in their code. In this sense, it is straightforward to write code in such a way that there is one unique source of \"truth\" about the way in which a particular data set should be interpreted. Conversely, the obsdim resolution requires that the obsdim keyword argument is passed around with the data every single time that you use it.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"The benefits of the AbstractVector approach are likely most strongly felt when writing a substantial amount of code on top of KernelFunctions.jl – in the same way that using AbstractVectors inside KernelFunctions.jl removes the need for large amounts of keyword argument propagation, the same will be true of other code.","category":"page"},{"location":"design/#Why-We-Have-Support-for-Both","page":"Design","title":"Why We Have Support for Both","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"In short: many people like matrices, and are familiar with obsdim-style keyword arguments.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"All internals are implemented using AbstractVectors though, and the obsdim interface is just a thin layer of utility functionality which sits on top of this. To avoid confusion and silent errors, we do not favour a specific convention (rows or columns) but instead it is necessary to specify the obsdim keyword argument explicitly.","category":"page"},{"location":"design/#inputs_for_multiple_outputs","page":"Design","title":"Kernels for Multiple-Outputs","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"There are two equally-valid perspectives on multi-output kernels: they can either be treated as matrix-valued kernels, or standard kernels on an extended input domain. Each of these perspectives are convenient in different circumstances, but the latter greatly simplifies the incorporation of multi-output kernels in KernelFunctions.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"More concretely, let k_mat be a matrix-valued kernel, mapping pairs of inputs of type T to matrices of size P x P to describe the covariance between P outputs. Given inputs x and y of type T, and integers p and q, we can always find an equivalent standard kernel k mapping from pairs of inputs of type Tuple{T, Int} to the Reals as follows:","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"k((x, p), (y, q)) = k_mat(x, y)[p, q]","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"This ability to treat multi-output kernels as single-output kernels is very helpful, as it means that there is no need to introduce additional concepts into the API of KernelFunctions.jl, just additional kernels! This in turn simplifies downstream code as they don't need to \"know\" about the existence of multi-output kernels in addition to standard kernels. For example, GP libraries built on top of KernelFunctions.jl just need to know about Kernels, and they get multi-output kernels, and hence multi-output GPs, for free.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"Where there is the need to specialise implementations for multi-output kernels, this is done in an encapsulated manner – parts of KernelFunctions that have nothing to do with multi-output kernels know nothing about the existence of multi-output kernels.","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"EditURL = \"../../../../examples/support-vector-machine/script.jl\"","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/master/examples/support-vector-machine/script.jl\"","category":"page"},{"location":"examples/support-vector-machine/#Support-Vector-Machine","page":"Support Vector Machine","title":"Support Vector Machine","text":"","category":"section"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"(Image: )","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"In this notebook we show how you can use KernelFunctions.jl to generate kernel matrices for classification with a support vector machine, as implemented by LIBSVM.","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"using Distributions\nusing KernelFunctions\nusing LIBSVM\nusing LinearAlgebra\nusing Plots\nusing Random\n\n# Set seed\nRandom.seed!(1234);","category":"page"},{"location":"examples/support-vector-machine/#Generate-half-moon-dataset","page":"Support Vector Machine","title":"Generate half-moon dataset","text":"","category":"section"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"Number of samples per class:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"n1 = n2 = 50;","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"We generate data based on SciKit-Learn's sklearn.datasets.make_moons function:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"angle1 = range(0, π; length=n1)\nangle2 = range(0, π; length=n2)\nX1 = [cos.(angle1) sin.(angle1)] .+ 0.1 .* randn.()\nX2 = [1 .- cos.(angle2) 1 .- sin.(angle2) .- 0.5] .+ 0.1 .* randn.()\nX = [X1; X2]\nx_train = RowVecs(X)\ny_train = vcat(fill(-1, n1), fill(1, n2));","category":"page"},{"location":"examples/support-vector-machine/#Training","page":"Support Vector Machine","title":"Training","text":"","category":"section"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"We create a kernel function:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"k = SqExponentialKernel() ∘ ScaleTransform(1.5)","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"Squared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t- Scale Transform (s = 1.5)","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"LIBSVM can make use of a pre-computed kernel matrix. KernelFunctions.jl can be used to produce that using kernelmatrix:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"model = svmtrain(kernelmatrix(k, x_train), y_train; kernel=LIBSVM.Kernel.Precomputed)","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"LIBSVM.SVM{Int64, LIBSVM.Kernel.KERNEL}(LIBSVM.SVC, LIBSVM.Kernel.Precomputed, nothing, 100, 100, 2, [-1, 1], Int32[1, 2], Float64[], Int32[], LIBSVM.SupportVectors{Vector{Int64}, Matrix{Float64}}(23, Int32[11, 12], [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1.0 0.8982223633317491 0.9596163170022011 0.8681749917956418 0.7405298560587654 0.6670753594660519 0.1779671467515013 0.12581804740739566 0.05707943398657384 0.02764121723161683 0.033765857073249396 0.2680295766735067 0.29939058530607915 0.37151489965630213 0.3524014409758097 0.2908959282977835 0.3880509811446821 0.8766234308310106 0.82681374480545 0.8144257681324784 0.6772129558340088 0.6360692761241019 0.27226866397259536; 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1.0; 1.0; 1.0; 0.4545873718969774; 0.36172853884920114; 1.0; 1.0; 0.9976825435225717; 1.0; 1.0; -1.0; -1.0; -1.0; -1.0; -0.5005315477488701; -0.21806563021962358; -1.0; -0.3833339180359196; -1.0; -0.7120673582643366; -1.0; -1.0;;], Float64[], Float64[], [-0.015075000482567661], 3, 0.01, 200.0, 0.001, 1.0, 0.5, 0.1, true, false)","category":"page"},{"location":"examples/support-vector-machine/#Prediction","page":"Support Vector Machine","title":"Prediction","text":"","category":"section"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"For evaluation, we create a 100×100 2D grid based on the extent of the training data:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"test_range = range(floor(Int, minimum(X)), ceil(Int, maximum(X)); length=100)\nx_test = ColVecs(mapreduce(collect, hcat, Iterators.product(test_range, test_range)));","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"Again, we pass the result of KernelFunctions.jl's kernelmatrix to LIBSVM:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"y_pred, _ = svmpredict(model, kernelmatrix(k, x_train, x_test));","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"We can see that the kernelized, non-linear classification successfully separates the two classes in the training data:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"plot(; lim=extrema(test_range), aspect_ratio=1)\ncontourf!(\n test_range,\n test_range,\n y_pred;\n levels=1,\n color=cgrad(:redsblues),\n alpha=0.7,\n colorbar_title=\"prediction\",\n)\nscatter!(X1[:, 1], X1[:, 2]; color=:red, label=\"training data: class –1\")\nscatter!(X2[:, 1], X2[:, 2]; color=:blue, label=\"training data: class 1\")","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"
      \n
      Package and system information
      \n
      \nPackage information (click to expand)\n
      \nStatus `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/support-vector-machine/Project.toml`\n  [31c24e10] Distributions v0.25.102\n  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`\n  [b1bec4e5] LIBSVM v0.8.0\n  [98b081ad] Literate v2.15.0\n  [91a5bcdd] Plots v1.39.0\n  [37e2e46d] LinearAlgebra\n
      \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
      \n
      \nSystem information (click to expand)\n
      \nJulia Version 1.9.3\nCommit bed2cd540a1 (2023-08-24 14:43 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-14.0.6 (ORCJIT, skylake-avx512)\n  Threads: 1 on 2 virtual cores\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
      \n
      ","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"This page was generated using Literate.jl.","category":"page"},{"location":"metrics/#Metrics","page":"Metrics","title":"Metrics","text":"","category":"section"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"SimpleKernel implementations rely on Distances.jl for efficiently computing the pairwise matrix. This requires a distance measure or metric, such as the commonly used SqEuclidean and Euclidean.","category":"page"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"The metric used by a given kernel type is specified as","category":"page"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"KernelFunctions.metric(::CustomKernel) = SqEuclidean()","category":"page"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"However, there are kernels that can be implemented efficiently using \"metrics\" that do not respect all the definitions expected by Distances.jl. For this reason, KernelFunctions.jl provides additional \"metrics\" such as DotProduct (langle x y rangle) and Delta (delta(xy)).","category":"page"},{"location":"metrics/#Adding-a-new-metric","page":"Metrics","title":"Adding a new metric","text":"","category":"section"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"If you want to create a new \"metric\" just implement the following:","category":"page"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"struct Delta <: Distances.PreMetric\nend\n\n@inline function Distances._evaluate(::Delta,a::AbstractVector{T},b::AbstractVector{T}) where {T}\n @boundscheck if length(a) != length(b)\n throw(DimensionMismatch(\"first array has length $(length(a)) which does not match the length of the second, $(length(b)).\"))\n end\n return a==b\nend\n\n@inline (dist::Delta)(a::AbstractArray,b::AbstractArray) = Distances._evaluate(dist,a,b)\n@inline (dist::Delta)(a::Number,b::Number) = a==b","category":"page"},{"location":"transform/#input_transforms","page":"Input Transforms","title":"Input Transforms","text":"","category":"section"},{"location":"transform/#Overview","page":"Input Transforms","title":"Overview","text":"","category":"section"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"Transforms are designed to change input data before passing it on to a kernel object.","category":"page"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"It can be as standard as IdentityTransform returning the same input, or multiplying the data by a scalar with ScaleTransform or by a vector with ARDTransform. There is a more general FunctionTransform that uses a function and applies it to each input.","category":"page"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"You can also create a pipeline of Transforms via ChainTransform, e.g.,","category":"page"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"LowRankTransform(rand(10, 5)) ∘ ScaleTransform(2.0)","category":"page"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"A transformation t can be applied to a single input x with t(x) and to multiple inputs xs with map(t, xs).","category":"page"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"Kernels can be coupled with input transformations with ∘ or its alias compose. It falls back to creating a TransformedKernel but allows more optimized implementations for specific kernels and transformations.","category":"page"},{"location":"transform/#List-of-Input-Transforms","page":"Input Transforms","title":"List of Input Transforms","text":"","category":"section"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"Transform\nIdentityTransform\nScaleTransform\nARDTransform\nARDTransform(::Real, ::Integer)\nLinearTransform\nFunctionTransform\nSelectTransform\nChainTransform\nPeriodicTransform","category":"page"},{"location":"transform/#KernelFunctions.Transform","page":"Input Transforms","title":"KernelFunctions.Transform","text":"Transform\n\nAbstract type defining a transformation of the input.\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.IdentityTransform","page":"Input Transforms","title":"KernelFunctions.IdentityTransform","text":"IdentityTransform()\n\nTransformation that returns exactly the input.\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.ScaleTransform","page":"Input Transforms","title":"KernelFunctions.ScaleTransform","text":"ScaleTransform(l::Real)\n\nTransformation that multiplies the input elementwise with l.\n\nExamples\n\njulia> l = rand(); t = ScaleTransform(l); X = rand(100, 10);\n\njulia> map(t, ColVecs(X)) == ColVecs(l .* X)\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.ARDTransform","page":"Input Transforms","title":"KernelFunctions.ARDTransform","text":"ARDTransform(v::AbstractVector)\n\nTransformation that multiplies the input elementwise by v.\n\nExamples\n\njulia> v = rand(10); t = ARDTransform(v); X = rand(10, 100);\n\njulia> map(t, ColVecs(X)) == ColVecs(v .* X)\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.ARDTransform-Tuple{Real, Integer}","page":"Input Transforms","title":"KernelFunctions.ARDTransform","text":"ARDTransform(s::Real, dims::Integer)\n\nCreate an ARDTransform with vector fill(s, dims).\n\n\n\n\n\n","category":"method"},{"location":"transform/#KernelFunctions.LinearTransform","page":"Input Transforms","title":"KernelFunctions.LinearTransform","text":"LinearTransform(A::AbstractMatrix)\n\nLinear transformation of the input realised by the matrix A.\n\nThe second dimension of A must match the number of features of the target.\n\nExamples\n\njulia> A = rand(10, 5); t = LinearTransform(A); X = rand(5, 100);\n\njulia> map(t, ColVecs(X)) == ColVecs(A * X)\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.FunctionTransform","page":"Input Transforms","title":"KernelFunctions.FunctionTransform","text":"FunctionTransform(f)\n\nTransformation that applies function f to the input.\n\nMake sure that f can act on an input. For instance, if the inputs are vectors, use f(x) = sin.(x) instead of f = sin.\n\nExamples\n\njulia> f(x) = sum(x); t = FunctionTransform(f); X = randn(100, 10);\n\njulia> map(t, ColVecs(X)) == ColVecs(sum(X; dims=1))\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.SelectTransform","page":"Input Transforms","title":"KernelFunctions.SelectTransform","text":"SelectTransform(dims)\n\nTransformation that selects the dimensions dims of the input.\n\nExamples\n\njulia> dims = [1, 3, 5, 6, 7]; t = SelectTransform(dims); X = rand(100, 10);\n\njulia> map(t, ColVecs(X)) == ColVecs(X[dims, :])\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.ChainTransform","page":"Input Transforms","title":"KernelFunctions.ChainTransform","text":"ChainTransform(transforms)\n\nTransformation that applies a chain of transformations ts to the input.\n\nThe transformation first(ts) is applied first.\n\nExamples\n\njulia> l = rand(); A = rand(3, 4); t1 = ScaleTransform(l); t2 = LinearTransform(A);\n\njulia> X = rand(4, 10);\n\njulia> map(ChainTransform([t1, t2]), ColVecs(X)) == ColVecs(A * (l .* X))\ntrue\n\njulia> map(t2 ∘ t1, ColVecs(X)) == ColVecs(A * (l .* X))\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.PeriodicTransform","page":"Input Transforms","title":"KernelFunctions.PeriodicTransform","text":"PeriodicTransform(f)\n\nTransformation that maps the input elementwise onto the unit circle with frequency f.\n\nSamples from a GP with a kernel with this transformation applied to the inputs will produce samples with frequency f.\n\nExamples\n\njulia> f = rand(); t = PeriodicTransform(f); x = rand();\n\njulia> t(x) == [sinpi(2 * f * x), cospi(2 * f * x)]\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#Convenience-functions","page":"Input Transforms","title":"Convenience functions","text":"","category":"section"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"with_lengthscale\nmedian_heuristic_transform","category":"page"},{"location":"transform/#KernelFunctions.with_lengthscale","page":"Input Transforms","title":"KernelFunctions.with_lengthscale","text":"with_lengthscale(kernel::Kernel, lengthscale::Real)\n\nConstruct a transformed kernel with lengthscale.\n\nExamples\n\njulia> kernel = with_lengthscale(SqExponentialKernel(), 2.5);\n\njulia> x = rand(2);\n\njulia> y = rand(2);\n\njulia> kernel(x, y) ≈ (SqExponentialKernel() ∘ ScaleTransform(0.4))(x, y)\ntrue\n\n\n\n\n\nwith_lengthscale(kernel::Kernel, lengthscales::AbstractVector{<:Real})\n\nConstruct a transformed \"ARD\" kernel with different lengthscales for each dimension.\n\nExamples\n\njulia> kernel = with_lengthscale(SqExponentialKernel(), [0.5, 2.5]);\n\njulia> x = rand(2);\n\njulia> y = rand(2);\n\njulia> kernel(x, y) ≈ (SqExponentialKernel() ∘ ARDTransform([2, 0.4]))(x, y)\ntrue\n\n\n\n\n\n","category":"function"},{"location":"transform/#KernelFunctions.median_heuristic_transform","page":"Input Transforms","title":"KernelFunctions.median_heuristic_transform","text":"median_heuristic_transform(distance, x::AbstractVector)\n\nCreate a ScaleTransform that divides the input elementwise by the median distance of the data points in x.\n\nThe distance has to support pairwise evaluation with KernelFunctions.pairwise. All PreMetrics of the package Distances.jl such as Euclidean satisfy this requirement automatically.\n\nExamples\n\njulia> using Distances, Statistics\n\njulia> x = ColVecs(rand(100, 10));\n\njulia> t = median_heuristic_transform(Euclidean(), x);\n\njulia> y = map(t, x);\n\njulia> median(euclidean(y[i], y[j]) for i in 1:10, j in 1:10 if i != j) ≈ 1\ntrue\n\n\n\n\n\n","category":"function"},{"location":"userguide/#User-guide","page":"User guide","title":"User guide","text":"","category":"section"},{"location":"userguide/#Kernel-Creation","page":"User guide","title":"Kernel Creation","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"To create a kernel object, choose one of the pre-implemented kernels, see Kernel Functions, or create your own, see Creating your own kernel. For example, a squared exponential kernel is created by","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":" k = SqExponentialKernel()","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"tip: How do I set the lengthscale(s)?\nInstead of having lengthscale(s) for each kernel we use Transform objects which act on the inputs before passing them to the kernel. Note that the transforms such as ScaleTransform and ARDTransform multiply the input by a scale factor, which corresponds to the inverse of the lengthscale. For example, a lengthscale of 0.5 is equivalent to premultiplying the input by 2.0, and you can create the corresponding kernel in either of the following equivalent ways: k = SqExponentialKernel() ∘ ScaleTransform(2.0)\n k = compose(SqExponentialKernel(), ScaleTransform(2.0))Alternatively, you can use the convenience function with_lengthscale:k = with_lengthscale(SqExponentialKernel(), 0.5)with_lengthscale also works with vector-valued lengthscales for multiple-dimensional inputs, and is equivalent to pre-composing with an ARDTransform:length_scales = [1.0, 2.0]\nk = with_lengthscale(SqExponentialKernel(), length_scales)\nk = SqExponentialKernel() ∘ ARDTransform(1 ./ length_scales)Check the Input Transforms page for more details.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"tip: How do I set the kernel variance?\nTo premultiply the kernel by a variance, you can use * with a scalar number: k = 3.0 * SqExponentialKernel()","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"tip: How do I use a Mahalanobis kernel?\nThe MahalanobisKernel(; P=P), defined byk(x x P) = expbig(- (x - x)^top P (x - x)big)for a positive definite matrix P = Q^top Q, was removed in 0.9. Instead you can use a squared exponential kernel together with a LinearTransform of the inputs:k = SqExponentialKernel() ∘ LinearTransform(sqrt(2) .* Q)Analogously, you can combine other kernels such as the PiecewisePolynomialKernel with a LinearTransform of the inputs to obtain a kernel that is a function of the Mahalanobis distance between inputs.","category":"page"},{"location":"userguide/#Using-a-Kernel-Function","page":"User guide","title":"Using a Kernel Function","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"To evaluate the kernel function on two vectors you simply call the kernel object:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"k = SqExponentialKernel()\nx1 = rand(3)\nx2 = rand(3)\nk(x1, x2)","category":"page"},{"location":"userguide/#Creating-a-Kernel-Matrix","page":"User guide","title":"Creating a Kernel Matrix","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"Kernel matrices can be created via the kernelmatrix function or kernelmatrix_diag for only the diagonal. For example, for a collection of 10 Real-valued inputs:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"k = SqExponentialKernel()\nx = rand(10)\nkernelmatrix(k, x) # 10x10 matrix","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"If your inputs are multi-dimensional, it is common to represent them as a matrix. For example","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"X = rand(10, 5)","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"However, it is ambiguous whether this represents a collection of 10 5-dimensional row-vectors, or 5 10-dimensional column-vectors. Therefore, we require users to provide some more information.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"You can write RowVecs(X) to declare that X contains 10 5-dimensional row-vectors, or ColVecs(X) to declare that X contains 5 10-dimensional column-vectors, then","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"kernelmatrix(k, RowVecs(X)) # returns a 10×10 matrix -- each row of X treated as input\nkernelmatrix(k, ColVecs(X)) # returns a 5×5 matrix -- each column of X treated as input","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"This is the mechanism used throughout KernelFunctions.jl to handle multi-dimensional inputs.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"You can utilise the obsdim keyword argument if you prefer:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"kernelmatrix(k, X; obsdim=1) # same as RowVecs(X)\nkernelmatrix(k, X; obsdim=2) # same as ColVecs(X)","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"This is similar to the convention used in Distances.jl.","category":"page"},{"location":"userguide/#So-what-type-should-I-use-to-represent-a-collection-of-inputs?","page":"User guide","title":"So what type should I use to represent a collection of inputs?","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"The central assumption made by KernelFunctions.jl is that all collections of N inputs are represented by AbstractVectors of length N. Abstraction is then used to ensure that efficiency is retained, ColVecs and RowVecs being the most obvious examples of this.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"Concretely:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"For Real-valued inputs (scalars), a Vector{<:Real} is fine.\nFor vector-valued inputs, consider a ColVecs or RowVecs.\nFor a new input type, simply represent collections of inputs of this type as an AbstractVector.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"See Input Types and Design for a more thorough discussion of the considerations made when this design was adopted.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"The obsdim kwarg mentioned above is a special case for vector-valued inputs stored in a matrix. It is implemented as a lightweight wrapper that constructs either a RowVecs or ColVecs from your inputs, and passes this on.","category":"page"},{"location":"userguide/#Output-Types","page":"User guide","title":"Output Types","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"In addition to plain Matrix-like output, KernelFunctions.jl supports specific output types:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"For a positive-definite matrix object of type PDMat from PDMats.jl, you can call the following:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"using PDMats\nk = SqExponentialKernel()\nK = kernelpdmat(k, RowVecs(X)) # PDMat\nK = kernelpdmat(k, X; obsdim=1) # PDMat","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"It will create a matrix and in case of bad conditioning will add some diagonal noise until the matrix is considered positive-definite; it will then return a PDMat object. For this method to work in your code you need to include using PDMats first.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"For a Kronecker matrix, we rely on Kronecker.jl. Here are two examples:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"using Kronecker\nx = range(0, 1; length=10)\ny = range(0, 1; length=50)\nK = kernelkronmat(k, [x, y]) # Kronecker matrix\nK = kernelkronmat(k, x, 5) # Kronecker matrix","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"Make sure that k is a kernel compatible with such constructions (with iskroncompatible(k)). Both methods will return a Kronecker matrix. For those methods to work in your code you need to include using Kronecker first.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"For a Nystrom approximation: kernelmatrix(nystrom(k, X, ρ, obsdim=1)) where ρ is the fraction of data samples used in the approximation.","category":"page"},{"location":"userguide/#Composite-Kernels","page":"User guide","title":"Composite Kernels","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"Sums and products of kernels are also valid kernels. They can be created via KernelSum and KernelProduct or using simple operators + and *. For example:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"k1 = SqExponentialKernel()\nk2 = Matern32Kernel()\nk = 0.5 * k1 + 0.2 * k2 # KernelSum\nk = k1 * k2 # KernelProduct","category":"page"},{"location":"userguide/#Kernel-Parameters","page":"User guide","title":"Kernel Parameters","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"What if you want to differentiate through the kernel parameters? This is easy even in a highly nested structure such as:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"k = (\n 0.5 * SqExponentialKernel() * Matern12Kernel() +\n 0.2 * (LinearKernel() ∘ ScaleTransform(2.0) + PolynomialKernel())\n) ∘ ARDTransform([0.1, 0.5])","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"One can access the named tuple of trainable parameters via Functors.functor from Functors.jl. This means that in practice you can implicitly optimize the kernel parameters by calling:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"using Flux\nkernelparams = Flux.params(k)\nFlux.gradient(kernelparams) do\n # ... some loss function on the kernel ....\nend","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"EditURL = \"../../../../examples/gaussian-process-priors/script.jl\"","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/master/examples/gaussian-process-priors/script.jl\"","category":"page"},{"location":"examples/gaussian-process-priors/#Gaussian-process-prior-samples","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"","category":"section"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"(Image: )","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"The kernels defined in this package can also be used to specify the covariance of a Gaussian process prior. A Gaussian process (GP) is defined by its mean function m(cdot) and its covariance function or kernel k(cdot cdot):","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":" f sim mathcalGPbig(m(cdot) k(cdot cdot)big)","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"In this notebook we show how the choice of kernel affects the samples from a GP (with zero mean).","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"# Load required packages\nusing KernelFunctions, LinearAlgebra\nusing Plots, Plots.PlotMeasures\ndefault(; lw=1.0, legendfontsize=8.0)\nusing Random: seed!\nseed!(42); # reproducibility","category":"page"},{"location":"examples/gaussian-process-priors/#Evaluation-at-finite-set-of-points","page":"Gaussian process prior samples","title":"Evaluation at finite set of points","text":"","category":"section"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"The function values mathbff = f(x_n)_n=1^N of the GP at a finite number N of points X = x_n_n=1^N follow a multivariate normal distribution mathbff sim mathcalMVN(mathbfm mathrmK) with mean vector mathbfm and covariance matrix mathrmK, where","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"beginaligned\n mathbfm_i = m(x_i) \n mathrmK_ij = k(x_i x_j)\nendaligned","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"with 1 le i j le N.","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"We can visualize the infinite-dimensional GP by evaluating it on a fine grid to approximate the dense real line:","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"num_inputs = 101\nxlim = (-5, 5)\nX = range(xlim...; length=num_inputs);","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"Given a kernel k, we can compute the kernel matrix as K = kernelmatrix(k, X).","category":"page"},{"location":"examples/gaussian-process-priors/#Random-samples","page":"Gaussian process prior samples","title":"Random samples","text":"","category":"section"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"To sample from the multivariate normal distribution p(mathbff) = mathcalMVN(0 mathrmK), we could make use of Distributions.jl and call rand(MvNormal(K)). Alternatively, we could use the AbstractGPs.jl package and construct a GP object which we evaluate at the points of interest and from which we can then sample: rand(GP(k)(X)).","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"Here, we will explicitly construct samples using the Cholesky factorization mathrmL = operatornamecholesky(mathrmK), with mathbff = mathrmL mathbfv, where mathbfv sim mathcalN(0 mathbfI) is a vector of standard-normal random variables.","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"We will use the same randomness mathbfv to generate comparable samples across different kernels.","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"num_samples = 7\nv = randn(num_inputs, num_samples);","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"Mathematically, a kernel matrix is by definition positive semi-definite, but due to finite-precision inaccuracies, the computed kernel matrix might not be exactly positive definite. To avoid Cholesky errors, we add a small \"nugget\" term on the diagonal:","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"function mvn_sample(K)\n L = cholesky(K + 1e-6 * I)\n f = L.L * v\n return f\nend;","category":"page"},{"location":"examples/gaussian-process-priors/#Visualization","page":"Gaussian process prior samples","title":"Visualization","text":"","category":"section"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"We now define a function that visualizes a kernel for us.","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"function visualize(k::Kernel)\n K = kernelmatrix(k, X)\n f = mvn_sample(K)\n\n p_kernel_2d = heatmap(\n X,\n X,\n K;\n yflip=true,\n colorbar=false,\n ylabel=string(nameof(typeof(k))),\n ylim=xlim,\n yticks=([xlim[1], 0, xlim[end]], [\"\\u22125\", raw\"$x'$\", \"5\"]),\n vlim=(0, 1),\n title=raw\"$k(x, x')$\",\n aspect_ratio=:equal,\n left_margin=5mm,\n )\n\n p_kernel_cut = plot(\n X,\n k.(X, 0.0);\n title=string(raw\"$k(x, x_\\mathrm{ref})$\"),\n label=raw\"$x_\\mathrm{ref}=0.0$\",\n legend=:topleft,\n foreground_color_legend=nothing,\n )\n plot!(X, k.(X, 1.5); label=raw\"$x_\\mathrm{ref}=1.5$\")\n\n p_samples = plot(X, f; c=\"blue\", title=raw\"$f(x)$\", ylim=(-3, 3), label=nothing)\n\n return plot(\n p_kernel_2d,\n p_kernel_cut,\n p_samples;\n layout=(1, 3),\n xlabel=raw\"$x$\",\n xlim=xlim,\n xticks=collect(xlim),\n )\nend;","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"We can now visualize a kernel and show samples from a Gaussian process with a given kernel:","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"plot(visualize(SqExponentialKernel()); size=(800, 210), bottommargin=5mm, topmargin=5mm)","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/gaussian-process-priors/#Kernel-comparison","page":"Gaussian process prior samples","title":"Kernel comparison","text":"","category":"section"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"This also allows us to compare different kernels:","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"kernels = [\n Matern12Kernel(),\n Matern32Kernel(),\n Matern52Kernel(),\n SqExponentialKernel(),\n WhiteKernel(),\n ConstantKernel(),\n LinearKernel(),\n compose(PeriodicKernel(), ScaleTransform(0.2)),\n NeuralNetworkKernel(),\n GibbsKernel(; lengthscale=x -> sum(exp ∘ sin, x)),\n]\nplot(\n [visualize(k) for k in kernels]...;\n layout=(length(kernels), 1),\n size=(800, 220 * length(kernels) + 100),\n)","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"
      \n
      Package and system information
      \n
      \nPackage information (click to expand)\n
      \nStatus `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/gaussian-process-priors/Project.toml`\n  [31c24e10] Distributions v0.25.102\n  [ec8451be] KernelFunctions v0.10.57 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#dff053f`\n  [98b081ad] Literate v2.15.0\n  [91a5bcdd] Plots v1.39.0\n  [37e2e46d] LinearAlgebra\n  [9a3f8284] Random\n
      \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
      \n
      \nSystem information (click to expand)\n
      \nJulia Version 1.9.3\nCommit bed2cd540a1 (2023-08-24 14:43 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 2 × Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-14.0.6 (ORCJIT, skylake-avx512)\n  Threads: 1 on 2 virtual cores\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
      \n
      ","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"This page was generated using Literate.jl.","category":"page"},{"location":"#KernelFunctions.jl","page":"Home","title":"KernelFunctions.jl","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"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:","category":"page"},{"location":"","page":"Home","title":"Home","text":"Flexibility: operations between kernels should be fluid and easy without breaking, with a user-friendly API.\nPlug-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.\nAutomatic Differentiation compatibility: all kernel functions which ought to be differentiable using AD packages like ForwardDiff.jl or Zygote.jl should be.","category":"page"},{"location":"","page":"Home","title":"Home","text":"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.","category":"page"},{"location":"","page":"Home","title":"Home","text":"See the User guide for a brief introduction.","category":"page"}] +[{"location":"create_kernel/#Custom-Kernels","page":"Custom Kernels","title":"Custom Kernels","text":"","category":"section"},{"location":"create_kernel/#Creating-your-own-kernel","page":"Custom Kernels","title":"Creating your own kernel","text":"","category":"section"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"KernelFunctions.jl contains the most popular kernels already but you might want to make your own!","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"Here are a few ways depending on how complicated your kernel is:","category":"page"},{"location":"create_kernel/#SimpleKernel-for-kernel-functions-depending-on-a-metric","page":"Custom Kernels","title":"SimpleKernel for kernel functions depending on a metric","text":"","category":"section"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"If your kernel function is of the form k(x, y) = f(d(x, y)) where d(x, y) is a PreMetric, you can construct your custom kernel by defining kappa and metric for your kernel. Here is for example how one can define the SqExponentialKernel again:","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"struct MyKernel <: KernelFunctions.SimpleKernel end\n\nKernelFunctions.kappa(::MyKernel, d2::Real) = exp(-d2)\nKernelFunctions.metric(::MyKernel) = SqEuclidean()","category":"page"},{"location":"create_kernel/#Kernel-for-more-complex-kernels","page":"Custom Kernels","title":"Kernel for more complex kernels","text":"","category":"section"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"If your kernel does not satisfy such a representation, all you need to do is define (k::MyKernel)(x, y) and inherit from Kernel. For example, we recreate here the NeuralNetworkKernel:","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"struct MyKernel <: KernelFunctions.Kernel end\n\n(::MyKernel)(x, y) = asin(dot(x, y) / sqrt((1 + sum(abs2, x)) * (1 + sum(abs2, y))))","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"Note that the fallback implementation of the base Kernel evaluation does not use Distances.jl and can therefore be a bit slower.","category":"page"},{"location":"create_kernel/#Additional-Options","page":"Custom Kernels","title":"Additional Options","text":"","category":"section"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"Finally there are additional functions you can define to bring in more features:","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"KernelFunctions.iskroncompatible(k::MyKernel): if your kernel factorizes in dimensions, you can declare your kernel as iskroncompatible(k) = true to use Kronecker methods.\nKernelFunctions.dim(x::MyDataType): by default the dimension of the inputs will only be checked for vectors of type AbstractVector{<:Real}. If you want to check the dimensionality of your inputs, dispatch the dim function on your datatype. Note that 0 is the default.\ndim is called within KernelFunctions.validate_inputs(x::MyDataType, y::MyDataType), which can instead be directly overloaded if you want to run special checks for your input types.\nkernelmatrix(k::MyKernel, ...): you can redefine the diverse kernelmatrix functions to eventually optimize the computations.\nBase.print(io::IO, k::MyKernel): if you want to specialize the printing of your kernel.","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"KernelFunctions uses Functors.jl for specifying trainable kernel parameters in a way that is compatible with the Flux ML framework. You can use Functors.@functor if all fields of your kernel struct are trainable. Note that optimization algorithms in Flux are not compatible with scalar parameters (yet), and hence vector-valued parameters should be preferred.","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"import Functors\n\nstruct MyKernel{T} <: KernelFunctions.Kernel\n a::Vector{T}\nend\n\nFunctors.@functor MyKernel","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"If only a subset of the fields are trainable, you have to specify explicitly how to (re)construct the kernel with modified parameter values by implementing Functors.functor(::Type{<:MyKernel}, x) for your kernel struct:","category":"page"},{"location":"create_kernel/","page":"Custom Kernels","title":"Custom Kernels","text":"import Functors\n\nstruct MyKernel{T} <: KernelFunctions.Kernel\n n::Int\n a::Vector{T}\nend\n\nfunction Functors.functor(::Type{<:MyKernel}, x::MyKernel)\n function reconstruct_mykernel(xs)\n # keep field `n` of the original kernel and set `a` to (possibly different) `xs.a`\n return MyKernel(x.n, xs.a)\n end\n return (a = x.a,), reconstruct_mykernel\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"EditURL = \"../../../../examples/train-kernel-parameters/script.jl\"","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/master/examples/train-kernel-parameters/script.jl\"","category":"page"},{"location":"examples/train-kernel-parameters/#Train-Kernel-Parameters","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"(Image: )","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Here we show a few ways to train (optimize) the kernel (hyper)parameters at the example of kernel-based regression using KernelFunctions.jl. All options are functionally identical, but differ a little in readability, dependencies, and computational cost.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"We load KernelFunctions and some other packages. Note that while we use Zygote for automatic differentiation and Flux.optimise for optimization, you should be able to replace them with your favourite autodiff framework or optimizer.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"using KernelFunctions\nusing LinearAlgebra\nusing Distributions\nusing Plots\nusing BenchmarkTools\nusing Flux\nusing Flux: Optimise\nusing Zygote\nusing Random: seed!\nseed!(42);","category":"page"},{"location":"examples/train-kernel-parameters/#Data-Generation","page":"Train Kernel Parameters","title":"Data Generation","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"We generate a toy dataset in 1 dimension:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"xmin, xmax = -3, 3 # Bounds of the data\nN = 50 # Number of samples\nx_train = rand(Uniform(xmin, xmax), N) # sample the inputs\nσ = 0.1\ny_train = sinc.(x_train) + randn(N) * σ # evaluate a function and add some noise\nx_test = range(xmin - 0.1, xmax + 0.1; length=300)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Plot the data","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"scatter(x_train, y_train; label=\"data\")\nplot!(x_test, sinc; label=\"true function\")","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/train-kernel-parameters/#Manual-Approach","page":"Train Kernel Parameters","title":"Manual Approach","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"The first option is to rebuild the parametrized kernel from a vector of parameters in each evaluation of the cost function. This is similar to the approach taken in Stheno.jl.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"To train the kernel parameters via Zygote.jl, we need to create a function creating a kernel from an array. A simple way to ensure that the kernel parameters are positive is to optimize over the logarithm of the parameters.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"function kernel_creator(θ)\n return (exp(θ[1]) * SqExponentialKernel() + exp(θ[2]) * Matern32Kernel()) ∘\n ScaleTransform(exp(θ[3]))\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"From theory we know the prediction for a test set x given the kernel parameters and normalization constant:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"function f(x, x_train, y_train, θ)\n k = kernel_creator(θ[1:3])\n return kernelmatrix(k, x, x_train) *\n ((kernelmatrix(k, x_train) + exp(θ[4]) * I) \\ y_train)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Let's look at our prediction. With starting parameters p0 (picked so we get the right local minimum for demonstration) we get:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"p0 = [1.1, 0.1, 0.01, 0.001]\nθ = log.(p0)\nŷ = f(x_test, x_train, y_train, θ)\nscatter(x_train, y_train; label=\"data\")\nplot!(x_test, sinc; label=\"true function\")\nplot!(x_test, ŷ; label=\"prediction\")","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"We define the following loss:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"function loss(θ)\n ŷ = f(x_train, x_train, y_train, θ)\n return norm(y_train - ŷ) + exp(θ[4]) * norm(ŷ)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"The loss with our starting point:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"loss(θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"2.613933959118708","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Computational cost for one step:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"@benchmark let\n θ = log.(p0)\n opt = Optimise.ADAGrad(0.5)\n grads = only((Zygote.gradient(loss, θ)))\n Optimise.update!(opt, θ, grads)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"BenchmarkTools.Trial: 5900 samples with 1 evaluation.\n Range (min … max): 722.880 μs … 4.704 ms ┊ GC (min … max): 0.00% … 18.77%\n Time (median): 782.641 μs ┊ GC (median): 0.00%\n Time (mean ± σ): 844.350 μs ± 226.566 μs ┊ GC (mean ± σ): 5.48% ± 11.02%\n\n ▃██▇▆▅▃▂ ▁▁▁▁▁ ▂\n ▄█████████▇▇▅▅▅▁▃▃▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▃▁▁▃▁▃▁▁▁▁▁▁▁▃▄▆▇████████▇▇▆ █\n 723 μs Histogram: log(frequency) by time 1.75 ms <\n\n Memory estimate: 2.98 MiB, allocs estimate: 1563.","category":"page"},{"location":"examples/train-kernel-parameters/#Training-the-model","page":"Train Kernel Parameters","title":"Training the model","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Setting an initial value and initializing the optimizer:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"θ = log.(p0) # Initial vector\nopt = Optimise.ADAGrad(0.5)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Optimize","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"anim = Animation()\nfor i in 1:15\n grads = only((Zygote.gradient(loss, θ)))\n Optimise.update!(opt, θ, grads)\n scatter(\n x_train, y_train; lab=\"data\", title=\"i = $(i), Loss = $(round(loss(θ), digits = 4))\"\n )\n plot!(x_test, sinc; lab=\"true function\")\n plot!(x_test, f(x_test, x_train, y_train, θ); lab=\"Prediction\", lw=3.0)\n frame(anim)\nend\ngif(anim, \"train-kernel-param.gif\"; show_msg=false, fps=15);","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"(Image: )","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Final loss","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"loss(θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"0.5241118228076058","category":"page"},{"location":"examples/train-kernel-parameters/#Using-ParameterHandling.jl","page":"Train Kernel Parameters","title":"Using ParameterHandling.jl","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Alternatively, we can use the ParameterHandling.jl package to handle the requirement that all kernel parameters should be positive. The package also allows arbitrarily nesting named tuples that make the parameters more human readable, without having to remember their position in a flat vector.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"using ParameterHandling\n\nraw_initial_θ = (\n k1=positive(1.1), k2=positive(0.1), k3=positive(0.01), noise_var=positive(0.001)\n)\n\nflat_θ, unflatten = ParameterHandling.value_flatten(raw_initial_θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"4-element Vector{Float64}:\n 0.09531016625781467\n -2.3025852420056685\n -4.6051716761053205\n -6.907770180254354","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"We define a few relevant functions and note that compared to the previous kernel_creator function, we do not need explicit exps.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"function kernel_creator(θ)\n return (θ.k1 * SqExponentialKernel() + θ.k2 * Matern32Kernel()) ∘ ScaleTransform(θ.k3)\nend\n\nfunction f(x, x_train, y_train, θ)\n k = kernel_creator(θ)\n return kernelmatrix(k, x, x_train) *\n ((kernelmatrix(k, x_train) + θ.noise_var * I) \\ y_train)\nend\n\nfunction loss(θ)\n ŷ = f(x_train, x_train, y_train, θ)\n return norm(y_train - ŷ) + θ.noise_var * norm(ŷ)\nend\n\ninitial_θ = ParameterHandling.value(raw_initial_θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"The loss at the initial parameter values:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"(loss ∘ unflatten)(flat_θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"2.613933959118708","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Cost per step","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"@benchmark let\n θ = flat_θ[:]\n opt = Optimise.ADAGrad(0.5)\n grads = (Zygote.gradient(loss ∘ unflatten, θ))[1]\n Optimise.update!(opt, θ, grads)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"BenchmarkTools.Trial: 4901 samples with 1 evaluation.\n Range (min … max): 882.109 μs … 4.975 ms ┊ GC (min … max): 0.00% … 21.56%\n Time (median): 963.350 μs ┊ GC (median): 0.00%\n Time (mean ± σ): 1.017 ms ± 247.795 μs ┊ GC (mean ± σ): 4.88% ± 10.50%\n\n ▄▇▆██▇▅▃▁ ▁▁▁▁ ▂\n ██████████▇▆▅▄▅▄▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▄▅▆███████ █\n 882 μs Histogram: log(frequency) by time 2.1 ms <\n\n Memory estimate: 3.08 MiB, allocs estimate: 2228.","category":"page"},{"location":"examples/train-kernel-parameters/#Training-the-model-2","page":"Train Kernel Parameters","title":"Training the model","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Optimize","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"opt = Optimise.ADAGrad(0.5)\nfor i in 1:15\n grads = (Zygote.gradient(loss ∘ unflatten, flat_θ))[1]\n Optimise.update!(opt, flat_θ, grads)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Final loss","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"(loss ∘ unflatten)(flat_θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"0.524117624126251","category":"page"},{"location":"examples/train-kernel-parameters/#Flux.destructure","page":"Train Kernel Parameters","title":"Flux.destructure","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"If we don't want to write an explicit function to construct the kernel, we can alternatively use the Flux.destructure function. Again, we need to ensure that the parameters are positive. Note that the exp function is now part of the loss function, instead of part of the kernel construction.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"We could also use ParameterHandling.jl here. To do so, one would remove the exps from the loss function below and call loss ∘ unflatten as above.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"θ = [1.1, 0.1, 0.01, 0.001]\n\nkernel = (θ[1] * SqExponentialKernel() + θ[2] * Matern32Kernel()) ∘ ScaleTransform(θ[3])\n\nparams, kernelc = Flux.destructure(kernel);","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"This returns the trainable params of the kernel and a function to reconstruct the kernel.","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"kernelc(params)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Sum of 2 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- σ² = 1.1\n\tMatern 3/2 Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- σ² = 0.1\n\t- Scale Transform (s = 0.01)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"From theory we know the prediction for a test set x given the kernel parameters and normalization constant","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"function f(x, x_train, y_train, θ)\n k = kernelc(θ[1:3])\n return kernelmatrix(k, x, x_train) * ((kernelmatrix(k, x_train) + (θ[4]) * I) \\ y_train)\nend\n\nfunction loss(θ)\n ŷ = f(x_train, x_train, y_train, exp.(θ))\n return norm(y_train - ŷ) + exp(θ[4]) * norm(ŷ)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Cost for one step","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"@benchmark let θt = θ[:], optt = Optimise.ADAGrad(0.5)\n grads = only((Zygote.gradient(loss, θt)))\n Optimise.update!(optt, θt, grads)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"BenchmarkTools.Trial: 6044 samples with 1 evaluation.\n Range (min … max): 709.496 μs … 3.264 ms ┊ GC (min … max): 0.00% … 33.73%\n Time (median): 779.306 μs ┊ GC (median): 0.00%\n Time (mean ± σ): 824.141 μs ± 220.509 μs ┊ GC (mean ± σ): 4.92% ± 10.45%\n\n ▄▆▆█▇▅▄▂ ▁ ▁ ▂\n ████████▇▆▆▆▆▅▃▃▁▅▆▄▄▄▃▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▄▅▇██████ █\n 709 μs Histogram: log(frequency) by time 1.88 ms <\n\n Memory estimate: 2.98 MiB, allocs estimate: 1558.","category":"page"},{"location":"examples/train-kernel-parameters/#Training-the-model-3","page":"Train Kernel Parameters","title":"Training the model","text":"","category":"section"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"The loss at our initial parameter values:","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"θ = log.([1.1, 0.1, 0.01, 0.001]) # Initial vector\nloss(θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"2.613933959118708","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Initialize optimizer","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"opt = Optimise.ADAGrad(0.5)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Optimize","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"for i in 1:15\n grads = only((Zygote.gradient(loss, θ)))\n Optimise.update!(opt, θ, grads)\nend","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"Final loss","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"loss(θ)","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"0.5241118228076058","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"
      \n
      Package and system information
      \n
      \nPackage information (click to expand)\n
      \nStatus `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/train-kernel-parameters/Project.toml`\n  [6e4b80f9] BenchmarkTools v1.4.0\n  [31c24e10] Distributions v0.25.107\n  [587475ba] Flux v0.14.11\n  [f6369f11] ForwardDiff v0.10.36\n  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`\n  [98b081ad] Literate v2.16.1\n  [2412ca09] ParameterHandling v0.4.10\n  [91a5bcdd] Plots v1.40.1\n  [e88e6eb3] Zygote v0.6.69\n  [37e2e46d] LinearAlgebra\n
      \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
      \n
      \nSystem information (click to expand)\n
      \nJulia Version 1.10.0\nCommit 3120989f39b (2023-12-25 18:01 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 4 × AMD EPYC 7763 64-Core Processor\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\n  Threads: 1 on 4 virtual cores\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
      \n
      ","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"","category":"page"},{"location":"examples/train-kernel-parameters/","page":"Train Kernel Parameters","title":"Train Kernel Parameters","text":"This page was generated using Literate.jl.","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"EditURL = \"../../../../examples/kernel-ridge-regression/script.jl\"","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/master/examples/kernel-ridge-regression/script.jl\"","category":"page"},{"location":"examples/kernel-ridge-regression/#Kernel-Ridge-Regression","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"(Image: )","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"Building on linear regression, we can fit non-linear data sets by introducing a feature space. In a higher-dimensional feature space, we can overfit the data; ridge regression introduces regularization to avoid this. In this notebook we show how we can use KernelFunctions.jl for kernel ridge regression.","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"# Loading and setup of required packages\nusing KernelFunctions\nusing LinearAlgebra\nusing Distributions\n\n# Plotting\nusing Plots;\ndefault(; lw=2.0, legendfontsize=11.0, ylims=(-150, 500));\n\nusing Random: seed!\nseed!(42);","category":"page"},{"location":"examples/kernel-ridge-regression/#Toy-data","page":"Kernel Ridge Regression","title":"Toy data","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"Here we use a one-dimensional toy problem. We generate data using the fourth-order polynomial f(x) = (x+4)(x+1)(x-1)(x-3):","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"f_truth(x) = (x + 4) * (x + 1) * (x - 1) * (x - 3)\n\nx_train = -5:0.5:5\nx_test = -7:0.1:7\n\nnoise = rand(Uniform(-20, 20), length(x_train))\ny_train = f_truth.(x_train) + noise\ny_test = f_truth.(x_test)\n\nplot(x_test, y_test; label=raw\"$f(x)$\")\nscatter!(x_train, y_train; seriescolor=1, label=\"observations\")","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/#Linear-regression","page":"Kernel Ridge Regression","title":"Linear regression","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"For training inputs mathrmX=(mathbfx_n)_n=1^N and observations mathbfy=(y_n)_n=1^N, the linear regression weights mathbfw using the least-squares estimator are given by","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = (mathrmX^top mathrmX)^-1 mathrmX^top mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"We predict at test inputs mathbfx_* using","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"haty_* = mathbfx_*^top mathbfw","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"This is implemented by linear_regression:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"function linear_regression(X, y, Xstar)\n weights = (X' * X) \\ (X' * y)\n return Xstar * weights\nend;","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"A linear regression fit to the above data set:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"y_pred = linear_regression(x_train, y_train, x_test)\nscatter(x_train, y_train; label=\"observations\")\nplot!(x_test, y_pred; label=\"linear fit\")","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/#Featurization","page":"Kernel Ridge Regression","title":"Featurization","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"We can improve the fit by including additional features, i.e. generalizing to tildemathrmX = (phi(x_n))_n=1^N, where phi(x) constructs a feature vector for each input x. Here we include powers of the input, phi(x) = (1 x x^2 dots x^d):","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"function featurize_poly(x; degree=1)\n return repeat(x, 1, degree + 1) .^ (0:degree)'\nend\n\nfunction featurized_fit_and_plot(degree)\n X = featurize_poly(x_train; degree=degree)\n Xstar = featurize_poly(x_test; degree=degree)\n y_pred = linear_regression(X, y_train, Xstar)\n scatter(x_train, y_train; legend=false, title=\"fit of order $degree\")\n return plot!(x_test, y_pred)\nend\n\nplot((featurized_fit_and_plot(degree) for degree in 1:4)...)","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"Note that the fit becomes perfect when we include exactly as many orders in the features as we have in the underlying polynomial (4).","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"However, when increasing the number of features, we can quickly overfit to noise in the data set:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"featurized_fit_and_plot(20)","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/#Ridge-regression","page":"Kernel Ridge Regression","title":"Ridge regression","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"To counteract this unwanted behaviour, we can introduce regularization. This leads to ridge regression with L_2 regularization of the weights (Tikhonov regularization). Instead of the weights in linear regression,","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = (mathrmX^top mathrmX)^-1 mathrmX^top mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"we introduce the ridge parameter lambda:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = (mathrmX^top mathrmX + lambda mathbb1)^-1 mathrmX^top mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"As before, we predict at test inputs mathbfx_* using","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"haty_* = mathbfx_*^top mathbfw","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"This is implemented by ridge_regression:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"function ridge_regression(X, y, Xstar, lambda)\n weights = (X' * X + lambda * I) \\ (X' * y)\n return Xstar * weights\nend\n\nfunction regularized_fit_and_plot(degree, lambda)\n X = featurize_poly(x_train; degree=degree)\n Xstar = featurize_poly(x_test; degree=degree)\n y_pred = ridge_regression(X, y_train, Xstar, lambda)\n scatter(x_train, y_train; legend=false, title=\"\\$\\\\lambda=$lambda\\$\")\n return plot!(x_test, y_pred)\nend\n\nplot((regularized_fit_and_plot(20, lambda) for lambda in (1e-3, 1e-2, 1e-1, 1))...)","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/#Kernel-ridge-regression","page":"Kernel Ridge Regression","title":"Kernel ridge regression","text":"","category":"section"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"Instead of constructing the feature matrix explicitly, we can use kernels to replace inner products of feature vectors with a kernel evaluation: langle phi(x) phi(x) rangle = k(x x) or tildemathrmX tildemathrmX^top = mathrmK, where mathrmK_ij = k(x_i x_j).","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"To apply this \"kernel trick\" to ridge regression, we can rewrite the ridge estimate for the weights","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = (mathrmX^top mathrmX + lambda mathbb1)^-1 mathrmX^top mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"using the matrix inversion lemma as","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = mathrmX^top (mathrmX mathrmX^top + lambda mathbb1)^-1 mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"where we can now replace the inner product with the kernel matrix,","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"mathbfw = mathrmX^top (mathrmK + lambda mathbb1)^-1 mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"And the prediction yields another inner product,","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"haty_* = mathbfx_*^top mathbfw = langle mathbfx_* mathbfw rangle = mathbfk_* (mathrmK + lambda mathbb1)^-1 mathbfy","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"where (mathbfk_*)_n = k(x_* x_n).","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"This is implemented by kernel_ridge_regression:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"function kernel_ridge_regression(k, X, y, Xstar, lambda)\n K = kernelmatrix(k, X)\n kstar = kernelmatrix(k, Xstar, X)\n return kstar * ((K + lambda * I) \\ y)\nend;","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"Now, instead of explicitly constructing features, we can simply pass in a PolynomialKernel object:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"function kernelized_fit_and_plot(kernel, lambda=1e-4)\n y_pred = kernel_ridge_regression(kernel, x_train, y_train, x_test, lambda)\n if kernel isa PolynomialKernel\n title = string(\"order \", kernel.degree)\n else\n title = string(nameof(typeof(kernel)))\n end\n scatter(x_train, y_train; label=nothing)\n return plot!(x_test, y_pred; label=nothing, title=title)\nend\n\nplot((kernelized_fit_and_plot(PolynomialKernel(; degree=degree, c=1)) for degree in 1:4)...)","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"However, we can now also use kernels that would have an infinite-dimensional feature expansion, such as the squared exponential kernel:","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"kernelized_fit_and_plot(SqExponentialKernel())","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"
      \n
      Package and system information
      \n
      \nPackage information (click to expand)\n
      \nStatus `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/kernel-ridge-regression/Project.toml`\n  [31c24e10] Distributions v0.25.107\n  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`\n  [98b081ad] Literate v2.16.1\n  [91a5bcdd] Plots v1.40.1\n  [37e2e46d] LinearAlgebra\n
      \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
      \n
      \nSystem information (click to expand)\n
      \nJulia Version 1.10.0\nCommit 3120989f39b (2023-12-25 18:01 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 4 × AMD EPYC 7763 64-Core Processor\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\n  Threads: 1 on 4 virtual cores\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
      \n
      ","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"","category":"page"},{"location":"examples/kernel-ridge-regression/","page":"Kernel Ridge Regression","title":"Kernel Ridge Regression","text":"This page was generated using Literate.jl.","category":"page"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":" CurrentModule = KernelFunctions","category":"page"},{"location":"kernels/#Kernel-Functions","page":"Kernel Functions","title":"Kernel Functions","text":"","category":"section"},{"location":"kernels/#base_kernels","page":"Kernel Functions","title":"Base Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"These are the basic kernels without any transformation of the data. They are the building blocks of KernelFunctions.","category":"page"},{"location":"kernels/#Constant-Kernels","page":"Kernel Functions","title":"Constant Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"ZeroKernel\nConstantKernel\nWhiteKernel\nEyeKernel","category":"page"},{"location":"kernels/#KernelFunctions.ZeroKernel","page":"Kernel Functions","title":"KernelFunctions.ZeroKernel","text":"ZeroKernel()\n\nZero kernel.\n\nDefinition\n\nFor inputs x x, the zero kernel is defined as\n\nk(x x) = 0\n\nThe output type depends on x and x.\n\nSee also: ConstantKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.ConstantKernel","page":"Kernel Functions","title":"KernelFunctions.ConstantKernel","text":"ConstantKernel(; c::Real=1.0)\n\nKernel of constant value c.\n\nDefinition\n\nFor inputs x x, the kernel of constant value c geq 0 is defined as\n\nk(x x) = c\n\nSee also: ZeroKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.WhiteKernel","page":"Kernel Functions","title":"KernelFunctions.WhiteKernel","text":"WhiteKernel()\n\nWhite noise kernel.\n\nDefinition\n\nFor inputs x x, the white noise kernel is defined as\n\nk(x x) = delta(x x)\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.EyeKernel","page":"Kernel Functions","title":"KernelFunctions.EyeKernel","text":"EyeKernel()\n\nAlias of WhiteKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Cosine-Kernel","page":"Kernel Functions","title":"Cosine Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"CosineKernel","category":"page"},{"location":"kernels/#KernelFunctions.CosineKernel","page":"Kernel Functions","title":"KernelFunctions.CosineKernel","text":"CosineKernel(; metric=Euclidean())\n\nCosine kernel with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the cosine kernel is defined as\n\nk(x x) = cos(pi d(x x))\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Exponential-Kernels","page":"Kernel Functions","title":"Exponential Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"ExponentialKernel\nGibbsKernel\nLaplacianKernel\nSqExponentialKernel\nSEKernel\nGaussianKernel\nRBFKernel\nGammaExponentialKernel","category":"page"},{"location":"kernels/#KernelFunctions.ExponentialKernel","page":"Kernel Functions","title":"KernelFunctions.ExponentialKernel","text":"ExponentialKernel(; metric=Euclidean())\n\nExponential kernel with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the exponential kernel is defined as\n\nk(x x) = expbig(- d(x x)big)\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nSee also: GammaExponentialKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.GibbsKernel","page":"Kernel Functions","title":"KernelFunctions.GibbsKernel","text":"GibbsKernel(; lengthscale)\n\nGibbs Kernel with lengthscale function lengthscale.\n\nThe Gibbs kernel is a non-stationary generalisation of the squared exponential kernel. The lengthscale parameter l becomes a function of position l(x).\n\nDefinition\n\nFor inputs x x, the Gibbs kernel with lengthscale function l(cdot) is defined as\n\nk(x x l) = sqrtleft(frac2 l(x) l(x)l(x)^2 + l(x)^2right)\nquad expleft(-frac(x - x)^2l(x)^2 + l(x)^2right)\n\nFor a constant function l equiv c, one recovers the SqExponentialKernel with lengthscale c.\n\nReferences\n\nMark N. Gibbs. \"Bayesian Gaussian Processes for Regression and Classication.\" PhD thesis, 1997\n\nChristopher J. Paciorek and Mark J. Schervish. \"Nonstationary Covariance Functions for Gaussian Process Regression\". NeurIPS, 2003\n\nSami Remes, Markus Heinonen, Samuel Kaski. \"Non-Stationary Spectral Kernels\". arXiV:1705.08736, 2017\n\nSami Remes, Markus Heinonen, Samuel Kaski. \"Neural Non-Stationary Spectral Kernel\". arXiv:1811.10978, 2018\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.LaplacianKernel","page":"Kernel Functions","title":"KernelFunctions.LaplacianKernel","text":"LaplacianKernel()\n\nAlias of ExponentialKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.SqExponentialKernel","page":"Kernel Functions","title":"KernelFunctions.SqExponentialKernel","text":"SqExponentialKernel(; metric=Euclidean())\n\nSquared exponential kernel with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the squared exponential kernel is defined as\n\nk(x x) = expbigg(- fracd(x x)^22bigg)\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nSee also: GammaExponentialKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.SEKernel","page":"Kernel Functions","title":"KernelFunctions.SEKernel","text":"SEKernel()\n\nAlias of SqExponentialKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.GaussianKernel","page":"Kernel Functions","title":"KernelFunctions.GaussianKernel","text":"GaussianKernel()\n\nAlias of SqExponentialKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.RBFKernel","page":"Kernel Functions","title":"KernelFunctions.RBFKernel","text":"RBFKernel()\n\nAlias of SqExponentialKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.GammaExponentialKernel","page":"Kernel Functions","title":"KernelFunctions.GammaExponentialKernel","text":"GammaExponentialKernel(; γ::Real=1.0, metric=Euclidean())\n\nγ-exponential kernel with respect to the metric and with parameter γ.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the γ-exponential kernel[RW] with parameter gamma in (0 2 is defined as\n\nk(x x gamma) = expbig(- d(x x)^gammabig)\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nSee also: ExponentialKernel, SqExponentialKernel\n\n[RW]: C. E. Rasmussen & C. K. I. Williams (2006). Gaussian Processes for Machine Learning.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Exponentiated-Kernel","page":"Kernel Functions","title":"Exponentiated Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"ExponentiatedKernel","category":"page"},{"location":"kernels/#KernelFunctions.ExponentiatedKernel","page":"Kernel Functions","title":"KernelFunctions.ExponentiatedKernel","text":"ExponentiatedKernel()\n\nExponentiated kernel.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the exponentiated kernel is defined as\n\nk(x x) = exp(x^top x)\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Fractional-Brownian-Motion-Kernel","page":"Kernel Functions","title":"Fractional Brownian Motion Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"FBMKernel","category":"page"},{"location":"kernels/#KernelFunctions.FBMKernel","page":"Kernel Functions","title":"KernelFunctions.FBMKernel","text":"FBMKernel(; h::Real=0.5)\n\nFractional Brownian motion kernel with Hurst index h.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the fractional Brownian motion kernel with Hurst index h in 01 is defined as\n\nk(x x h) = fracx_2^2h + x_2^2h - x - x^2h2\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Gabor-Kernel","page":"Kernel Functions","title":"Gabor Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"gaborkernel","category":"page"},{"location":"kernels/#KernelFunctions.gaborkernel","page":"Kernel Functions","title":"KernelFunctions.gaborkernel","text":"gaborkernel(;\n sqexponential_transform=IdentityTransform(), cosine_tranform=IdentityTransform()\n)\n\nConstruct a Gabor kernel with transformations sqexponential_transform and cosine_transform of the inputs of the underlying squared exponential and cosine kernel, respectively.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the Gabor kernel with transformations f and g of the inputs to the squared exponential and cosine kernel, respectively, is defined as\n\nk(x x f g) = expbigg(- frac f(x) - f(x)_2^22bigg)\n cosbig(pi g(x) - g(x)_2 big)\n\n\n\n\n\n","category":"function"},{"location":"kernels/#Matérn-Kernels","page":"Kernel Functions","title":"Matérn Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"MaternKernel\nMatern12Kernel\nMatern32Kernel\nMatern52Kernel","category":"page"},{"location":"kernels/#KernelFunctions.MaternKernel","page":"Kernel Functions","title":"KernelFunctions.MaternKernel","text":"MaternKernel(; ν::Real=1.5, metric=Euclidean())\n\nMatérn kernel of order ν with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the Matérn kernel of order nu 0 is defined as\n\nk(xxnu) = frac2^1-nuGamma(nu)big(sqrt2nu d(x x)big) K_nubig(sqrt2nu d(x x)big)\n\nwhere Gamma is the Gamma function and K_nu is the modified Bessel function of the second kind of order nu. By default, d is the Euclidean metric d(x x) = x - x_2.\n\nA Gaussian process with a Matérn kernel is lceil nu rceil - 1-times differentiable in the mean-square sense.\n\nnote: Note\nDifferentiation with respect to the order ν is not currently supported.\n\nSee also: Matern12Kernel, Matern32Kernel, Matern52Kernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.Matern12Kernel","page":"Kernel Functions","title":"KernelFunctions.Matern12Kernel","text":"Matern12Kernel()\n\nAlias of ExponentialKernel.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.Matern32Kernel","page":"Kernel Functions","title":"KernelFunctions.Matern32Kernel","text":"Matern32Kernel(; metric=Euclidean())\n\nMatérn kernel of order 32 with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the Matérn kernel of order 32 is given by\n\nk(x x) = big(1 + sqrt3 d(x x) big) expbig(- sqrt3 d(x x) big)\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nSee also: MaternKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.Matern52Kernel","page":"Kernel Functions","title":"KernelFunctions.Matern52Kernel","text":"Matern52Kernel(; metric=Euclidean())\n\nMatérn kernel of order 52 with respect to the metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the Matérn kernel of order 52 is given by\n\nk(x x) = bigg(1 + sqrt5 d(x x) + frac53 d(x x)^2bigg)\n expbig(- sqrt5 d(x x) big)\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nSee also: MaternKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Neural-Network-Kernel","page":"Kernel Functions","title":"Neural Network Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"NeuralNetworkKernel","category":"page"},{"location":"kernels/#KernelFunctions.NeuralNetworkKernel","page":"Kernel Functions","title":"KernelFunctions.NeuralNetworkKernel","text":"NeuralNetworkKernel()\n\nKernel of a Gaussian process obtained as the limit of a Bayesian neural network with a single hidden layer as the number of units goes to infinity.\n\nDefinition\n\nConsider the single-layer Bayesian neural network f colon mathbbR^d to mathbbR with h hidden units defined by\n\nf(x b v u) = b + sqrtfracpi2 sum_i=1^h v_i mathrmerfbig(u_i^top xbig)\n\nwhere mathrmerf is the error function, and with prior distributions\n\nbeginaligned\nb sim mathcalN(0 sigma_b^2)\nv sim mathcalN(0 sigma_v^2 mathrmI_hh)\nu_i sim mathcalN(0 mathrmI_d2) qquad (i = 1ldotsh)\nendaligned\n\nAs h to infty, the neural network converges to the Gaussian process\n\ng(cdot) sim mathcalGPbig(0 sigma_b^2 + sigma_v^2 k(cdot cdot)big)\n\nwhere the neural network kernel k is given by\n\nk(x x) = arcsinleft(fracx^top xsqrtbig(1 + x^2_2big) big(1 + x_2^2big)right)\n\nfor inputs x x in mathbbR^d.[CW]\n\n[CW]: C. K. I. Williams (1998). Computation with infinite neural networks.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Periodic-Kernel","page":"Kernel Functions","title":"Periodic Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"PeriodicKernel\nPeriodicKernel(::DataType, ::Int)","category":"page"},{"location":"kernels/#KernelFunctions.PeriodicKernel","page":"Kernel Functions","title":"KernelFunctions.PeriodicKernel","text":"PeriodicKernel(; r::AbstractVector=ones(Float64, 1))\n\nPeriodic kernel with parameter r.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the periodic kernel with parameter r_i 0 is defined[DM] as\n\nk(x x r) = expbigg(- frac12 sum_i=1^d bigg(fracsinbig(pi(x_i - x_i)big)r_ibigg)^2bigg)\n\n[DM]: D. J. C. MacKay (1998). Introduction to Gaussian Processes.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.PeriodicKernel-Tuple{DataType, Int64}","page":"Kernel Functions","title":"KernelFunctions.PeriodicKernel","text":"PeriodicKernel([T=Float64, dims::Int=1])\n\nCreate a PeriodicKernel with parameter r=ones(T, dims).\n\n\n\n\n\n","category":"method"},{"location":"kernels/#Piecewise-Polynomial-Kernel","page":"Kernel Functions","title":"Piecewise Polynomial Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"PiecewisePolynomialKernel","category":"page"},{"location":"kernels/#KernelFunctions.PiecewisePolynomialKernel","page":"Kernel Functions","title":"KernelFunctions.PiecewisePolynomialKernel","text":"PiecewisePolynomialKernel(; dim::Int, degree::Int=0, metric=Euclidean())\nPiecewisePolynomialKernel{degree}(; dim::Int, metric=Euclidean())\n\nPiecewise polynomial kernel of degree degree for inputs of dimension dim with support in the unit ball with respect to the metric.\n\nDefinition\n\nFor inputs x x of dimension m and metric d(cdot cdot), the piecewise polynomial kernel of degree v in 0123 is defined as\n\nk(x x v) = max(1 - d(x x) 0)^alpha(vm) f_vm(d(x x))\n\nwhere alpha(v m) = lfloor fracm2rfloor + 2v + 1 and f_vm are polynomials of degree v given by\n\nbeginaligned\nf_0m(r) = 1 \nf_1m(r) = 1 + (j + 1) r \nf_2m(r) = 1 + (j + 2) r + big((j^2 + 4j + 3) 3big) r^2 \nf_3m(r) = 1 + (j + 3) r + big((6 j^2 + 36j + 45) 15big) r^2 + big((j^3 + 9 j^2 + 23j + 15) 15big) r^3\nendaligned\n\nwhere j = lfloor fracm2rfloor + v + 1. By default, d is the Euclidean metric d(x x) = x - x_2.\n\nThe kernel is 2v times continuously differentiable and the corresponding Gaussian process is hence v times mean-square differentiable.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Polynomial-Kernels","page":"Kernel Functions","title":"Polynomial Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"LinearKernel\nPolynomialKernel","category":"page"},{"location":"kernels/#KernelFunctions.LinearKernel","page":"Kernel Functions","title":"KernelFunctions.LinearKernel","text":"LinearKernel(; c::Real=0.0)\n\nLinear kernel with constant offset c.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the linear kernel with constant offset c geq 0 is defined as\n\nk(x x c) = x^top x + c\n\nSee also: PolynomialKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.PolynomialKernel","page":"Kernel Functions","title":"KernelFunctions.PolynomialKernel","text":"PolynomialKernel(; degree::Int=2, c::Real=0.0)\n\nPolynomial kernel of degree degree with constant offset c.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the polynomial kernel of degree nu in mathbbN with constant offset c geq 0 is defined as\n\nk(x x c nu) = (x^top x + c)^nu\n\nSee also: LinearKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Rational-Kernels","page":"Kernel Functions","title":"Rational Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"RationalKernel\nRationalQuadraticKernel\nGammaRationalKernel","category":"page"},{"location":"kernels/#KernelFunctions.RationalKernel","page":"Kernel Functions","title":"KernelFunctions.RationalKernel","text":"RationalKernel(; α::Real=2.0, metric=Euclidean())\n\nRational kernel with shape parameter α and given metric.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the rational kernel with shape parameter alpha 0 is defined as\n\nk(x x alpha) = bigg(1 + fracd(x x)alphabigg)^-alpha\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nThe ExponentialKernel is recovered in the limit as alpha to infty.\n\nSee also: GammaRationalKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.RationalQuadraticKernel","page":"Kernel Functions","title":"KernelFunctions.RationalQuadraticKernel","text":"RationalQuadraticKernel(; α::Real=2.0, metric=Euclidean())\n\nRational-quadratic kernel with respect to the metric and with shape parameter α.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the rational-quadratic kernel with shape parameter alpha 0 is defined as\n\nk(x x alpha) = bigg(1 + fracd(x x)^22alphabigg)^-alpha\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nThe SqExponentialKernel is recovered in the limit as alpha to infty.\n\nSee also: GammaRationalKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.GammaRationalKernel","page":"Kernel Functions","title":"KernelFunctions.GammaRationalKernel","text":"GammaRationalKernel(; α::Real=2.0, γ::Real=1.0, metric=Euclidean())\n\nγ-rational kernel with respect to the metric with shape parameters α and γ.\n\nDefinition\n\nFor inputs x x and metric d(cdot cdot), the γ-rational kernel with shape parameters alpha 0 and gamma in (0 2 is defined as\n\nk(x x alpha gamma) = bigg(1 + fracd(x x)^gammaalphabigg)^-alpha\n\nBy default, d is the Euclidean metric d(x x) = x - x_2.\n\nThe GammaExponentialKernel is recovered in the limit as alpha to infty.\n\nSee also: RationalKernel, RationalQuadraticKernel\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Spectral-Mixture-Kernels","page":"Kernel Functions","title":"Spectral Mixture Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"spectral_mixture_kernel\nspectral_mixture_product_kernel","category":"page"},{"location":"kernels/#KernelFunctions.spectral_mixture_kernel","page":"Kernel Functions","title":"KernelFunctions.spectral_mixture_kernel","text":"spectral_mixture_kernel(\n h::Kernel=SqExponentialKernel(),\n αs::AbstractVector{<:Real},\n γs::AbstractMatrix{<:Real},\n ωs::AbstractMatrix{<:Real},\n)\n\nwhere αs are the weights of dimension (A, ), γs is the covariance matrix of dimension (D, A) and ωs are the mean vectors and is of dimension (D, A). Here, D is input dimension and A is the number of spectral components.\n\nh is the kernel, which defaults to SqExponentialKernel if not specified.\n\nwarning: Warning\nIf you want to make sure that the constructor is type-stable, you should provide StaticArrays arguments: αs as a StaticVector, γs and ωs as StaticMatrix.\n\nGeneralised Spectral Mixture kernel function. This family of functions is dense in the family of stationary real-valued kernels with respect to the pointwise convergence.[1]\n\n κ(x y) = αs (h(-(γs * t)^2) * cos(π * ωs * t) t = x - y\n\nReferences:\n\n[1] Generalized Spectral Kernels, by Yves-Laurent Kom Samo and Stephen J. Roberts\n[2] SM: Gaussian Process Kernels for Pattern Discovery and Extrapolation,\n ICML, 2013, by Andrew Gordon Wilson and Ryan Prescott Adams,\n[3] Covariance kernels for fast automatic pattern discovery and extrapolation\n with Gaussian processes, Andrew Gordon Wilson, PhD Thesis, January 2014.\n http://www.cs.cmu.edu/~andrewgw/andrewgwthesis.pdf\n[4] http://www.cs.cmu.edu/~andrewgw/pattern/.\n\n\n\n\n\n","category":"function"},{"location":"kernels/#KernelFunctions.spectral_mixture_product_kernel","page":"Kernel Functions","title":"KernelFunctions.spectral_mixture_product_kernel","text":"spectral_mixture_product_kernel(\n h::Kernel=SqExponentialKernel(),\n αs::AbstractMatrix{<:Real},\n γs::AbstractMatrix{<:Real},\n ωs::AbstractMatrix{<:Real},\n)\n\nwhere αs are the weights of dimension (D, A), γs is the covariance matrix of dimension (D, A) and ωs are the mean vectors and is of dimension (D, A). Here, D is input dimension and A is the number of spectral components.\n\nSpectral Mixture Product Kernel. With enough components A, the SMP kernel can model any product kernel to arbitrary precision, and is flexible even with a small number of components [1]\n\nh is the kernel, which defaults to SqExponentialKernel if not specified.\n\n κ(x y) = Πᵢ₁ᴷ Σ(αsᵢᵀ * (h(-(γsᵢᵀ * tᵢ)²) * cos(ωsᵢᵀ * tᵢ))) tᵢ = xᵢ - yᵢ\n\nReferences:\n\n[1] GPatt: Fast Multidimensional Pattern Extrapolation with GPs,\n arXiv 1310.5288, 2013, by Andrew Gordon Wilson, Elad Gilboa,\n Arye Nehorai and John P. Cunningham\n\n\n\n\n\n","category":"function"},{"location":"kernels/#Wiener-Kernel","page":"Kernel Functions","title":"Wiener Kernel","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"WienerKernel","category":"page"},{"location":"kernels/#KernelFunctions.WienerKernel","page":"Kernel Functions","title":"KernelFunctions.WienerKernel","text":"WienerKernel(; i::Int=0)\nWienerKernel{i}()\n\nThe i-times integrated Wiener process kernel function.\n\nDefinition\n\nFor inputs x x in mathbbR^d, the i-times integrated Wiener process kernel with i in -1 0 1 2 3 is defined[SDH] as\n\nk_i(x x) = begincases\n delta(x x) textif i=-1\n minbig(x_2 x_2big) textif i=0\n a_i1^-1 minbig(x_2 x_2big)^2i + 1\n + a_i2^-1 x - x_2 r_ibig(x_2 x_2big) minbig(x_2 x_2big)^i + 1\n textotherwise\nendcases\n\nwhere the coefficients a are given by\n\na = beginbmatrix\n3 2 \n20 12 \n252 720\nendbmatrix\n\nand the functions r_i are defined as\n\nbeginaligned\nr_1(t t) = 1\nr_2(t t) = t + t - fracmin(t t)2\nr_3(t t) = 5 max(t t)^2 + 2 tt + 3 min(t t)^2\nendaligned\n\nThe WhiteKernel is recovered for i = -1.\n\n[SDH]: Schober, Duvenaud & Hennig (2014). Probabilistic ODE Solvers with Runge-Kutta Means.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Composite-Kernels","page":"Kernel Functions","title":"Composite Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"The modular design of KernelFunctions uses base kernels as building blocks for more complex kernels. There are a variety of composite kernels implemented, including those which transform the inputs to a wrapped kernel to implement length scales, scale the variance of a kernel, and sum or multiply collections of kernels together.","category":"page"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"TransformedKernel\n∘(::Kernel, ::Transform)\nScaledKernel\nKernelSum\nKernelProduct\nKernelTensorProduct\nNormalizedKernel","category":"page"},{"location":"kernels/#KernelFunctions.TransformedKernel","page":"Kernel Functions","title":"KernelFunctions.TransformedKernel","text":"TransformedKernel(k::Kernel, t::Transform)\n\nKernel derived from k for which inputs are transformed via a Transform t.\n\nThe preferred way to create kernels with input transformations is to use the composition operator ∘ or its alias compose instead of TransformedKernel directly since this allows optimized implementations for specific kernels and transformations.\n\nSee also: ∘\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Base.:∘-Tuple{Kernel, Transform}","page":"Kernel Functions","title":"Base.:∘","text":"kernel ∘ transform\n∘(kernel, transform)\ncompose(kernel, transform)\n\nCompose a kernel with a transformation transform of its inputs.\n\nThe prefix forms support chains of multiple transformations: ∘(kernel, transform1, transform2) = kernel ∘ transform1 ∘ transform2.\n\nDefinition\n\nFor inputs x x, the transformed kernel widetildek derived from kernel k by input transformation t is defined as\n\nwidetildek(x x k t) = kbig(t(x) t(x)big)\n\nExamples\n\njulia> (SqExponentialKernel() ∘ ScaleTransform(0.5))(0, 2) == exp(-0.5)\ntrue\n\njulia> ∘(ExponentialKernel(), ScaleTransform(2), ScaleTransform(0.5))(1, 2) == exp(-1)\ntrue\n\nSee also: TransformedKernel\n\n\n\n\n\n","category":"method"},{"location":"kernels/#KernelFunctions.ScaledKernel","page":"Kernel Functions","title":"KernelFunctions.ScaledKernel","text":"ScaledKernel(k::Kernel, σ²::Real=1.0)\n\nScaled kernel derived from k by multiplication with variance σ².\n\nDefinition\n\nFor inputs x x, the scaled kernel widetildek derived from kernel k by multiplication with variance sigma^2 0 is defined as\n\nwidetildek(x x k sigma^2) = sigma^2 k(x x)\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.KernelSum","page":"Kernel Functions","title":"KernelFunctions.KernelSum","text":"KernelSum <: Kernel\n\nCreate a sum of kernels. One can also use the operator +.\n\nThere are various ways in which you create a KernelSum:\n\nThe simplest way to specify a KernelSum would be to use the overloaded + operator. This is equivalent to creating a KernelSum by specifying the kernels as the arguments to the constructor. \n\njulia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5);\n\njulia> (k = k1 + k2) == KernelSum(k1, k2)\ntrue\n\njulia> kernelmatrix(k1 + k2, X) == kernelmatrix(k1, X) .+ kernelmatrix(k2, X)\ntrue\n\njulia> kernelmatrix(k, X) == kernelmatrix(k1 + k2, X)\ntrue\n\nYou could also specify a KernelSum by providing a Tuple or a Vector of the kernels to be summed. We suggest you to use a Tuple when you have fewer components and a Vector when dealing with a large number of components.\n\njulia> KernelSum((k1, k2)) == k1 + k2\ntrue\n\njulia> KernelSum([k1, k2]) == KernelSum((k1, k2)) == k1 + k2\ntrue\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.KernelProduct","page":"Kernel Functions","title":"KernelFunctions.KernelProduct","text":"KernelProduct <: Kernel\n\nCreate a product of kernels. One can also use the overloaded operator *.\n\nThere are various ways in which you create a KernelProduct:\n\nThe simplest way to specify a KernelProduct would be to use the overloaded * operator. This is equivalent to creating a KernelProduct by specifying the kernels as the arguments to the constructor. \n\njulia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5);\n\njulia> (k = k1 * k2) == KernelProduct(k1, k2)\ntrue\n\njulia> kernelmatrix(k1 * k2, X) == kernelmatrix(k1, X) .* kernelmatrix(k2, X)\ntrue\n\njulia> kernelmatrix(k, X) == kernelmatrix(k1 * k2, X)\ntrue\n\nYou could also specify a KernelProduct by providing a Tuple or a Vector of the kernels to be multiplied. We suggest you to use a Tuple when you have fewer components and a Vector when dealing with a large number of components.\n\njulia> KernelProduct((k1, k2)) == k1 * k2\ntrue\n\njulia> KernelProduct([k1, k2]) == KernelProduct((k1, k2)) == k1 * k2\ntrue\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.KernelTensorProduct","page":"Kernel Functions","title":"KernelFunctions.KernelTensorProduct","text":"KernelTensorProduct\n\nTensor product of kernels.\n\nDefinition\n\nFor inputs x = (x_1 ldots x_n) and x = (x_1 ldots x_n), the tensor product of kernels k_1 ldots k_n is defined as\n\nk(x x k_1 ldots k_n) = Big(bigotimes_i=1^n k_iBig)(x x) = prod_i=1^n k_i(x_i x_i)\n\nConstruction\n\nThe simplest way to specify a KernelTensorProduct is to use the overloaded tensor operator or its alias ⊗ (can be typed by \\otimes).\n\njulia> k1 = SqExponentialKernel(); k2 = LinearKernel(); X = rand(5, 2);\n\njulia> kernelmatrix(k1 ⊗ k2, RowVecs(X)) == kernelmatrix(k1, X[:, 1]) .* kernelmatrix(k2, X[:, 2])\ntrue\n\nYou can also specify a KernelTensorProduct by providing kernels as individual arguments or as an iterable data structure such as a Tuple or a Vector. Using a tuple or individual arguments guarantees that KernelTensorProduct is concretely typed but might lead to large compilation times if the number of kernels is large.\n\njulia> KernelTensorProduct(k1, k2) == k1 ⊗ k2\ntrue\n\njulia> KernelTensorProduct((k1, k2)) == k1 ⊗ k2\ntrue\n\njulia> KernelTensorProduct([k1, k2]) == k1 ⊗ k2\ntrue\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.NormalizedKernel","page":"Kernel Functions","title":"KernelFunctions.NormalizedKernel","text":"NormalizedKernel(k::Kernel)\n\nA normalized kernel derived from k.\n\nDefinition\n\nFor inputs x x, the normalized kernel widetildek derived from kernel k is defined as\n\nwidetildek(x x k) = frack(x x)sqrtk(x x) k(x x)\n\n\n\n\n\n","category":"type"},{"location":"kernels/#Multi-output-Kernels","page":"Kernel Functions","title":"Multi-output Kernels","text":"","category":"section"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"Kernelfunctions implements multi-output kernels as scalar kernels on an extended output domain. For more details on this read the section on inputs for multi-output GPs.","category":"page"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"For a function f(x) rightarrow y denote the inputs as x x, such that we compute the covariance between output components y_p and y_p. The total number of outputs is m.","category":"page"},{"location":"kernels/","page":"Kernel Functions","title":"Kernel Functions","text":"MOKernel\nIndependentMOKernel\nLatentFactorMOKernel\nIntrinsicCoregionMOKernel\nLinearMixingModelKernel","category":"page"},{"location":"kernels/#KernelFunctions.MOKernel","page":"Kernel Functions","title":"KernelFunctions.MOKernel","text":"MOKernel\n\nAbstract type for kernels with multiple outpus.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.IndependentMOKernel","page":"Kernel Functions","title":"KernelFunctions.IndependentMOKernel","text":"IndependentMOKernel(k::Kernel)\n\nKernel for multiple independent outputs with kernel k each.\n\nDefinition\n\nFor inputs x x and output dimensions p p, the kernel widetildek for independent outputs with kernel k each is defined as\n\nwidetildekbig((x p) (x p)big) = begincases\n k(x x) textif p = p \n 0 textotherwise\nendcases\n\nMathematically, it is equivalent to a matrix-valued kernel defined as\n\nwidetildeK(x x) = mathrmdiagbig(k(x x) ldots k(x x)big) in mathbbR^m times m\n\nwhere m is the number of outputs.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.LatentFactorMOKernel","page":"Kernel Functions","title":"KernelFunctions.LatentFactorMOKernel","text":"LatentFactorMOKernel(g::AbstractVector{<:Kernel}, e::MOKernel, A::AbstractMatrix)\n\nKernel associated with the semiparametric latent factor model.\n\nDefinition\n\nFor inputs x x and output dimensions p_x p_x, the kernel is defined as[STJ]\n\nkbig((x p_x) (x p_x)big) = sum^Q_q=1 A_p_xqg_q(x x)A_p_xq\n + ebig((x p_x) (x p_x)big)\n\nwhere g_1 ldots g_Q are Q kernels, one for each latent process, e is a multi-output kernel for m outputs, and A is a matrix of weights for the kernels of size m times Q.\n\n[STJ]: M. Seeger, Y. Teh, & M. I. Jordan (2005). Semiparametric Latent Factor Models.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.IntrinsicCoregionMOKernel","page":"Kernel Functions","title":"KernelFunctions.IntrinsicCoregionMOKernel","text":"IntrinsicCoregionMOKernel(; kernel::Kernel, B::AbstractMatrix)\n\nKernel associated with the intrinsic coregionalization model.\n\nDefinition\n\nFor inputs x x and output dimensions p p, the kernel is defined as[ARL]\n\nkbig((x p) (x p) B tildekbig) = B_p p tildekbig(x xbig)\n\nwhere B is a positive semidefinite matrix of size m times m, with m being the number of outputs, and tildek is a scalar-valued kernel shared by the latent processes.\n\n[ARL]: M. Álvarez, L. Rosasco, & N. Lawrence (2012). Kernels for Vector-Valued Functions: a Review.\n\n\n\n\n\n","category":"type"},{"location":"kernels/#KernelFunctions.LinearMixingModelKernel","page":"Kernel Functions","title":"KernelFunctions.LinearMixingModelKernel","text":"LinearMixingModelKernel(k::Kernel, H::AbstractMatrix)\nLinearMixingModelKernel(Tk::AbstractVector{<:Kernel},Th::AbstractMatrix)\n\nKernel associated with the linear mixing model, taking a vector of Q kernels and a Q × m mixing matrix H for a function with m outputs. Also accepts a single kernel k for use across all Q basis vectors. \n\nDefinition\n\nFor inputs x x and output dimensions p p, the kernel is defined as[BPTHST]\n\nkbig((x p) (x p)big) = H_pK(x x)H_p\n\nwhere K(x x) = Diag(k_1(x x) k_Q(x x)) with zero off-diagonal entries. H_p is the p-th column (p-th output) of H in mathbbR^Q times m representing Q basis vectors for the m dimensional output space of f. k_1 ldots k_Q are Q kernels, one for each latent process, H is a mixing matrix of Q basis vectors spanning the output space.\n\n[BPTHST]: Wessel P. Bruinsma, Eric Perim, Will Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner (2020). Scalable Exact Inference in Multi-Output Gaussian Processes.\n\n\n\n\n\n","category":"type"},{"location":"api/#API-Library","page":"API","title":"API Library","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"CurrentModule = KernelFunctions","category":"page"},{"location":"api/#Functions","page":"API","title":"Functions","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"The KernelFunctions API comprises the following four functions.","category":"page"},{"location":"api/","page":"API","title":"API","text":"kernelmatrix\nkernelmatrix!\nkernelmatrix_diag\nkernelmatrix_diag!","category":"page"},{"location":"api/#KernelFunctions.kernelmatrix","page":"API","title":"KernelFunctions.kernelmatrix","text":"kernelmatrix(κ::Kernel, x::AbstractVector)\n\nCompute the kernel κ for each pair of inputs in x. Returns a matrix of size (length(x), length(x)) satisfying kernelmatrix(κ, x)[p, q] == κ(x[p], x[q]).\n\nkernelmatrix(κ::Kernel, x::AbstractVector, y::AbstractVector)\n\nCompute the kernel κ for each pair of inputs in x and y. Returns a matrix of size (length(x), length(y)) satisfying kernelmatrix(κ, x, y)[p, q] == κ(x[p], y[q]).\n\nkernelmatrix(κ::Kernel, X::AbstractMatrix; obsdim)\nkernelmatrix(κ::Kernel, X::AbstractMatrix, Y::AbstractMatrix; obsdim)\n\nIf obsdim=1, equivalent to kernelmatrix(κ, RowVecs(X)) and kernelmatrix(κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix(κ, ColVecs(X)) and kernelmatrix(κ, ColVecs(X), ColVecs(Y)), respectively.\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"api/#KernelFunctions.kernelmatrix!","page":"API","title":"KernelFunctions.kernelmatrix!","text":"kernelmatrix!(K::AbstractMatrix, κ::Kernel, x::AbstractVector)\nkernelmatrix!(K::AbstractMatrix, κ::Kernel, x::AbstractVector, y::AbstractVector)\n\nIn-place version of kernelmatrix where pre-allocated matrix K will be overwritten with the kernel matrix.\n\nkernelmatrix!(K::AbstractMatrix, κ::Kernel, X::AbstractMatrix; obsdim)\nkernelmatrix!(\n K::AbstractMatrix,\n κ::Kernel,\n X::AbstractMatrix,\n Y::AbstractMatrix;\n obsdim,\n)\n\nIf obsdim=1, equivalent to kernelmatrix!(K, κ, RowVecs(X)) and kernelmatrix(K, κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix!(K, κ, ColVecs(X)) and kernelmatrix(K, κ, ColVecs(X), ColVecs(Y)), respectively.\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"api/#KernelFunctions.kernelmatrix_diag","page":"API","title":"KernelFunctions.kernelmatrix_diag","text":"kernelmatrix_diag(κ::Kernel, x::AbstractVector)\n\nCompute the diagonal of kernelmatrix(κ, x) efficiently.\n\nkernelmatrix_diag(κ::Kernel, x::AbstractVector, y::AbstractVector)\n\nCompute the diagonal of kernelmatrix(κ, x, y) efficiently. Requires that x and y are the same length.\n\nkernelmatrix_diag(κ::Kernel, X::AbstractMatrix; obsdim)\nkernelmatrix_diag(κ::Kernel, X::AbstractMatrix, Y::AbstractMatrix; obsdim)\n\nIf obsdim=1, equivalent to kernelmatrix_diag(κ, RowVecs(X)) and kernelmatrix_diag(κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix_diag(κ, ColVecs(X)) and kernelmatrix_diag(κ, ColVecs(X), ColVecs(Y)), respectively.\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"api/#KernelFunctions.kernelmatrix_diag!","page":"API","title":"KernelFunctions.kernelmatrix_diag!","text":"kernelmatrix_diag!(K::AbstractVector, κ::Kernel, x::AbstractVector)\nkernelmatrix_diag!(K::AbstractVector, κ::Kernel, x::AbstractVector, y::AbstractVector)\n\nIn place version of kernelmatrix_diag.\n\nkernelmatrix_diag!(K::AbstractVector, κ::Kernel, X::AbstractMatrix; obsdim)\nkernelmatrix_diag!(\n K::AbstractVector,\n κ::Kernel,\n X::AbstractMatrix,\n Y::AbstractMatrix;\n obsdim\n)\n\nIf obsdim=1, equivalent to kernelmatrix_diag!(K, κ, RowVecs(X)) and kernelmatrix_diag!(K, κ, RowVecs(X), RowVecs(Y)), respectively. If obsdim=2, equivalent to kernelmatrix_diag!(K, κ, ColVecs(X)) and kernelmatrix_diag!(K, κ, ColVecs(X), ColVecs(Y)), respectively.\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"api/#Input-Types","page":"API","title":"Input Types","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"The above API operates on collections of inputs. All collections of inputs in KernelFunctions.jl are represented as AbstractVectors. To understand this choice, please see the design notes on collections of inputs. The length of any such AbstractVector is equal to the number of inputs in the collection. For example, this means that","category":"page"},{"location":"api/","page":"API","title":"API","text":"size(kernelmatrix(k, x)) == (length(x), length(x))","category":"page"},{"location":"api/","page":"API","title":"API","text":"is always true, for some Kernel k, and AbstractVector x.","category":"page"},{"location":"api/#Univariate-Inputs","page":"API","title":"Univariate Inputs","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"If each input to your kernel is Real-valued, then any AbstractVector{<:Real} is a valid representation for a collection of inputs. More generally, it's completely fine to represent a collection of inputs of type T as, for example, a Vector{T}. However, this may not be the most efficient way to represent collection of inputs. See Vector-Valued Inputs for an example.","category":"page"},{"location":"api/#Vector-Valued-Inputs","page":"API","title":"Vector-Valued Inputs","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"We recommend that collections of vector-valued inputs are stored in an AbstractMatrix{<:Real} when possible, and wrapped inside a ColVecs or RowVecs to make their interpretation clear:","category":"page"},{"location":"api/","page":"API","title":"API","text":"ColVecs\nRowVecs","category":"page"},{"location":"api/#KernelFunctions.ColVecs","page":"API","title":"KernelFunctions.ColVecs","text":"ColVecs(X::AbstractMatrix)\n\nA lightweight wrapper for an AbstractMatrix which interprets it as a vector-of-vectors, in which each column of X represents a single vector.\n\nThat is, by writing x = ColVecs(X), you are saying \"x is a vector-of-vectors, each of which has length size(X, 1). The total number of vectors is size(X, 2).\"\n\nPhrased differently, ColVecs(X) says that X should be interpreted as a vector of horizontally-concatenated column-vectors, hence the name ColVecs.\n\njulia> X = randn(2, 5);\n\njulia> x = ColVecs(X);\n\njulia> length(x) == 5\ntrue\n\njulia> X[:, 3] == x[3]\ntrue\n\nColVecs is related to RowVecs via transposition:\n\njulia> X = randn(2, 5);\n\njulia> ColVecs(X) == RowVecs(X')\ntrue\n\n\n\n\n\n","category":"type"},{"location":"api/#KernelFunctions.RowVecs","page":"API","title":"KernelFunctions.RowVecs","text":"RowVecs(X::AbstractMatrix)\n\nA lightweight wrapper for an AbstractMatrix which interprets it as a vector-of-vectors, in which each row of X represents a single vector.\n\nThat is, by writing x = RowVecs(X), you are saying \"x is a vector-of-vectors, each of which has length size(X, 2). The total number of vectors is size(X, 1).\"\n\nPhrased differently, RowVecs(X) says that X should be interpreted as a vector of vertically-concatenated row-vectors, hence the name RowVecs.\n\nInternally, the data continues to be represented as an AbstractMatrix, so using this type does not introduce any kind of performance penalty.\n\njulia> X = randn(5, 2);\n\njulia> x = RowVecs(X);\n\njulia> length(x) == 5\ntrue\n\njulia> X[3, :] == x[3]\ntrue\n\nRowVecs is related to ColVecs via transposition:\n\njulia> X = randn(5, 2);\n\njulia> RowVecs(X) == ColVecs(X')\ntrue\n\n\n\n\n\n","category":"type"},{"location":"api/","page":"API","title":"API","text":"These types are specialised upon to ensure good performance e.g. when computing Euclidean distances between pairs of elements. The benefit of using this representation, rather than using a Vector{Vector{<:Real}}, is that optimised matrix-matrix multiplication functionality can be utilised when computing pairwise distances between inputs, which are needed for kernelmatrix computation.","category":"page"},{"location":"api/#Inputs-for-Multiple-Outputs","page":"API","title":"Inputs for Multiple Outputs","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"KernelFunctions.jl views multi-output GPs as GPs on an extended input domain. For an explanation of this design choice, see the design notes on multi-output GPs.","category":"page"},{"location":"api/","page":"API","title":"API","text":"An input to a multi-output Kernel should be a Tuple{T, Int}, whose first element specifies a location in the domain of the multi-output GP, and whose second element specifies which output the inputs corresponds to. The type of collections of inputs for multi-output GPs is therefore AbstractVector{<:Tuple{T, Int}}.","category":"page"},{"location":"api/","page":"API","title":"API","text":"KernelFunctions.jl provides the following helper functions to reduce the cognitive load associated with working with multi-output kernels by dealing with transforming data from the formats in which it is commonly found into the format required by KernelFunctions. The intention is that users can pass their data to these functions, and use the returned values throughout their code, without having to worry further about correctly formatting their data for KernelFunctions' sake:","category":"page"},{"location":"api/","page":"API","title":"API","text":"prepare_isotopic_multi_output_data(x::AbstractVector, y::ColVecs)\nprepare_isotopic_multi_output_data(x::AbstractVector, y::RowVecs)\nprepare_heterotopic_multi_output_data","category":"page"},{"location":"api/#KernelFunctions.prepare_isotopic_multi_output_data-Tuple{AbstractVector, ColVecs}","page":"API","title":"KernelFunctions.prepare_isotopic_multi_output_data","text":"prepare_isotopic_multi_output_data(x::AbstractVector, y::ColVecs)\n\nUtility functionality to convert a collection of N = length(x) inputs x, and a vector-of-vectors y (efficiently represented by a ColVecs) into a format suitable for use with multi-output kernels.\n\ny[n] is the vector-valued output corresponding to the input x[n]. Consequently, it is necessary that length(x) == length(y).\n\nFor example, if outputs are initially stored in a num_outputs × N matrix:\n\njulia> x = [1.0, 2.0, 3.0];\n\njulia> Y = [1.1 2.1 3.1; 1.2 2.2 3.2]\n2×3 Matrix{Float64}:\n 1.1 2.1 3.1\n 1.2 2.2 3.2\n\njulia> inputs, outputs = prepare_isotopic_multi_output_data(x, ColVecs(Y));\n\njulia> inputs\n6-element KernelFunctions.MOInputIsotopicByFeatures{Float64, Vector{Float64}, Int64}:\n (1.0, 1)\n (1.0, 2)\n (2.0, 1)\n (2.0, 2)\n (3.0, 1)\n (3.0, 2)\n\njulia> outputs\n6-element Vector{Float64}:\n 1.1\n 1.2\n 2.1\n 2.2\n 3.1\n 3.2\n\nSee also prepare_heterotopic_multi_output_data.\n\n\n\n\n\n","category":"method"},{"location":"api/#KernelFunctions.prepare_isotopic_multi_output_data-Tuple{AbstractVector, RowVecs}","page":"API","title":"KernelFunctions.prepare_isotopic_multi_output_data","text":"prepare_isotopic_multi_output_data(x::AbstractVector, y::RowVecs)\n\nUtility functionality to convert a collection of N = length(x) inputs x and output vectors y (efficiently represented by a RowVecs) into a format suitable for use with multi-output kernels.\n\ny[n] is the vector-valued output corresponding to the input x[n]. Consequently, it is necessary that length(x) == length(y).\n\nFor example, if outputs are initial stored in an N × num_outputs matrix:\n\njulia> x = [1.0, 2.0, 3.0];\n\njulia> Y = [1.1 1.2; 2.1 2.2; 3.1 3.2]\n3×2 Matrix{Float64}:\n 1.1 1.2\n 2.1 2.2\n 3.1 3.2\n\njulia> inputs, outputs = prepare_isotopic_multi_output_data(x, RowVecs(Y));\n\njulia> inputs\n6-element KernelFunctions.MOInputIsotopicByOutputs{Float64, Vector{Float64}, Int64}:\n (1.0, 1)\n (2.0, 1)\n (3.0, 1)\n (1.0, 2)\n (2.0, 2)\n (3.0, 2)\n\njulia> outputs\n6-element Vector{Float64}:\n 1.1\n 2.1\n 3.1\n 1.2\n 2.2\n 3.2\n\nSee also prepare_heterotopic_multi_output_data.\n\n\n\n\n\n","category":"method"},{"location":"api/#KernelFunctions.prepare_heterotopic_multi_output_data","page":"API","title":"KernelFunctions.prepare_heterotopic_multi_output_data","text":"prepare_heterotopic_multi_output_data(\n x::AbstractVector, y::AbstractVector{<:Real}, output_indices::AbstractVector{Int},\n)\n\nUtility functionality to convert a collection of inputs x, observations y, and output_indices into a format suitable for use with multi-output kernels. Handles the situation in which only one (or a subset) of outputs are observed at each feature. Ensures that all arguments are compatible with one another, and returns a vector of inputs and a vector of outputs.\n\ny[n] should be the observed value associated with output output_indices[n] at feature x[n].\n\njulia> x = [1.0, 2.0, 3.0];\n\njulia> y = [-1.0, 0.0, 1.0];\n\njulia> output_indices = [3, 2, 1];\n\njulia> inputs, outputs = prepare_heterotopic_multi_output_data(x, y, output_indices);\n\njulia> inputs\n3-element Vector{Tuple{Float64, Int64}}:\n (1.0, 3)\n (2.0, 2)\n (3.0, 1)\n\njulia> outputs\n3-element Vector{Float64}:\n -1.0\n 0.0\n 1.0\n\nSee also prepare_isotopic_multi_output_data.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"The input types returned by prepare_isotopic_multi_output_data can also be constructed manually:","category":"page"},{"location":"api/","page":"API","title":"API","text":"MOInput","category":"page"},{"location":"api/#KernelFunctions.MOInput","page":"API","title":"KernelFunctions.MOInput","text":"MOInput(x::AbstractVector, out_dim::Integer)\n\nA data type to accommodate modelling multi-dimensional output data. MOInput(x, out_dim) has length length(x) * out_dim.\n\njulia> x = [1, 2, 3];\n\njulia> MOInput(x, 2)\n6-element KernelFunctions.MOInputIsotopicByOutputs{Int64, Vector{Int64}, Int64}:\n (1, 1)\n (2, 1)\n (3, 1)\n (1, 2)\n (2, 2)\n (3, 2)\n\nAs shown above, an MOInput represents a vector of tuples. The first length(x) elements represent the inputs for the first output, the second length(x) elements represent the inputs for the second output, etc. See Inputs for Multiple Outputs in the docs for more info.\n\nMOInput will be deprecated in version 0.11 in favour of MOInputIsotopicByOutputs, and removed in version 0.12.\n\n\n\n\n\n","category":"type"},{"location":"api/","page":"API","title":"API","text":"As with ColVecs and RowVecs for vector-valued input spaces, this type enables specialised implementations of e.g. kernelmatrix for MOInputs in some situations.","category":"page"},{"location":"api/","page":"API","title":"API","text":"To find out more about the background, read this review of kernels for vector-valued functions.","category":"page"},{"location":"api/#Generic-Utilities","page":"API","title":"Generic Utilities","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"KernelFunctions also provides miscellaneous utility functions.","category":"page"},{"location":"api/","page":"API","title":"API","text":"nystrom\nNystromFact","category":"page"},{"location":"api/#KernelFunctions.nystrom","page":"API","title":"KernelFunctions.nystrom","text":"nystrom(k::Kernel, X::AbstractVector, S::AbstractVector{<:Integer})\n\nCompute a factorization of a Nystrom approximation of the square kernel matrix of data vector X with respect to kernel k, using indices S. Returns a NystromFact struct which stores a Nystrom factorization satisfying:\n\nmathbfK approx mathbfC^intercalmathbfWmathbfC\n\n\n\n\n\nnystrom(k::Kernel, X::AbstractVector, r::Real)\n\nCompute a factorization of a Nystrom approximation of the square kernel matrix of data vector X with respect to kernel k using a sample ratio of r. Returns a NystromFact struct which stores a Nystrom factorization satisfying:\n\nmathbfK approx mathbfC^intercalmathbfWmathbfC\n\n\n\n\n\nnystrom(k::Kernel, X::AbstractMatrix, S::AbstractVector{<:Integer}; obsdim)\n\nIf obsdim=1, equivalent to nystrom(k, RowVecs(X), S). If obsdim=2, equivalent to nystrom(k, ColVecs(X), S).\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\nnystrom(k::Kernel, X::AbstractMatrix, r::Real; obsdim)\n\nIf obsdim=1, equivalent to nystrom(k, RowVecs(X), r). If obsdim=2, equivalent to nystrom(k, ColVecs(X), r).\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"api/#KernelFunctions.NystromFact","page":"API","title":"KernelFunctions.NystromFact","text":"NystromFact\n\nType for storing a Nystrom factorization. The factorization contains two fields: W and C, two matrices satisfying:\n\nmathbfK approx mathbfC^intercalmathbfWmathbfC\n\n\n\n\n\n","category":"type"},{"location":"api/#Conditional-Utilities","page":"API","title":"Conditional Utilities","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"To keep the dependencies of KernelFunctions lean, some functionality is only available if specific other packages are explicitly loaded (using).","category":"page"},{"location":"api/#Kronecker.jl","page":"API","title":"Kronecker.jl","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"https://github.com/MichielStock/Kronecker.jl","category":"page"},{"location":"api/","page":"API","title":"API","text":"kronecker_kernelmatrix\nkernelkronmat","category":"page"},{"location":"api/#KernelFunctions.kronecker_kernelmatrix","page":"API","title":"KernelFunctions.kronecker_kernelmatrix","text":"kronecker_kernelmatrix(\n k::Union{IndependentMOKernel,IntrinsicCoregionMOKernel}, x::MOI, y::MOI\n) where {MOI<:IsotopicMOInputsUnion}\n\nRequires Kronecker.jl: Computes the kernelmatrix for the IndependentMOKernel and the IntrinsicCoregionMOKernel, but returns a lazy kronecker product. This object can be very efficiently inverted or decomposed. See also kernelmatrix.\n\n\n\n\n\n","category":"function"},{"location":"api/#KernelFunctions.kernelkronmat","page":"API","title":"KernelFunctions.kernelkronmat","text":"kernelkronmat(κ::Kernel, X::AbstractVector{<:Real}, dims::Int) -> KroneckerPower\n\nReturn a KroneckerPower matrix on the D-dimensional input grid constructed by otimes_i=1^D X, where D is given by dims.\n\nwarning: Warning\nRequires Kronecker.jl and for iskroncompatible(κ) to return true.\n\n\n\n\n\nkernelkronmat(κ::Kernel, X::AbstractVector{<:AbstractVector}) -> KroneckerProduct\n\nReturns a KroneckerProduct matrix on the grid built with the collection of vectors X_i_i=1^D: otimes_i=1^D X_i.\n\nwarning: Warning\nRequires Kronecker.jl and for iskroncompatible(κ) to return true.\n\n\n\n\n\n","category":"function"},{"location":"api/#PDMats.jl","page":"API","title":"PDMats.jl","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"https://github.com/JuliaStats/PDMats.jl","category":"page"},{"location":"api/","page":"API","title":"API","text":"kernelpdmat","category":"page"},{"location":"api/#KernelFunctions.kernelpdmat","page":"API","title":"KernelFunctions.kernelpdmat","text":"kernelpdmat(k::Kernel, X::AbstractVector)\n\nCompute a positive-definite matrix in the form of a PDMat matrix (see PDMats.jl), with the Cholesky decomposition precomputed. The algorithm adds a diagonal \"nugget\" term to the kernel matrix which is increased until positive definiteness is achieved. The algorithm gives up with an error if the nugget becomes larger than 1% of the largest value in the kernel matrix.\n\n\n\n\n\nkernelpdmat(k::Kernel, X::AbstractMatrix; obsdim)\n\nIf obsdim=1, equivalent to kernelpdmat(k, RowVecs(X)). If obsdim=2, equivalent to kernelpdmat(k, ColVecs(X)).\n\nSee also: ColVecs, RowVecs\n\n\n\n\n\n","category":"function"},{"location":"design/#Design","page":"Design","title":"Design","text":"","category":"section"},{"location":"design/#why_abstract_vectors","page":"Design","title":"Why AbstractVectors Everywhere?","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"To understand the advantages of using AbstractVectors everywhere to represent collections of inputs, first consider the following properties that it is desirable for a collection of inputs to satisfy.","category":"page"},{"location":"design/#Unique-Ordering","page":"Design","title":"Unique Ordering","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"There must be a clearly-defined first, second, etc element of an input collection. If this were not the case, it would not be possible to determine a unique mapping between a collection of inputs and the output of kernelmatrix, as it would not be clear what order the rows and columns of the output should appear in.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"Moreover, ordering guarantees that if you permute the collection of inputs, the ordering of the rows and columns of the kernelmatrix are correspondingly permuted.","category":"page"},{"location":"design/#Generality","page":"Design","title":"Generality","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"There must be no restriction on the domain of the input. Collections of Reals, vectors, graphs, finite-dimensional domains, or really anything else that you fancy should be straightforwardly representable. Moreover, whichever input class is chosen should not prevent optimal performance from being obtained.","category":"page"},{"location":"design/#Unambiguously-Defined-Length","page":"Design","title":"Unambiguously-Defined Length","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"Knowing the length of a collection of inputs is important. For example, a well-defined length guarantees that the size of the output of kernelmatrix, and related functions, are predictable. It also makes it possible to perform internal error-checking that ensures that e.g. there are the same number of inputs in two collections of inputs.","category":"page"},{"location":"design/#AbstractMatrices-Do-Not-Cut-It","page":"Design","title":"AbstractMatrices Do Not Cut It","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"Notably, while AbstractMatrix objects are often used to represent collections of vector-valued inputs, they do not immediately satisfy these properties as it is unclear whether a matrix of size P x Q represents a collection of P Q-dimensional inputs (each row is an input), or Q P-dimensional inputs (each column is an input).","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"Moreover, they occasionally add some aesthetic inconvenience. For example, a collection of Real-valued inputs, which might be straightforwardly represented as an AbstractVector{<:Real}, must be reshaped into a matrix.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"There are two commonly used ways to partly resolve these shortcomings:","category":"page"},{"location":"design/#Resolution-1:-Specify-a-Convention","page":"Design","title":"Resolution 1: Specify a Convention","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"One way that these shortcomings can be partly resolved is by specifying a convention that everyone adheres to regarding the interpretation of rows vs columns. However, opinions about the choice of convention are often surprisingly strongly held, and users regularly have to remind themselves which convention has been chosen. While this resolves the ordering problem, and in principle defines the \"length\" of a collection of inputs, AbstractMatrixs already have a length defined in Julia, which would generally disagree with our internal notion of length. This isn't a show-stopper, but it isn't an especially clean situation.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"There is also the opportunity for some kinds of silent bugs. For example, if an input matrix happens to be square because the number of input dimensions is the same as the number of inputs, it would be hard to know whether the correct kernelmatrix has been computed. This kind of bug seems unlikely, but it exists regardless.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"Finally, suppose that your inputs are some type T that is not simply a vector of real numbers, say a graph. In this situation, how should a collection of inputs be represented? A N x 1 or 1 x N matrix is the only obvious candidate, but the additional singular dimension seems somewhat redundant.","category":"page"},{"location":"design/#Resolution-2:-Always-Specify-An-obsdim-Argument","page":"Design","title":"Resolution 2: Always Specify An obsdim Argument","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"Another way to partly resolve these problems is to not commit to a convention, and instead to propagate some additional information through the codebase that specifies how the input data is to be interpreted. For example, a kernel k that represents the sum of two other kernels might implement kernelmatrix as follows:","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"function kernelmatrix(k::KernelSum, x::AbstractMatrix; obsdim=1)\n return kernelmatrix(k.kernels[1], x; obsdim=obsdim) +\n kernelmatrix(k.kernels[2], x; obsdim=obsdim)\nend","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"While this prevents this package from having to pre-specify a convention, it doesn't resolve the length issue, or the issue of representing collections of inputs which aren't immediately represented as vectors. Moreover, it complicates the internals; in contrast, consider what this function looks like with an AbstractVector:","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"function kernelmatrix(k::KernelSum, x::AbstractVector)\n return kernelmatrix(k.kernels[1], x) + kernelmatrix(k.kernels[2], x)\nend","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"This code is clearer (less visual noise), and has removed a possible bug – if the implementer of kernelmatrix forgets to pass the obsdim kwarg into each subsequent kernelmatrix call, it's possible to get the wrong answer.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"This being said, we do support matrix-valued inputs – see Why We Have Support for Both.","category":"page"},{"location":"design/#AbstractVectors","page":"Design","title":"AbstractVectors","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"Requiring all collections of inputs to be AbstractVectors resolves all of these problems, and ensures that the data is self-describing to the extent that KernelFunctions.jl requires.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"Firstly, the question of how to interpret the columns and rows of a matrix of inputs is resolved. Users must wrap matrices which represent collections of inputs in either a ColVecs or RowVecs, both of which have clearly defined semantics which are hard to confuse.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"By design, there is also no discrepancy between the number of inputs in the collection, and the length function – the length of a ColVecs, RowVecs, or Vector{<:Real} is equal to the number of inputs.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"There is no loss of performance.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"A collection of N Real-valued inputs can be represented by an AbstractVector{<:Real} of length N, rather than needing to use an AbstractMatrix{<:Real} of size either N x 1 or 1 x N. The same can be said for any other input type T, and new subtypes of AbstractVector can be added if particularly efficient ways exist to store collections of inputs of type T. A good example of this in practice is using Tuple{S, Int}, for some input type S, as the Inputs for Multiple Outputs.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"This approach can also lead to clearer user code. A user need only wrap their inputs in a ColVecs or RowVecs once in their code, and this specification is automatically re-used everywhere in their code. In this sense, it is straightforward to write code in such a way that there is one unique source of \"truth\" about the way in which a particular data set should be interpreted. Conversely, the obsdim resolution requires that the obsdim keyword argument is passed around with the data every single time that you use it.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"The benefits of the AbstractVector approach are likely most strongly felt when writing a substantial amount of code on top of KernelFunctions.jl – in the same way that using AbstractVectors inside KernelFunctions.jl removes the need for large amounts of keyword argument propagation, the same will be true of other code.","category":"page"},{"location":"design/#Why-We-Have-Support-for-Both","page":"Design","title":"Why We Have Support for Both","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"In short: many people like matrices, and are familiar with obsdim-style keyword arguments.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"All internals are implemented using AbstractVectors though, and the obsdim interface is just a thin layer of utility functionality which sits on top of this. To avoid confusion and silent errors, we do not favour a specific convention (rows or columns) but instead it is necessary to specify the obsdim keyword argument explicitly.","category":"page"},{"location":"design/#inputs_for_multiple_outputs","page":"Design","title":"Kernels for Multiple-Outputs","text":"","category":"section"},{"location":"design/","page":"Design","title":"Design","text":"There are two equally-valid perspectives on multi-output kernels: they can either be treated as matrix-valued kernels, or standard kernels on an extended input domain. Each of these perspectives are convenient in different circumstances, but the latter greatly simplifies the incorporation of multi-output kernels in KernelFunctions.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"More concretely, let k_mat be a matrix-valued kernel, mapping pairs of inputs of type T to matrices of size P x P to describe the covariance between P outputs. Given inputs x and y of type T, and integers p and q, we can always find an equivalent standard kernel k mapping from pairs of inputs of type Tuple{T, Int} to the Reals as follows:","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"k((x, p), (y, q)) = k_mat(x, y)[p, q]","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"This ability to treat multi-output kernels as single-output kernels is very helpful, as it means that there is no need to introduce additional concepts into the API of KernelFunctions.jl, just additional kernels! This in turn simplifies downstream code as they don't need to \"know\" about the existence of multi-output kernels in addition to standard kernels. For example, GP libraries built on top of KernelFunctions.jl just need to know about Kernels, and they get multi-output kernels, and hence multi-output GPs, for free.","category":"page"},{"location":"design/","page":"Design","title":"Design","text":"Where there is the need to specialise implementations for multi-output kernels, this is done in an encapsulated manner – parts of KernelFunctions that have nothing to do with multi-output kernels know nothing about the existence of multi-output kernels.","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"EditURL = \"../../../../examples/support-vector-machine/script.jl\"","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/master/examples/support-vector-machine/script.jl\"","category":"page"},{"location":"examples/support-vector-machine/#Support-Vector-Machine","page":"Support Vector Machine","title":"Support Vector Machine","text":"","category":"section"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"(Image: )","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"In this notebook we show how you can use KernelFunctions.jl to generate kernel matrices for classification with a support vector machine, as implemented by LIBSVM.","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"using Distributions\nusing KernelFunctions\nusing LIBSVM\nusing LinearAlgebra\nusing Plots\nusing Random\n\n# Set seed\nRandom.seed!(1234);","category":"page"},{"location":"examples/support-vector-machine/#Generate-half-moon-dataset","page":"Support Vector Machine","title":"Generate half-moon dataset","text":"","category":"section"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"Number of samples per class:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"n1 = n2 = 50;","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"We generate data based on SciKit-Learn's sklearn.datasets.make_moons function:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"angle1 = range(0, π; length=n1)\nangle2 = range(0, π; length=n2)\nX1 = [cos.(angle1) sin.(angle1)] .+ 0.1 .* randn.()\nX2 = [1 .- cos.(angle2) 1 .- sin.(angle2) .- 0.5] .+ 0.1 .* randn.()\nX = [X1; X2]\nx_train = RowVecs(X)\ny_train = vcat(fill(-1, n1), fill(1, n2));","category":"page"},{"location":"examples/support-vector-machine/#Training","page":"Support Vector Machine","title":"Training","text":"","category":"section"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"We create a kernel function:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"k = SqExponentialKernel() ∘ ScaleTransform(1.5)","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"Squared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t- Scale Transform (s = 1.5)","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"LIBSVM can make use of a pre-computed kernel matrix. KernelFunctions.jl can be used to produce that using kernelmatrix:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"model = svmtrain(kernelmatrix(k, x_train), y_train; kernel=LIBSVM.Kernel.Precomputed)","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"LIBSVM.SVM{Int64, LIBSVM.Kernel.KERNEL}(LIBSVM.SVC, LIBSVM.Kernel.Precomputed, nothing, 100, 100, 2, [-1, 1], Int32[1, 2], Float64[], Int32[], LIBSVM.SupportVectors{Vector{Int64}, Matrix{Float64}}(23, Int32[11, 12], [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1.0 0.8982223633317491 0.9596163170022011 0.8681749917956418 0.7405298560587654 0.6670753594660519 0.1779671467515013 0.12581804740739566 0.05707943398657384 0.02764121723161683 0.033765857073249396 0.2680295766735067 0.29939058530607915 0.37151489965630213 0.3524014409758097 0.2908959282977835 0.3880509811446821 0.8766234308310106 0.82681374480545 0.8144257681324784 0.6772129558340088 0.6360692761241019 0.27226866397259536; 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1.0; 1.0; 1.0; 0.4545873718969774; 0.36172853884920114; 1.0; 1.0; 0.9976825435225717; 1.0; 1.0; -1.0; -1.0; -1.0; -1.0; -0.5005315477488701; -0.21806563021962358; -1.0; -0.3833339180359196; -1.0; -0.7120673582643366; -1.0; -1.0;;], Float64[], Float64[], [-0.015075000482567661], 3, 0.01, 200.0, 0.001, 1.0, 0.5, 0.1, true, false)","category":"page"},{"location":"examples/support-vector-machine/#Prediction","page":"Support Vector Machine","title":"Prediction","text":"","category":"section"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"For evaluation, we create a 100×100 2D grid based on the extent of the training data:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"test_range = range(floor(Int, minimum(X)), ceil(Int, maximum(X)); length=100)\nx_test = ColVecs(mapreduce(collect, hcat, Iterators.product(test_range, test_range)));","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"Again, we pass the result of KernelFunctions.jl's kernelmatrix to LIBSVM:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"y_pred, _ = svmpredict(model, kernelmatrix(k, x_train, x_test));","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"We can see that the kernelized, non-linear classification successfully separates the two classes in the training data:","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"plot(; lim=extrema(test_range), aspect_ratio=1)\ncontourf!(\n test_range,\n test_range,\n y_pred;\n levels=1,\n color=cgrad(:redsblues),\n alpha=0.7,\n colorbar_title=\"prediction\",\n)\nscatter!(X1[:, 1], X1[:, 2]; color=:red, label=\"training data: class –1\")\nscatter!(X2[:, 1], X2[:, 2]; color=:blue, label=\"training data: class 1\")","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"
      \n
      Package and system information
      \n
      \nPackage information (click to expand)\n
      \nStatus `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/support-vector-machine/Project.toml`\n  [31c24e10] Distributions v0.25.107\n  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`\n  [b1bec4e5] LIBSVM v0.8.0\n  [98b081ad] Literate v2.16.1\n  [91a5bcdd] Plots v1.40.1\n  [37e2e46d] LinearAlgebra\n
      \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
      \n
      \nSystem information (click to expand)\n
      \nJulia Version 1.10.0\nCommit 3120989f39b (2023-12-25 18:01 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 4 × AMD EPYC 7763 64-Core Processor\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\n  Threads: 1 on 4 virtual cores\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
      \n
      ","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"","category":"page"},{"location":"examples/support-vector-machine/","page":"Support Vector Machine","title":"Support Vector Machine","text":"This page was generated using Literate.jl.","category":"page"},{"location":"metrics/#Metrics","page":"Metrics","title":"Metrics","text":"","category":"section"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"SimpleKernel implementations rely on Distances.jl for efficiently computing the pairwise matrix. This requires a distance measure or metric, such as the commonly used SqEuclidean and Euclidean.","category":"page"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"The metric used by a given kernel type is specified as","category":"page"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"KernelFunctions.metric(::CustomKernel) = SqEuclidean()","category":"page"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"However, there are kernels that can be implemented efficiently using \"metrics\" that do not respect all the definitions expected by Distances.jl. For this reason, KernelFunctions.jl provides additional \"metrics\" such as DotProduct (langle x y rangle) and Delta (delta(xy)).","category":"page"},{"location":"metrics/#Adding-a-new-metric","page":"Metrics","title":"Adding a new metric","text":"","category":"section"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"If you want to create a new \"metric\" just implement the following:","category":"page"},{"location":"metrics/","page":"Metrics","title":"Metrics","text":"struct Delta <: Distances.PreMetric\nend\n\n@inline function Distances._evaluate(::Delta,a::AbstractVector{T},b::AbstractVector{T}) where {T}\n @boundscheck if length(a) != length(b)\n throw(DimensionMismatch(\"first array has length $(length(a)) which does not match the length of the second, $(length(b)).\"))\n end\n return a==b\nend\n\n@inline (dist::Delta)(a::AbstractArray,b::AbstractArray) = Distances._evaluate(dist,a,b)\n@inline (dist::Delta)(a::Number,b::Number) = a==b","category":"page"},{"location":"transform/#input_transforms","page":"Input Transforms","title":"Input Transforms","text":"","category":"section"},{"location":"transform/#Overview","page":"Input Transforms","title":"Overview","text":"","category":"section"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"Transforms are designed to change input data before passing it on to a kernel object.","category":"page"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"It can be as standard as IdentityTransform returning the same input, or multiplying the data by a scalar with ScaleTransform or by a vector with ARDTransform. There is a more general FunctionTransform that uses a function and applies it to each input.","category":"page"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"You can also create a pipeline of Transforms via ChainTransform, e.g.,","category":"page"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"LowRankTransform(rand(10, 5)) ∘ ScaleTransform(2.0)","category":"page"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"A transformation t can be applied to a single input x with t(x) and to multiple inputs xs with map(t, xs).","category":"page"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"Kernels can be coupled with input transformations with ∘ or its alias compose. It falls back to creating a TransformedKernel but allows more optimized implementations for specific kernels and transformations.","category":"page"},{"location":"transform/#List-of-Input-Transforms","page":"Input Transforms","title":"List of Input Transforms","text":"","category":"section"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"Transform\nIdentityTransform\nScaleTransform\nARDTransform\nARDTransform(::Real, ::Integer)\nLinearTransform\nFunctionTransform\nSelectTransform\nChainTransform\nPeriodicTransform","category":"page"},{"location":"transform/#KernelFunctions.Transform","page":"Input Transforms","title":"KernelFunctions.Transform","text":"Transform\n\nAbstract type defining a transformation of the input.\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.IdentityTransform","page":"Input Transforms","title":"KernelFunctions.IdentityTransform","text":"IdentityTransform()\n\nTransformation that returns exactly the input.\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.ScaleTransform","page":"Input Transforms","title":"KernelFunctions.ScaleTransform","text":"ScaleTransform(l::Real)\n\nTransformation that multiplies the input elementwise with l.\n\nExamples\n\njulia> l = rand(); t = ScaleTransform(l); X = rand(100, 10);\n\njulia> map(t, ColVecs(X)) == ColVecs(l .* X)\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.ARDTransform","page":"Input Transforms","title":"KernelFunctions.ARDTransform","text":"ARDTransform(v::AbstractVector)\n\nTransformation that multiplies the input elementwise by v.\n\nExamples\n\njulia> v = rand(10); t = ARDTransform(v); X = rand(10, 100);\n\njulia> map(t, ColVecs(X)) == ColVecs(v .* X)\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.ARDTransform-Tuple{Real, Integer}","page":"Input Transforms","title":"KernelFunctions.ARDTransform","text":"ARDTransform(s::Real, dims::Integer)\n\nCreate an ARDTransform with vector fill(s, dims).\n\n\n\n\n\n","category":"method"},{"location":"transform/#KernelFunctions.LinearTransform","page":"Input Transforms","title":"KernelFunctions.LinearTransform","text":"LinearTransform(A::AbstractMatrix)\n\nLinear transformation of the input realised by the matrix A.\n\nThe second dimension of A must match the number of features of the target.\n\nExamples\n\njulia> A = rand(10, 5); t = LinearTransform(A); X = rand(5, 100);\n\njulia> map(t, ColVecs(X)) == ColVecs(A * X)\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.FunctionTransform","page":"Input Transforms","title":"KernelFunctions.FunctionTransform","text":"FunctionTransform(f)\n\nTransformation that applies function f to the input.\n\nMake sure that f can act on an input. For instance, if the inputs are vectors, use f(x) = sin.(x) instead of f = sin.\n\nExamples\n\njulia> f(x) = sum(x); t = FunctionTransform(f); X = randn(100, 10);\n\njulia> map(t, ColVecs(X)) == ColVecs(sum(X; dims=1))\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.SelectTransform","page":"Input Transforms","title":"KernelFunctions.SelectTransform","text":"SelectTransform(dims)\n\nTransformation that selects the dimensions dims of the input.\n\nExamples\n\njulia> dims = [1, 3, 5, 6, 7]; t = SelectTransform(dims); X = rand(100, 10);\n\njulia> map(t, ColVecs(X)) == ColVecs(X[dims, :])\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.ChainTransform","page":"Input Transforms","title":"KernelFunctions.ChainTransform","text":"ChainTransform(transforms)\n\nTransformation that applies a chain of transformations ts to the input.\n\nThe transformation first(ts) is applied first.\n\nExamples\n\njulia> l = rand(); A = rand(3, 4); t1 = ScaleTransform(l); t2 = LinearTransform(A);\n\njulia> X = rand(4, 10);\n\njulia> map(ChainTransform([t1, t2]), ColVecs(X)) == ColVecs(A * (l .* X))\ntrue\n\njulia> map(t2 ∘ t1, ColVecs(X)) == ColVecs(A * (l .* X))\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#KernelFunctions.PeriodicTransform","page":"Input Transforms","title":"KernelFunctions.PeriodicTransform","text":"PeriodicTransform(f)\n\nTransformation that maps the input elementwise onto the unit circle with frequency f.\n\nSamples from a GP with a kernel with this transformation applied to the inputs will produce samples with frequency f.\n\nExamples\n\njulia> f = rand(); t = PeriodicTransform(f); x = rand();\n\njulia> t(x) == [sinpi(2 * f * x), cospi(2 * f * x)]\ntrue\n\n\n\n\n\n","category":"type"},{"location":"transform/#Convenience-functions","page":"Input Transforms","title":"Convenience functions","text":"","category":"section"},{"location":"transform/","page":"Input Transforms","title":"Input Transforms","text":"with_lengthscale\nmedian_heuristic_transform","category":"page"},{"location":"transform/#KernelFunctions.with_lengthscale","page":"Input Transforms","title":"KernelFunctions.with_lengthscale","text":"with_lengthscale(kernel::Kernel, lengthscale::Real)\n\nConstruct a transformed kernel with lengthscale.\n\nExamples\n\njulia> kernel = with_lengthscale(SqExponentialKernel(), 2.5);\n\njulia> x = rand(2);\n\njulia> y = rand(2);\n\njulia> kernel(x, y) ≈ (SqExponentialKernel() ∘ ScaleTransform(0.4))(x, y)\ntrue\n\n\n\n\n\nwith_lengthscale(kernel::Kernel, lengthscales::AbstractVector{<:Real})\n\nConstruct a transformed \"ARD\" kernel with different lengthscales for each dimension.\n\nExamples\n\njulia> kernel = with_lengthscale(SqExponentialKernel(), [0.5, 2.5]);\n\njulia> x = rand(2);\n\njulia> y = rand(2);\n\njulia> kernel(x, y) ≈ (SqExponentialKernel() ∘ ARDTransform([2, 0.4]))(x, y)\ntrue\n\n\n\n\n\n","category":"function"},{"location":"transform/#KernelFunctions.median_heuristic_transform","page":"Input Transforms","title":"KernelFunctions.median_heuristic_transform","text":"median_heuristic_transform(distance, x::AbstractVector)\n\nCreate a ScaleTransform that divides the input elementwise by the median distance of the data points in x.\n\nThe distance has to support pairwise evaluation with KernelFunctions.pairwise. All PreMetrics of the package Distances.jl such as Euclidean satisfy this requirement automatically.\n\nExamples\n\njulia> using Distances, Statistics\n\njulia> x = ColVecs(rand(100, 10));\n\njulia> t = median_heuristic_transform(Euclidean(), x);\n\njulia> y = map(t, x);\n\njulia> median(euclidean(y[i], y[j]) for i in 1:10, j in 1:10 if i != j) ≈ 1\ntrue\n\n\n\n\n\n","category":"function"},{"location":"userguide/#User-guide","page":"User guide","title":"User guide","text":"","category":"section"},{"location":"userguide/#Kernel-Creation","page":"User guide","title":"Kernel Creation","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"To create a kernel object, choose one of the pre-implemented kernels, see Kernel Functions, or create your own, see Creating your own kernel. For example, a squared exponential kernel is created by","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":" k = SqExponentialKernel()","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"tip: How do I set the lengthscale(s)?\nInstead of having lengthscale(s) for each kernel we use Transform objects which act on the inputs before passing them to the kernel. Note that the transforms such as ScaleTransform and ARDTransform multiply the input by a scale factor, which corresponds to the inverse of the lengthscale. For example, a lengthscale of 0.5 is equivalent to premultiplying the input by 2.0, and you can create the corresponding kernel in either of the following equivalent ways: k = SqExponentialKernel() ∘ ScaleTransform(2.0)\n k = compose(SqExponentialKernel(), ScaleTransform(2.0))Alternatively, you can use the convenience function with_lengthscale:k = with_lengthscale(SqExponentialKernel(), 0.5)with_lengthscale also works with vector-valued lengthscales for multiple-dimensional inputs, and is equivalent to pre-composing with an ARDTransform:length_scales = [1.0, 2.0]\nk = with_lengthscale(SqExponentialKernel(), length_scales)\nk = SqExponentialKernel() ∘ ARDTransform(1 ./ length_scales)Check the Input Transforms page for more details.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"tip: How do I set the kernel variance?\nTo premultiply the kernel by a variance, you can use * with a scalar number: k = 3.0 * SqExponentialKernel()","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"tip: How do I use a Mahalanobis kernel?\nThe MahalanobisKernel(; P=P), defined byk(x x P) = expbig(- (x - x)^top P (x - x)big)for a positive definite matrix P = Q^top Q, was removed in 0.9. Instead you can use a squared exponential kernel together with a LinearTransform of the inputs:k = SqExponentialKernel() ∘ LinearTransform(sqrt(2) .* Q)Analogously, you can combine other kernels such as the PiecewisePolynomialKernel with a LinearTransform of the inputs to obtain a kernel that is a function of the Mahalanobis distance between inputs.","category":"page"},{"location":"userguide/#Using-a-Kernel-Function","page":"User guide","title":"Using a Kernel Function","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"To evaluate the kernel function on two vectors you simply call the kernel object:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"k = SqExponentialKernel()\nx1 = rand(3)\nx2 = rand(3)\nk(x1, x2)","category":"page"},{"location":"userguide/#Creating-a-Kernel-Matrix","page":"User guide","title":"Creating a Kernel Matrix","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"Kernel matrices can be created via the kernelmatrix function or kernelmatrix_diag for only the diagonal. For example, for a collection of 10 Real-valued inputs:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"k = SqExponentialKernel()\nx = rand(10)\nkernelmatrix(k, x) # 10x10 matrix","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"If your inputs are multi-dimensional, it is common to represent them as a matrix. For example","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"X = rand(10, 5)","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"However, it is ambiguous whether this represents a collection of 10 5-dimensional row-vectors, or 5 10-dimensional column-vectors. Therefore, we require users to provide some more information.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"You can write RowVecs(X) to declare that X contains 10 5-dimensional row-vectors, or ColVecs(X) to declare that X contains 5 10-dimensional column-vectors, then","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"kernelmatrix(k, RowVecs(X)) # returns a 10×10 matrix -- each row of X treated as input\nkernelmatrix(k, ColVecs(X)) # returns a 5×5 matrix -- each column of X treated as input","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"This is the mechanism used throughout KernelFunctions.jl to handle multi-dimensional inputs.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"You can utilise the obsdim keyword argument if you prefer:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"kernelmatrix(k, X; obsdim=1) # same as RowVecs(X)\nkernelmatrix(k, X; obsdim=2) # same as ColVecs(X)","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"This is similar to the convention used in Distances.jl.","category":"page"},{"location":"userguide/#So-what-type-should-I-use-to-represent-a-collection-of-inputs?","page":"User guide","title":"So what type should I use to represent a collection of inputs?","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"The central assumption made by KernelFunctions.jl is that all collections of N inputs are represented by AbstractVectors of length N. Abstraction is then used to ensure that efficiency is retained, ColVecs and RowVecs being the most obvious examples of this.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"Concretely:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"For Real-valued inputs (scalars), a Vector{<:Real} is fine.\nFor vector-valued inputs, consider a ColVecs or RowVecs.\nFor a new input type, simply represent collections of inputs of this type as an AbstractVector.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"See Input Types and Design for a more thorough discussion of the considerations made when this design was adopted.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"The obsdim kwarg mentioned above is a special case for vector-valued inputs stored in a matrix. It is implemented as a lightweight wrapper that constructs either a RowVecs or ColVecs from your inputs, and passes this on.","category":"page"},{"location":"userguide/#Output-Types","page":"User guide","title":"Output Types","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"In addition to plain Matrix-like output, KernelFunctions.jl supports specific output types:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"For a positive-definite matrix object of type PDMat from PDMats.jl, you can call the following:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"using PDMats\nk = SqExponentialKernel()\nK = kernelpdmat(k, RowVecs(X)) # PDMat\nK = kernelpdmat(k, X; obsdim=1) # PDMat","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"It will create a matrix and in case of bad conditioning will add some diagonal noise until the matrix is considered positive-definite; it will then return a PDMat object. For this method to work in your code you need to include using PDMats first.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"For a Kronecker matrix, we rely on Kronecker.jl. Here are two examples:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"using Kronecker\nx = range(0, 1; length=10)\ny = range(0, 1; length=50)\nK = kernelkronmat(k, [x, y]) # Kronecker matrix\nK = kernelkronmat(k, x, 5) # Kronecker matrix","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"Make sure that k is a kernel compatible with such constructions (with iskroncompatible(k)). Both methods will return a Kronecker matrix. For those methods to work in your code you need to include using Kronecker first.","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"For a Nystrom approximation: kernelmatrix(nystrom(k, X, ρ, obsdim=1)) where ρ is the fraction of data samples used in the approximation.","category":"page"},{"location":"userguide/#Composite-Kernels","page":"User guide","title":"Composite Kernels","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"Sums and products of kernels are also valid kernels. They can be created via KernelSum and KernelProduct or using simple operators + and *. For example:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"k1 = SqExponentialKernel()\nk2 = Matern32Kernel()\nk = 0.5 * k1 + 0.2 * k2 # KernelSum\nk = k1 * k2 # KernelProduct","category":"page"},{"location":"userguide/#Kernel-Parameters","page":"User guide","title":"Kernel Parameters","text":"","category":"section"},{"location":"userguide/","page":"User guide","title":"User guide","text":"What if you want to differentiate through the kernel parameters? This is easy even in a highly nested structure such as:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"k = (\n 0.5 * SqExponentialKernel() * Matern12Kernel() +\n 0.2 * (LinearKernel() ∘ ScaleTransform(2.0) + PolynomialKernel())\n) ∘ ARDTransform([0.1, 0.5])","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"One can access the named tuple of trainable parameters via Functors.functor from Functors.jl. This means that in practice you can implicitly optimize the kernel parameters by calling:","category":"page"},{"location":"userguide/","page":"User guide","title":"User guide","text":"using Flux\nkernelparams = Flux.params(k)\nFlux.gradient(kernelparams) do\n # ... some loss function on the kernel ....\nend","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"EditURL = \"../../../../examples/gaussian-process-priors/script.jl\"","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/blob/master/examples/gaussian-process-priors/script.jl\"","category":"page"},{"location":"examples/gaussian-process-priors/#Gaussian-process-prior-samples","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"","category":"section"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"(Image: )","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"The kernels defined in this package can also be used to specify the covariance of a Gaussian process prior. A Gaussian process (GP) is defined by its mean function m(cdot) and its covariance function or kernel k(cdot cdot):","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":" f sim mathcalGPbig(m(cdot) k(cdot cdot)big)","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"In this notebook we show how the choice of kernel affects the samples from a GP (with zero mean).","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"# Load required packages\nusing KernelFunctions, LinearAlgebra\nusing Plots, Plots.PlotMeasures\ndefault(; lw=1.0, legendfontsize=8.0)\nusing Random: seed!\nseed!(42); # reproducibility","category":"page"},{"location":"examples/gaussian-process-priors/#Evaluation-at-finite-set-of-points","page":"Gaussian process prior samples","title":"Evaluation at finite set of points","text":"","category":"section"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"The function values mathbff = f(x_n)_n=1^N of the GP at a finite number N of points X = x_n_n=1^N follow a multivariate normal distribution mathbff sim mathcalMVN(mathbfm mathrmK) with mean vector mathbfm and covariance matrix mathrmK, where","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"beginaligned\n mathbfm_i = m(x_i) \n mathrmK_ij = k(x_i x_j)\nendaligned","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"with 1 le i j le N.","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"We can visualize the infinite-dimensional GP by evaluating it on a fine grid to approximate the dense real line:","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"num_inputs = 101\nxlim = (-5, 5)\nX = range(xlim...; length=num_inputs);","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"Given a kernel k, we can compute the kernel matrix as K = kernelmatrix(k, X).","category":"page"},{"location":"examples/gaussian-process-priors/#Random-samples","page":"Gaussian process prior samples","title":"Random samples","text":"","category":"section"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"To sample from the multivariate normal distribution p(mathbff) = mathcalMVN(0 mathrmK), we could make use of Distributions.jl and call rand(MvNormal(K)). Alternatively, we could use the AbstractGPs.jl package and construct a GP object which we evaluate at the points of interest and from which we can then sample: rand(GP(k)(X)).","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"Here, we will explicitly construct samples using the Cholesky factorization mathrmL = operatornamecholesky(mathrmK), with mathbff = mathrmL mathbfv, where mathbfv sim mathcalN(0 mathbfI) is a vector of standard-normal random variables.","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"We will use the same randomness mathbfv to generate comparable samples across different kernels.","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"num_samples = 7\nv = randn(num_inputs, num_samples);","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"Mathematically, a kernel matrix is by definition positive semi-definite, but due to finite-precision inaccuracies, the computed kernel matrix might not be exactly positive definite. To avoid Cholesky errors, we add a small \"nugget\" term on the diagonal:","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"function mvn_sample(K)\n L = cholesky(K + 1e-6 * I)\n f = L.L * v\n return f\nend;","category":"page"},{"location":"examples/gaussian-process-priors/#Visualization","page":"Gaussian process prior samples","title":"Visualization","text":"","category":"section"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"We now define a function that visualizes a kernel for us.","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"function visualize(k::Kernel)\n K = kernelmatrix(k, X)\n f = mvn_sample(K)\n\n p_kernel_2d = heatmap(\n X,\n X,\n K;\n yflip=true,\n colorbar=false,\n ylabel=string(nameof(typeof(k))),\n ylim=xlim,\n yticks=([xlim[1], 0, xlim[end]], [\"\\u22125\", raw\"$x'$\", \"5\"]),\n vlim=(0, 1),\n title=raw\"$k(x, x')$\",\n aspect_ratio=:equal,\n left_margin=5mm,\n )\n\n p_kernel_cut = plot(\n X,\n k.(X, 0.0);\n title=string(raw\"$k(x, x_\\mathrm{ref})$\"),\n label=raw\"$x_\\mathrm{ref}=0.0$\",\n legend=:topleft,\n foreground_color_legend=nothing,\n )\n plot!(X, k.(X, 1.5); label=raw\"$x_\\mathrm{ref}=1.5$\")\n\n p_samples = plot(X, f; c=\"blue\", title=raw\"$f(x)$\", ylim=(-3, 3), label=nothing)\n\n return plot(\n p_kernel_2d,\n p_kernel_cut,\n p_samples;\n layout=(1, 3),\n xlabel=raw\"$x$\",\n xlim=xlim,\n xticks=collect(xlim),\n )\nend;","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"We can now visualize a kernel and show samples from a Gaussian process with a given kernel:","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"plot(visualize(SqExponentialKernel()); size=(800, 210), bottommargin=5mm, topmargin=5mm)","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/gaussian-process-priors/#Kernel-comparison","page":"Gaussian process prior samples","title":"Kernel comparison","text":"","category":"section"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"This also allows us to compare different kernels:","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"kernels = [\n Matern12Kernel(),\n Matern32Kernel(),\n Matern52Kernel(),\n SqExponentialKernel(),\n WhiteKernel(),\n ConstantKernel(),\n LinearKernel(),\n compose(PeriodicKernel(), ScaleTransform(0.2)),\n NeuralNetworkKernel(),\n GibbsKernel(; lengthscale=x -> sum(exp ∘ sin, x)),\n]\nplot(\n [visualize(k) for k in kernels]...;\n layout=(length(kernels), 1),\n size=(800, 220 * length(kernels) + 100),\n)","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"
      \n
      Package and system information
      \n
      \nPackage information (click to expand)\n
      \nStatus `~/work/KernelFunctions.jl/KernelFunctions.jl/examples/gaussian-process-priors/Project.toml`\n  [31c24e10] Distributions v0.25.107\n  [ec8451be] KernelFunctions v0.10.60 `/home/runner/work/KernelFunctions.jl/KernelFunctions.jl#e6b42a9`\n  [98b081ad] Literate v2.16.1\n  [91a5bcdd] Plots v1.40.1\n  [37e2e46d] LinearAlgebra\n  [9a3f8284] Random\n
      \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
      \n
      \nSystem information (click to expand)\n
      \nJulia Version 1.10.0\nCommit 3120989f39b (2023-12-25 18:01 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 4 × AMD EPYC 7763 64-Core Processor\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\n  Threads: 1 on 4 virtual cores\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
      \n
      ","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"","category":"page"},{"location":"examples/gaussian-process-priors/","page":"Gaussian process prior samples","title":"Gaussian process prior samples","text":"This page was generated using Literate.jl.","category":"page"},{"location":"#KernelFunctions.jl","page":"Home","title":"KernelFunctions.jl","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"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:","category":"page"},{"location":"","page":"Home","title":"Home","text":"Flexibility: operations between kernels should be fluid and easy without breaking, with a user-friendly API.\nPlug-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.\nAutomatic Differentiation compatibility: all kernel functions which ought to be differentiable using AD packages like ForwardDiff.jl or Zygote.jl should be.","category":"page"},{"location":"","page":"Home","title":"Home","text":"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.","category":"page"},{"location":"","page":"Home","title":"Home","text":"See the User guide for a brief introduction.","category":"page"}] } diff --git a/previews/PR530/transform/index.html b/previews/PR530/transform/index.html index 79cd91a35..b44851a06 100644 --- a/previews/PR530/transform/index.html +++ b/previews/PR530/transform/index.html @@ -1,20 +1,20 @@ -Input Transforms · KernelFunctions.jl
      Edit on GitHub

      Input Transforms

      Overview

      Transforms are designed to change input data before passing it on to a kernel object.

      It can be as standard as IdentityTransform returning the same input, or multiplying the data by a scalar with ScaleTransform or by a vector with ARDTransform. There is a more general FunctionTransform that uses a function and applies it to each input.

      You can also create a pipeline of Transforms via ChainTransform, e.g.,

      LowRankTransform(rand(10, 5)) ∘ ScaleTransform(2.0)

      A transformation t can be applied to a single input x with t(x) and to multiple inputs xs with map(t, xs).

      Kernels can be coupled with input transformations with ∘ or its alias compose. It falls back to creating a TransformedKernel but allows more optimized implementations for specific kernels and transformations.

      List of Input Transforms

      KernelFunctions.ScaleTransform — Type
      ScaleTransform(l::Real)

      Transformation that multiplies the input elementwise with l.

      Examples

      julia> l = rand(); t = ScaleTransform(l); X = rand(100, 10);
+Input Transforms · KernelFunctions.jl
      Edit on GitHub
      Input Transforms
      Overview
      Transforms are designed to change input data before passing it on to a kernel object.
      It can be as standard as IdentityTransform returning the same input, or multiplying the data by a scalar with ScaleTransform or by a vector with ARDTransform. There is a more general FunctionTransform that uses a function and applies it to each input.
      You can also create a pipeline of Transforms via ChainTransform, e.g.,
      LowRankTransform(rand(10, 5)) ∘ ScaleTransform(2.0)
      A transformation t can be applied to a single input x with t(x) and to multiple inputs xs with map(t, xs).
      Kernels can be coupled with input transformations with ∘ or its alias compose. It falls back to creating a TransformedKernel but allows more optimized implementations for specific kernels and transformations.
      List of Input Transforms
      KernelFunctions.ScaleTransform — Type
      ScaleTransform(l::Real)
      Transformation that multiplies the input elementwise with l.
      Examples
      julia> l = rand(); t = ScaleTransform(l); X = rand(100, 10);
 
 julia> map(t, ColVecs(X)) == ColVecs(l .* X)
-true
      source
      KernelFunctions.ARDTransform — Type
      ARDTransform(v::AbstractVector)
      Transformation that multiplies the input elementwise by v.
      Examples
      julia> v = rand(10); t = ARDTransform(v); X = rand(10, 100);
+true
      source
      KernelFunctions.ARDTransform — Type
      ARDTransform(v::AbstractVector)
      Transformation that multiplies the input elementwise by v.
      Examples
      julia> v = rand(10); t = ARDTransform(v); X = rand(10, 100);
 
 julia> map(t, ColVecs(X)) == ColVecs(v .* X)
-true
      source
      KernelFunctions.LinearTransform — Type
      LinearTransform(A::AbstractMatrix)
      Linear transformation of the input realised by the matrix A.
      The second dimension of A must match the number of features of the target.
      Examples
      julia> A = rand(10, 5); t = LinearTransform(A); X = rand(5, 100);
+true
      source
      KernelFunctions.LinearTransform — Type
      LinearTransform(A::AbstractMatrix)
      Linear transformation of the input realised by the matrix A.
      The second dimension of A must match the number of features of the target.
      Examples
      julia> A = rand(10, 5); t = LinearTransform(A); X = rand(5, 100);
 
 julia> map(t, ColVecs(X)) == ColVecs(A * X)
-true
      source
      KernelFunctions.FunctionTransform — Type
      FunctionTransform(f)
      Transformation that applies function f to the input.
      Make sure that f can act on an input. For instance, if the inputs are vectors, use f(x) = sin.(x) instead of f = sin.
      Examples
      julia> f(x) = sum(x); t = FunctionTransform(f); X = randn(100, 10);
+true
      source
      KernelFunctions.FunctionTransform — Type
      FunctionTransform(f)
      Transformation that applies function f to the input.
      Make sure that f can act on an input. For instance, if the inputs are vectors, use f(x) = sin.(x) instead of f = sin.
      Examples
      julia> f(x) = sum(x); t = FunctionTransform(f); X = randn(100, 10);
 
 julia> map(t, ColVecs(X)) == ColVecs(sum(X; dims=1))
-true
      source
      KernelFunctions.SelectTransform — Type
      SelectTransform(dims)
      Transformation that selects the dimensions dims of the input.
      Examples
      julia> dims = [1, 3, 5, 6, 7]; t = SelectTransform(dims); X = rand(100, 10);
+true
      source
      KernelFunctions.SelectTransform — Type
      SelectTransform(dims)
      Transformation that selects the dimensions dims of the input.
      Examples
      julia> dims = [1, 3, 5, 6, 7]; t = SelectTransform(dims); X = rand(100, 10);
 
 julia> map(t, ColVecs(X)) == ColVecs(X[dims, :])
-true
      source
      KernelFunctions.ChainTransform — Type
      ChainTransform(transforms)
      Transformation that applies a chain of transformations ts to the input.
      The transformation first(ts) is applied first.
      Examples
      julia> l = rand(); A = rand(3, 4); t1 = ScaleTransform(l); t2 = LinearTransform(A);
+true
      source
      KernelFunctions.ChainTransform — Type
      ChainTransform(transforms)
      Transformation that applies a chain of transformations ts to the input.
      The transformation first(ts) is applied first.
      Examples
      julia> l = rand(); A = rand(3, 4); t1 = ScaleTransform(l); t2 = LinearTransform(A);
 
 julia> X = rand(4, 10);
 
@@ -22,24 +22,24 @@
 true
 
 julia> map(t2 ∘ t1, ColVecs(X)) == ColVecs(A * (l .* X))
-true
      source
      KernelFunctions.PeriodicTransform — Type
      PeriodicTransform(f)
      Transformation that maps the input elementwise onto the unit circle with frequency f.
      Samples from a GP with a kernel with this transformation applied to the inputs will produce samples with frequency f.
      Examples
      julia> f = rand(); t = PeriodicTransform(f); x = rand();
+true
      source
      KernelFunctions.PeriodicTransform — Type
      PeriodicTransform(f)
      Transformation that maps the input elementwise onto the unit circle with frequency f.
      Samples from a GP with a kernel with this transformation applied to the inputs will produce samples with frequency f.
      Examples
      julia> f = rand(); t = PeriodicTransform(f); x = rand();
 
 julia> t(x) == [sinpi(2 * f * x), cospi(2 * f * x)]
-true
      source
      Convenience functions
      KernelFunctions.with_lengthscale — Function
      with_lengthscale(kernel::Kernel, lengthscale::Real)
      Construct a transformed kernel with lengthscale.
      Examples
      julia> kernel = with_lengthscale(SqExponentialKernel(), 2.5);
+true
      source
      Convenience functions
      KernelFunctions.with_lengthscale — Function
      with_lengthscale(kernel::Kernel, lengthscale::Real)
      Construct a transformed kernel with lengthscale.
      Examples
      julia> kernel = with_lengthscale(SqExponentialKernel(), 2.5);
 
 julia> x = rand(2);
 
 julia> y = rand(2);
 
 julia> kernel(x, y) ≈ (SqExponentialKernel() ∘ ScaleTransform(0.4))(x, y)
-true
      source
      with_lengthscale(kernel::Kernel, lengthscales::AbstractVector{<:Real})
      Construct a transformed "ARD" kernel with different lengthscales for each dimension.
      Examples
      julia> kernel = with_lengthscale(SqExponentialKernel(), [0.5, 2.5]);
+true
      source
      with_lengthscale(kernel::Kernel, lengthscales::AbstractVector{<:Real})
      Construct a transformed "ARD" kernel with different lengthscales for each dimension.
      Examples
      julia> kernel = with_lengthscale(SqExponentialKernel(), [0.5, 2.5]);
 
 julia> x = rand(2);
 
 julia> y = rand(2);
 
 julia> kernel(x, y) ≈ (SqExponentialKernel() ∘ ARDTransform([2, 0.4]))(x, y)
-true
      source
      KernelFunctions.median_heuristic_transform — Function
      median_heuristic_transform(distance, x::AbstractVector)
      Create a ScaleTransform that divides the input elementwise by the median distance of the data points in x.
      The distance has to support pairwise evaluation with KernelFunctions.pairwise. All PreMetrics of the package Distances.jl such as Euclidean satisfy this requirement automatically.
      Examples
      julia> using Distances, Statistics
+true
      source
      KernelFunctions.median_heuristic_transform — Function
      median_heuristic_transform(distance, x::AbstractVector)
      Create a ScaleTransform that divides the input elementwise by the median distance of the data points in x.
      The distance has to support pairwise evaluation with KernelFunctions.pairwise. All PreMetrics of the package Distances.jl such as Euclidean satisfy this requirement automatically.
      Examples
      julia> using Distances, Statistics
 
 julia> x = ColVecs(rand(100, 10));
 
@@ -48,4 +48,4 @@
 julia> y = map(t, x);
 
 julia> median(euclidean(y[i], y[j]) for i in 1:10, j in 1:10 if i != j) ≈ 1
-true
      source
      
+true
      source
      diff --git a/previews/PR530/userguide/index.html b/previews/PR530/userguide/index.html index 99c656055..f62f51c12 100644 --- a/previews/PR530/userguide/index.html +++ b/previews/PR530/userguide/index.html @@ -26,4 +26,4 @@ kernelparams = Flux.params(k) Flux.gradient(kernelparams) do # ... some loss function on the kernel .... -end +end