Skip to content

Latest commit

 

History

History
99 lines (79 loc) · 3.81 KB

api.md

File metadata and controls

99 lines (79 loc) · 3.81 KB
Raw

API Library

CurrentModule = KernelFunctions

Functions

The KernelFunctions API comprises the following four functions.

kernelmatrix
kernelmatrix!
kernelmatrix_diag
kernelmatrix_diag!

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](@ref why_abstract_vectors). 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:

ColVecs
RowVecs

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](@ref inputs_for_multiple_outputs).

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:

prepare_isotopic_multi_output_data(x::AbstractVector, y::ColVecs)
prepare_isotopic_multi_output_data(x::AbstractVector, y::RowVecs)
prepare_heterotopic_multi_output_data

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

MOInput

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.

kernelpdmat
nystrom
NystromFact

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

kronecker_kernelmatrix