Docs for new kernels

Author: theogf

docs/make.jl

@@ -12,6 +12,7 @@ makedocs(
              "Transform"=>"transform.md",
              "Metrics"=>"metrics.md",
              "Theory"=>"theory.md",
+             "Custom Kernels"=>"create_kernel.md"
              "API"=>"api.md"]
 )
 

docs/src/create_kernel.md

@@ -0,0 +1,20 @@
+## Creating your own kernel
+
+KernelFunctions.jl contains the most popular kernels already but you might want to make your own!
+
+Here is for example how one can define the Squared Exponential Kernel again :
+
+```julia
+struct MyKernel <: Kernel end
+
+KernelFunctions.kappa(::MyKernel, d2::Real) = exp(-d2)
+KernelFunctions.metric(::MyKernel) = SqEuclidean()
+```
+
+For a "Base" kernel, where the kernel function is simply a function applied on some metric between two vectors of real, you only need to:
+ - Define your struct inheriting from `Kernel`.
+ - Define a `kappa` function.
+ - Define the metric used `SqEuclidean`, `DotProduct` etc. Note that the term "metric" is here overabused.
+ - Optional : Define any parameter of your kernel as `trainable` by Flux.jl if you want to perform optimization on the parameters. We recommend wrapping all parameters in arrays to allow them to be mutable.
+
+Once these functions are defined, you can use all the wrapping functions of KernelFuntions.jl