Docs improvements

Author: theogf

docs/src/create_kernel.md

@@ -8,7 +8,7 @@ Here are a few ways depending on how complicated your kernel is :
 
 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:
+Here is for example how one can define the `SqExponentialKernel` again :
 
 ```julia
 struct MyKernel <: KernelFunctions.SimpleKernel end
@@ -28,15 +28,15 @@ struct MyKernel <: KernelFunctions.Kernel end
 (::MyKernel)(x, y) = asin(dot(x, y) / sqrt((1 + sum(abs2, x)) * (1 + sum(abs2, y))))
 ```
 
-Note that `Kernel` do not use `Distances.jl` and can therefore be a bit slower.
+Note that `BaseKernel` do not use `Distances.jl` and can therefore be a bit slower.
 
 ### Additional Options
 
 Finally there are additional functions you can define to bring in more features:
- - Define the trainable parameters of your kernel with `KernelFunctions.trainable(k)` which should return a `Tuple` of your parameters.
-This parameters will be then passed to `Flux.params` function
- - `KernelFunctions.iskroncompatible(k)`, if your kernel factorizes in the dimensions. You can declare your kernel as `iskroncompatible(k) = true`
- - `KernelFunctions.dim`: by default the dimension of the inputs will only be checked for vectors of `AbstractVector{<:Real}`.
-If you want to check the dimensions of your inputs, dispatch the `dim` function on your kernel. Note that `0` is the default.
-- You can also redefine the `kernelmatrix(k, x, y)` functions for your kernel to eventually optimize the computations of your kernel.
- - `Base.show(io::IO, k::Kernel)`, if you want to specialize the printing of your kernel
+ - `KernelFunctions.trainable(k::MyKernel)`: it defines the trainable parameters of your kernel, it should return a `Tuple` of your parameters.
+These parameters will be passed to the `Flux.params` function. For some examples see the `trainable.jl` file in `src/`
+ - `KernelFunctions.iskroncompatible(k::MyKernel)`: if your kernel factorizes in dimensions, you can declare your kernel as `iskroncompatible(k) = true` to use Kronecker methods.
+ - `KernelFunctions.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.
+ - You can also directly overload `KernelFunctions.validate_inputs(x::MyDataType, y::MyDataType)` if you want to run special checks for your input types.
+ - `kernelmatrix(k::MyKernel, ...)`: you can redefine the diverse `kernelmatrix` functions to eventually optimize the computations.
+ - `Base.print(io::IO, k::MyKernel)`: if you want to specialize the printing of your kernel