Solved raised points

Author: theogf

src/generic.jl

@@ -14,8 +14,6 @@ _scale(t::ScaleTransform, metric, x, y) = evaluate(metric, apply(t, x), apply(t,
 printshifted(io::IO,κ::Kernel,shift::Int) = print(io,"$κ")
 Base.show(io::IO,κ::Kernel) = print(io,nameof(typeof(κ)))
 
-_trainable(x) = ()
-
 ### Syntactic sugar for creating matrices and using kernel functions
 for k in subtypes(BaseKernel)
     @eval begin

src/transform/scaletransform.jl

@@ -16,7 +16,6 @@ function ScaleTransform(s::T=1.0) where {T<:Real}
 end
 
 set!(t::ScaleTransform,ρ::Real) = t.s .= [ρ]
-_trainable(t::ScaleTransform) = (t.s,)
 dim(str::ScaleTransform) = 1
 
 apply(t::ScaleTransform,x::AbstractVecOrMat;obsdim::Int=defaultobs) = first(t.s) * x

src/utils.jl

@@ -31,11 +31,12 @@ base_kernel(k::Kernel) = eval(nameof(typeof(k)))
 base_transform(t::Transform) = eval(nameof(typeof(t)))
 
 """
+Will be implemented at some point
 ```julia
     params(k::Kernel)
     params(t::Transform)
 ```
 For a kernel return a tuple with parameters of the transform followed by the specific parameters of the kernel
 For a transform return its parameters, for a `ChainTransform` return a vector of `params(t)`.
 """
-params
+#params