Errors
ANOVAapprox.get_L2error
— Methodget_L2error( a::approx, norm::Float64, bc_fun::Function, λ::Float64 )::Float64
This function computes the relative $L_2$ error of the function given the norm norm
and a function that returns the basis coefficients bc_fun
for regularization parameter λ
.
ANOVAapprox.get_L2error
— Methodget_L2error( a::approx, norm::Float64, bc_fun::Function )::Dict{Float64,Float64}
This function computes the relative $L_2$ error of the function given the norm norm
and a function that returns the basis coefficients bc_fun
for all regularization parameters.
ANOVAapprox.get_l2error
— Methodget_l2error( a::approx, λ::Float64 )::Float64
This function computes the relative $\ell_2$ error on the training nodes for regularization parameter λ
.
ANOVAapprox.get_l2error
— Methodget_l2error( a::approx, X::Matrix{Float64}, y::Union{Vector{ComplexF64},Vector{Float64}}, λ::Float64 )::Float64
This function computes the relative $\ell_2$ error on the data X
and y
for regularization parameter λ
.
ANOVAapprox.get_l2error
— Methodget_l2error( a::approx, X::Matrix{Float64}, y::Union{Vector{ComplexF64},Vector{Float64}}, )::Dict{Float64,Float64}
This function computes the relative $\ell_2$ error on the data X
and y
for all regularization parameters.
ANOVAapprox.get_l2error
— Methodget_l2error( a::approx )::Dict{Float64,Float64}
This function computes the relative $\ell_2$ error on the training nodes for all regularization parameters.
ANOVAapprox.get_mad
— Methodget_mad( a::approx, λ::Float64 )::Float64
This function computes the mean absolute deviation (mad) on the training nodes for regularization parameter λ
.
ANOVAapprox.get_mad
— Methodget_mad( a::approx, X::Matrix{Float64}, y::Union{Vector{ComplexF64},Vector{Float64}}, λ::Float64 )::Float64
This function computes the mean absolute deviation (mad) on the data X
and y
for regularization parameter λ
.
ANOVAapprox.get_mad
— Methodget_mse( a::approx, X::Matrix{Float64}, y::Union{Vector{ComplexF64},Vector{Float64}}, )::Dict{Float64,Float64}
This function computes the mean absolute deviation (mad) on the data X
and y
for all regularization parameters.
ANOVAapprox.get_mad
— Methodget_mad( a::approx )::Dict{Float64,Float64}
This function computes the mean absolute deviation (mad) on the training nodes for all regularization parameters.
ANOVAapprox.get_mse
— Methodget_mse( a::approx, λ::Float64 )::Float64
This function computes the mean square error (mse) on the training nodes for regularization parameter λ
.
ANOVAapprox.get_mse
— Methodget_mse( a::approx, X::Matrix{Float64}, y::Union{Vector{ComplexF64},Vector{Float64}}, λ::Float64 )::Float64
This function computes the mean square error (mse) on the data X
and y
for regularization parameter λ
.
ANOVAapprox.get_mse
— Methodget_mse( a::approx, X::Matrix{Float64}, y::Union{Vector{ComplexF64},Vector{Float64}}, )::Dict{Float64,Float64}
This function computes the mean square error (mse) on the data X
and y
for all regularization parameters.
ANOVAapprox.get_mse
— Methodget_mse( a::approx )::Dict{Float64,Float64}
This function computes the mean square error (mse) on the training nodes for all regularization parameters.