Surrogate

Every surrogate has a different definition depending on the parameters needed. However, they have in common:

add_point!(::AbstractSurrogate,x_new,y_new)
AbstractSurrogate(value)

The first function adds a sample point to the surrogate, thus changing the internal coefficients. The second one calculates the approximation at value.

Linear surrogate

Surrogates.LinearSurrogate — Method

LinearSurrogate(x,y,lb,ub)

Builds a linear surrogate using GLM.jl

source

Radial basis function surrogate

Surrogates.RadialBasis — Method

RadialBasis(x,y,lb,ub,rad::RadialFunction, scale_factor::Float = 1.0)

Constructor for RadialBasis surrogate

source

Kriging surrogate

Surrogates.Kriging — Method

Kriging(x,y,lb,ub;p=collect(one.(x[1])),theta=collect(one.(x[1])))

Constructor for Kriging surrogate.

(x,y): sampled points
p: array of values 0<=p<2 modeling the smoothness of the function being approximated in the i-th variable. low p -> rough, high p -> smooth
theta: array of values > 0 modeling how much the function is changing in the i-th variable.

source

Lobachevsky surrogate

Surrogates.LobachevskySurrogate — Method

LobachevskySurrogate(x,y,alpha,n::Int,lb,ub,sparse = false)

Build the Lobachevsky surrogate with parameters alpha and n.

source

Surrogates.lobachevsky_integral — Method

lobachevsky_integral(loba::LobachevskySurrogate,lb,ub)

Calculates the integral of the Lobachevsky surrogate, which has a closed form.

source

Support vector machine surrogate, requires using LIBSVM

Missing docstring.

Missing docstring for SVMSurrogate(x,y,lb::Number,ub::Number). Check Documenter's build log for details.

Random forest surrogate, requires using XGBoost

Surrogates.RandomForestSurrogate — Method

RandomForestSurrogate(x,y,lb,ub,num_round)

Build Random forest surrogate. num_round is the number of trees.

source

Neural network surrogate, requires using Flux

Surrogates.NeuralSurrogate — Method

NeuralSurrogate(x,y,lb,ub,model,loss,opt,n_echos)

model: Flux layers
loss: loss function
opt: optimization function

source

Creating another surrogate

It's great that you want to add another surrogate to the library! You will need to:

Define a new mutable struct and a constructor function
Define add_point!(your_surrogate::AbstactSurrogate,x_new,y_new)
Define your_surrogate(value) for the approximation

Example

mutable struct NewSurrogate{X,Y,L,U,C,A,B} <: AbstractSurrogate
  x::X
  y::Y
  lb::L
  ub::U
  coeff::C
  alpha::A
  beta::B
end

function NewSurrogate(x,y,lb,ub,parameters)
    ...
    return NewSurrogate(x,y,lb,ub,calculated\_coeff,alpha,beta)
end

function add_point!(NewSurrogate,x\_new,y\_new)

  nothing
end

function (s::NewSurrogate)(value)
  return s.coeff*value + s.alpha
end