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API

SurrogatesBase.AbstractDeterministicSurrogate — Type
abstract type AbstractDeterministicSurrogate <: Function end

An abstract type for deterministic surrogates.

(s::AbstractDeterministicSurrogate)(xs)

Subtypes of AbstractDeterministicSurrogate are callable with a Vector of points xs. The result is a Vector of evaluations of the surrogate at points xs, corresponding to approximations of the underlying function at points xs respectively.

Examples

julia> struct ZeroSurrogate <: AbstractDeterministicSurrogate end

julia> (::ZeroSurrogate)(xs) = 0

julia> s = ZeroSurrogate();

julia> s([4]) == 0
true
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SurrogatesBase.finite_posterior — Function
finite_posterior(s::AbstractStochasticSurrogate, xs::AbstractVector)

Return a posterior distribution at points xs.

An AbstractStochasticSurrogate might implement some or all of the following methods on the returned object:

  • mean(finite_posterior(s,xs)) returns a Vector of posterior means at xs
  • var(finite_posterior(s,xs)) returns a Vector of posterior variances at xs
  • mean_and_var(finite_posterior(s,xs)) returns a Tuple consisting of a Vector of posterior means and a Vector of posterior variances at xs
  • rand(finite_posterior(s,xs)) returns a Vector, which is a sample from the joint

posterior at points xs

Use mean(finite_posterior(s, eachslice(X, dims = 2))) if X is a matrix.

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SurrogatesBase.update! — Function
update!(s, new_xs::AbstractVector, new_ys::AbstractVector)

Include data new_ys at points new_xs into the surrogate s, i.e., refit the surrogate s to incorporate new data points.

If the surrogate s is a deterministic surrogate, the new_ys correspond to function evaluations, if s is a stochastic surrogate, the new_ys are samples from a conditional probability distribution.

Use update!(s, eachslice(X, dims = 2), new_ys) if X is a matrix.

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