diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index fc52c0a..df62838 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.11.2","generation_timestamp":"2024-12-24T13:05:07","documenter_version":"1.8.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.11.2","generation_timestamp":"2024-12-31T13:05:23","documenter_version":"1.8.0"}} \ No newline at end of file diff --git a/dev/api/index.html b/dev/api/index.html index 44eaf61..9d62456 100644 --- a/dev/api/index.html +++ b/dev/api/index.html @@ -6,4 +6,4 @@ julia> s = ZeroSurrogate(); julia> s([4]) == 0 -truesource
SurrogatesBase.AbstractStochasticSurrogate — Type
abstract type AbstractStochasticSurrogate end

An abstract type for stochastic surrogates.

See also finite_posterior.

source
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.

source
SurrogatesBase.hyperparameters — Function
hyperparameters(s)

Returns current values of hyperparameters.

See also update_hyperparameters!.

source
SurrogatesBase.parameters — Function
parameters(s)

Returns current values of parameters used in surrogate s.

source
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.

source
SurrogatesBase.update_hyperparameters! — Function
update_hyperparameters!(s, prior)

Update the hyperparameters of the surrogate s by performing hyperparameter optimization using the information in prior. After changing hyperparameters of s, fit s to past data.

See also hyperparameters.

source
+truesource
SurrogatesBase.AbstractStochasticSurrogate — Type
abstract type AbstractStochasticSurrogate end

An abstract type for stochastic surrogates.

See also finite_posterior.

source
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.

source
SurrogatesBase.hyperparameters — Function
hyperparameters(s)

Returns current values of hyperparameters.

See also update_hyperparameters!.

source
SurrogatesBase.parameters — Function
parameters(s)

Returns current values of parameters used in surrogate s.

source
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.

source
SurrogatesBase.update_hyperparameters! — Function
update_hyperparameters!(s, prior)

Update the hyperparameters of the surrogate s by performing hyperparameter optimization using the information in prior. After changing hyperparameters of s, fit s to past data.

See also hyperparameters.

source
diff --git a/dev/assets/Manifest.toml b/dev/assets/Manifest.toml index 49d8156..e4a5f61 100644 --- a/dev/assets/Manifest.toml +++ b/dev/assets/Manifest.toml @@ -89,9 +89,9 @@ version = "1.11.0" [[deps.JLLWrappers]] deps = ["Artifacts", "Preferences"] -git-tree-sha1 = "be3dc50a92e5a386872a493a10050136d4703f9b" +git-tree-sha1 = "a007feb38b422fbdab534406aeca1b86823cb4d6" uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210" -version = "1.6.1" +version = "1.7.0" [[deps.JSON]] deps = ["Dates", "Mmap", "Parsers", "Unicode"] diff --git a/dev/index.html b/dev/index.html index 7fa681f..0e30a95 100644 --- a/dev/index.html +++ b/dev/index.html @@ -19,7 +19,7 @@ [e30172f5] Documenter v1.8.0 [d7ba0133] Git v1.3.1 [b5f81e59] IOCapture v0.2.5 - [692b3bcd] JLLWrappers v1.6.1 + [692b3bcd] JLLWrappers v1.7.0 [682c06a0] JSON v0.21.4 [0e77f7df] LazilyInitializedFields v1.3.0 [d0879d2d] MarkdownAST v0.1.2 @@ -68,4 +68,4 @@ [efcefdf7] PCRE2_jll v10.42.0+1 [83775a58] Zlib_jll v1.2.13+1 [8e850ede] nghttp2_jll v1.59.0+0 - [3f19e933] p7zip_jll v17.4.0+2

You can also download the manifest file and the project file.

+ [3f19e933] p7zip_jll v17.4.0+2

You can also download the manifest file and the project file.

diff --git a/dev/interface/index.html b/dev/interface/index.html index 30e92b3..270fc7d 100644 --- a/dev/interface/index.html +++ b/dev/interface/index.html @@ -55,4 +55,4 @@ # Fit a Gaussian process that uses the updated hyperparameters to past # samples and save it in g.gp_process. return g -end +end