From d6ca89524c949bd620f7011bd1b8c37c0af16bc7 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Thu, 17 Oct 2024 17:27:25 +0000 Subject: [PATCH] build based on f87e6e7 --- dev/.documenter-siteinfo.json | 2 +- dev/api/index.html | 2 +- dev/assets/Manifest.toml | 6 +++--- dev/index.html | 8 ++++---- dev/interface/index.html | 2 +- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 66a2cfc..6afc288 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.11.0","generation_timestamp":"2024-10-15T13:07:11","documenter_version":"1.7.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-10-17T17:27:21","documenter_version":"1.7.0"}} \ No newline at end of file diff --git a/dev/api/index.html b/dev/api/index.html index ad4fbed..d2163c1 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 b6ac7cf..74e4550 100644 --- a/dev/assets/Manifest.toml +++ b/dev/assets/Manifest.toml @@ -1,6 +1,6 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.11.0" +julia_version = "1.11.1" manifest_format = "2.0" project_hash = "de226a46ac96f2c6398e379aa8421cb01c51318d" @@ -89,9 +89,9 @@ version = "1.11.0" [[deps.JLLWrappers]] deps = ["Artifacts", "Preferences"] -git-tree-sha1 = "f389674c99bfcde17dc57454011aa44d5a260a40" +git-tree-sha1 = "be3dc50a92e5a386872a493a10050136d4703f9b" uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210" -version = "1.6.0" +version = "1.6.1" [[deps.JSON]] deps = ["Dates", "Mmap", "Parsers", "Unicode"] diff --git a/dev/index.html b/dev/index.html index 5bf36ad..f2a88d7 100644 --- a/dev/index.html +++ b/dev/index.html @@ -2,8 +2,8 @@ Home · SurrogatesBase.jl
GitHub

SurrogatesBase.jl: A Common Interface for Surrogate Libraries

API for deterministic and stochastic surrogates.

Given data $((x_1, y_1), \ldots, (x_N, y_N))$ obtained by evaluating a function $y_i = f(x_i)$ or sampling from a conditional probability density $p_{Y|X}(Y = y_i|X = x_i)$, a deterministic surrogate is a function $s(x)$ (e.g. a radial basis function interpolator) that uses the data to approximate $f$ or some statistic of $p_{Y|X}$ (e.g. the mean), whereas a stochastic surrogate is a stochastic process (e.g. a Gaussian process approximation) that uses the data to approximate $f$ or $p_{Y|X}$ and quantify the uncertainty of the approximation.

Installation

To install SurrogatesBase.jl, use the Julia package manager:

using Pkg
 Pkg.add("SurrogatesBase")

Contributing

Reproducibility

The documentation of this SciML package was built using these direct dependencies,
Status `~/work/SurrogatesBase.jl/SurrogatesBase.jl/docs/Project.toml`
   [e30172f5] Documenter v1.7.0
-  [89f642e6] SurrogatesBase v1.1.0 `~/work/SurrogatesBase.jl/SurrogatesBase.jl`
and using this machine and Julia version.
Julia Version 1.11.0
-Commit 501a4f25c2b (2024-10-07 11:40 UTC)
+  [89f642e6] SurrogatesBase v1.1.0 `~/work/SurrogatesBase.jl/SurrogatesBase.jl`
and using this machine and Julia version.
Julia Version 1.11.1
+Commit 8f5b7ca12ad (2024-10-16 10:53 UTC)
 Build Info:
   Official https://julialang.org/ release
 Platform Info:
@@ -19,7 +19,7 @@
   [e30172f5] Documenter v1.7.0
   [d7ba0133] Git v1.3.1
   [b5f81e59] IOCapture v0.2.5
-  [692b3bcd] JLLWrappers v1.6.0
+  [692b3bcd] JLLWrappers v1.6.1
   [682c06a0] JSON v0.21.4
   [0e77f7df] LazilyInitializedFields v1.2.2
   [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 22fd484..910f110 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