From 8fc8e56d1dc088c5a92ee5640057737aca7c2407 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Sun, 10 Mar 2024 00:12:48 +0000 Subject: [PATCH] build based on 07d5f00 --- previews/PR387/.documenter-siteinfo.json | 1 + previews/PR387/api/index.html | 95 + previews/PR387/assets/documenter.js | 1050 ++ .../PR387/assets/themes/documenter-dark.css | 7 + .../PR387/assets/themes/documenter-light.css | 9 + previews/PR387/assets/themeswap.js | 84 + previews/PR387/assets/warner.js | 52 + previews/PR387/concrete_features/index.html | 26 + .../PR387/examples/0-intro-1d/Manifest.toml | 1645 +++ previews/PR387/examples/0-intro-1d/index.html | 2089 ++++ .../PR387/examples/0-intro-1d/notebook.ipynb | 5163 +++++++++ .../PR387/examples/1-mauna-loa/Manifest.toml | 1607 +++ .../PR387/examples/1-mauna-loa/index.html | 2423 ++++ .../PR387/examples/1-mauna-loa/notebook.ipynb | 5297 +++++++++ .../2-deep-kernel-learning/Manifest.toml | 1715 +++ .../2-deep-kernel-learning/index.html | 539 + .../2-deep-kernel-learning/notebook.ipynb | 9767 +++++++++++++++++ .../2-deep-kernel-learning/train-dkl.gif | Bin 0 -> 443282 bytes .../Manifest.toml | 1735 +++ .../3-parametric-heteroscedastic/index-22.png | Bin 0 -> 53064 bytes .../3-parametric-heteroscedastic/index.html | 127 + .../notebook.ipynb | 360 + previews/PR387/index.html | 2 + previews/PR387/objects.inv | Bin 0 -> 2040 bytes previews/PR387/search_index.js | 3 + previews/PR387/siteinfo.js | 1 + 26 files changed, 33797 insertions(+) create mode 100644 previews/PR387/.documenter-siteinfo.json create mode 100644 previews/PR387/api/index.html create mode 100644 previews/PR387/assets/documenter.js create mode 100644 previews/PR387/assets/themes/documenter-dark.css create mode 100644 previews/PR387/assets/themes/documenter-light.css create mode 100644 previews/PR387/assets/themeswap.js create mode 100644 previews/PR387/assets/warner.js create mode 100644 previews/PR387/concrete_features/index.html create mode 100644 previews/PR387/examples/0-intro-1d/Manifest.toml create mode 100644 previews/PR387/examples/0-intro-1d/index.html create mode 100644 previews/PR387/examples/0-intro-1d/notebook.ipynb create mode 100644 previews/PR387/examples/1-mauna-loa/Manifest.toml create mode 100644 previews/PR387/examples/1-mauna-loa/index.html create mode 100644 previews/PR387/examples/1-mauna-loa/notebook.ipynb create mode 100644 previews/PR387/examples/2-deep-kernel-learning/Manifest.toml create mode 100644 previews/PR387/examples/2-deep-kernel-learning/index.html create mode 100644 previews/PR387/examples/2-deep-kernel-learning/notebook.ipynb create mode 100644 previews/PR387/examples/2-deep-kernel-learning/train-dkl.gif create mode 100644 previews/PR387/examples/3-parametric-heteroscedastic/Manifest.toml create mode 100644 previews/PR387/examples/3-parametric-heteroscedastic/index-22.png create mode 100644 previews/PR387/examples/3-parametric-heteroscedastic/index.html create mode 100644 previews/PR387/examples/3-parametric-heteroscedastic/notebook.ipynb create mode 100644 previews/PR387/index.html create mode 100644 previews/PR387/objects.inv create mode 100644 previews/PR387/search_index.js create mode 100644 previews/PR387/siteinfo.js diff --git a/previews/PR387/.documenter-siteinfo.json b/previews/PR387/.documenter-siteinfo.json new file mode 100644 index 00000000..c4a9197c --- /dev/null +++ b/previews/PR387/.documenter-siteinfo.json @@ -0,0 +1 @@ +{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-03-10T00:12:24","documenter_version":"1.3.0"}} \ No newline at end of file diff --git a/previews/PR387/api/index.html b/previews/PR387/api/index.html new file mode 100644 index 00000000..60ac19dc --- /dev/null +++ b/previews/PR387/api/index.html @@ -0,0 +1,95 @@ + +The Main APIs · AbstractGPs.jl
GitHub

FiniteGP and AbstractGP

Intended Audience

This page is best read once you have a sense of what this package is trying to achieve. Therefore, we recommend reading (or at least skimming) some of the examples before reading these docs.

Introduction

AbstractGPs provides the abstract type AbstractGP, and the concrete type FiniteGP. An AbstractGP, f, should be thought of as a distribution over functions. This means that the output of rand(f) would be a real-valued function. It's not usually possible to implement this though, so we don't.

A FiniteGP fx = f(x) represents the distribution over functions at the finite collection of points specified in x. fx is a multivariate Normal distribution, so rand(fx) produces a Vector of Reals.

A FiniteGP is the interesting object computationally, so if you create a new subtype MyNewGP of AbstractGP, and wish to make it interact well with the rest of the GP ecosystem, the methods that you must implement are not those directly involving MyNewGP, but rather those involving

FiniteGP{<:MyNewGP}

We provide two ways in which to do this. The first is to implement methods directly on FiniteGP{<:MyNewGP} – this is detailed in the FiniteGP APIs. The second is to implement some methods directly involving MyNewGP, and utilise default FiniteGP methods implemented in terms of these – this is detailed in the Internal AbstractGPs API. For example, the first method involves implementing methods like AbstractGPs.mean(fx::FiniteGP{<:MyNewGP}), while the second involves AbstractGPs.mean(f::MyNewGP, x::AbstractVector).

The second interface is generally easier to implement, but isn't always the best choice. See Which API should I implement? for further discussion.

FiniteGP APIs

Let f be an AbstractGP, x an AbstractVector representing a collection of inputs, and Σ a positive-definite matrix of size (length(x), length(x)). A FiniteGP represents the multivariate Gaussian induced by "indexing" into f at each point in x, and adding independent zero-mean noise with covariance matrix Σ:

fx = f(x, Σ)
+
+# The code below is equivalent to the above, and is just for reference.
+# When writing code, prefer the above syntax.
+fx = AbstractGPs.FiniteGP(f, x, Σ)

The FiniteGP has two API levels. The Primary Public API should be supported by all FiniteGPs, while the Secondary Public API will only be supported by a subset. Use only the primary API when possible.

Primary Public API

These are user-facing methods. You can expect them to be implemented whenever you encounter a FiniteGP. If you are building something on top of AbstractGPs, try to implement it in terms of these functions.

Required Methods

Base.rand — Function
rand(rng::AbstractRNG, f::FiniteGP, N::Int=1)

Obtain N independent samples from the marginals f using rng. Single-sample methods produce a length(f) vector. Multi-sample methods produce a length(f) × N Matrix.

julia> f = GP(Matern32Kernel());
+
+julia> x = randn(11);
+
+julia> rand(f(x)) isa Vector{Float64}
+true
+
+julia> rand(MersenneTwister(123456), f(x)) isa Vector{Float64}
+true
+
+julia> rand(f(x), 3) isa Matrix{Float64}
+true
+
+julia> rand(MersenneTwister(123456), f(x), 3) isa Matrix{Float64}
+true
source
Random.rand! — Function
rand!(rng::AbstractRNG, f::FiniteGP, y::AbstractVecOrMat{<:Real})

Obtain sample(s) from the marginals f using rng and write them to y.

If y is a matrix, then each column corresponds to an independent sample.

julia> f = GP(Matern32Kernel());
+
+julia> x = randn(11);
+
+julia> y = similar(x);
+
+julia> rand!(f(x), y);
+
+julia> rand!(MersenneTwister(123456), f(x), y);
+
+julia> ys = similar(x, length(x), 3);
+
+julia> rand!(f(x), ys);
+
+julia> rand!(MersenneTwister(123456), f(x), ys);
source
AbstractGPs.marginals — Function
marginals(f::FiniteGP)

Compute a vector of Normal distributions representing the marginals of f efficiently. In particular, the off-diagonal elements of cov(f(x)) are never computed.

julia> f = GP(Matern32Kernel());
+
+julia> x = randn(11);
+
+julia> fs = marginals(f(x));
+
+julia> mean.(fs) == mean(f(x))
+true
+
+julia> std.(fs) == sqrt.(diag(cov(f(x))))
+true
source
Distributions.logpdf — Method
logpdf(f::FiniteGP, y::AbstractVecOrMat{<:Real})

The logpdf of y under f if y isa AbstractVector. The logpdf of each column of y if y isa Matrix.

julia> f = GP(Matern32Kernel());
+
+julia> x = randn(11);
+
+julia> y = rand(f(x));
+
+julia> logpdf(f(x), y) isa Real
+true
+
+julia> Y = rand(f(x), 3);
+
+julia> logpdf(f(x), Y) isa AbstractVector{<:Real}
+true
source
AbstractGPs.posterior — Method
posterior(fx::FiniteGP, y::AbstractVector{<:Real})

Construct the posterior distribution over fx.f given observations y at fx.x made under noise fx.Σy. This is another AbstractGP object. See chapter 2 of [1] for a recap on exact inference in GPs. This posterior process has mean function

m_posterior(x) = m(x) + k(x, fx.x) inv(cov(fx)) (y - mean(fx))

and kernel

k_posterior(x, z) = k(x, z) - k(x, fx.x) inv(cov(fx)) k(fx.x, z)

where m and k are the mean function and kernel of fx.f, respectively.

source
Statistics.mean — Method
mean(fx::FiniteGP)

Compute the mean vector of fx.

julia> f = GP(Matern52Kernel());
+
+julia> x = randn(11);
+
+julia> mean(f(x)) == zeros(11)
+true
source
Statistics.var — Method
var(f::FiniteGP)

Compute only the diagonal elements of cov(f).

Examples

julia> fx = GP(Matern52Kernel())(randn(10), 0.1);
+
+julia> var(fx) == diag(cov(fx))
+true
source

Optional methods

Default implementations are provided for these, but you may wish to specialise for performance.

StatsBase.mean_and_var — Method
mean_and_var(f::FiniteGP)

Compute both mean(f) and the diagonal elements of cov(f).

Sometimes more efficient than computing them separately, particularly for posteriors.

Examples

julia> fx = GP(SqExponentialKernel())(range(-3.0, 3.0; length=10), 0.1);
+
+julia> mean_and_var(fx) == (mean(fx), var(fx))
+true
source

Secondary Public API

Observe that the Primary Public API does not include a function to compute the covariance matrix of a FiniteGP. While the covariance matrix of any multivariate Gaussian is defined, it is not always a good idea to actually compute it. Fortunately, it's often the case that you're not actually interested in the covariance matrix per-se, rather the other quantities that you might use it to compute (logpdf, rand, posterior). This is similar to the well-known observation that you rarely need the inverse of a matrix, you just need to compute the inverse multiplied by something, so it's considered good practice to avoid ever explicitly computing the inverse of a matrix so as to avoid the numerical issues associated with it. This is important, for example, as TemporalGPs.jl is able to implement all of the Primary Public API in linear time in the dimension of the FiniteGP, as it never needs to evaluate the covariance matrix.

However, for many (probably the majority of) GPs, this acceleration isn't possible, and there is really nothing lost by explicitly evaluating the covariance matrix. We call this the Secondary Public API, because it's available a large proportion of the time, but should be avoided if at all possible.

Required Methods

Statistics.cov — Method
cov(f::FiniteGP)

Compute the covariance matrix of fx.

Noise-free observations

julia> f = GP(Matern52Kernel());
+
+julia> x = randn(11);
+
+julia> cov(f(x)) == kernelmatrix(Matern52Kernel(), x)
+true

Isotropic observation noise

julia> cov(f(x, 0.1)) == kernelmatrix(Matern52Kernel(), x) + 0.1 * I
+true

Independent anisotropic observation noise

julia> s = rand(11);
+
+julia> cov(f(x, s)) == kernelmatrix(Matern52Kernel(), x) + Diagonal(s)
+true

Correlated observation noise

julia> A = randn(11, 11); S = A'A;
+
+julia> cov(f(x, S)) == kernelmatrix(Matern52Kernel(), x) + S
+true
source

Optional Methods

Default implementations are provided for these, but you may wish to specialise for performance.

StatsBase.mean_and_cov — Method
mean_and_cov(f::FiniteGP)

Equivalent to (mean(f), cov(f)), but sometimes more efficient to compute them jointly than separately.

julia> fx = GP(SqExponentialKernel())(range(-3.0, 3.0; length=10), 0.1);
+
+julia> mean_and_cov(fx) == (mean(fx), cov(fx))
+true
source

Internal AbstractGPs API

This functionality is not intended to be used directly by the users, or those building functionality on top of this package – they should interact with Primary Public API. If you believe you really do need to interact with this level of the API, please open an issue to discuss as you may have a use-case that was missed during the design of this API.

As discussed at the top of this page, instances of subtypes of AbstractGP represent Gaussian processes – collections of jointly-Gaussian random variables, which may be infinite-dimensional.

Implementing the following API for your own AbstractGP subtype automatically implements both the Primary and Secondary public APIs above in terms of them.

Existing implementations of this interface include

  1. GP
  2. PosteriorGP
  3. ApproxPosteriorGP
  4. WrappedGP
  5. CompositeGP
  6. GaussianProcessProbabilisticProgramme

Required Methods

Statistics.mean — Method
mean(f::AbstractGP, x::AbstractVector)

Compute the mean vector of the multivariate Normal f(x).

source
Statistics.cov — Method
cov(f::AbstractGP, x::AbstractVector, y::AbstractVector)

Compute the length(x) by length(y) cross-covariance matrix between f(x) and f(y).

source
Statistics.var — Method
var(f::AbstractGP, x::AbstractVector)

Compute only the diagonal elements of cov(f(x)).

source

Optional Methods

Default implementations are provided for these, but you may wish to specialise for performance.

Statistics.cov — Method
cov(f::AbstractGP, x::AbstractVector)

Compute the length(x) by length(x) covariance matrix of the multivariate Normal f(x).

source
StatsBase.mean_and_cov — Method
mean_and_cov(f::AbstractGP, x::AbstractVector)

Compute both mean(f(x)) and cov(f(x)). Sometimes more efficient than computing them separately, particularly for posteriors.

source
StatsBase.mean_and_var — Method
mean_and_var(f::AbstractGP, x::AbstractVector)

Compute both mean(f(x)) and the diagonal elements of cov(f(x)). Sometimes more efficient than computing them separately, particularly for posteriors.

source

Note that, while we could provide a default implementation for var(f, x) as diag(cov(f, x)), this is generally such an inefficient fallback, that we find it preferable to error if it's not implemented than to ever hit a fallback.

Which API should I implement?

To answer this question, you need to need to know whether or not the default implementations of the FiniteGP APIs work for your use case. There are a couple of reasons of which we are aware for why this might not be the case (see below) – possibly there are others.

If you are unsure, please open an issue to discuss.

You want to avoid computing the covariance matrix

We've already discussed this a bit on this page. The default implementations of the FiniteGP APIs rely on computing the covariance matrix. If your AbstractGP subtype needs to avoid computing the covariance matrix for performance reasons, then do not implement the Internal AbstractGPs API. Do implement the Primary Public API. Do not implement the Secondary Public API.

TemporalGPs.jl is an example of a package that does this – see the LTISDE implementation for an example. The same is true of the BayesianLinearRegressor type.

You don't want to use the default implementations

Perhaps you just don't like the default implementations because you don't want to make use of Cholesky factorisations. We don't have an example of this yet in Julia, however GPyTorch avoids the Cholesky factorisation in favour of iterative solvers.

In this situation, implement both the Internal AbstractGPs API and the FiniteGP APIs.

In this situation you will benefit less from code reuse inside AbstractGPs, but will continue to benefit from the ability of others use your code, and to take advantage of any existing functionality which requires types which adhere to the AbstractGPs API.

Mean functions

We define an API for prior mean functions with the abstract type MeanFunction and the mean_vector function.

AbstractGPs.MeanFunction — Type
abstract type MeanFunction end

MeanFunction introduces an API for treating the prior mean function appropriately. On the abstract level, all MeanFunction are functions. However we generally want to evaluate them on a collection of inputs. To this effect, we provide the mean_vector(::MeanFunction, ::AbstractVector) function, which is equivalent to map but with possibilities of optimizations (for ZeroMean and ConstMean for example).

source
AbstractGPs.mean_vector — Function
mean_vector(m::MeanFunction, x::AbstractVector)::AbstractVector{<:Real}

mean_vector is the function to call to apply a MeanFunction to a collection of inputs.

source

We provide standard mean functions like ZeroMean and ConstMean as well as CustomMean to simply wrap a function.

AbstractGPs.CustomMean — Type
CustomMean{Tf} <: MeanFunction

A wrapper around whatever unary function you fancy. Must be able to be mapped over an AbstractVector of inputs.

Warning

CustomMean is generally sufficient for testing purposes, but care should be taken if attempting to differentiate through mean_vector with a CustomMean when using Zygote.jl. In particular, mean_vector(m::CustomMean, x) is implemented as map(m.f, x), which when x is a ColVecs or RowVecs will not differentiate correctly.

In such cases, you should implement mean_vector directly for your custom mean. For example, if f(x) = sum(x), you might implement mean_vector as

mean_vector(::CustomMean{typeof(f)}, x::ColVecs) = vec(sum(x.X; dims=1))
+mean_vector(::CustomMean{typeof(f)}, x::RowVecs) = vec(sum(x.X; dims=2))

which avoids ever applying map to a ColVecs or RowVecs.

source

Testing Utilities

AbstractGPs provides several consistency tests in the AbstractGPs.TestUtils module. These tests will ensure that, for example, the size and type of everything produced by an implementation of the API is correct, and consistent with the other methods. It will not ensure correctness in any absolute sense though (e.g. that logpdf indeed computes what you wanted it to compute). Consequently, these tests should be seen as a set of necessary conditions for your code to be correct. They are not, however, sufficient.

You should only need to run one of the following test suites.

  1. If you implement the Primary Public API, run test_finitegp_primary_public_interface.
  2. If you implement both the Primary Public API and the Secondary Public API, then run test_finitegp_primary_and_secondary_public_interface.
  3. If you implement the Internal AbstractGPs API, run test_internal_abstractgps_interface.

Also see Which API should I implement? for more information about the most appropriate API to implement.

AbstractGPs.TestUtils.test_finitegp_primary_public_interface — Function
test_finitegp_primary_public_interface(
+    rng::AbstractRNG, fx::FiniteGP; atol::Real=1e-12
+)

Basic consistency tests for the Primary Public FiniteGP API. You should run these tests if you only implement the Primary Public API – see API section of docs for details about this API.

These are consistency checks, not correctness tests in the absolute sense. For example, these tests ensure that samples generated by rand are of the correct size, but does not check that they come from the intended distribution.

source
AbstractGPs.TestUtils.test_finitegp_primary_and_secondary_public_interface — Function
test_finitegp_primary_and_secondary_public_interface(
+    rng::AbstractRNG, fx::FiniteGP; atol::Real=1e-12
+)

Basic consistency tests for both the Primary and Secondary Public FiniteGP APIs. Runs test_finitegp_primary_public_interface as part of these tests. You should run these tests if you implement both the primary and secondary public APIs – see API section of the docs for more information about these APIs.

These are consistency checks, not correctness tests in the absolute sense. For example, these tests ensure that samples generated by rand are of the correct size, but does not check that they come from the intended distribution.

source
AbstractGPs.TestUtils.test_internal_abstractgps_interface — Function
test_internal_abstractgps_interface(
+    rng::AbstractRNG,
+    f::AbstractGP,
+    x::AbstractVector,
+    z::AbstractVector;
+    atol=1e-12,
+    σ²::Real=1e-1,
+    jitter::Real=AbstractGPs.default_σ²,
+)

Basic consistency tests for the Internal AbstractGPs API. Runs test_finitegp_primary_and_secondary_public_interface as part of these tests. Run these tests if you implement the Internal AbstractGPs API – see the API section of the docs for more information about this API.

These are consistency checks, not correctness tests in the absolute sense. For example, these tests ensure that samples generated by rand are of the correct size, but does not check that they come from the intended distribution.

kwargs

  • atol: the minimum eigenvalue of cov(f, x) must be greater than this.
  • σ²: the observation noise under which things are observed.
  • jitter: constant added to diagonal of cov(f, x) and cov(f, z) before inversion.
source
diff --git a/previews/PR387/assets/documenter.js b/previews/PR387/assets/documenter.js new file mode 100644 index 00000000..c6562b55 --- /dev/null +++ b/previews/PR387/assets/documenter.js @@ -0,0 +1,1050 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min', + 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min', + 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/contrib/auto-render.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min', + 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', + 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min', + }, + shim: { + "highlight-julia": { + "deps": [ + "highlight" + ] + }, + "katex-auto-render": { + "deps": [ + "katex" + ] + }, + "headroom-jquery": { + "deps": [ + "jquery", + "headroom" + ] + }, + "highlight-julia-repl": { + "deps": [ + "highlight" + ] + } +} +}); +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'katex', 'katex-auto-render'], function($, katex, renderMathInElement) { +$(document).ready(function() { + renderMathInElement( + document.body, + { + "delimiters": [ + { + "left": "$", + "right": "$", + "display": false + }, + { + "left": "$$", + "right": "$$", + "display": true + }, + { + "left": "\\[", + "right": "\\]", + "display": true + } + ] +} + + ); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) { +$(document).ready(function() { + hljs.highlightAll(); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let timer = 0; +var isExpanded = true; + +$(document).on("click", ".docstring header", function () { + let articleToggleTitle = "Expand docstring"; + + debounce(() => { + if ($(this).siblings("section").is(":visible")) { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + } else { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + articleToggleTitle = "Collapse docstring"; + } + + $(this) + .find(".docstring-article-toggle-button") + .prop("title", articleToggleTitle); + $(this).siblings("section").slideToggle(); + }); +}); + +$(document).on("click", ".docs-article-toggle-button", function (event) { + let articleToggleTitle = "Expand docstring"; + let navArticleToggleTitle = "Expand all docstrings"; + let animationSpeed = event.noToggleAnimation ? 0 : 400; + + debounce(() => { + if (isExpanded) { + $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + + isExpanded = false; + + $(".docstring section").slideUp(animationSpeed); + } else { + $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + isExpanded = true; + articleToggleTitle = "Collapse docstring"; + navArticleToggleTitle = "Collapse all docstrings"; + + $(".docstring section").slideDown(animationSpeed); + } + + $(this).prop("title", navArticleToggleTitle); + $(".docstring-article-toggle-button").prop("title", articleToggleTitle); + }); +}); + +function debounce(callback, timeout = 300) { + if (Date.now() - timer > timeout) { + callback(); + } + + clearTimeout(timer); + + timer = Date.now(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require([], function() { +function addCopyButtonCallbacks() { + for (const el of document.getElementsByTagName("pre")) { + const button = document.createElement("button"); + button.classList.add("copy-button", "fa-solid", "fa-copy"); + button.setAttribute("aria-label", "Copy this code block"); + button.setAttribute("title", "Copy"); + + el.appendChild(button); + + const success = function () { + button.classList.add("success", "fa-check"); + button.classList.remove("fa-copy"); + }; + + const failure = function () { + button.classList.add("error", "fa-xmark"); + button.classList.remove("fa-copy"); + }; + + button.addEventListener("click", function () { + copyToClipboard(el.innerText).then(success, failure); + + setTimeout(function () { + button.classList.add("fa-copy"); + button.classList.remove("success", "fa-check", "fa-xmark"); + }, 5000); + }); + } +} + +function copyToClipboard(text) { + // clipboard API is only available in secure contexts + if (window.navigator && window.navigator.clipboard) { + return window.navigator.clipboard.writeText(text); + } else { + return new Promise(function (resolve, reject) { + try { + const el = document.createElement("textarea"); + el.textContent = text; + el.style.position = "fixed"; + el.style.opacity = 0; + document.body.appendChild(el); + el.select(); + document.execCommand("copy"); + + resolve(); + } catch (err) { + reject(err); + } finally { + document.body.removeChild(el); + } + }); + } +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks); +} else { + addCopyButtonCallbacks(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { + +// Manages the top navigation bar (hides it when the user starts scrolling down on the +// mobile). +window.Headroom = Headroom; // work around buggy module loading? +$(document).ready(function () { + $("#documenter .docs-navbar").headroom({ + tolerance: { up: 10, down: 10 }, + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let meta = $("div[data-docstringscollapsed]").data(); + + if (meta?.docstringscollapsed) { + $("#documenter-article-toggle-button").trigger({ + type: "click", + noToggleAnimation: true, + }); + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +/* +To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc + +PSEUDOCODE: + +Searching happens automatically as the user types or adjusts the selected filters. +To preserve responsiveness, as much as possible of the slow parts of the search are done +in a web worker. Searching and result generation are done in the worker, and filtering and +DOM updates are done in the main thread. The filters are in the main thread as they should +be very quick to apply. This lets filters be changed without re-searching with minisearch +(which is possible even if filtering is on the worker thread) and also lets filters be +changed _while_ the worker is searching and without message passing (neither of which are +possible if filtering is on the worker thread) + +SEARCH WORKER: + +Import minisearch + +Build index + +On message from main thread + run search + find the first 200 unique results from each category, and compute their divs for display + note that this is necessary and sufficient information for the main thread to find the + first 200 unique results from any given filter set + post results to main thread + +MAIN: + +Launch worker + +Declare nonconstant globals (worker_is_running, last_search_text, unfiltered_results) + +On text update + if worker is not running, launch_search() + +launch_search + set worker_is_running to true, set last_search_text to the search text + post the search query to worker + +on message from worker + if last_search_text is not the same as the text in the search field, + the latest search result is not reflective of the latest search query, so update again + launch_search() + otherwise + set worker_is_running to false + + regardless, display the new search results to the user + save the unfiltered_results as a global + update_search() + +on filter click + adjust the filter selection + update_search() + +update_search + apply search filters by looping through the unfiltered_results and finding the first 200 + unique results that match the filters + + Update the DOM +*/ + +/////// SEARCH WORKER /////// + +function worker_function(documenterSearchIndex, documenterBaseURL, filters) { + importScripts( + "https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min.js" + ); + + let data = documenterSearchIndex.map((x, key) => { + x["id"] = key; // minisearch requires a unique for each object + return x; + }); + + // list below is the lunr 2.1.3 list minus the intersect with names(Base) + // (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) + // ideally we'd just filter the original list but it's not available as a variable + const stopWords = new Set([ + "a", + "able", + "about", + "across", + "after", + "almost", + "also", + "am", + "among", + "an", + "and", + "are", + "as", + "at", + "be", + "because", + "been", + "but", + "by", + "can", + "cannot", + "could", + "dear", + "did", + "does", + "either", + "ever", + "every", + "from", + "got", + "had", + "has", + "have", + "he", + "her", + "hers", + "him", + "his", + "how", + "however", + "i", + "if", + "into", + "it", + "its", + "just", + "least", + "like", + "likely", + "may", + "me", + "might", + "most", + "must", + "my", + "neither", + "no", + "nor", + "not", + "of", + "off", + "often", + "on", + "or", + "other", + "our", + "own", + "rather", + "said", + "say", + "says", + "she", + "should", + "since", + "so", + "some", + "than", + "that", + "the", + "their", + "them", + "then", + "there", + "these", + "they", + "this", + "tis", + "to", + "too", + "twas", + "us", + "wants", + "was", + "we", + "were", + "what", + "when", + "who", + "whom", + "why", + "will", + "would", + "yet", + "you", + "your", + ]); + + let index = new MiniSearch({ + fields: ["title", "text"], // fields to index for full-text search + storeFields: ["location", "title", "text", "category", "page"], // fields to return with results + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + + word = word.toLowerCase(); + } + + return word ?? null; + }, + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not + // find anything if searching for "add!", only for the entire qualification + tokenize: (string) => string.split(/[\s\-\.]+/), + // options which will be applied during the search + searchOptions: { + prefix: true, + boost: { title: 100 }, + fuzzy: 2, + }, + }); + + index.addAll(data); + + /** + * Used to map characters to HTML entities. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const htmlEscapes = { + "&": "&", + "<": "<", + ">": ">", + '"': """, + "'": "'", + }; + + /** + * Used to match HTML entities and HTML characters. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const reUnescapedHtml = /[&<>"']/g; + const reHasUnescapedHtml = RegExp(reUnescapedHtml.source); + + /** + * Escape function from lodash + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + function escape(string) { + return string && reHasUnescapedHtml.test(string) + ? string.replace(reUnescapedHtml, (chr) => htmlEscapes[chr]) + : string || ""; + } + + /** + * Make the result component given a minisearch result data object and the value + * of the search input as queryString. To view the result object structure, refer: + * https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult + * + * @param {object} result + * @param {string} querystring + * @returns string + */ + function make_search_result(result, querystring) { + let search_divider = `
`; + let display_link = + result.location.slice(Math.max(0), Math.min(50, result.location.length)) + + (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div + + if (result.page !== "") { + display_link += ` (${result.page})`; + } + + let textindex = new RegExp(`${querystring}`, "i").exec(result.text); + let text = + textindex !== null + ? result.text.slice( + Math.max(textindex.index - 100, 0), + Math.min( + textindex.index + querystring.length + 100, + result.text.length + ) + ) + : ""; // cut-off text before and after from the match + + text = text.length ? escape(text) : ""; + + let display_result = text.length + ? "..." + + text.replace( + new RegExp(`${escape(querystring)}`, "i"), // For first occurrence + '$&' + ) + + "..." + : ""; // highlights the match + + let in_code = false; + if (!["page", "section"].includes(result.category.toLowerCase())) { + in_code = true; + } + + // We encode the full url to escape some special characters which can lead to broken links + let result_div = ` + +
+
${escape(result.title)}
+
${result.category}
+
+

+ ${display_result} +

+
+ ${display_link} +
+ + ${search_divider} + `; + + return result_div; + } + + self.onmessage = function (e) { + let query = e.data; + let results = index.search(query, { + filter: (result) => { + // Only return relevant results + return result.score >= 1; + }, + }); + + // Pre-filter to deduplicate and limit to 200 per category to the extent + // possible without knowing what the filters are. + let filtered_results = []; + let counts = {}; + for (let filter of filters) { + counts[filter] = 0; + } + let present = {}; + + for (let result of results) { + cat = result.category; + cnt = counts[cat]; + if (cnt < 200) { + id = cat + "---" + result.location; + if (present[id]) { + continue; + } + present[id] = true; + filtered_results.push({ + location: result.location, + category: cat, + div: make_search_result(result, query), + }); + } + } + + postMessage(filtered_results); + }; +} + +// `worker = Threads.@spawn worker_function(documenterSearchIndex)`, but in JavaScript! +const filters = [ + ...new Set(documenterSearchIndex["docs"].map((x) => x.category)), +]; +const worker_str = + "(" + + worker_function.toString() + + ")(" + + JSON.stringify(documenterSearchIndex["docs"]) + + "," + + JSON.stringify(documenterBaseURL) + + "," + + JSON.stringify(filters) + + ")"; +const worker_blob = new Blob([worker_str], { type: "text/javascript" }); +const worker = new Worker(URL.createObjectURL(worker_blob)); + +/////// SEARCH MAIN /////// + +// Whether the worker is currently handling a search. This is a boolean +// as the worker only ever handles 1 or 0 searches at a time. +var worker_is_running = false; + +// The last search text that was sent to the worker. This is used to determine +// if the worker should be launched again when it reports back results. +var last_search_text = ""; + +// The results of the last search. This, in combination with the state of the filters +// in the DOM, is used compute the results to display on calls to update_search. +var unfiltered_results = []; + +// Which filter is currently selected +var selected_filter = ""; + +$(document).on("input", ".documenter-search-input", function (event) { + if (!worker_is_running) { + launch_search(); + } +}); + +function launch_search() { + worker_is_running = true; + last_search_text = $(".documenter-search-input").val(); + worker.postMessage(last_search_text); +} + +worker.onmessage = function (e) { + if (last_search_text !== $(".documenter-search-input").val()) { + launch_search(); + } else { + worker_is_running = false; + } + + unfiltered_results = e.data; + update_search(); +}; + +$(document).on("click", ".search-filter", function () { + if ($(this).hasClass("search-filter-selected")) { + selected_filter = ""; + } else { + selected_filter = $(this).text().toLowerCase(); + } + + // This updates search results and toggles classes for UI: + update_search(); +}); + +/** + * Make/Update the search component + */ +function update_search() { + let querystring = $(".documenter-search-input").val(); + + if (querystring.trim()) { + if (selected_filter == "") { + results = unfiltered_results; + } else { + results = unfiltered_results.filter((result) => { + return selected_filter == result.category.toLowerCase(); + }); + } + + let search_result_container = ``; + let modal_filters = make_modal_body_filters(); + let search_divider = `
`; + + if (results.length) { + let links = []; + let count = 0; + let search_results = ""; + + for (var i = 0, n = results.length; i < n && count < 200; ++i) { + let result = results[i]; + if (result.location && !links.includes(result.location)) { + search_results += result.div; + count++; + links.push(result.location); + } + } + + if (count == 1) { + count_str = "1 result"; + } else if (count == 200) { + count_str = "200+ results"; + } else { + count_str = count + " results"; + } + let result_count = `
${count_str}
`; + + search_result_container = ` +
+ ${modal_filters} + ${search_divider} + ${result_count} +
+ ${search_results} +
+
+ `; + } else { + search_result_container = ` +
+ ${modal_filters} + ${search_divider} +
0 result(s)
+
+
No result found!
+ `; + } + + if ($(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").removeClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(search_result_container); + } else { + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(` +
Type something to get started!
+ `); + } +} + +/** + * Make the modal filter html + * + * @returns string + */ +function make_modal_body_filters() { + let str = filters + .map((val) => { + if (selected_filter == val.toLowerCase()) { + return `${val}`; + } else { + return `${val}`; + } + }) + .join(""); + + return ` +
+ Filters: + ${str} +
`; +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Modal settings dialog +$(document).ready(function () { + var settings = $("#documenter-settings"); + $("#documenter-settings-button").click(function () { + settings.toggleClass("is-active"); + }); + // Close the dialog if X is clicked + $("#documenter-settings button.delete").click(function () { + settings.removeClass("is-active"); + }); + // Close dialog if ESC is pressed + $(document).keyup(function (e) { + if (e.keyCode == 27) settings.removeClass("is-active"); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let search_modal_header = ` +
+
+

+ + + + +

+
+
+ +
+
+ `; + + let initial_search_body = ` +
Type something to get started!
+ `; + + let search_modal_footer = ` + + `; + + $(document.body).append( + ` + + ` + ); + + document.querySelector(".docs-search-query").addEventListener("click", () => { + openModal(); + }); + + document + .querySelector(".close-search-modal") + .addEventListener("click", () => { + closeModal(); + }); + + $(document).on("click", ".search-result-link", function () { + closeModal(); + }); + + document.addEventListener("keydown", (event) => { + if ((event.ctrlKey || event.metaKey) && event.key === "/") { + openModal(); + } else if (event.key === "Escape") { + closeModal(); + } + + return false; + }); + + // Functions to open and close a modal + function openModal() { + let searchModal = document.querySelector("#search-modal"); + + searchModal.classList.add("is-active"); + document.querySelector(".documenter-search-input").focus(); + } + + function closeModal() { + let searchModal = document.querySelector("#search-modal"); + let initial_search_body = ` +
Type something to get started!
+ `; + + searchModal.classList.remove("is-active"); + document.querySelector(".documenter-search-input").blur(); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".documenter-search-input").val(""); + $(".search-modal-card-body").html(initial_search_body); + } + + document + .querySelector("#search-modal .modal-background") + .addEventListener("click", () => { + closeModal(); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Manages the showing and hiding of the sidebar. +$(document).ready(function () { + var sidebar = $("#documenter > .docs-sidebar"); + var sidebar_button = $("#documenter-sidebar-button"); + sidebar_button.click(function (ev) { + ev.preventDefault(); + sidebar.toggleClass("visible"); + if (sidebar.hasClass("visible")) { + // Makes sure that the current menu item is visible in the sidebar. + $("#documenter .docs-menu a.is-active").focus(); + } + }); + $("#documenter > .docs-main").bind("click", function (ev) { + if ($(ev.target).is(sidebar_button)) { + return; + } + if (sidebar.hasClass("visible")) { + sidebar.removeClass("visible"); + } + }); +}); + +// Resizes the package name / sitename in the sidebar if it is too wide. +// Inspired by: https://github.com/davatron5000/FitText.js +$(document).ready(function () { + e = $("#documenter .docs-autofit"); + function resize() { + var L = parseInt(e.css("max-width"), 10); + var L0 = e.width(); + if (L0 > L) { + var h0 = parseInt(e.css("font-size"), 10); + e.css("font-size", (L * h0) / L0); + // TODO: make sure it survives resizes? + } + } + // call once and then register events + resize(); + $(window).resize(resize); + $(window).on("orientationchange", resize); +}); + +// Scroll the navigation bar to the currently selected menu item +$(document).ready(function () { + var sidebar = $("#documenter .docs-menu").get(0); + var active = $("#documenter .docs-menu .is-active").get(0); + if (typeof active !== "undefined") { + sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Theme picker setup +$(document).ready(function () { + // onchange callback + $("#documenter-themepicker").change(function themepick_callback(ev) { + var themename = $("#documenter-themepicker option:selected").attr("value"); + if (themename === "auto") { + // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'); + window.localStorage.removeItem("documenter-theme"); + } else { + // set_theme(themename); + window.localStorage.setItem("documenter-theme", themename); + } + // We re-use the global function from themeswap.js to actually do the swapping. + set_theme_from_local_storage(); + }); + + // Make sure that the themepicker displays the correct theme when the theme is retrieved + // from localStorage + if (typeof window.localStorage !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if (theme !== null) { + $("#documenter-themepicker option").each(function (i, e) { + e.selected = e.value === theme; + }); + } + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// update the version selector with info from the siteinfo.js and ../versions.js files +$(document).ready(function () { + // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the + // siteinfo.js file, we just return immediately and not display the version selector. + if ( + typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" && + DOCUMENTER_VERSION_SELECTOR_DISABLED + ) { + return; + } + + var version_selector = $("#documenter .docs-version-selector"); + var version_selector_select = $("#documenter .docs-version-selector select"); + + version_selector_select.change(function (x) { + target_href = version_selector_select + .children("option:selected") + .get(0).value; + window.location.href = target_href; + }); + + // add the current version to the selector based on siteinfo.js, but only if the selector is empty + if ( + typeof DOCUMENTER_CURRENT_VERSION !== "undefined" && + $("#version-selector > option").length == 0 + ) { + var option = $( + "" + ); + version_selector_select.append(option); + } + + if (typeof DOC_VERSIONS !== "undefined") { + var existing_versions = version_selector_select.children("option"); + var existing_versions_texts = existing_versions.map(function (i, x) { + return x.text; + }); + DOC_VERSIONS.forEach(function (each) { + var version_url = documenterBaseURL + "/../" + each + "/"; + var existing_id = $.inArray(each, existing_versions_texts); + // if not already in the version selector, add it as a new option, + // otherwise update the old option with the URL and enable it + if (existing_id == -1) { + var option = $( + "" + ); + version_selector_select.append(option); + } else { + var option = existing_versions[existing_id]; + option.value = version_url; + option.disabled = false; + } + }); + } + + // only show the version selector if the selector has been populated + if (version_selector_select.children("option").length > 0) { + version_selector.toggleClass("visible"); + } +}); + +}) diff --git a/previews/PR387/assets/themes/documenter-dark.css b/previews/PR387/assets/themes/documenter-dark.css new file mode 100644 index 00000000..53889fb9 --- /dev/null +++ b/previews/PR387/assets/themes/documenter-dark.css @@ -0,0 +1,7 @@ +html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark .file-cta,html.theme--documenter-dark .file-name,html.theme--documenter-dark .select select,html.theme--documenter-dark .textarea,html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark .button{-moz-appearance:none;-webkit-appearance:none;align-items:center;border:1px solid transparent;border-radius:.4em;box-shadow:none;display:inline-flex;font-size:1rem;height:2.5em;justify-content:flex-start;line-height:1.5;padding-bottom:calc(0.5em - 1px);padding-left:calc(0.75em - 1px);padding-right:calc(0.75em - 1px);padding-top:calc(0.5em - 1px);position:relative;vertical-align:top}html.theme--documenter-dark 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kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-primary,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}html.theme--documenter-dark 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h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content 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html.theme--documenter-dark .navbar-dropdown,html.theme--documenter-dark .navbar-dropdown.is-boxed{border-radius:8px;border-top:none;box-shadow:0 8px 8px rgba(10,10,10,0.1), 0 0 0 1px rgba(10,10,10,0.1);display:block;opacity:0;pointer-events:none;top:calc(100% + (-4px));transform:translateY(-5px);transition-duration:86ms;transition-property:opacity, transform}html.theme--documenter-dark .navbar-dropdown.is-right{left:auto;right:0}html.theme--documenter-dark .navbar-divider{display:block}html.theme--documenter-dark .navbar>.container .navbar-brand,html.theme--documenter-dark .container>.navbar .navbar-brand{margin-left:-.75rem}html.theme--documenter-dark .navbar>.container .navbar-menu,html.theme--documenter-dark .container>.navbar .navbar-menu{margin-right:-.75rem}html.theme--documenter-dark .navbar.is-fixed-bottom-desktop,html.theme--documenter-dark .navbar.is-fixed-top-desktop{left:0;position:fixed;right:0;z-index:30}html.theme--documenter-dark 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a.navbar-item.is-active:not(:focus):not(:hover),html.theme--documenter-dark .navbar-link.is-active:not(:focus):not(:hover){background-color:rgba(0,0,0,0)}html.theme--documenter-dark .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar-item.has-dropdown.is-active .navbar-link{background-color:rgba(0,0,0,0)}}html.theme--documenter-dark .hero.is-fullheight-with-navbar{min-height:calc(100vh - 4rem)}html.theme--documenter-dark .pagination{font-size:1rem;margin:-.25rem}html.theme--documenter-dark .pagination.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination{font-size:.75rem}html.theme--documenter-dark .pagination.is-medium{font-size:1.25rem}html.theme--documenter-dark .pagination.is-large{font-size:1.5rem}html.theme--documenter-dark .pagination.is-rounded .pagination-previous,html.theme--documenter-dark #documenter .docs-sidebar 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+ Maintainer: @highlightjs/core-team + Website: https://highlightjs.org/ + License: see project LICENSE + Touched: 2021 +*/pre code.hljs{display:block;overflow-x:auto;padding:1em}code.hljs{padding:3px 5px}.hljs{background:#F3F3F3;color:#444}.hljs-comment{color:#697070}.hljs-tag,.hljs-punctuation{color:#444a}.hljs-tag .hljs-name,.hljs-tag .hljs-attr{color:#444}.hljs-keyword,.hljs-attribute,.hljs-selector-tag,.hljs-meta .hljs-keyword,.hljs-doctag,.hljs-name{font-weight:bold}.hljs-type,.hljs-string,.hljs-number,.hljs-selector-id,.hljs-selector-class,.hljs-quote,.hljs-template-tag,.hljs-deletion{color:#880000}.hljs-title,.hljs-section{color:#880000;font-weight:bold}.hljs-regexp,.hljs-symbol,.hljs-variable,.hljs-template-variable,.hljs-link,.hljs-selector-attr,.hljs-operator,.hljs-selector-pseudo{color:#ab5656}.hljs-literal{color:#695}.hljs-built_in,.hljs-bullet,.hljs-code,.hljs-addition{color:#397300}.hljs-meta{color:#1f7199}.hljs-meta .hljs-string{color:#38a}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold}.gap-4{gap:1rem} diff --git a/previews/PR387/assets/themeswap.js b/previews/PR387/assets/themeswap.js new file mode 100644 index 00000000..9f5eebe6 --- /dev/null +++ b/previews/PR387/assets/themeswap.js @@ -0,0 +1,84 @@ +// Small function to quickly swap out themes. Gets put into the tag.. +function set_theme_from_local_storage() { + // Initialize the theme to null, which means default + var theme = null; + // If the browser supports the localstorage and is not disabled then try to get the + // documenter theme + if (window.localStorage != null) { + // Get the user-picked theme from localStorage. May be `null`, which means the default + // theme. + theme = window.localStorage.getItem("documenter-theme"); + } + // Check if the users preference is for dark color scheme + var darkPreference = + window.matchMedia("(prefers-color-scheme: dark)").matches === true; + // Initialize a few variables for the loop: + // + // - active: will contain the index of the theme that should be active. Note that there + // is no guarantee that localStorage contains sane values. If `active` stays `null` + // we either could not find the theme or it is the default (primary) theme anyway. + // Either way, we then need to stick to the primary theme. + // + // - disabled: style sheets that should be disabled (i.e. all the theme style sheets + // that are not the currently active theme) + var active = null; + var disabled = []; + var primaryLightTheme = null; + var primaryDarkTheme = null; + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // To distinguish the default (primary) theme, it needs to have the data-theme-primary + // attribute set. + if (ss.ownerNode.getAttribute("data-theme-primary") !== null) { + primaryLightTheme = themename; + } + // Check if the theme is primary dark theme so that we could store its name in darkTheme + if (ss.ownerNode.getAttribute("data-theme-primary-dark") !== null) { + primaryDarkTheme = themename; + } + // If we find a matching theme (and it's not the default), we'll set active to non-null + if (themename === theme) active = i; + // Store the style sheets of inactive themes so that we could disable them + if (themename !== theme) disabled.push(ss); + } + var activeTheme = null; + if (active !== null) { + // If we did find an active theme, we'll (1) add the theme--$(theme) class to + document.getElementsByTagName("html")[0].className = "theme--" + theme; + activeTheme = theme; + } else { + // If we did _not_ find an active theme, then we need to fall back to the primary theme + // which can either be dark or light, depending on the user's OS preference. + var activeTheme = darkPreference ? primaryDarkTheme : primaryLightTheme; + // In case it somehow happens that the relevant primary theme was not found in the + // preceding loop, we abort without doing anything. + if (activeTheme === null) { + console.error("Unable to determine primary theme."); + return; + } + // When switching to the primary light theme, then we must not have a class name + // for the tag. That's only for non-primary or the primary dark theme. + if (darkPreference) { + document.getElementsByTagName("html")[0].className = + "theme--" + activeTheme; + } else { + document.getElementsByTagName("html")[0].className = ""; + } + } + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // we'll disable all the stylesheets, except for the active one + ss.disabled = !(themename == activeTheme); + } +} +set_theme_from_local_storage(); diff --git a/previews/PR387/assets/warner.js b/previews/PR387/assets/warner.js new file mode 100644 index 00000000..3f6f5d00 --- /dev/null +++ b/previews/PR387/assets/warner.js @@ -0,0 +1,52 @@ +function maybeAddWarning() { + // DOCUMENTER_NEWEST is defined in versions.js, DOCUMENTER_CURRENT_VERSION and DOCUMENTER_STABLE + // in siteinfo.js. + // If either of these are undefined something went horribly wrong, so we abort. + if ( + window.DOCUMENTER_NEWEST === undefined || + window.DOCUMENTER_CURRENT_VERSION === undefined || + window.DOCUMENTER_STABLE === undefined + ) { + return; + } + + // Current version is not a version number, so we can't tell if it's the newest version. Abort. + if (!/v(\d+\.)*\d+/.test(window.DOCUMENTER_CURRENT_VERSION)) { + return; + } + + // Current version is newest version, so no need to add a warning. + if (window.DOCUMENTER_NEWEST === window.DOCUMENTER_CURRENT_VERSION) { + return; + } + + // Add a noindex meta tag (unless one exists) so that search engines don't index this version of the docs. + if (document.body.querySelector('meta[name="robots"]') === null) { + const meta = document.createElement("meta"); + meta.name = "robots"; + meta.content = "noindex"; + + document.getElementsByTagName("head")[0].appendChild(meta); + } + + const div = document.createElement("div"); + div.classList.add("outdated-warning-overlay"); + const closer = document.createElement("button"); + closer.classList.add("outdated-warning-closer", "delete"); + closer.addEventListener("click", function () { + document.body.removeChild(div); + }); + const href = window.documenterBaseURL + "/../" + window.DOCUMENTER_STABLE; + div.innerHTML = + 'This documentation is not for the latest stable release, but for either the development version or an older release.
Click here to go to the documentation for the latest stable release.'; + div.appendChild(closer); + document.body.appendChild(div); +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", maybeAddWarning); +} else { + maybeAddWarning(); +} diff --git a/previews/PR387/concrete_features/index.html b/previews/PR387/concrete_features/index.html new file mode 100644 index 00000000..c1e88226 --- /dev/null +++ b/previews/PR387/concrete_features/index.html @@ -0,0 +1,26 @@ + +Concrete Features · AbstractGPs.jl
GitHub

Features

Setup

using AbstractGPs, Random
+rng = MersenneTwister(0)
+
+# Construct a zero-mean Gaussian process with a matern-3/2 kernel.
+f = GP(Matern32Kernel())
+
+# Specify some input and target locations.
+x = randn(rng, 10)
+y = randn(rng, 10)

Finite dimensional projection

Look at the finite-dimensional projection of f at x, under zero-mean observation noise with variance 0.1.

fx = f(x, 0.1)

Sample from GP from the prior at x under noise.

y_sampled = rand(rng, fx)

Compute the log marginal probability of y.

logpdf(fx, y)

Construct the posterior process implied by conditioning f at x on y.

f_posterior = posterior(fx, y)

A posterior process follows the AbstractGP interface, so the same functions which work on the posterior as on the prior.

rand(rng, f_posterior(x))
+logpdf(f_posterior(x), y)

Compute the VFE approximation to the log marginal probability of y.

Here, z is a set of pseudo-points.

z = randn(rng, 4)

Evidence Lower BOund (ELBO)

We provide a ready implentation of elbo w.r.t to the pseudo points. We can perform Variational Inference on pseudo-points by maximizing the ELBO term w.r.t pseudo-points z and any kernel parameters. For more information, see examples.

elbo(VFE(f(z)), fx, y)

Construct the approximate posterior process implied by the VFE approximation.

The optimal pseudo-points obtained above can be used to create a approximate/sparse posterior. This can be used like a regular posterior in many cases.

f_approx_posterior = posterior(VFE(f(z)), fx, y)

An approximate posterior process is yet another AbstractGP, so you can do things with it like

marginals(f_approx_posterior(x))

Sequential Conditioning

Sequential conditioning allows you to compute your posterior in an online fashion. We do this in an efficient manner by updating the cholesky factorisation of the covariance matrix and avoiding recomputing it from original covariance matrix.

# Define GP prior
+f = GP(SqExponentialKernel())

Exact Posterior

Generate posterior with the first batch of data by conditioning the prior on them:

p_fx = posterior(f(x[1:3], 0.1), y[1:3])

Generate posterior with the second batch of data, considering the previous posterior p_fx as the prior:

p_p_fx = posterior(p_fx(x[4:10], 0.1), y[4:10])

Approximate Posterior

Adding observations in a sequential fashion

Z1 = rand(rng, 4)
+Z2 = rand(rng, 3)
+Z = vcat(Z1, Z2)
+p_fx1 = posterior(VFE(f(Z)), f(x[1:7], 0.1), y[1:7])
+u_p_fx1 = update_posterior(p_fx1, f(x[8:10], 0.1), y[8:10])

Adding pseudo-points in a sequential fashion

p_fx2 = posterior(VFE(f(Z1)), f(x, 0.1), y)
+u_p_fx2 = update_posterior(p_fx2, f(Z2))

Plotting

Plots.jl

We provide functions for plotting samples and predictions of Gaussian processes with Plots.jl. You can see some examples in the One-dimensional regression tutorial.

RecipesBase.plot — Method
plot(x::AbstractVector, f::FiniteGP; ribbon_scale=1, kwargs...)
+plot!([plot, ]x::AbstractVector, f::FiniteGP; ribbon_scale=1, kwargs...)

Plot the predictive mean for the projection f of a Gaussian process and a ribbon of ribbon_scale standard deviations around it versus x.

Note

Make sure to load Plots.jl before you use this function.

Examples

Plot the mean and a ribbon of 3 standard deviations:

using Plots
+
+gp = GP(SqExponentialKernel())
+plot(gp(rand(5)); ribbon_scale=3)
source
RecipesBase.plot — Method
plot(f::FiniteGP; kwargs...)
+plot!([plot, ]f::FiniteGP; kwargs...)

Plot the predictive mean and a ribbon around it for the projection f of a Gaussian process versus f.x.

source
RecipesBase.plot — Method
plot(x::AbstractVector, gp::AbstractGP; kwargs...)
+plot!([plot, ]x::AbstractVector, gp::AbstractGP; kwargs...)

Plot the predictive mean and a ribbon around it for the projection gp(x) of the Gaussian process gp.

source
AbstractGPs.sampleplot — Function
sampleplot([x::AbstractVector=f.x, ]f::FiniteGP; samples=1, kwargs...)

Plot samples from the projection f of a Gaussian process versus x.

Note

Make sure to load Plots.jl before you use this function.

When plotting multiple samples, these are treated as a single series (i.e., only a single entry will be added to the legend when providing a label).

Example

using Plots
+
+gp = GP(SqExponentialKernel())
+sampleplot(gp(rand(5)); samples=10, linealpha=1.0)

The given example plots 10 samples from the projection of the GP gp. The linealpha is modified from default of 0.35 to 1.

sampleplot(x::AbstractVector, gp::AbstractGP; samples=1, kwargs...)

Plot samples from the finite projection gp(x, 1e-9) versus x.

source

Makie.jl

You can use the Julia package AbstractGPsMakie.jl to plot Gaussian processes with Makie.jl.

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GitHub

Intro to AbstractGPs: one-dimensional regression

You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.

Setup

Loading the necessary packages.

using AbstractGPs
+using Distributions
+using FillArrays
+using StatsFuns
+
+using Plots
+default(; legend=:outertopright, size=(700, 400))
+
+using Random
+Random.seed!(42)  # setting the seed for reproducibility of this notebook

Load toy regression dataset taken from GPflow examples.

x = [
+    0.8658165855998895,
+    0.6661700880180962,
+    0.8049218148148531,
+    0.7714303440386239,
+    0.14790478354654835,
+    0.8666105548197428,
+    0.007044577166530286,
+    0.026331737288148638,
+    0.17188596617099916,
+    0.8897812990554013,
+    0.24323574561119998,
+    0.028590102134105955,
+]
+y = [
+    1.5255314337144372,
+    3.6434202968230003,
+    3.010885733911661,
+    3.774442382979625,
+    3.3687639483798324,
+    1.5506452040608503,
+    3.790447985799683,
+    3.8689707574953,
+    3.4933565751758713,
+    1.4284538820635841,
+    3.8715350915692364,
+    3.7045949061144983,
+]
+scatter(x, y; xlabel="x", ylabel="y", legend=false)
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

We split the observations into train and test data.

x_train = x[1:8]
+y_train = y[1:8]
+x_test = x[9:end]
+y_test = y[9:end]

We instantiate a Gaussian process with a Matern kernel. The kernel has fixed variance and length scale parameters of default value 1.

f = GP(Matern52Kernel())

We create a finite dimensional projection at the inputs of the training dataset observed under Gaussian noise with variance $noise_var = 0.1$, and compute the log-likelihood of the outputs of the training dataset.

noise_var = 0.1
+fx = f(x_train, noise_var)
+logpdf(fx, y_train)
-25.53057444906228

We compute the posterior Gaussian process given the training data, and calculate the log-likelihood of the test dataset.

p_fx = posterior(fx, y_train)
+logpdf(p_fx(x_test, noise_var), y_test)
-2.878533694097381

We plot the posterior Gaussian process (its mean and a ribbon of 2 standard deviations around it) on a grid along with the observations.

scatter(
+    x_train,
+    y_train;
+    xlim=(0, 1),
+    xlabel="x",
+    ylabel="y",
+    title="posterior (default parameters)",
+    label="Train Data",
+)
+scatter!(x_test, y_test; label="Test Data")
+plot!(0:0.001:1, p_fx; label=false, ribbon_scale=2)
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Markov Chain Monte Carlo

Previously we computed the log likelihood of the untuned kernel parameters of the GP. We now also perform approximate inference over said kernel parameters using different Markov chain Monte Carlo (MCMC) methods. I.e., we approximate the posterior distribution of the kernel parameters with samples from a Markov chain.

We define a function which returns the log-likelihood of the data for different variance and inverse lengthscale parameters of the Matern kernel. We ensure that these parameters are positive with the softplus function

\[f(x) = \log (1 + \exp x).\]

function gp_loglikelihood(x, y)
+    function loglikelihood(params)
+        kernel =
+            softplus(params[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(params[2])))
+        f = GP(kernel)
+        fx = f(x, noise_var)
+        return logpdf(fx, y)
+    end
+    return loglikelihood
+end
+
+const loglik_train = gp_loglikelihood(x_train, y_train)

We define a Gaussian prior for the joint distribution of the two transformed kernel parameters. We assume that both parameters are independent with mean 0 and variance 1.

logprior(params) = logpdf(MvNormal(Eye(2)), params)

Hamiltonian Monte Carlo

We start with a Hamiltonian Monte Carlo (HMC) sampler. More precisely, we use the No-U-Turn sampler (NUTS), which is provided by the Julia packages AdvancedHMC.jl and DynamicHMC.jl.

AdvancedHMC

We start with performing inference with AdvancedHMC.

using AdvancedHMC
+using ForwardDiff

Set the number of samples to draw and warmup iterations.

n_samples = 2_000
+n_adapts = 1_000

AdvancedHMC and DynamicHMC below require us to implement the LogDensityProblems interface for the log joint probability.

using LogDensityProblems
+
+struct LogJointTrain end
+
+# Log joint density
+LogDensityProblems.logdensity(::LogJointTrain, θ) = loglik_train(θ) + logprior(θ)
+
+# The parameter space is two-dimensional
+LogDensityProblems.dimension(::LogJointTrain) = 2
+
+# `LogJointTrain` does not allow to evaluate derivatives of the log density function
+function LogDensityProblems.capabilities(::Type{LogJointTrain})
+    return LogDensityProblems.LogDensityOrder{0}()
+end

We use ForwardDiff.jl to compute the derivatives of the log joint density with automatic differentiation.

using LogDensityProblemsAD
+
+const logjoint_train = ADgradient(Val(:ForwardDiff), LogJointTrain())

We define an Hamiltonian system of the log joint probability.

metric = DiagEuclideanMetric(2)
+hamiltonian = Hamiltonian(metric, logjoint_train)

Define a leapfrog solver, with initial step size chosen heuristically.

initial_params = rand(2)
+initial_ϵ = find_good_stepsize(hamiltonian, initial_params)
+integrator = Leapfrog(initial_ϵ)

Define an HMC sampler, with the following components:

  • multinomial sampling scheme,
  • generalised No-U-Turn criteria, and
  • windowed adaption for step-size and diagonal mass matrix
proposal = HMCKernel(Trajectory{MultinomialTS}(integrator, GeneralisedNoUTurn()))
+adaptor = StanHMCAdaptor(MassMatrixAdaptor(metric), StepSizeAdaptor(0.8, integrator))

We draw samples from the posterior distribution of kernel parameters. These samples are in the unconstrained space $\mathbb{R}^2$.

samples, _ = sample(
+    hamiltonian, proposal, initial_params, n_samples, adaptor, n_adapts; progress=false
+)
┌ Info: Finished 1000 adapation steps
+│   adaptor =
+│    StanHMCAdaptor(
+│        pc=WelfordVar,
+│        ssa=NesterovDualAveraging(γ=0.05, t_0=10.0, κ=0.75, δ=0.8, state.ϵ=0.729267980509611),
+│        init_buffer=75, term_buffer=50, window_size=25,
+│        state=window(76, 950), window_splits(100, 150, 250, 450, 950)
+│    )
+│   κ.τ.integrator = Leapfrog(ϵ=0.729)
+└   h.metric = DiagEuclideanMetric([0.44910857845910196, 0.511 ...])
+┌ Info: Finished 2000 sampling steps for 1 chains in 1.592507863 (s)
+│   h = Hamiltonian(metric=DiagEuclideanMetric([0.44910857845910196, 0.511 ...]), kinetic=AdvancedHMC.GaussianKinetic())
+│   κ = AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}(AdvancedHMC.FullMomentumRefreshment(), Trajectory{AdvancedHMC.MultinomialTS}(integrator=Leapfrog(ϵ=0.729), tc=AdvancedHMC.GeneralisedNoUTurn{Float64}(10, 1000.0)))
+│   EBFMI_est = 1.073491835278073
+└   average_acceptance_rate = 0.8645436053764034
+

We transform the samples back to the constrained space and compute the mean of both parameters:

samples_constrained = [map(softplus, p) for p in samples]
+mean_samples = mean(samples_constrained)
2-element Vector{Float64}:
+ 2.281171532295106
+ 2.2420363237188656

We plot a histogram of the samples for the two parameters. The vertical line in each graph indicates the mean of the samples.

histogram(
+    reduce(hcat, samples_constrained)';
+    xlabel="sample",
+    ylabel="counts",
+    layout=2,
+    title=["variance" "inverse length scale"],
+    legend=false,
+)
+vline!(mean_samples'; linewidth=2)
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

We approximate the log-likelihood of the test data using the posterior Gaussian processes for kernels with the sampled kernel parameters. We can observe that there is a significant improvement over the log-likelihood of the test data with respect to the posterior Gaussian process with default kernel parameters of value 1.

function gp_posterior(x, y, p)
+    kernel = softplus(p[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(p[2])))
+    f = GP(kernel)
+    return posterior(f(x, noise_var), y)
+end
+
+mean(logpdf(gp_posterior(x_train, y_train, p)(x_test, noise_var), y_test) for p in samples)
-0.9982165090824527

We sample 5 functions from each posterior GP given by the final 100 samples of kernel parameters.

plt = plot(; xlim=(0, 1), xlabel="x", ylabel="y", title="posterior (AdvancedHMC)")
+for (i, p) in enumerate(samples[(end - 100):end])
+    sampleplot!(
+        plt,
+        0:0.02:1,
+        gp_posterior(x_train, y_train, p);
+        samples=5,
+        seriescolor="red",
+        label=(i == 1 ? "samples" : nothing),
+    )
+end
+scatter!(plt, x_train, y_train; label="Train Data", markercolor=1)
+scatter!(plt, x_test, y_test; label="Test Data", markercolor=2)
+plt
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

DynamicHMC

We repeat the inference with DynamicHMC.

using DynamicHMC
+
+samples =
+    mcmc_with_warmup(
+        Random.GLOBAL_RNG, logjoint_train, n_samples; reporter=NoProgressReport()
+    ).posterior_matrix

We transform the samples back to the constrained space and compute the mean of both parameters:

samples_constrained = map(softplus, samples)
+mean_samples = vec(mean(samples_constrained; dims=2))
2-element Vector{Float64}:
+ 2.2964420066634905
+ 2.279998092861483

We plot a histogram of the samples for the two parameters. The vertical line in each graph indicates the mean of the samples.

histogram(
+    samples_constrained';
+    xlabel="sample",
+    ylabel="counts",
+    layout=2,
+    title=["variance" "inverse length scale"],
+    legend=false,
+)
+vline!(mean_samples'; linewidth=2)
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Again we can observe that there is a significant improvement over the log-likelihood of the test data with respect to the posterior Gaussian process with default kernel parameters.

mean(logpdf(gp_posterior(x_train, y_train, p)(x_test), y_test) for p in eachcol(samples))
-8.367931279409353

We sample a function from the posterior GP for the final 100 samples of kernel parameters.

plt = plot(; xlim=(0, 1), xlabel="x", ylabel="y", title="posterior (DynamicHMC)")
+scatter!(plt, x_train, y_train; label="Train Data")
+scatter!(plt, x_test, y_test; label="Test Data")
+for i in (n_samples - 100):n_samples
+    p = @view samples[:, i]
+    sampleplot!(plt, 0:0.02:1, gp_posterior(x_train, y_train, p); seriescolor="red")
+end
+plt
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Elliptical slice sampling

Instead of HMC, we use elliptical slice sampling which is provided by the Julia package EllipticalSliceSampling.jl.

using EllipticalSliceSampling

We draw 2000 samples from the posterior distribution of kernel parameters.

samples = sample(ESSModel(
+    MvNormal(Eye(2)), # Gaussian prior
+    loglik_train,
+), ESS(), n_samples; progress=false)

We transform the samples back to the constrained space and compute the mean of both parameters:

samples_constrained = [map(softplus, p) for p in samples]
+mean_samples = mean(samples_constrained)
2-element Vector{Float64}:
+ 2.323481323947446
+ 2.3243119513909822

We plot a histogram of the samples for the two parameters. The vertical line in each graph indicates the mean of the samples.

histogram(
+    reduce(hcat, samples_constrained)';
+    xlabel="sample",
+    ylabel="counts",
+    layout=2,
+    title=["variance" "inverse length scale"],
+)
+vline!(mean_samples'; layout=2, labels="mean")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Again we can observe that there is a significant improvement over the log-likelihood of the test data with respect to the posterior Gaussian process with default kernel parameters.

mean(logpdf(gp_posterior(x_train, y_train, p)(x_test), y_test) for p in samples)
-31.841050394184858

We sample a function from the posterior GP for the final 100 samples of kernel parameters.

plt = plot(;
+    xlim=(0, 1), xlabel="x", ylabel="y", title="posterior (EllipticalSliceSampling)"
+)
+scatter!(plt, x_train, y_train; label="Train Data")
+scatter!(plt, x_test, y_test; label="Test Data")
+for p in samples[(end - 100):end]
+    sampleplot!(plt, 0:0.02:1, gp_posterior(x_train, y_train, p); seriescolor="red")
+end
+plt
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Variational Inference

Sanity check for the Evidence Lower BOund (ELBO) implemented according to M. K. Titsias's Variational learning of inducing variables in sparse Gaussian processes.

elbo(VFE(f(rand(5))), fx, y_train)
-25.927861625387596

We use the LBFGS algorithm to maximize the given ELBO. It is provided by the Julia package Optim.jl.

using Optim

We define a function which returns the negative ELBO for different variance and inverse lengthscale parameters of the Matern kernel and different pseudo-points. We ensure that the kernel parameters are positive with the softplus function

\[f(x) = \log (1 + \exp x),\]

and that the pseudo-points are in the unit interval $[0,1]$ with the logistic function

\[f(x) = \frac{1}{1 + \exp{(-x)}}.\]

jitter = 1e-6  # "observing" the latent process with some (small) amount of jitter improves numerical stability
+
+function objective_function(x, y)
+    function negative_elbo(params)
+        kernel =
+            softplus(params[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(params[2])))
+        f = GP(kernel)
+        fx = f(x, noise_var)
+        z = logistic.(params[3:end])
+        approx = VFE(f(z, jitter))
+        return -elbo(approx, fx, y)
+    end
+    return negative_elbo
+end

We randomly initialize the kernel parameters and 5 pseudo points, and minimize the negative ELBO with the LBFGS algorithm and obtain the following optimal parameters:

x0 = rand(7)
+opt = optimize(objective_function(x_train, y_train), x0, LBFGS())
 * Status: success
+
+ * Candidate solution
+    Final objective value:     1.086925e+01
+
+ * Found with
+    Algorithm:     L-BFGS
+
+ * Convergence measures
+    |x - x'|               = 1.38e-08 ≰ 0.0e+00
+    |x - x'|/|x'|          = 1.65e-09 ≰ 0.0e+00
+    |f(x) - f(x')|         = 0.00e+00 ≤ 0.0e+00
+    |f(x) - f(x')|/|f(x')| = 0.00e+00 ≤ 0.0e+00
+    |g(x)|                 = 9.39e-09 ≤ 1.0e-08
+
+ * Work counters
+    Seconds run:   0  (vs limit Inf)
+    Iterations:    33
+    f(x) calls:    90
+    ∇f(x) calls:   90
+
opt.minimizer
7-element Vector{Float64}:
+  8.379380163263326
+  3.932737537022372
+  1.2763097368903071
+  1.8479571188160673
+ -1.7583267679064027
+ -4.133679971062581
+  0.6917102791672016

The optimized value of the variance is

softplus(opt.minimizer[1])
8.3796096891065

and of the inverse lengthscale is

softplus(opt.minimizer[2])
3.952138094098159

We compute the log-likelihood of the test data for the resulting approximate posterior. We can observe that there is a significant improvement over the log-likelihood with the default kernel parameters of value 1.

opt_kernel =
+    softplus(opt.minimizer[1]) *
+    (Matern52Kernel() ∘ ScaleTransform(softplus(opt.minimizer[2])))
+opt_f = GP(opt_kernel)
+opt_fx = opt_f(x_train, noise_var)
+ap = posterior(VFE(opt_f(logistic.(opt.minimizer[3:end]), jitter)), opt_fx, y_train)
+logpdf(ap(x_test, noise_var), y_test)
-2.068599765069256

We visualize the approximate posterior with optimized parameters.

scatter(
+    x_train,
+    y_train;
+    xlim=(0, 1),
+    xlabel="x",
+    ylabel="y",
+    title="posterior (VI with sparse grid)",
+    label="Train Data",
+)
+scatter!(x_test, y_test; label="Test Data")
+plot!(0:0.001:1, ap; label=false, ribbon_scale=2)
+vline!(logistic.(opt.minimizer[3:end]); label="Pseudo-points")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Exact Gaussian Process Inference

Here we use Type-II MLE to train the hyperparameters of the Gaussian process. This means that our loss function is the negative log marginal likelihood.

We re-calculate the log-likelihood of the test dataset with the default kernel parameters of value 1 for the sake of comparison.

logpdf(p_fx(x_test), y_test)
-232.51565975779937

We define a function which returns the negative log marginal likelihood for different variance and inverse lengthscale parameters of the Matern kernel and different pseudo-points. We ensure that the kernel parameters are positive with the softplus function $f(x) = \log (1 + \exp x)$.

function loss_function(x, y)
+    function negativelogmarginallikelihood(params)
+        kernel =
+            softplus(params[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(params[2])))
+        f = GP(kernel)
+        fx = f(x, noise_var)
+        return -logpdf(fx, y)
+    end
+    return negativelogmarginallikelihood
+end
+

We randomly initialize the kernel parameters, and minimize the negative log marginal likelihood with the LBFGS algorithm and obtain the following optimal parameters:

θ0 = randn(2)
+opt = Optim.optimize(loss_function(x_train, y_train), θ0, LBFGS())
 * Status: success (objective increased between iterations)
+
+ * Candidate solution
+    Final objective value:     1.085252e+01
+
+ * Found with
+    Algorithm:     L-BFGS
+
+ * Convergence measures
+    |x - x'|               = 5.44e-08 ≰ 0.0e+00
+    |x - x'|/|x'|          = 6.48e-09 ≰ 0.0e+00
+    |f(x) - f(x')|         = 7.64e-14 ≰ 0.0e+00
+    |f(x) - f(x')|/|f(x')| = 7.04e-15 ≰ 0.0e+00
+    |g(x)|                 = 1.11e-10 ≤ 1.0e-08
+
+ * Work counters
+    Seconds run:   0  (vs limit Inf)
+    Iterations:    15
+    f(x) calls:    51
+    ∇f(x) calls:   51
+
opt.minimizer
2-element Vector{Float64}:
+ 8.385520525672359
+ 3.968794221481056

The optimized value of the variance is

softplus(opt.minimizer[1])
8.385748646622107

and of the inverse lengthscale is

softplus(opt.minimizer[2])
3.987514109926022

We compute the log-likelihood of the test data for the resulting optimized posterior. We can observe that there is a significant improvement over the log-likelihood with the default kernel parameters of value 1.

opt_kernel =
+    softplus(opt.minimizer[1]) *
+    (Matern52Kernel() ∘ ScaleTransform(softplus(opt.minimizer[2])))
+
+opt_f = GP(opt_kernel)
+opt_fx = opt_f(x_train, noise_var)
+opt_p_fx = posterior(opt_fx, y_train)
+logpdf(opt_p_fx(x_test, noise_var), y_test)
-2.092647856958113

We visualize the posterior with optimized parameters.

scatter(
+    x_train,
+    y_train;
+    xlim=(0, 1),
+    xlabel="x",
+    ylabel="y",
+    title="posterior (optimized parameters)",
+    label="Train Data",
+)
+scatter!(x_test, y_test; label="Test Data")
+plot!(0:0.001:1, opt_p_fx; label=false, ribbon_scale=2)
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
Package and system information
+
+Package information (click to expand) +
+Status `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/0-intro-1d/Project.toml`
+  [99985d1d] AbstractGPs v0.5.20 `/home/runner/work/AbstractGPs.jl/AbstractGPs.jl#07d5f00`
+  [0bf59076] AdvancedHMC v0.6.1
+  [31c24e10] Distributions v0.25.107
+  [bbc10e6e] DynamicHMC v3.4.7
+⌅ [cad2338a] EllipticalSliceSampling v1.1.0
+  [1a297f60] FillArrays v1.9.3
+  [f6369f11] ForwardDiff v0.10.36
+  [98b081ad] Literate v2.16.1
+  [6fdf6af0] LogDensityProblems v2.1.1
+  [996a588d] LogDensityProblemsAD v1.7.0
+  [429524aa] Optim v1.9.2
+  [91a5bcdd] Plots v1.40.2
+  [4c63d2b9] StatsFuns v1.3.1
+  [9a3f8284] Random
+Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated`
+
+To reproduce this notebook's package environment, you can + +download the full Manifest.toml. +
+
+System information (click to expand) +
+Julia Version 1.10.2
+Commit bd47eca2c8a (2024-03-01 10:14 UTC)
+Build Info:
+  Official https://julialang.org/ release
+Platform Info:
+  OS: Linux (x86_64-linux-gnu)
+  CPU: 4 × AMD EPYC 7763 64-Core Processor
+  WORD_SIZE: 64
+  LIBM: libopenlibm
+  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
+Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)
+Environment:
+  JULIA_DEBUG = Documenter
+  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src
+
+

This page was generated using Literate.jl.

diff --git a/previews/PR387/examples/0-intro-1d/notebook.ipynb b/previews/PR387/examples/0-intro-1d/notebook.ipynb new file mode 100644 index 00000000..aff12a7a --- /dev/null +++ b/previews/PR387/examples/0-intro-1d/notebook.ipynb @@ -0,0 +1,5163 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Intro to AbstractGPs: one-dimensional regression\n", + "\n", + "*You are seeing the\n", + "notebook output generated by\n", + "[Literate.jl](https://github.com/fredrikekre/Literate.jl) from the\n", + "[Julia source file](https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/master/examples/0-intro-1d/script.jl).\n", + "The rendered HTML can be viewed [in the docs](https://juliagaussianprocesses.github.io/AbstractGPs.jl/dev/examples/0-intro-1d/).*" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "Loading the necessary packages." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Random.TaskLocalRNG()" + }, + "metadata": {}, + "execution_count": 1 + } + ], + "cell_type": "code", + "source": [ + "using AbstractGPs\n", + "using Distributions\n", + "using FillArrays\n", + "using StatsFuns\n", + "\n", + "using Plots\n", + "default(; legend=:outertopright, size=(700, 400))\n", + "\n", + "using Random\n", + "Random.seed!(42) # setting the seed for reproducibility of this notebook" + ], + "metadata": {}, + "execution_count": 1 + }, + { + "cell_type": "markdown", + "source": [ + "Load toy regression\n", + "[dataset](https://github.com/GPflow/GPflow/blob/7705cee6723f78066981f27954130daaede55dfc/doc/sphinx/notebooks/basics/data/regression_1D.csv)\n", + "taken from GPflow examples." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=1}", + "image/png": 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"code", + "source": [ + "x = [\n", + " 0.8658165855998895,\n", + " 0.6661700880180962,\n", + " 0.8049218148148531,\n", + " 0.7714303440386239,\n", + " 0.14790478354654835,\n", + " 0.8666105548197428,\n", + " 0.007044577166530286,\n", + " 0.026331737288148638,\n", + " 0.17188596617099916,\n", + " 0.8897812990554013,\n", + " 0.24323574561119998,\n", + " 0.028590102134105955,\n", + "]\n", + "y = [\n", + " 1.5255314337144372,\n", + " 3.6434202968230003,\n", + " 3.010885733911661,\n", + " 3.774442382979625,\n", + " 3.3687639483798324,\n", + " 1.5506452040608503,\n", + " 3.790447985799683,\n", + " 3.8689707574953,\n", + " 3.4933565751758713,\n", + " 1.4284538820635841,\n", + " 3.8715350915692364,\n", + " 3.7045949061144983,\n", + "]\n", + "scatter(x, y; xlabel=\"x\", ylabel=\"y\", legend=false)" + ], + "metadata": {}, + "execution_count": 2 + }, + { + "cell_type": "markdown", + "source": [ + "We split the observations into train and test data." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "4-element Vector{Float64}:\n 3.4933565751758713\n 1.4284538820635841\n 3.8715350915692364\n 3.7045949061144983" + }, + "metadata": {}, + "execution_count": 3 + } + ], + "cell_type": "code", + "source": [ + "x_train = x[1:8]\n", + "y_train = y[1:8]\n", + "x_test = x[9:end]\n", + "y_test = y[9:end]" + ], + "metadata": {}, + "execution_count": 3 + }, + { + "cell_type": "markdown", + "source": [ + "We instantiate a Gaussian process with a Matern kernel. The kernel has\n", + "fixed variance and length scale parameters of default value 1." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.Matern52Kernel{Distances.Euclidean}}(AbstractGPs.ZeroMean{Float64}(), Matern 5/2 Kernel (metric = Distances.Euclidean(0.0)))" + }, + "metadata": {}, + "execution_count": 4 + } + ], + "cell_type": "code", + "source": [ + "f = GP(Matern52Kernel())" + ], + "metadata": {}, + "execution_count": 4 + }, + { + "cell_type": "markdown", + "source": [ + "We create a finite dimensional projection at the inputs of the training dataset\n", + "observed under Gaussian noise with variance $noise_var = 0.1$, and compute the\n", + "log-likelihood of the outputs of the training dataset." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "-25.53057444906228" + }, + "metadata": {}, + "execution_count": 5 + } + ], + "cell_type": "code", + "source": [ + "noise_var = 0.1\n", + "fx = f(x_train, noise_var)\n", + "logpdf(fx, y_train)" + ], + "metadata": {}, + "execution_count": 5 + }, + { + "cell_type": "markdown", + "source": [ + "We compute the posterior Gaussian process given the training data, and calculate the\n", + "log-likelihood of the test dataset." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "-2.878533694097381" + }, + "metadata": {}, + "execution_count": 6 + } + ], + "cell_type": "code", + "source": [ + "p_fx = posterior(fx, y_train)\n", + "logpdf(p_fx(x_test, noise_var), y_test)" + ], + "metadata": {}, + "execution_count": 6 + }, + { + "cell_type": "markdown", + "source": [ + "We plot the posterior Gaussian process (its mean and a ribbon of 2 standard deviations\n", + "around it) on a grid along with the observations." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=3}", + "image/png": 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "cell_type": "code", + "source": [ + "scatter(\n", + " x_train,\n", + " y_train;\n", + " xlim=(0, 1),\n", + " xlabel=\"x\",\n", + " ylabel=\"y\",\n", + " title=\"posterior (default parameters)\",\n", + " label=\"Train Data\",\n", + ")\n", + "scatter!(x_test, y_test; label=\"Test Data\")\n", + "plot!(0:0.001:1, p_fx; label=false, ribbon_scale=2)" + ], + "metadata": {}, + "execution_count": 7 + }, + { + "cell_type": "markdown", + "source": [ + "## Markov Chain Monte Carlo\n", + "\n", + "Previously we computed the log likelihood of the untuned kernel parameters of the GP.\n", + "We now also perform approximate inference over said kernel parameters using different\n", + "Markov chain Monte Carlo (MCMC) methods. I.e., we approximate the posterior distribution\n", + "of the kernel parameters with samples from a Markov chain.\n", + "\n", + "We define a function which returns the log-likelihood of the data for different variance\n", + "and inverse lengthscale parameters of the Matern kernel. We ensure that these parameters are\n", + "positive with the softplus function\n", + "$$\n", + "f(x) = \\log (1 + \\exp x).\n", + "$$" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "(::Main.var\"##240\".var\"#loglikelihood#1\"{Vector{Float64}, Vector{Float64}}) (generic function with 1 method)" + }, + "metadata": {}, + "execution_count": 8 + } + ], + "cell_type": "code", + "source": [ + "function gp_loglikelihood(x, y)\n", + " function loglikelihood(params)\n", + " kernel =\n", + " softplus(params[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(params[2])))\n", + " f = GP(kernel)\n", + " fx = f(x, noise_var)\n", + " return logpdf(fx, y)\n", + " end\n", + " return loglikelihood\n", + "end\n", + "\n", + "const loglik_train = gp_loglikelihood(x_train, y_train)" + ], + "metadata": {}, + "execution_count": 8 + }, + { + "cell_type": "markdown", + "source": [ + "We define a Gaussian prior for the joint distribution of the two transformed kernel\n", + "parameters. We assume that both parameters are independent with mean 0 and variance 1." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "logprior (generic function with 1 method)" + }, + "metadata": {}, + "execution_count": 9 + } + ], + "cell_type": "code", + "source": [ + "logprior(params) = logpdf(MvNormal(Eye(2)), params)" + ], + "metadata": {}, + "execution_count": 9 + }, + { + "cell_type": "markdown", + "source": [ + "### Hamiltonian Monte Carlo\n", + "\n", + "We start with a Hamiltonian Monte Carlo (HMC) sampler. More precisely, we use the\n", + "[No-U-Turn sampler (NUTS)](http://www.jmlr.org/papers/volume15/hoffman14a/hoffman14a.pdf),\n", + "which is provided by the Julia packages\n", + "[AdvancedHMC.jl](https://github.com/TuringLang/AdvancedHMC.jl/) and\n", + "[DynamicHMC.jl](https://github.com/tpapp/DynamicHMC.jl/).\n", + "\n", + "#### AdvancedHMC\n", + "\n", + "We start with performing inference with AdvancedHMC." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using AdvancedHMC\n", + "using ForwardDiff" + ], + "metadata": {}, + "execution_count": 10 + }, + { + "cell_type": "markdown", + "source": [ + "Set the number of samples to draw and warmup iterations." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "1000" + }, + "metadata": {}, + "execution_count": 11 + } + ], + "cell_type": "code", + "source": [ + "n_samples = 2_000\n", + "n_adapts = 1_000" + ], + "metadata": {}, + "execution_count": 11 + }, + { + "cell_type": "markdown", + "source": [ + "AdvancedHMC and DynamicHMC below require us to implement the LogDensityProblems interface for the log joint probability." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using LogDensityProblems\n", + "\n", + "struct LogJointTrain end\n", + "\n", + "# Log joint density\n", + "LogDensityProblems.logdensity(::LogJointTrain, θ) = loglik_train(θ) + logprior(θ)\n", + "\n", + "# The parameter space is two-dimensional\n", + "LogDensityProblems.dimension(::LogJointTrain) = 2\n", + "\n", + "# `LogJointTrain` does not allow to evaluate derivatives of the log density function\n", + "function LogDensityProblems.capabilities(::Type{LogJointTrain})\n", + " return LogDensityProblems.LogDensityOrder{0}()\n", + "end" + ], + "metadata": {}, + "execution_count": 12 + }, + { + "cell_type": "markdown", + "source": [ + "We use [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) to compute the derivatives of the log joint density with automatic differentiation." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "ForwardDiff AD wrapper for Main.var\"##240\".LogJointTrain(), w/ chunk size 2" + }, + "metadata": {}, + "execution_count": 13 + } + ], + "cell_type": "code", + "source": [ + "using LogDensityProblemsAD\n", + "\n", + "const logjoint_train = ADgradient(Val(:ForwardDiff), LogJointTrain())" + ], + "metadata": {}, + "execution_count": 13 + }, + { + "cell_type": "markdown", + "source": [ + "We define an Hamiltonian system of the log joint probability." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Hamiltonian(metric=DiagEuclideanMetric([1.0, 1.0]), kinetic=AdvancedHMC.GaussianKinetic())" + }, + "metadata": {}, + "execution_count": 14 + } + ], + "cell_type": "code", + "source": [ + "metric = DiagEuclideanMetric(2)\n", + "hamiltonian = Hamiltonian(metric, logjoint_train)" + ], + "metadata": {}, + "execution_count": 14 + }, + { + "cell_type": "markdown", + "source": [ + "Define a leapfrog solver, with initial step size chosen heuristically." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Leapfrog(ϵ=3.2)" + }, + "metadata": {}, + "execution_count": 15 + } + ], + "cell_type": "code", + "source": [ + "initial_params = rand(2)\n", + "initial_ϵ = find_good_stepsize(hamiltonian, initial_params)\n", + "integrator = Leapfrog(initial_ϵ)" + ], + "metadata": {}, + "execution_count": 15 + }, + { + "cell_type": "markdown", + "source": [ + "Define an HMC sampler, with the following components:\n", + "- multinomial sampling scheme,\n", + "- generalised No-U-Turn criteria, and\n", + "- windowed adaption for step-size and diagonal mass matrix" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "StanHMCAdaptor(\n pc=WelfordVar,\n ssa=NesterovDualAveraging(γ=0.05, t_0=10.0, κ=0.75, δ=0.8, state.ϵ=3.2),\n init_buffer=75, term_buffer=50, window_size=25,\n state=window(0, 0), window_splits()\n)" + }, + "metadata": {}, + "execution_count": 16 + } + ], + "cell_type": "code", + "source": [ + "proposal = HMCKernel(Trajectory{MultinomialTS}(integrator, GeneralisedNoUTurn()))\n", + "adaptor = StanHMCAdaptor(MassMatrixAdaptor(metric), StepSizeAdaptor(0.8, integrator))" + ], + "metadata": {}, + "execution_count": 16 + }, + { + "cell_type": "markdown", + "source": [ + "We draw samples from the posterior distribution of kernel parameters. These samples\n", + "are in the unconstrained space $\\mathbb{R}^2$." + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "┌ Info: Finished 1000 adapation steps\n", + "│ adaptor =\n", + "│ StanHMCAdaptor(\n", + "│ pc=WelfordVar,\n", + "│ ssa=NesterovDualAveraging(γ=0.05, t_0=10.0, κ=0.75, δ=0.8, state.ϵ=0.729267980509611),\n", + "│ init_buffer=75, term_buffer=50, window_size=25,\n", + "│ state=window(76, 950), window_splits(100, 150, 250, 450, 950)\n", + "│ )\n", + "│ κ.τ.integrator = Leapfrog(ϵ=0.729)\n", + "└ h.metric = DiagEuclideanMetric([0.44910857845910196, 0.511 ...])\n", + "┌ Info: Finished 2000 sampling steps for 1 chains in 0.310669411 (s)\n", + "│ h = Hamiltonian(metric=DiagEuclideanMetric([0.44910857845910196, 0.511 ...]), kinetic=AdvancedHMC.GaussianKinetic())\n", + "│ κ = AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}(AdvancedHMC.FullMomentumRefreshment(), Trajectory{AdvancedHMC.MultinomialTS}(integrator=Leapfrog(ϵ=0.729), tc=AdvancedHMC.GeneralisedNoUTurn{Float64}(10, 1000.0)))\n", + "│ EBFMI_est = 1.073491835278073\n", + "└ average_acceptance_rate = 0.8645436053764034\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": "([[0.6293451231426089, 0.4503389405961936], [0.6293451231426089, 0.4503389405961936], [1.26444921901017, 0.42394743345987346], [1.3765698887739206, 2.224992727432125], [2.265634865470127, 2.4125160303201536], [2.265634865470127, 2.4125160303201536], [2.1563132453718783, 2.2373255514266823], [2.0552356411412536, 2.20906072308031], [1.7316243693084552, 2.3100897977252988], [2.874902122829152, 1.992661807463025] … [1.7361006954337663, 1.0269066390319421], [1.3159677135595074, -0.7652074278731631], [0.6694620362737291, -0.3977732720246032], [0.6492232757070063, 1.3771606959028748], [2.382776854934076, 1.0806984316735297], [2.601107411791877, 1.3125565611010142], [1.7169967754833444, 3.1758820894032915], [2.7244267806224562, 1.2803250050026527], [2.2311210806990838, 2.564183002892208], [1.8625449806798902, 1.5666823683641407]], NamedTuple[(n_steps = 1, is_accept = true, acceptance_rate = 0.0, log_density = -27.548797865093082, hamiltonian_energy = 28.16498485690657, hamiltonian_energy_error = 0.0, max_hamiltonian_energy_error = 789.8321116797612, tree_depth = 1, numerical_error = false, step_size = 3.2, nom_step_size = 3.2, is_adapt = true), (n_steps = 1, is_accept = true, acceptance_rate = 0.0, log_density = -27.548797865093082, hamiltonian_energy = 28.556455551293244, hamiltonian_energy_error = 0.0, max_hamiltonian_energy_error = 103250.09338231794, tree_depth = 0, numerical_error = true, step_size = 7.472207330909228, nom_step_size = 7.472207330909228, is_adapt = true), (n_steps = 1, is_accept = true, acceptance_rate = 1.0, log_density = -26.12578440335334, hamiltonian_energy = 27.652259693524268, hamiltonian_energy_error = -0.4422837368164849, max_hamiltonian_energy_error = -0.4422837368164849, tree_depth = 1, numerical_error = false, step_size = 0.7367547106735891, nom_step_size = 0.7367547106735891, is_adapt = true), (n_steps = 3, is_accept = true, acceptance_rate = 0.9601998762004014, log_density = -23.358113972067258, hamiltonian_energy = 26.206629301670095, hamiltonian_energy_error = -0.6730723877715086, max_hamiltonian_energy_error = -0.6730723877715086, tree_depth = 2, numerical_error = false, step_size = 0.7673324977229767, nom_step_size = 0.7673324977229767, is_adapt = true), (n_steps = 11, is_accept = true, acceptance_rate = 0.9934452178653669, log_density = -22.719136026598207, hamiltonian_energy = 23.096685770801326, hamiltonian_energy_error = -0.47078885514320135, max_hamiltonian_energy_error = -0.479895391611457, tree_depth = 3, numerical_error = false, step_size = 0.9263064447759997, nom_step_size = 0.9263064447759997, is_adapt = true), (n_steps = 1, is_accept = true, acceptance_rate = 0.0005398633622842218, log_density = -22.719136026598207, hamiltonian_energy = 24.192019244159088, hamiltonian_energy_error = 0.0, max_hamiltonian_energy_error = 7.5241944832311525, tree_depth = 1, numerical_error = false, step_size = 1.4135557028483503, nom_step_size = 1.4135557028483503, is_adapt = true), (n_steps = 15, is_accept = true, acceptance_rate = 0.9992763568778837, log_density = -22.612318932641216, hamiltonian_energy = 23.07900225218923, hamiltonian_energy_error = -0.0007259383728559499, max_hamiltonian_energy_error = 0.0023060288193832434, tree_depth = 3, numerical_error = false, step_size = 0.11237707015439995, nom_step_size = 0.11237707015439995, is_adapt = true), (n_steps = 15, is_accept = true, acceptance_rate = 0.9988327973792217, log_density = -22.607670753976635, hamiltonian_energy = 22.756177373414697, hamiltonian_energy_error = -0.0001245974981713971, max_hamiltonian_energy_error = 0.0024837090868992107, tree_depth = 3, numerical_error = false, step_size = 0.19026062539893943, nom_step_size = 0.19026062539893943, is_adapt = true), (n_steps = 7, is_accept = true, acceptance_rate = 0.9929137605283122, log_density = -22.836416387089745, hamiltonian_energy = 22.86403545629423, hamiltonian_energy_error = 0.016398202480797153, max_hamiltonian_energy_error = 0.0170195386553047, tree_depth = 3, numerical_error = false, step_size = 0.3382517739730839, nom_step_size = 0.3382517739730839, is_adapt = true), (n_steps = 3, is_accept = true, acceptance_rate = 0.8903451367195928, log_density = -23.203288626045914, hamiltonian_energy = 23.616291436834633, hamiltonian_energy_error = 0.11108253115788358, max_hamiltonian_energy_error = 0.1776776240993705, tree_depth = 2, numerical_error = false, step_size = 0.6084956268395714, nom_step_size = 0.6084956268395714, is_adapt = true) … (n_steps = 7, is_accept = true, acceptance_rate = 0.9756246416162744, log_density = -23.96708800074765, hamiltonian_energy = 24.276684702434274, hamiltonian_energy_error = 0.07099105073226042, max_hamiltonian_energy_error = -0.1043445563584271, tree_depth = 3, numerical_error = false, step_size = 0.729267980509611, nom_step_size = 0.729267980509611, is_adapt = false), (n_steps = 3, is_accept = true, acceptance_rate = 0.5438390879750715, log_density = -30.22602342114209, hamiltonian_energy = 32.967988214770266, hamiltonian_energy_error = 0.24837064694474265, max_hamiltonian_energy_error = 1.221079494048567, tree_depth = 2, numerical_error = false, step_size = 0.729267980509611, nom_step_size = 0.729267980509611, is_adapt = false), (n_steps = 3, is_accept = true, acceptance_rate = 0.9098179463366484, log_density = -30.012975585612715, hamiltonian_energy = 31.609256357514656, hamiltonian_energy_error = 0.0062271504303623715, max_hamiltonian_energy_error = 0.30698499296834925, tree_depth = 2, numerical_error = false, step_size = 0.729267980509611, nom_step_size = 0.729267980509611, is_adapt = false), (n_steps = 3, is_accept = true, acceptance_rate = 0.8772415033116264, log_density = -25.935029604196778, hamiltonian_energy = 32.93443002060721, hamiltonian_energy_error = -0.3072881074536298, max_hamiltonian_energy_error = -0.8110587603904662, tree_depth = 2, numerical_error = false, step_size = 0.729267980509611, nom_step_size = 0.729267980509611, is_adapt = false), (n_steps = 15, is_accept = true, acceptance_rate = 1.0, log_density = -23.89086875475129, hamiltonian_energy = 26.350237839708093, hamiltonian_energy_error = -0.22244499244872884, max_hamiltonian_energy_error = -0.4323251599408806, tree_depth = 3, numerical_error = false, step_size = 0.729267980509611, nom_step_size = 0.729267980509611, is_adapt = false), (n_steps = 23, is_accept = true, acceptance_rate = 0.9972709490459822, log_density = -23.56304891524305, hamiltonian_energy = 24.653384054382652, hamiltonian_energy_error = -0.05255630024674218, max_hamiltonian_energy_error = -0.1817631161420259, tree_depth = 4, numerical_error = false, step_size = 0.729267980509611, nom_step_size = 0.729267980509611, is_adapt = false), (n_steps = 7, is_accept = true, acceptance_rate = 0.9406766802613317, log_density = -23.92479483193981, hamiltonian_energy = 24.624990728339338, hamiltonian_energy_error = 0.0866296832499458, max_hamiltonian_energy_error = 0.16829894717819727, tree_depth = 3, numerical_error = false, step_size = 0.729267980509611, nom_step_size = 0.729267980509611, is_adapt = false), (n_steps = 7, is_accept = true, acceptance_rate = 0.9669421277817528, log_density = -23.77363858278736, hamiltonian_energy = 24.98936929494635, hamiltonian_energy_error = -0.02083986314008257, max_hamiltonian_energy_error = 0.1264170359331409, tree_depth = 3, numerical_error = false, step_size = 0.729267980509611, nom_step_size = 0.729267980509611, is_adapt = false), (n_steps = 3, is_accept = true, acceptance_rate = 0.9900283350175633, log_density = -22.827042366173895, hamiltonian_energy = 24.035561631487862, hamiltonian_energy_error = -0.13362576804009052, max_hamiltonian_energy_error = -0.13362576804009052, tree_depth = 2, numerical_error = false, step_size = 0.729267980509611, nom_step_size = 0.729267980509611, is_adapt = false), (n_steps = 3, is_accept = true, acceptance_rate = 0.8849418974151785, log_density = -22.934286580238467, hamiltonian_energy = 24.436122553841958, hamiltonian_energy_error = -0.03736086127784688, max_hamiltonian_energy_error = 0.19459521119631162, tree_depth = 1, numerical_error = false, step_size = 0.729267980509611, nom_step_size = 0.729267980509611, is_adapt = false)])" + }, + "metadata": {}, + "execution_count": 17 + } + ], + "cell_type": "code", + "source": [ + "samples, _ = sample(\n", + " hamiltonian, proposal, initial_params, n_samples, adaptor, n_adapts; progress=false\n", + ")" + ], + "metadata": {}, + "execution_count": 17 + }, + { + "cell_type": "markdown", + "source": [ + "We transform the samples back to the constrained space and compute the mean of both\n", + "parameters:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "2-element Vector{Float64}:\n 2.281171532295106\n 2.2420363237188656" + }, + "metadata": {}, + "execution_count": 18 + } + ], + "cell_type": "code", + "source": [ + "samples_constrained = [map(softplus, p) for p in samples]\n", + "mean_samples = mean(samples_constrained)" + ], + "metadata": {}, + "execution_count": 18 + }, + { + "cell_type": "markdown", + "source": [ + "We plot a histogram of the samples for the two parameters.\n", + "The vertical line in each graph indicates the mean of the samples." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=4}", + "image/png": 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We can observe that there is a significant\n", + "improvement over the log-likelihood of the test data with respect to the posterior\n", + "Gaussian process with default kernel parameters of value 1." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "-0.9982165090824527" + }, + "metadata": {}, + "execution_count": 20 + } + ], + "cell_type": "code", + "source": [ + "function gp_posterior(x, y, p)\n", + " kernel = softplus(p[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(p[2])))\n", + " f = GP(kernel)\n", + " return posterior(f(x, noise_var), y)\n", + "end\n", + "\n", + "mean(logpdf(gp_posterior(x_train, y_train, p)(x_test, noise_var), y_test) for p in samples)" + ], + "metadata": {}, + "execution_count": 20 + }, + { + "cell_type": "markdown", + "source": [ + "We sample 5 functions from each posterior GP given by the final 100 samples of kernel\n", + "parameters." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=103}", + "image/png": 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+ "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ], + "cell_type": "code", + "source": [ + "plt = plot(; xlim=(0, 1), xlabel=\"x\", ylabel=\"y\", title=\"posterior (AdvancedHMC)\")\n", + "for (i, p) in enumerate(samples[(end - 100):end])\n", + " sampleplot!(\n", + " plt,\n", + " 0:0.02:1,\n", + " gp_posterior(x_train, y_train, p);\n", + " samples=5,\n", + " seriescolor=\"red\",\n", + " label=(i == 1 ? \"samples\" : nothing),\n", + " )\n", + "end\n", + "scatter!(plt, x_train, y_train; label=\"Train Data\", markercolor=1)\n", + "scatter!(plt, x_test, y_test; label=\"Test Data\", markercolor=2)\n", + "plt" + ], + "metadata": {}, + "execution_count": 21 + }, + { + "cell_type": "markdown", + "source": [ + "#### DynamicHMC\n", + "\n", + "We repeat the inference with DynamicHMC." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "2×2000 Matrix{Float64}:\n 2.02056 2.10188 2.10188 1.80528 … 2.22443 2.67033 2.58862 2.6571\n 3.41688 2.19242 2.19242 1.56786 0.96448 3.08627 1.59562 1.21985" + }, + "metadata": {}, + "execution_count": 22 + } + ], + "cell_type": "code", + "source": [ + "using DynamicHMC\n", + "\n", + "samples =\n", + " mcmc_with_warmup(\n", + " Random.GLOBAL_RNG, logjoint_train, n_samples; reporter=NoProgressReport()\n", + " ).posterior_matrix" + ], + "metadata": {}, + "execution_count": 22 + }, + { + "cell_type": "markdown", + "source": [ + "We transform the samples back to the constrained space and compute the mean of both\n", + "parameters:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "2-element Vector{Float64}:\n 2.2964420066634905\n 2.279998092861483" + }, + "metadata": {}, + "execution_count": 23 + } + ], + "cell_type": "code", + "source": [ + "samples_constrained = map(softplus, samples)\n", + "mean_samples = vec(mean(samples_constrained; dims=2))" + ], + "metadata": {}, + "execution_count": 23 + }, + { + "cell_type": "markdown", + "source": [ + "We plot a histogram of the samples for the two parameters.\n", + "The vertical line in each graph indicates the mean of the samples." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=4}", + "image/png": 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kernel\n", + "parameters." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "-8.367931279409353" + }, + "metadata": {}, + "execution_count": 25 + } + ], + "cell_type": "code", + "source": [ + "mean(logpdf(gp_posterior(x_train, y_train, p)(x_test), y_test) for p in eachcol(samples))" + ], + "metadata": {}, + "execution_count": 25 + }, + { + "cell_type": "markdown", + "source": [ + "We sample a function from the posterior GP for the final 100 samples of kernel\n", + "parameters." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=103}", + "image/png": 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label=\"Test Data\")\n", + "for i in (n_samples - 100):n_samples\n", + " p = @view samples[:, i]\n", + " sampleplot!(plt, 0:0.02:1, gp_posterior(x_train, y_train, p); seriescolor=\"red\")\n", + "end\n", + "plt" + ], + "metadata": {}, + "execution_count": 26 + }, + { + "cell_type": "markdown", + "source": [ + "### Elliptical slice sampling\n", + "\n", + "Instead of HMC, we use\n", + "[elliptical slice sampling](http://proceedings.mlr.press/v9/murray10a/murray10a.pdf)\n", + "which is provided by the Julia package\n", + "[EllipticalSliceSampling.jl](https://github.com/TuringLang/EllipticalSliceSampling.jl/)." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using EllipticalSliceSampling" + ], + "metadata": {}, + "execution_count": 27 + }, + { + "cell_type": "markdown", + "source": [ + "We draw 2000 samples from the posterior distribution of kernel parameters." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "2000-element Vector{Vector{Float64}}:\n [1.5670775306118085, -1.1898078050983412]\n [0.830516503968974, -0.10972053545433325]\n [1.3914720784374213, -0.19794362061420429]\n [1.422092963034582, 0.6281630832642413]\n [1.3574819823872004, 0.4940701010479628]\n [1.1666504240958266, 0.6773678932395444]\n [1.1560506733083034, 0.6896113035173512]\n [1.3032650862646378, 0.6191734581028139]\n [1.5067775628002513, 0.42722346976756176]\n [1.7299105037770988, 0.415975059704452]\n ⋮\n [2.0627507394990183, 2.0444139149233336]\n [2.727919394310928, 1.5281278990089076]\n [2.6590785381515647, 1.5209513820714424]\n [2.6613136166441898, 1.522434907135991]\n [2.6897143422268384, 1.6246875146204829]\n [2.1938473103834824, 2.3514883065741765]\n [2.449330658309207, 2.3300012041578597]\n [2.2835408926715832, 2.634535434182729]\n [2.2446316386256817, 2.7274640931689844]" + }, + "metadata": {}, + "execution_count": 28 + } + ], + "cell_type": "code", + "source": [ + "samples = sample(ESSModel(\n", + " MvNormal(Eye(2)), # Gaussian prior\n", + " loglik_train,\n", + "), ESS(), n_samples; progress=false)" + ], + "metadata": {}, + "execution_count": 28 + }, + { + "cell_type": "markdown", + "source": [ + "We transform the samples back to the constrained space and compute the mean of both\n", + "parameters:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "2-element Vector{Float64}:\n 2.323481323947446\n 2.3243119513909822" + }, + "metadata": {}, + "execution_count": 29 + } + ], + "cell_type": "code", + "source": [ + "samples_constrained = [map(softplus, p) for p in samples]\n", + "mean_samples = mean(samples_constrained)" + ], + "metadata": {}, + "execution_count": 29 + }, + { + "cell_type": "markdown", + "source": [ + "We plot a histogram of the samples for the two parameters.\n", + "The vertical line in each graph indicates the mean of the samples." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=4}", + "image/png": 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improvement over the log-likelihood\n", + "of the test data with respect to the posterior Gaussian process with default kernel\n", + "parameters." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "-31.841050394184858" + }, + "metadata": {}, + "execution_count": 31 + } + ], + "cell_type": "code", + "source": [ + "mean(logpdf(gp_posterior(x_train, y_train, p)(x_test), y_test) for p in samples)" + ], + "metadata": {}, + "execution_count": 31 + }, + { + "cell_type": "markdown", + "source": [ + "We sample a function from the posterior GP for the final 100 samples of kernel\n", + "parameters." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=103}", + "image/png": 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sampleplot!(plt, 0:0.02:1, gp_posterior(x_train, y_train, p); seriescolor=\"red\")\n", + "end\n", + "plt" + ], + "metadata": {}, + "execution_count": 32 + }, + { + "cell_type": "markdown", + "source": [ + "## Variational Inference\n", + "\n", + "Sanity check for the Evidence Lower BOund (ELBO) implemented according to\n", + "M. K. Titsias's _Variational learning of inducing variables in sparse Gaussian processes_." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "-25.927861625387596" + }, + "metadata": {}, + "execution_count": 33 + } + ], + "cell_type": "code", + "source": [ + "elbo(VFE(f(rand(5))), fx, y_train)" + ], + "metadata": {}, + "execution_count": 33 + }, + { + "cell_type": "markdown", + "source": [ + "We use the LBFGS algorithm to maximize the given ELBO. It is provided by the Julia\n", + "package [Optim.jl](https://github.com/JuliaNLSolvers/Optim.jl)." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using Optim" + ], + "metadata": {}, + "execution_count": 34 + }, + { + "cell_type": "markdown", + "source": [ + "We define a function which returns the negative ELBO for different variance and inverse\n", + "lengthscale parameters of the Matern kernel and different pseudo-points. We ensure that\n", + "the kernel parameters are positive with the softplus function\n", + "$$\n", + "f(x) = \\log (1 + \\exp x),\n", + "$$\n", + "and that the pseudo-points are in the unit interval $[0,1]$ with the logistic function\n", + "$$\n", + "f(x) = \\frac{1}{1 + \\exp{(-x)}}.\n", + "$$" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "objective_function (generic function with 1 method)" + }, + "metadata": {}, + "execution_count": 35 + } + ], + "cell_type": "code", + "source": [ + "jitter = 1e-6 # \"observing\" the latent process with some (small) amount of jitter improves numerical stability\n", + "\n", + "function objective_function(x, y)\n", + " function negative_elbo(params)\n", + " kernel =\n", + " softplus(params[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(params[2])))\n", + " f = GP(kernel)\n", + " fx = f(x, noise_var)\n", + " z = logistic.(params[3:end])\n", + " approx = VFE(f(z, jitter))\n", + " return -elbo(approx, fx, y)\n", + " end\n", + " return negative_elbo\n", + "end" + ], + "metadata": {}, + "execution_count": 35 + }, + { + "cell_type": "markdown", + "source": [ + "We randomly initialize the kernel parameters and 5 pseudo points, and minimize the\n", + "negative ELBO with the LBFGS algorithm and obtain the following optimal parameters:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": " * Status: success\n\n * Candidate solution\n Final objective value: 1.086925e+01\n\n * Found with\n Algorithm: L-BFGS\n\n * Convergence measures\n |x - x'| = 1.38e-08 ≰ 0.0e+00\n |x - x'|/|x'| = 1.65e-09 ≰ 0.0e+00\n |f(x) - f(x')| = 0.00e+00 ≤ 0.0e+00\n |f(x) - f(x')|/|f(x')| = 0.00e+00 ≤ 0.0e+00\n |g(x)| = 9.39e-09 ≤ 1.0e-08\n\n * Work counters\n Seconds run: 0 (vs limit Inf)\n Iterations: 33\n f(x) calls: 90\n ∇f(x) calls: 90\n" + }, + "metadata": {}, + "execution_count": 36 + } + ], + "cell_type": "code", + "source": [ + "x0 = rand(7)\n", + "opt = optimize(objective_function(x_train, y_train), x0, LBFGS())" + ], + "metadata": {}, + "execution_count": 36 + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "7-element Vector{Float64}:\n 8.379380163263326\n 3.932737537022372\n 1.2763097368903071\n 1.8479571188160673\n -1.7583267679064027\n -4.133679971062581\n 0.6917102791672016" + }, + "metadata": {}, + "execution_count": 37 + } + ], + "cell_type": "code", + "source": [ + "opt.minimizer" + ], + "metadata": {}, + "execution_count": 37 + }, + { + "cell_type": "markdown", + "source": [ + "The optimized value of the variance is" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "8.3796096891065" + }, + "metadata": {}, + "execution_count": 38 + } + ], + "cell_type": "code", + "source": [ + "softplus(opt.minimizer[1])" + ], + "metadata": {}, + "execution_count": 38 + }, + { + "cell_type": "markdown", + "source": [ + "and of the inverse lengthscale is" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "3.952138094098159" + }, + "metadata": {}, + "execution_count": 39 + } + ], + "cell_type": "code", + "source": [ + "softplus(opt.minimizer[2])" + ], + "metadata": {}, + "execution_count": 39 + }, + { + "cell_type": "markdown", + "source": [ + "We compute the log-likelihood of the test data for the resulting approximate\n", + "posterior. We can observe that there is a significant improvement over the\n", + "log-likelihood with the default kernel parameters of value 1." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "-2.068599765069256" + }, + "metadata": {}, + "execution_count": 40 + } + ], + "cell_type": "code", + "source": [ + "opt_kernel =\n", + " softplus(opt.minimizer[1]) *\n", + " (Matern52Kernel() ∘ ScaleTransform(softplus(opt.minimizer[2])))\n", + "opt_f = GP(opt_kernel)\n", + "opt_fx = opt_f(x_train, noise_var)\n", + "ap = posterior(VFE(opt_f(logistic.(opt.minimizer[3:end]), jitter)), opt_fx, y_train)\n", + "logpdf(ap(x_test, noise_var), y_test)" + ], + "metadata": {}, + "execution_count": 40 + }, + { + "cell_type": "markdown", + "source": [ + "We visualize the approximate posterior with optimized parameters." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=4}", + "image/png": 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+ "source": [ + "We re-calculate the log-likelihood of the test dataset with the\n", + "default kernel parameters of value 1 for the sake of comparison." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "-232.51565975779937" + }, + "metadata": {}, + "execution_count": 42 + } + ], + "cell_type": "code", + "source": [ + "logpdf(p_fx(x_test), y_test)" + ], + "metadata": {}, + "execution_count": 42 + }, + { + "cell_type": "markdown", + "source": [ + "We define a function which returns the negative log marginal\n", + "likelihood for different variance and inverse lengthscale parameters\n", + "of the Matern kernel and different pseudo-points. We ensure that the\n", + "kernel parameters are positive with the softplus function\n", + "$f(x) = \\log (1 + \\exp x)$." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "loss_function (generic function with 1 method)" + }, + "metadata": {}, + "execution_count": 43 + } + ], + "cell_type": "code", + "source": [ + "function loss_function(x, y)\n", + " function negativelogmarginallikelihood(params)\n", + " kernel =\n", + " softplus(params[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(params[2])))\n", + " f = GP(kernel)\n", + " fx = f(x, noise_var)\n", + " return -logpdf(fx, y)\n", + " end\n", + " return negativelogmarginallikelihood\n", + "end" + ], + "metadata": {}, + "execution_count": 43 + }, + { + "cell_type": "markdown", + "source": [ + "We randomly initialize the kernel parameters, and minimize the\n", + "negative log marginal likelihood with the LBFGS algorithm\n", + "and obtain the following optimal parameters:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": " * Status: success (objective increased between iterations)\n\n * Candidate solution\n Final objective value: 1.085252e+01\n\n * Found with\n Algorithm: L-BFGS\n\n * Convergence measures\n |x - x'| = 5.44e-08 ≰ 0.0e+00\n |x - x'|/|x'| = 6.48e-09 ≰ 0.0e+00\n |f(x) - f(x')| = 7.64e-14 ≰ 0.0e+00\n |f(x) - f(x')|/|f(x')| = 7.04e-15 ≰ 0.0e+00\n |g(x)| = 1.11e-10 ≤ 1.0e-08\n\n * Work counters\n Seconds run: 0 (vs limit Inf)\n Iterations: 15\n f(x) calls: 51\n ∇f(x) calls: 51\n" + }, + "metadata": {}, + "execution_count": 44 + } + ], + "cell_type": "code", + "source": [ + "θ0 = randn(2)\n", + "opt = Optim.optimize(loss_function(x_train, y_train), θ0, LBFGS())" + ], + "metadata": {}, + "execution_count": 44 + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "2-element Vector{Float64}:\n 8.385520525672359\n 3.968794221481056" + }, + "metadata": {}, + "execution_count": 45 + } + ], + "cell_type": "code", + "source": [ + "opt.minimizer" + ], + "metadata": {}, + "execution_count": 45 + }, + { + "cell_type": "markdown", + "source": [ + "The optimized value of the variance is" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "8.385748646622107" + }, + "metadata": {}, + "execution_count": 46 + } + ], + "cell_type": "code", + "source": [ + "softplus(opt.minimizer[1])" + ], + "metadata": {}, + "execution_count": 46 + }, + { + "cell_type": "markdown", + "source": [ + "and of the inverse lengthscale is" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "3.987514109926022" + }, + "metadata": {}, + "execution_count": 47 + } + ], + "cell_type": "code", + "source": [ + "softplus(opt.minimizer[2])" + ], + "metadata": {}, + "execution_count": 47 + }, + { + "cell_type": "markdown", + "source": [ + "We compute the log-likelihood of the test data for the resulting optimized\n", + "posterior. We can observe that there is a significant improvement over the\n", + "log-likelihood with the default kernel parameters of value 1." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "-2.092647856958113" + }, + "metadata": {}, + "execution_count": 48 + } + ], + "cell_type": "code", + "source": [ + "opt_kernel =\n", + " softplus(opt.minimizer[1]) *\n", + " (Matern52Kernel() ∘ ScaleTransform(softplus(opt.minimizer[2])))\n", + "\n", + "opt_f = GP(opt_kernel)\n", + "opt_fx = opt_f(x_train, noise_var)\n", + "opt_p_fx = posterior(opt_fx, y_train)\n", + "logpdf(opt_p_fx(x_test, noise_var), y_test)" + ], + "metadata": {}, + "execution_count": 48 + }, + { + "cell_type": "markdown", + "source": [ + "We visualize the posterior with optimized parameters." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=3}", + "image/png": 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\n", + "
Package and system information
\n", + "
\n", + "Package information (click to expand)\n", + "
\n",
+    "Status `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/0-intro-1d/Project.toml`\n",
+    "  [99985d1d] AbstractGPs v0.5.20 `/home/runner/work/AbstractGPs.jl/AbstractGPs.jl#07d5f00`\n",
+    "  [0bf59076] AdvancedHMC v0.6.1\n",
+    "  [31c24e10] Distributions v0.25.107\n",
+    "  [bbc10e6e] DynamicHMC v3.4.7\n",
+    "⌅ [cad2338a] EllipticalSliceSampling v1.1.0\n",
+    "  [1a297f60] FillArrays v1.9.3\n",
+    "  [f6369f11] ForwardDiff v0.10.36\n",
+    "  [98b081ad] Literate v2.16.1\n",
+    "  [6fdf6af0] LogDensityProblems v2.1.1\n",
+    "  [996a588d] LogDensityProblemsAD v1.7.0\n",
+    "  [429524aa] Optim v1.9.2\n",
+    "  [91a5bcdd] Plots v1.40.2\n",
+    "  [4c63d2b9] StatsFuns v1.3.1\n",
+    "  [9a3f8284] Random\n",
+    "Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated`\n",
+    "
\n", + "To reproduce this notebook's package environment, you can\n", + "\n", + "download the full Manifest.toml.\n", + "
\n", + "
\n", + "System information (click to expand)\n", + "
\n",
+    "Julia Version 1.10.2\n",
+    "Commit bd47eca2c8a (2024-03-01 10:14 UTC)\n",
+    "Build Info:\n",
+    "  Official https://julialang.org/ release\n",
+    "Platform Info:\n",
+    "  OS: Linux (x86_64-linux-gnu)\n",
+    "  CPU: 4 × AMD EPYC 7763 64-Core Processor\n",
+    "  WORD_SIZE: 64\n",
+    "  LIBM: libopenlibm\n",
+    "  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\n",
+    "Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)\n",
+    "Environment:\n",
+    "  JULIA_DEBUG = Documenter\n",
+    "  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n",
+    "
\n", + "
" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.2" + }, + "kernelspec": { + "name": "julia-1.10", + "display_name": "Julia 1.10.2", + "language": "julia" + } + }, + "nbformat": 4 +} diff --git a/previews/PR387/examples/1-mauna-loa/Manifest.toml b/previews/PR387/examples/1-mauna-loa/Manifest.toml new file mode 100644 index 00000000..60283e51 --- /dev/null +++ b/previews/PR387/examples/1-mauna-loa/Manifest.toml @@ -0,0 +1,1607 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.10.2" +manifest_format = "2.0" +project_hash = "318bab8e4339b9c93b5e5df5fc82f14be1ef2a4e" + +[[deps.AbstractFFTs]] +deps = ["LinearAlgebra"] 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GitHub

Mauna Loa time series example

You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.

In this notebook, we apply Gaussian process regression to the Mauna Loa CO₂ dataset. This showcases a rich combination of kernels, and how to handle and optimize all their parameters.

Setup

We make use of the following packages:

using CSV, DataFrames  # data loading
+using AbstractGPs  # exact GP regression
+using ParameterHandling  # for nested and constrained parameters
+using Optim  # optimization
+using Zygote  # auto-diff gradient computation
+using Plots  # visualisation

Let's load and visualize the dataset.

Tip

The let block creates a new scope, so any utility variables we define in here won't leak outside. This is particularly helpful to keep notebooks tidy! The return value of the block is given by its last expression.

(xtrain, ytrain), (xtest, ytest) = let
+    data = CSV.read(joinpath("/home/runner/work/AbstractGPs.jl/AbstractGPs.jl/examples/1-mauna-loa", "CO2_data.csv"), Tables.matrix; header=0)
+    year = data[:, 1]
+    co2 = data[:, 2]
+
+    # We split the data into training and testing set:
+    idx_train = year .< 2004
+    idx_test = .!idx_train
+
+    (year[idx_train], co2[idx_train]), (year[idx_test], co2[idx_test])  # block's return value
+end
+# The utility variables such as `idx_train` and `idx_test` are not available outside the `let` scope
+
+function plotdata()
+    plot(; xlabel="year", ylabel="CO₂ [ppm]", legend=:bottomright)
+    scatter!(xtrain, ytrain; label="training data", ms=2, markerstrokewidth=0)
+    return scatter!(xtest, ytest; label="test data", ms=2, markerstrokewidth=0)
+end
+
+plotdata()
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Prior

We will model this dataset using a sum of several kernels which describe

  • smooth trend: squared exponential kernel with long lengthscale;
  • seasonal component: periodic covariance function with period of one year, multiplied with a squared exponential kernel to allow decay away from exact periodicity;
  • medium-term irregularities: rational quadratic kernel;
  • noise terms: squared exponential kernel with short lengthscale and uncorrelated observation noise.

For more details, see Rasmussen & Williams (2005), chapter 5.

We will use ParameterHandling.jl for handling the (hyper)parameters of our model. It provides functions such as positive with which we can put constraints on the hyperparameters, and allows us to represent all required parameters as a nested NamedTuple:

# initial values to match http://stor-i.github.io/GaussianProcesses.jl/latest/mauna_loa/
+θ_init = (;
+    se_long = (;
+        σ = positive(exp(4.0)),
+        ℓ = positive(exp(4.0)),
+    ),
+    seasonal = (;
+        # product kernels only need a single overall signal variance
+        per = (;
+            ℓ = positive(exp(0.0)),  # relative to period!
+            p = fixed(1.0),  # 1 year, do not optimize over
+        ),
+        se = (;
+            σ = positive(exp(1.0)),
+            ℓ = positive(exp(4.0)),
+        ),
+    ),
+    rq = (;
+        σ = positive(exp(0.0)),
+        ℓ = positive(exp(0.0)),
+        α = positive(exp(-1.0)),
+    ),
+    se_short = (;
+        σ = positive(exp(-2.0)),
+        ℓ = positive(exp(-2.0)),
+    ),
+    noise_scale = positive(exp(-2.0)),
+)

We define a couple of helper functions to simplify the kernel construction:

SE(θ) = θ.σ^2 * with_lengthscale(SqExponentialKernel(), θ.ℓ)
+Per(θ) = with_lengthscale(PeriodicKernel(; r=[θ.ℓ / 2]), θ.p)  # NOTE- discrepancy with GaussianProcesses.jl
+RQ(θ) = θ.σ^2 * with_lengthscale(RationalQuadraticKernel(; α=θ.α), θ.ℓ)

This allows us to write a function that, given the nested tuple of parameter values, constructs the GP prior:

function build_gp_prior(θ)
+    k_smooth_trend = SE(θ.se_long)
+    k_seasonality = Per(θ.seasonal.per) * SE(θ.seasonal.se)
+    k_medium_term_irregularities = RQ(θ.rq)
+    k_noise_terms = SE(θ.se_short) + θ.noise_scale^2 * WhiteKernel()
+    kernel = k_smooth_trend + k_seasonality + k_medium_term_irregularities + k_noise_terms
+    return GP(kernel)  # [`ZeroMean`](@ref) mean function by default
+end

Posterior

To construct the posterior, we need to first build a FiniteGP, which represents the infinite-dimensional GP at a finite number of input features:

function build_finite_gp(θ)
+    f = build_gp_prior(θ)
+    return f(xtrain)
+end
`WhiteKernel` vs `FiniteGP` observation noise

In this notebook, we already included observation noise through the WhiteKernel as part of the GP prior covariance in build_gp_prior. We therefore call f(xtrain) which implies zero (additional) observation noise.

Alternatively, we could have omitted the θ.noise_scale^2 * WhiteKernel() term and instead passed the noise variance as a second argument to the GP call in build_finite_gp, f(xtrain, θ.noise_scale^2).

These two approaches have slightly different semantics: In the first one, the WhiteKernel contributes non-zero variance to the [i, j] element of the covariance matrix of the FiniteGP if xtrain[i] == xtrain[j] (based on the values of the features). In the second one, the observation noise variance passed to FiniteGP only contributes to the diagonal elements of the covariance matrix, i.e. for i == j.

Moreover, the variance (uncertainty) of the posterior predictions includes the variance from the WhiteKernel, but does not include the variance of the observation noise passed to the FiniteGP. To include the observation noise in posterior predictions from the second approach, call fpost_opt(xtest, noise_scale^2).

Tip

For most use-cases and if in any doubt, we recommend that you pass in observation noise to the FiniteGP, and omit the explicit WhiteKernel. This is slightly faster (no need to check xtrain[i] == xtrain[j] for all pairs i, j), and WhiteKernel will not give stable gradients if you wish to compute the gradient of the log marginal likelihood logpdf(f(x), y) w.r.t. x.

We obtain the posterior, conditioned on the (finite) observations, by calling posterior:

function build_posterior_gp(θ)
+    fx = build_finite_gp(θ)
+    return posterior(fx, ytrain)
+end

Now we can put it all together to obtain a PosteriorGP. The call to ParameterHandling.value is required to replace the constraints (such as positive in our case) with concrete numbers:

fpost_init = build_posterior_gp(ParameterHandling.value(θ_init))
AbstractGPs.PosteriorGP{AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.TransformedKernel{KernelFunctions.PeriodicKernel{Float64}, KernelFunctions.ScaleTransform{Float64}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}, @NamedTuple{α::Vector{Float64}, C::LinearAlgebra.Cholesky{Float64, Matrix{Float64}}, x::Vector{Float64}, δ::Vector{Float64}}}(AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.TransformedKernel{KernelFunctions.PeriodicKernel{Float64}, KernelFunctions.ScaleTransform{Float64}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}(AbstractGPs.ZeroMean{Float64}(), Sum of 5 kernels:
+	Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
+			- Scale Transform (s = 0.018315638888734182)
+			- σ² = 2980.957987041728
+	Product of 2 kernels:
+			Periodic Kernel, length(r) = 1
+					- Scale Transform (s = 1.0)
+			Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
+					- Scale Transform (s = 0.018315638888734182)
+					- σ² = 7.3890560989306495
+	Rational Quadratic Kernel (α = 0.36787944117144233, metric = Distances.Euclidean(0.0))
+			- Scale Transform (s = 1.0)
+			- σ² = 1.0
+	Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
+			- Scale Transform (s = 7.3890560989306495)
+			- σ² = 0.018315638888734182
+	White Kernel
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2001.5416666666667, 2001.625, 2001.7083333333333, 2001.7916666666667, 2001.875, 2001.9583333333333, 2002.0416666666667, 2002.125, 2002.2083333333333, 2002.2916666666667, 2002.375, 2002.4583333333333, 2002.5416666666667, 2002.625, 2002.7083333333333, 2002.7916666666667, 2002.875, 2002.9583333333333, 2003.0416666666667, 2003.125, 2003.2083333333333, 2003.2916666666667, 2003.375, 2003.4583333333333, 2003.5416666666667, 2003.625, 2003.7083333333333, 2003.7916666666667, 2003.875, 2003.9583333333333], δ = [315.71, 317.45, 317.5, 317.1, 315.86, 314.93, 313.2, 312.66, 313.33, 314.67, 315.62, 316.38, 316.71, 317.72, 318.29, 318.15, 316.54, 314.8, 313.84, 313.26, 314.8, 315.58, 316.43, 316.97, 317.58, 319.02, 320.03, 319.59, 318.18, 315.91, 314.16, 313.83, 315.0, 316.19, 316.93, 317.7, 318.54, 319.48, 320.58, 319.77, 318.57, 316.79, 314.8, 315.38, 316.1, 317.01, 317.94, 318.56, 319.68, 320.63, 321.01, 320.55, 319.58, 317.4, 316.26, 315.42, 316.69, 317.69, 318.74, 319.08, 319.86, 321.39, 322.25, 321.47, 319.74, 317.77, 316.21, 315.99, 317.12, 318.31, 319.57, 320.07, 320.73, 321.77, 322.25, 321.89, 320.44, 318.7, 316.7, 316.79, 317.79, 318.71, 319.44, 320.44, 320.89, 322.13, 322.16, 321.87, 321.39, 318.81, 317.81, 317.3, 318.87, 319.42, 320.62, 321.59, 322.39, 323.87, 324.01, 323.75, 322.39, 320.37, 318.64, 318.1, 319.79, 321.08, 322.07, 322.5, 323.04, 324.42, 325.0, 324.09, 322.55, 320.92, 319.31, 319.31, 320.72, 321.96, 322.57, 323.15, 323.89, 325.02, 325.57, 325.36, 324.14, 322.03, 320.41, 320.25, 321.31, 322.84, 324.0, 324.42, 325.64, 326.66, 327.34, 326.76, 325.88, 323.67, 322.38, 321.78, 322.85, 324.11, 325.03, 325.99, 326.87, 328.13, 328.07, 327.66, 326.35, 324.69, 323.1, 323.16, 323.98, 325.13, 326.17, 326.68, 327.18, 327.78, 328.92, 328.57, 327.34, 325.46, 323.36, 323.57, 324.8, 326.01, 326.77, 327.63, 327.75, 329.72, 330.07, 329.09, 328.05, 326.32, 324.93, 325.06, 326.5, 327.55, 328.54, 329.56, 330.3, 331.5, 332.48, 332.07, 330.87, 329.31, 327.51, 327.18, 328.16, 328.64, 329.35, 330.71, 331.48, 332.65, 333.19, 332.12, 330.99, 329.17, 327.41, 327.21, 328.34, 329.5, 330.68, 331.41, 331.85, 333.29, 333.91, 333.4, 331.74, 329.88, 328.57, 328.36, 329.33, 330.59, 331.66, 332.75, 333.46, 334.78, 334.78, 334.06, 332.95, 330.64, 328.96, 328.77, 330.18, 331.65, 332.69, 333.23, 334.97, 336.03, 336.82, 336.1, 334.79, 332.53, 331.19, 331.21, 332.35, 333.47, 335.1, 335.26, 336.61, 337.77, 338.01, 337.98, 336.48, 334.37, 332.33, 332.41, 333.76, 334.83, 336.21, 336.65, 338.13, 338.94, 339.0, 339.2, 337.6, 335.56, 333.93, 334.12, 335.26, 336.78, 337.8, 338.28, 340.04, 340.86, 341.47, 341.26, 339.34, 337.45, 336.1, 336.05, 337.21, 338.29, 339.36, 340.51, 341.57, 342.56, 343.01, 342.49, 340.68, 338.49, 336.92, 337.12, 338.59, 339.9, 340.92, 341.69, 342.85, 343.92, 344.67, 343.78, 342.23, 340.11, 338.32, 338.39, 339.48, 340.88, 341.64, 342.87, 343.59, 345.25, 345.96, 345.52, 344.15, 342.25, 340.17, 340.3, 341.53, 343.07, 344.05, 344.77, 345.46, 346.77, 347.55, 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Let's visualize what the GP fitted to the data looks like, for the initial choice of kernel hyperparameters.

We use the following function to plot a GP f on a specific range, using the AbstractGPs plotting recipes. By setting ribbon_scale=2 we visualize the uncertainty band with $\pm 2$ (instead of the default $\pm 1$) standard deviations.

plot_gp!(f; label) = plot!(f(1920:0.2:2030); ribbon_scale=2, linewidth=1, label)
+
+plotdata()
+plot_gp!(fpost_init; label="posterior f(⋅)")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

A reasonable fit to the data, but poor extrapolation away from the observations!

Hyperparameter Optimization

To improve the fit, we want to maximize the (log) marginal likelihood with respect to the hyperparameters. Optim.jl expects to minimize a loss, so we define it as the negative log marginal likelihood:

function loss(θ)
+    fx = build_finite_gp(θ)
+    lml = logpdf(fx, ytrain)  # this computes the log marginal likelihood
+    return -lml
+end
Work-in-progress

In the future, we are planning to provide the optimize_loss utility function as part of JuliaGaussianProcesses – for now, we just define it inline.

The L-BFGS parameters were chosen because they seem to work well empirically. You could also try with the defaults.

default_optimizer = LBFGS(;
+    alphaguess=Optim.LineSearches.InitialStatic(; scaled=true),
+    linesearch=Optim.LineSearches.BackTracking(),
+)
+
+function optimize_loss(loss, θ_init; optimizer=default_optimizer, maxiter=1_000)
+    options = Optim.Options(; iterations=maxiter, show_trace=true)
+
+    θ_flat_init, unflatten = ParameterHandling.value_flatten(θ_init)
+    loss_packed = loss ∘ unflatten
+
+    # https://julianlsolvers.github.io/Optim.jl/stable/#user/tipsandtricks/#avoid-repeating-computations
+    function fg!(F, G, x)
+        if F !== nothing && G !== nothing
+            val, grad = Zygote.withgradient(loss_packed, x)
+            G .= only(grad)
+            return val
+        elseif G !== nothing
+            grad = Zygote.gradient(loss_packed, x)
+            G .= only(grad)
+            return nothing
+        elseif F !== nothing
+            return loss_packed(x)
+        end
+    end
+
+    result = optimize(Optim.only_fg!(fg!), θ_flat_init, optimizer, options; inplace=false)
+
+    return unflatten(result.minimizer), result
+end

We now run the optimization:

θ_opt, opt_result = optimize_loss(loss, θ_init)
+opt_result
 * Status: success
+
+ * Candidate solution
+    Final objective value:     1.152862e+02
+
+ * Found with
+    Algorithm:     L-BFGS
+
+ * Convergence measures
+    |x - x'|               = 0.00e+00 ≤ 0.0e+00
+    |x - x'|/|x'|          = 0.00e+00 ≤ 0.0e+00
+    |f(x) - f(x')|         = 0.00e+00 ≤ 0.0e+00
+    |f(x) - f(x')|/|f(x')| = 0.00e+00 ≤ 0.0e+00
+    |g(x)|                 = 3.78e-02 ≰ 1.0e-08
+
+ * Work counters
+    Seconds run:   28  (vs limit Inf)
+    Iterations:    61
+    f(x) calls:    95
+    ∇f(x) calls:   61
+

The final value of the log marginal likelihood is:

-opt_result.minimum
-115.28622096603124
Warning

To avoid bad local optima, we could (and should) have carried out several random restarts with different initial values for the hyperparameters, and then picked the result with the highest marginal likelihood. We omit this for simplicity. For more details on how to fit GPs in practice, check out A Practical Guide to Gaussian Processes.

Let's construct the posterior GP with the optimized hyperparameters:

fpost_opt = build_posterior_gp(ParameterHandling.value(θ_opt))

This is the kernel with the point-estimated hyperparameters:

fpost_opt.prior.kernel
Sum of 5 kernels:
+	Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
+			- Scale Transform (s = 0.006456502042458794)
+			- σ² = 191273.47839039602
+	Product of 2 kernels:
+			Periodic Kernel, length(r) = 1
+					- Scale Transform (s = 1.0)
+			Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
+					- Scale Transform (s = 0.012139119857609751)
+					- σ² = 5.99003503589132
+	Rational Quadratic Kernel (α = 0.0007411653885252467, metric = Distances.Euclidean(0.0))
+			- Scale Transform (s = 0.04622167413324385)
+			- σ² = 178.92175101210103
+	Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
+			- Scale Transform (s = 8.752621585494843)
+			- σ² = 0.028315070269881937
+	White Kernel
+			- σ² = 0.03576077215040964

Let's print the optimized values of the hyperparameters in a more helpful format:

Work-in-progress

This is another utility function we would eventually like to move out of this notebook:

using Printf
+
+show_params(nt::Union{Dict,NamedTuple}) = String(take!(show_params(IOBuffer(), nt)))
+function show_params(io, nt::Union{Dict,NamedTuple}, indent::Int=0)
+    for (s, v) in pairs(nt)
+        if v isa Union{Dict,NamedTuple}
+            println(io, " "^indent, s, ":")
+            show_params(io, v, indent + 4)
+        else
+            println(io, " "^indent, s, " = ", @sprintf("%.3f", v))
+        end
+    end
+    return io
+end
+
+print(show_params(ParameterHandling.value(θ_opt)))
se_long:
+    σ = 437.348
+    ℓ = 154.883
+seasonal:
+    per:
+        ℓ = 1.456
+        p = 1.000
+    se:
+        σ = 2.447
+        ℓ = 82.378
+rq:
+    σ = 13.376
+    ℓ = 21.635
+    α = 0.001
+se_short:
+    σ = 0.168
+    ℓ = 0.114
+noise_scale = 0.189
+

And, finally, we can visualize our optimized posterior GP:

plotdata()
+plot_gp!(fpost_opt; label="optimized posterior f(⋅)")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
Package and system information
+
+Package information (click to expand) +
+Status `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/1-mauna-loa/Project.toml`
+  [99985d1d] AbstractGPs v0.5.20 `/home/runner/work/AbstractGPs.jl/AbstractGPs.jl#07d5f00`
+  [336ed68f] CSV v0.10.13
+  [a93c6f00] DataFrames v1.6.1
+  [98b081ad] Literate v2.16.1
+  [429524aa] Optim v1.9.2
+⌅ [2412ca09] ParameterHandling v0.4.10
+  [91a5bcdd] Plots v1.40.2
+  [e88e6eb3] Zygote v0.6.69
+Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated`
+
+To reproduce this notebook's package environment, you can + +download the full Manifest.toml. +
+
+System information (click to expand) +
+Julia Version 1.10.2
+Commit bd47eca2c8a (2024-03-01 10:14 UTC)
+Build Info:
+  Official https://julialang.org/ release
+Platform Info:
+  OS: Linux (x86_64-linux-gnu)
+  CPU: 4 × AMD EPYC 7763 64-Core Processor
+  WORD_SIZE: 64
+  LIBM: libopenlibm
+  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
+Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)
+Environment:
+  JULIA_DEBUG = Documenter
+  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src
+
+

This page was generated using Literate.jl.

diff --git a/previews/PR387/examples/1-mauna-loa/notebook.ipynb b/previews/PR387/examples/1-mauna-loa/notebook.ipynb new file mode 100644 index 00000000..431bb793 --- /dev/null +++ b/previews/PR387/examples/1-mauna-loa/notebook.ipynb @@ -0,0 +1,5297 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Mauna Loa time series example\n", + "\n", + "*You are seeing the\n", + "notebook output generated by\n", + "[Literate.jl](https://github.com/fredrikekre/Literate.jl) from the\n", + "[Julia source file](https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/master/examples/1-mauna-loa/script.jl).\n", + "The rendered HTML can be viewed [in the docs](https://juliagaussianprocesses.github.io/AbstractGPs.jl/dev/examples/1-mauna-loa/).*" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "In this notebook, we apply Gaussian process regression to the Mauna Loa CO₂\n", + "dataset. This showcases a rich combination of kernels, and how to handle and\n", + "optimize all their parameters." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## Setup\n", + "\n", + "We make use of the following packages:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using CSV, DataFrames # data loading\n", + "using AbstractGPs # exact GP regression\n", + "using ParameterHandling # for nested and constrained parameters\n", + "using Optim # optimization\n", + "using Zygote # auto-diff gradient computation\n", + "using Plots # visualisation" + ], + "metadata": {}, + "execution_count": 1 + }, + { + "cell_type": "markdown", + "source": [ + "Let's load and visualize the dataset." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "!!! tip\n", + " The `let` block [creates a new\n", + " scope](https://docs.julialang.org/en/v1/manual/variables-and-scoping/#scope-of-variables),\n", + " so any utility variables we define in here won't leak outside. This is\n", + " particularly helpful to keep notebooks tidy! The return value of the\n", + " block is given by its last expression." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=2}", + "image/png": 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\"CO2_data.csv\"), Tables.matrix; header=0)\n", + " year = data[:, 1]\n", + " co2 = data[:, 2]\n", + "\n", + " # We split the data into training and testing set:\n", + " idx_train = year .< 2004\n", + " idx_test = .!idx_train\n", + "\n", + " (year[idx_train], co2[idx_train]), (year[idx_test], co2[idx_test]) # block's return value\n", + "end\n", + "# The utility variables such as `idx_train` and `idx_test` are not available outside the `let` scope\n", + "\n", + "function plotdata()\n", + " plot(; xlabel=\"year\", ylabel=\"CO₂ [ppm]\", legend=:bottomright)\n", + " scatter!(xtrain, ytrain; label=\"training data\", ms=2, markerstrokewidth=0)\n", + " return scatter!(xtest, ytest; label=\"test data\", ms=2, markerstrokewidth=0)\n", + "end\n", + "\n", + "plotdata()" + ], + "metadata": {}, + "execution_count": 2 + }, + { + "cell_type": "markdown", + "source": [ + "## Prior\n", + "\n", + "We will model this dataset using a sum of several kernels which describe\n", + "\n", + "- smooth trend: squared exponential kernel with long lengthscale;\n", + "- seasonal component: periodic covariance function with period of one year,\n", + " multiplied with a squared exponential kernel to allow decay away from exact\n", + " periodicity;\n", + "- medium-term irregularities: rational quadratic kernel;\n", + "- noise terms: squared exponential kernel with short lengthscale\n", + " and uncorrelated observation noise.\n", + "\n", + "For more details, see [Rasmussen & Williams (2005), chapter 5](http://www.gaussianprocess.org/gpml/chapters/RW5.pdf)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "We will use\n", + "[ParameterHandling.jl](https://invenia.github.io/ParameterHandling.jl/) for\n", + "handling the (hyper)parameters of our model. It provides functions such as\n", + "`positive` with which we can put constraints on the hyperparameters, and\n", + "allows us to represent all required parameters as a nested NamedTuple:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "(se_long = (σ = ParameterHandling.Positive{Float64, typeof(exp), Float64}(3.9999999997270757, exp, 1.4901161193847656e-8), ℓ = ParameterHandling.Positive{Float64, typeof(exp), Float64}(3.9999999997270757, exp, 1.4901161193847656e-8)), seasonal = (per = (ℓ = ParameterHandling.Positive{Float64, typeof(exp), Float64}(-1.490116130486996e-8, exp, 1.4901161193847656e-8), p = ParameterHandling.Fixed{Float64}(1.0)), se = (σ = ParameterHandling.Positive{Float64, typeof(exp), Float64}(0.9999999945181691, exp, 1.4901161193847656e-8), ℓ = ParameterHandling.Positive{Float64, typeof(exp), Float64}(3.9999999997270757, exp, 1.4901161193847656e-8))), rq = (σ = ParameterHandling.Positive{Float64, typeof(exp), Float64}(-1.490116130486996e-8, exp, 1.4901161193847656e-8), ℓ = ParameterHandling.Positive{Float64, typeof(exp), Float64}(-1.490116130486996e-8, exp, 1.4901161193847656e-8), α = ParameterHandling.Positive{Float64, typeof(exp), Float64}(-1.0000000405055565, exp, 1.4901161193847656e-8)), se_short = (σ = ParameterHandling.Positive{Float64, typeof(exp), Float64}(-2.000000110105522, exp, 1.4901161193847656e-8), ℓ = ParameterHandling.Positive{Float64, typeof(exp), Float64}(-2.000000110105522, exp, 1.4901161193847656e-8)), noise_scale = ParameterHandling.Positive{Float64, typeof(exp), Float64}(-2.000000110105522, exp, 1.4901161193847656e-8))" + }, + "metadata": {}, + "execution_count": 3 + } + ], + "cell_type": "code", + "source": [ + "# initial values to match http://stor-i.github.io/GaussianProcesses.jl/latest/mauna_loa/\n", + "θ_init = (;\n", + " se_long = (;\n", + " σ = positive(exp(4.0)),\n", + " ℓ = positive(exp(4.0)),\n", + " ),\n", + " seasonal = (;\n", + " # product kernels only need a single overall signal variance\n", + " per = (;\n", + " ℓ = positive(exp(0.0)), # relative to period!\n", + " p = fixed(1.0), # 1 year, do not optimize over\n", + " ),\n", + " se = (;\n", + " σ = positive(exp(1.0)),\n", + " ℓ = positive(exp(4.0)),\n", + " ),\n", + " ),\n", + " rq = (;\n", + " σ = positive(exp(0.0)),\n", + " ℓ = positive(exp(0.0)),\n", + " α = positive(exp(-1.0)),\n", + " ),\n", + " se_short = (;\n", + " σ = positive(exp(-2.0)),\n", + " ℓ = positive(exp(-2.0)),\n", + " ),\n", + " noise_scale = positive(exp(-2.0)),\n", + ")" + ], + "metadata": {}, + "execution_count": 3 + }, + { + "cell_type": "markdown", + "source": [ + "We define a couple of helper functions to simplify the kernel construction:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "RQ (generic function with 1 method)" + }, + "metadata": {}, + "execution_count": 4 + } + ], + "cell_type": "code", + "source": [ + "SE(θ) = θ.σ^2 * with_lengthscale(SqExponentialKernel(), θ.ℓ)\n", + "Per(θ) = with_lengthscale(PeriodicKernel(; r=[θ.ℓ / 2]), θ.p) # NOTE- discrepancy with GaussianProcesses.jl\n", + "RQ(θ) = θ.σ^2 * with_lengthscale(RationalQuadraticKernel(; α=θ.α), θ.ℓ)" + ], + "metadata": {}, + "execution_count": 4 + }, + { + "cell_type": "markdown", + "source": [ + "This allows us to write a function that, given the nested tuple of parameter values, constructs the GP prior:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "build_gp_prior (generic function with 1 method)" + }, + "metadata": {}, + "execution_count": 5 + } + ], + "cell_type": "code", + "source": [ + "function build_gp_prior(θ)\n", + " k_smooth_trend = SE(θ.se_long)\n", + " k_seasonality = Per(θ.seasonal.per) * SE(θ.seasonal.se)\n", + " k_medium_term_irregularities = RQ(θ.rq)\n", + " k_noise_terms = SE(θ.se_short) + θ.noise_scale^2 * WhiteKernel()\n", + " kernel = k_smooth_trend + k_seasonality + k_medium_term_irregularities + k_noise_terms\n", + " return GP(kernel) # `ZeroMean` mean function by default\n", + "end" + ], + "metadata": {}, + "execution_count": 5 + }, + { + "cell_type": "markdown", + "source": [ + "## Posterior\n", + "\n", + "To construct the posterior, we need to first build a `FiniteGP`,\n", + "which represents the infinite-dimensional GP at a finite number of input\n", + "features:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "build_finite_gp (generic function with 1 method)" + }, + "metadata": {}, + "execution_count": 6 + } + ], + "cell_type": "code", + "source": [ + "function build_finite_gp(θ)\n", + " f = build_gp_prior(θ)\n", + " return f(xtrain)\n", + "end" + ], + "metadata": {}, + "execution_count": 6 + }, + { + "cell_type": "markdown", + "source": [ + "!!! info \"`WhiteKernel` vs `FiniteGP` observation noise\"\n", + " In this notebook, we already included observation noise through the\n", + " `WhiteKernel` as part of the GP prior covariance in `build_gp_prior`. We\n", + " therefore call `f(xtrain)` which implies zero (additional) observation\n", + " noise.\n", + "\n", + " Alternatively, we could have omitted the `θ.noise_scale^2 *\n", + " WhiteKernel()` term and instead passed the noise variance as a second\n", + " argument to the GP call in `build_finite_gp`, `f(xtrain,\n", + " θ.noise_scale^2)`.\n", + "\n", + " These two approaches have slightly different semantics: In the first one,\n", + " the `WhiteKernel` contributes non-zero variance to the `[i, j]` element\n", + " of the covariance matrix of the `FiniteGP` if `xtrain[i] == xtrain[j]`\n", + " (based on the values of the features). In the second one, the observation\n", + " noise variance passed to `FiniteGP` only contributes to the diagonal\n", + " elements of the covariance matrix, i.e. for `i == j`.\n", + "\n", + " Moreover, the variance (uncertainty) of the posterior predictions\n", + " includes the variance from the `WhiteKernel`, but does not include the\n", + " variance of the observation noise passed to the `FiniteGP`. To include\n", + " the observation noise in posterior predictions from the second approach,\n", + " call `fpost_opt(xtest, noise_scale^2)`.\n", + "\n", + "!!! tip\n", + " For most use-cases and if in any doubt, we recommend that you pass in\n", + " observation noise to the `FiniteGP`, and omit the explicit `WhiteKernel`.\n", + " This is slightly faster (no need to check `xtrain[i] == xtrain[j]` for\n", + " all pairs `i`, `j`), and `WhiteKernel` will not give stable gradients if\n", + " you wish to compute the gradient of the log marginal likelihood\n", + " `logpdf(f(x), y)` w.r.t. `x`." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "We obtain the posterior, conditioned on the (finite) observations, by calling\n", + "`posterior`:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "build_posterior_gp (generic function with 1 method)" + }, + "metadata": {}, + "execution_count": 7 + } + ], + "cell_type": "code", + "source": [ + "function build_posterior_gp(θ)\n", + " fx = build_finite_gp(θ)\n", + " return posterior(fx, ytrain)\n", + "end" + ], + "metadata": {}, + "execution_count": 7 + }, + { + "cell_type": "markdown", + "source": [ + "Now we can put it all together to obtain a `PosteriorGP`.\n", + "The call to `ParameterHandling.value` is required to replace the constraints\n", + "(such as `positive` in our case) with concrete numbers:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "AbstractGPs.PosteriorGP{AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.TransformedKernel{KernelFunctions.PeriodicKernel{Float64}, KernelFunctions.ScaleTransform{Float64}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}, @NamedTuple{α::Vector{Float64}, C::LinearAlgebra.Cholesky{Float64, Matrix{Float64}}, x::Vector{Float64}, δ::Vector{Float64}}}(AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.TransformedKernel{KernelFunctions.PeriodicKernel{Float64}, KernelFunctions.ScaleTransform{Float64}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}(AbstractGPs.ZeroMean{Float64}(), Sum of 5 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.018315638888734182)\n\t\t\t- σ² = 2980.957987041728\n\tProduct of 2 kernels:\n\t\t\tPeriodic Kernel, length(r) = 1\n\t\t\t\t\t- Scale Transform (s = 1.0)\n\t\t\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t\t\t- Scale Transform (s = 0.018315638888734182)\n\t\t\t\t\t- σ² = 7.3890560989306495\n\tRational Quadratic Kernel (α = 0.36787944117144233, metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 1.0)\n\t\t\t- σ² = 1.0\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 7.3890560989306495)\n\t\t\t- σ² = 0.018315638888734182\n\tWhite Kernel\n\t\t\t- σ² = 0.018315638888734182), (α = [-9.508930877725136, 19.389883608061695, -7.197900325224828, -4.730615302853674, -15.23471364637827, 18.62620628181198, 7.526138666092433, -0.03155846836128887, -13.788654737243691, 1.6524756606264315 … -4.04659725976334, -2.9334457010779236, 2.099011615147155, 4.200011736661871, 5.696790416685783, -11.011994620197953, 9.404617195251152, -5.495561190687362, 4.121759676625851, -1.6600889945065676], C = LinearAlgebra.Cholesky{Float64, Matrix{Float64}}([54.67525650985495 54.657791476465945 … 38.45054512284434 38.41521522921301; 2988.428769237939 1.381848520498082 … 2.1695516564590633 1.8834148423466848; … ; 2102.293417535266 2104.6198692274925 … 0.2317707151386545 0.1990883709260124; 2100.3617465385078 2102.293417535265 … 2988.4287692372563 0.2317686017127864], 'U', 0), x = [1958.2083333333333, 1958.2916666666667, 1958.375, 1958.4583333333333, 1958.5416666666667, 1958.625, 1958.7083333333333, 1958.7916666666667, 1958.875, 1958.9583333333333 … 2003.2083333333333, 2003.2916666666667, 2003.375, 2003.4583333333333, 2003.5416666666667, 2003.625, 2003.7083333333333, 2003.7916666666667, 2003.875, 2003.9583333333333], δ = [315.71, 317.45, 317.5, 317.1, 315.86, 314.93, 313.2, 312.66, 313.33, 314.67 … 376.48, 377.74, 378.5, 378.18, 376.72, 374.31, 373.2, 373.1, 374.64, 375.93]))" + }, + "metadata": {}, + "execution_count": 8 + } + ], + "cell_type": "code", + "source": [ + "fpost_init = build_posterior_gp(ParameterHandling.value(θ_init))" + ], + "metadata": {}, + "execution_count": 8 + }, + { + "cell_type": "markdown", + "source": [ + "Let's visualize what the GP fitted to the data looks like, for the initial choice of kernel hyperparameters.\n", + "\n", + "We use the following function to plot a GP `f` on a specific range, using the\n", + "AbstractGPs [plotting\n", + "recipes](https://juliagaussianprocesses.github.io/AbstractGPs.jl/dev/concrete_features/#Plotting).\n", + "By setting `ribbon_scale=2` we visualize the uncertainty band with $\\pm 2$\n", + "(instead of the default $\\pm 1$) standard deviations." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=3}", + "image/png": 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label=\"posterior f(⋅)\")" + ], + "metadata": {}, + "execution_count": 9 + }, + { + "cell_type": "markdown", + "source": [ + "A reasonable fit to the data, but poor extrapolation away from the observations!" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## Hyperparameter Optimization\n", + "\n", + "To improve the fit, we want to maximize the (log) marginal likelihood with\n", + "respect to the hyperparameters.\n", + "[Optim.jl](https://julianlsolvers.github.io/Optim.jl/stable/) expects to\n", + "minimize a loss, so we define it as the negative log marginal likelihood:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "loss (generic function with 1 method)" + }, + "metadata": {}, + "execution_count": 10 + } + ], + "cell_type": "code", + "source": [ + "function loss(θ)\n", + " fx = build_finite_gp(θ)\n", + " lml = logpdf(fx, ytrain) # this computes the log marginal likelihood\n", + " return -lml\n", + "end" + ], + "metadata": {}, + "execution_count": 10 + }, + { + "cell_type": "markdown", + "source": [ + "!!! note \"Work-in-progress\"\n", + " In the future, we are planning to provide the `optimize_loss` utility\n", + " function as part of JuliaGaussianProcesses -- for now, we just define it\n", + " inline.\n", + "\n", + "The L-BFGS parameters were chosen because they seem to work well empirically.\n", + "You could also try with the defaults." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "optimize_loss (generic function with 1 method)" + }, + "metadata": {}, + "execution_count": 11 + } + ], + "cell_type": "code", + "source": [ + "default_optimizer = LBFGS(;\n", + " alphaguess=Optim.LineSearches.InitialStatic(; scaled=true),\n", + " linesearch=Optim.LineSearches.BackTracking(),\n", + ")\n", + "\n", + "function optimize_loss(loss, θ_init; optimizer=default_optimizer, maxiter=1_000)\n", + " options = Optim.Options(; iterations=maxiter, show_trace=true)\n", + "\n", + " θ_flat_init, unflatten = ParameterHandling.value_flatten(θ_init)\n", + " loss_packed = loss ∘ unflatten\n", + "\n", + " # https://julianlsolvers.github.io/Optim.jl/stable/#user/tipsandtricks/#avoid-repeating-computations\n", + " function fg!(F, G, x)\n", + " if F !== nothing && G !== nothing\n", + " val, grad = Zygote.withgradient(loss_packed, x)\n", + " G .= only(grad)\n", + " return val\n", + " elseif G !== nothing\n", + " grad = Zygote.gradient(loss_packed, x)\n", + " G .= only(grad)\n", + " return nothing\n", + " elseif F !== nothing\n", + " return loss_packed(x)\n", + " end\n", + " end\n", + "\n", + " result = optimize(Optim.only_fg!(fg!), θ_flat_init, optimizer, options; inplace=false)\n", + "\n", + " return unflatten(result.minimizer), result\n", + "end" + ], + "metadata": {}, + "execution_count": 11 + }, + { + "cell_type": "markdown", + "source": [ + "We now run the optimization:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iter Function value Gradient norm \n", + " 0 2.285662e+02 3.466984e+02\n", + " * time: 5.1021575927734375e-5\n", + " 1 1.597968e+02 5.811062e+01\n", + " * time: 0.8650219440460205\n", + " 2 1.516902e+02 4.934826e+01\n", + " * time: 1.06327486038208\n", + " 3 1.311976e+02 4.436589e+01\n", + " * time: 1.2967119216918945\n", + " 4 1.250485e+02 2.431020e+01\n", + " * time: 1.8010060787200928\n", + " 5 1.208493e+02 7.951632e+00\n", + " * time: 2.3028769493103027\n", + " 6 1.190798e+02 9.555997e+00\n", + " * time: 2.5056169033050537\n", + " 7 1.172102e+02 7.308235e+00\n", + " * time: 2.738455057144165\n", + " 8 1.161961e+02 5.340673e+00\n", + " * time: 3.251149892807007\n", + " 9 1.157434e+02 2.651007e+00\n", + " * time: 3.756255865097046\n", + " 10 1.156009e+02 1.236205e+00\n", + " * time: 3.9578590393066406\n", + " 11 1.155254e+02 7.740985e-01\n", + " * time: 4.198113918304443\n", + " 12 1.154964e+02 3.221338e-01\n", + " * time: 4.719902038574219\n", + " 13 1.154896e+02 1.037538e+00\n", + " * time: 5.225795030593872\n", + " 14 1.154860e+02 1.208455e+00\n", + " * time: 5.4291510581970215\n", + " 15 1.154829e+02 3.337283e-01\n", + " * time: 5.6775429248809814\n", + " 16 1.154811e+02 2.493247e-01\n", + " * time: 6.187587022781372\n", + " 17 1.154783e+02 5.225326e-01\n", + " * time: 6.692312002182007\n", + " 18 1.154739e+02 6.714885e-01\n", + " * time: 6.9231650829315186\n", + " 19 1.154657e+02 2.448683e+00\n", + " * time: 7.683696985244751\n", + " 20 1.154479e+02 1.482835e+00\n", + " * time: 7.899996042251587\n", + " 21 1.154179e+02 4.570831e-01\n", + " * time: 8.137176036834717\n", + " 22 1.153882e+02 1.255304e+00\n", + " * time: 8.652770042419434\n", + " 23 1.153624e+02 9.739080e-01\n", + " * time: 9.17615795135498\n", + " 24 1.153578e+02 1.674349e+00\n", + " * time: 9.379494905471802\n", + " 25 1.153252e+02 2.614272e+00\n", + " * time: 9.623292922973633\n", + " 26 1.153176e+02 8.495608e-01\n", + " * time: 10.132621049880981\n", + " 27 1.153147e+02 4.125077e-01\n", + " * time: 10.640310049057007\n", + " 28 1.153091e+02 4.314195e-01\n", + " * time: 10.84328007698059\n", + " 29 1.153032e+02 4.390014e-01\n", + " * time: 11.080934047698975\n", + " 30 1.153011e+02 2.784737e-01\n", + " * time: 11.639296054840088\n", + " 31 1.152962e+02 1.852778e-01\n", + " * time: 11.863555908203125\n", + " 32 1.152937e+02 5.503039e-01\n", + " * time: 12.627807855606079\n", + " 33 1.152920e+02 1.513503e-01\n", + " * time: 12.832826852798462\n", + " 34 1.152915e+02 5.230940e-02\n", + " * time: 13.07210898399353\n", + " 35 1.152910e+02 7.497965e-02\n", + " * time: 13.581198930740356\n", + " 36 1.152903e+02 1.413038e-01\n", + " * time: 14.090049028396606\n", + " 37 1.152899e+02 2.784662e-01\n", + " * time: 14.29173994064331\n", + " 38 1.152893e+02 1.700334e-01\n", + " * time: 14.527704954147339\n", + " 39 1.152889e+02 1.211075e-01\n", + " * time: 15.054534912109375\n", + " 40 1.152886e+02 1.414145e-01\n", + " * time: 15.561893939971924\n", + " 41 1.152884e+02 1.137853e-01\n", + " * time: 15.82743787765503\n", + " 42 1.152883e+02 1.153485e-01\n", + " * time: 16.334455013275146\n", + " 43 1.152880e+02 4.809486e-02\n", + " * time: 17.067939043045044\n", + " 44 1.152879e+02 4.391792e-02\n", + " * time: 17.305831909179688\n", + " 45 1.152878e+02 5.320242e-02\n", + " * time: 17.81175684928894\n", + " 46 1.152876e+02 4.006674e-02\n", + " * time: 18.283094882965088\n", + " 47 1.152875e+02 8.294305e-02\n", + " * time: 18.768867015838623\n", + " 48 1.152874e+02 2.073692e-02\n", + " * time: 19.003319025039673\n", + " 49 1.152873e+02 4.791444e-02\n", + " * time: 19.75844407081604\n", + " 50 1.152872e+02 4.894094e-02\n", + " * time: 19.9752459526062\n", + " 51 1.152871e+02 9.555718e-02\n", + " * time: 20.20474100112915\n", + " 52 1.152870e+02 9.137966e-02\n", + " * time: 20.71038508415222\n", + " 53 1.152867e+02 1.583593e-01\n", + " * time: 21.243731021881104\n", + " 54 1.152866e+02 1.252607e-01\n", + " * time: 21.54708504676819\n", + " 55 1.152866e+02 1.092765e-01\n", + " * time: 21.782137870788574\n", + " 56 1.152864e+02 3.678196e-02\n", + " * time: 22.307750940322876\n", + " 57 1.152863e+02 2.918578e-02\n", + " * time: 22.830111026763916\n", + " 58 1.152863e+02 2.032871e-02\n", + " * time: 23.041857957839966\n", + " 59 1.152862e+02 3.781791e-02\n", + " * time: 23.281810998916626\n", + " 60 1.152862e+02 3.781791e-02\n", + " * time: 24.520040035247803\n", + " 61 1.152862e+02 3.781791e-02\n", + " * time: 24.990450859069824\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": " * Status: success\n\n * Candidate solution\n Final objective value: 1.152862e+02\n\n * Found with\n Algorithm: L-BFGS\n\n * Convergence measures\n |x - x'| = 0.00e+00 ≤ 0.0e+00\n |x - x'|/|x'| = 0.00e+00 ≤ 0.0e+00\n |f(x) - f(x')| = 0.00e+00 ≤ 0.0e+00\n |f(x) - f(x')|/|f(x')| = 0.00e+00 ≤ 0.0e+00\n |g(x)| = 3.78e-02 ≰ 1.0e-08\n\n * Work counters\n Seconds run: 25 (vs limit Inf)\n Iterations: 61\n f(x) calls: 95\n ∇f(x) calls: 61\n" + }, + "metadata": {}, + "execution_count": 12 + } + ], + "cell_type": "code", + "source": [ + "θ_opt, opt_result = optimize_loss(loss, θ_init)\n", + "opt_result" + ], + "metadata": {}, + "execution_count": 12 + }, + { + "cell_type": "markdown", + "source": [ + "The final value of the log marginal likelihood is:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "-115.28622096603124" + }, + "metadata": {}, + "execution_count": 13 + } + ], + "cell_type": "code", + "source": [ + "-opt_result.minimum" + ], + "metadata": {}, + "execution_count": 13 + }, + { + "cell_type": "markdown", + "source": [ + "!!! warning\n", + " To avoid bad local optima, we could (and should) have carried out several\n", + " random restarts with different initial values for the hyperparameters,\n", + " and then picked the result with the highest marginal likelihood. We omit\n", + " this for simplicity. For more details on how to fit GPs in practice,\n", + " check out [A Practical Guide to Gaussian\n", + " Processes](https://infallible-thompson-49de36.netlify.app/).\n", + "\n", + "Let's construct the posterior GP with the optimized hyperparameters:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "AbstractGPs.PosteriorGP{AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.TransformedKernel{KernelFunctions.PeriodicKernel{Float64}, KernelFunctions.ScaleTransform{Float64}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}, @NamedTuple{α::Vector{Float64}, C::LinearAlgebra.Cholesky{Float64, Matrix{Float64}}, x::Vector{Float64}, δ::Vector{Float64}}}(AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.TransformedKernel{KernelFunctions.PeriodicKernel{Float64}, KernelFunctions.ScaleTransform{Float64}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}(AbstractGPs.ZeroMean{Float64}(), Sum of 5 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.006456502042458794)\n\t\t\t- σ² = 191273.47839039602\n\tProduct of 2 kernels:\n\t\t\tPeriodic Kernel, length(r) = 1\n\t\t\t\t\t- Scale Transform (s = 1.0)\n\t\t\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t\t\t- Scale Transform (s = 0.012139119857609751)\n\t\t\t\t\t- σ² = 5.99003503589132\n\tRational Quadratic Kernel (α = 0.0007411653885252467, metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.04622167413324385)\n\t\t\t- σ² = 178.92175101210103\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 8.752621585494843)\n\t\t\t- σ² = 0.028315070269881937\n\tWhite Kernel\n\t\t\t- σ² = 0.03576077215040964), (α = [-6.406094979444605, 9.479119219537516, -3.948734481727562, -2.2929110041795133, -7.264548896805682, 10.568699772266585, 4.7636505832994915, -0.45049595724000985, -5.304176095351258, 0.6863897391900999 … -2.4085413345738695, -1.8519725706491437, -0.08804437329108424, 2.9187163418293287, 3.859296814723736, -4.852329282110473, 5.787914028013686, -2.597113724382437, 1.6293634786512665, -1.556529064227645], C = LinearAlgebra.Cholesky{Float64, Matrix{Float64}}([437.5596579351054 437.5586565555823 … 418.9550496116569 418.8901303626153; 191458.01608900484 0.9361226204096542 … 31.030631101781392 30.79245365912258; … ; 183317.82819826168 183346.45714095386 … 0.283876246183141 0.18253163358441124; 183289.42215385765 183317.82819826689 … 191458.01608900103 0.2838619481426396], 'U', 0), x = [1958.2083333333333, 1958.2916666666667, 1958.375, 1958.4583333333333, 1958.5416666666667, 1958.625, 1958.7083333333333, 1958.7916666666667, 1958.875, 1958.9583333333333 … 2003.2083333333333, 2003.2916666666667, 2003.375, 2003.4583333333333, 2003.5416666666667, 2003.625, 2003.7083333333333, 2003.7916666666667, 2003.875, 2003.9583333333333], δ = [315.71, 317.45, 317.5, 317.1, 315.86, 314.93, 313.2, 312.66, 313.33, 314.67 … 376.48, 377.74, 378.5, 378.18, 376.72, 374.31, 373.2, 373.1, 374.64, 375.93]))" + }, + "metadata": {}, + "execution_count": 14 + } + ], + "cell_type": "code", + "source": [ + "fpost_opt = build_posterior_gp(ParameterHandling.value(θ_opt))" + ], + "metadata": {}, + "execution_count": 14 + }, + { + "cell_type": "markdown", + "source": [ + "This is the kernel with the point-estimated hyperparameters:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Sum of 5 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.006456502042458794)\n\t\t\t- σ² = 191273.47839039602\n\tProduct of 2 kernels:\n\t\t\tPeriodic Kernel, length(r) = 1\n\t\t\t\t\t- Scale Transform (s = 1.0)\n\t\t\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t\t\t- Scale Transform (s = 0.012139119857609751)\n\t\t\t\t\t- σ² = 5.99003503589132\n\tRational Quadratic Kernel (α = 0.0007411653885252467, metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.04622167413324385)\n\t\t\t- σ² = 178.92175101210103\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 8.752621585494843)\n\t\t\t- σ² = 0.028315070269881937\n\tWhite Kernel\n\t\t\t- σ² = 0.03576077215040964" + }, + "metadata": {}, + "execution_count": 15 + } + ], + "cell_type": "code", + "source": [ + "fpost_opt.prior.kernel" + ], + "metadata": {}, + "execution_count": 15 + }, + { + "cell_type": "markdown", + "source": [ + "Let's print the optimized values of the hyperparameters in a more helpful format:" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "!!! note \"Work-in-progress\"\n", + " This is another utility function we would eventually like to move out of this notebook:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "se_long:\n", + " σ = 437.348\n", + " ℓ = 154.883\n", + "seasonal:\n", + " per:\n", + " ℓ = 1.456\n", + " p = 1.000\n", + " se:\n", + " σ = 2.447\n", + " ℓ = 82.378\n", + "rq:\n", + " σ = 13.376\n", + " ℓ = 21.635\n", + " α = 0.001\n", + "se_short:\n", + " σ = 0.168\n", + " ℓ = 0.114\n", + "noise_scale = 0.189\n" + ] + } + ], + "cell_type": "code", + "source": [ + "using Printf\n", + "\n", + "show_params(nt::Union{Dict,NamedTuple}) = String(take!(show_params(IOBuffer(), nt)))\n", + "function show_params(io, nt::Union{Dict,NamedTuple}, indent::Int=0)\n", + " for (s, v) in pairs(nt)\n", + " if v isa Union{Dict,NamedTuple}\n", + " println(io, \" \"^indent, s, \":\")\n", + " show_params(io, v, indent + 4)\n", + " else\n", + " println(io, \" \"^indent, s, \" = \", @sprintf(\"%.3f\", v))\n", + " end\n", + " end\n", + " return io\n", + "end\n", + "\n", + "print(show_params(ParameterHandling.value(θ_opt)))" + ], + "metadata": {}, + "execution_count": 16 + }, + { + "cell_type": "markdown", + "source": [ + "And, finally, we can visualize our optimized posterior GP:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=3}", + "image/png": 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HlERiz+wYADi6/Khy5zi148UBUqfgkDjRXjKmWE3hEByysHdq6AdG6b+u5AX1CCGEppaslnOLLrCXj2G7l48Z+1zQXvmsNdZhH+xO9zZ6672SN68XRnPRoLx7Pstp9R8D2He/JI5yIUfZUG4EAPzS3hkxe6fJSF7DMkr8Fd8He4QIIYTex578CQAZNQsAiqEyYPs8F4wrCd3UNVPTTK0vPdDgq6WEVLkq3o3sKPbhKCH2o76x/JK3OEHGXqRG2h2Eu2fElMkBB++ASYRBiBBCaC/N1BJK0n6d1XMAYDErrWZUSwWAt4Y22921jkT3zEBjQPLHlGQ8nwg7QwAwLzQrpab3r3CgY1aQDsr+Yub5JA8AOIT39QgFTgjIvlJ+xX1hECKEENorpWU0U7cYA4DCnl5grJBQdFUxlCda/z6SiwBAT6qvwVcXcATieXuCjB8AjgrPAYCAvG8QljnKiq99ktfuAgpUsFcN9YhujnAiJxTP2b8fWVIYhAghhPbKqFkGTDNVxpi257lgJB9jwNoTXRaz7Cmj3am+Rl9dyBGIFKI5PW8vBBN2hi6bt8K93/bxla6wPfNT5iSREyVeJECKZfIc4Sr2nHBIYBAihNB0p5iqvmd+ir18jH3E7hcCgL2OTFu8U+bluJLIarmCXgg7wyFn2c5Yq0/yFrfPW1h+1D4XF6jgFlz2cKg9FkqASLw0tjqwyl1R2m94UBiECCE03aWUtL2CqMWsvF4AAMVQ7cmieaOQUtP2ae2JrmMqF8TzidFCtNwdpoTMCjb3pgaCjn0rHAghxXIIv+y1qwMBoDgLxi04HWOWz5ZKsILoh4dBiBBC011KTduxZ2+3CwAJJaUaCgC82rf+2a4XASCjZRVTmVPWElcSiXzCnhoakH3Vnsrgfg8F3YKrGIRece/UUInuDrygIzB24bRDC+sIEUJoukupGXsNz2KNREpNOwUZANriHQInAEB3qq/eW1cmB2JKIq4ky/aE30nVi8fOc7EFZD9Pd2+fa++sZG+oVKyUD8i+/ddpO1QwCBFCaFpTDFW3dOX9y8dYzBrNRfN6fjgbcYkOAOhJ9Tb66/yyP6NmR3KRo8Kz7TMXVy2yX0icqJq7t6oPyD7N1AFA4IQ9g6IyASLS3ZHJEa4Uy2d/NDg0ihBC01FKzdgvipsrwZ7lY0zLAADdMjoS3bOCTXm9oFvGYHa0xl3FEVrnqd4Za9+/RqLSVW6/4Cnn5B12/sl7xj+9otcpOA7h1NCDwCBECKFphwGLFHYvH2NXzRe329Ut447Xf2F3ELtSvY3+hoDsSxSSCSVhD4fOC802mVH2/qp5Skj5nhIIr+QlhEicRAgpBiElpJiUhxsMQoQQmnZyej6n5e3XWS0HAKqpKYaiGGp3slcx1XghAQC96f5GX11ADkQK0Yya88leAJgXni3zkr0YabGH5xbdAuXtigiv6AYASohIhbHLZ5e7QpP6JT80DEKEEJp2Umqm+DhQMRT7RSQf0y29LdEBAPFCQjW0SD5W464scwQ6kt0+yWMv+BKUA98+6Xp710A7GgHAN2ZqaLFGwit5x26odHiOiwIGIUIITRNszEYQGS1rMlO3DPu/9sHhXAQA2uOdDb76eCExkBuqcpdzlK9xV747un1ssaCTdwBAQPYVRz59sg/2LJxdXEo0KPsPnxqJg8AgRAihaSGlpItZaG8roZqq3S9cN/DWhsFNuqUXDCWhphaE58aVZCKfKHMEAWB22aycnt+/WDAg+eyco4S6BRcAyJxkPx20T/BLXocwqftIfDQYhAghNC2k1LQde6qp6ZYOAAVdsefIbB3d3pvuB4C+dH+NpyrsDCYKyWKxoFt01ntrgs4AANiBBwAEiFfy2J0/p+Cw95dwCk6RCsW9JjjKFffgPZxhECKE0LSQ0nY/F7RnxwBASksrpqoYan+6315Kuydlz47xx5VEvJAI7BkOPXfG8gXhuQBQ5dm9KKhTcPCUt4OwuECaX/YePtWBHx4GIUIIHflMy8xpeXV31fzu2TGJQlIx1M5kd9ARKE4TrffWBmV/Rs8O5oaLxYINvtqgHJA4sbjprr1ezO49dfeMhRIgFYdrjcRBYBAihNARy7RM+0VOzzNgBV0BAMVUTct4bNdTumXEConuVO/C8gV5vWBYRiQfCzvDHOVn+BpGc9Ggw0+A8HuGN/2yjyO7RzvtpUR5ygmUH1sjMcl76k4IDEKEEDpixZSE/cJePka1NABQDLUr1bdp6G3FUHRL70n1NwXqfZI3Wohntd07C84JzRIo7xHdXsld3CbCLhCU3j811D9m7ugUhUGIEEJHIsbiG/4xuPYPRnQQADJ6DgAKegEAVEPtSHQBQKyQMC1jKDda664qkwMdiW6f5LF3FpxXNvu4qkUEiF/2FzPPK3lhz0Bo8WBgitRIHMQUmM+DEELon5Vd9/feJ+6KiSSyeWPVLauzexYUVQxVNbW2RKdHdCeUpGGZ5c4ykRPLXMGd8TZ7aigB4hZdn2o5FwD8ktceX5U40Z4UI/GSQHmO7N5cwi95uT0bTUxR2CNECKEjUPbFx3MCaByYyYg61K2Z+o5Y20BmaCQ3WjCUSD42Pzw3XkiM5iP27vDN/qaOeJddL1F8zjd2+WznnopAr+iWx+ypy1P+sF0y5kPCIEQIoSNHcSMkZhg5CjoBEyDb9Q4AvNL3xo5Y22g+2pceqHVXhR1lcSUZLyQCsh8AmgNNHOWCcoAAKXeF7YvYy2fbY6HF8U+f5B27ufwRAIMQIYSOEFYu3fv6nwsd7wKA+/wrVYEDAN0hWQ2zNFPrS/XHCwndMvrS/fW+2qDTb1fNBx0BABA5sTkwo9xV5hFd7j21gGNnxzj2TA2lhFbsScojAz4jRAihI0Tk7hv6Mz00z6q/dFvkzWeYZfGhSvfF/2W4/Z2dWwQqJJQEAPSk+k+qWRyUg9FC3GU4y+SAwAmmZX5u/iUcoT7JK1CBAGHA7C0mRE7gCDd2RoxdO3HEwCBECKEjATMNdbgn5yUqZeq29Ynud00n2amOBlf/2DP/xHazY2HFUTuibQAwlBup8VR5JLdu6YPZkYAc8Eu+nJ6zdAsAvJKHECILUkFX7CFQAsQnead6jcRB4NAoQggdCQjHG43NFgGNI8QT1CVhwEGfK6eJzEjs1Se7h3fOylh5o5DXC4qheCQ3ATIrOJOj1C06/XtyjgCxe3sSlSihAhXsiwcde4sojjwYhAghNLWltQwAqO3vpAbbAUAjkN/4glXV2OPmNQopEZISjEqkIpn3y76OZFfA4SdAKCGzy5rLZD8A+GWfyIkA4BKc9qaDDkEam3xlcsA+fkTCoVGEEJrCNFNLKmmv6DEiAzkeAEDlwEpGMtpwTwPPM8iFK1LJSEAHz7xjgomNHYnugBQAgDJHcF5oToWz3Ck4hD3LZxdrJNyCWzX14qdM9UrBgztiEx4hhKaD2MDOTMfbYJlq1/YcR4ZlEpWIZehZniVFaCpAQk2OiFDDuYWqGXXe2ndGtwWdfoHyIUeQI7TcFfKKHhhvvZgjrEbiILBHiBBCU5WybX3XQ7cVCFRu2Zzb8abqghfCdH6azU2zAQctV5hfZUlm5UXiSab14e7ZwZnPd78clP0+yVucBeoZUyNRPMhTLuQIHqrvNckwCBFCaIoxLdMeq0w/92iOMoMQZcdGzTJUSoYlUiewiEQGJVKtMI/BRiWS56C6AJ5AlehyOQVnQAoU99SFPXvtOniZEjL2ueBU3Fnwo8GhUYQQmmKKe0qoQ50KJToF09R0l7vXQQiBXHll2uMakkmdszzorUwJJC1A0BEMVTaLnHhhy7lNgXqv6KGECpxAgNh9QUqoV/LK02Y4dCwMQoQQmmIi+RgDBgB6ebVBYUQiKjBVzfY5ycwCjWcjlq6OOGjzorPKc9qIRJIC8eXyAdkvc9KC8nk+0WNPipGoKHICJbtXCi2TAwKdjsOEGIQIITSVWIyllZSiqwBgLTmnxyv9rZLm3Q6VwpBM5qWMFMcKYJjMIlvWOVMpn8EoA0+oziO67M5fcV0YhyBLY8ZCw86yQ/KNDrnpGP4IITR19T/6i8jOF/q9dfUXXj/65P91V9MUTxOmanIkJpEZxKPTbFQkfh2ILFeEZzbm2rpdEF5wGgFi76NUXDXUI7qpni9e+QiuFDw4DEKEEJoC8nrBKTjMdCK2ZS3IJDvUk/7rapWwPgexCAzLkOehTGXOinqfvrPXZfkZHzrp/Lry5sWbHitj2dCcE2FPdURxaqhf8tl7DU5zGIQIIXRYY2oh+vwfE7n4zI9faaSjOQ7iIoQ0ovXsSHhIlieVKkuIZFgmVSpjEgspxi4XnZnTXdGIXL+odtZJtQAewQXFYsE9QSjzkn/P1oPTGQYhQggd1pJ/uXdg8zMahfjIqDjjqBwla6r4c0eMulGtW6ZVCnMQPu2ABG+Ucy6Pq6xxlO30wLEp8LvDMrd7FNQtugHAITgIIbKwd2ro9KmROIhpOiKMEEJTA2P5TS9leVA50Ac68q1vxyUSF2FYohqwIQepUphPMVKgpwTwpDM1M49r4YMEIBRqKJt3skB5SghPOZ5yACBQ3sU77SeFqKiEQWgYRjKZ3P+4aZqZTGb/45lMRlXV0rUHIYSmEItZAMAsE0wtxxOVgFXIZYc6ehxAABIixAUYkkmtJXl1lhIgxZMAkcJNi5ovu3FeePbs0z4rUJ4QInLS2K0EK1xhAuTQfa3DUamC8FOf+pTP52tpaQkEAj/60Y/sg6ZpXn/99ZWVlbNmzVq2bNno6Kh9PJFInHnmmY2NjRUVFbfeemuJmoQQQlNIrJAAAMLxpHlBjodBJwUA1dL6nKQ+z1ICRGQyIpOKjFph8sMSyQik+RNfdnrKJE783FGX1ntr7OvI3Pv2kZi2NRIHUaogvOOOO1KpVCQSefXVV3/0ox+tW7cOAO67774XX3yxra1tcHDwqKOOuuGGG+yTb7nlFq/XG4lEtm3b9utf//r1118vUasQQmiqiBRiAKBsfyvZtXWnm/6jgnKNLSolgzKdl2EZrzcuUtEClzc0NwsZgcgm4196GvZMCnXwu/eR8EmesctnT9saiYMo1d/IggULeJ4HgPnz51dVVUUiEQB48cUXL7nkEr/fTwi55pprHnnkkUKhAACrV6/+xje+QSmtqam57LLLVq9eXaJWIYTQlJB6bU3Po7+I//W3ZiaR50ifE1I8yeYyaR5yPDTz/qSRS8tiwOKqln7Goar1eSugMTkWhT01EsWpoX7ZJx25m8tPiBLOGn355Ze3bdv22muvzZkz55xzzgEAp9OZSqXsP02lUoqiDAwMlJWVJRKJOXPm2Mdnz579xBNPHOiahmGMjIx0dnbaP3o8nnA4XLqvgBBCk8+IDfevuVd3Qay9u+yYjxco9DpInoPR3EinX6wwqJxXqJ8N8bpXAWHbRtfRpzYPvzEkQ+WSiwDAngtTHA517ukaogMpYRBGIpHt27e3trbOmTPHNE0AuOKKKy699NIlS5bU1tZ+73vf4zgulUrZHUenc/cUXrfbPe4UG1tra+v69etXrlxp/+jxeF577TVKS9jTz+VyhOCD5YmkaRoAiCLOW5tIeKNOuMm/URkw1dBkXtJ3bslTa0jmGgrgbN085CSUgU+HPpl0S2ZlznRpLKizdjdpyjEn73SefeXxo6cQizlqF2ezWTCZoih6QcuqVvHiWTU7aV/kICb/RpVl2U6ZgyhhEF588cUXX3yxZVknnXTSqlWr/uM//mP58uUPPPDAgw8+yBj71re+tWLFirq6OpfLBQCpVMp+kUwmD9LJmzdv3hVXXLFixYrSNXsfjDG32z1pHzcdYBCWAt6oE27yb9RY2ybdUENzT4lu35DlyV+quZDGKqizy5mpKUDBKUacEBFZY55VhWY05FvXBekxKVbz8c9y3qAj55J52eP2AIAb3GVq0O/1T1rLP7zD80YteUE9pXTGjBnRaNT+8YILLrjgggsA4E9/+lNjY2N5eTkANDY2bt3M8R4AACAASURBVNq0qbq6GgA2bty4YMGCUrcKIYQOK5kXHut+9n5g4Fr2Wa3rvV43KXAwIhE9k+0PQpVOYxaX4qykSAJpEhA8M9JsXRDKFWaNDoihaoHy8pgHgZWu8kP4XaackgRhLBZbvXr10qVLZVl++eWXn3766RdffBEARkdH161bN2fOnPfee+8///M/f/GLX9jnf+UrX7n55ptbWlra2trWrFmzcePGUrQKIYQOW8rOjVkORAuyr/3V8Pg7nQoBGHaJymhqWBIWpA2T5VI8pASoVEzvcWc0PrFRNqFeF8T6FgCQ3l8jMX02l58QJQlCURS3bdu2evVqRVFmzZr19NNPH3/88QBgWdaqVava29urq6vvvvvuiy66yD7/W9/6VjabveSSS3w+30MPPdTS0lKKViGE0OGmuNc8J7lGZKITUhdNKbLQK5OGHItxepynCZHUaDyA/nKI0ylpOvrjjmOXNpR7v925ad55Z1K3HwB8spcnXPGy9jXRh1SSIPR4PKtWrdr/eGVl5dNPP73/cY7jbrvttttuu60UjUEIocOTER8Z2LWhdu7J1OVLdWx5u5KMSmRpUshnYsP1wtKouc1H273EZ0CAcT5DeEjWyjTmZHkAkL1h58yFTs/u6nif5LFXokEfAVZWIoTQIaDs3DRy+1Wdf71n6IdfNBOjGT3f66AJkYBlDTkIx1iNQlISNyKBT7d8vNObzpVrzKuDNDwIe8oEi9vqekWPA8skPioMQoQQOgSyL/1ZISzHAzO0zNpHMyI/LEOKB4VYXU5SpUK9I5wlVkIgPgMqTrlQJFxjnoV0FjjpXABwCU5KiEgF+2qU0LHLx6B/CgYhQghNHqYWmK4BgFjXkuPJZh9XoJDf+HyrywqrwDEYlqHbRSpVCA2NeHXocpKgCnxsuOrfvr/Uf9Sn5pzvPvUCAHCJTpfgwuLRCYH7ESKE0CRJPfX/J198zOT46qu/7znr8mT7qy+EIh9LcLPTZreTr1aYyZFeB42IZHHS8guOuoL6rpeeFrXEGfMd804I+rjidFACpNwZOrRf54iBPUKEEJoMVjaVeeGxLG+pTEs//0j8gR+9qw4xgGEJgKP9DlJVYGFXxYjflRBIlSHITUfNzFmMQB31OI9dRoBIvCRhjUQJYBAihNBkMLMpYFaGIyolxkhvbtuGdhflGYxIoIE17CBHVc4LDA9HzXxGgHpHOTONWVnGMaiMpfShbgDwSZ6xVfNYIzFRMAgRQmgyCBV1YuPcrT66roxa2bRKod9BZmbZqERGJMoAyts7qgqsy0VkE9wDfa4Tzw5o8MUe020RKjkBwCt6ZAFnxEw8DEKEECotk5kAAIS4Tr/obT+33Us5fyglkIQAs3Ms7nP1OEiZxnyyf3aODTiIX2eB2jmOY5aGz/9iqGF+9ef/iwuWA4Bf9socLtI78XCyDEIIlQrT1Mj/3jAU7aqbdbLjmFP7Vv+4d4HgNykA63BCuQozpdCbstnjFwOMVJzxWfWxX3h1VqYzyvMAEF58VmJGs6tqkX01YU+xBJpYe4Pw1Vdfve+++z7k2+68887KysrSNAkhhI4Q8T/+Qu3dFXeRsi0vK21be1xEp5AglpmMddXxlQqbmadxJRYhrCwHLBkRqdBQYGEFxIaZAOCVPE4By+RLbm8Qtre3//nPf25oaPjA9+zYseP73/8+BiFCCB2cGR9Jc+RnLcLdWzUxl95eQ2bmWJubZHnodZHZGeYfGhQrhF4H/XjEoE53/TfvWv7c/9bWN3jP+gIAECBhZ9mh/hJHvvcNjR533HGvvPLKB76H43CqEkIIHZCZGCWSkzpclpLb5qNZHkZkrjZvdjv5xpw14hB6nWRUhLPyJscJ9QW23UNqLYfrxLOJKNWceUWNp4oIu58FYo3EJNgbhNXV1YsXL/4w7znzzDPtTXQRQgjtI3bfDzI73uQoF/r8jcZI344mDgCGJKs2D/0yWRa12sPSQIBPCtqMijmckJ2ZHdjuoTWpgplJ8GWVPskjjamRoASnNJbc3iA8++yzzz777A/znmeeeaZk7UEIoSlMH+wqbH+z00MchuXc9DIXrGhzxv0aG5FpTiApgcyRq9anRlp54BmtcIWpIc6ODD5XzoIgcv4QAHglj2NM1TyaBPi7BkIITRimqwDwVCX/QpiaqdF8anRYJsekYVQiHU4rrDJPXqvLs51uEtCY8c56/6e/Uhdq/PdkWcW//5hwPAD4RI/IYRBOqgOWT1iWtXPnzqGhIcbY2ONnnnlm6VuFEEJTktgwR6hraXP3MCB6R2erhwQ01pSzNlV7+pVMWGOir2xervAnUVuQtsS6WUJVY+O1PyKpfql8rn0FGTeRmHTjB+HGjRs///nP79y5c/8/2icXEUJomtMs+MZ6881R6/qjuIszr3RHO7IVfEwkxO1pd2frC6zGEP5h5kZkWsm53addUPfQne46PqQyrqwcADyCG2skDq3xg/DSSy8VRfGPf/xjc3MzbvOBEEIH8XCH9evtZhW39qqXl59qvvKel8zKskEn4dyBTmemOcsqM0qSF4ck67h4hql5wWKNeVajMKGiDgA4ypXJgUP9Jaa1cYIwGo12dXU9//zzZ5xxxuQ3CCGEppYnepiL9jaL9w0XTje6d7RWk+OSZpebz8X6B8rIJ4bNACfzzOxy0k8OGcywfMef9ameN5qrF3jOuty+QsDhP7RfYZobJwi9Xq/D4RAEXMsHIYQ+WHeG+ehOApYXhmQl1e3kPjnE1mpshDPiolCfZ8Gr/6vpjZVbXUad5ZDnHe8+7YKmxEWV7r1rkhDAgbdDaZxZo6Iofu1rX/vVr35lGMbkNwghhKaWJg/x0h0AcKLyukpZXIS6AgR1tt1DJMacJuTeenZmShUsCBgWHywHe0MlrJE4bIz/jPDHP/7xF7/4xfnz5y9btszvf1+f/Sc/+cmkNAwhhKaA4QI81WstFDuzrMlJo10uqFEYx1ilxm3xk4BGnCecqbZuOdpkrS4LVIOZJqFcUA5gpfzhY/wg/N3vfvf73//eNM3e3l5Zft9cXgxChBD69Tbrxo2GkyM3H0stpkgQ7Tc+lXFm1wTnerTBuNczKzVwfyO/MGVZelysbandsf6zA0yee7y9dhqPe+oeTsYJQl3Xv/nNb5577rn33HNPbW3t5LcJIYQOc/+5wazlHszos2948yQ36SywWg1qK6VX/xFo0eHEJYX++SN9JoEKlRnZgfL/73+F158mghlccumhbjgaxzhBGIvF0un097//fUxBhBDa3xsjTLcgJG4kjCXMk6u5nRlrpkOsdNKIk5pRa37wovPqN7Ay7ZUqjXqWX0adbu/yyxyWTnBDwcPSOIPU4XA4HA5Ho9HJbw1CCB22EipsTzIAGMwznmQdZMhBh3nCvLQ1bc2O6jWaPuQggxmzosA5Czs2nj1sLYzr1OW1347b6h62xglCjuN+9rOf3XTTTT09PZPfIIQQOgxtirKaP+pHPaZf+ZK5KcK8ZJfB3DIZVRm4SG+OtaR1ucCqZTJcsCqqetZZmeTHYkZIA617nCW60GFl/MkyTz/99NDQUEtLy5w5cyoqKsb+0XPPPTcpDUMIocPIV143TTMxT/zN79u/fVQA/Px7EevkMH2VMEsk8ZxVIVNIWXNlEv18i3t+GR8D8OoAAI5FSw5129EHOOCi2wsWLJjMdiCE0OFsVxrC9I0gt4kB25YgC6W2Xv3TYfENF+3SwSNxvMCRpHmsj7Y+3s1+e9qJnjM/o3Ztc590jlg/+1C3HX2A8YPw0UcfneR2IITQ4cZikNLAJ0JnhmVV1iBtpKCLkNTA7yTdadYccIZm8e9aZujnJ3I/fNsa1BalrZkCx4AQ3/lXH+rmow8LKzoRQmgcQwWY+RgJPRj3P6g93csYGF7SqrDKBlfUSfsN8BrM05mrsfQNGTP8UIe1Zjk3w0tDDt/vlmKN4BRzwCBsa2v76le/esoppzQ0NJx44olf/OIXt27dOpktQwihSaZZ8Lc+ti3BAODJXhgswHzpJ6K57vYtlpt2KxDOsvqkOuqlbVmzySdC0lzgpW0qq/CKsDhMOi7lBy/nL2nCDsYUM/4/2PPPP79o0aLf/va3Ho9n6dKloVDoscceW7x48SOPPDLJ7UMIoUlzzt+NTzxjHPN4/LsbzWoncFDw0DYn7VcM5qW7slaTYpU7YMhJ+nJQVyGThLUIgDmFql+fgr3AKWycZ4SMsWuuueboo49es2ZNeXm5fTCVSn3uc5+77rrrPvnJTzqdzsltJEIITYZXR1gZ3dQgPLRy688vaSRB7m0A5qRDGk+8+s6kuUDimQvaiZWLWic3uqA1HVZZaEAt789Bo+dQtx59VOP0CEdGRrq6un7+858XUxAAfD7fXXfdlUqlduzYMYnNQwih0srq8Pc+1p9jAFDpJCH+NZlETAZvRSHIbUyYiyQYHcoxN+1Ks9kLQ+VlQsTFRRp9FUurKABs076bYnPfjLBD/T3QRzdOEHo8Hp7n9+/22UcCAdxJGSF0hDAsOHGNcd4/9NmPGmt6rMGcFeTe5ojCk3xvFrx015CxXCKjFHSJRAus9h+DDZbRzUPk7UT5eXWkzkWyVoNX4FY04oaCU9g4QehyuT7zmc+sXLnSsqyxx1euXHnaaafNmDFjstqGEEKl1Z9j25OsRVrFrMFvbzCdpNtgzgKrkkgEICVAIm4dK5CMk/ZrLMgYp7KgAQ5KNI35GIOez/Kdn+GHLhcaPRiEU9j4dYSnnnrq9773vblz51544YWVlZXRaPTvf/97W1vbTTfddN9999nnnHbaaXPmzJnEpiKE0ARbP8oAIExfS5HZQ/lqH92esWbxkJHJqAzDWTaTAG+RshD/jsoqvzqPvjLE0rnZbtp5ejU9qZwAQBNG4NQ3fhDefPPN0Wg0Go2uXLly7PHvfOc7xderVq3CIEQITWmtaZBIjCc5JzcYN6Ge354wF9U4OmvYaEaLZaw5FU5IGTVl5JWUNas7y7qzTLaO46HQGTvUTUcTZ/wg3LVr1z7jovvDuaMIoanuwkbym3e6AIiDDBoWuElHt/XZy6oKz/YNOmg8qp9SAEKsRc3i/cPm6eUyKZgsbZ0aM49v9h/qpqOJM34Q+ny+SW4HQghNDsWEe3ZYaQ2+Oo9+7y2Lh9a0OVsmIzzJCzTDaM3W5ADPtvKQ1Gnll+bAvduOAQCDVN+4kJsfJDe9ZZY7HE8ux8LBI8cBF91mjL322mtbtmwZGBiorKycP3/+GWecwXH4b48Qmtq+/Jr5QJsFjG2Kce8mmId2jhpLGoVHZDKsWBWKSd6IzVgkdVPQDKMyoUDeqspbtUmjYVPU+sZ8+o35uHDMkWb8IIxEIhdffPErr7wCAIIg6LoOAAsXLnziiScaGxsns30IITSBTAZ/7LDctDNEN2yOXs4RcJLeLvgCT3/vp50KC59YTtaPhikzgUDG9NR5CADbpP0KgBwXwnkxR6bxf7W56qqr3n777fvuuy8Wi2malkql/vjHP0YikYsuuogxrBtFCE0xnRl2305rR5LpFlgMyrnXyri3YgoMZrM8yWXNqqxVFaDrNai4aRF349E0zxpVq/zcGvjBIu5XJ3GfaqAvnMfN8mEQHpnG6REmk8m///3vq1evvuKKK+wjXq/3sssuq6ysPP3003ft2oWTRRFCU0hChYV/Nnjz3bQ17+mzxS/P415v3yaSuGIyP+3KW7UAJG3OruKe6zAW/7HDei/BMlaLk/QnNeApfH0+/ToOhx7RxvnX1XWdMbZ48eJ9jttHVFWdjHYhhNAE+XOPldON+dKPXbTz9q3spcG8k/ZzRJGI4qYdOasxJEOBLCDELFiVi8rIO3E2aJzTq6+IqTgANi2ME4ThcHj+/Plr167d5/hzzz1XXl4+d+7cSWkYQgh9dGkdfvi29cO3rZQGN28y/XQbBdVBRkZyrD+1K2/VqlaIg6iL9mZZY4uPjupzAYgCVUsrSVAClZVlWMt/zMOx0Glh/Mkyd9111xVXXDE8PLxixQp7ZZm//e1v//M//3PPPffkcrlcLgcATqdTkqTJbS1CCH0o175qPtppcFB4J+aOFKCe22CC5KCDUZUF6fYMm+WEfplEJIhHWEXeYDrzdepXFqxyIPD8efzv2qz5XvPK5kP9NdCkGD8IL7vsspGRkTvuuOOOO+4Ye/zTn/508fWqVau+9KUvlbZ1CCH0z1NM+HO3Vc3/w0N3/rnnmwTAx+2ImKc4yFCPBo3SziHzHLeQn+OKEDV2RlXFceV0a8zsNy5o8ZITwgQAflXGaZp5qL8HmiTjB+Gdd95ZKBQO/s4lS5aUoD0IIfSvGi0w3QIf/55IEoYFBAwHGe42r6jlnwBgbtKVMmdfWNvXnh42WHRpdfgr82itCyIKXNWCk2Kmo/GD8POf//wktwMhhP5FcRW2xtlxIVLnJieUU0h3EjDn+Mhgpk+1Qi6pIUgjshaxQNAhsKavtppf4yTC19eLlzfD55oxAqevA64s8y/asGHD+vXrk8lkfX39JZdc4na7i3+0cePGZ599luO4ZcuWnXjiifbBrVu3PvXUU36///LLLw8GgyVqFULoSDWQg7mP6Rkd6lxk5Yl0SyRxvJSjoLemjAquI8caPHKZUUi5aG+eVfEEktacOfTnOateoCBgCE5ve//9N23adO+9936Y93znO9+JRqMHP+eXv/xld3c3z/MPP/zwokWLksmkfXzlypUXXHBBIpFIp9MPPfSQffD5559funSpYRjr1q074YQTMpnMR/ouCKHp69tvmhkdZgn/O5BXn+lnXtqas5o08Ikk5uHa0lZza5IqLFTGbVCsyo/XUI4LKKwcaPBPH+c8wqFuPTqk9vYI33nnnT/84Q/XXXfdB75n5cqVV199dSgUOsg5Dz/8sP3iO9/5zsyZM1966aULL7xw586dt95663vvvdfU1DT25B/+8Ie33nrr17/+dQA47bTT/vCHP3z5y1/+KN8GITTN7EwyJw/1bqKaIJNIJf/CgPnJ14cbPKQ1zWb6mOmkozIZVIWTLQVyVmOIvtlrXPyJOlLnoq92zjJBbk8f6u+ADrX3DY22trZeddVVH/ief2qVtcHBwVQqZe9r//TTT5955pn5fP7+++9vbm5eunQpAOi6/vLLLxc7o+eee+7atWsxCBFCH+j2LdYPNpkcgdXLuPPqyPr+twFAIqMD+YaZtGPIXN7oSgxlRh0k2qZWXNJE1/XNDJF1eau6NcV+22pJ7EJCzD91WriO9jS3Nwj9fn8gENiwYcMHvmf27NkfpoLwtttue+SRR7q7u3/1q18dffTRANDV1dXa2vqlL31p2bJld95557Jly/7v//5vdHTUsqyKigr7XZWVlUNDQwe65uDg4AMPPLBu3Tr7R4fD8d3vfpeQEha9qqoqimLprj8NaZoG/+SvU+gDTZMbVTXhytfoKyNwfi2sOsX6fTt10gHKtJ+929SdZpX0bYM5XGQkaTCXozerz1wQ7BhS+gRIqFb45LC5tq8FAAhXdXalcdc2akAjMGh2W+MumIU3ailM/o3K8/wH7pu0Nwgvuuiiiy66aAI//itf+cqll176+uuv33jjjSeccMLChQsZY/F4fPPmzQ6H4/rrr29sbPzWt75lz6Mp7gNsmuZBGi0IgsfjCQQC9o+BQKDUO0NRSinF3xYnkv33iX+rE+sIvlEf6oRvvwWVDnh4GTw7CGt6oYp/dnXHWceHaU8GGvgnCMDW2FcZkBapLWqeLJIRniQp0xQWXjPYXA6/18AXEPlHuiBpzDB5R1yvqPfQT9bBU33Q7IGfnUAoHeeXabxRS2Hyb9QP01Mq1axRAAiHw+FweM6cOc8///wjjzyycOHC6urqWbNmORwOAKiurq6oqOjs7DzzzDM5jhsaGvL7/QAwNDRUVVV1kGuuWLFixYoVpWv2PgRBEAR8kj6R7F+x8W91Yh3BN+rXNxgpzYoo5J5ddGscHHSoRbg3Yi7ZGHOrluUjOxQW5ghYLMOTfIItKqevOkl/ntW4eDKkzG5wDKWsuWkDGtz0jVHn+sIqIFyZk3/ybJJQwS/Bgf43iTdqKRyeN2pJktk0zbE9vF27dtnZdv7557e2ttql+oODgyMjI83NzRzHnX322U888QQAWJb15JNPnnfeeaVoFUJoylk3ylIaaxQequafaUszrwh++g4AOLlYnQsEknLQYZnETqwgbtKVs+pUq1ymUZkOK6zSwRMNAooVUln5zcdy95/GfWYGXRB0PriUq3YSAAgcOAXRtFKSHuHQ0NCyZctOPfVUl8v18ssvOxyOq6++GgAWLVp01llnnXbaaWecccaaNWu+9rWvzZw5EwC+//3vn3vuub29vZ2dnaZpXnrppaVoFUJoqhjMs/Y0nFy+O6eC9O201fLaMCuTwc+9A0BuX5RoLzT46XsZq9lF+wZz4OHac6yxxht2aTEnN6haFVe1kJ+9yzIwK29VvxtnDh4ePqO0T1LQFFWSIKytrf3LX/6yZcsWTdM+/elPL126tDgo/OCDD65du7anp+d3v/vdSSedZB886aSTtm7d+uyzz55xxhnnn3++PXaKEJqetsbZyU8aBQNOqyR/OJ2rcuou1qOyYFiC7iw7Ud6VtmZtGI70G+Cn2+LWcQ7S35PJzBLa4+bxibQvIFpe2DVonePkgQB0aF8wmWRYh/pbocNYqZ4RLliwYMGCBfsfJ4QsX758/+P19fW4hDdCCABeGGQFg4W49a8Mn/y9TVZeaWMS5+YT/zaPfu+tGAdq0lrwxshIt24dJ+9q174Ypq9JEHWQIZ3WqzrJsOYA3drJ/q3SSe5YzP383UClk/zkBJzzgg4Ibw6E0KGX0WF1u/XGCAOAvizz0LY5wq8IYQUD/Ny2uLmQsPg925mX7sqyJsUKSyRCiSaToQxr1qBMplGJRFJG+elVJGnOB2DHV1R+YRb97iIa+7yw7dN8sxefBqID+oAgfPzxx++///5ikc0Pf/jD0jcJITS9WAzmPqZ/4aX0kqe0v/ax1W1WiHuTEkOERFIDL22LmScLkOnOGT66LW3N1kiZROIBrqtgVc3zSwtD4QqxDQipcHpPqSQJa6EJ8ovD3rxxqL8YmiIOFoSrVq269tpr77rrriVLliQSCQC4++67J6thCKHp4uk+ayAHs8W7KvgXvvOWGVUhQLdaIDi40bUDlpP0pNksjXlFSHpoR8o86sLGch8fl6Anz+o0C54fbnJZb6isfK4fnuqxslbTNvU7ADBecSBC4zhYED7xxBOPP/74li1bLrzwwuXLl9tZiBBCE8sOLC/d5SQDXRnGEcVBBhLmQhFGOZbhSK5gVfJ8QCJRBxnMs7rHe0PMijpJd57VxlVIsXkOOqhY4bcibIaXApCkNX9JBfEd+SvtoIlxsMkyhmHU1tYSQm666SYAOO+88+w1hxBC6F8UUeDKl4zuLPzgGPqZGfTsqsFCMi2R0YBAeLO9wGoKrNoBwzrtyFsNAKTWHexQ+ihoKvOrGlHlsjJuc7d59TcX0Fs21ZngKFiVV7TQ/1pEgyIIFG45Disl0Id1sCA87rjjXn755ebmZgC46aabFEVZv379ZDUMIXQku/FN8/n+dK2w5t9e/hwAvBd7t44GZRJtzbNafkfGalFYlZfuMkHOWfUOHt6M1YbpehUqltfSwRzkcg0hbn3WqEoqTLNIxppZgGqRY3Uucv9pGIHon3OwodHvfve7F1xwQfHH2267bfPmzaVvEkLoyPdkr+Xn3qnh/65b8B/rTA+8N2qeKpGoycBDW1PW3FOrK4LCqIv25a3aoARpa7afe0exyrvS8PAyLstmApACq6x2EQBo0748ap6mWfhUEH0UBwxCXdez2ayu68Upo4SQY445ZrIahhA6koVl4qfvUVAEkooq4KFto+YSHnI80d2kK23NfmG42rKGZDJUYDUXNdCM1UzAzLPKBUFyzetm2mxRWRkj4rVz6cVNlOOrjg46bsfhUPSRjBOEa9euPeecc3w+X1VVVU1NjcfjOfXUUx955JHJbxxC6EiydoBd8rx5+xbLsGCOj3hpqwWCgwwJJC1AMmc1ykJAggGeZBUWjmhhAOahHXlWfWI58YgBnfkMqL3tONqXg5Q1b6f2TS9PHBz86eNc5t+EzRfxwQ/eHQ6hcez7jPAHP/jBHXfcIUnS4sWLm5qaeJ7v7u7evHnzZZdd9tRTTz3wwAOl3vYIIXRE0iy4cK2h6tnHulz9ee7JXuVkeShhLnLQQdFK56yZc/3UxYfKlHdUFg7LRLMgY7YEuLcXl5dndYipMCqcGjNm/6GdXdlMfryVy7DZ956ES4KgCfC+IPzTn/50++23f+5zn/vv//7vYDBYPJ7P52+99daVK1cuWLDgxhtvnPRGIoSmvIEc5HRYKK3sNy54tGOxi/QoVkWONQT4AcXkMqy5QiTbEg0h+qpiVVw1h3Zn4Y3eBU7Sn9PI/AABgA7tKiDkuBBc3MR9dib1itDgxoeCaAK87/eplStXnn322Q8++ODYFAQAp9P505/+9Lrrrvv5z39umubkthAhNIU9P8ge7rAUE57qtQCYm3Q5ST9HwUM7c6yep7UiDDrIUM6q256EtDXHTTvzrOqVYfa3XitintJvnt+ZZksqyQNLueW19H8/Ri9uogCwIEgwBdFE2RuEmqZt2rTp2muvPdB+vtddd10kEmlvb5+stiGEprb7d1ln/s347IvmFS+aM71EJqMcyTvJ8DFlxE3aFTZzSKuToM9BhhVWfVEDybDZAFBgNWdW03o30VhgyDjnE3UUAK5soc+ey//7XHw0gybe3iDMZrOMsfLy8gOdWlFRAQDpdHoy2oUQmvoe7rRkGmkQHn2q16pxwjH+TotJIh1dO8BctCdhzcxDnUgSDjKgQuWSClKwwjp4C6x6phfWLOe+No/+5HjuIdxEEJXY3iD0+XyyRBxougAAIABJREFULO/YseNAp27fvh0A7L3mEULoQOIqMAAG8MYoC5AtFdyLugVXv2yOZnbG2TEyiQAwJx3ImY0LgkLeqmOEUyzfhggDgF3a9SlrjleAFh+56xTuxoVUxAkxqMT23mIcx5111lkrV64cd01RVVVvueWW+fPn19bWTmLzEEJTicXgwufMstX6gseNpAoeHrzcLhHiAmVZAzxc+6i+RCJRiSRMJkq8K6exjNWsWOXVTvLVedTJQdw8dkFAWtGE6Ycmz/vutttvv31gYODkk09es2aNoij2QU3TXnrppWXLlq1bt+6nP/3poWgkQmhqaE+xNT1WHf+XtmR85bvWPUu4EN9FiXl1c6IzY7poT5ItoET20l0qK88b0JlhcXNxyjxKMWFhGem/XHj30/yWFTxOg0GT6X3lE0cfffQTTzxxxRVXXHjhhQBQVVXF8/zg4KBpmg6H4ze/+c155513iNqJEDp8vZdgBQOOD5PH/x97bx4nV1Xn/X/PubXv1d3Vtfbene7sC2EJkJAQgiACIvAY0RGQGRUdAQf0mUedB/WHMzIy88A4+og6oAgqIjjKpkCQSCAJ2dPpfV+qa9/vvXXrLuf8/rjdlSIKPiIh23n/kde59566fapSVZ/6rmeKIqS1GH8p0sgLs3Xf7ZOXG+ICaf75aNKKSjJxq9RRJj4vt1+ijR/rwmMFeD25KktW3taNAMBrBq+ZiSDjvebYgvpLL710eHj4oYce2rZt2+zsrKZpF1100UUXXXTLLbeEw+ETskQGg3Ey84NB8skdGgB8aRX++Rh1wigGyYridWag2qSEG0UatOCkAco86cAI5UiXn3slql6Rq9BXrzS8Gsc+K1riOdFPg3EG8yd2n/B6vXfeeeedd9753q+GwWCccvxohDrRaJ1h/3/0/Q+nEbzcQY2avcZYvQncuK9EOlVw2lDcBNkS7Wy0QEFeFuJ+K2rhNfUII7goyExAxgnmTTFCWZafeOKJffv2/fG8oaGhJ554Ip/Pv1cLYzAYJzUUAAAUAntTxGfYWY/38Arc0IE9XH9aW0dJ8olJ6uYGC3SZ1xq0c3ELnhNpZGU9ypMlAEhGga0dLCOGcVLwpjfi97///RtvvNHj+RNOCp/Pd/vtt3/jG994rxbGYDBOUlQC176kGf5Luey3qqCCBuDCgyacsXDwi3FqR1MpcoEF0oSCE40WyeJxIWKiM1aUwDhk4ahC3XHt4pLW1J+jJ/qpMBgAxwjhQw89dNNNN3V0dPzxvLq6ujvvvPOhhx4ihLxXa2MwGCcjb6ToU5MkzP3qpWj5/iOk2UbteMoIAlA5KaYocEXSY8YpE+QQkiXiL2rtNhw1okJW9V0aRgAwLH/GYrBtDjOLkHFScPSNWKlUDh06tGXLlreaumXLlmw2y1qsMRhnIITCczP0d7OUAjw7QxBoLcbH7Wjq8XGSEidl6qnQBgwpJx7laQtCNgycixuWSMBqAIosFdoo03oE3NUt+NUrDf95AZ67wegxnehnxWAAQG2yDM/zhJBj2m3X4vV6AYCFCRmMM5Av7dXuPUQA4O41OFcBO57GoFhRTFC63bi/RDpNKGeBpBMP8VqnRoEnwQZulwKhe9bif9hFSqTNBIVLwjhsR2E7XOhnXdMYJxFvarHGcdz09PRbTZ2amgKAhoaG92JdDAbjZOKXE9SGo168//kZ+pklOGweAwArjs2J4Mb9BW2p1eCz4IQDz5ZoG4eAp+313O6iFh4tgIWDtHp+jq6ZE1hQkHEyclQIDQbDOeec88gjj7zV1EceeSQQCLS1tb0nC2MwGCcLcyIdL9IQ99uQ4XeHs/Q/jhCqDvGk1YrmNApOPJInS1s9QZ8pZoakRAP/axUu0x4OKiJtshjg2jacIWfPylcs9rJKCcbJyJuC1bfffvtLL710xx13yLJce54Q8u///u8//OEPb7vttrfapInBYJxOyASufUlz/1j59A6NAgIEbtxvRllJg4dHiIObSGkXWFDSBDmMpAoEt8WbqTZlwckN4XDAgjJqFwAQFLpzOffdC7i71+Avr+a+cz7ziDJORt5UUP/hD394x44dDzzwwOOPP3755Ze3t7frztLf/e53ExMTV1xxxRe+8IUTtVAGg/Fe8sAR8tQkiRh+/f3BKy0cYFq2olkV7BeH8e/nFCuaS5N1Lcbf2HC0TMImjEqkw4UHVbA9N2OSVFqmwQJZkleDo0W6IYC+uoZJIOPk5djOMt/+9rfXrVt37733Pvzww9WT7e3tDzzwwGc/+1mOY+9mBuOM4MUoNaNsu/GRtHbezlTAhYcE2m7HE9vnJBuKy8RbJgFKZQcel2hgiQf15es0atf3kfhoF7wcQ4cqXzdi1OViPiTGyc6faLF2ww033HDDDel0empqStO0SCQSCoXe+5UxGIz3mD0p+vevaxTgkY0cr1IvdwAALCilkoCbGyiSRUZUMEHaiUcE2gaARBpp5HaU8dk/2cj9Wy/ZM9WiUufty9AnFnHtTrwtSj7SiYO2E/2sGIw/x58QQp2GhgaWIMpgnFFcv00riH0A2oVPL89K0GM6QMC41J06UqTNaDSmXWLHEzacdKLREm2vtyCBtAW4bTPy1T8cJj8aIUFuLaGGZ6boF1fAxiDaGGQOJMapAevswGAwAACiAp3madDwvJ97JSOBhQMXHs5qa6N8oiCDnZvkSWeXx2dFKRue4bX2zSHEGRYB0DINWDkgFKLK5THt0i4384UyTjGYEDIYDACAgxlKAZx4zIyTHU6kaiUOxBxZYUEJE8ohqknUN1BsNKGoFSXKNGxEdEZqBwCKAnet4G5ehA0cOseH/pOlhjJONZgQMhhnOlGBllV4YY4aUdGMUlaUvq4dObgJkTZJxG9CGTuaEmmT0wgJucOFhwyIX17v68tTgTTH1MsEzR4T6UMbOOUTxt1XG6xvGW9hME5SmBAyGGcuyTIEH1O6fz7e+Kgia+DEwyXSZYTctw4rDjwq0Naz/Y0eQ8aGYxIN2IyIJ20uPCLRxgketoQxBW5E+buQDRax1FDGqQwTQgbjzOW6l9SclF1jvktUZZsBne0ZKmiLFXCZUNaJxkraoqLWADTrwFMVCN57Nt4Q8qnUIdFARYNvrOV+sZl7YB0e/7DRwL5IGKcy7P3LYJxZbE/QRU8oy59S92XoQIF68UGENAtKvhKnsdIoD10V6rOiuA3PCLR1T8ookkYv3sdr4SgP22NEIC0iCb2/CRsxXN+Gb1vKmVlMkHGKw4SQwTiDyFRg8zNqRXwiWXj10ufUtARe7iAA8hrj+1PExs2USIfF2GjFcxaUEklQo1AinSaULdMAr1KZQJxcnNbOLcp//m8xGKcKTAgZjNOfOZHuiFOVwOsJolHwcbtcuE9QwGYAFx7Ma8sRiZlQHlNZIg2zUms93qVSh8VguW0JJ9AlAGhTOPiFlVyDBSXUiwTac+cK9tXBOH1gCV4MxmnOgQw9/zeqpMGlYXT3GmxEFRuariDvta3cUCHDCZUsWWNDUTueEGmzmcNF0tNmfLRAFls5OJglGXVxhPM+O2tCANNbDYeydKkHOdmeuozTCPazjsE4zXligsiaHDH8+oUo/XYftcOwBlYrSv5sXJvMj/C0VYaQ2xC3oahIQl1ukFEbAJJocLEXxcu0TEN7pX/HAGYMVgOc18hUkHG6wYSQwTjNeWGWOvB4m/ExBCQhUY/hSFo7z4xTFKgLD5ZIpwJBE8StKCrS8FIPkjSTSJoE0ny2D315NcchAOT82lkcKxBknK4wIWQwTk+SZSAUAKCogAsPINBMKDtWpE40kqdrNGrxmfNOPFokPXk1AMA78bjNHL6uDQPApPqRtHaeRuHjnbhwo7F0k/HLq9h3BeO0hb25GYzTjbQEDT9R/I/Jzh+rbyTprEDdeAgA2VB8mgcHN1lQu9yWRq8haUezZmMbBSyQZicemxUDJYVe34bKaG2Xp/5LKzkAsBvAwgokGKc1zNnBYJwmjBbpnAgX+NHT0yRTgXbjI1ntrHsPLS+r4LBMlki7GSXMKAAUKrR+SmwUtLFGkzhXrr+mDR+e7XDikQr1L/agmxexrwXGmQV7xzMYpwMvJ7hrt6sapR9s4f5xFQagAcPLKrUPFpZtDPByns+RDVY85zDZK9AWsCJeaW0wvC7RRqcRRXlaIEsa6C4zZzy3kTVLY5xxMNcog3Gq8sgIWfcb9dbXNJnAv/djIyR7TPf/epo8NkJceMgAvBXHeBWIOkm4pg2hSJM1rqiTebVpsQd40unCQxLxB20wWoSctnJC+bhC58OKDMYZBbMIGYxTidcTNFuByyKopMDNf9CAyruSpvP96ECOqzPsr8d7KYWfjZMG7g2BtJpRalwEKo1YUPOryUAjJK2Iy2srPWb0vtb2uRgRabjHhS5vQg8csaW09XetwBwzCBlnHkwIGYxThoeGyS1/0ADo5U3cVS2IUFhl/tqU+mFBXt3tImKpl0OSnSulJWfYPBzXLg4bnlEIOIwTeW15stIcMkXtiMTh8ksj6Iu7XMsMrjKNfHEld1EA/W039pggYmcyyDgTYa5RBuOU4dlp4sSjS0z/9ttZkixTDkQnHrahmeejtKKBE49o1LHCk+QQtaOptLbOhHIcInaYKpF2jVgq0GDHUwIJHUjTkkpT2gUFrXtblALAMi9iKsg4YzleQvj3f//3nZ2d9fX1q1ev/ulPf6qfPHDgwPr16wOBQDgc3rp1azQa1c9LkvSpT33K7/d3dXX9+Mc/Pk5LYjBOdQ5mwIMPufAgpbA/DV58CAGx4rl9aTJcyHFIzpPFo4W4CWIa2GzGervRaYS0BScqtClsQzzpUKmFgnt9AAHAqHyLSCObQ0z/GGc6x0sI3//+97/00kvj4+Nf+9rXbrnllsOHDwOAx+P5l3/5l/7+/t27dxNCPvnJT+qTv/nNbw4MDPT39z/66KO33357b2/vcVoVg3HqolGY4qkLD5tQ3oTKOxK0jttXJD0WlEiVkQcfKZKuMgnYYM6JR3naqhKqQIOb61OpU6EmXqVF0iNBo92IPtaJf3mJ4YZO7vn3cRcFmRAyznSOoxC2tra63e6rrrqqtbV1ZGQEANra2i688MK6urpIJPI3f/M3Q0ND+uQf/OAHX/7yl+vr688999zrr7/+oYceOk6rYjBOOWYF+vAw6c9TlUDEjhx4QgG7GeYyEnXi0bi6yYJSMqFu3JvXlnGGsNc450BjJdLpMEFUivjwzgr1f3ElpxJIaeumlK36ba9tRY9t4i5rYsERBuN4Jsv09/dPT0/v2LHDZDJdeuml+klFUbZv357L5e6777477rgDAIrF4tzc3KpVq/QJK1eufO65547fqhiMU4hMBXqeUAWVmjD6l7PxnJAPm8t5bbkVz4pa2IKTGe3cDtNDANTFDc/J77dxZVC22XEppm1RVIRIZ7vhoRS5YIan37uA+9xON6BzHl7P+sQwGG/iOArh9u3bn3nmmQMHDtx88802m00/KUnSgw8+ODMzw/P8WWedBQCZTAYAnE6nPsHlcqVSqbe65+HDh5966qnqYTAYHBgYwPg4/qrlef743fzMRJZlADCZ2BYGb4lGgVIwYLjnkEFQDT2m+yeVG34x6nPiUYG2iqS50ThlMvjKNLyu0Y54k4NLmlEa4dZxUTrXMqNQl6aFbutW/rO/DSO1TCIxXr06IF99zfz9S6UT+vROEdgb9Xjw3n+jWiwWo9H49nOOoxDeeuutt956qyAIZ599dkdHxyc+8QkAcDqdTzzxBAD8/Oc/v+aaa2ZnZ+vr6wGgWCzqYlksFhsaGt7qnitWrLj33ns/9KEPHb9l/zFVkWa8K7Dvl7fnxSi9bpsqq/DdCzmHhXIg+rjX09p5USngxoMl2smTZgf9PSL2EnQcSeOlRr8bvSERf0nFADaFus2QzmvBVX6zOtBKKVZw+Otnm5xO84l+ZqcY7I16nDgJv1GPe4TAbrevWbNmdHT0mPMbNmxIJBKlUsnlcjU2Nvb19enn+/r6urq6jveqGIyTBwrw83F6124SFQAAvnVYI+p4PX761tfIJ3u4kGUYAbHhaFqiTjxSUHsE2mpHMy48XCSLVQoCbWnkdkg0sMyLIg7Ea20V8AEybIvSkmoq0kV5tTklsYYxDMZbclyEsFAo/OY3vykWi7Isv/jii88+++zmzZsBYPv27YODg6qqJpPJu++++6yzzvJ6vQDwiU984t577xUEoa+v7xe/+MVNN910PFbFYJycfOsw+cjL6kP9B9ofr6QlKMng517xc69WNPrMjFbPHZSp14ZmJQ0caJKnXWv9IQMUXXikSLrW+RFPOp14VKBN3R40y9Mi7S7ToMcIG4IYAA5V7imToEpO9JNkME5ijosQEkIeeOCBlpYWt9t95513PvDAA7oQTk5OXnXVVXa7ffny5YVC4cknn9Tnf+UrX/H5fOFweMuWLffcc8+aNWuOx6oYjJOH3ix93/PqB36njhXpKzGKobLU9M9GmPnSXm1flnq4PjNK2Azw2dcIUvvj2iYLSphRlgIiUJcuQxlCCOQyDf5dN1JRBwAItLneDB1OFNcuGVNu7nShD7ejL6zAbU78t934Q60sO5TBeEuOS4zQ6/Vu27btj8/feOONN9544x+ft9vtjz322PFYCYNxcvKZ17U9iaQJ8p/e0b3Wh/bEDmJQrCjWn28jWtliigOArJUwGG1oelC7PcC9ZEVzZRqgCAYK0G1sIdjSaMW7U5BWmijHiVrk4134m2ejO3Y5ABxfXYMB4F/P4f71nBP9VBmMk57Tv9doRZPNHAt3M04iBBV2J2mz4RkLSm6P/8/dKQiiNzSw2vHsWIF6uF6BtGBUsaEYh0SRNsk0YEAVJx6RSGBtAzqUoRltrZmmIw6wcUDANK7eJNKmvAxeM/z4IlYdwWD8ZZz+DpPR3DilLFOAceKRNBgqUI3CNE8VAi48aMEJhUBZBQ83EFc3WlE0XqYN3O6stkahwXpjrI47kCfLAZBAmv3cdpE2Lfei/3shV0Tr0uQD/7gK37OWW1mP5rT3n9eAWLM0BuOdcZpbhCkxU6iUBFV0GO36GUIJRqe//DNONjIVWP6kmhRFn816/7kGjBQbngEKABSAN0Axo53TavwZAPXiQ9PKtW6TDMqMDU9G1asAIE+WNhn+W1TDK+rRjV14azumABYOAODgNQZCQeBLFo4VSDAY74TTWRJEpTySGweAXDmvnxEUMSVmTuiiGGcQj44S94+V1p+rBzL0P/u0hFg5z/q3aVH4117NgcbKNKCB2YxyTjwq0hYFhe04YUNRCgaCQzGp1YEmbCgmksjN3ShPVgOA0xy8ZREGADM3r4I6mJmCDMZfwekshBTmPaIlWdAHCSGVrxSOTmAuU8bx5PO7SFktRvnSt/vIjgR1ohEOJCueqzMhD9df0hYRFGi2zXnQQIl0alAHUHFz/QJtNiEQoMOBx42oZDH6ZnhU0BZVaP2UGDiYZW9aBuNd5nQWwiq8wgNAtBSLC8lCpVQVyJiQOKHrYpxu9Odp4DHF8F/y9du0/Wmalmib8ZFmwy93pajLiLzcYQCworlX4sSFhopkcaM9iLQZBx4tkMWShkTSFOB+z5OW97fgy5oCCBSJNBZk3GijBExvVB7E2NTpYtYfg/Euc0YIoUo0QRFnSnMAoBJVkEUAIJTMlubKqnSiV8c4ffjo77W0JK0w/c9fTmivxCkAeHC/FceHcvTZGeLGAwJpseMZlYADjxVIz8F8M9UmbXhOoC09biiRDiceEUhrjwuFHKhMm8o0eGMXvv9cw0c70NkN+Ln3cT7LiX6SDMZpxxkhhAAwkZ8mdL67hqCKhNLR3IRKtLw07ynViMbCh4y/Eo2CCw848ZgJ5SeL1GdOW1DSgpJdbiQT1YHHYuoWG5ozoywGtUL9POlwcqNGVNDAv7UDFWkPABVIiwHT7/YTnrSJtGlagDozPLrJsPtqw5bwmfKBZTDeS86Uz1VRPtpvn5eFTDmTLmcBoFCZP58uZ7NSrjqHhQ8ZtfAKPDlBehfic4ez9MkJIqgAABqFXUmalwEAblmEG/ABAHDh2I4ENWj7C9pSM0rmKtSNBss0WKJdjea4HU0IpKXThUTS6kBTEvUHrNyTk7SoLSLUUKbBlfWIUhhX/mZKuS5gPWHPmsE4QzjNyyf+JLwsVD2ievhQ0ipRPqYSlQJFgAAgIab8dp8+ZjA2P6e+kaIYwe+vMMgaXPq8iqHc6rLt/aCh43GFKL0y1H9nfdMX39CWmQZkWs+h+MHMkiWm/Qmyvh1P5KRSs3FPjqykOKSocTueFSFkwKAgp0w9IgmlFLolgnqz/sPKPS6T4dIw/txS+siIdYkH/ZBtH8hgHGfOFIuwFlEpFxcMQVlTFKKM5iYktaISTQ8fAsAcnxAUsfoQwgzEM5iKBntS1InHMJR+OUnu2q1Z0exay+1jRfpvvVq2Aq3GnzVyv//sa5pKFCuKJrULbShKgbrxQE5bU6GNFhx34/6stoZXbQp11HH7VIh8tBNRCiJtEmjzhhD6vxcY/s95+Oae7uH/YTBz8B/ruPzHja9fZbCeiT9WGYz3lDNRCKtZozppMVvVRV4RAGCOj0uqVJCK+klCyRwff48XyTixZCpw/UvahU+rL0XpKzFKATqNP2jAu16O0pQE9dwbZpTlkGTigIOyE4/a0bSogh1PSsQvkFaPIebEYzK4NFxnMDRYUdyKYwJpNWAQaIsLDxTU8PmN2GZA0+qHkurGJhsyYrhjGXf/Oq6RpcMwGO8tZ6IQHsNMKVodC4ooquXJwgwAFBbCiplyNls+Gj5UiPIer5Dx3vD1AyTwmHLlC6qowtaX1V9P5aYyv7r6RXVWoAg0O56ycjEOoKyClzsEAGFLbFcCvIZDCnVZcRwAXGiIp+0uS8gCCSca40mn1whJuamO26tR01q/43sXGAStDQERIXwoS+9cjvLacsQFPtLBPokMxgmDffxAJVp1zMtCSkgvjHkAUIgyU5oTFFGj89PmSnFJrbz362S860QF+qGXND3+l5Lg7n1askyemaa/nCA74rQBv9Fk+LWogsuMFjsnMcheLnFdOyrIshOPFEi3KMefnyF1eG9cvcSCElaD5jIMFUnPpBjCNGbDUwJpshggT5bUc3vKNDhZpD8bI0XSRcCo0IYPtqCvn8VNbTVEbzBezNqEMhgnDiaEb0JUyqlyVh+rRJNUaboUldQKBVqSeQCgQFPlo65UAKgdM04t7jlAfjVJXpmrXPmCasLAIVhh/qqbG3gxSgFBHd5nREUOxO8PaIXyQJmEKUl98yBx4mGJBHnSacNRCuDGA2lyrsVY1+lIOtBEkSxSqE0FZx13qAJN31jLWU1dHMhlGt4UwiNFWqTdSe0ijSK3CQFAswN52OYoDMYJhQnhm6BAZU2uHhYqpbw8HynkZQEAJvLTsiZXvaYVTdbr9HWqpYqMU4Ijeajj9i8z/UuyDPf1apQqTjzsQON700RSqZsblKnbhudeT1AP7k1oG8woJWpQxx3Ik2UVCNcbZ80oawReQa0NtmCiNGZGaQXCq+uRSCIWFOe1YLJMExVHmfoF0vyRdnzXCqyCa1i+dakXeVmLbAbj5IAJ4dsR5WPaguOUlwVJleJCEgB06xAA4kKyWNOzLVPOSaxVzcmNpMHuJC0pQCgcTBM37rPiKEIwJ4IDj2FQrGjWbUIOPKOCrUS6rSgqqtSNhxLaRgOSjEjy4CNZbbXREKJawsMdLpJuWUO70u2N3CsV6pOJ4fo2VIZWCths9BsxaATmtMuz2ppnZ8jnluCJDxt+e5lh7wdZMiiDcbLAhPDtqI0FlmQhveA1lVRJJVpR5udKcQq02tQ7KaYLzGt6EiNp0PNL9YKnS3WPKC/MUgLgxKMmlHMYtNcT1Mv1SsRnxfHdSerGvUWyiHJNbbYZB55UwVahDRZjg5NL2PEUGHpmpSYbijrRaJEushigSHq83OEyCXlM6MFBmtc6K6RudYPp2lZs5iCqfKBCQx/twADQ4kDviyALKw5kME4amBD+v6IQJS6kqof8ggoCQKnCA0BRLhUrpeKCsahRTc8+nT8kmlaTlcN4z9iTppueU7e+rKUleHqaTJXoWebPm2Hyawe0i0PIgSY1alPVxHiJulF/QttshRgAeHBvXltuMjSb0awHH85ri90mSEh+O9qvUHdOthDqpmCs4w7wWsdSL+JpBwKNJy3/31oMAHmyfErb+nqChuwo/lHjTzZyUx8xrg+yjBgG42SECeFfwJvCh3KpuqOToAiU0tHcBAVako/2bBNUUSWqfpiV8lWHKvxRLSPjXUeP11Y02PC0uiueeHFy7/pn1D/EqBUnLChl56aMGF6aiRMwlmibFcUUjTi4kZh2iRnnEahObiRPlo8KzWV51onHSnSxRlCJtvq5P4gkuMiNvnMBJ9CIBcV52rqmHsnUI1OvQFvObkA3LUIqdSTUTR4zIACPCT7WiSP2E/2KMBiMt4AJ4TskzierqTG8LJQUXvejSmpFIaqsydOFWUqPek3T5UxJEaoPj5ZitXdjuvjXo1FN1o6WeOot1HMySBoEuReajU8N5ul4iXrxXgDkwTPpCvXiA0XSXaGBNXUxB5qQqVeh3gqp83D9iBIZNQokbEYZBxrnSWuHG0palw1NizTc6USHs5QnbSp1Iuy9ogkDwIT60by24rUE/ceV3BdW4Gta8ZObWSCQwTgFYEL4DqmWFQKApFUSC9WHACAoQlxIKUSFhbQaXhHyUrEaMlQ0JbrgVgUASZV056oO6/f9Nhzzi6FQKR4dS0VRne+KRyl95MjjFU0OWOG8RuzBvTY0ixA8P0Pr8IGstsYEMwM5qOP2Z7XVFmO4UI56uINFshgQCNDsxy+LNIIomDiuTAJWHBdI0z1nYY1rBwCRtNiN8IMhUiJdAm02ILiqBS3xooS6iSDnBX5sRpG1AAAgAElEQVRk4eBfz+GeuoQ73898oQzGKQATwneHdM0WTrwsVDvRCIoAAGO5SQqUVwT9ezwhpjR6tK9pppyr3RwjXX7TblDMWKwtSuFlodZBHeOT1XFWKvAL9ndMSDw7+uJYdiIlgZmr2PEMgGqGAiDNjYei6hUWHEdAPHgwq501W45U1BkXHi1oS7wmKJKuBsMugbSc5UOyBiI0SbQRIfNLUZpXGlSwCrTpY52owYLS2rr+yl2dbgQAu64y/HIz13ut4dxGJn4MxikGE8J3h1q5ypRzolrWx7wsSFpF79+tEa2sSBVNni3FAKCkVCv0M9XwIaV0uhit3k0hak7KV+98htQpKtqbmtilan4ZFCrF6s4holIuykctwl+PPPeTI7/QxzujeynQ0fzEj4bJ4WSfSMIiabagKRceqtCGEl1sRQkbmlbAoSKvQJvtaM6KowJt+cpqTqQ9GBSedHY4kdeMSlq7SCPnNOKwAwDQkPw5nnSUFNh+BfehNsNHF3l2fMAAAE4jXNuGF3uYCjIYpx5MCN99aretUIgar7FaBEVIixldz3TzJSGkRKVcFcJCpVjR5Kplky3nijVe0xifqP1DsnYKdz09xtKtfdEyUq6qhQpRazdMzkuFqhDmK4VXpl+XiQwAklbpzwxPFKb1S/vjh51m50R+ym+FOrw/ry0TocmBJ+rxnixZ0WgxVWidz/C6QJqsBkC4HiHFAgnMhR1GyKvtAKhEWi+NoHMbUVy7dFy+qSTTf1jGnetDGe3cxV7TB5pxlxv9cjP38AbOYTyurxODwTjuMCE87ug1+DolWcguWHgLO10kYKGdGwBMl6JQU7CfLmf5mhSbuJCsLDgGZU1J1Ny5NloGACdJqcYxalep8WpmavqYa0RL1JSm5KWjZl+hUiwr8+a1RrVCpcQvvDjD2fFnR1+YKcwBQFrMThVm46UEAChEHcqOXNKyYboU/VgnXuLoc9nW8LTdgcddeCRHVsXKUCLtQW6bSFrW+9HWDlwmoQr1FRXT7+eoBtYx+WYJNb0/gj/eiSjYRBra2oE5BLuuNkg3G49ca3CzpmgMxmkEE8LjTq0/syAXq9aepFYERZSObhEsFirzV/V/izKvn9HlpCjzFU2uamRWyr1ZI1NVA5ECjdVoZFHmawWp1o48xtd6jG7Vpu0cc6l2g0b1zaIrLugWvFntVKLVRlIz5WxVFwtyqepMpkCLNYc5qaAQVc88ykvF58Ze+tXw8wCgUe1Q8ggAjOTGAeBA4lCjrV4ilXylOJ6bFBXpio4ts8WYpJYrauwb65ZKtN2Opq1oViCtLhMqkiVGlC+R9s0h/JMRIpJImYaXeJGe3hLVrrjQb2i0wtYOPLHVMHy94cur5j8pZlYIz2CcdjAhfE8pK1JVUSiltX1KBUXISfOFibwiAkC0NAcAhBJdWqaLs7CgkQCQEFLVMaEkXylUTaWCVKw1EDPlTFWcKNAof7RyIy8VJO1o95xaoSqrUrmmXVytpFU0uVhz/7hw1GErKGKhJm6XEFJV27RQKVb9nxRooVKsriovFarjYoVXiaofUqApMf3fw8/ph1kpN5IbnynOAkCxUhrLTdiN9onCFAAcSvYvru/y2xqHs2N74gd76jpaPc3ZcrY31d/kDL+/yfzx7lYbjgLAyoa6C/yoQBYDAHCtH+vEhEKanJfSzjMg+pnF+GebuP9zHvfUJfOVD0121OVmkT8G43SGCeGJpHabQ14Wq3kxkiopRKl2a+NlQVBEvfpCT0PNSQVBERWi6sWLSTGtEa1ap5iRckKNsZgt56sKVJL5/ILcAkCuUqgmr2pEm6uJQWbLuaO6RWnt1sQ5KV97KVZTUpmvFGtvWHqTbZevjkuyoBKt9pJKVD0dNCflY3wiJiT01Y7mJnbP7UuV0wAQLc0lxVRMSMiaXFKEsfzk2aHV08VoRZMHMyOr/SvCzuBobvxgondVYIURGxrtvufGtrV7Wwfz9P4+o0QbRRrZm6Z1ZiTQ5rS2Lq34UxLdFEQZ7eyEtmljCGMEWzvwHctwHeuIzWCcMTAhPFkoVkq1Ftgcn6iqi6CIVfXiZZFSWo2oCYpIKZ0rxWEh30TPLqkGHfNSoTb7JlPOSVpFt9Io0Gw5X/WvFuSiqJSrfzRXKVSFSlBEXhaUhS45OakgLixVUESFKFV7Llcjn/lKkVBate1yUr6sSLpPVf8FoF8SFLGiyZJa0f9cVso/P/7SqzO7YMHsA4DR3ISiKQOZ0S5vR66cz1dKg+lhm9G6qnHZXCmWEJMpIb3av7zV3TSen5wsTK9qXAYA7d7WndE3Fnk7EAIAEEirQFptBnTHMgSU66/cacDIY4ZnLzP8cD33Xxu4fz2b+T0ZjDMRJoQnC8cE4WpzTXlFqG78pFFN0irVsgFeEUS1rLs3hYU01PmsVEUEAN200sWJUJISM5RSQRUBIFfOK0QRF3QrKWQozOuWrMmlCn9UPiVdt0QAUImalwrVR+mNyPX764ZaWZ13/+oFkbq8FSslhagUaFktA0Cukq/eMFvOxfjEf+x9UFDKClEFmZ8qzMRKCUmt8LI4lptssNVP5KcEVRzJji2u7/JY3BP5yQPx3kXejpWNSxJCald0X7MrUm+r66nvGkyPFiql7vpOAOj2dlY0eVFdZ7cbNTvQrHplTHvfZRE4qwE9spG7cRH35GauyY4sHNzSjT+xCLP4H4NxZsKE8CSltnONuOAX1YnzyWp+iqCI+YVwnULUiiYf7YAqCyrR9KuCIlKg6XJW732qey/1HuL8vB2p6I5Z3UBMimkKVBcqCjQppGFB7TLlnK5nhFJKqS6Eutqly1lKKaFEUiqEEj3kqctzNVdWVMqSWqlo8m9GfnskNUAoFRRxJDeekwopIS0owlQpigEnxJSoiqlyKiWmzwqsnCnOFWV+ODu23LfYb28cyo4NZEeXNHQ3uyImzrR7bl+TO2zhzEvqu2dLc36Hz8yZAGB5Yw8C1OltBYBHLuLavT1rGpv/eS0HAB/rxD+6iLuqhb3/GQwGE8JTAUJpbXpnslzTzk0WavNiipVStVsbr4jFSlHP/CSUlBWpOpNXBEKJblYqmiJrsi5vsKCRurzpylqslBSiQFUIpZy+JFERBVXUA3u62lUlWVBFXhH1NQtKGfRuO1Lud+Mv84ogqCKl9FDiyERhuqyWS7Iwlp3ACI/lJ3lZGMtOnBVcmSlnsuX8keRgu7c15AwkhOShxBGLwdzubW1yhcfykzE+trShx4ANEVfocLK/09sGAD57vdvibnU26ctYUt/9ga732YxWALgoiA5cY/jDBwyLWOYLg8F4M0wITz1qawQVohak2gTOZNXFKtQ4VOcPF8xKQRZLMl8tgeAVsVqVwSuCSrRqzK/WGNULOaozhZpHCWqZAq26UgVF0PX41ZldWSknKmVeFvrTQ2/EDgiyICjCHB8X1XJCSGXLOUktT5dmlzR0zxSjhUppKDu6rGGxw+gYzIwMZccW1XV0ulvjQmIoO9buaXWZnJ3etiOpAYvBGnIEAKDV3Sxr8pKGbgBAgFrdTZ11bfoyjJzxM6tv/qtfbwaDcZrDuuOf8tQGF2t3elKJVt1JGADS5Wy1S2dZlWpzRwVZqKqdqJRrSyN4Rawmo0pqhZcFrcYrW91kSiNaXiqoRM1Kucn8jNvsMmCOUPLy5B8cJtuS+kUU6Gh2UlTEmJDymb2jhcl2T0uCTybF9FhhKmBvbHE3xcXkLD+Xk/LN7kjA0TjHx0dz45e2bepu6AJAg5nhpQ09brOzp64rLWaWNHR7LG4A6PK2GznTIm+7vpKbl98QtDdW16+bgwwGg/E2MIvwdKa2aWeuRvko0KR41L+alfLV2nYKNCG+af/hqq+VAq3dmrhqEf5hZqekSnoDnYOJIztmdoqKyMvCTHFO0ipxPpksZ1SqTRWng47AXClWkoWR3NiFkfOyUk5Qy4Ppke76roC9Mc6nhtKjXd52j8UdcgQGs6MaJWFnwGl0hBzBsdxEm6fFxJkizqDdaG92RUycEQBW+ZdtbrnQapgXvJDDX2fzvNsvJIPBOJ1hFuEZSrUWAt7c5xNqQn0AUKgUa5vIZMoZAJgpRpNi+uzgKkJpRZVfGH85YPdZjVYAGMmNJ8V0WasYEDeWG/fbfDEhoWjKZH7aZ2todoXjQtJvbUgIyUV17V6rJyVmBjPDN674iMvkiPHxWVt9szviNbvbPC3be3/e4o54LC6LwRxxhUZyY4vrFwGA3WRr9USWNCzSlxSwN36g830IzUf+PBaXEbPun6cAN9xww89+9rMTvQrGqYTNZsvlcibTu9/hkFmEjGOp7aymqyAF+siRxyW1oh/uix/aEzuoj8fzkxolc3yCUlpR5Rgfd5vdKSENAIPZ0fXN6/Q6kP7M0JKGbr+9MS4khnJjHZ42DhuCDv9QZqSiKX6br9kVsRjMfenBsCPgMjsX1bUrmhy2BzxmNwB0eJobrQ2NtgYAMGLjJ1Z87JzgGn2FFs7sNDmqCzZxrA3oqUGxWHzmmWcog/H/DEJIUY7LTgNMCBlHSdZsL/yH6Z2/m/j9wvnUQHo4ttCbbSQ7nhASemxyJD8edPjjfAIARnJjLc6mJncoxicKlWKmnF3lX64SlZfF/tTQ0obukCMQK8UnC9PtnhYDNoQdoT1zB8LOgIkz1lu9YWdAVMSIM+ww2UOOoMNkC7uCLrMTANYGVl+/+Oqq4DmMNpvRpo8RQo32hvfqFWIwGKchTAjPaMYLU9HSvLyVZP6Bvd+rdrfpzwxPFWb18XB2HAGK8gkASJezGtUsBktOylOgg+nhjc0X6GX7R9IDS3zdQUcgLiT600M99V0cwgFHoC89gBDy2RoCdl9Gys0UZ8P2YIO1LuIKZqVc2BF0m902gzVoD9Zb6332BgTIZrRuadu0xr8CAQIAj8Xd7mkx4PmKd7vRZl8QQgCwGVhGDIPBeOcwITz9SYmZmmZpwnf3P1S9tH3ytT2xA/p4ODtGKNV1UdIqc3w8xsd1s284N9Zd3xXj4wDQnxrsqe8KOvxzpfhsac7IGZf6FuvbBA5lxpY29AQd/mgpNp6fWlTXCQARZ2D33L6wIwgAdpOjwVqflfJ+u89r8XS42xCgkDPgMbvMnHlxfdfawCqP2QUAdqPtnOCawEL+p8Not9UoX72triqKDAaD8Vdy+gth7baupzFpMVOtjihUik8NPVO99JuR5/fOHVW7mWJUr52QNWW8MKX3KdUvOUz2uVIMAEayYx2eZovBnJPyolKeLc5d1LROn3k41b/ctyToCMT4xJHU4DLfEg5hn61hb+xgg7XOYbKHHYE5PjFTjDa5QgAQcgZjfCLiChkw57PWhZyBRpvPiI0uk6Pe6u2qa291N3vMLoRQV137huZ1elDQzJmMnNFhsutrs5tszoUxMBOQwWC8q5zmQiiplW/v/f7Uwsblo9nxZ8derF49khqobkJEKC3WlJ+fJGiUVKvUAeBIaqDquoyWYk8M/rp66af9T/Ym+/Vxf3pof/yQnhdaUeXJ/PTswn5PQ9lRDhn0nZgmCpN+uy8upjRKNKKO5sbXR9bpu0wcTvYva1gccgSipXhvqr+7rqPJFcpKuaSQLlZK7Z6WsCMQLcXHc5OLvB0AEHIE3pjb1+QKA4DdaLcYzApRvBaPy+wMOwIAEHGGXGaXw2Rvc7e0epocRhuHOZvJdvOKGwL2RiNnBACHyW7EBothft+HNwUCATXY6o/by8xgMM5oTnMh7E8PEUqOpIb0w11z+/bGDmqUAEBFlX8x8N99yQH90qHkkYcP/bT6wJ/2PzmRn9THRbn0XI189qUHUwtFeISSqvwAQEJM5cr56uFgZvToJSEZq9nJaMfsrmrXtPH8VNU/CQDfO/BwteZvb+zA4wO/0scaJU8OPj28cM8jqYEjqUH9JoVKMc4nq7sbDmdHKYDu5BzNTXgsbl0INUpGshNrgytni3MA0JscWO1f4bW4kkJyIDMScgS66zuifFxSpdHcxNLGxWFHYK4UO5IeXO5bymFDg7V+59yedk8LQijsDM6UogkxHXIGACDsDKbL2YgzBAAY4bAzEHGEEKCAvdFn83mtnogz6DG7rAbrmsCK97dt8Vg8sGDY6XXxoEf+asw+t9llrkkBtXBsYyTGmyir8FqC/naW9uXon5/NYLw1p7MQqkTbNbd3Y8v6I+lBClRSpbHchNfsmshNAkB/ZojD3EBmRJ98OHkkIaR0BeJl8UhqoDc9qF86GO99bfaN6lbyz42+tDd+UB+P5sZ/PvCr6qVnRl/YMbtbH8f4xI97f1bt2PLy1I7t06/r43Q5++zoi9UslTdi+/YsuC4TQnKqMDu+oMGDmeHpYlRXu4n8lKRVporzCSwDmREAmhCSADCYGfHZ66eLUQCQNXkyP7OycelsKQoAR1ID50fOqaiyoAiD6ZGAzbekYdEsH5M1uT89tLJxWdgRipbiBxK9q/0rGm2+klzqTfW3e1osnDnkDM2UorPFaJu3GQDCzuCBxOGIKwwAbrMLI9RoqzdiAwDoEhhxhjjM1Vu9He62zrp2hJDb7HKYbF8893MWg8VjcVsMZg5xAOC1uAHAYbRV/wUAp8lhr/F5+mwsF5TxpxFVuGu31vCocuHT6uW/VZc9qXb9Qn1qkvz5R76rjI+Pz8zMvM2E3t7eXC73NhP+ImZnZ3t7e9+tuzFqOZ2F8NG+XziMtotbLgSAtJj97+HnVvqXrQwsH8iMaJTsjO65rH3zWG5CJSovC5P5maW+nsHMMAD0pQdCjsBQet72OpzqtxttI7lxAJjj4yVFGMlO6Jd6UwMAMJafBABRLU/mpvT98wCgLz2EAOmXNKIOZ0bH81P6pf7UIEZ4Ij8FACrVhjNjcTGlR/gG0sM2o3WqMAMLkmY32WJ8AgAGM8PddV36PvWZclZUxOW+Jbr4HU70bW7ZoDdRO5Ts6/C2dXrbZ0tzolIeyo6s8i8PuYKzpbl98QNrQ6tCjtBcMdafGW51NzlMtogrOFuaG89N9jR0IYQC9sDO6N4WVwQAws7AeH7KaXLoplvYGayosn4JAMKOkK5/Zs4UsPu667oabPUuk8NutK2LnL2+6TybwWrEBj2902qw6Cad3Wg1YoN+QyNnNHOmqhVoNVid5qMVgbrEMhjHwCuw6Vn133qJeLQnBIwW6bUvafceeudauH///h//+Md/0UPuv//+hx9++G0m3Hrrra+99to7XtIxPPPMM//8z//8NhO+8Y1vJBKJt5nAeCtOZyG8rH3zDUuvwwi3uMO/n3o1zic/0Hlpm7t5sjD9+6k/OEz2c0JrfPaG6VL0icHfnBc5e4VvyUhuXFKl7dOvXdFxiUa1tJiZLkV5Wbio+YLh7BgA7I0fPD+0tiAVeFmQNbk/PXxeaO1obgIA+lND3Q2dJZkvyTwFeiTZv7JxmW7bjeTGgw4/hw165s6R1ODZwdUThSkAGM1O+O2+sDMwXYxSoIeTfZtbN0wWZgCgN9nf6mnp8rRPFWdlTTmY6Ht/xyVJMSNr8q7ovrMCK1vckenibFxIpqXsUt/igN03V4rvntt3TnhNkys0W4wdTvb11HdZDZaIMzSZnxnNTy1t6HGYbCaDaX/8UIe3HQDCjtDhZJ/T7NTFKeIMxPhEsysCAE6Tw2ly6JE/AAg7gwbEBR1+/XBtcOVK/zIA0J/aTSu2YoTcZle1sKHO6gUA/VAfA4DD5KjN/3SbXVWfJ0bIY2bd0Rh/hn/Yrb2R+tO+0P+1R3s1/g7dpL29vU8++eRf9JCvf/3rd9xxx9tMeOqppy655JJ3tp53wHe+8510Ov3n5zH+iNNZCAP2RgM2AECLq+lg4sia4EoDNoQdgXQ5+8bcgcvbNyNALa6m12Z256TcltaLWtxN04Xoy1OvLqrrbPO0trlbJgrTT0/87qquyzrr2ibz09FS7EhyYEPzujZv83h+8uXJV7vrOtYGV47nJ2VN2Tb16vmRc9s8LeO5ySPJAQNn2Nh6wXhuigJ9eWrHueG17Z7micLUaG5C0qTNrRsmC7OE0u3TO84LrW11N08WZoYyYwjhdaGzS5WSqIh/mN25vum8FnfTTGH2QOJwqyfSaG8I2H1Thdn9iUPnhddGXJHZ0tyB+OG1wdUcwhFnqD8zlJeKnZ72emu9IAt96cGuug4AiDiDe+MHAzaf3ngl7AiOZida3BEACDr8FU3WxwAQdoY4hMOukH7Y5Ay3uud3NQo7gh9eco0BGzjMOYz2Zb7Fbe5mvUCwar15zO6qztVZPLAghNWcT7vRVuv/9Nt9tf9lGLE9khhvR7wMDw29pdlHAb55SHurq2/D7Ozst771rZ07d27ZsuUjH/kIANx1110//elPr7322p6ennK5/L3vfW/jxo1Lly695pprjhw5oj/q8ccff/bZZwFgx44dd9111/33379ixYpNmzbt2bNHn/Ctb33r8OHDAPCjH/3ogQce+PznP6/fYXZ2PsDR19d3xRVXLFu27J/+6Z++8pWvvPjii8csTBCEz372s8uXL9+6dWsyOb9ZtyRJt95669q1a1evXn3HHXcUi0UA+OpXv5rNZv/u7/5uy5Yt27ZtS6fTt9xyy+rVq9euXfvlL39ZkqR38LKcOZzOQlhF/ypf4VsMABw2BB0Bm8Gqh6Ca3eH+9NCKxiUYYZfZaeSMe2IH1oXX6pdem33DgAxLGrp91gZeEbdN/WFD0zqb0dbsbBrNTeyO7bu845KA3V+slF6f3R12BNrdLc3uyHQx+tLU9svaNzfaGiRVOpjoVYm23Lek2RWZLka3T7+2uWWD0+SwG20H4od4RVzRuLTFFZ4pzu6M7lnfvA4hFHGFXo/uRYDbPS0RZ2imNHckNXBWYDUARJyh16O7G20NbrPLb2vIS8WBzEiXtx0AmlyhPXMHWt1NGCGMUNAZHM1NtLqb9UfxstDimZe0iCtk4IxBRwAATJzRb2toXnB4trmbVgdWVIXtup6r1gRWAQCHOYTQMt9iAHCbnI4aYTNig+7etBgsNqPViA168YMugXajDQGyG+fnO4z22oyY2u5oDMafZXuMaG9r8v0+Rt+BSej3+2+66aYVK1Y8+OCD9957LwDs37//f//v//25z33u2WefNZlMfr//hz/84fbt26+88sqrr75ab/TV19c3MjICAMlk8rvf/a4kSb/97W+vvPLKj370o/ptd+7cqavX2NjY3XfffeGFF7744ot1dXV33XUXAJTL5UsvvfSqq6565ZVXfD7ffffdF4vFjlnY5z73uWQy+eKLL37qU5/6zne+o5/UNG3jxo1PP/30008/nclkvvSlLwHArbfe6na777777gcffPDcc8/VNO2DH/zg888//8QTTxw6dOi+++77y1+VM4gzQggDDv+HF39Qz1QEgEXetlWB5fq4xd0EAEsbeuYPXWGH0e63NwJAsyuSEJI93k4AwAg1uUKD6ZElvm4AaHKH98d7w86g0+RACIUcgVdndul+wmZnuC89WFakDm8rAhRxhbZNvrqqcRlGqNkVHs9PzpSiS3w9ANDkDL88tWNJwyKEUNgZmi3OTRdneuaL0EM7o2901bUDQIOtXpCFqWK0zd0MAE2u0FBmrNXTAgAY4ZDDX6gUIq4QAIQdIUmrtHqa9efS5Aq5zS49LcVtdjlM9qpt1+Zu6vS0cGj+f//KRZfrCgcAHovn2u4rqy+dxWDWp/ltR003/W762Dtv9lkBoH7B/2k32aspMPovjGr+p8VgdhqPCiGD8ReR+XOGTVkF/i/vRmk0Gn0+n91ub29vb26e/wR9+tOf3rhxY0dHB8dx11xzTaVS2blzp9PpLJVKY2Njx9yhqanpH//xH0Oh0G233TY+Pp7P54+ZcPnll1977bWhUOizn/2sbjJu27atsbHxU5/6VENDw2233dbW1nbMQ1RVffTRR++7775AILBp06atW7fq5+12+zXXXDM+Pr579+7ly5dv27YNAPx+P8dxkUikvb3d4XD4/f7NmzePjIzs27dv5cqV+hzGW3FG5CMgQLpK6VzcuqE6dpmcH1nyId02AoDF9YtCC+Ogw2/iTLoyAUCzq6koleosXgAIO4IApKeua+FSZKow0+VtA4CQM1iS+dWB+d5gTa7IcHZMlzS/vZGXxRZXWLe3mtyhg8neLk87ADhNDpPB5DQ6LAYLAESc4VemX+vwtAEARijsCsqqohfYRZwhCrTD3aL/6YgzZMAGA+IAoMFWb+HMul4CQKenjdbsa3/Noiv0bdwBoMXd/PHlzdVL1YfUYsAcRpyewoMAhRyBGD/fX9RjcVdrP7xmNwDoBp/L5NRPOoy22l/ltf5PhBDri814xwRsf2aC0wiOd2n3kZaW+U8ZIeT666+PxWLnn3++wWAghPxxKC4Umo8mGAwGi8XC87zH86aAdzAY1Ad2u53neQCIx+ORSKQ6oamp6Zh7JhIJQkhVmNvb23X7cnp6etOmTWvXrm1ra8tms38yLnjkyJErrrhiw4YN4XB4ZmaGxQ7fnuMlhLFYbN++fblcrrm5ef369RjPGx+EkFdeeSUaja5fv761tbU6P5FIbNu2zePxXHLJJcdjl423YUXj0uq4Vi8xwnes/ZQF5lM5VjUu1ZMkAcDEGVcFVixdsKLaPC2zfEzXMBNnDDr83XUd+qVmV9hpcgQcjfoNI45A18IWsk3OsBEbWqoGnDNUv1Az3uQOGRBXNeBaXE3V3XfrrfVtntbmhUur/Mt5hV9YMPr0mpsaF1Snq65dF2AdfQ/3twcBqv4ht9lFAbJlGQDsJpuJM1oM5rIqWQ0Wq8FCKMUIGfF885djCiEcJjutcVDpvx4YjL+eTUFs5rTKW8cBL4vgdxZnxhgTQo45ow8GBwd37do1PT3NcZyqqt/73vfewf3RH8W/m5qaqpYlpXR0dPSYCT6fDwBisZiustVSjZ/85CdbtmzRl6bByEEAACAASURBVPHCCy/86le/qi64+rn7/ve/f9NNN33ta1/T5+uhSsZbcbyE8Lrrrquvr/f5fDt37qyrq3vxxRetVisAXH/99aOjo2vXrv385z//k5/85PLLLweA/fv3b9my5YorrhgfH7/nnnteeeWV91gL3wqv1VMuz3fpbLDV1zY3ua7Gf7iormPRgvIBwMeXf9hpnA99dXjbbly+VbcOAeCKzku91vnfiRFn6OPLt1ajce9rv9jMWfSxy+T8h3M+U+2xsrl1Q/UOGKFPrvqb6t8KO4O1C/bXbM7+DvDZ66sbULjNLpWo2XIOFtJeHCZ7WZX0/E+MkM1oqyZ8WgwWu9GmN4gBALvRXmuMshQYxruF1wx3LMNvVSZhxPDl1e8w3NPc3Dw4ONjX1+d0OqtGmI7dbi8WiyMjI62trXfffbcoim91k7+Iiy++WFXVu++++/rrr3/sscf+2GgzmUzXX3/9/9/efQdEcbQNAH/2ej+OznFIEzCIFMHKG7tEwKAEArZPIVERY0+iJur3JSoaY4uJYjQIlhAMMVZERSCiGPVVohFFEThBpFsoJ3D9+2N0vYAaRYHDm99fe3O7e3PnyLM7O/PMkiVLYmNj8/PzExMThwwZAgB8Pv/GjRuPHj2qq6tbuXKl7lfIyMjgcDjm5uZ8Pj83N1cul9+9exc/IPxX7fWM8OzZs4cPH96xY8dff/0llUpRD/XZs2fPnj2bnZ29Y8eO9evXo2e8ALB8+fLZs2fv3r371KlTjY2N+/bta6dadQwBg09e/VEJim6ssuZbkXkyCYIg+yoBwIRtzGM87fch4yUAUAhK68vJN4JOoeuGWLJbGACMWEJyhAu6pUODX0RPHrXyGVyuztM+3f5POoWG+z+xdrLCmxpo84z/DjQK/PQu1cO4jf9TBg0aNGnSpLlz586YMQMAnJ2dRaLHPRm2trYxMTEBAQG9evUyNTUNDg7m8XgAQD5QFIlEzs7O5Km8vLzQpby7u7uxsTEAiMVisheUxWJ5eHgAAJ1Oz8zMLC8v//TTT21tbb29vdHOutAYHGdn54ULFy5atAg9R5w6dapEInF2dh47dmxUVBQ6GwB89913Z86cmTBhwpkzZxYsWKDRaBwcHKZMmbJgwQJXV9e2/SwGgtC2ZYzVK5DJZI6OjgcPHhwwYMDixYsrKirQrFWZTCYQCIqLi62trdls9uXLl3v27AkAX331VX5+/vOWrg4JCZk4ceIHH3zwMh/9SNn4d/X116x/U1MTupd9axDE0390M45Jk7JZpnwEAAIm3820x38r/lJp1DwG193MValWXqy8wqaxvCx6AUC9ouF6TX4/cW8KQQGA6sZ7LCoTrRcIAGqNmvpyK0IoFAoA0JOb/rdGQ0MDn8/v7Fq8gtGjR0dHRwcGBrbhWLUWfriu+e6apkSmBQAKAYMtiZg+1AHmXazvQS6XM5lMALhz546Hh8fVq1dbPynESDwer6qqist986Pt2nGwzNatW48dO3b58uXFixcPGDAAAMrKysg+Bx6PJxQKy8rK6HS6Uqm0tn48a1sikWRmZj7vnA8ePEhLSyPn03A4HHKkcmtqtbpFp38baDSa1z+JXjFhi+43PU77xKfxCC2B8sAJ6Xy1Ws2hcWqb64zoArVaTQEKnaDx6Fy1Wg0AbAqLTWNpNVo1PH7JpDLQW4ju9gug3V5yZ+wlqdXqrvWTvs4lOJWAeW6UeW6UOzJtrQJsuISoa2ainTVrVlFREZ/PP3fu3LJly3AU/FdtaOcUyr/3qLVjIBw8eLCtrW12dvb69evHjh1rb2+vVqvJ588AQKPRVCqVSqUCACr18c0Eehz9vHM2NjYWFhaSOxgZGYWGhuqeU5dSqXzBqV4SWcOui0FlkCs0UQmqKdOkqqEGAAiC4FLYKuLxrySg8ZVKJQPoKpWKR+WimVIsCpMJDLQNAEY0AblNB5pGpdHAK18loDO0U2evwVIqleQ/TZfw+n1RmuZH5mX5ZvImqpEZ2DhBF2xR27ZtKygoaGpqsre3FwqFnV2dLqAN7ZxOp5Px5XnaMRC6urq6uroGBATcvHkzISFh+fLlVlZWNTU16F2FQvHw4UOxWGxpaUkQRHV1NerYqa6uJscZtyaRSF6+a1RN1bx+/5tare7qnXhWXIuH8tpmlRwAjNkiU74xq4Gp0WoFTD6fy6cx6Xcay/kMnpArAABTrfFDVZ2IZ4QClTHP2IgpYNEfj+KRMMS0104Bii5cuvqvqm+USiWLxersWryC512/vgytvKn2cFzj+RNa9eOLVKqRqfD9jzjew95Q7d6wW7du0Wg0BweHFuUUCsXF5d+Hc2MkFovVHu28IybUP3jwAAW5wYMHZ2RkoBvbzMxMiURiZ2dHp9N9fX3T0tLQzmlpaWhkFPY6dGcsGLGEZAIXY5YRAQSbxgYAM44JALCoTCpBJR/18RhcLp1D3q4JGXwO/ekj0tePghj2mjTNj6q///TR2aNkFAQAde29B3u+rT+2p82nPXfuHJm65eXl5+frjtt8nq1bt+7evft57zY1NUVFRb1lj2C6lnYJhGVlZSNGjFixYsWGDRtGjx4tlUonT54MAIGBgTweLzw8/Pvvv58+ffoXX3yB7li/+OKLJUuWrFu3LioqqqCgAO2MvRLdUZpMKsOc+3gNIwIIIZPPo3PRNpoIIWDyAMCYaQQABEHwGFzBk0jJoDJM2U+HrvEYXHJYKYbpg9rftyrLpM98qz7tF3nBlbad9tatWydOnHjVo8rKyl41VXdrCoVi+/bt7T1uEXuBdgmEFhYWs2fPVqlUNTU1wcHBeXl5FhYWAEClUrOysnx9fcvKyn766aeoqCi0f0BAQGpq6v379+3t7f/73//ivvI2sOSakbdr/7gFZBtRCAqa9i5iG6F9uDQui8okp/3xGVyezkQIM7wWPKav1HX3Gy89P1uYVtuQ8VsbTts66bZcLl+2bJmHh4eHh8e3336Lbtdu3rwZEBBga2vbs2fPb775Ri6Xf/7550VFRSNHjhw5cmSLSFZaWjpmzBh7e/uIiAiUSgYVhoaGOjs7u7m5ffHFF2gE9Zw5cwDgvffeGzlyZH5+fl5eXlBQkKOjo6en56pVq/CdYgdol54uGo02ZsyYMWPGtH5LIBDMnz+/dXn//v379+/fHpV5WxEEYcwS3W96gF4aMYX1clmtvA4ARCwjOoXGorGaVc0otziPwaUQhBFTgHbmMbi6K/8Zs0RkUAQAtHYuhukheVEuvPDOSV54FbTaVx04Y2Vl9cknnxw+fPiXX35BDy/nzZtXV1eXkZGhVCrDwsJEItG0adNmzZoVFBSUkpJSV1d38+ZNBoPxf//3f0uXLk1OToZ/jv/SarUffPCBv79/UlJSRkZGWFjYokWL0FuzZ8/u27dvbW1tREREbGzsvHnzYmJidu/evXfvXiqVyufzpVLpl19+6enpWVlZGR4ebmdnN2HChFf+pbBXYRBJt98aTJ3+Tw6Nbfxk0j1a6gF1eNKpdJRom0/nUgiKEVMIAAQQHDqHvO1j01ioHOExcBZsrGvQNMpevINWpdQqXnnJISqVyuFw6HS6SCQSCoVyuTwuLu7rr7+mUqksFmvmzJlklo+ioqLy8nKRSDRgwACCIHg8HpVKFYlE5Ox7JD8/v7CwcNmyZRwO5/333x86dCgqt7Gx6dWrV2ZmZnZ2ds+ePf/44w8AQEMo0EloNJqzs7OdnV16evrFixd79ux56tSpV/062KvCgVDf6T6is+ZbkenKjFhCMpGbKduYIAjUHWrCEqFD+Ewej84h9xcyBdwnmWsIgjDltMxhgWH6jyr8l3ZLYXEI5utmwCgrK9NoNDNnzgwLCwsLC4uPj0cToLdt21ZdXe3u7u7u7n748OEXnAElCKXTH3e0kPm7z5w54+7ufvTo0Vu3bt2/f//Bgwetjz148GC/fv1OnjxZWFhYW1v7zH2wNwsPAtRr6JbuQfPj+e8iltG9pgdo/rsp25hFY6I5guZP+j8JgjBiPen/pHPlDDl5KguumW5MxUNgsK6I6eRB0BlapeJ5O7Bc+7btzFQqlXwaZ21tTaFQduzY0SLpqKOjY1JSklKp3Lt374QJE2pra3WP0iUWi8vLy5VKJYqFxcXFaJzE9u3bFy5ciB4Kfvfdd1KpFJ7MJNFoNGjwYGxs7LfffhseHg4Aixcvbp2MG3vj8B2h3kGrWCBGTAEZ2Lh0DpPKQMM7uXTOk5XfeSwaE21TCSqbxiKHyXDoHOGTYwGATJCNYV0XhcXlD/vwee8SNDrfr42P0+zs7K5fv37p0qU7d+4wmcyoqKhp06YVFBQ8evTo4sWLqampABAfH19cXKzVavl8PuoUtbW1LSkpOXv2LAppJBcXF2dn56+//rq+vv7AgQNZWVmo3NjY+Ny5czKZ7NKlS5s3b0aFfD7f1NT0999/LywslMvlIpEoOzu7sbExKysLJaTE2hsOhHpB9/7Mhi8mdPo/yUX+0IwIHoMHOgM7eXSu7iLvpmxjOuVxbwyFIISMp4EQw94Ogvcmsj3ebV1OUGmiCZ/SLZ+xuObL8PX1jY6OXr58ORrVsnHjxmHDhk2aNMnLy2vp0qVon5s3b37wwQeurq7btm07dOgQQRB2dnbr1q1DU79ajBrdv39/Xl6et7d3SkrKihUr0Gz6JUuWKJVKT0/Pr776asWKFT4+PmjnX3755cCBA9HR0cXFxWvWrJFKpb169dqyZcvq1avd3d3b9o2wl9fuSbffrLcy6TaLxmTTWA+b6wCAAKKfuPe1ezdlikcEED5WHnQK/WLFZZVG7WPlSafQlBrVxYrLHuY90V1gvULWqGy0fLL6klKjouv9nHecdLs9GFTSbdBqH5071pB1QFVVCgAElcZ06S0MmEKXOP7roVjX1SWTbmMvwKVzHikfr2pmxBSyaEwUCEUsIYWg8Ok8meKRMdsI3d7xGTyVVo0iHJ1C4zG4ZLYXHp1D05ntoP9REMPeAILgDgzgDgzQyOo08kaqwISg4+sqrO1w12gHIYDQXZ/WViAht0X/6P80gyeZX9AQGADgMbjkDgBgxTXXWUSQopsCDcMMCoUnpJlY4SiIvSYcCDsIn8HjPU1jRjdiCVEAo1PoQqaQS+fQKDQmlSFiCtHOBEHwnwQ/PoMn0HkQaMLGmV8wTF/U1NS8/FQ/qVSak5PT5s8qKSnJzs5u8+FvytWrV/Pz89t2rEwmS09PT0lJQS/Pnz9fVVXVYp8jR450cD4dHAjbEZnJGgBELKHoyRx2E7YxAKCbPEueOYUgCILg0blGLCEaJsOgMkRMIe3JOrctRsRQuuByMxj2Npk/fz65kM6VK1c+++yzlzwwJSVl06ZNbf7cM2fOrFmzps2HP9Pp06e3b9/+Sof89NNPv/76axs+Sy6Xe3t779ixA4XzqqqqyMhIgaDlmL7U1NSdO3e24fxthh8pvUk0Ck2tUWtBCwAEEPbCbuRoHRHbCF3jEARhxTUHAAGTX/momsxwzWdwWbSnMxzEfEty+yVXfscwrGPs2rVr1qxZZmZmADB8+PA///zzJQ/85JNP9C136I0bNzIyMqZPn/7yh2zcuLFt64nm5+fL5fKkpCT0csOGDRMnTmw9GnHevHmBgYERERGvs1bXK8F3hG+SiCUk05XxGTwuncOmswBAwORzaGwug0OlUIVMAZopyGfw6FQ6+8msQT6DJ2A+vTLSfSiIYVh7e/jwYWRkpJ2dXa9evX788UdUGB8fv3Tp0vHjx1tbWw8bNqygoAAA5syZU19fP3bsWB8fn/T09EuXLk2bNg0A1Gq1j4/Pzz//7OLiYmNjExcXl5OT069fP7FYvHjxYnTCffv2xcTEAEBMTIyPjj179gBAeXn5uHHj7OzsPDw8EhMT0SH19fWTJk2SSCSDBw9+5uT6I0eOzJkzZ8aMGdbW1gMHDiS7Xu/evRsSEmJjY+Pt7b1//35UmJ6e7uPjI5FInJ2d4+PjCwoKVq1ahQqDg4MBQCaTzZ0718nJycXFhUz5HRcX97//+7//8z//Y2Fhcfz48e+++468Y9u5c6enp6etre2kSZPQXXJ9fb2Pj88vv/zi7Oz84YdPJ31euXIlPDy8srLSx8dn5cqVWq129+7dISEhrb+Ri4sLl8s9c+bMa/x7vhp8R/i6TNii+02PM78YMYXN1OYGhQye9H8KGYImZbOYZwEABBA8Opec28ekMsx0nvYJmALc54kZsnpFg0zxqGM+y4xj2mKI9fTp05lMZl5eXklJyciRI8VicVBQUEVFxbp16w4ePPjzzz9v2LAhODj46tWrq1at2rNnz65du+zt7blcblZW1vXr1wFAq9Xm5ORkZGTk5ORcu3Zt2LBh77333sGDB9Vqdd++fceMGTNgwICqqio09X7+/PkzZ84EgNzc3ODg4HfeeUetVgcGBk6ZMmXPnj2oDt27d+/Xr9/8+fPVanVBQcGdO3f8/PxaTyu8f/9+bGxsfHz8li1bkpKSRo8eXVRUxGazQ0JChgwZkpSUdOnSpYCAAHt7ey8vr4iIiD179gwdOrSurq60tNTBwWHevHlZWVkJCQno9isqKorP51+5cqW5uXns2LEWFhYff/xxRUXF+vXrk5OTt2/frtFojh492tjYCACZmZmLFi1KT0/v3r377Nmzp0yZkpqaqlKpcnJyTp48ef78ed1bOjc3t++//37GjBknT55kMpk3b95samrq0aMHevfBgwcNDQ1kLrp+/fqdOnVq8ODB7dQAWsCB8JUxqQy5+nGGJzqF1k0gQYGQAELEMmpWNZc2lNOpdAuuKQDwGbzqxhoyw7WAyRfqPDhEARLBURAzcFtydlytzuuYz5rZO/JdmwHky+bm5v3795eUlHA4nHfeeWfmzJmJiYlBQUEAMHjw4FGjRgHAggUL1q5de/XqVU9PT4IghEJhi0TbyLJly3g8Xv/+/SUSycSJE62srABg0KBBf//994ABTz+Rw+FwOJyKiorJkydv3rzZx8fnzz//vHfvXlBQUGlpKYVCGT169KFDh/r27bt3794rV66w2WwXF5fIyMhnjrXp0aMHWsZ10qRJ33777alTp5ycnK5du3b69GkGgzFw4MCwsLCkpCQvLy8Gg3H69GlnZ2dra2u04B2Hw2EwGOi71NfX792796+//kIDWMaNG3fw4MGPP/4YAEaMGNF60mdiYuLUqVN79eoFAGvWrDE1NSUzo65YscLY+B+JYWk0Gp/PRznKAaCkpEQsfpo8ZOfOnb/99tu5c+fQS7FYXFJS8pL/mq8PB8KXQgCBnvwBgCnHpLa5Ds0CNGIK2TQWWvDIgmtGo1BR/6cpy5hCUABAwOTzGTy0DQAilhBNhEd0V9PFMAP3xYB5nfXR1dXVACAWi9FLGxub48ePo21ra2u0QaVSxWJxRUWFp6fnC05lavp41hObzdbdRrdQupqamoKDg2fOnIlWQCwtLZXJZOQqrQDg5+fX0NDQ2NhI1kEikTwzEEokEt3tiooKPp9vZmbGZD4edtCtWzc0zvPAgQMxMTFubm729vbffffdoEGDdM9z9+5dCoWiO/bHycmp9UeQKisr+/Tpg7ZNTEy4XG5FRQWK/WSdn4dCoajVavLlmDFjyFOBTubVjoED4Uux4JpVPqpG28YsIwpBQYHQgmcOACKWoEL2z/5Plk7/55PpgACgu/4thmF6wtLSkkKh3L5929HREQCKiopsbGzQW7dv30YbSqXyzp07qPx5ubZfnkajmTBhgpub28KFC1GJnZ0dm80+duwYjfaPP8sCgUAqlbq5uQFAi4ymJN1yqVRqY2MjkUiqqqpkMhmPxwOAwsJCVHMPD4/k5GSFQvH9999HR0dfv35d97t069ZNo9Hs2rWLvCYgPXN0jEQiKSoqQtsVFRWNjY02NjYqlep5++uys7OrqKjQaDSo+9TR0RH9+EhZWZm9vf2Lz/AG4cEyz8ahPR3IxGNwrbiP+zCZVAaPzkUTIQRMPprex6PzWDQWmSxb+M/+T7zgO4bpOQaDMXny5Hnz5hUXF//xxx9btmxB418A4MKFC7t27aqoqFiyZImTk1PPnj0BwMHBITk5OScn5+HDh237xKVLlxYXF3/++edSqVQqldbW1vbp06d79+7R0dFSqbS8vPzQoUPnz58HgMjIyIULF5aUlGRlZe3evfuZZystLV27dm1lZeW6desUCsWQIUPs7e19fX3nz59fWlq6f//+/fv3R0REKBSKTZs2SaXShoYGuVyORr06Ojpevnz59OnT165d4/F4UVFRU6ZMyc3NraqqOnXqFDnh75k+/vjj+Pj4tLS0O3fuzJo1KywsrPVciOdxdnYWCoV5ec/uDD9//jy5iGMHwIHwKd3M11Y8C7IPU8QyYtNZaPynhC8mCAL1f5IRTsDki3TWebDgmOku8o4XPMIw/bdlyxZ3d/ewsLDVq1fHxcUNGTIElY8fP/7y5cujRo26e/cuSrQNAHFxcXfu3FmzZk1+fr65uTnamSCIDz/8kFyD0M/PD0UaAOjTp4+zszMAODo6ent7AwCTyXRyclq2bNnixYsXL158+vRpCoVy9OhRU1PTSZMmvf/++/v370fP8NasWePq6jp27NjNmzevX7/e19e3deVHjBghk8lGjRp19uzZtLQ0lMh33759TCYzODg4Pj4+NTXV2dmZQqHk5+dPnDhx6NChBQUFaGmLIUOGzJ49e9u2bWg24ffff+/v7z937tyRI0du3rwZJbB1dXX18vIiP87d3R0Ncunbt29iYuKGDRtCQkIcHBx27NgBAAwGQ3ewqC4TExN/f3/yZWRkZHJycuvdrl27plarBw4c+LL/eK8NJ91+jEJQrHgWZQ0V6GUfK89KWXVpQ7m8Wf4f+350Cl1aW1LTeL+v2AsFtrz7txyMbMm1jR4pG3Uf/mEvgJNutwfDSrrdUWJiYsrKymJjYzu7Ii+yc+fOAwcOHDp0qLMr8soePnw4YMCAixcvtmi6H3300ciRI9GjU13tl3TboO8IdTO/GDGFZG5PI6aQTqGLWEYAYM42RZmvBUy+kCkgb+/M2Ca6K/zhKIhhGPZKRCLRvn370JWxroiIiHHjxnVkTQxusAyNQlNpVGhbwrMq1qgalU0AYMIRsWksNo3VrJbbCiUAgPo/ybxoAgaPPBAATDnGrc6NYdhb5ZNPPkFDP/RZSEgImuDRFaFBQC20GMvaAQzrjpBFY5JL91EJqoApQBnOOHQ22hAyBeTi7wQQRkwBOc6TQWWY6sx/x0/+MOytZ2RkRE6B0Ft8Pt/S0vLf98Oe7+0PhCbsp5NejVkiMpmLJdeMQhBoqrs13woFNgGTT05+BwAbvrVuwKPhnJ8YhmFvnbc/EHYTSMgV+0zZxmw6i01j0Sk0CV8MAFwGh0ahkcFPyOSLWE8DIV7qD8Mw7K33lgdC9NgPdXuacUxRRmwhU2DEEqIlHQggrLjmZNZBOoWuu+ARhmEY9tZ7ywfL2AisAcCIJbxTX2b15Omg7mBRABDzcPc6hnWCwsLC11mlFjM07beC1dscCNk0Fhr2wqVz2DQWuUCSkMHXTf+DV/vDsI7n5eW1Z88etPyQfkJzrNu28B72PGROtTbw9vYms6e+WW9zICRTXRNA2AptyHI6ld5JNcIw7LEVK1asWLGis2vxIjjzQ3vQz8wPb/kzQpIxy6izq4BhGIbpI0MJhBiGYRj2TDgQYhiGYQYNB0IMwzDMoOFA+C/IhaqxN6WgoODWrVudXYu3DW6obxxuqO1BPxsqDoT/Ijw8vGutVKX/kpOTf/31186uxdsGN9Q3DjfU9jBu3Lj2mw7YZjgQYh0N/73GugTcUA0HDoQYhmGYQcOBEMMwDDNoRNe6/e/du7dGozEzM+uwT8zIyBg+fHiHfZwhkEqlAODg4NDZFXmr4Ib6xuGG2h4yMjKGDRvWkYnrgoODZ86c+eJ9ulgg/OOPPxobG9sp3dwz3b59297evsM+zhDU1tZqtVqRSPTvu2IvDTfUNw431PbQ8Q3V3t7e0dHxxft0sUCIYRiGYW8WfkaIYRiGGTQcCDEMwzCDhgMhhmEYZtBwIMQwDMMM2tu8MO+LNTc35+bmqtXq/v3765afO3fu1q1bbm5u3t7eqESr1ebk5OTl5ZmYmAwdOpTD4aDyCxcuNDQ0oG2hUNinT5+OrL9+Kioqun37dt++fQUCAVlYWlp69uxZNpv93nvvsVgsVJiZmUlmWhKLxa6urmhbJpMdP35cqVSOGjUKD9iDf2uoPXv29PHxQSW5ublVVVXkDkwm89133wXcUFt58ODB2bNna2trvby83NzcyHKFQpGWlvbw4cPhw4eLxWKy/Pbt26dPn7aysho+fDiVSkWFarU6IyOjoqJi0KBBeLwuAFy/fv3vv/9ms9mDBw82NjYmy8vLyzMyMkQikZ+fH7nKcUFBQU5ODp1O9/X1tbS0RIU3b968e/cueeCIESM6rvZag/Tzzz8zGAwTE5NevXrplk+ZMqVnz55z5sxxcHD4+uuvUWF4ePg777wzefLkQYMGSSSS27dvo3JPT8/evXuPGDFixIgRc+bM6eCvoG/kcrlIJDI2NqZQKBcvXiTLjx49ampqGhUVFRAQ4OHhUV9fj8oZDMa7776Lfr1169ahwvv37zs5OQUEBISHh1tZWRUXF3fCN9EniYmJqKG6ubnplj+zoa5cuXLEE926devTpw8q9/LyIhvq7NmzO/o76Jnr16/z+Xx/f/8pU6aYmpouWrQIlSsUCl9fX19f38jISGNj4/Pnz6Py48ePGxsbT5061dvbOzAwUKPRaLVajUYTGBjYu3fvqVOnmpiYnDhxotO+j35YsmSJnZ3d+PHjAwMDRSLRuXPnUPmFCxeMjY0jIyPRbyuXy7Va7caNG8VicVhYWHBwsEAgOHbsGNo5Ojra0dGRbMMdWX8DDYQ1NTW1tbXJ4QDg/QAADLRJREFUycm6gTA7O1skEtXV1Wm12rt37/L5/KqqKq1WW1hYSO4TFBQ0d+5ctO3p6Zment6xFddfarVaKpVqtVoej6cbCJ2cnHbt2oW2AwMDN27ciLYZDEZ5eXmLk6xatcrf3x9tT506ddasWe1eb/2GGupvv/2mGwizs7ONjIzQLDfUUCsrK1sc2Lt37y1btqBtLy8v3FBJtbW1NTU1aPvvv/8GgOrqaq1W++uvv/b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Package and system information
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+    "Status `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/1-mauna-loa/Project.toml`\n",
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"1.4.1+1" diff --git a/previews/PR387/examples/2-deep-kernel-learning/index.html b/previews/PR387/examples/2-deep-kernel-learning/index.html new file mode 100644 index 00000000..d7df1dab --- /dev/null +++ b/previews/PR387/examples/2-deep-kernel-learning/index.html @@ -0,0 +1,539 @@ + +Deep Kernel Learning with Flux · AbstractGPs.jl
GitHub

Deep Kernel Learning with Flux

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# Background

This example trains a GP whose inputs are passed through a neural network. This kind of model has been considered previously [Calandra] [Wilson], although it has been shown that some care is needed to avoid substantial overfitting [Ober]. In this example we make use of the FunctionTransform from KernelFunctions.jl to put a simple Multi-Layer Perceptron built using Flux.jl inside a standard kernel.

Package loading

We use a couple of useful packages to plot and optimize the different hyper-parameters

using AbstractGPs
+using Distributions
+using Flux
+using KernelFunctions
+using LinearAlgebra
+using Plots
+default(; legendfontsize=15.0, linewidth=3.0);

Data creation

We create a simple 1D Problem with very different variations

xmin, xmax = (-3, 3)  # Limits
+N = 150
+noise_std = 0.01
+x_train_vec = rand(Uniform(xmin, xmax), N) # Training dataset
+x_train = collect(eachrow(x_train_vec)) # vector-of-vectors for Flux compatibility
+target_f(x) = sinc(abs(x)^abs(x)) # We use sinc with a highly varying value
+y_train = target_f.(x_train_vec) + randn(N) * noise_std
+x_test_vec = range(xmin, xmax; length=200) # Testing dataset
+x_test = collect(eachrow(x_test_vec)) # vector-of-vectors for Flux compatibility
+
+plot(xmin:0.01:xmax, target_f; label="ground truth")
+scatter!(x_train_vec, y_train; label="training data")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Model definition

We create a neural net with 2 layers and 10 units each. The data is passed through the NN before being used in the kernel.

neuralnet = Chain(Dense(1, 20), Dense(20, 30), Dense(30, 5))
Chain(
+  Dense(1 => 20),                       # 40 parameters
+  Dense(20 => 30),                      # 630 parameters
+  Dense(30 => 5),                       # 155 parameters
+)                   # Total: 6 arrays, 825 parameters, 3.598 KiB.

We use the Squared Exponential Kernel:

k = SqExponentialKernel() ∘ FunctionTransform(neuralnet)
Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
+	- Function Transform: Chain(Dense(1 => 20), Dense(20 => 30), Dense(30 => 5))

We now define our model:

gpprior = GP(k)  # GP Prior
+fx = AbstractGPs.FiniteGP(gpprior, x_train, noise_std^2)  # Prior at the observations
+fp = posterior(fx, y_train)  # Posterior of f given the observations
AbstractGPs.PosteriorGP{AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.FunctionTransform{Flux.Chain{Tuple{Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}}}}, @NamedTuple{α::Vector{Float64}, C::LinearAlgebra.Cholesky{Float64, Matrix{Float64}}, x::Vector{SubArray{Float64, 1, Matrix{Float64}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}, δ::Vector{Float64}}}(AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.FunctionTransform{Flux.Chain{Tuple{Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}}}}(AbstractGPs.ZeroMean{Float64}(), Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
+	- Function Transform: Chain(Dense(1 => 20), Dense(20 => 30), Dense(30 => 5))), (α = [-701.1388050972998, 484.7628436219038, -1469.7818185173296, 598.4896099252254, -28.37330126200084, -1204.9104474162616, -368.92333277448313, -526.4255972350581, -199.92519155937478, -1626.7497780328176, 542.7341811143751, 504.21553444458897, -70.6248456998835, 73.75787171813063, -637.6002584964182, 110.33892243470423, -474.18996320833907, -660.5467484683887, -659.7326121860807, 672.2091629543114, 665.6601342518854, 1120.5438249474564, -546.7134141035283, 1245.7237350856647, -227.70765217332402, 879.7242710791257, 876.3055702798916, -218.70441049135363, -152.06133901326265, 848.9544310441407, -313.1315330357074, -754.0189412136584, 188.96327955336375, -827.7453089403782, 263.2940773111813, -1215.9123838858677, 569.7609342101207, -374.2744442832052, 745.8508168662794, 120.4296738562412, -487.93811821919303, -292.88941964656874, 127.09987527121002, -139.28671472970413, 251.8507846017912, 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-0.18582961703438408, 0.004374204772543916, 0.10783157043411237, -0.05704431445175237]))

This computes the negative log evidence of y (the negative log marginal likelihood of the neural network parameters), which is going to be used as the objective:

loss(y) = -logpdf(fx, y)
+
+@info "Initial loss = $(loss(y_train))"
┌ Warning: Layer with Float32 parameters got Float64 input.
+│   The input will be converted, but any earlier layers may be very slow.
+│   layer = Dense(1 => 20)      # 40 parameters
+│   summary(x) = "1-element view(::Matrix{Float64}, 1, :) with eltype Float64"
+└ @ Flux ~/.julia/packages/Flux/qZO6T/src/layers/stateless.jl:60
+[ Info: Initial loss = 4090.717331659456
+

Flux will automatically extract all the parameters of the kernel

ps = Flux.params(k)
Params([Float32[-0.267783; -0.45613554; -0.51431525; 0.36201957; -0.06702539; 0.31383085; -0.016083641; -0.3159858; -0.28528228; 0.43957305; 0.45529974; -0.50481147; -0.23413388; -0.41008896; 0.39742747; 0.1611269; -0.19932841; -0.35804477; 0.10675026; -0.08516879;;], Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], Float32[0.12864004 -0.14245832 0.071479864 0.16269171 0.19467425 0.27578887 -0.16545948 0.17726882 -0.31125942 0.18201688 0.011603197 -0.2971016 0.3289558 0.31981733 -0.116226636 -0.23015061 0.2284964 -0.238933 0.24398346 -0.03505947; 0.10848042 0.11245278 0.29866865 0.32251987 0.04924185 -0.13928242 -0.014168998 -0.077437915 -0.04602333 -0.13059947 -0.25386176 -0.33714315 0.15307344 0.3114677 -0.31656373 0.061651707 -0.25939843 -0.2939725 -0.033230994 0.28005722; -0.117163666 -0.06855673 0.1374514 0.027878545 0.071886085 -0.09881992 0.31111118 -0.21509764 0.22342224 0.28379446 0.0083606485 -0.20008382 0.29914466 -0.25262272 0.048486143 0.24638961 -0.28296173 0.18608062 -0.2718269 -0.29707497; 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We show the initial prediction with the untrained model

p_init = plot(; title="Loss = $(round(loss(y_train); sigdigits=6))")
+plot!(vcat(x_test...), target_f; label="true f")
+scatter!(vcat(x_train...), y_train; label="data")
+pred_init = marginals(fp(x_test))
+plot!(vcat(x_test...), mean.(pred_init); ribbon=std.(pred_init), label="Prediction")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Training

nmax = 200
+opt = Flux.ADAM(0.1)
+
+anim = Animation()
+for i in 1:nmax
+    grads = gradient(ps) do
+        loss(y_train)
+    end
+    Flux.Optimise.update!(opt, ps, grads)
+
+    if i % 10 == 0
+        L = loss(y_train)
+        @info "iteration $i/$nmax: loss = $L"
+
+        p = plot(; title="Loss[$i/$nmax] = $(round(L; sigdigits=6))")
+        plot!(vcat(x_test...), target_f; label="true f")
+        scatter!(vcat(x_train...), y_train; label="data")
+        pred = marginals(posterior(fx, y_train)(x_test))
+        plot!(vcat(x_test...), mean.(pred); ribbon=std.(pred), label="Prediction")
+        frame(anim)
+        display(p)
+    end
+end
+gif(anim, "train-dkl.gif"; fps=3)
┌ Warning: Layer with Float32 parameters got Float64 input.
+│   The input will be converted, but any earlier layers may be very slow.
+│   layer = Dense(1 => 20)      # 40 parameters
+│   summary(x) = "1-element view(::Matrix{Float64}, 1, :) with eltype Float64"
+└ @ Flux ~/.julia/packages/Flux/qZO6T/src/layers/stateless.jl:60
+[ Info: iteration 10/200: loss = 76.0354504227719
+[ Info: iteration 20/200: loss = -0.9394704761251926
+[ Info: iteration 30/200: loss = 32.64950825879501
+[ Info: iteration 40/200: loss = 51.34619725383423
+[ Info: iteration 50/200: loss = -186.9573985312025
+[ Info: iteration 60/200: loss = 90.20641080950064
+[ Info: iteration 70/200: loss = 125.57395643728562
+[ Info: iteration 80/200: loss = 128.52814346820105
+[ Info: iteration 90/200: loss = 128.96777494909188
+[ Info: iteration 100/200: loss = 128.69756159631837
+[ Info: iteration 110/200: loss = 128.11952169143362
+[ Info: iteration 120/200: loss = 127.33786275210127
+[ Info: iteration 130/200: loss = 126.35728321434976
+[ Info: iteration 140/200: loss = 125.1327243923329
+[ Info: iteration 150/200: loss = 123.56936409299618
+[ Info: iteration 160/200: loss = 121.48655719765259
+[ Info: iteration 170/200: loss = 118.51590542776988
+[ Info: iteration 180/200: loss = 113.74951112525171
+[ Info: iteration 190/200: loss = 104.23131367025238
+[ Info: iteration 200/200: loss = 71.02513873978248
+[ Info: Saved animation to /home/runner/work/AbstractGPs.jl/AbstractGPs.jl/docs/src/examples/2-deep-kernel-learning/train-dkl.gif
+

+
Package and system information
+
+Package information (click to expand) +
+Status `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/2-deep-kernel-learning/Project.toml`
+  [99985d1d] AbstractGPs v0.5.20 `/home/runner/work/AbstractGPs.jl/AbstractGPs.jl#07d5f00`
+  [31c24e10] Distributions v0.25.107
+  [587475ba] Flux v0.14.13
+  [ec8451be] KernelFunctions v0.10.63
+  [98b081ad] Literate v2.16.1
+  [cc2ba9b6] MLDataUtils v0.5.4
+  [91a5bcdd] Plots v1.40.2
+  [e88e6eb3] Zygote v0.6.69
+  [37e2e46d] LinearAlgebra
+
+To reproduce this notebook's package environment, you can + +download the full Manifest.toml. +
+
+System information (click to expand) +
+Julia Version 1.10.2
+Commit bd47eca2c8a (2024-03-01 10:14 UTC)
+Build Info:
+  Official https://julialang.org/ release
+Platform Info:
+  OS: Linux (x86_64-linux-gnu)
+  CPU: 4 × AMD EPYC 7763 64-Core Processor
+  WORD_SIZE: 64
+  LIBM: libopenlibm
+  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
+Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)
+Environment:
+  JULIA_DEBUG = Documenter
+  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src
+
+

This page was generated using Literate.jl.

diff --git a/previews/PR387/examples/2-deep-kernel-learning/notebook.ipynb b/previews/PR387/examples/2-deep-kernel-learning/notebook.ipynb new file mode 100644 index 00000000..be058add --- /dev/null +++ b/previews/PR387/examples/2-deep-kernel-learning/notebook.ipynb @@ -0,0 +1,9767 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Deep Kernel Learning with Flux\n", + "\n", + "*You are seeing the\n", + "notebook output generated by\n", + "[Literate.jl](https://github.com/fredrikekre/Literate.jl) from the\n", + "[Julia source file](https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/master/examples/2-deep-kernel-learning/script.jl).\n", + "The rendered HTML can be viewed [in the docs](https://juliagaussianprocesses.github.io/AbstractGPs.jl/dev/examples/2-deep-kernel-learning/).*" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "# Background" + ], + "metadata": {}, + "execution_count": 1 + }, + { + "cell_type": "markdown", + "source": [ + "This example trains a GP whose inputs are passed through a neural network.\n", + "This kind of model has been considered previously [^Calandra] [^Wilson], although it has been shown that some care is needed to avoid substantial overfitting [^Ober].\n", + "In this example we make use of the `FunctionTransform` from [KernelFunctions.jl](github.com/JuliaGaussianProcesses/KernelFunctions.jl/) to put a simple Multi-Layer Perceptron built using Flux.jl inside a standard kernel." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "[^Calandra]: Calandra, R., Peters, J., Rasmussen, C. E., & Deisenroth, M. P. (2016, July). [Manifold Gaussian processes for regression.](https://ieeexplore.ieee.org/abstract/document/7727626) In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 3338-3345). IEEE." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "[^Wilson]: Wilson, A. G., Hu, Z., Salakhutdinov, R. R., & Xing, E. P. (2016). [Stochastic variational deep kernel learning.](https://proceedings.neurips.cc/paper/2016/hash/bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html) Advances in Neural Information Processing Systems, 29." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "[^Ober]: Ober, S. W., Rasmussen, C. E., & van der Wilk, M. (2021, December). [The promises and pitfalls of deep kernel learning.](https://proceedings.mlr.press/v161/ober21a.html) In Uncertainty in Artificial Intelligence (pp. 1206-1216). PMLR." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Package loading\n", + "We use a couple of useful packages to plot and optimize\n", + "the different hyper-parameters" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "using AbstractGPs\n", + "using Distributions\n", + "using Flux\n", + "using KernelFunctions\n", + "using LinearAlgebra\n", + "using Plots\n", + "default(; legendfontsize=15.0, linewidth=3.0);" + ], + "metadata": {}, + "execution_count": 2 + }, + { + "cell_type": "markdown", + "source": [ + "## Data creation\n", + "We create a simple 1D Problem with very different variations" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=2}", + "image/png": 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"plot(xmin:0.01:xmax, target_f; label=\"ground truth\")\n", + "scatter!(x_train_vec, y_train; label=\"training data\")" + ], + "metadata": {}, + "execution_count": 3 + }, + { + "cell_type": "markdown", + "source": [ + "## Model definition\n", + "We create a neural net with 2 layers and 10 units each.\n", + "The data is passed through the NN before being used in the kernel." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Chain(\n Dense(1 => 20), \u001b[90m# 40 parameters\u001b[39m\n Dense(20 => 30), \u001b[90m# 630 parameters\u001b[39m\n Dense(30 => 5), \u001b[90m# 155 parameters\u001b[39m\n) \u001b[90m # Total: 6 arrays, \u001b[39m825 parameters, 3.598 KiB." + }, + "metadata": {}, + "execution_count": 4 + } + ], + "cell_type": "code", + "source": [ + "neuralnet = Chain(Dense(1, 20), Dense(20, 30), Dense(30, 5))" + ], + "metadata": {}, + "execution_count": 4 + }, + { + "cell_type": "markdown", + "source": [ + "We use the Squared Exponential Kernel:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Squared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t- Function Transform: Chain(Dense(1 => 20), Dense(20 => 30), Dense(30 => 5))" + }, + "metadata": {}, + "execution_count": 5 + } + ], + "cell_type": "code", + "source": [ + "k = SqExponentialKernel() ∘ FunctionTransform(neuralnet)" + ], + "metadata": {}, + "execution_count": 5 + }, + { + "cell_type": "markdown", + "source": [ + "We now define our model:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "┌ Warning: Layer with Float32 parameters got Float64 input.\n", + "│ The input will be converted, but any earlier layers may be very slow.\n", + "│ layer = Dense(1 => 20) # 40 parameters\n", + "│ summary(x) = \"1-element view(::Matrix{Float64}, 1, :) with eltype Float64\"\n", + "└ @ Flux ~/.julia/packages/Flux/qZO6T/src/layers/stateless.jl:60\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": "AbstractGPs.PosteriorGP{AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.FunctionTransform{Flux.Chain{Tuple{Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}}}}, @NamedTuple{α::Vector{Float64}, C::LinearAlgebra.Cholesky{Float64, Matrix{Float64}}, x::Vector{SubArray{Float64, 1, Matrix{Float64}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}, δ::Vector{Float64}}}(AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.FunctionTransform{Flux.Chain{Tuple{Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}}}}(AbstractGPs.ZeroMean{Float64}(), Squared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t- Function Transform: Chain(Dense(1 => 20), Dense(20 => 30), Dense(30 => 5))), (α = [91.7665344140111, 479.23922536544893, -564.2150269715258, -40.2642849901063, -906.7954738060807, 534.0158293358137, -152.9094531034823, 757.1314771946985, 433.8778611938516, -498.53681479643956 … -721.5096388486259, -904.6159242292871, 1395.3249692391598, -310.2059044251377, -948.0871917655168, 335.557163980973, 418.47890479474376, -211.6033551315803, -646.2826179974485, -400.8465645788945], C = LinearAlgebra.Cholesky{Float64, Matrix{Float64}}([1.0000499987500624 0.5530042122626816 … 0.13629577575175186 0.10517795518291023; 0.553031861782074 0.8332384659986185 … 0.7042340200700025 0.6334788135352111; … ; 0.13630259037017822 0.6621670126914978 … 0.010424129756756976 0.0007692368028730984; 0.10518321394920349 0.586002767086029 … 0.9921119809150696 0.010389128574212474], 'U', 0), x = SubArray{Float64, 1, Matrix{Float64}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}[[-0.6596862392175811], [0.40143610672279273], [2.2237304363741472], [-2.039833211583813], [-2.16028626270872], [-0.27904842635686933], [-2.84147413640246], [2.5200297096526194], [-0.4171171511997711], [-1.176057495267508] … [0.08878734979504266], [2.1549157900360782], [-1.6929620984344447], [2.2227830442041085], [-2.1820021984042555], [0.6627737427849927], [-0.3105458065884181], [-1.1039209426690533], [1.2866615439881697], [1.409355371159723]], δ = [0.281855675053829, 0.3900132769955219, -0.03145999606943251, 0.05848017234557851, -0.0537548576110714, 0.3806060893247067, 0.0021995034266906986, 0.041137837556069964, 0.3823148659704494, -0.16192727782250677 … 0.22787083571708247, -0.04410591774831145, 0.13008650929895132, -0.0058016026488830474, -0.06406813466153205, 0.30964043212953346, 0.3732964960380299, -0.09431529514693379, -0.2127079294566562, -0.17958826269096365]))" + }, + "metadata": {}, + "execution_count": 6 + } + ], + "cell_type": "code", + "source": [ + "gpprior = GP(k) # GP Prior\n", + "fx = AbstractGPs.FiniteGP(gpprior, x_train, noise_std^2) # Prior at the observations\n", + "fp = posterior(fx, y_train) # Posterior of f given the observations" + ], + "metadata": {}, + "execution_count": 6 + }, + { + "cell_type": "markdown", + "source": [ + "This computes the negative log evidence of `y` (the negative log marginal likelihood of\n", + "the neural network parameters), which is going to be used as the objective:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "┌ Warning: Layer with Float32 parameters got Float64 input.\n", + "│ The input will be converted, but any earlier layers may be very slow.\n", + "│ layer = Dense(1 => 20) # 40 parameters\n", + "│ summary(x) = \"1-element view(::Matrix{Float64}, 1, :) with eltype Float64\"\n", + "└ @ Flux ~/.julia/packages/Flux/qZO6T/src/layers/stateless.jl:60\n", + "[ Info: Initial loss = 2722.023214090015\n" + ] + } + ], + "cell_type": "code", + "source": [ + "loss(y) = -logpdf(fx, y)\n", + "\n", + "@info \"Initial loss = $(loss(y_train))\"" + ], + "metadata": {}, + "execution_count": 7 + }, + { + "cell_type": "markdown", + "source": [ + "Flux will automatically extract all the parameters of the kernel" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Params([Float32[0.4398817; -0.19868113; … ; 0.2330839; 0.22354896;;], Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], Float32[-0.12032974 -0.22805598 … 0.1822656 0.14429498; -0.30137697 0.11233472 … -0.27775398 -0.19642217; … ; 0.0056175245 -0.22974274 … -0.03468071 0.121390365; 0.11585907 0.076146446 … -0.0072538513 -0.20977497], Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], Float32[-0.19119956 0.3656751 … 0.200278 0.10543245; 0.045965303 0.1618123 … -0.40140027 0.13227594; … ; 0.3398019 0.19245917 … 0.34413797 0.1357459; 0.010359611 0.26487392 … 0.4062629 -0.07145348], Float32[0.0, 0.0, 0.0, 0.0, 0.0]])" + }, + "metadata": {}, + "execution_count": 8 + } + ], + "cell_type": "code", + "source": [ + "ps = Flux.params(k)" + ], + "metadata": {}, + "execution_count": 8 + }, + { + "cell_type": "markdown", + "source": [ + "We show the initial prediction with the untrained model" + ], + "metadata": {} + }, + { + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "┌ Warning: Layer with Float32 parameters got Float64 input.\n", + "│ The input will be converted, but any earlier layers may be very slow.\n", + "│ layer = Dense(1 => 20) # 40 parameters\n", + "│ summary(x) = \"1-element view(::Matrix{Float64}, 1, :) with eltype Float64\"\n", + "└ @ Flux ~/.julia/packages/Flux/qZO6T/src/layers/stateless.jl:60\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=3}", + "image/png": 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"output_type": "stream", + "text": [ + "┌ Warning: Layer with Float32 parameters got Float64 input.\n", + "│ The input will be converted, but any earlier layers may be very slow.\n", + "│ layer = Dense(1 => 20) # 40 parameters\n", + "│ summary(x) = \"1-element view(::Matrix{Float64}, 1, :) with eltype Float64\"\n", + "└ @ Flux ~/.julia/packages/Flux/qZO6T/src/layers/stateless.jl:60\n", + "[ Info: iteration 10/200: loss = 4.806795171966006\n", + "[ Info: iteration 20/200: loss = -162.83053965341526\n", + "[ Info: iteration 30/200: loss = -21.87084462766412\n", + "[ Info: iteration 40/200: loss = -98.52513915635485\n", + "[ Info: iteration 50/200: loss = -128.08553484947677\n", + "[ Info: iteration 60/200: loss = -169.56799778645413\n", + "[ Info: iteration 70/200: loss = -159.23199444056016\n", + "[ Info: iteration 80/200: loss = -200.11024677645878\n", + "[ Info: iteration 90/200: loss = -199.39638805165856\n", + "[ Info: iteration 100/200: loss = -200.66056214368092\n", + "[ Info: iteration 110/200: loss = -200.6020168145976\n", + "[ Info: iteration 120/200: loss = -200.70342301078205\n", + "[ Info: iteration 130/200: loss = -200.72290507011775\n", + "[ Info: iteration 140/200: loss = -200.74110242767466\n", + "[ Info: iteration 150/200: loss = -200.73771597384572\n", + "[ Info: iteration 160/200: loss = -200.7521456998893\n", + "[ Info: iteration 170/200: loss = -200.73742458454842\n", + "[ Info: iteration 180/200: loss = -200.75012350588543\n", + "[ Info: iteration 190/200: loss = -200.75867539549898\n", + "[ Info: iteration 200/200: loss = -200.74450033594132\n", + "[ Info: Saved animation to /home/runner/work/AbstractGPs.jl/AbstractGPs.jl/docs/src/examples/2-deep-kernel-learning/train-dkl.gif\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": "Plot{Plots.GRBackend() n=3}", + "image/png": 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ubuzYsUKh8OLFi2PHjlWmTZKGhgZyRgQFHf2AUevmDcQHQ+v7CIVCIelYce/ePVmhQCAgA06eP3+eLKmrq1O4MC0tDQB8fHwIgsBxvL6+XqHC6dOnAWDhwoXkxw7uIywrK1OIXkYQRFJSkpaWFoZh6enpssI5c+YAwNq1a2UlRUVFZNCTgoICsoR0r9++fbtCg5GRkZqamtra2lFRUa3YSV7es2dP+ZiZU6dOVWizrKxMS0tLS0tLfnc/uYU/JCSkabPvwz7CtWvXAoCzs7MsRkGzkJ66c+bMkZVwuVwrKysMwxISEmSFZMw2Ly8v2U5NHMfJfZlHjhyRVaupqdm7d6984DQZQqGQHHa3FOTh+fPnLBaLSqWGhYW1YrAsBpA85Hfh5ubWyoWI9xw0IkS0gb1795JvJXkGDRoUGhq6c+fO2bNnh4SEbNu2bciQISUlJVu3biV3uU2ZMoWsOXz4cEdHx4kTJzo6OhoYGGRlZW3atAkAyIgtYrG4R48eoaGhgYGBDg4ONBrt+fPnq1evxjAsJCTknbYdPnz4+fPnAEB69CUnJy9YsIA8deDAAXLccPz48UOHDn3yySeOjo5mZmaVlZWxsbFhYWFCofCrr76SjxO9cePGiIiI7du3SySS8ePHFxcXr1+/vra2dvXq1bLpLzKkiMJUW0lJyYQJEwQCgaen57lz586dOyd/durUqbI5N39//4kTJ169ejUoKGjNmjU6OjrHjx8PDw93dHSUX3szMzNbs2bNhg0bRo8evWXLFltb21u3bm3dulVPT2/z5s3vfCxdz4ULF8joLc7OzuT3K8+GDRtk0cOXLVt2+vTpU6dOaWpqzpo1q6amZvPmzUVFRbNnz5afTJ48efKwYcMePnw4bty4pUuXamhoHDx4MCoqytPTU97/s7Ky8vvvvweApn5AV69erays7N+/v6enZ1ODBQLByJEjORyOs7Pz/fv3yR8oMgIDA8lfJwAwceJEOp0eFBRka2urq6tbUFBw6dKlR48eaWho7N69uz0PC/GeoG4lRnwYNF3lkiHLAnHkyBGFpaDJkyfLR+acMGGCgjcBnU5fuXIlGRyksbGxaboiJpMpn7ehlRGhTG6bIhty/f33303zHBkaGm7evLnpSPHJkyfyU38UCmXx4sXy1RwcHHr27KlwlWwneLPs2rVLvnJtba3sJUvi5eWVlZWl0CaO46tWrZJ3FbG0tHzw4EGz35TaR4Stb8xQCOn58uVLMvicjFmzZjVNK1FZWamw8XHIkCEK47OsrCzyVFOTyGsPHjzYrMGk92lLLFu2TFZzzpw5TcMmuLm53b59u50PC/F+gBEoQz1CCQoLC8kN4E0xMDCQ+YvW1dVFR0cXFRXp6Oj4+vqSCRzkYbPZCQkJpaWlYrHYysqqf//+CluqCwsLX7x4UVZWRqfTbWxsBg4cKB+2bcmSJXv27ImIiBg/frxCy+Xl5S1tqrO3t5c57kskkqSkpNzc3OrqagaD0bNnT29v75ZWdxobG6Ojo3NycnR0dIYPHy7vQpmSkuLh4bFixYpffvlF4ZJWHDKNjIyauo1kZGTEx8c3Njb26tVr0KBBLW0XKSkpiY6Orq2ttbe39/f3b7oBkSQ0NDQsLOzJkydNg9V1DbW1tVVVVS2dtbKyUrAcx/FHjx5lZmbS6XQ/P7+mfzMyXrx4kZSUhON43759mzr7NDY25uXlYRhGBlGTJy8vD8dxS0vLZpMd4jiel5fXUqcGBgbya5x1dXXPnj0rKSkhtzn27du36RI44oMDCSHiQ4IUwuDgYHt7excXFzXu3Nq8efOGDRsePXo0ePBgddkgT0NDw+rVqwHg9u3bmZmZahRCBOKDAwkh4kNi8+bNpPsfAAwePPjs2bPqsoTH44lEIiaT2Rn7/dsBl8uVD8dz5cqVpvENEAhEsyAhRCAQCES3BsVBQCAQCES3BgkhAoFAILo1SAgRCAQC0a1BQohAIBCIbg0SQgQCgUB0a5AQIhAIBKJbg4QQgUAgEN0aJIQIBAKB6Na8X0KYlZV15swZ5euLxeLOM+b9B92+uk1QJ9359gmCkEgk6rZCbeA4juO4uq1QG2SMftW2qQIhrKqqGjt2rK6urqmp6YkTJ1qqhuP4+PHjPTw8WmkqJSXlypUrynfN5/PbYOh/DnT76jZBbeA4LhQK1W2F2pBIJCKRSN1WqA2xWNydfwaJRCKV/wxSQT7CVatWGRgYcDictLS04cOHDxs2rGkyHQA4ePAgh8NpJco7AoFAIBBdT0dHhEKh8Ny5c2vWrKHRaF5eXmPHjiVTiiuQl5d35MiRjRs3drA7BAKBQCBUS0dHhMXFxXw+383Njfzo5ub2+vVrhToEQSxYsGD37t26urrvbFAkEnE4HNnH9ye6PwKBQCD+k3RUCDkcDoPBkGVt1tfXr66uVqjz559/mpubjx49Oi4urvXWcnJybt265eDgICs5efLkyJEjW6rP4/G6s0yi2++2t4/juEAg6LapY8RicWNjY7f1lyHXR1vKzPyfh8/n02g0mei8E21tbQrlHXOfHRVCExMTgUAgEonIb4XD4ciSlZNwudwffvjhwIEDd+/ezcjIEIvFd+/e9fX1bXZ06ODgMH78+PDwcCV7JwhCmVHmfxV0+9329nEcp1KpOjo6nddFSUlJ1L17lRXl7p5eI0aMeK9+c5BCqKWlpW5D1EM3F0IqldomIVSGjrZlbm5uYGCQnJw8cOBAAHjx4gV5IEMoFHp5eR07dgwAuFyuSCTasWPHkSNHuu0rDIF4//l972/HD+wdZ6NnSMP+Dj+xUUQLu3rd2tpa3XYhEJ1CR4WQTqfPmTNn48aNp06dSkhIiIqKOnDgAABkZ2d///33ERERpqamd+7cISvHxcUFBQXJPiIQ/xmuX4v45ccNvNpakURibWs/bFTgqNFB3t7e6rbr3XC53JycHEtLS9lczqOYmMtH9oePcdGgYAAwGSCxjPtpyOT7cfFqtRSB6CxUMLrctm3b4sWLvby8zM3Nw8LCrKysAADH8draWoWaBgYG/v7+He8RgXiv2LHlx7jwv/cPtDbWtsAJIuxl8fHfdjy+cErPwfXU+fD3dgqLw+EsWfhlxvN4Z5ZuSb2QwjT+/cRpJyenk38c/LaPKamCJP3NDTQzcnNzc+3t7dVoMALRSahACHV0dP7880+FQmdn55iYGIVCV1fXiIiIjveIQLw/cLnccyeOXQ12pWIYAFAwbGYfK66gUZ+hwefmbd24/sftO9RtYzMQBBESPGYaq3HL2F5kSWpFbcjHQTGJL4oKC2x7aSvUt9GjFxUVISFE/CdR5XojAtENSUxMHGimR/1fX5JAB5MDSXnbRvYeHx4W++hhVUUFQ0tr6IhAEZ+X9+aNo7PLZ18vkm06UgsJCQmGQu5YR1tZSV9T/YnW9Sf/Pm5lY1NQm2es/T8D2cJ6kaWlZZeb2QYKCgoyMzPVbUVXQIaVUa23yAeBg4OD/J4CFdLtHiUCoVoIgmjg8eRLJBhkG9PqR1j+pNlgFmAyg6k9QuQkEksOxd54mFd5YIx7bvGzLyZ/PHPh919/v1hdZqenp3sYKP77Wzno3amK9Z7ht++XlKOmBrLZ0aRybgNDr5PeQapiw4YNCQkJFhYW6jYE0SlUVlY6OjpeunSpMxpHQohAdAgTE5MnhdU4QVAwDACq6Fh4D+oTpoaEplcEgIFhhAhqqyRjyrClPo5CsSS5jDve2WKwtdEnB/cET5psY2OjFrP19PS4/xuuMkUfS9Kj1+uKbiXcZNszAmNfmlYI+xrrvKoVl4qpp8LOq8VO5cFxfNWqVbNnz1a3IYhO4erVq6dOneqkxt+v7BMIxAeHUCg0MdBZfvclV9hYrAW7nDViTCgS2v//Z3HocM2ccsqWIqTAGEez2CIOAGhQsAk2erdu3lSX2f7+/neLuEKJNIlBmSb2lEVJs9epNxU39qSzAqzMpzqBjW5CKTevkuOmhS+ZM2Pj6pXqshaB6FSQECIQHcLGxoaupRNga/zpnZRl1kShlnQ6kRDjVKGEAgQAiCnYIyPqLCr3j+f5daJGsoIhnVrDrlKX2SwWa/HaDbMiM2MKqqsaREdZkmgjCjCkL4RGDYxipcf6vM+eub63Zw6iEvg4K63cqGsnjh5Vl8EIROeBhBCB6BBmZmYsG4fqRgnzsz5UAwYAAAH6VcLhGTxhZL79q1rgSacgNT2Ne09wSq2oq2oQAUBardi1j7saLZ8z//OT124/MXZfRscyLDUJCgYAdJzAhNLQZbUa2HYXjYucGh069Uhifj9D+olDB9RoMALRSSAhRCA6yryvvr5siolt9MiPVgW8wkNJUzmYy6v6u/sTvi2UGIkIAMABy+yt//lY1yPP8+JLOPFc/KOPPlKr4eDi4vLroT80hkodTPA68YgKScODooFsnEIAANRrYPdHmg7ytd0d2Ce1ovZNzhve/3oGIRD/AZAQIhAdZcPOjYZj7Mhjlzp8eyVtoaPl7wm5cz2tnVk6vnXYp/kSKgEAwKZhpT5mNwurj1VrXfr3No1GU6PZJOufnBWIawEAJxiceHYvtgR/UzOkUhxQLQGcAACxJvVfPybLhbVpeK9v+tlsXveDuk1GIFQMEkIEokM8zanU+chEjAEA6DXCkmwxhYAgR9O7OZUZlXXk/sIBNfjwSul840sm1mOM2417D96H0J1x5fWPCv4hjyUaXnP8Pj10NcudB/nPSubkSXi388k1zhoadtBB454JJcjL+q/jR336uJ7563S3zX2B+O+Btk8gEO2HLYQ59zPN7KVZID7Ll+iJAQAoGFApWGwx51V1Ha9RokOjzi7Ek5gUDh0TUzAtH6s8bqGdgZqFkC+Gb6JO64IAACSgf3rM9z0NdDysHTeuWnYr9k1pvVCSXT7V3fqiJZUAqGRg/5pT2ZqUHkMsf3G2/mPPljeZrzds3qreW0AgVAIaESIQ7YQA+CRKrEm/DhgAgAMP9+ZIh31Pizn9LZhb/F1X+jl9eSOpqI5Px4lxpdKzIn3J48J4AtQ5oqqtrf05mauD3yM/TnOd2tNABwBGBwXFJqdll1YaffxppVAyprAx4O1YtlwTe8DC6Ja6yRYa/lN6Xk+5HvMmNpuTU1JfxhOhhUPEBwwaESIQ7eTIK/xpSbaXZjoAAAHjMwXkP1RqRe2q6ExHE8OHBdV2TG0HY4OQy0mGTAOMTtdZ1pemg2MgvvImcYi1t22XDwrZbPYPyxbHPozWY9A43izj0eYAQJPoMNO4mbqZzs7OZDVtbe3la9aKRI3b/w1bS7Gt1cASDCkAwKVjWn49SuqJHnzCxt7gXvoDW1tbAKBRaGMdR7G0DLv4dhAIlYCEEIFoD8U8YtUziQP9NPnRUsNsx+3HhIgvIcDCxvbfmLiqysobl8M51VW+n8/+fe48MovsF3fPZZafBYDy+syYopQuFsLGxsaPRwyfZ01fOdaloJ6/azizDgAA7J/m573affKXhp79fQ8ePUGj0QQCwQ/Lljy49Y+GWDTq79hB9sa6Y63qnQyBAo10ShwLTIWEkEMtKSnhcrkmJiYWFhaRufc/7jlaj47yjCI+PJAQIhDt4ZsnuKix1EAzDQAwwNaNWuE2/U+hUEij0SgUCgBAr15Dhg5VuOrXYZPHXrxGgXoKCC9kJgTY9LPU67rYmJfCwz21xfdzaw7GZ1sOsebRDAEA+OKpdZTefXvM7wsHkl9s2bDux+07Fs6b41CZETHODQPgCBof5FX+eiot+OuBr3rQRRQMACoYGPTRz617w8oVvIoTCmmak0I+uZN7f5zTRzSK+l1hEYg2gdYIEYg2czkPj8jHbWjh5EdXYyc3Y2cAYDAYUhVsAT063d3Mjzyu5mU+KX3Z2abKE//4YWxO6Qg743+m+xkGSRMq8WKKUktryOOv3HtcvXi+oqLiddKzeX16kDFyDDVpk3r1WO9hn3E1K7Bc4lBPYG8XN0V6GmUOuixPlrs+XDp/jiusy+cWdeUdIRAqAWWW3TkAACAASURBVAkhAtE2JASsuldOTb9hjEkzbs5yC1H+8hXeEwDoAKCB1UVkpdQIuZ1iZXOUlpe7meiN6Wn2Wg8r0sYAgEpA3zcNr6ulri4aFIyGwatXr/oYK85wepkbVFbil6JK0uIrqp9UGHEbKW/lsEgLe+WoTdWSsNnsglokhIgPDySECETbmL3sR9Emv/HC3VSKBADoIqpPj/7KX25rYG2m50ge53Kzcjj5nWJlc7CMTX0tDQHgoqV0TWQABx/G0qe+zaVIAAhxgslk1ryNsiajRtDo6uYW90/S6Z//0qQaelVLgsol9jxpzG4+FcPd9MvY5cV1pWJcDIgPCqFQ+OrVq6ioqLt379bX16vbHDWAhBCBaAPHjx+vu3fmwWQXGCD1kDR9Xrllw3rlW8AAm+EyAgADACpeGpGb3imGNoe7p2etUJynjb3SwwAAA5hYKqkVik11GGSF86/KRgZ91KdPn0yugAyIKiMsmz1h+kwMw1yNnZ3qDfF6kZaE8KrB/apxhgQAAKdieZJiMS4urivtsjtCdJzdu3cbGRm5urqOHDkyMDDwzZs36rZIDSAhRCDawO7de7b7Wb9kUqrpZIhq2KChe+nc2TY1MtTam65hAgAYhj8pSqnmszvF1iaMChwdUcQPsybDvYFLPW7dQJx5WUwA/JtdvvxxfrSEtW3XbxQKZc8fR+dFvfnnTUU5T/iysm5NbD7XxPGT6TPIdr6auyDqRmFPHoFhmIUA92VLyCFlhagql1uAZkflCQkJwTDswoUL6jakeWJiYpYvX66hofHDDz8cPnz48OHDVlZW6jZKDSCvUQRCWQQS4HDrmJo9blhQyRI/Nq5DgD6N2tDQoK2trWQ7xtqs/hZ9YwvvAQBfmBNflvuRPauzjJbDwcFBHDglTe81+dEnrf6bh8V9ho/WdetdLZEsHO4/fPhw8pT/iBE3Hz3dv+uX64nxZuaWU9csnzBxkqydYcOHD544c9mpv+YMNOL01C0WSMQiTaqlFgA8LXlux7QebOVDwdCP7A+A+/fvA8DSpUs3bNigblvUCRJCBEJZzmTjjZjGHW7tKz0jAMCAGF3YCIDVChvJbYLKM9XFP7boERBCKtbwV8bjILt+GIa9+7KOUcwjxH7agGEAQFSI7hbpLfntcMCIEc1WNjc337rr15aa2rb1p3LPGT9euMzMybXqz5of0OtGTiSvsUEgFiSWvvjIfkQP3a7bFoJoN3l5eQBARkXoziAhRCCUggDYk4bjmnon9SXkCp9+qWDh6WdTelv19/Ftq4w5GzqY6zqW1aUDQBE3o7KhylTHpFPsluNkxmtDSip5vG3qpiFLfDvS2uaPe/1dt7Ich0ZKQo04d6CF1/2CxwCQxy3I4xYiIayurt65c2dqaioAnD179vnz52T5xx9/PHToUIIg1qxZAwDbt29/+fLloUOHUlJSxGLx9evXjY2N16xZQxDEzz//rNDm3r17S0tLFy9ebG5uLl/+8uXLEydOJCYm1tXVWVhYjB49+vPPP2/9x9k///wTExPz9OlTAAgPD8/IyAAAT0/P6dOnq+4ZfDAgIUQglOJOMZERdSXIoKGxv5MQAAC+rKMxx3pMj0hOeRPT1tb06XojbLzOvswAIAAvfVKaObFn5wohTuB3c05jIAEAXYb5EOsOqSAA9NDGpjlQ/s7GObhXfDUn2IpgaNCFYlG9qCG1ImOQ5UBVWN1+GnGIryQautCD1UwL+rL+//dQTU3Njh07yOOIiIiIiAjy2MTEZOjQoQBAnu3fv//s2bOFQiGFQsFxnM/nk6eaFcITJ068ePEiNDRUXgi3bdu2YcMGiURCpVJZLFZiYuKNGzcOHToUGRnZyoJfVFTUr79KR/w3bty4ceMGAISGhiIhRCAQLfJbqoT57NSgIMtIKgYArEbwqMUxpra3g2VFRQWL1eZFvqFWnmfTbwFRDYBfyXo8sefgTrD6/4kreSkRpZMD11m9Z6ikzSV9KH9n4wRQIysGBJjftdW3zmS/AYCUynScwNW4TJjFJYJuSXLrujqs+Rhr7MooDQYVAMDR0ZEgiJCQkPDw8PPnz0+bNq3ZS+bPn//111+vXLnS3Nw8Ly/PyMioTT0eO3Zs7dq1pqame/bsmTx5MoPBKC8vX7RoUXh4+PTp02NiYlqaq9i9e/fu3btnzZp15syZ06dPz549u433+p8CLWgjEO/m3O1HT/YtoxU+f2gt3WkwpkxMBlgx0dKoqqpqR5sOTFsLfQfyuKDmZaOkUUXGNs/B5+cwDAcAHLOY4Rqgkjb7GWPDzDEAaJAYvuRayBJL5dbk1whrVdJF+/g2Vg0qCAA3C4mD6XibLpk0adKvv/5KjvDs7OyUd7kCAD6fv2bNGgqFcu7cuRkzZjAYDAAwMzMLCwvr06fP48ePSV8YxDtBQohAtAaO45/NmnFs+fwD9MfLpjkJ9DQAgCYhAiqk77tMToOjo2M7WtbU0BxtN4AAKgDgOOdxyQsVmq1ALie/qFYazq2PRRvi4LyTJX2l75BrJW6mOqYMKgMAeI0NCaVJKuylrSRVqy3F1fM2dv3ll1+2u6979+5VVlZ6eHgEBPzPLxsNDY1Zs2YBwJ07d9rdeLcCTY0iEK1x+uQJjZwXJ0Y6AMA6N2k4adGzMi0NIwC48abSyM7JwqKdjiHDLHsdT+lBJQoB4Ez6PX/rASqyWpGjKWcAcABoICy/cG/eTbR9jLOh2OvhuXVEmYBVJrC0NbDMZOcAQExh3Gh71Yw720EfQyyKrx4t7GvYNrcpJyendveVlJQEAI2NjatXr1Y4RTq/FBQUtLvxbgUSQgSiNS6ePrnB1RQAMvWwXB1pfM76e4XbDXmv60RGDr2O/32m3Y3b6lv10OtZXlsIANnsZJwgKJ2wiYIv4scWJ5LHDdQpXkaqnAeiYjDPmbIhUQIAj6rdhjOTSSFMKk8jgMCg0/eENMteP+rom+LShq7ud6g59m3vtj1eE5P2O0nV1NQAQFpaWlpaWrMVGhq6/BF8mCAhRCBao5rNNu1rCQBX3m6i92bjWZr6vWYsXB4cbG9v35HGaVTaGAe/Y8mxVBDgOC+96nUfk14qMFqOG9evbT6xSXOKFQBIeGLjhBjikxFYqyky2spcZ+zH5yAh4G4pa6adqyY1ViAR1Inq3nDyehp26Pm0mz6GWNY02sNSol7cdePCHtqYnylGaaP0t7LxhiAIhbMCgUD+o46ODgB88cUXR44caVuviP8FCSEC0TwcDmf9ymXFhQWv2QYGNgap+hQAwAAml0gW1Yvmzp2rp6fX8V7GO7gcTjanQh4AXM6KVa0QPn70aMfy7zy+603GkhnBw0pSLmz7EV/342YV9mKtgwVaYreKCJyA1Bp7c13TPG4BAKRUvFSXEAKAjgaMscZATUNSGWRaLoJomx5raWk1NDRUVVXJjxclEgm5/12Gq6srAMTHx6vA0O4NcpZBIJqBz+cHDRvsWpqM4ZJN0a/CLSgEBgDgwcXvxxdoMVkqUUEAMNIyNNCWrhIllCWqpE0ZuzZv+sHP+o2BdCz7UQW+bqDN+b9OSSSKySU6yDxn6ZvkUoGVibYxefyiovn5um6FmZkZALDZbQsna2dnBwAJCQnyhWfOnBEKhfIlQUFBurq6ycnJ0dHRHTW0e4OEEIFohtMnTwQaU4tqeDQqxdKWmWAo/U9J/Cs1toijp6OYrq8jeFsMJwADAC6/qE6kyiQ4+Xl53J56YgwAwExI2PAJDQpmpa9dXl6uwl4AYKIdxVgTACC3nq6tKdX119XdMY+BAj179gSA06dPp6enczgcDoejML3ZLGPHjgWANWvWFBcXkyWRkZHLli2j0+ny1Vgs1rp16wAgJCQkIiJCftyZkJDw1VdfvXjRia7I/yWQECIQzfA0+r61NvVOTsWdmYNcp7kCBQMAVhmfnc3ZOMxFrNI9fxMcXEVA7sfHE0qTVdgyXZPx4G2sE7+3bv1cgUhXV5VCDgB0CoQ6Sl8mmbwBpMtPeUMFr7G7O2uEhoZaW1vHxcX17t2bxWKxWKyDBw++86qVK1daWlq+ePHCzs7OxcWFyWQGBQXNmjWLnAtVqLl06dLKysqJEyfq6+v379+/d+/e2tra3t7ehw8fRs4ySoLWCBGIZqBSqdH5VYu8HXialCcs6dTi11yN42YG59JLBw4Zp8K++pnQGgg3BhYDAJF5zwJsh6iq5cBJwTH66QCAAQRUiQHgVVWdNstEX19fVV3I+NSJsu8lDgC3S20CdA2r+WycIF5XZ/czd1d5Xx8QxsbGKSkply5dysnJIQMvuLtLH0grOwhNTEzi4uI2bdoUFRXF5XLd3d0XLFgwc+bMzZs3+/j4yEefwTBs9+7dn3zyyZEjR2JjYwsLCw0MDAYMGODt7T1p0iQfH5/WzQsICNDR0enIFo7/BkgIEYhmGBY0Zm/0nS/0ta71oEooAAAOPNy1lrDS17yQw3l2ZYUK+6JiYKY/WFQXAwBJ5aqcywqYHfwwLgMAjOslwirBueKav7K5567/q8IuZPQ3xpwMsCwuUSPS0GXYkkkW06pedXMhBAAmk/nZZ58pFGIYdvjw4VausrKyOnr0qELh+vXNp4AeOHDgwIHtCe762WefNbWtG4KmRhGIZpgROrOikZIlEN43lg4Hp5RIAOAVh7/v8J+mpqaq7W6Y9UAcGADAb+Tk1uSrqtkifgl5kJlHCUmmiPwmxDx/0XR6TVWE2EunYTmS3uRBakV6J/WFQKgQJIQIRDNsWrPKREfrJF3YSAEAsOQTHlwis7q+jKoXHBys8u6GmlNrJdI4bc9UF5zsDSePPKh0XjR6z50fNv7YGZOiMj5xkL5PnrI9yYPnhS+mjxu7dvnSN2+Q4wzi/QUJIQKhSGJi4tObV69McDPwl2axsUiu/iEqY+Xzyr8vXaWodDc6iY8JxiW8yeOk8teqajanJo88qCdsh5p3+o46dxbmxsQAoFJshxEUABBrSGZa8npmRYeOGXnquOJEHwLxnoCEEIFQ5MaVS9Ps9FP1KXxtDQDQEONa6Rwhga3Y8JOLi0tn9KilAZYGfcjjV2zVTI0KJaKiulIAIIDSQFgPMeuKreUhDhgAQFmWuJRHloh66ATam5wNct67bbPKt20gECoBCSECoQiXw2Zq0v4xl/53jKwmvnK39TLVravtxNRCgyxsyN2EHH6JUCLqeIM5NXk4gQMAn+hhpkV31O8KISRnRxnJV53qpSlxs3UwAGBQKcE2+nciI7vAhnYgFovv37+/dOnS4ODgAQMGDB48eMqUKT///HN6Olrj7BaoxmsUx/HExESxWOzt7a2h0UybVVVVr1+/BoC+fft26ioFAtFxXNz7PYl4mqFHAQCMgJEVOACk1uKLevfuvE4HmTEeZplpYmUAeD630JnVntRO8uS8dbrh4TZ+5l30k9eVifVlYXkNVY7VjTkAAJCrTQGQAACLhrGrq7vGDOXBcfzcuXPr1q3Lzc1VOHX58uU1a9aMGDFix44dAwZ0VmIQxPuACv49eDze4MGDv/jiiyVLlnh6ejZNUvro0SNXV9cffvhh5cqVNjY24eHhHe8Ugeg8egTMiLTQxDEAAIcGwlJA3MmrKqFo+/n5dV6nnkZYPW5LHqvEcTSbI32z83A7T6OuC7k5yRZrsOhbnCOdGi3UlnadXifp5ebWZWYoQ319/aRJk2bOnJmbm6tL1xjnZP7j8F6HxnrsDeq7erCznxWLimFRUVE+Pj6//PKLCvu9f/8+i8WaOnWqCttEdAQVCOHRo0dpNFpiYmJcXJyzs/OePXsUKgwcOLC8vDw6Ovrx48e//vrr8uXLO94pAtF53K3W1hvWkzyuj6365FZmJNYj/MatVhIFdBw7PUwsNieP/0mMFovFHWzw/0eEhK0Hq4ONtYHxthRJv8kXU7gaEgIA6jWgloY9K6lJ4WEjRqgyFWIHqa+vHz58+LVr1zQolG+9HZ7NH/77WI/5nrbBTuaTe/X4ZoD9hSne92YPDnQwxXF81apV33//vaq6bmxs5HA49fXtCad3/PjxadOm3bx5U1XGIEAlQhgeHj5z5kwqlQoAn376adMBH51OlznasVisZudOEYj3h1v5+QwNLgDQKPTvv9x1/UlC2JVrxsbGndrp1UvhlIv7yOPM7MeDvdzJ3KrtJuft3on6rh0R9jPGrFi61Qtv1FdKtfy7jNLj1Zrh/9x6f/73CYKYPXv28+fPDRi0s5P6rxzkpMdoxjZHQ53j47zWDHbGAPbt29f6FviuISkp6eLFi1lZWeo25D+FCv4uCwsLbW2lUzp2dnaFhYVN60gkkq+//prH4718+fLEiRMtNSUUCouLiy9cuEB+pFAoAQEBhoaGLdXHcRzH8Y6Z/wGDbr8zbv8FmxALooAGADDQop+fjx/Zl8o7kicnJ2fT8sXuNlplAADQwNSYYIrNmjwh9kVas+KBv6WlBisaqmpFdQAgBm0tmpGlNoHjXZeZL9ga/uA5cWmBmvAAAKavXTbfZyao7jE2e/ttSnV04cKFq1evalAohz/29LN6x3j56wH29SLx/vicZcuWjR8/3sLCoj1GIzoMQRC4HEpepcx+JxUIoUAgkMVEZzAYQqEQx3GFvjEM69+/f319fXJy8s2bN4cOHdpsU1VVVYWFhefOnZOVmJiY9O/fv6Wu+Xw+ORLtnqDb74zbv/xGw5gaRx4P6+HTNWGLjxw80FBbM0Lf5BIOQgpg+vTHVVxtEf7gwYNBgwY1rY/juEAgaGWqNqM8kzzgSez7Mgl+1wZfHm1G+eMVjUfYkB+rcY5qH6NYLG5sbFRQPuVzS+E4TiZt+MLLdrC1UrPGy/163sutTK+q27p164EDB9pkrUgkunXr1osXL3R0dHx8fAYPHtxSzby8vNjY2JKSEh6P16NHj1GjRpH5mGRmJyUlVVRUAEBhYWFiojRpl5mZmZWVdMNrXV1dTExMTk5OdXW1vr6+j4+Pn59fp07pdyU4jjc0NPD5fBqNpvzsgra29ju1UAVCaG5uXv3WGayqqsrMzKxprxQKhYwwO3XqVDs7u++//55M06WApaWlr6+v8t40BEGoPI7+BwS6/c64/dvFxVqUCgCgYBrD7Adp07RU3kVTHkff/8zT9mNHs1g+kauDAcB3wX2/PhRTUlLS7D3iOE6lUskE5c1SnE+OLaGesA0w1dDVZXSS5c0y1gH0njQ24Nbkx6KGUtV+U6QQamn9z1ej/JsxLi4uOztbS4P6jbeyeYMpGLbMr+dn15PCwsL27NmjfF9ZWVkTJkyQn+UeO3bs559/rlCtsbGxX79+aWn/k8GRSqUuXLjwt99+I7vj8/ky59Vdu3bt2rWLPF6xYgXpy7NixYp9+/aJRP+z98bb2/vixYuySbsPGiqVqqurS6VS2ySEyqCCtnx8fB4+fBgSEgIA0dHRrcc7p1AoBEF053EM4n2mpIGorL1rQwMAcDft0zUqCAC8utqR/WwBwPqtEJboYJ7mBsrkrmuWnLcuow2EjQerqwcEDCoEWVGu56nSCVaF3LhxAwCG2xobMGjKXxVga6xL12Cz2bGxsS3NaSlQX18fFBSUm5s7cuTIVatWmZmZxcfHr127tukKn0QiKSkp+e677/z8/GxsbEQi0bNnz3bu3HngwAFjY+ONGzcCAIPBuHDhwvHjx2/dujVv3rwxY8aQ18qCPGRlZQUFBU2YMMHOzk5HR+fNmzd79+6Nj4+fMGFCYmIieuu2ggqE8Ntvvx00aJCLi4u2tvbu3buvXbtGltvb2x84cODjjz/+888/S0tLnZyc6urqDh06FBIS0tl+BwhE+/ingDDWeEoeB9kp9bJTCcYmJuQ8n00DTrqwFWpTRBLw8PBoX4P5tdKErjzcxr3LhRAAxtti4bksCWhRgV8nqq/mc4y0Wlzs72JIHepvYdCmq2hUioeZ/uNC9uvXr5UUwj179uTm5vr5+d28eZNGowGAu7u7t7e3t7e3Qk0Gg1FcXKypqSkr8ff3HzNmzMCBA3/77bfVq1czGAwNDY2QkJCHDx8CgKenJzn2kCcsLEx+lOzr6xsSEjJkyJD4+Phbt259/PHHbbrfboUKvEb79Olz586dlJSUR48eXb58WfYnsmDBAkdHRwAYPHiwUCi8cePG8+fPlyxZcvbs2Y53ikB0Bv/ms7WxIgAAwAZbvyOXmwoJ/Hjc3fxqALDmS0vytSCbT3h5ebWvwfKGSvJADKa9DdUghGOsKBQM472dHc3jFnS9DS1RWloKAKY6bZ4uJi8hL1eGsLAwAFi/fj2pgiTu7u5NNQzDMHkVJOnbt6+3tzeXy1Uy0bzCXDEA0On0WbNmAUB0dLSSNndPVDPN6uPj03RGdPXq1eSBm5vb1q1bVdIRAtF5PIl9mhZ1ooc7AICdgaMBo+tCIH393WL/M38zNSuGuUoTPGXTYNHyFU1fjsogkojqhfUAQAC1J9OQoY4pMWNN6GeE1XKt9SmZAJBbU9DfvJ2jW5VDPlWBuM0urEIxLrv8nfB4PHJpsOn4z9vb+8yZM00vuX///t27dwsLC8vLy0mvyMzMTAAoKSlR0sKcnJzw8PCcnJzi4mJyXp10rlFevLsn78u2HgRCjeA4/tW8T/OfP+s3z64MNAEg+9rTIs8imTNeZ6Onp3fvydNNa1YduHKX5eRC1dEABmXqJzPb11pGwWsCCAAQiXU9WGqLJ/yRNXaiRjoizOW+R8uE5P6Hojr+O2sqUFjLBwBLS0tlKrPZbIIgtLS05BPKk9jY2CiU8Pn8Tz755Pr16wCgp6dnbGzMZDIpFEpjYyMAKLlUvH///mXLljU2NjIYDGNjY0NDQwaDQW7bb/diczcBBd1GIODE0aOUrOd/jnTgGkt/7M8lsC9nh3alDUwmc8+hw9Fpb3ha0oimxXXtydXw246fv/hyOnlMLyvN2Di2rq5OZVa2hSBLSsN7OTVKJnOPzlcMBtk6VQ2il5V10NwIr1lIt0aRSNR0Xwefr6jBO3bsuH79uq+v79OnT7lcbk5OzvPnzxMSEoYMGaKkeS9evFi8eLG2tnZYWBiHwykqKkpNTU1ISNi0aZOSLXRnkBAiEHDu1PEve5tl62J8KgCAjhg+0tXllhbV1NR0sSUmmkChSF3J0qor23r59WsRD8KOLR4qddf009acrVv1zWfzVGmi0viaYlSQBo3L5uSTg9T3gfHjx2MYllpem1rRhnQiYS+LcILo3bu3k5OTMvWNjY01NTUlEknTacmCAsWfBZcuXQKAXbt2DRw4UH7bX3Z2tpLmXb16FcfxefPmTZ8+XX6xEKVEVgYkhAgEsNlsEx3G07eziK51BEaAma5m0wjyXYA+QyqEWTVt7v3I3l9XelnUMKRvUpaImOxkkp6U0DVhARS4cOa01sahIMABQCDmb9+1uettaBZbW9sJEyYQAFtiXuPKxaMp5wkPJeYCwHfffadkLzQajfScuHXrlsKpppFCa2trAaBHjx7yhW/evCGT9sjDZDIBoGmcUnIzt0LUG4IgUFRSZUBCiECAjY11DoeXri/9d/DiSgAgn9ug8GLqGkx1pEJYWNdmISwpKbEx0Oa8dVFkiQgAsNbX7npfiRvXr53e8WPkuJ4ub4OHJ8Vd3ffrri42oyW2bdtGo9GeFLF3PHl30E6BWPLljeQ6odjNzW3+/PnK97JgwQIA2LRpk3zgyVOnTpFbIORxdnYGAPlYIvX19fPmzWs6rWptbQ0AsbGxCjHGyBg0V69elb/k559/fvbsmfIGd1uQECIQ8NWSFesTS4s1pQMpdy5+7nW5l88gbW3trjfGRs+EPKjit3lq1MjIuIInZMtGhI0AAGU8vomJieoMVIrdW37c6mejS9fowZcOuUZ5mR37/WAXm9ESrq6uZKS03xNy197PELcciLWsXhByKf55WY2+vv6FCxfaFNBk+vTpwcHBJSUlXl5eS5cu3bFjx5QpU+bNmzdu3DiFmvPmzQOAVatWTZ06dfv27atWrXJzc8vKyho2bJhCzTFjxujo6Ny4ccPe3t7f3z8wMPDIkSMAMHPmTENDw9jY2H79+m3cuHHr1q3Dhg1bu3bttGnTlDe424K8RhEIGP3RR988mGaJpQEAo16y7OYbM5c+R44cVYsxLoYmz/IBAOqFbR4Rzvzsi98P78S9pTmkWCLicRHb1Mah67Nh13A4ZjomAGD5VgjLtSm6NKyhoUEtPy+a8uWXX5aWlv7444+nUwoeFlQt8XEMcjTTof3/XpPSesH5l8WHn+fVi8T6+vrh4eG925iZGcOwCxcurFix4ujRo7/99hsAsFis3377zd3d/dGjR/Jh50JDQ6uqqjZu3Hjp0qVLly5RKBR/f/8//vhjx44dqampsmDOAGBtbX337t1ff/319evX6enpYrGYjEZrZmZ2+/btefPmpaSkpKSkAICtre358+c1NTXv3LnTSjQ+BCAhRCAAIKOGoLlKfdzt9JxWXN6jpENEZ+BhYvIXAABIJG0Wwk/nznv+NC6ZKASgAcCxuJI8Dhb+TzNb1jobDKOIcUKDglkIpUJYqonxRGIGo0ujnrbOxo0bXVxcvvnmmzw2+/vbqQyNl72N9U106BKcKKzlZ1bXk6b37dv33Llzbu3KKqylpXXgwIFt27ZlZmZqaWk5OTmRqsZmsxVqfvfdd998801eXh6Hw7G1tSUH8UePHj16VPEHma+vryxFjzze3t5paWmVlZV5eXksFsvBwYH0u2naF0IBJIQIBESVEPrUV+TxrCGfONmoTQUBwN3YACdoFKyRAjyOUGDIaMOeegzDdv9+aGTYZAACAIxDfjr9xThl0tConIDA0deyYyc7m1m+3SmQxwAH517vW8TL6dOnBwUF/fLLL3/99VdxcfHzsv/xE/by8lq0aNGcOXM6aLa+vr4sXnYrUKlUMhpXRzAxMen6mfAPHSSECATcK6pjYBUAHJjmSQAAIABJREFUgAGlv4WaA6AwKBiOsShQDgD7z/89f8RHbdrXX81nkyooIgynTRxPoagnBc+Grds/HjG8oKF4jDWT1pvZSMUaGJSf9uxXizGtY2houH379m3btiUnJ6enp5eXl9NoNCsrq379+v03kjYg3gkSQkR3BycgrSK2B0YAgJluDx2amlewGhsb8fJqMAMAKIk8MnXL1sCJU37asVPJrHIVDdIJVSFh7GSgtkR0BgYG0c8STx47evL2vw0cnGYMAJAsJpTai64OMAzz8vJqd3BXxAcN8hpFdHdesAk6nkwe9zdvzzqQavlx7ZoetdIJusG9jC+McS6OvnFYaX/L7BppclAcY5m0J1KpyqBSqZ99ueCvSxHm9tJA/I9KC1u/BIFQC0gIEd2dqBJCF5Pmc/fr8e6FnM7mavjFQTrSyCDVDIyCYSv7W508fEjJy19zpJsudOiKIS7VhStLOrX7urpIvZYgEM2ChBDR3Ykq4jAo1UAuEKo7Q4JQKNShUYxFUk/LajoGALp0DRFf2dAw+XVSF0EjrfdFCAf3kEYcrRcWVqHgz4j3DySEiG6NGIf0yngMyAVCiy5LSd8SDAaDJxIbNUqFkE0HABBJcIqGsrnUy3nSNUJLvfdFCJ0NpSNCbUpxdGmbkx8h2s2CBQumTZvG4XBkJevWrZs+fXpOTo6quvj111+nT5+ekJCgqgbVAhJCRLcmoYrQgrcLhGau6jWGZPBw/5Rs6aiOTccA4MTLsgkhysYHqX27Dd+RadwZ5rUDG31LAAwANKHsQnS8us3pIt68eYPJQaFQWCyWl5fXqlWrlM8v2EFu3Lhx8eJF+RxMkZGR58+fJwOTKk9aWtqRI0ceP37c9FRMTMz58+eLij7sSW8khIhuzYNSQo8iDfA/sEd/9RpDsnPfwful0vFfBQ2+fZj3Wtd61boNSl4ubJSKaG/We7GZTCKRfPf551AjAgAMI4pPfB46eaJQKFS3XV2Hp6dn//79PTw8MAxLTk7+5Zdf3N3dk5OT1WKMpaWlnZ1dW8MaREVFLViw4Ny5c01PmZmZ2dnZfeiRa5AQIro1D0u5mm93EA606KducwAA9PX179x7BDgdAMRUrO/qfReu/SMfZKsVJASBEeQ8GNbP+L2YGt398zbtN4meb3dqLR1h3as2d8OqFeq1qiuJjo5OSEhISkqqrq5+8uSJra1tdXX1nDlzCOUSX6iWK1eu5Obmuru7q6rBP/74Izc3NzAwUFUNqgUkhIjuC05ARsVz8thMR/0LhPJoaZqRB402DspflcHmYCAGAAnommkrpZ2dzYUzf33jYdlDIF0aLNGCub3N/70eoV6r1IWfn9/BgwcBICUl5dWrV00rsNnslJQUhSSCBEHk5uYmJibm5ua20jhBEFlZWSkpKe3Oo5mfn5+YmJifny8SidrXgjxCoTAjIyM5OVmZdGZ8Pv/ly5epqalqmS1AQojovqSwCQYkkcdeZi7qNUYBpqZ0hS+H24aIo8mV0rUfKvW9GA4CgKRRxKBSZDkoijQpFAxjUDCxWNz6hf9VZAklSI+VO3fusFiskJCQgoKC4OBgU1NTDw+PyZMnk3VEItG2bdssLS0dHBwGDBjg4OBgZ2d36tSpps1GREQ4ODg4Ozt7eHiYmprOnDlT3kdGRmBgoJGRUVJSknyhQCDYtGmThYWFnZ3dgAED7OzsDA0NQ0JCSEHt3bv3mjVrAODPP/9kvWXmzJnktZ9++qmRkdG///4r3yCHw1m4cKGRkZGbm5uXl5epqengwYOfPHmiYIyJiQmLxeLz+Rs2bDA3N+/Tp4+7u7uJicnu3bvb/lw7BIosg+i+xJQRBpj0V/lAi/digVCGuY5JaS0AQBmvDcmYsrhSIdSivS9CSKXRhRK8h0D6m7tQG8MJQogTbcpn1A5iixPuFzwSirtueGGoyZzkPNbWwLr1ajwejzwgp7sbGxs5HE5hYaG/v399fX1oaKi2tnZdXR15aty4cZGRkQYGBrNnz7azs8vOzr58+fLcuXNLS0tXr14ta/Py5cshISE4jo8fP37gwIHl5eVnzpwZN25c018bXC6XzWbLl/N4vMDAwNjYWF1d3dmzZzs6OlZUVKSkpFy6dGnHjh1MJjM4OPjRo0dPnjzp2bPn4MGDyas8PKQbjerq6thstvwIsra2dvjw4ampqRYWFp9//jmTybx3796jR48CAgKuXr06ZswYWU0OhyORSBYuXHjhwoXAwEAHB4f09PTIyMjly5ezWCwyNVXXgIQQ0X2JLuFqUioAgAIUPyv1b6WXx0bPOKkUAKBW0IYRYUm91FOGqcnqDKvaQUjorEORF770lspDOQP7M61s1NjxndrpP2/u/BKnhrimt3LuHf5od+taePHiRQDAMKxPnz6ywqdPnw4ZMuSff/6RT5i1ZcuWyMjIAQMGXL9+3dzcnCxMTk4eOnTounXrJk2a5OLiAgD19fULFizAcfzQoUNfffUVWW358uW+vr4VFRXvtHnFihWxsbGurq6RkZHyUW1fv35NBu/esWPHvn37njx5EhAQsH//u5/qunXrUlNT+/btGx0dbWhoCACbNm1av379li1b5s6dm5OTo+BZ8++//7548UKW72Xbtm1r167duXNnVwohmhpFdF9SK1LJAxMdcy2N92iBEAAcDaU+nyJxVctZYxWp4ksXh4y0mJ1hVTtYtvqHGhv37+5kazZIAKCRAn9i1oFLfunUTs+lX+nU9luCLxZEZN1SKKypqeFwONXV1cnJyVu2bFm1ahUATJw40cLCQlYHw7Djx4/LqyCfz9+7dy8AnD59WqaCAODp6bls2TKJRHLy5EmyJDw8vKqqytfXV6aCAGBjY7N58+Z3GlxdXX38+HEAOHPmjEJsdxcXFz09PWXvXM5sssHff/+dVEGSTZs2ubi4VFRUNE0g9eOPP8pnPVu8eDGDwcjIyGjrHo+OgIQQ0U3JriUwSSJ57Gn6XuwglMdKV7pGqIFxyvmt1/1/agS15IGZdldn4m0JDQ2No3+f/eXvS0ya1P0Hn7nyKadzHXkaxEo/ss7v2tbWlsViGRsbe3l5rV+/ns/nDxkyRCHLoI2NjUIKzKdPn3K5XGdnZ1dXxT/OoUOHAoBsD3tMTAwAyE85kowdO/ad1j58+FAoFLq4uKgq2vjz5895PJ6RkdGQIUPky6lUanBwMNmjwiUKHqfa2trW1tYAUF5erhKTlAFNjSK6KTFlhAGWTh77Wr4XGyfkYWlJf03TsZpCHmGhrVQeiYZGLhln20bPoNNMaw/u7u4jJMPPpl8CAD1KVkzZoE7tbpTtsHMZahgUYoCNtB2qUOjv708uiDKZTFtb28DAwMDAQIUkkTY2NgpXkQ6iubm5LJbiLLdEIgEAmSsmuZm9acYoc3NzTU1N+d30TcnPzweAnj17vvvelKOwsLBZYwDA3t5eZq08pOzJQ45EVeK5qiRICBHdlAfF/8feeQZGUa19/Dkz23ezLbvpPSShdxAERToIKgLiRYH7Wq69F66oiB0UUUQUG4oFrxVEBQu99w5JIAnpbbObbO87836Ys8sSUjYhZEvm9+lkMjvzTLIz/3nOeYqeWSBEQAwLgVrbjVAK8NwmDwzlZnqoOiAhdHkMAgQAkClTtLZvZ5OhwA9HCXHhQB3tpIB31Sak7u0/FyG0tWSXzd15tU1VQuXsntOHxDd2rTZs2OA/59kklyekMwKWmJg4YcKEJj+SkJDADJjIF4GgcasRhBCfz29ZCJnPCoUdti7AiHSTB2Q2ulyuRtuD0ji6EawQsnRRjteeVgANAGpxQkhlEDJI+VEISBo8HGQuNbsBWp9LrDG7SVcds2OCJLQ8QgDIkKcxAxGqsLnhmJYeFnO12iVyCc4DA/7vgQH/d5WO3wnExMQAgEQi+eSTT1reU6VSgdcV88doNBoMhkDO0nJ6YpuIjo5u0hgAKCsrA6+1oUbwpZiFpfOptYHdcZIZD4nvE1xjmgQB4nEZN4IuMbaSH+10Ol96bv7o3ukiNy5iWVNcfZUNbDMp0iQOQQIAD+lJsO6tDUJdlTBi2LBhCKG8vLxWl8oGDhwIAEePHm20/fDh1su6Dh8+HADOnj3bcmQK48wFkuo+cOBAhFBFRcXl9VQPHDgAAIMHh9zsC7BCyNI12a+h5AQOGb0heWhwjWkOCQ/PjpabWnmvf+Q/d7sP/rlpWk9CSAIAouHBWbOrq0NLC7kEJ17CRD/SEqJ8v4YVwpZITEy8+eabPR7Po48+ysw3+uN0On3SNWvWLJIk161bd/r0ad8OHo/n9ddfb/UsWVlZo0ePttvtTz755OUl3ygK1wNiAkoD6VkRExMzfvx4iqIWLlzov3379u3//PMPh8OZPXt2qwfpfFghZOmK7KqsFzILhIjTPzYUPUIAUPLxOp/G2kSJEB8ajeb0gb0P9Uu0cRGFAABEHngwW/HR8nc7wcg20U2By8VJybz9rEfYGitXroyLi/vpp59GjBixevXq/fv3Hzhw4Pvvv3/mmWeSkpJ8xVzS09Mfe+wxp9M5YcKENWvW5Ofnb9u2berUqWfPnm11bRIAVq1aJZVKv/nmm/Hjx69fv/7UqVPbt29fsWJF3759S0pKmH0GDhzI4/G2b98+b968xYsXv/XWW+vWrWvugMuWLRMKhV988cWcOXN27tx58uTJd999d9q0aTRNz58/v8k4mqDDrhGydEWO1J5lehCqRCk8MiRqcl6OWiQrqAcA0Nlb8gjz8vL6qSUAYOTgJTeZix4UJ/vzaMg1PMpSpG8v3Q0AElSYZ6VLzXSq5GotE0YASUlJBw4cuOeee7Zu3Xrw4EH/X6Wmpvoryttvv200GlevXu1LQler1evWrbv99tuNRmPLZ8nJydm9e/fcuXO3bt26detW33aZTOZrUhEbG7t69eqnnnrqm2++Ybbccsstvjpwjejdu/dff/01d+7ctWvXrl27ltnI5XJfeOGFV199tQ3X34mwQsjStaioqDh28pSmYl+sEgBgaEKHleHvcJIkeGrU5tK7KOA2M30jEonMbgoAjFwsKlI3bXK6xZLQCxz1xsuIUTkA7K+NZCFMSkpiUv1ablE0YsSII0eONOe6paambtmyJTc3d8eOHTU1NUKhMDExsXfv3sy6oA8Oh/P5558//PDDmzdvtlgsWVlZN910k0wm+/PPP10ul398yurVq00mU6PcxL59+x4/fnzfvn2HDh3S6XQxMTFZWVljx47179Y0Z86cO+64o7a2tqamhqIouRx/OZcsWfLss892797d/4DXX3/9+fPnt2zZcuLECYfDkZycPHny5EYJ+wBw6NAhmm6i2N7atWutVmt2dnYLf7eOhRVClq6Cw+F44sH7Tu/fPUgtSZiV6AEuAIxLCcWle4ZoEX7WcMBQZW1WM/r373+yzmJ0uI0c7NpK3bDuQsOUB+7rJEMDJlORxgwEqA6A2q8h/pUZVIOuJnw+f9Cg1gvYymSyVnfr2bNnz549Wz3UgAEDGuXFX95uqU+fphcCCIIYOXJkoyz4y/eJj4/3L4gDAEyZt8vh8/lTpkyZMmVKCwdsJOc+Lq8hcLVh1whZugpPP/JQXNnx7ydmPzg0gVZwAQAoWnO6k3qFtwO/VEJ9ubnZ3bhc7hvvLp+3ueC4Cdc0OV9hqpDE33HnnE4wsk3EiFRCrgAACOSQoGI2XoYlRGCFkKVLYLfb92zbfHfvBADIj0JMMFy8xfPJO0uDa1gLKLxCyEX6cktLmnHzLdO+/3v7Ok8M82Nar2G//rU5FPKULydFiufHZGTuCR1t7aK9mFhCi1C8VVhYOpyamppkmZiZW8yPwl/7wWZ0xi/iPNTwCSEP6cstrewsik9398ON7kYNvR6hEF176yZPZwZSosBFwTEt6xSyBB9WCFm6BAqFQmfF6cC5XiFM0bkoh33tN18Hz66WuDg1igzl5lYEo9wMPISDSxWCkCsr46N/LO49JCaKgabZ2VGWUIAVQpYugUwmE6tij9UYbCQqEyIAIGjYsr3o2kT5J++/F2zrmkYukDGOHRcZyy2NU6obUW6huQgHyoeyEPaN6cUMhKiOh/SsELKEAqwQsnQVsnv2fuqf00ura2kEAEDU2qrrrSdqDebW6rYECwIRIg6uslZhbiUbrMICPIQrsclCWAjjxDFSXhQAIHDJibP7a6lgW8TCwgohS5fh1NHD624b6knDvUb7mKhVN/ZLkAgoCNHlNABQePvr1lpbKTdabqE5yMSM5fzQFUIASJPjlkMyzukaG5S1NuvLwnK16Zg8QovFsnbt2qqqqvHjx48YMeLyHY4ePbpnzx6TyTRkyJCJEyd2yElZWNqEy+WS8DjudJzdPBrxASgawbjJLaU6BZcYkbzCWAoAVofe5gZh8/drmdnDBTMAIIRk/DY3Fu9MekZnn9KcBYAoVAQ0fbiOTonctHqWsKADPEKPxzN69OjffvuNy+XOnDnTV4PHx86dO2fMmFFYWOhyue6///4HH3zwyk/KwtJWBgwavK+6oUKIn7lZZtrlofLqba+8sTi4hrWAb7WPgwyV1pY8pwqzAYAGADFXSqCQnunp510mFBAaLjIcZgNHWYJNB3iEmzZtqq+v37dvH4fD6dWr1/z58+fMmeMfvT148ODCwkKmjs706dMHDhz49ttvMz2IWVg6jWcXLrrl31OUCABA5aAJi+uZvReeXvBiyxWwgovy0gyKbs2XUK61GJg+3zLvR0KWfrG9OQTHTbk5YJERhYc0Q4JtEUtXpwPeHLdt2zZ+/HhG5yZNmlRUVMQ0YPQhFot91eRIkiQI4vLiciwsV5uMjIzUB+5lxrUFxtlbS25+4sUnnp0fXKtaxi+n3lDTvEdIA5ideBFRJWi94UBwEXNFsWKc+y8jTx3R0hTrE7IElQ4QpOrqal+5OYFAIJPJqqqqmuy1QVHUM8888+CDDzJtHi9Hq9WePn36mWee8W2ZM2dOo3Ku/tjtdi6Xe2XmhzHs5bf18vWEhqQAAEaNu2/hohnMQa6GbR1FFIm9VR7SVxpddjtWDIqi7HY7SZLMj/VOhGgcVirnRYX4RQFAhjSl0lQFAFGosMhJn9TYe7QlvsftdrtcrkZFAy5v2scSYTBfe7vd7vF4AvemeDxeq1WWOkAICYLw7+jo8Xh896c/NE0/8sgjVqt1yZIlzR2Ky+XyeDylUsn8SJKkVCpt8mi+HVr4bcTDXn6bLt/qpihPJYkAAG7M7hcWfzqlCHeQ4CKDzkX4TEYI+V++1glcXza9UBb6l9ZH3WN35QEA4BN1fFR/vCG6t7INH6dpmqKoRpcZmiXlWDoQ39e+Tfd+IFWWOkAIExISfL2wzWazyWRKSEi4fLenn3762LFj//zzj0gkau5QMpksJyfn+eefD/DUXC63K7tE7OW36fJ3VpR7U86JIQnZXDIM/nRqCW6gwwO91kFwufjmpyjK7Xb7Lr/eRXO9uRNKoSL0vxXDkoasOvklTQMP9GKi+Fi9+u7ubRBvXGfg0stECFmt1oaGlpoYs4QvFosFIcTlcplvfseur3XAsSZPnnzXXXfZbDahULhhw4Y+ffowfaeKior4fD4zfv7557dv375169ZAOiazsFwNNpceYQY8bmrINuNthH+wjKb5+U6NjfZl08tDOJveR7I0QS1UaaxahKho8uihug6Il0lMTFywYMGCBQuu/FAsocmdd955lY7cAUI4ZsyYHj16jBkzZsiQId99990XX3zBbJ8/f35GRsbSpUs3b968ePHiQYMG3X777cyvPv300/T09Cs/NQtL4BTo8plBsqzpDmohiFwgR4BooLlgrLVRAE27TRo7cMFXaDTUo0YBgEBEj+hsjVULAFJ07pTO7fBw+Fc2obt48eLFi0M3E6YDcTqdAMDjhcfLXFjQAUKIENq4cePGjRs1Gs3evXt9gTOLFi1igmL69++/efNm/4+o1eorPy8LS+BQNG1ylDJf98Fxnd32s91wCY6IK7a4zAh5dFYzQNNN5zW2i4VG5fzwmHQZEj9gZ/k+ABCiKkRrT9YnDlWzafUswaFjplk5HM4tt9zSaKOvObJarR43blyHnIiFpX2UGHUErWPGE1PDxiMEAIVAbnGZAaDBoW9OCOvsF4Nl5OHgEQJAn5geEp7Y7LQQyBVNHD5cl8AKIUuwYOOsWLoEm4rzCWDaMAkz5PFBtqYtqLzlRj0evaWZNrYaG3DB6xGGfB4hQ4xInRyVyIyV5PEjbH0ZluDBCiFLl+BITS4zEPCSUAhX2b4cX91tHtJrbE2rRa3VwUFWACAJMoon6TzjrgARV5ilzGDGEqLwqJbNAmQJGqwQskQ+FE1VmkqZcZI0Lai2tBmlwJdKqK+1Nb2PzoZDRqN48jCS+f6xvbgEFwC4yFxpyLU24++ysFxtWCFkiXx0tnqrC6eXDYjJCq4xbcW/T31zHqHeYfDuHAa5Ez6So5ISo+KYsZLcd6qenR1lCQ6sELJEPuUmA01hIRyTkh1cY9rKxalRaGgyldDuAacbC6FvQTEsSJMlp0qTmLGCOHWoxpmfn19SUuJfqYqFpRNgi1+zRD4Hamq5yAoAAESWIiXI1rQRv6lRg6apqVGNjeYReGpUGVZCyCN51yQO2lV+kAZKiKren9Vjt1Rod1OVVvfi5SsmTpocbANZugqsELJEPsc0RUyvPi6p4hJh9p33mxpt0NibcJU0duABFsKwyKb3p7eqh0yg0ts1ADB/XvcpGgoAtFbnw48/KP3yu+HXXhtsA1m6BOzUKEvkU2EqZwYqcRNVcEMc5SVRo03soLH5VdwONyFMliZmK3CnmmNK/DhSiXivDk1a+uqi4NnF0rVghZAlwnF4nEZ7HTPOUYZfYT+myhoAeKusNabWRvt5hOEULAMABCIGyHGhnwIxYfM+kHKiJaUlxUEzi6WLwQohS4RTYdZTFNaJkYlhFikDuMqaBAAQ8mhtpst30NiAS+Bs+rDzCAFgWNIgyuwGAA8BZ2U498Pu9nBCvocGS8TACiFLhHOgpoHnrcM5IKbZJs+hjE/eGuxN9Biqs9Phu0YIAOnqNE8dzqY/rMBPpJ/P1026qXHVRhaWqwQrhCwRzuHaUgQuACAIgUoUHWxz2oMvKcLi1HsuC5fR2OBixe1wmxoFAITQmAF3MOOjUqLEaF1+onKTnpz/wsLgGsbSdWCFkCXCKawvYgYSXrj2PIn2CiEH6XWXpRJq7B5GCBEgOT/8hBAAHh8z20MLAMDGRe+4xN1nP7x9/yGxWBxsu1i6CqwQskQ4OlsVM0jxlngOO5RCnErIo5vIoKizGpjkEBE3ikNcWU+/IKEW8twEjmNSXzfw5lunk2RYXghLmMIKIUsk02C3uz24+1JfdWpwjWk3xWcLmUH0/mUP3zq2oKDA/7f19jCeF2U4dvSoZeN2ZqzXHJh07dC1X60JqkUsXQtWCFkimQO1ep63dXtPdUZwjWkfS994vXDrNmY8tXf0/HjbzMnjKyoqmC00gMWJI2Wiw6qsjA+n03n37FmvReOmGfYk0Q+Ts1a8vuj8+fPBNYyl68AKIUskc7ROR4IFABBAtrxbsM1pMw6H4+vVn96TimN8DBzUL1b2eM/od5e8wWxpcACisRCqw1MI9+zZM1QtHETyRB4aAIxcZBBz7spRfv/NV8E2jaWrwAohSyRzWnsBIQoAOKREJVYG25w2U1hY2EMVpfD2JzJwAQBGJCuPHjzIbNHYaB7CQhguvekbUV1dncBHiIYcM17+zI9CyVGCytKSoNrF0oVghZAlkqk04uJqcoGCROEXf8Hj8ZweSu4VQj0XAYDD7eHxcLJ5rV9v+rArK8OQlJRUZqcAoIcJC+E5CSox2lMyws+DZwlTWCFkiVg8NFicOGQ0OSo2uMa0j8zMzGKjHZldzI1q4gCFYGOxbuykKcwOGvtFjzAcs+kBYMSIEScanIUNlhwjFsJcCbE6X3fn/90VXMNYug6sELJELLkNLo635EqGLD64xrQPgiBeW/rePZsL+E4KACgEnxTW/VrjfuTJp5gd/Ctuh1cPJh8cDufbXzb895jui21lpJsCAK0APb30jbS0tGCbxtJVYIWQJWI5rDFxvCGjafJwzZ24edq01es3OrzlqP8XN2rHwSMSCY6xrHcAzyuEcn5YCiEA9OzZc+/xUykPLKvX47CguEFpQbWIpWvBCiFLxHJcq+cCrlKdrcwMrjFXQu/evXtl9WPG9KjZQqHQ96sGB81F4dp6wh8OhzNv2gS9bALz42lNbnDt6RB+Xb/u/nl3zr55yttvvm40GoNtDkuzsELIErHk6sqYkFEuKYwVq4JtzhUR4y0u4/QY/LfrnWEfLOOjmxS5CNweJFd3IbjGXCEul2vapPEb3nrxZteFBxVGtPOn6wb2PXXqVLDtYmmaMOvWzcISODWmMhUCAJALZBJueBeujBbhaU+CbrC4Qeh9g9XbLQRyAQCXEAg4gmCZ1yEQCNLlaYwPX2wooYFmGjGGI599vKqbrfqJYSnMjxkK8TVx0gf/PWfvcVYLQxHWI2SB6urqjz5c+dyTj6/+/DODwdD6B8KBCgtNUzhkNF4czSHC+51PKfD1qTfoHRfLjeodeF5UwgvXBUJ/BqiULloGAE63rdZSF2xz2s/677+7I/uSIu8ZCrHIbauurg6WSSwtwAphV2ftV2umXDfM9Osn/Ur3Vf7v/esH9du6ZXOwjeoATtaDANUy40RJXHCNuXL8hFCvd17c7quvJgvP3Al/zpw5c2TBJNcF/PpyoiyMnSeTyagU8vy3bCqsrdU1/N9t019+YYFOpwuWYSxNwgphl6a4uHj564t+mpwzu3fidSnRd/dJ+mZsxpP332syNdEJPbw4rvPwEW5jmypLCq4xV47CmxrBhUuF0IWFMExzJ3xs3bL57lunLEqyTJfgCd5F77xQVVUVXKvaTXZOzmkNXrv0qVIuAAAgAElEQVR1U/Q9e/M2y+jFY7JfyeFFH/9r9NCBh7y1gVhCAVYIuzS//PjDHZlyh4DYpiY8CABAKeRNSJJu2bIl2KZdKae0Jo43iqSbIizLbfujFHg7MRGXCKHTjaeyo8M8UmbBE499Ojqzt1qabMMTvz2zpK+/+HxwrWo3jz/34pvHqhvsLgBYYWkQj0uWXZ90rr88KUp4a3bspzekPXLP/wXZRBY/WCHs0tRWlkfL+HuUpIED5yU4MCGeBzXV4fom7uNcfRmB3ADAIwSJUWGZTe9PrBgvOAlRbYODYsY0gIeqZ8bxYVhJ1UdDQ4OAcqpEPABItmIhdKgEh/bvC6pd7WfQoEEL31v57+2lj1TpDg1Q6HorCyTEhnjOGSkBAElRwhguXVxcHGwzWTCsEHZpUrOz98VwbSQNAOclBJO0fcFGp6WHtwtlc0ODFcffywVRMr40uPZcOVE8CUlIAYAAR60FrzCZXcAHbw05aRivg3o8Hg6Bn0VJNpoZVQsQjRp3IQ4jrrt+1F3PP9lwczyp4Ps27o3GlynjcyImMC0CYIWwSzP65gkHzM58Ab05lpMXhc7IUGGDZW+dfcyYMcE27Yo4q6cFCJfbjhYqRBxhy/uHBSIe9murzTXMwOCkhQiPk6ISgmNWR6BSqeqdlMnhBgA+BTF2GgAoBNkj+wTbtHby4/++G5DT7cv1HzAJID5BPyonKAQAkK81ZWaGcZGHCIMVwi5NLbiooSNylVwTB05LiY8cttuP6G9651c+n9/6h0OYkzpa5FWIeEksQuGajuaPVICFUGetZAYNTlpI4HD8JGkYCyEAvPDaGw/vKKox2wHAt0w4+a7bgmpUOzl69Oi7Lz0ncNvG34Clzl3QIHTTAGDhQL4YVp6oGDF2QlRUVFDNZLlIeCdXsVwhn53ZK5d6FyoQWLrH2DPf3010D6pRHcCpepqPcBZaijQxuMZ0FNGihEo9AIDejjW+3FhPgh0AaBQl5YX3U/W22/+ljFY9+d+nLUaDc0Rs9PhYALDw7cG2qz18+v57Y2KFFiWvUIo9jds09Pfaat61CQDwnNE0fuC4ZW8tDaqNLJfAeoRdl+Oagsr6vxFcXIbhkvZk7u8HNLTN3cLnwoCT9RSPwFEk3RTpwTWmo4iTYJ/P6u0tVWrEA5IM+2ggABg7btzuoycP5RVaxr7DbMnVlQbXpPZRfKEwis8RRgu1fAQAfAqm8sT9tPimEvWSb99/mI2UCSlYIey6LD/6K1OKk4KoQXH9mY1K4giXvrBPE8ZBCgBwvr6SABcA8EhesjTskwgZfLGvDheeDq00YSEU8MJ7XtQfDoeTKk9jxhf0JcE0pb2o1TFSPue0CN9EWWb69Z35ogqL0EMDACkh7x/AmXnjBI1GE1QzWS7CCmHXpdyAC/ynKQb3VuckSGIBACEqmdywvYoKqmlXRLmFpjxFzFgukEZAyChDuncVkPbUUDQNABorVsQoXiR4hD76RsdTwAcAs7O+waYPtjltZvbd/9lRYzPHi5gfkxtcZzTGp4ZkDtJjaazPkPwnW/HR++8Fz0aWS2CFsIuisWo9Hh0A0EBmylWJkoSBcX2ZX8nIM9urw9gjPKGjxaiEGSv4MilPElRzOow4sYipwwngrLNqAaDBhj1ChThyPEIA6COnbHqSGU+YOuLOGdPCq8TM1Jtu6jbqRjoD1/rZsr0oJ1oCANfU4/fL43JiaLzsxOFDQTOR5VJYIeyi7Ks4xgwctPqGpF4TM0YPjhvAI3kAwEP6U9pKkyuo9l0Bp+pBiCqYcYxYTRJkcO3pKOQ8ZKOx51dprnY6nQ167PiqRRHlEf7z6t0JtXja8LHRqVOhauqYUWazObhWtYkX336TiBEAAE2BRT4ChBIA6GWkmDyKagEyuN1CUSRk9UQGrBB2UTaXHWYGFJk9KmUAAIxMHpbsnXyTotN7a8PVKTxVTwsJXG47YkJGAUDBBxuNs+Z3HNs1ZvhQN40jYzc9eVd5eXnwTOtIysvLq0/uGxeFRaJGgK5LVk5LEn65+rPgGtYmTtXlMvePGWV+tPKTE1qL2enmU6By4hTJ7xqsk6fNDK6RLD46RggPHjw4c+bMsWPHrlixgqabeIBu2rTp5Zdfvv/++/Py8jrkjCxXSIEunxkkKKYyXd+4BGd0ynXMRiVxLHyXCU/X03zQMuN0b9hFBMAjwA3Y8/tp43fLJiTTJAIAqYt+KZm6/eYpFBWu/zJ/Tpw4MSxGHO9NJawRIgAYHhd1dO/uoNrVNraX4wedC/XoH8t/ecnS/9tSeLiqIcmC/0eHohR3zp0bPANZLqEDhLCqqmrixIljx45dtGjRqlWrVq5cefk+y5Yta2ho+OGHHyorK6/8jCxXSLmx0uE2AIAH+INju/m2j0oZzgxkRO726rB8qto9UGJoIJEdALgkJ5I8QgAgSOyyp6VKnHLcpSHODoPjZGlc9+HDh4NnWofB4/EcFMTZsRBWCRAA2N0Ujx9ObYeP1WAhzFD0QAAzZ92+5re//uCkHczHheBh8hySjJBJ+wigA4Twiy++uOGGGx588MHrr79+yZIlK1asuHyfrVu3vv/++2wlhRDhWC3u9GamMvspL34HUqRJUr4MAEhkPVdfZHA2/fFQJk9PCxGuMirjyRTh36XPH743TcIl59cKcLmcOAcNAFkSsqioKGiWdRzDhg3bXWVQW/FymoaHKAQby4zjb5oWbNPagM6Ko3tGJuDiMt27d1/93Q+vPfsG8yNBlZdbwnX1IfLoACE8fvz48OHYkxg+fHhhYaHRaGz5IyzB5Uj1SWZQ7xnYP/qS8mMDY3F1Ryk6fao+/G7U0/W0hMR6oBDKI0wIJTy8RlgvIg8ovd1C7DQAaJy0Wq1u9pPhg0wmu+/xp+77q0DipADATcBbBbVlAtWMmeGxouZ0Old9vJLy1AMATROTUmL8f5suT2EGIlR2Qhd+91ek0gEl1mpraxUK3CxNqVQyW6TS9iRvlZSUbN26deDAgb4tb7755siRI5vb32KxREYZyfbRvsungT5ac5oZu8mBSrD4h+MNiO69o2wPACjIUydqpw6QeDrI2I6nycs/WssRe8ttq/gKh9Xu6HTDrh5RHJ4JFDxooAmoEOK32Fg7rbU691SbXhswILxCK5tj3t33yjL6Lj+zTJQMACCfdOP/5s63Wq2+Hdxut8vl8nhC7stZWVl5x603j8yRwMxEAOAYHU9Pu2HtL+uFQhz7o0QyBCQNHiFRd7DSOFrZniew0+kEAB6P14GWhxE2m43L5XI4gf7pRCIRQbTi8nWAEEokEpvNxoyZL2v7VBAAkpOTBw4c+Pbbb/u29OrVSyBodm2ApmmJJEKyxNpB+y6/2lxrcZkAwE2LuitTJRKu/29HpA197/jHACAj8oqsKJT/vE1efr7JLUR4HbpbdEYo298OokUeTX08j2gAAKP33j10RvPqMe0Hn32pUqmCaVyHcsfUMW82nBHBFgDIumGYTHZJ22FGCH3qEjo89/gjL/RWcHoqTwAAQA9ExvJMH3/w/sJXX/ftEyWIN9orAOizBo1Ekt2Os3RxISRJsk1CGAgdMDWampp64QJelblw4YJQKGz3DUmSpEKhGORHCyrI0j7KTVgnbHR8/+jGXwC1SBXFVwMAAc6z2vArh3i6gRYinDvRIzoruMZ0OHIe2OhLc+dp+Fg0a+fREzeEeeesRiAAhRAHOp3Rhkc2vdPpLC08PzRBXuft3RJjp+/oHvPHul/8d0uX4dnRC/qwrKQakXSAEP7rX//6+eeftVotAHz88cezZs1ioqG+//77I0eOXPnxWTqWciMWQguV0i+6iZnVbCXWjypzWeeZ1RHUO6DepiORDQB4JDdLGd7thS9HzoNq9zg3iHxbTHRW1KSn5PKIWgpl8EX8lhorgmtJgJjNZpmABwAaPr6tYpwg4JCMA+ejrzoV7+8oN4Zt2YoIowOE8IYbbpg+fXrPnj179eq1Z8+e1157jdn+wQcf7NixgxmPHj1aqVRWVVXdeuutSqUyNzf3ys/L0j7KvS0LrHRyP2UTQthfjePcHM6S8LpRT9VfLK4WLVAoBYqgmtPxyHjIRGWdsC+Ok00ZFHtdjXvsGccL8gidIeutwr5vvbekaoijUCjqrA4PTV8UQgddZbKrLg1i6qa4GC9zOgzj0SKSDphmRQh98MEHL730UkNDQ1ZWli9+YdeuXb7x9u3br/xELB3CBT2OJXGixB7yJoSwmxK/sYqJ0nw9PVQdNuFIp+tpKXmeGcdLYjlEpLXbZDTPCTI3REl4Ah2V5YIoKbe1j4UnQ+Pi/8olEfK4PXVOj5Op/xfKIIRmzr7z3Z2/1fXBUxFSq3vRofInFi/33y1VlswMRETZqXp6RGzY3F8RTIeVWFOr1dnZ2f5RfCRJthqrw9L5lBrwRJNKmMhvKqM301uNJcqd/+vmnZ1lVwdwpoGWEHi5Ol2eGlxjrgaMELpdPFI4PVUx0kKnAYCc3/KHwpX+Sq6dZqIN6HJjeDiFC199XThsYgUX+3nPbiya9fh/p0y9yX+f5KhEAnEAQIB0BXpLEKxkuQxWqLoWP6//yeBgMpwggSu6fAe32z3/nofB4QEAkuc+teqRqeNGh0tQ/sl6WoxwAEJPVXvi8UKc4398o3qlV9qbfQ7d1X3Nk4/ZdU4AUPAi06VQCcDjxCU4ftizNbjGBAhJkjc98yLiEABAUfy9B07dde99jfbhEKRMwJTKo4v1bKWtkIAVwjBg08Y/Jl1/7YCczDHXDP7y88+brObaKi6Xa9qk8etWv8F8WGxyFzwx5OzZs412W7bkzbia/Gwn/mI8NCZlLFf/3yceu6IL6BRogPwGKx/VAwCBUK/oHsG2qIN5+41XL3z1xq6pKbun5+yZ3v0RuVa1fDSY6iLSI/R4PPfNmyM8dZD5cdfGz8PlheyopoYZcHlJYrG4yX2SpWnMoNYSZvFokQorhKHOwvnPrH7xqVezeL9Pznp/gOzQl+/9a9rN7TjOxys/yLZVTx+KAxC6U8SXo5Ie/PecRrv99L+1/+kdl+IteVwmQrflxO7atuVKLqFzKDbRtKcUgAYAuUAeI4mcpDoAsNls367+fOnI9Cg+XvgckaR4oa9cvGVZRAbLrHj3HXHxiXtjcfrgkB7KCXzDM48+FFyrAiFPh7N3ZILY5vbJUuCAWIMjPKZ8Ix5WCEOakpKSbb/9snxURkKUAAAUAu4LQ5KhsqAdwUcbfvr+jpyYal+BSjudpRRz7GaNRuO/m8fpFHDIZBuuuF0uRAAg4ZG+mgkhy5l6WkLgxMc4cQyJIqqicV5eXt8YKYe4ZBZ0dIpCeGGvlBuBU6M/fP3VA33j4+z4xxo+mp4Vs3fnjvZNh3QmFSasbQmSuOb26SbHGkl5NOawCsyOVFghDGl27949PkHc6Dl3Y6J4258b23ooo9GoEHJ9QphgpwFAKeQ2NDT478YXCM1Od4q3mlW5EFE0bXJ6QrCKRyPONIBPCDMiLlKGw+G4qcYa4PJQiCAjcmrU4bCLuWScA7+QMRXG5QKuyWQKql2to7PiqdFsZbNCmCDBBUgFSFNqDnVp7wqwQhjSuJxOHtH4fZ9HEg6Hvcn9WyArKzu3zlQl8FVqBhqgqN6SkpLiv9vce+9773hlkrf2f6UQfXym5qZp09t5AZ3ImQZaQuBy2/3UPYNrTIfTo0ePM1qT1XVJdc3fLjSYekxWROLUqFAkMjncSgdwKQAAAxesBDTYnCHewcZDg8ONPcL+6vjmdouTYI9QgDQlYbDuGfmwQhjSDBw0aJ+2cdXovRrr0JGj2nqoxxe8+PrRqmpvxbp4O/3hiYrRk25s5Oo99Njj0iFj7/nzPN/iBgA3gu0xWa8sefvyA4YaZxrcIm+V0UEJ/YNrTIfD5XIXvPzavVsLCxssAOCm6DVna1cWueyjH5HzI3BqdO699797vJIAUDuxw/RFVf24yTeGeJH9YhPN81b4y5I3u0aoFkUjIAGAh/RFhkgqCx+usEIY0vTv31+UkvXpqSrftNgv+dU7a6y3TGtzb7ahQ4c++tZiO4kAADmpm37IpfqNfmt54+aRCKGlK1Z+tXELl4vTftVzHg7x8r5ut/vzzz9v+GoeAS4AiOJJowXKYBvV8dw5799Lv/p+7kk06OfzQzaUvicc2zB/H/DFskhMqH/g4UfkQ8fO/vu8U4On6fdL45a8+35wrWqV0/UOHhgAAICMETXbFYtEpIAbDQAAdEGDprndAmThUQ/3C9cDe0KuF0cYwQphqPPNT+v0OcOHrdl980+HJqzdt7tMe41KcOOYUXZ7m2dHe1zblxkYPfEZ7x19+/0PuNymH6IZGRlDu1/PjOssIV0auLq6esTAfvnfvP9oFn6gWMp1/i17Iolrrrkm4ZVtmpfPaReerr95MfDFEKEJ9cwL2Zo/Npuj8Jc2ZcYdoV+C/4y2lolb5nKiOURL4VpyIV4mLDPVXckZLW5YeopyU/BJPsV2+m03rBB2NhRFfbrqoxH9+wzIzrh+cP/vvv225UA4Pp9/7PDh728d9OzdQz79z7XLJ/R5eVjqCJ55xbKlbT11ubd4sY2XkxXXSh1OX2lguzOkhfCJ++99Mlv8xIBEZyKuD5Cisy159eWgGnUVuTxZQh6hCfUAkJmZec2gCcy4xlwTXGMCocjgy51oNlKGIUGMJ07rbO30CI8dOzZtwtgh3TOjXu4j+eUZsJv+KmeFsJ2wQtjZzL71ltNrP1x9bezvN2Z/ODj67xWvP3LfPS3sX19f7zQZSrOjPu7Of6UHj6nn+6+cmN/X/dzWU18st00lpEpaeXr2VeOpUS5dpXe2vG/QoCgq/8zpkUlKADgnwV/mGYi/6bdfg2rXVeTyFcGIzCP00U+FQ05M4ZByV23B7l2sKKblPdNkeOLUaKttx4nW//Lzo7OnPxbr+Gdq1uFbUl+gdineHv77OX07DsUCrBB2Mrt376YrC/47OFnC4wCAQsB949q0c/t35eXlNfcRk8kk5HL3K0kAcBKwNoEAADGPU1fX5hmVKu87tQ0S0lprWJsgiQNAACAgNIUGd1vP1TlYrdYoPhcAHARUCBEAIIAeNsLjDlGDr5zoSydCJVxosmBsxDAsDueeI0fx+ytXHT58OLj2tEyDTccMkqJaqeeQJccuo4dqcyohRVGvv/j86rHdsqMlAMAh0Jwesc92F+5Z87aLarPNLMAKYSezc8vmcXGNE/LGxQl37dzR3EcSEhLO15vO8fAX/Eg0ce/u3B0lWspm/W1D2/yeWivWTgelSotqxSMUcPiIiAYABJ5Tuitdz79KSCQSvcPtpugLYkQhAAC1g7aYHNJI7M/H8O8swqeFJILHe0X4LRwvUdEUAQA8nt385yeLH5h70/gxBoMh2HY1AQ1gdeJbrJu8FSGMj/KlEta1NZXwwoULGTKBhMdxEHBIQRg4AAA3ZUSTZzfvqw3oUA0NDa8ufGH6pHH3zJ71048/tOnsEUmE30Whhsvl5F6WF8glwO1uNuLrvJlEciEl8L72I5Q+M+e/W88uGJ6xcumSNp1dY9EyAwfduhACgIiPX1rP1YfurNTsef9eerTcNy/azUgtOlT26LPPBdeqq8c1MUg7l+u5mzTPdrrv4b4+OKL9QYD1v/xM1uOqRqP6x6+4Lm2qwPDIvXcF16om0diApLEQpklbE0KxTwhr25pK6Ha7uQQBAEYuKhfC1hjynARxSQI87r8rWncJjx09esOQAbKjm+YnuG4jKv5Z/upN48e4XF26wg0rhJ3KsOtG7dY0jvbcrXVeM2xYcx/57M8D3btf4t8UJAtnDU03ONz2ttQgdlFug8MAADQQJEceG0ChGJUIL8+UGkO3Rv6Cl1429xy9Ro9dhD+2Fo+/6+GZt80KrlUsHcUXK1d09z6mavgIAKZkqgvPnLJYQq6BUYmJ5hN4alQlim55Z7VIhRCTSmhoayphZmZmvs7k8lB6LgAADXQdH3aW1Tsyrv2zonWP8KG75q26LnVGdmySVNhbLV00NLm7s+6zj1e1yYYIgxXCTmXSpEllHNm3uTUUTQOAm6I/OlnJTcwcPHhwcx/ZcuhMWsYlquVCQIyIy9ea3G2ZUNHZ6pmTOml5ipgTSKBhihQvz9RZq9pwps7l5IkT+zf/oczC7woTuMK1X35hNBqDaxVLR1FdU5Pmxo+pWm+gUKJUWFvbnhiTq8oFE81H+IVMJWwlk5VAhIDLxMvQhfq2XQuXy73noUefOly2JpE8qCQtHMi12hce05kmLTipo6usLT0XKioqpOBKkV3ySJmdrdrw4/dtsiHCYIWwU0EIbfh7i6nf2Js2np++KX/aXwXSMbet/aXZpT6bGwocYp03U2yQHs97nE8Uam2ugUOGBH5qjTeezUlHtxopw5CjwB6hJYQD9p64/96lN2Y4+AQACD3wRKr6tnjOW5GbPtHVUKlVIgOetavxFgisMdtVqpDrLlKgtxFgBwACcaP4rd9jCiEOHC0ztjnw7eHHn1Ddf1uJlKgUor9UxH6lOOO1v0AWTwP806JTaDQa5YLG2cMKATc0l107DVYIOxuxWPzG0mUnC4v3nDl/4vyF5xa+1ELdliNa2tnteq0cd96ZVuVhSoCahESuk/vSm28Fft46K560cdCqVnMnGPrHYI+Q9lSFZoKS0Wh0m/S2ePx6m2mhEQ3Tuqm3/P1ncA1j6Sjm/eeB3Wfx2natAABgZ3l9Qka2VCoNpllNUWTEdgp5SgSt32K+VMJaa3u8W6MKf+0RSTgTiBT1eubHvS3Gy6SmphboTI32OFFr7NmrVztsiBhYIexsCgz08N9cN/3jdqLW87/KLTRHLuXI+QBAekBl8IiY+GgS/X14T0xMK7lK/misvkiZ6EAiZQAgR44zKPigLQ/JbjE2m03E4xRE4a9xdxMFADyScDlD0VqWdnDn3Hlx6UOZcSmHXrCv7LMK6sMv1gTVqKapMOF3TaUgIG81XYbvX6O9PVHZOmuJ/481xv0cZAKAluvLiMXiGybduPJ4BeWt46G1Opccr378uRfaYUPEwAphZ/PeGSq37tymMsf/ilqP76qwgBiVAKIBgGMhHtivqW/AGnZW07bpSo0vd4KOTg1sapRH8iikBgCEPMfqQm5JBgDUanWV2ZHnbQOeZaYB4EydMTOrWzDNYuk4EELLl65CwAUAu4CMuu/t7QcOq9XNlvEMIhoLFsJ4cUBC6KvKTVMaaxsTX+1uh9tdCwA0TQi5uCOHCFUBQGVrUURvL//AM3D8iF/yHtpdMXtL2d27y99c9Xm/fv0CP3ttbe26deu+/fbbs2fPts3uUIUTbAO6HGcaXFIy3+WRHtAk/qd7KztXWGgxUcKMxwwYY5tQ+ZfuAMTKAOD5Z++ZNWT6cwsXBXhev6lRdXLjFofNIuDGOZ0aAMjVVU9LTwrwU50GQRDj/+/B44J9AIBoyLRQVSb7okOVH33/QbBNY+kwCIQk/HiTowwA7Jk5odmAwkOD1Vmn5AAApEhbCRlliPfrSlhpobNkbbiuUmM5DTQAuEEiFyhsLhMAiFClEXIqWqs4yuVyp85/d1nmworqfBArCh/KypS3QQjeXbL4kw+WR4Hb4nQByUnu3mv9xr/EYnHrnwxhWI+wsynTn4ghd6Zxvzusbf0lsMJysdls+dHC2h2/3RSNvbn7hiUeW7/26zVfBnhevyRCZWLAX1qFMIEZFBtCNHA0evJE5ltMa50P/lP02OG6pZ9/PWjQoGDbxdKRxEfh72GRIUQzecrNNMfbgClBEpAQ+roS8pGmso1V4osNZczATsdFC7BHGEVWAoDeCabWVgYO1dEgiIL0IRDT7ZypDamo69f98vnytwfIuUvH9tww65o3r8+uP39m6rjRbbM+9GCFsFOpsrij6R8kRKma3Fdn3NvquluFhZYgXPP6wK/b5g9OVnk/ohegRdckr1q+LMBT+8rKOGlVvCjQd88ECQ4crTGHqBCerjvNDGKSxny/ff+BU2evH9XmZo0sIU623JvJY6kIriXNUWwGAcK3mEoYkBCqhEpvKqGxzNS2VMJz9VgIjTCgrzqHGSu4+I9T2ZpTeLju4g55+jZEwi159eUcufCd8b2zlGIRlxySIP9xxpDi/Nzq6tANLA8EVgg7lV+LDkqIC8xYSpw8rmvpK3jy5MnS714R0RcAAAEiamw8koh24I/oeEjG5zoCSyt2eVwGuxEAaJqU8hWCgF8Buynwm7jREaJCWGc6yQwGpw0LwZB6lg6hlwoLIbgra21BNaUZik00HxqYsbq1bHoGAhFcEnclvGDQtel05/T4/VjCTx+Xjt/8BN7G1K36l+0Wwrrqqpk9Evy38Ehialbc77//3txHCgsLF7/2ykN3zXvvnaVarTbwc3UmrBB2KjtK/mLalQGAnMjz/zo24vmnnnjyjulPk38TJA0AHr1D32ABgGjXRSF0UzSFAvoP1tl0zIqCE+QJ4jbMhPRV4S+92x2Kb3xuyuNx4yfC6OSewTWG5eqRKsPr00Ki6mxDKObyFJtoLoFT8QIUQgCI4uM9y01tU4gKE/7aq8WpSVEJBLNu6tEgcANAy8uEdXbwr26a25a/J5/HYxoG+CPjkyTZ9FNl5XvL5k0ZLz+wfpLtvGfzt+OHDf5z08bAT9dpsELYYVjc8K9tnvF/uksaZ+lg9A6DxnwxyEqAag9qGprcc9PGjed3bPp6fFb3bOzi5LiRy+n6p1irdOKD13Ph5/O1E2+cEohtfrkTqgRRgBcEFRUVXy14jqZoACApTaUm5AJHj9UWIXACgBNUg2JYdzBiSZFiIRQRVWfqQ7HDwgWjhwMWAEBAKIWtNPv0oRBgIay1tsEj1DuMNpcRADw0PyUqlk/yYkRMGK1HiGqgtcDRI5e+f7fsET5xwDPyd/d+Dd5n6PWjD1Y2fmrtqTQMHTr08s/m5uZ+/8nK75skbdoAACAASURBVCZm35QVNzBO/q8e8d+Oz/zvow+ZTKZWrrDTYYWww/i5mPrhArWlkl58sukb9ae832jaP0CGztU23X3p+y8/v6e7CgC03p6ryRTRN1G1stC8+kAlk1Ov58JGi2Dh628GYpt/ue3EwEJGT5w4MWXUtWPN59UuAACEYPLNo8rLywP5bKexuRTPiyJOLzIUYwlZOoYonoTPkQEAAY6T2lDshVJq0jKTPSKelESBTrrEeRMtGuxt8AiLDdgdtNApyRIEACky3D1URFRCax7hYS3+rQBpAEDvhOaqsu2tpd8/Q+2tpRcewV0BPlj18Xfn6g5X4caHNMA3pytEqdl9+vS5/OPrfvj+zkw5h0AA4CIAAGR87rgk6fbt2wO/2M6BFcIOo8T7lrOlsolvlcPj/PX8JmZsp3EWlM2eW9/UGnldXV28RAAAem/OvdIFcWLeex99Mvie+S4rAgAaoVU/fieRBJQSWOeXTZ8YWKTMMw/dv2Jk6vUp0fF2fDl3DFa/NP/pQD7baZzyRsrERrHzohFOrBg7hef1oRgvU23CflJ0wO4gACR7Ey2szjZ4hL6QUQuVyqRCpfk8ZlQBABUteoSH6ygAiEJFWdxPYji7ACC36Zkp2FyJ3+mLvQ83qVS688jxlZUw5Zfj9/6dO+Hn49VZw3/6fVOTH9fWVseKeADgQfBXLPF3LHFCjlQcqNOE3KsMK4Rtxmq1Hj58ePfu3Xr9Jf2gfW9hF0z05bOjRQ0lZpcFANwgrvDcyWyUkWePaJtQzfTMzIJ6MwDouFi0lE4o0tsyMzP/PW+uW4yzxQsM9QHa7F9WJimA3AmPx6Orrc5UiAEgxtueXpUoPX70SIBn7ARooOvMBcy4p4oVwginmwI/62tNIZdB4aTA4sIho4mSNtR7ypRhj9Dt1noCXqor8QqhlU5JwR7hxTVUAKhsse724Toagac3/3UFeSKb87GcPNvc7Oh2b2nFSivt2yM1NXXr3gN784re/3XzqeKKFR9/1tzreGaPnuf0NgAoF4ITgZmEIhHKI4mY2Ni///573bp1ZWVlgV7zVYYVwrbx1RefD+/b88PH7/p2wcNjBvdbtOC/FIVfmvynI7ZXN/5iVXrTD2xUYqx0MFOKUAyl+2uaiIG777En3z1Va3a6670eYUm5XhSfkpSUBABcDnYoi0zNvMhdhp9HqEoKYGrU4/FwCPzdUHnjVLV8BHQIxSlUmqs9FPNuIRoaG3LJ/iwdS49o/C+mqcrqNmbdXW3KzTQPaphxvKQNa9WJ3p15SFdrC/Tm8vcIUyQA/muo2CNs9lClZrrWBlLyPBcZAYBAjhzu+2frm3AhrW446F1NdHhA69c+zk25Txly49ISuNzG9bv9mX3n3P9dMFSa7MUiwk2gWj46zKfz0vmLnnxk05vPHPpg0Z0Tb3jgrnlOp7OFg3QOrBC2gd9/2/DDe4t/npT1+tCkhYMT1k/prt31+2JvowP/6YjtlxWprjLh+8RMZfSQi+XCZABAiDpUk3/5ifr37//0629P+qPwPIWn5g/oxV/98DMzlvDwzVNuCnQ6xW+NMDqQbHoejwc8gd7uAgC1NzynBNyJSckBnrETOKs7xwxMnuw+SvabHMkcOXLk01dwyqys4vcNe44H155GlFnwehsEnETI4Isv5SNdq6XRGCiaLjHipXoLncKsdPiEUExUAkCdDRzNtPpmItXlxCnfFj7S5VV/cvmee2pp/4P4chMb7PrfC//eWrJrU9Hmlk1VqVSff/fjo2e1X0mp3+OJvSqyXMUVXx+36M6ezw5IfKRf4v8mZseUnljw9JOBXPhVhX18BEpeXt5zjz38yjXJQg5eCScRenZQ8v++XkPTNMAlC87bLvMIK0w4/cBEpfeQo74xvZkfywxNx8vMnHV7wrsnnFJcYP77b39VKnGHs2hvq7Nae6BC6N96IiGwNcLnX3vj8V0X6qwOtdcjPO3xLHrrnQDP2Akc12IhtEDvtMD6abCEI7t37Xpk9oxHvBXI4uI5Hz884/ChQ8G1yp8yMy1A+BYLPHcC8L1MAgAXGcvMATlGOpvO7rYDgIuOiuJJJVwAAIVAFsWVAAABNh5qoKHZ+JejWkYIz1yy1bXrYNXRRntur7ok6M/3or+/8uifRVu3lOz6+PiaPRUHWrZ26DXXPPTJC65oHu3XjuOvuIvBRP/pm7B54+8eTzO63VmwQggAUO+ACX+6r/3NXdxM5sOKZUvvmz7FbTYwMSw+OARSCXl6vd7qBv+wl0oLfc5wyaEqvUJop+N6yOG6JNz0hPDkOpuJBq9z0wThBgAhRyTmXkx68JUo1NsCEkKHx2lwGAGABpJDypT8QD4Et06f8eyyjx48oHluUxE2NU7RZJB0sMivx6UJVOJsgtXByGXhM08uvz5toEDIowAATHzi/bHpry6YH2y7LlJmBh7hy6Zvw9QogQgOyQTX0IWB5dT7vdTG+BcNTpbhmgMtx8uczi8gT/0iRfkAQCBkN+DHwdKf32/UknDbpdNazLqjk3L+nL/hXH0hs/GbMz+1bK2Lcu2rOsyMBRwJk0SdH0XovdEPCCBWItDpmr52XxfVq00XFUKDEx7d53nusIcRoe+KqM2V9H4N/WFuE6JUUFDw/acffjM+S8QlHZ7GOxgdLolEcnlNo8ZfI68Q2uj4ngrUP6YH82MUKqi2NnFSGkBrw/OZjd4x03yRZu6AgmW0VpxN76CVCaI2ZNNPmjz5wKmzG7Ydo2geABCE3ewKlcUZp8epszN/UpStzAyyNSxXE72uLjFKuC6vyl6Dn+5fVWtqa0Ko1FG5heYCDp1rtTd9I8S+lQ5jW4VQkewXpJLaWgaFwWCYdfMU7Rs3zdW+ixAFAI5K67XHcOSRG9VdP3hASUkJ3tkJxy6te8Uc8NU97xTpS3wbz+kKy4wtBfEWNZQU6/FErhmGgdENABSC50zar0+V2d0eANBZHTKZrIkrNWs/3vHFG2vf2nNgry8U4yrRVYTQ6oYPc6lN5fhfu/wMtTKXeusk9V0hBQA+R7DQ2MRnf1u/7rZ0GYdAY9LU/ztzyX/9YFVDWlYOl8utuEwg/IXQ4rLqHQYAoGgehZTdpEgtUtFIBAAkshfqm+gNrbEBQeNvfOylXV2y5ThYxu0JKPeozus4OgNbIGxErFziQtgH/fav30+cOOFyBb/bX2FDCU1TAGCn1H2iw7vyPUvL0ABv7T1/rEY/lIfnY2JTFXVand1ub/mDnUaZmeICzjBo09QoAMgFWDhrLAHdzlrbxWWOFL8VgVTvMqGQacZ02RPp7jtuH0vX/DwpM2ewN661QDfKiXCvbzn3pcGxzzz8APObXTWU+1LpqbRAhalqdzmeC6VoPgDQQC8/+ksL1u4s2++kHADgBnGDU+YuwmahIbEOip7+06Ef86oSsnp+cJ6be2nk6pHDh2+6c+Kxf34/f+yPd1Y/PXJgv6va8im8hdBFBdrF6+Vjnkf2eab87WamyH1Vmpi/fqkZ79ZoapQGOFRHV9fWRQs4APDgoLStxXWv7Mo/XmM4W2d693DZG2cMKz77AvxWknsp8FdzRzXlO9ZFdxDiMqUElwAA4JC49nyBvuZygystNN+76qC69NbqoVTQNAkAiDY5Pa2vK+hseNLGSSsCTCJsBIGwAXt+fG/p/Xde07vHP3//1Y7jdCDn6nHihJnO9P3NWSKSaHXc3vL6N0b3TPZ+2eMzFHN6JwXeeuVqU2E2IuQBAD5HIOAIWt3fnzgxfq/VBbbS4fMInbTSf2o0QRLHDASoFi7zCDUajaa4YEqmGgDOevty38aX/Hy8jImGcxMQly4vyMchC773eN/NVWGhT2lymTFBxla4b2HGh6r2HtTgZSEPDftqaV/vCw/lOVR9jBlbqNTafaVDj+v4FACAUUCMvC7tnfG9lx8pLZr5+bMHPRP+9PikV6/X3zNn1oDRMYpeiu6J0tFZykUDFXOm33L1Xn3CWwj1dv3lG1flUc8f9hguFQhfGOehOhoAiryCxySKlnp/bJT/t/CI55oN7tWWnnkGJwAIOOTX0wYNSVD8nFc1f/u5lcIbvt55kklp8EV8TUxCMUIAAK0dCrzLhBcXCKlYXxqfkIeFsNTQRHpphYXmIzzzGXOpECaKSRfgdYVAAkd1NnycNjVg8sd1BhdwuaFn9FvDk7+8IeWFR+7Py2s6zKdzOFOHo20NVI+e8iAawnLVmTH330MSFQggwVvboVJATMhQ79++NbiG+fC15FUK2pBNz5AUhT1CszOwCZ6LU6PRyX63c5zY292Q0MJla4QlJSXd5CIAMHKhXIgAgEPDaJpXarCle8NqSkQEiYAJ/Tvgrak2txvWiEoLnNTgEBs3kVnqnumgFQBAgv0/23Yw2+/d7Rnxu3voBrfFDQBQYaosM+B50XL3NGLbb//uph7agOVum5rsqYpKi1UxfaAqLfR5Iz7pul9+GTwhMVfNvSBG29Sklo/kseJRsYK///47kD9ROwhvITQ4G09lbq6kH9rrWXySWnDQUVeHF1o9NJzV+yY/aQAoMl6ifL4StEYX6LwxL2cb6LdOUQCg7zN9fZnlnM4MAHYC0gfE9RiX3pCT6pq1rM6NE/18719JYtRd5nuHwofyXyD0BW0qBPgNrtrSRD3rCgvwkW+N8JKpUQIBTeAtufWtLybrrFgInbS8HR5hbW0tUYWrjGr5AADRQt5TfWI+ei+YEaRntNgj9BAZaVGsRxjJZGZm8oQiAIj3ZtpVCcFD0SHSoVfnAMrju1XbtkAIABlS7BE63QF5hNqLQqhM9psajZXg4/BRHVzWiUmlUmmsTgDIjSJoBACQaaEbjHaZgANV+DmVz6d5IjHzV/U9u8YlXvQIT2rw5GStZyINZI1nPPMj17WRCRXcWEYBQL6eXnzCAwBbSnY5KRcAuGhpvWcAbTUqhdzJNThA9IiCcBEgF/BpK56yOuVdlSw+l1fTB68aOgnYE03uUBGZIrKo4Hwgf6J2EN5CaHZZXJ5L1qv+qqCgvlzx4dSNd2TPHjuid0bKW6+/mq9z2bxzqAUG0NppQ1UJFOwBfeWFDauG9etNPJetenMgd/9XQNO+2dHH93tddb549gd/vJpvnb255DmjYaHT9GsMmTQjJv2vyT99/QXzAuWbkU8SQ4J3vqLa+6pVYfYJYVy8N/wzVow9wgZbEx5hpZXmEVjALl914HOwEBbqW3+L9JsabY9HeOHChSQPviKNt/ZpL3XUudzcNh+rg7C77bWWagCgAaVIM0Licchy1RgyZMi+ajNF04l2mnlg1fDRHxWmUZNuDLJlAABQaqKFBL7B2xQyypDmLS5D0jpDAAkUvipRTrjEI5TxpQIOHwA4YCHB2sgjzMjIaABeQb05Pwo/83sbqXcPFp2tM9Wcx8+ZLU7LEwteBACKhhobDQAIoLcCCTkAADa3ocasAQAuyS2z5QBAtXsyDSQASIjSk9o6q/ti0v2y01SRkd5fiQtR6ak+AASo08/pzKlWWuUEAHASkBtFFNabQYHjXTdu2/324jeWvf1WuUxv9muSQyH4M44sFnJi4+Jb/wO1i/AWQgBocFwa8lvYEP3+uM+yDHtn9FgzJuOPqT0qNq6dObynfO1/yOPrgabP5uXdOGLw4DVTZ2x7LPHNfjeffO+rkfFHZubsnBg77cQy6dd3Mz7iz8XUVr9oF2dMzo5DRysfeKpulJrfV8lPEnMV3Kxbouy/vzdjymSHw3Hu9Ako2AOW+iQx8rV3qPKqo1/uxEWPMFmKhdDsbKKrQ7kZ+BczkxrfXVHeSLMKc+uBozq7zyNUJAdWcdsfuVxuqcNuspaPP661On15jZ1PQcMF5v3DTsf1VAqDZQZL5xAdHT19zrwndl4wmp0xdhqYJyMhuuPOOcE2DQCgzEL7Jm98C36BczGnntC1XBoNAGigtd6VDhet9K8ShQB5e1CAgNBWW+lG0S6r//fjg/tqDrqw2K7fVXa+wbb5zmufS8et1oRJ0n82/gYAGju4HXYAUAmATwIzjSQj8png8wx5NwfFBQApX0pymHKP9J6KI2Xmi3Wn7B54ar+21Jv4r/GMBID68c+9cLDS7HQPbsBO4ddOa+yQCWDRy9fMjXlWtXP+lC2fr3D8802xDH8w2UpLXTQA0AB75cSkyZMD+ZO2gw4QQrvdPnfu3KioqOjo6CVLljS5z4cffqhWq6OiombMmNFRPTi2lu7+Ou/HnWX7fFuMLjj366onekRdk4BXjTgEeuza9CiV43X12ekHXpYtG2V6a8qSnsL1k9LvTBcPjRW/dn22gEcCgJTPefe61KzSf16ZOWLx6288vesSI48fOzZq1LAEwa/+7f/sHHTT+BRRbVHPzLSUr+bN3PZY6jtDv3zxgRgudlJ9Oa0V3uqINjre5xF2k2EhdLmbEMJKK81vPkVXKcIXWGdtYpW0EdqLU6OKhLZ7hN27d6/yLoZrvDmIX5/XzZgzr83H6iDO6XBqo5nq1lPOOoSRz4uvvPZ/ryx97Fh9ubf8M/3vhZwW63t1GmVmvCwHALFtF0KVSMk8h3mgLzO3Ev1ncpiZ+Dg3LVIJhdxLn9++ZUI+0nhoaFSzrUePHvf9eKouGj86SFr+0cTeAg4pddEKFwCAh4NOFR57cf6zY3qlpS/uG/NSN9HPz1gsFmYaSUrgGaAUGc6BjhchddRwZnxas98XcshwpHq/0+MCADctbvD0BwA6a6R49ptjfyvctRsv6FSmyaWTH4p997r31YUH5w7bNXfE7F5JP9NWUPABADw074JBpcPKnT1xYHR02yJyA6cDhHDp0qWlpaU1NTVHjhxZvnz5jh07Gu1w8uTJF198cfv27XV1dU6nc9GiRVd+Uoqm39zz7nn9hVWHvvj11/WMf7C3hhYXHxiRIGX2oQGKRWhzDJk2JKEwSzzutvThOcY7hiriYyVVQrQZ7Dlj0jbGEb/FE4cVyMhFAHB7TsxEvs6z8yfz8wNAW4I7+5SfLHxj5qCpclLEAQAuDb5iK9vVxIxM5XAl97Mbkt8bkbh7Ro/Ygn27lz7M/JapiGh32xtsBgCggeOgohmP0Gazbf7oA1y606V549WXHY5L+lBUW6wk2ACAR/KlvKhGlx/jFUKDvYnUi0b4gmVcSBEnbLNsIISWvPcl5aQAwEainZqGR3cVc7IGzLxtVlsP1VHk1+OlAoMnhw0Z7SLcfMu0XUdO3H87ju8niepycyv+U+dQbqZ50OwqRquQiCQJ5namC/WtLBP6zYsqL1/vj/HmWQmQFprKoKh1AV/gAAACkYZSbTclfi9Ot2Dn0akE8shfm6bm7Jqec/i27vdadt4ycVySCABAQeJIGakI50AniCBHNZgZ15nyik1u7xUBACgJXKrGTKdTgGMp3ENnmV8+9XevZR6KCwCkhMr97qlvxiSNSVVxCMQh0PgMdeaM7szOljLrb4YRh3R4sVCLDNRVq3XcAUK4Zs2aZ555RiwWp6enz5s378svG8c0r1mzZvr06b179xYIBP/973+/+uor+squx2w2T75hJG1zAoCHpDZ8/Mq4EcOMRuOuGori8pkkTQDIl6JjclTPA6uc5xSQVoIWJIm5fRT/xBD7FcgQLzRHce0E2In/Z++8A6Qo7z7+fWa293a9N3rviIKAUlSsRCyxY4u9JrHFklhjYomvqDH2jqKoINJBQUF6Ow44rrfd29277W2e94+Z3TuOa9ztcgLz+Wt2duaZZ7bM7/l1VCjJimRmv4ZICNHLJdcOTn3vjBTTR/P/kM8AMH3z1/svyN2bJmhDg5q58+s4Pvlmt45waknsdghw0/CMsl9XIOhFVCOsdtfxJgU/TaZg0lSglF40e0bS9mXGMAAQFo7fFl924ZzW92iNZtNb2vM6pKsFQegOdVF32x/2e0JeABxkZoVG2qMvfNbk8QFWSNddmzH4rhcX/PfDj/swVGFvNFLGRQsHH3OYnsgJTL4hl99QM+V7ultzPrFUeNAqm74nKosq2qe+vLkLl3+rJEJz+lFujlYaYTvxMgAqXbV800SNPNViMjV4hMV3TtR2ZcnT3zgsg+8gyBBy7eDUrHBTcNdyBgEVqeB3spL+/MHpKjLQnBmgZgAcDeyxCSrj3DxGRao0TBn/klcHeXbZaTOUNHeCg47i98iS3AMtLQv9agVp0EsAMBSO5XWNo56sKniMgwKAN+yNNWKMO70VhKFQqKysbMgQoXLm4MGDDx482OaYAwcOxA4YMmSI3W7vqKAOP6AjSnNze/ntwGN/fmCG0jMwJPwO5kxMv8gYeui+u9fW0qYhF3x60AnAx6JYzezRMSuSWFd/w+589bdprHuIqdwkrVUwh9VMOF+/N0vxXZrkuzTJD6mSbXpmZRKzAv6RKXoAI1L1SvvhC7MiDIE8UL59oNbPf1S+8B0Hw9MaIvkeCiBCyBIlNyRJF5sbAfqZ1LBXIuojbB0yCiBNSVatWpXss107OC0p6ls+a0QK01D+yy9CsmpTEJFIzOvQzl8rRysIwlC4C9Noq0gZQ4aqh183Q0Akgpt62vz5Z06d2rNx4oI35Kt185EyDGFzssUqo6cSBcZcfkNNyvZ22lr9uFHhplKmh9n0PHq5sNitdnehEbYKGTWnHeUcjxlmFbDiSI2Q47gvFy7cv/gf/MtkVfqlV1333z1CmF4sg8KY3zYV6fQkRXDfeh1zgIADkKnNsAWEejbpKuRoiD0ykn950LqU35ieHsmSbZAR/tHEWLlJsdFi0UB2bjS/oR7U8vAEsDZJiJHJcXPByrBBhmbav5kTGs9tr9+1sYGmf8H2W0g7KqbaMyS9PL+pqYnjuFg/Kq1We7SQczqdrQ8AYLfbLZZ2FJ3S0tKlS5fm5+fH9rz77rvTpk1rc9jyH5Z8d96Az73YrQOAgxrm8qLkBYtXHphII2PnLd7wlnFrdcFZ6T+mss2t7i/MEOhk9TpEPXKy2FteFqVqUqpm2bOzJS455+IYCrVMOoBpTFUatGONJdFQK83G+ndL3DeNyp1qjRzSSADsz1P9mRzRjsvuC0KpB1Dj4VwuV6mtjN/v41INMhr2udavWjHJogCQHKAlWgLAKiOTk+RrV68aPHgwgAPNJJZEaJDqj/aqpkmFyXNcU+c+14pGoRROkBoz5RGXq52uT91BIbUgCAD7Gqpc6fHx8vaM/Y5DnBApk1Kklbjj5HI+seA4zu/3J7ru1O8QDVQSRhHm/FLSvKW2wZXb90WFKlxMIQIAWMIyQcYVOrYfZHNzs3XHDuQAwO7P/vZ96IHJkyd3dHCVU1hVBzmzWRJ0uY7wKWqjDyI5YwNw2BHgD7DZbH+85MLhytC0KSmboAVQuv6XZ+95d+2KH29dve2SXB2xy1FoAeA0yWlVmLQSMUGOU0qJjhGShgcb+5c0BflC4SYmYELEzo1NwwoAXv9eLTnoooUh385U2SEapgCCSPVz7TRotEdGQUoAqsxVl9e4dZTZY22OEKwdISSVuX9qOOOSaxyWyA81bGNkHF8l/Jeq3zYUz3AGGWcQS0p983K6VapbpVKxbBelJXsrCI1GI8uyTU1NycnJAJxOJ7/RGrPZHCvnyjezTUpq36Wcn59//vnnL1y4sItJE8ISkuvheI32sBoEAEUwQsGwmU+szLH+53N2FRO9OV2IBlni70gd4loU40i6+j0/nWbFQHvERSjVNObLNobGp4UIB8AYpC+ozW+Em8/+cIPWoFY/NoZIGDZd3WgPqaJ+6QN2D6fUaiyp7hB8ERKRa63R3tM+Li1Dx2i1WoVSFaEcgOSor9GqIJSjKrWaXyg4m1vi0NL1qfzO1oxQqgACUAYupVod6x14ND67oHUGqTFXL9Fqu1dy+yhMylR7EADqAo6j53N8CIVC//n3vxZu/0pybgoAr10yNrvnd3RCw3GcRCJRq/teDBx/0rS5lU3FAA67q7TaMX07mSCHpkAl5BSATq7V63RdntKa5ubm88+edto5qTtz9ADm9JO/9OAddXc+eMPNt7R/fFiQsgEY84xyrfYIrTCPyeE3+J5Q1pCMP+Dma668OUc2LSdtQarwTMxzOP73xuvvfvr5hg0bnnp70S9l9oGhJqk0GJYxVhmJPZcALKvxnX/HeXsahEpSozKG/3xAwttX882KfsmMhw7hqIQhYdAmi+RnLiwJcodUbD2fU28PCw5FhoBrLV+p0cPlqJkyMOTmyjp2c82kLLMrX+uWEgCcJ7zOOuLg2395swQ/1EScnGBQ3GUr2eBiAMgYnJOv1PaoTla79NY0yrJsUVHRjh1C5ZGdO3cOGDCgzTEDBw6MHbBjx46UlBSjsVeOHVYu94Yi2VHVuFLJBCKcj0hACIApaWxwpIxRswAoB1+xC9uczK+O5vVWe5XB7UvxBbR+O+urjDgr1NWuyRXc3HpbPy6aIVCjIEuS2af8rvz5s7fV7VQx++UGDgABvagyLCNkTIZRZTBa5/3XRk7jT3mquXlLrbOk0f3untq7N1YveP+jWI5EjZfWuIUKan6k8CGjU6aftaLODyApaihokGFFnX/KmYLJsdrbuqxMO6qzSsJGqA4AAVfagQGZJ2YaDRwZbH2spEZbXlg97WQ9HgcopefPmF6/9MPzRgrWp9R92+reub9PJiPShww05fEbVk9Zn9tGqzwtLXlNymMucfTaS/+el6WcrBcWNAG1dMG0wn8/+3RHvWqtrX2ER4mBJJWZIQwAGXEShPlkjHA4vGf79mk5ZgC10fSni0y6RZ99CuC00067/C/POS9f4GGF5/bTFVa7LwigORB+/JcKbdGQmWdM0DBCnPYAc1EsKyxdRViCVLXSRfmq91RFapIkv6olIX9Q+Exs3Hh+Y5jpiNkaZLBxQiub5HNyv75swkOn99NOFQIRGqpTbn76dZ2cmZxKAHi4bAolAF/YY7Z9jEjosnzSzXZy3SQOwTLz589/5plnamtrIePZfwAAIABJREFUf/vttw8++OCGG24A4HK5zj///NraWgDXXXfdN998s2bNGqvV+tRTT9144429vOL1N//p2d8qU/yclG/LIsHfdtaGz5jPv1uo3rO24md+u5kObsi6fGvaBbuTp8gZmfbQweQdxfTnQ03FfmX2+b7Mc8OK1AiV+/UjbXsDgVJBbbXLiXeoQSXz2nz2oF9oAeq2Kh54t3zM12WLJQWLV/9UOGmWPTKWf8szYvR1dYXz9ptKRl/x87adgwYNiqUS1npR6xZssX6awn95EydOVOQOfGZzpTYaLb01FMocMW7YsGH8y2oPZF22N2OFYKoDzs5iBmIho0FqzOyF/pCtEwShK9BOssdx4NvFizP9ttuHZ9RFG2jcmmZo+OnrysrKPpmPSF8xJCmX35DQ8oq+Dhwtd0PJCA99s+KYM2vXr1w+PdtojgaM2+SQs8zwFN3eDqpVtCo0ak5XtX2XJWz0cUEVjNDp1+12G5SCJ6VOIQiP3CACAcFWxD+U3Fwu/zJnzoyZqx2jF5ZMWW4bdPV973+2UAI7386egyJJlV4TjcHhJ5CjQWNEEGl6plgjiVQ1V0eokDjh5IYBIAAv0mKcmcbUhmbwbW0ieunqFPZXI7Mv6oHyfLvpzsEsgFFmopEC9irPYeHhPNz3XtILo6Z7N3b10R4bvTWNArjrrrtqamrGjRunVqtfeOGF8ePHA6CU2mw2vt1i//7933rrrdtvv91ut19wwQUPP/xwL6946x13Pllfd+kXnxoK+sMkA7C6aFLN+PsAKIij1PoVHycplygd4RFWdoJPrbG8MvqpszNTUnSNMuIyk731ze+9+z/VTZ+VOk0RKLFv5bnbG/6Ukv2lLXTIIqEgjEoSUDV+d/BH/ophqi7V3eN4aKSEwfvXSiUM0ksjm6iwMqUaj+OqdwBce45EoyEA0lSEtx5UecPR7vDEzyXFkgg/XPjV22++8Zd33rAMzQYQTNK9+d6HsRus9lIFEVJt2tUIAUhZPe8hKnd1lkHRIghh7I1GOMCYwhtHulkIKu6s/fGH6elqAJVRa1CGj87K0GzYsGHevHl9MiWRPiEWOKphyvc4kKPp9OgEU+Gmcib6V1Ufc1mZCMcxBOagIFoapQQAATry/raur9auSpSsSqr3WAHIYa3xpgDQ6/WN3iBHqUdK3BIAUERouDmoMwj6K/9Q8kQFoTxDbfvLpjAHAtx+vZQQHHTE0nZzqjxo8AMAQ8DnYmWryc91Z+VIv2DhlxC3kd35a00Ff7yLFnFUCiBFiYFH5vtOzyBflxtrIrMyJYsBfJHBSgCOAECel6tpdCcpAEDCYFISt+lvc2ZdlfoTZADSM9WXXZr5z9uvnLlxc0pKyjF81p0SB41QIpG8+OKLlZWVxcXF119/Pb9Tp9Nt2LCBL0gNYN68ebt3766pqXn99dcVimOrzn40hJC//eOZ7376NdsiBPKqJg+VEC9Lwn/MXl/cKBTEOzNrwsDkOT6aLtn86Q1F2oFGjSmIIjcd5eT+KNcMsbs0FRURKAFo9yyZl64a5oJqeZVsp53l2iwziZWb6OSGAkhXEQkDAOkqeGkmnx9DOKuEuADEVK7YYq3UYYvQCIAgNXCQxX67DMPceMutj3y0hf+hUDboj7TkEVZ5qCIa05Ouab+qkEoq/I6rXJ1rhNGoUa5XGmF/o46DHACox9MXXQkjkbCEIQEGdhkBwFKa6qcsg3C4ux1IRE4O8g05AAGgIpW77H387Ve6hRBNdLxm7YSJk6esqXQYQpTleyFJiI/jdjQ0Dxo06OiD/WG/K+gGwFEpiM7S3nO0JYOCsbpCaA6BEHLehRct2FlTqxCe9il++vTmqlvuuo9/KWiENJd/WWI/zJekSVJCxgBArA1v5OcN5w/PNT05TLLp42Q55Z+EOVqEqaY2PJ0/hkQO+MIBAISoqsNCDbw8LWkT3T09nQAoD10a8UcAeCWkWSIsAi6q5vwux+7dQtriH+imSUY6gxOUWruMcDrZ1YXGjz94vzufcDc5gUusJScnnzVYCK8ykL1DVN//Y9gyJX4LRIIA9HLdjSOufnikEoC6ZsfopLaxxqMNrCLawYAN+RQSptEX/HlL5Qucdro1oo5QAOoIlTuCNmt6TehcCgnQsvxMVxFKWQ+Xzb/UkMMsQayAWUzgVbkEeebjUhFdfMVIVzMBCHFDMQsqgFp3A0NCALRyo0rafgkxvVwQhA2dFpdpKcgEU0YvNMJsNeEThgDUe7tVJj++TJgybX2dt0YhlAxODkBC8VODj7dAiJw6qKUqldQCgCCyw9pZV9jjQKWHyiCsNWONkLrPnfc98N4h1+rDjQa+kBjBHdurbrr9rna1hRa7KIwpCsK092+Old7m42X4VMKnnv+nM3/MQyXCE2Z/afP4uddcfuWV/EujHEoJvFwmvyi3ea0SeACkRYtv7G0QnpMPZ+iXXzhw7TkZZ//yD9W3j/E7s9UEQF1kVpC2uEgJSJZ+XEzLzNOS1oq7QYYBBqKWIAJlw56W8CKG4tKqSPWm6lHJ6vkXnbvoy4UApI2Hx5rYbB/lY1k9EkQI+pmUB/fu7vLj7T4nsCAE0N9YxG8YpM5bBhG9xLnXJtQcmZE3NUllPiOVLDidHZhpdgTarhwbQ8hKEmJ2XHmn/VTv3VbXNDnbYg5hVj2dVRe5oCY8sy4yuiHk323zcIIXN7au4XU+NydYRzXM4dEWoTotWgm8Bm/UQYhktBKQPKkq+DlBu28tCO0+IUg6XZPe0b3HPPN2X2emUWu0vppGZlQcQ3f6tmikiCBa6dvRB4Jw7h/+8JtHsigafpzu5Z76taJg1PjCwsLjPxmRviVLl8tvlDaV9ulEUOGmMkZYiSYdu2nUZDItW7/hJ01hbZ1gZTFcetsd997X7sGxRW2AM3dUKzFFFetBYUO0PZxUKv2/t9/JufQa/q28CVfe/9AR/qk0JaFgvTQTAAVVs2UAYpfYWSvY2HI9fHCsZMHUPGz8mE8B4B+JPi7Zzo3mNXUA/c2Fg5Oy+D5NAPK0yGmlEeZpCQHydQTAwfyXw64IAJajow66d35V/NGuqmenDn7nrKK/PXh/JBKxWCzWIKQcLCEAoECTlNR7gpa4FuA+sQVhtjaDj5IKcS4G4X22khAXAmBQ6K8cPJc/5uYBzOt3z/us9IjyPL5wZHlV8/TpQpRmZMylKxvp1romliEAUgJ0VJNgrGAJCVZQRKuotNYIAbhpiyCcndWqSWZU4Dn9UUFY1wyvs41/O01F/FQwZcQEYZhDICQIwlx9h192arTKmivYoUboDwe8IQ8AjkrTNb3NeZBJhf/5AWcfBI5KJJKla9ZV5+TyL5dttzecds3r/3vv+M9EpM8ZYsnlN5ze0rZ+jONLuRuSWG/6HpWVSUlJ+e+Hn+QNm8m/lPZrG3Ufo3VL3vQOSs23dCUUqqy1fDpeVmhIkW3p1+asNm5CDSkDhFrbjT5HgAkBkHM0LdoPkiHkjAzDtm3bAGRrgHCAVuxqLFe4w1kAjArDmNQRo5PT5KwgX/K0RCOFKZrolKclAPK1BABVGFPy/i9y0KNZU9P0/eGRFt2nl4zVyiVauaS/SVVcXDxlypS1Nc0Ofygrei9OFh8ccMy9/MqOPqgecGILQhkr48u9U4q9lft31ArKsqleqSIttoURI0ZMuWje9SsPbqiyVzX7lh+2zlpc8uDjTw3Niv5wJbLXF6+xZgz57qDwiM/10HPquelWzverVVp0VmyoFuOnGgDcnJD7ryGlMzNbPkxB4DXVubd+xO8Zt//HlGfHrPuqJSIGgE6KCBHMKYeahMLcdb6WgOxsXYcaYaZGEIS+UIeC0O6POgjR87IyMbQy4T/WZSGoBKHT6fLHCWlJ1jOeufqWOySSOER7iZxwDLMI/zsFDpT3aeBolYeTEkHAWHpUVoYnVhTG6u2ww6g16pIIwJyqbP+uk6PjyPniMq2aMXmCwhMm19B2ed1mWa9mygCkqoBWDsIs7xHSglKOL7K4a8XXSU8NnfXt9dNXLuDWbtZUyGfnTZcwbLYuNZYyUaAjQItSmKsFgMKoTXSQOVOxyfeU1PD4pP7nFqXE9Ak5ywQCAbVa/eLrb1614qC7QsgT2+jyXXTdzUOHDu3og+oBJ7YgBJBvEL68rc27KAsAmhDN+H7z9EkTWpexfuzvTz/33uc/W0ZcsUt6V/D0aS+vvOKqq3NbaevDc5I++err7LMuu3/dYW8oAkARwdq9DT/VhIec29JmIafFNEoI4KE5lLIAlEztMGPL5dJUBIDxzbn90oRP+MYMw/qL+7359KP8MiqGUiZ4GUsay/iNag+UpIbfztR2qBHm6YX0iXCkQ0HYKnfC1JtIGR5LtNhbnafrbsAJIlZs0MtlDtB1q66EyMlHPxOfuAYNKdvt6DNB6AggEHYCHACVRClnZV2e0hGZGuHP5eq4T32rQqPth4yiVR8oObEBNKYRcpRGwsLyeoAhs81ZUY1QyMfnNUL+EsWNgrOp0NMSyBrm6K+1zaNHj960adMrD9+z5vyCt6Zkvjgi5S+5Zktj3fLvv1dJlTm6zAeGMSoJJqeSiclHCEJeI5ybxyhYqCW4OJcZXTTm1+ojgv4ilO5qaOaz0s+eMXPlr1v12UI0QNqAvDvui3MC8QkvCEekDGmzZ7yTu6Uo5Uxd5K0Fr7feP3r06JcXvHlg04q9C19666JCAHoZxlgIgDEWopMCwLKPXp9+091XrCq/4Pvi85eUlGSNWbbu5zxjS+2S7KhpVMHCKAdHZV5kACDgDjvLYodppNA4D+dSh08ndIpJ8kMpYe8amvz2a6+0npVJJQjCKlc5X5u72ktjmUmdCMJCvWB/J9QZ6eBR0KoBk6E3uRM86dH/mMPfNxphIBKs9zQAoJSVSdIzjsqjEjlFSFZZJIwWAEv8m+tr+moalR6qIIIXQ684tpoybcjXC3+uQKjD9KRWplFLWgcaoUKi0Mt1ABgSkhFnTCO0eq1AEECI6vP0bRfFbQJHVaSSIMJLx53RrvS1B5y8d8nqDVy1/NCUK2/VarWvPv/0X0el6eSCYaa/mxuVrK06fHhC0miFRPGHPMZzrXTteRI5CwBT0wkACYPJqaSsrOy7fz5w7lcXzt9yr6Rm110P/uW1ffYd9UIEgDcUeWxj+dw/Xq1SCX9ys9l825VCVx83F/+o9RNeEF5YNLufOp/1RBhQAKYQPbuBA3BurnHF998efTxL0FokLD9Hsuhsdvk5whfJZzX8tq9kU/GhbSWlLy9402AwtA78be3yjSaiCirpgWi2DY/JU1lkUTSzACChMIUogAKjuuzQEUXJU9WWMNUC8Ic9fMZhlYeL5k6QDG2HptEklaARSklTna/9rKOW3AnEQSPM0wuLVk+gb1IJK5qrhCqjSB5gkInFtk9ljApBfVn23oNLlyzpkzlUuKGKrlmNimMuK9Oa/sZo1UlqDXZQQTaakQw/taR1XFclQyt4W1SkOlaZ+mB0me63hot/WdXmFF7mhamGZZMBMCSkYqr551vsRFhGj11UOnbh/hmrnRtm/OuW+/4C4EDJgUGtekdYgjCGkF4Xdte3U3D1zsHMinMkWy+U7Pru/XkzzswrXnWHqXls1cY7L7vww3f+++UPK96wKS/8vviPyw9euvzQpOvufOSJp1qfnqnLYBkGgCfk9YX9R4/fG054QShlpWM1w3V7mubURM5qiExviKT7KAA5y4SCgS5PN8hwYQ5jOMqk0bpIa0wLNMmhadUKtE3gaIn9CEGYlppSJQMf628OUoYCQI3Ln5qe0fqwVBViORilznIApU1WPndCLjEoJR3mXMpYGSUqAASRQ03udo+J2VKCXK+y6XkGGgUfYYSz8crrcaasSUjU9XDZYhvCU5mdO3eWLRWe5hNSrB8+ft+8C+bw5TuOJxXulnxfi/KYy8q0Jk0TM2k21hzVPomnPuqSCFBLuqrDP2CuXnieqJnyKg8F4PF4Hn3xUX7neGf9Bw/dPufsaT5fS/39mKGVY2LW0YMZKlS7alwBNwA5K3/pyZdGvrmv7vES2583cUNn86E0RqOh0XdENbiJjVxzpdtkav/TmJ5O0rjGF5987KMZRTPzkwuM6ik5lvfOLvr+w3cDgcDi5as27j2w6KfN20tKb7r1T20avUkZSaoqGQAFLW+Ocz2pE14QAhhWOKTWHWAodCGa4xGiPTdUO8dMPD0u4/PRTYiatmPw/cDcVPDblzQeIQgL+w+sVgg2Vb6RLwVe32P94/ybjxhERTyICcIyANXNgrHFoOxQHeRhhH6eKO2gylqrHkzGjF5rhAV6Ba+8ggad/s4KnCaIsibh1++lWaIgPJX507VX3WQQftCuFMVzp+UkN5a++/bbx3kaVR4qj5ZCTFG302Oh+xgUOhAZAAk8h5ratohxOBy33Hit098EgIap9KvnDeiwjUyhsSXmpcGHMIdHHrwvKUlYJZwhVT4/KWcSsT/x0F9ip8QyJZqjgaMWdkuyknx9QOisVGDMZQgzOxoPKGXAp/NfdNmV7xcfETFQZXMH5Zq0tA59OitWrJiVpVNIWjQNlpDL8nWLv1oIQCqVdlKJOlsvpLGVN4mC8CiyzJn9Bw9ZU+UMc5TvMLm51vlWifP2DtJxjpURZnJeNlFLcM+QIz6ubDUAeLg8PnumrLkixLVkK6arEDrnJn47bPN9WVx7wXf7T597RZtmfgMMLT7qUkf5/95688DCh/iXBtJFwoM8Wm60wt1+KmEsWCbQu/pqPCW/rIrUC3/7q2649MCBA70c8FipaBKyp700Y5AoCE9VKisr9QiNi7ZRq1ISSvDH/kmLPvmw8xPjToVHaHgEIE3Tq3JfBIRlBdfDfucRoiUcDp83/cw8zz4+SS8lgju9P9591dyOhiqIlaAjZRGKOh9d8cMPXLKQb5Hj5QBcPjBl6XeLY6fEcucrfEJWrpHd1uxvXFm2jn85I+9MALOiGWKZaiGd/4abbq4359//U/nmGkexzfXO7tq7f6lZ8P5Hndyp0+EwHhXrbVLI7Nauk7LyDYLOcDhqHIoXJ4MgNCtN0846O3P4+M92WW/7/sCF3xd/5DZ8s2J1R82ejhWW4NsZEve10isLj/i4ruvHjDCTEWZ1sioZQJiLlLaKl0lTEaUgp7CqOetB/ZXXvvXDQ397ss3ggw0kZhpduW3ljg9ePa9AMHps/3LRiuU/djIxjVS4QJ27/cDR2qhTQSExq3uXaPDFZ58+c+f8kdEO99OS/H+YfdbRTZgTSpVLCIvwcemDe+WRETmBcTqdJqXUEqTqMAAEGNTKiFkpdXRafT4RVLhbyspkdpzp1E2U0vb71H+9aNFIVXh4ofCuOYg/DU9HQ8WWLVvaHSc/2rtYxVQSEqn2QCInVjkBIKHI8FEALCEMbXFF8sVlANi5kQGaBIBB4ME1T/ImJRkrm5E3FUB/PblrCJOuIo+NFJ4DLMt+9s231z71r9WGIR8EUnSzr9qwbVe79eFi9Ovff5+7rRd0j8M3YNjIrj4hFBoF81tZvDXCkyENSyGRXzvsiisGz+Wu4YLeoFqt7rINY1zI1ZJtF0kAPP5TUUN5PYAS+6H+JmFJla6CIupIbxp0HTfwtHNHt/Np5+sIx/K1EykM7D2jM17WCYfdlmG8/7ZbthYfZDpoN6iT6W0eAGjwtS8IbdHEoyRVb9cE/3j04U+mF3wRIXxVfH2K+pGRqf949KF3Pvm8lyN3Ewpa5RKMxjJJWoaanJIdeUWQm5tb0ugGkOelu3UEwA4DSSpz9R8w8DjPpNKDNCIYYzKOvb5aG4yKJLcPAGrcR+hGWzf+PDFJaYuGricFOACnWWRbt2wZPXr00ePoZFqL0mzzNTIIKVFb7c1hM7R8sEKml0ooAPjDEUZyRGTEWAtZV0cpZSvDFxZK3wJw0HFYeCtthFoqRG++NIF9aULbK86aPXvW7NndvM0pU6Y87GN+qXZMyBBMoPsb3d9Uun/qRun8mNW3TPQRdoSMlSkkCp1Od3ykYGtiwm9/Y4uGNNREVBA0mNHJKavPkfBZpW1gCYp0Sj9NAkAZUqsgddGeYYWULdQpiouLO7quRSnYTpv87ZhGmwLNwUgAQATKDE2vUg1sNptJxmjlEnPUL94oJeMzjDuPzIlMKA0eWzDCx3/rBhj7tOOASJ+i1WrHnD757d01+dHMtp0qPLu19s4/P3Q8pxGhqPFEpMQNgID0oOJ2G5Kjq9VG3xFR2VK5IhDh+MYUAPj/YICjMlmHaYv9TNFCH/7t1R6MO1+oyZztE4IVXtxafdX8I9rhfTFdcv9QxiBDXXh6GEcEFMzOPwvxg2XZhd//8J5Ddf2qQ89urfnTurIni72fLl7SnXbfGdo0lrAArF6bO+jp8vjuc/IIwj4klt67L1rpFMAgA9FJBIXsk7PSTk/t0Kc12NhiHS1XkQY5AUAoUgLQyBiPp8PvOzWaQeEOtWMUaqlDQc29dBCyLBvmOACWWL8YOeEoxXH001VH1UEfTRcjZU5x/vXagobcMZ+uE3zGWwj3yL9fHTFixPGcQ52XMtTGd1tTyVSyXmTT82TpBFHqChzhI5w++5wl1Z7GqEZoCoECy6o9U848s6OhlF5hMrm7//mv2fletbBiKN3v/L8d1fN+2K8ff/Zd9z3Q+pRkJV4Yz1ZdIX3jDEWhZXpsf7o6ZULGmF7eWhsyMjK+XbH6rW9XXPz4S//6/Ls1m7YMHNgtbZ4lrEUlxKPG1zoqCsI40M9UQI6Kl2kOuEJcAIBSqjDI9Z2cPthIPNEcjJXJTJgBAH2YyiN0Z4Orf//+HZ2YqRZyeINh+9HvNsQEIZfUS0FoNBp9RGrzBmMaoV2GNRX28RMn9WbYY6LSJRSI8nFpoiA8xZHL5f989bX/vCyY5VmzZtAZXXuY4kuFB0pGkMTGTv/g3aRALwjCwJEtPydPnqwsGLLZJySDUYf/xpWHpp5/SW5ubrvj7Nq1a8mbH/Pb44cal80pqnIKlqpNKZeOvv3xz1esf/bfL7dJTuBRSzC/P/N/0y5XSATBOyN/qpRJiActJydn2rRphYWF7c6kI1JUQnRutSuehRREQRgHtDINHzzdOl6msll4cKepuwgnG2yEhwphwQfV0Z5hPvrilsqps87V6TqsWJGjif79OGfgqByqWPptAObeZ9M/89IrN60+ZK8T/HLVDP3Xbtsjf3+6t+N2m6oWjTBtiCgIRYDxef05qAAwCK4s33Wcr17ppqpoWRmTosOI/+4zIJZTz9nCR0aTvP/ZQlmWIAAe3hwecue/Huv4r/fc44/eEk1yr1ASuUwCvsstyIjTrr3kkktibWI7Qi1VX9zvPAnDZmrTLx14QY/uJlGMSRtBQNRS1SBLhxpCDxAFYXwYaBEaQsXS6jfXCf6zbF0XP7vBRhLLoOCiT/ite+1J0y957qVXOjwNMCtjxWWch11tE2xbaYS9NY0COOvsGW9/9d2bh+WgFIBbyS77eWN6em8j5bpPVXM0ZFQ0jYpEkUhy+Y3f6vZGuOOaUF/hgYII2fRJ6p6X246RoY3Vy7ZVe4+UhAR+SYjfrPvjt+fNntnJOPv27pmq1cgoADhk5CczE2IAwB82DjZ3d0X8xyFzrx5y6e2jb1BLe72IjiujUodeOfDiT+a8maXL6ProbiMKwl7h9Xr37NnT1NQUi5fZY93Pb2yt38lvDEvuLJgYQK6GMJI0vismj9ub/P6jXz/8+JOdd1eIWVxlxHXoqBDKFo2QWvJ724IJAIYOHfrpdyuCMAEgBER9XIOSDjcJglAhTU3poAeNyKlGslpYgNa6rdXuuuN56Uo3lRPBmRfrHdEb1FIViAoAQ4I7G48oWNEUaPaHAwDCUEegbDfsLoZUKguFIrGmRcui7RyOqR6TWqrO0WcNtnTYE6qvUElVClYRi2KNF6Ig7CEOh+OGP15++tABT1536TnjR3743AJ+/7b6nSEuTEEP2IWWoSNThnU+FEPQXy9pDI8CwAU5yQGP9t1frz7zjPXr1nV+ooKVM4wCAEGo2NG21EtttCBThFha99noDRYFQtH2vIePYzMmjnINHv4xRwqNx08NFfmd0z9qH3MH7eXxTrLunHI3FNGyMqlduT+6iUwijLPDeoQDrGVRy1k0UiQrOitwOOOcc785aOUDRAFUKYWHvJvpd0w+hQnpY3XyeKyg40onVSd7gygIewKldO65s8Y3lSyeM+jF07I+m9XvyrCLr75p9dqWH15TbDvgDfkAaKTqvGg1hE4YbCS1b+/O+c0x10ofCsveH5u+4LS0u6+/av/+/Z2fqIr2CCxxtm2NFMumT1YnSeL3PUtYYfG733782vPWeRoiNAwgSA1DTKI+KCIwMU2wxEQizrLmygg9ftbRSg+VEiF/N6vj4vjHhFkpmPsOOKpa729VZTQpX9tFbMn9Dz3yRW24+qCQUhWJHu2j+YWdqpIAvvt28dzZMyYMG3zlRedv29R+wn7fopIm5O8vCsKesGHDhuRw8zkFLfaQyUl6uTsMIEK5PbbiT/Z9xe8fYC4i3UgyyGMcxvKNd8jUMxu4/m7KgKRqFPcNS/7Pi893fmKSUphDeVN96/0cpc5oNlK+vrcZTq1RyYTw5cNNx08jPKKmjOggFIlyenpKGPyTMWDzOmM5NseBCjeVEsEhka2Nj78qVkuz1nWEIGyIduv1U0u+tovfv06n+3rZitIjYypdXFGydoS00+f9rddf++ETD96Z7PtkSsbVqsa/3XTVP5/+xzHeQcJRSkRB+Lth7549w3VtvXeZzULihN3nKHMKVprhKYO7M6DeeWiwRZMWgCbcYvQYlqwr3t1FLFyGTrClxLIGeRx+J69ChaDpp5e3c2ZPMSoEsVrjOX7NmCqjkTJeMWRUpBVaKeGIEIxW0eyIey3mjvCF0eT3MAgCkDASSzyCZQAMtgiC0BU4UhC2+PvrcFczAAAgAElEQVTNBV31PWxoaJg95fS5Qe/cqnCOlyuqD7jW1+/cPm2wsbPnwC+//FK9dcM/T8/NN6qlLDM4SfvW9MIv3n2rurq65/eTAGSMlGXiH50gCsKeoNZo3EfZYHRWIdFnv/1gnVcwG56eMb47Aw5L19mPSoBw+EM6XRf5Sf30gmnUE6jlWjkOYkvIAJdUpI+n5EhRC4LQ5j1+GmFlTCOkYhKhyBFoFEIVlVqPvaK5+vjEjpa7qYIRfpP6+DnSxqYIgpCLVIZaxY3Wx/7OsHQeKQPgmSceu7Wffk5B0gV13Dg7NzIk+bNOZ/j8ns7/OD98+835WUcEiLKEzMzUrl69ugc3kjgIIYlQCkVB2BOmTZv2Q1VzuJXkoUDxJitf/qfR5wiEgwDUMnWeMac7A8q8tkPV1bM+3njOJxtv/n57qcMD4JMDtgsuu6LzE7N0gmlUirpqb8t8Wi8h+8VXEEYraFirtm87XiXWShzCQ0clSz+6eaTIqUy2ToiXcQTswUiw2n08rKPlbqiIoCoZFHHIpucpMqRTsADkxFrS1NJOtXWwTJem0Z/Xrjk71wyAoRjt5GbXRyYF2VQ5l03bhhG0xut2qaVtrVxqFt6OK1v1FQo2niYuHlEQ9oTU1NTr77jnmuUHNtc4XIHwbmvzbasPDR0x7cJ+R1SejVWc6Zzvv/v2oflXLbp45A9XTFxy+cQ/jcm7ZcmO21YfsJnz/3j1NZ2fG4vbVhBraau40ZYlJE0q6sqW0n3Wr1u38K6r+G2N3PPAVXMfvv+euI3eMbEkwlx9PJOHRE4CxqQJ6UmBkIOCVjQfD1NeuZsqo9n0FmV87KIApKyUMEkACLjN9S1evtpoZoifJndpGuU4jmWEx06WDzIOABQsM8HSma48YtzE32xtexz+5ggNP76F67pDIuJlREHYQ/50592vfLpoqbzgT1sdn4XT733pzRde/s9F/c7N0LZ0pBzdVeIEz+N/fuC1M/OzdMK3OzxF/+z0QR615fPF33XUdyJGLG5bRuyHWuXUV7oEBx4lpoxeZ9PzhEKh22+45p2xWfyc/Ar2v2f327X8uzUJNp6EuHCzn5frZLA5PnHqIicNE1JTOSoHQGnQE/TWHJdswnI3lRPB/dH7ctut0SoE6+gem+DvDHMRZ6AJAAXDMcacrlKhho8ctbH6iOLDnlAkGAn1y+qwWS6AS+bOXWePrK2IPjeAL/bXu7XJ48d3y7lzPFGw8c+gEAVhzxk+fPibH3y8+tct73y2kG+3a1IaT88Yp5TIARCQKdldl+L0er2ScMCkPMLeNyrV0NjYLQ+cUWEgkACQEM9BZ8uC7nCToBGaVJZ4GUY3b9480qJOVcp0IQqAI3BKcW0/0xcfvhenK7RPrbuOIgLATy3DTPG3ioic0AwyMH4qeMrrvXZP0OMKuhN90TIXFCTWkre3DZhak6IWBGFZtA211WvjKAUQpMYslaTzyE8ADz35j2e21++sFwxENm/wrrWlDzzyWOclPeVy+bcrVn9H0y78vvi29eVzvt17OHvM54u/7+XtJIJEaIQnQz/C3w9qqcqsMk3LOWNnw97+5qLsbhQBIoRwR2XHUvCFzLqGIUQlN3gCNgAHmqyAurKy8q9331E61i3NUgHQOuLWrdThcFjkDABTEE4pANhlJEktt9XXd3Fm76hsVVxtiEmMlBE5AjkLhs0CrQRQ7bIXGLLqPQ1aWWIbdZW7qSyaRJgZpyRCngJj5kErANi8gkZ4wCGU5vDRtC7togAKCgo+X7L8/tturv6tWMIQRqF+5NmXzj1vTpcnpqamfrJocSAQqK6uzs7O7rysVR8i+ghPAEYkD01SWabnTj6noFtNvJRKJatS17n9rXdurHKMGDWqm1e0RFMJq5qtpaWl50094wKmNilVKEFkX/DXH5Yu6fb0O6OgoKC4KQDA1KoHxb5Gd/8hQ+MyfkeURb0+fi59oEEUhCJtMauFwFGr3wGg1p3wUg/lbkgZIWM9T99FMeFjYmQ0gyIQFFKwttRu5zdcXL8uI2V4+vXrt3j56s37S9fv2r9xx+7uSMEYcrk8Pz//dysFkZhUQlEQxpl+5gKL0gQgVZ3czVOe+fert6wp3WMVknPXVdr/vr3+8Wdf6Obp2dFUQpuv4amH//LwiJRxmaYmCQAwwFtjsx574N5juoWOGDBgQFBrWVneaAoKkd2VCL++13bDrbfFZfyO2G0TBKFKnq7+/f49RfqMIpNQcdQTdAKo8yTWRBHiUO8NSuABQEAydPHUCIfohEYWknDF8AGF/3ru2W0NQjJxU2RAl7kTrWEYppPmvScuiaiyJgrCOENAJmSMYQiT3O06vFOmTn336yULbMo5S0rmLD2wSlnww7oNHTUbO5oCveAD5yK127ZsnZhpdMhACQDogzRVIZNFQs3NbSuR9oyPv/pmccC0dI/gUf+w3vvSf9/LyelWikjPoJSW1AsNPTI1YsioSDuMS8unkALgOL8v5HMHPfFtX96GKg+VEcFcr5Nr+KSpuBAOh6+YNVPhjQAgErJgTmHljx+XO3lnIWnmBsSlev6JjkIUhCcESSrLhPTRx9TNcsiQIYuWrdhWUrpt/6H/fvjJMbU3StMIGqGaVIY4CqAx2sfCHOqep7HbWCyWL5csG3GRkDKRMv3cKVOnxvcSrVm/bt24IQNtjhL+peeb/wWDwc5PETkFGW3RBKmQzGf380phAq2jZW6oSTm/bVKa4jjy14sWDZMHC8PCY3mLhZ05KZPPwPLRtDDUhXHNCT5BYY6lkW93x4z7iCIA+puLjtu1UqOqp5KpTR8w4tdqR1k0XyLJTxt9wQAj7aS7bw8YlidkbnlD9jgO24Z9+/bdc/1V/z4jDWoWgITDuAPr773tlsRdUeQEpZ+eDUHwRNR5nQDq3Am0jpa7aCybPiWuuRO/rl9zerIq3S+4Hop1zCaj8IhuigwEkNc9H6HIsSIKwhOelKgzUoLmwTc/8tS2+p+lQuaszh66dfWhJ194Mb5XHGgU/vzhSAIF4SvPP3v/8GQa7TWREqB3jMjYuHplvMy8IicNDIFCJpjN6zwOJFgjrPBAGa2vlh7X3AkJK41QmhHtoGSVYmdUBXREhiUroZN2fLJILxAF4QlPksrC16+REO/mUGjxyrUHtIKH/NMt/pc+/GL2OefG94pDzWaAAGA4e5jjujy+Z+zbs3t4sr42GiCW5geAwUm6gwcPJuiKIicuqRrBUd3kdwBwBd2ehLkJy1xUEc2mz4pryOiUmbOW13iK3IIgrFMSm0wQhM10kBgynThEQXjCI2FYXbRVfU3z3jKphWgYAJQy/16wbNy4cXG/okEui1AdAEIi+51xy1Nsg06rbQqEauXCn5+3Fzn8ofiaeUVODgab8vnFWTDiDnIhADZfoswV5W4ay6bP03fdbbT7zJw5065LW/Rz1YhGwajDR70FqSlATZfmiY/rRCF+sicDqdEuMHJiv2f9SgIKIMJkTstIVA9bwgoxAru7VwGnB8z5w2WfHWisVQqCMNWPGpe/NkALCwsTdEWRE5ehSaYQ5VdI1OFvAmD3J2qJVu6OSNAMgBDkx1UQEkIWLf0x96Lrl39UxQVbbC1NXH+lBFcUio/rRCF+sicDqWrBUcESbziwid/O1hclzpKikApuwkPORHUlvG7+/FJV6pZwiH958FDjjWsO/+ftdxN0OZETmrHJugCEDDy71wHA7nMm4kIchdVTRwgHQCVV6+Rxtk9IJJLb777n++XbbOzk2E5HZPjcXEbsu5I4REF4MpChEwShlLjlUaPNeXlDEndFvVzQQStdidIIS0pKBg0eEoj++w8YTlv565YJEycm6HIiJzTZGjUhgiDk42UaE6MR1nqpBELfCZNC33kBzx6Tp2NStaO9XBYASlkHN/KG/uKzOoGIH+7JQIEhl9/QMKVyIqhoZ2QOSNwVU6LG2AZvQjTCfz//7C2XnJddvYHICADOH5k0eLjFEs9QdZGTCYYQvVyoLNHodwLwBD3+cKDTk3pCuRtqIhQ/i2MDpqO5csAoa2R8Y2T8gfCNGZqkyWlipEwCiZsg3Lt3708//eT3+zs6IBQKHT582Odr2/JKpPdMyTqNrzLMws+SAAAZo8yKa+WnNmRqBZnk8MdfEBYXF3/5vzc+mFFU2E940BSGyVvPPma1dtZZVOQUJ12bzcfLeIJOvl1DItyE5W6qZIS+EKmaBDYFu7LIGGEGubkcZ2T4/AGMKAYTShwEIcdx8+bNO++88x555JGioqKSkpKjj5k0aZJWqy0oKPj55597f0WRNkhZ6ZnZp7XeU2jK7U5P4B5TaBBElD8Uf9Po4kVfXpqnYwmpVQq/z3Q/zsnSLV++PO7XEjlp6Ge0hKkSAAXHN/Cz+xIhCKEkQsvD+PadaINSgisGDA1DpZKZru8nmu4SSxw+32XLlm3atGnHjh1r1qy57LLLHnvssaOPeeqpp2pqajIyxFqRieL0zAl6eUshwmFJCbSLAhhkEsrZcJHGOJdxA5xWq1khBVAbbbeSFuDMUtgbExWYI3ISMNCkDkXjZRw+BxKmEcaSCAv0CayyC+CZsfL/Tpu66xJFSqKiv0UE4iAIv/jii0suuUSr1QK45pprFi1aFA6H2xwzbdo0kymeRflE2qCVaYYnD469HJU6LKGXK9ALGqEU9lpvnEVh0ZBh+5wBAHWKltyJvW6u/4DESneRE5ohJl2QGvhtG59BkYDA0UPN4WgnQtLPnNhMHoZgVrYxXSWaRRNOHLraVFZWjhw5kt/Oy8sLBoMNDQ3HVDY6RiAQaGhoaG0BGz16tMFg6Oh4juO4hFU2+f3T+vZVEmWuLmeXotjhd0oZ6SBz/4R+MjJGBqIBdTMktMNqT80yxnHwiy+55IxnnpqW6a4cJgzrrGje6cZrU6a0vqlT+dvnovT1RPqGdm/fLFcqJHpwAFDvcVBKnf6mYDgoOZby911S3mxLBQdAIVEaZLo++Qr4i4rffjePZ5iu9b1u/UR279796KOPHr3/lVdeycrK8vv9sa5X/IbX6+3mFNtgtVr379//zDPP8C8ZhnnkkUfGjBnT0fE+n49l49YD5YSj9e0zYSYcDk3OmHDIWT4mZQQJwRvq4bfQJX6//1/PPh1MtcrSlAD++cRtQ554wWiMmyxkWfadTxfecdsN0pkmAIjQTw7Q9z7/MhgMtu4+cSp/+xzH+f3+BMXu//4Jh8OhUIjSI0wRbIQxyg1eHwA0BZyhUAhAraPOHL8GEWEODl91qhQATApDX4X+8f+Cow1vpwg+n08qlXa/dbBKpepSFnZrrLS0tOuvv/7o/fyzLzU11W4Xqhk1Njbye7o5xTZkZmaeccYZCxcu7ObxlFKNRtOza50EtL59iUIqq5XJZLIxKsP49FEJ/ViuvewPQ33VYy/O3gEAGC+tvHTOOes2b5VK41YSePTo0Xe8+8GCLQ8DCCFz7dpvjz7mVP72OY5jWVatVvf1RPoGXhAqlW1dZ1k60z6flEEowgXCJKySqvxsMI4/koPNVEaFctsZ2uS++vnxgvCkbLrbHViWPSZB2B26NZbZbJ4zZ05H744bN27VqlV//etfAaxbt27IkCGn7OOpD1FI5AqJnE+cMinjaahsw/bt2wNVh244M/9/QskXFOQaPHubFn311aXz5sXxQr81CNlaeu3x62klckKTr9PurDPIiRVAo9+pkqriGzha6oKKEX6WmVox9O/kIQ7BMtddd93mzZufeOKJL7744oEHHrj33nv5/TNmzHj11Vf57ffee++5555zuVyfffbZc889JyaEJYIp2ZMYwhAQsyKBgnDrli0TzHIA5oBgmLJLMTFZufnndfG90CFnGb+Ro8+N78giJysDjdpANF6GF4HxFYSHmqkqmkSYZ0hsyKjI8SQOgtBisfz8888NDQ1ff/31888/f9111/H7L7nkklgQTXNzs8PhuOWWW8xms8PhiEQivb+uSBvSNakTM8Zq5Ropm8CuZTK5nJeAxqhG2Cgn/jAnlyvieyG79zC/MSopL74ji5ysZGg0DCMIwnqPE4AzEM/ulYeaqYoI9dUGHcfm2yKJJj5m1v79+7/22mttdt58882x7TvuuCMuFxLpnH6mAo0ssX6jyZMnv/bUo/OH0phG2Cgl31d5brunQ+N5DwhzlEYq+FCQqVmiIBTpFlqZxiDX84XV7H4ngGAk6A35VNL4JOIdbPJKSTMAhjD5xty4jCnye0AsWHCyEd+W2UeTnZ0985J5t68pDdiFkLlDXNifPuDMM8+M41X+t3wJAz+AMKcp6jh/RkSkNVq5JlVt4AutBcKuMBcG0BQ/pbC8uQqgAHRyI0tO0YjlkxJREIocM48//eztz//njf1hUAAI62TXvNzdQN8u8fl8c8+b/dXbf+NfsmX1110+75SNFBc5JjRSdaqKDVEtAAo4/Lx1tCle4zu8QqRMhiYtXmOK/B4QBaFIT5gxc+b3K9azEi0AQug+Z9yeNQ/df8/YUO2ssUI54zkqZVLNnn8+83S8xhc5idHINGkqEqTRxoS8IPTH58dZ5wNLhUiZfKMYMnpSIQpCkZ6jkQkVRw864xYGvHzpkisGplZGy0pl+bibh6Uv/PjDeI0vchLDEJKt0YSh5182eOMpCA8101gDpkJDflzGFPmdIApCkZ6TpBSaMVXFqT1vOBxWsAwBKpRRQeilcpaJhIOdnygiwqOXazQyQSNs8DoQv8DR1rkTgyxiyOhJhSgIRXqOwiekUNT+8rHNFgdZKJFI/BHqYahVRgBIKDJ81BeOsDJ5l+eKiADQyjVJKiMfL+MOOjlK/WF/XDr0ljj9cmIDQMDkJbjvhMhxRhSEIj3k/jtu27/sR357Itl71vjRq1et6v2wcy686LVaByUAkO6jEopXttdceU07Ff5ERI5GI1Wnq+VCY0Ia4e2icYmXOeCs4UNGlTJDQlN1RY4/oiAU6QkrV6w49NPy+dGmE5Y0zbvTC+6+6Qa+0nFveOLZ57ekCa1LwjWeq5YfwKAJd93/QC+HFTlF0Mq0acpW8TI+O+KUQVHtEhyEyWoxZPRkQxSEIj3hq08+vKrQ2FJlTQaLSjYqWb1p06ZejiyTyZSnjeC3I4ZJbyxa+uqbb3enkYqICAC9XJuiQqxDb73PgTjFyzh8goOwn1Gs8HCyEc8C3iKnDvZGm0kv00XVP7uMADDLmFgfkh4ToZFgYB8fKnPb3Nv7ZaT0ckCRUwqdXCtliEpqQgQA6j0OxEMjdIeASCVYABiVUtDbWYr8zhAX2iI9od+gIftsLlOAEgoADinhCPY6A/379+/lyL/UHCLwAQjCMilNlIIix4aEkagkyhS10IPQHXRQcL3XCEuaqDrad6LAkNvL0UR+b4iCUKQnzL/1tjeK7TaXXxsGgAjBohoHTKn9+vXr5cjLKnbyG1LpUIn48xQ5dnRybbpaHqYqAByNNPld3rAvGOlVBs4uR0iBBgAEJFsnZtOfbIhPGpGekJWV9X/vf3LLxlqv3c/vecmj/+jLr3s5rN1uX7vxE367+etFq1au7OWAIqcgOrkuXUVC0X5MjT47pbSX1tHtDdWERABIJUaFJM6NVkT6HFEQivSQ7JwchUJJmgU/oYKre/vt//VmQIfDcdbp46VmYeX+vJF95s4b3/nvm72dqMgphl6mTVG2jZfppSA84CjnN5LVYgbhSYgoCEV6yNV/uPj+IuVUvYp/edcZmd//77X163renvelF56bM9oUkhAASQE6SCp/fWrBi0//Xay4LXJM6ORalkAlFdyEde7eBo7u3LmzYplQ7bZm3aZ9+/b1fpIivytEQSjSE8rLy6Ve55hUfVR/g1NO7huW8u6C//RgNLfbvXTp0oWff96cJhidBrooAIWEHWzR7t+/P06zFjkl0Mt1AI6Il6E91wg3bthww8VzJuQLj8rzOP+V583ctm1bXKYq8jtBFIQiPaG6ujpLIwdg9nP8nhVO1/pK2+HDZcc61A9Ll5w+ctgPzzx4eZZir074QQ5q5mIHcBzXwakiIu2gkWkYwmRqFBEoAHA03Bxo6rEgfOieO1+ZnGvXC6VkJkkVL07KfuTeu+I2XZHfAaIgFOkJ6enpVe6AOxh+edlefo8lQ6uSSA4eKDl8+HD3x6msrPzr7bc8ONT02+Gab8qtviwNv79/MwcgEOH22FwDBgyI+/xFTmIYQrQyTbqKBDhBKbT67K6gJ8JFejCaw9ZgMSht0eK3aX4UmTTVlRXxnLFIXyMKQpGekJubG1Dqn1i3/xyzEJvnVLI3jMx57ayBd910DHVBP37/vclJ8pc3lf7z7CF3Xz+OSAiASKVr0a+HGzyB21YfuvvBv0qlYl1HkWNDJ9cmKRAiQneUGreNo1xTwNWz0apUhC9+m+qjLKUAKKVxmqnI7wJREIr0kPe/+Gp9tfPy3BRFhALwsfBIMDJVX3W4tPsVRw8V791QYfv32UOzdMoNZqH10sUB6VvbK+/e5rjnn6/Nv+XWRN2AyMmLTqZlCLSxeBmvDUBTsCfWUUtK2nYI4Vo5Pgqg2ObKzhWrrJ1UiIJQpIdkZWUlJSVJWcYSjZdpkBEAWrnU6/V2Z4T9+/cv//HHpkA4W69slmKvlgHAAGd5mCSDYe3mbTNnzUrY9EVOZvRyLYB0rYXvx+QNNgciwZ65CZ956dUPnW5+O8tHt9Y5799Y+fRLr8ZxtiJ9jigIRXqOJTm5yuVLjpbetspJKMLZ/WG9Xt+d0/903VV/G5/tDYUBbDIyHAGAIhdnCoJIWEJIwiYucpKjk+sAZGvYaBsKavM19iyDYvTYcXTkOH777XWVH3pMC5etGjp0aNzmKvI7QBSEIj3n/kcef2Rjpd4nRHXWy/DIxorrbrqlO+c2NTX57baz8pMzdcottc6fzCy/f5KdFttcqRmZiZq0yCmATq4FkK0hfmrm9zR4GnumEf56oEYhreW3l3yx6vNvlxQWFsZrniK/E0RBKNJzZs6adeOjf1+y08q/XFDpLBlx5T0P/rk757pcLr1CCuDFs4c8vPXQQQ0AMBSBHbY/Ld3Z7O6WcVVEpF1UEqWUkWgkkLKCIKxy25r8zRw9hlQcm80274Lz7rtympT1A+D8XE1JZUKmK9LXiIJQpFfMu/yK55/8N7/NjT/LP/uv3TRppqSkVDb7IpTm6FXXXD2KggAIHHC8tGrfJxePSYK/uLg4gfMWOakhhOgVOgApKiFw1OlrDNOwJ9TdBRal9Lyzps5C7SPnCHExeX7u6ksutNlsiZiwSN8iCkKR3pKpFxrKK1hbSRMNd2/NLZVKL/zDvH9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marginals(posterior(fx, y_train)(x_test))\n", + " plot!(vcat(x_test...), mean.(pred); ribbon=std.(pred), label=\"Prediction\")\n", + " frame(anim)\n", + " display(p)\n", + " end\n", + "end\n", + "gif(anim, \"train-dkl.gif\"; fps=3)\n", + "nothing #hide" + ], + "metadata": {}, + "execution_count": 10 + }, + { + "cell_type": "markdown", + "source": [ + "![](train-dkl.gif)\n", + "
\n", + "
Package and system information
\n", + "
\n", + "Package information (click to expand)\n", + "
\n",
+    "Status `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/2-deep-kernel-learning/Project.toml`\n",
+    "  [99985d1d] AbstractGPs v0.5.20 `/home/runner/work/AbstractGPs.jl/AbstractGPs.jl#07d5f00`\n",
+    "  [31c24e10] Distributions v0.25.107\n",
+    "  [587475ba] Flux v0.14.13\n",
+    "  [ec8451be] KernelFunctions v0.10.63\n",
+    "  [98b081ad] Literate v2.16.1\n",
+    "  [cc2ba9b6] MLDataUtils v0.5.4\n",
+    "  [91a5bcdd] Plots v1.40.2\n",
+    "  [e88e6eb3] Zygote v0.6.69\n",
+    "  [37e2e46d] LinearAlgebra\n",
+    "
\n", + "To reproduce this notebook's package environment, you can\n", + "\n", + "download the full Manifest.toml.\n", + "
\n", + "
\n", + "System information (click to expand)\n", + "
\n",
+    "Julia Version 1.10.2\n",
+    "Commit bd47eca2c8a (2024-03-01 10:14 UTC)\n",
+    "Build Info:\n",
+    "  Official https://julialang.org/ release\n",
+    "Platform Info:\n",
+    "  OS: Linux (x86_64-linux-gnu)\n",
+    "  CPU: 4 × AMD EPYC 7763 64-Core Processor\n",
+    "  WORD_SIZE: 64\n",
+    "  LIBM: libopenlibm\n",
+    "  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\n",
+    "Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)\n",
+    "Environment:\n",
+    "  JULIA_DEBUG = Documenter\n",
+    "  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n",
+    "
\n", + "
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docs](https://juliagaussianprocesses.github.io/AbstractGPs.jl/dev/examples/3-parametric-heteroscedastic/).*" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "This example is a small extension of the standard GP regression problem, in which the\n", + "observation noise variance is a function of the input.\n", + "It is assumed to be a simple quadratic form, with a single unknown\n", + "scaling parameter, in addition to the usual lengthscale and variance parameters\n", + "of the GP.\n", + "A point estimate of all parameters is obtained using type-II maximum likelihood,\n", + "as per usual." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Random.TaskLocalRNG()" + }, + "metadata": {}, + "execution_count": 1 + } + ], + "cell_type": "code", + "source": [ + "using AbstractGPs\n", + "using AbstractGPsMakie\n", + "using CairoMakie\n", + "using KernelFunctions\n", + "using Optim\n", + "using ParameterHandling\n", + "using Zygote\n", + "\n", + "using LinearAlgebra\n", + "using Random\n", + "Random.seed!(42) # setting the seed for reproducibility of this notebook" + ], + "metadata": {}, + "execution_count": 1 + }, + { + "cell_type": "markdown", + "source": [ + "In this example we work with a simple GP with a Gaussian kernel and heteroscedastic observation variance." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "observation_variance(θ, x::AbstractVector{<:Real}) = Diagonal(0.01 .+ θ.σ² .* x .^ 2)\n", + "function build_gpx(θ, x::AbstractVector{<:Real})\n", + " Σ = observation_variance(θ, x)\n", + " return GP(0, θ.s * with_lengthscale(SEKernel(), θ.l))(x, Σ)\n", + "end;" + ], + "metadata": {}, + "execution_count": 2 + }, + { + "cell_type": "markdown", + "source": [ + "We specify the following hyperparameters:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "const flat_θ, unflatten = ParameterHandling.value_flatten((\n", + " s=positive(1.0), l=positive(3.0), σ²=positive(0.1)\n", + "));\n", + "θ = unflatten(flat_θ);" + ], + "metadata": {}, + "execution_count": 3 + }, + { + "cell_type": "markdown", + "source": [ + "We generate some observations:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "const x = 0.0:0.1:10.0\n", + "const y = rand(build_gpx(θ, x));" + ], + "metadata": {}, + "execution_count": 4 + }, + { + "cell_type": "markdown", + "source": [ + "We specify the objective function:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "function objective(flat_θ)\n", + " θ = unflatten(flat_θ)\n", + " fx = build_gpx(θ, x)\n", + " return -logpdf(fx, y)\n", + "end;" + ], + "metadata": {}, + "execution_count": 5 + }, + { + "cell_type": "markdown", + "source": [ + "We use L-BFGS for optimising the objective function.\n", + "It is a first-order method and hence requires computing the gradient of the objective function.\n", + "We do not derive and implement the gradient function manually here but instead use reverse-mode automatic differentiation with Zygote.\n", + "When computing gradients with Zygote, the objective function is evaluated as well.\n", + "We can exploit this and [avoid re-evaluating the objective function](https://julianlsolvers.github.io/Optim.jl/stable/#user/tipsandtricks/#avoid-repeating-computations) in such cases." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "function objective_and_gradient(F, G, flat_θ)\n", + " if G !== nothing\n", + " val_grad = Zygote.withgradient(objective, flat_θ)\n", + " copyto!(G, only(val_grad.grad))\n", + " if F !== nothing\n", + " return val_grad.val\n", + " end\n", + " end\n", + " if F !== nothing\n", + " return objective(flat_θ)\n", + " end\n", + " return nothing\n", + "end;" + ], + "metadata": {}, + "execution_count": 6 + }, + { + "cell_type": "markdown", + "source": [ + "We optimise the hyperparameters using initializations close to the values that the observations were generated with." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": " * Status: success\n\n * Candidate solution\n Final objective value: 1.576205e+02\n\n * Found with\n Algorithm: L-BFGS\n\n * Convergence measures\n |x - x'| = 3.97e-08 ≰ 0.0e+00\n |x - x'|/|x'| = 1.58e-08 ≰ 0.0e+00\n |f(x) - f(x')| = 0.00e+00 ≤ 0.0e+00\n |f(x) - f(x')|/|f(x')| = 0.00e+00 ≤ 0.0e+00\n |g(x)| = 6.34e-09 ≤ 1.0e-08\n\n * Work counters\n Seconds run: 0 (vs limit Inf)\n Iterations: 10\n f(x) calls: 13\n ∇f(x) calls: 11\n" + }, + "metadata": {}, + "execution_count": 7 + } + ], + "cell_type": "code", + "source": [ + "flat_θ_init = flat_θ + 0.01 * randn(length(flat_θ))\n", + "result = optimize(\n", + " Optim.only_fg!(objective_and_gradient),\n", + " flat_θ_init,\n", + " LBFGS(;\n", + " alphaguess=Optim.LineSearches.InitialStatic(; scaled=true),\n", + " linesearch=Optim.LineSearches.BackTracking(),\n", + " ),\n", + " Optim.Options(; show_every=100),\n", + ")" + ], + "metadata": {}, + "execution_count": 7 + }, + { + "cell_type": "markdown", + "source": [ + "The optimal model parameters are:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "(s = 0.385143986830958, l = 2.3342748073768, σ² = 0.08082582089918736)" + }, + "metadata": {}, + "execution_count": 8 + } + ], + "cell_type": "code", + "source": [ + "θ_final = unflatten(result.minimizer)" + ], + "metadata": {}, + "execution_count": 8 + }, + { + "cell_type": "markdown", + "source": [ + "We compute the posterior GP with these optimal model parameters:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "fx_final = build_gpx(θ_final, x)\n", + "f_post = posterior(fx_final, y);" + ], + "metadata": {}, + "execution_count": 9 + }, + { + "cell_type": "markdown", + "source": [ + "We visualize the results with [AbstractGPsMakie](https://github.com/JuliaGaussianProcesses/AbstractGPsMakie.jl):" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Figure()", + "image/png": 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" + }, + "metadata": {}, + "execution_count": 10 + } + ], + "cell_type": "code", + "source": [ + "using CairoMakie.Makie.ColorSchemes: Set1_4\n", + "\n", + "with_theme(\n", + " Theme(;\n", + " palette=(color=Set1_4,),\n", + " patchcolor=(Set1_4[2], 0.2),\n", + " Axis=(limits=((0, 10), nothing),),\n", + " ),\n", + ") do\n", + " plot(\n", + " x,\n", + " f_post(x, observation_variance(θ_final, x));\n", + " bandscale=3,\n", + " label=\"posterior + noise\",\n", + " color=(:orange, 0.3),\n", + " )\n", + " plot!(x, f_post; bandscale=3, label=\"posterior\")\n", + " gpsample!(x, f_post; samples=10, color=Set1_4[3])\n", + " scatter!(x, y; label=\"y\")\n", + " axislegend()\n", + " current_figure()\n", + "end" + ], + "metadata": {}, + "execution_count": 10 + }, + { + "cell_type": "markdown", + "source": [ + "
    \n", + "
    Package and system information
    \n", + "
    \n", + "Package information (click to expand)\n", + "
    \n",
+    "Status `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/3-parametric-heteroscedastic/Project.toml`\n",
+    "  [99985d1d] AbstractGPs v0.5.20 `/home/runner/work/AbstractGPs.jl/AbstractGPs.jl#07d5f00`\n",
+    "  [7834405d] AbstractGPsMakie v0.2.6\n",
+    "⌃ [13f3f980] CairoMakie v0.10.12\n",
+    "  [ec8451be] KernelFunctions v0.10.63\n",
+    "  [98b081ad] Literate v2.16.1\n",
+    "  [429524aa] Optim v1.9.2\n",
+    "⌅ [2412ca09] ParameterHandling v0.4.10\n",
+    "  [e88e6eb3] Zygote v0.6.69\n",
+    "  [37e2e46d] LinearAlgebra\n",
+    "  [9a3f8284] Random\n",
+    "Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated`\n",
+    "
    \n", + "To reproduce this notebook's package environment, you can\n", + "\n", + "download the full Manifest.toml.\n", + "
    \n", + "
    \n", + "System information (click to expand)\n", + "
    \n",
+    "Julia Version 1.10.2\n",
+    "Commit bd47eca2c8a (2024-03-01 10:14 UTC)\n",
+    "Build Info:\n",
+    "  Official https://julialang.org/ release\n",
+    "Platform Info:\n",
+    "  OS: Linux (x86_64-linux-gnu)\n",
+    "  CPU: 4 × AMD EPYC 7763 64-Core Processor\n",
+    "  WORD_SIZE: 64\n",
+    "  LIBM: libopenlibm\n",
+    "  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\n",
+    "Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)\n",
+    "Environment:\n",
+    "  JULIA_DEBUG = Documenter\n",
+    "  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n",
+    "
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    " + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.2" + }, + "kernelspec": { + "name": "julia-1.10", + "display_name": "Julia 1.10.2", + "language": "julia" + } + }, + "nbformat": 4 +} diff --git a/previews/PR387/index.html b/previews/PR387/index.html new file mode 100644 index 00000000..696405bd --- /dev/null +++ b/previews/PR387/index.html @@ -0,0 +1,2 @@ + +Home · AbstractGPs.jl
    GitHub

    AbstractGPs.jl

    Abstract types and methods for Gaussian Processes.

    AbstractGPs.jl is a package that defines a low-level API for working with Gaussian processes (GPs), and basic functionality for working with them in the simplest cases. While it is aimed more at developers and researchers who are interested in using it as a building block than end-users of GPs, much can be achieved with it as an end-user.

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It's not usually possible to implement this though, so we don't.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"A FiniteGP fx = f(x) represents the distribution over functions at the finite collection of points specified in x. fx is a multivariate Normal distribution, so rand(fx) produces a Vector of Reals.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"A FiniteGP is the interesting object computationally, so if you create a new subtype MyNewGP of AbstractGP, and wish to make it interact well with the rest of the GP ecosystem, the methods that you must implement are not those directly involving MyNewGP, but rather those involving","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"FiniteGP{<:MyNewGP}","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"We provide two ways in which to do this. 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Multi-sample methods produce a length(f) × N Matrix.\n\njulia> f = GP(Matern32Kernel());\n\njulia> x = randn(11);\n\njulia> rand(f(x)) isa Vector{Float64}\ntrue\n\njulia> rand(MersenneTwister(123456), f(x)) isa Vector{Float64}\ntrue\n\njulia> rand(f(x), 3) isa Matrix{Float64}\ntrue\n\njulia> rand(MersenneTwister(123456), f(x), 3) isa Matrix{Float64}\ntrue\n\n\n\n\n\n","category":"function"},{"location":"api/#Random.rand!","page":"The Main APIs","title":"Random.rand!","text":"rand!(rng::AbstractRNG, f::FiniteGP, y::AbstractVecOrMat{<:Real})\n\nObtain sample(s) from the marginals f using rng and write them to y.\n\nIf y is a matrix, then each column corresponds to an independent sample.\n\njulia> f = GP(Matern32Kernel());\n\njulia> x = randn(11);\n\njulia> y = similar(x);\n\njulia> rand!(f(x), y);\n\njulia> rand!(MersenneTwister(123456), f(x), y);\n\njulia> ys = similar(x, length(x), 3);\n\njulia> rand!(f(x), ys);\n\njulia> rand!(MersenneTwister(123456), f(x), ys);\n\n\n\n\n\n","category":"function"},{"location":"api/#AbstractGPs.marginals","page":"The Main APIs","title":"AbstractGPs.marginals","text":"marginals(f::FiniteGP)\n\nCompute a vector of Normal distributions representing the marginals of f efficiently. 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We call this the Secondary Public API, because it's available a large proportion of the time, but should be avoided if at all possible.","category":"page"},{"location":"api/#Required-Methods-2","page":"The Main APIs","title":"Required Methods","text":"","category":"section"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"cov(::AbstractGPs.FiniteGP)","category":"page"},{"location":"api/#Statistics.cov-Tuple{AbstractGPs.FiniteGP}","page":"The Main APIs","title":"Statistics.cov","text":"cov(f::FiniteGP)\n\nCompute the covariance matrix of fx.\n\nNoise-free observations\n\njulia> f = GP(Matern52Kernel());\n\njulia> x = randn(11);\n\njulia> cov(f(x)) == kernelmatrix(Matern52Kernel(), x)\ntrue\n\nIsotropic observation noise\n\njulia> cov(f(x, 0.1)) == kernelmatrix(Matern52Kernel(), x) + 0.1 * I\ntrue\n\nIndependent anisotropic observation noise\n\njulia> s = rand(11);\n\njulia> cov(f(x, s)) == kernelmatrix(Matern52Kernel(), x) + Diagonal(s)\ntrue\n\nCorrelated observation noise\n\njulia> A = randn(11, 11); S = A'A;\n\njulia> cov(f(x, S)) == kernelmatrix(Matern52Kernel(), x) + S\ntrue\n\n\n\n\n\n","category":"method"},{"location":"api/#Optional-Methods","page":"The Main APIs","title":"Optional Methods","text":"","category":"section"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"Default implementations are provided for these, but you may wish to specialise for performance.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"mean_and_cov(::AbstractGPs.FiniteGP)","category":"page"},{"location":"api/#StatsBase.mean_and_cov-Tuple{AbstractGPs.FiniteGP}","page":"The Main APIs","title":"StatsBase.mean_and_cov","text":"mean_and_cov(f::FiniteGP)\n\nEquivalent to (mean(f), cov(f)), but sometimes more efficient to compute them jointly than separately.\n\njulia> fx = GP(SqExponentialKernel())(range(-3.0, 3.0; length=10), 0.1);\n\njulia> mean_and_cov(fx) == (mean(fx), cov(fx))\ntrue\n\n\n\n\n\n","category":"method"},{"location":"api/#Internal-AbstractGPs-API","page":"The Main APIs","title":"Internal AbstractGPs API","text":"","category":"section"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"This functionality is not intended to be used directly by the users, or those building functionality on top of this package – they should interact with Primary Public API. If you believe you really do need to interact with this level of the API, please open an issue to discuss as you may have a use-case that was missed during the design of this API.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"As discussed at the top of this page, instances of subtypes of AbstractGP represent Gaussian processes – collections of jointly-Gaussian random variables, which may be infinite-dimensional.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"Implementing the following API for your own AbstractGP subtype automatically implements both the Primary and Secondary public APIs above in terms of them.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"Existing implementations of this interface include","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"GP\nPosteriorGP\nApproxPosteriorGP\nWrappedGP\nCompositeGP\nGaussianProcessProbabilisticProgramme","category":"page"},{"location":"api/#Required-Methods-3","page":"The Main APIs","title":"Required Methods","text":"","category":"section"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"mean(::AbstractGPs.AbstractGP, ::AbstractVector)\ncov(::AbstractGPs.AbstractGP, ::AbstractVector, ::AbstractVector)\nvar(::AbstractGPs.AbstractGP, ::AbstractVector)","category":"page"},{"location":"api/#Statistics.mean-Tuple{AbstractGPs.AbstractGP, AbstractVector}","page":"The Main APIs","title":"Statistics.mean","text":"mean(f::AbstractGP, x::AbstractVector)\n\nCompute the mean vector of the multivariate Normal f(x).\n\n\n\n\n\n","category":"method"},{"location":"api/#Statistics.cov-Tuple{AbstractGPs.AbstractGP, AbstractVector, AbstractVector}","page":"The Main APIs","title":"Statistics.cov","text":"cov(f::AbstractGP, x::AbstractVector, y::AbstractVector)\n\nCompute the length(x) by length(y) cross-covariance matrix between f(x) and f(y).\n\n\n\n\n\n","category":"method"},{"location":"api/#Statistics.var-Tuple{AbstractGPs.AbstractGP, AbstractVector}","page":"The Main APIs","title":"Statistics.var","text":"var(f::AbstractGP, x::AbstractVector)\n\nCompute only the diagonal elements of cov(f(x)).\n\n\n\n\n\n","category":"method"},{"location":"api/#Optional-Methods-2","page":"The Main APIs","title":"Optional Methods","text":"","category":"section"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"Default implementations are provided for these, but you may wish to specialise for performance.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"cov(::AbstractGPs.AbstractGP, ::AbstractVector)\nmean_and_cov(::AbstractGPs.AbstractGP, ::AbstractVector)\nmean_and_var(::AbstractGPs.AbstractGP, ::AbstractVector)","category":"page"},{"location":"api/#Statistics.cov-Tuple{AbstractGPs.AbstractGP, AbstractVector}","page":"The Main APIs","title":"Statistics.cov","text":"cov(f::AbstractGP, x::AbstractVector)\n\nCompute the length(x) by length(x) covariance matrix of the multivariate Normal f(x).\n\n\n\n\n\n","category":"method"},{"location":"api/#StatsBase.mean_and_cov-Tuple{AbstractGPs.AbstractGP, AbstractVector}","page":"The Main APIs","title":"StatsBase.mean_and_cov","text":"mean_and_cov(f::AbstractGP, x::AbstractVector)\n\nCompute both mean(f(x)) and cov(f(x)). Sometimes more efficient than computing them separately, particularly for posteriors.\n\n\n\n\n\n","category":"method"},{"location":"api/#StatsBase.mean_and_var-Tuple{AbstractGPs.AbstractGP, AbstractVector}","page":"The Main APIs","title":"StatsBase.mean_and_var","text":"mean_and_var(f::AbstractGP, x::AbstractVector)\n\nCompute both mean(f(x)) and the diagonal elements of cov(f(x)). Sometimes more efficient than computing them separately, particularly for posteriors.\n\n\n\n\n\n","category":"method"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"Note that, while we could provide a default implementation for var(f, x) as diag(cov(f, x)), this is generally such an inefficient fallback, that we find it preferable to error if it's not implemented than to ever hit a fallback.","category":"page"},{"location":"api/#Which-API-should-I-implement?","page":"The Main APIs","title":"Which API should I implement?","text":"","category":"section"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"To answer this question, you need to need to know whether or not the default implementations of the FiniteGP APIs work for your use case. There are a couple of reasons of which we are aware for why this might not be the case (see below) – possibly there are others.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"If you are unsure, please open an issue to discuss.","category":"page"},{"location":"api/#You-want-to-avoid-computing-the-covariance-matrix","page":"The Main APIs","title":"You want to avoid computing the covariance matrix","text":"","category":"section"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"We've already discussed this a bit on this page. The default implementations of the FiniteGP APIs rely on computing the covariance matrix. If your AbstractGP subtype needs to avoid computing the covariance matrix for performance reasons, then do not implement the Internal AbstractGPs API. Do implement the Primary Public API. Do not implement the Secondary Public API.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"TemporalGPs.jl is an example of a package that does this – see the LTISDE implementation for an example. The same is true of the BayesianLinearRegressor type.","category":"page"},{"location":"api/#You-don't-want-to-use-the-default-implementations","page":"The Main APIs","title":"You don't want to use the default implementations","text":"","category":"section"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"Perhaps you just don't like the default implementations because you don't want to make use of Cholesky factorisations. We don't have an example of this yet in Julia, however GPyTorch avoids the Cholesky factorisation in favour of iterative solvers.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"In this situation, implement both the Internal AbstractGPs API and the FiniteGP APIs.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"In this situation you will benefit less from code reuse inside AbstractGPs, but will continue to benefit from the ability of others use your code, and to take advantage of any existing functionality which requires types which adhere to the AbstractGPs API.","category":"page"},{"location":"api/#Mean-functions","page":"The Main APIs","title":"Mean functions","text":"","category":"section"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"We define an API for prior mean functions with the abstract type MeanFunction and the mean_vector function.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"AbstractGPs.MeanFunction\nmean_vector","category":"page"},{"location":"api/#AbstractGPs.MeanFunction","page":"The Main APIs","title":"AbstractGPs.MeanFunction","text":"abstract type MeanFunction end\n\nMeanFunction introduces an API for treating the prior mean function appropriately. On the abstract level, all MeanFunction are functions. However we generally want to evaluate them on a collection of inputs. To this effect, we provide the mean_vector(::MeanFunction, ::AbstractVector) function, which is equivalent to map but with possibilities of optimizations (for ZeroMean and ConstMean for example).\n\n\n\n\n\n","category":"type"},{"location":"api/#AbstractGPs.mean_vector","page":"The Main APIs","title":"AbstractGPs.mean_vector","text":"mean_vector(m::MeanFunction, x::AbstractVector)::AbstractVector{<:Real}\n\nmean_vector is the function to call to apply a MeanFunction to a collection of inputs.\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"We provide standard mean functions like ZeroMean and ConstMean as well as CustomMean to simply wrap a function.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"ZeroMean\nConstMean\nCustomMean","category":"page"},{"location":"api/#AbstractGPs.ZeroMean","page":"The Main APIs","title":"AbstractGPs.ZeroMean","text":"ZeroMean{T<:Real} <: MeanFunction\n\nReturns zero(T) everywhere, T is Float64 by default.\n\n\n\n\n\n","category":"type"},{"location":"api/#AbstractGPs.ConstMean","page":"The Main APIs","title":"AbstractGPs.ConstMean","text":"ConstMean{T<:Real} <: MeanFunction\n\nReturns c everywhere.\n\n\n\n\n\n","category":"type"},{"location":"api/#AbstractGPs.CustomMean","page":"The Main APIs","title":"AbstractGPs.CustomMean","text":"CustomMean{Tf} <: MeanFunction\n\nA wrapper around whatever unary function you fancy. Must be able to be mapped over an AbstractVector of inputs.\n\nWarning\n\nCustomMean is generally sufficient for testing purposes, but care should be taken if attempting to differentiate through mean_vector with a CustomMean when using Zygote.jl. In particular, mean_vector(m::CustomMean, x) is implemented as map(m.f, x), which when x is a ColVecs or RowVecs will not differentiate correctly.\n\nIn such cases, you should implement mean_vector directly for your custom mean. For example, if f(x) = sum(x), you might implement mean_vector as\n\nmean_vector(::CustomMean{typeof(f)}, x::ColVecs) = vec(sum(x.X; dims=1))\nmean_vector(::CustomMean{typeof(f)}, x::RowVecs) = vec(sum(x.X; dims=2))\n\nwhich avoids ever applying map to a ColVecs or RowVecs.\n\n\n\n\n\n","category":"type"},{"location":"api/#Testing-Utilities","page":"The Main APIs","title":"Testing Utilities","text":"","category":"section"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"AbstractGPs provides several consistency tests in the AbstractGPs.TestUtils module. These tests will ensure that, for example, the size and type of everything produced by an implementation of the API is correct, and consistent with the other methods. It will not ensure correctness in any absolute sense though (e.g. that logpdf indeed computes what you wanted it to compute). Consequently, these tests should be seen as a set of necessary conditions for your code to be correct. They are not, however, sufficient.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"You should only need to run one of the following test suites.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"If you implement the Primary Public API, run test_finitegp_primary_public_interface.\nIf you implement both the Primary Public API and the Secondary Public API, then run test_finitegp_primary_and_secondary_public_interface.\nIf you implement the Internal AbstractGPs API, run test_internal_abstractgps_interface.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"Also see Which API should I implement? for more information about the most appropriate API to implement.","category":"page"},{"location":"api/","page":"The Main APIs","title":"The Main APIs","text":"AbstractGPs.TestUtils.test_finitegp_primary_public_interface\nAbstractGPs.TestUtils.test_finitegp_primary_and_secondary_public_interface\nAbstractGPs.TestUtils.test_internal_abstractgps_interface","category":"page"},{"location":"api/#AbstractGPs.TestUtils.test_finitegp_primary_public_interface","page":"The Main APIs","title":"AbstractGPs.TestUtils.test_finitegp_primary_public_interface","text":"test_finitegp_primary_public_interface(\n rng::AbstractRNG, fx::FiniteGP; atol::Real=1e-12\n)\n\nBasic consistency tests for the Primary Public FiniteGP API. You should run these tests if you only implement the Primary Public API – see API section of docs for details about this API.\n\nThese are consistency checks, not correctness tests in the absolute sense. For example, these tests ensure that samples generated by rand are of the correct size, but does not check that they come from the intended distribution.\n\n\n\n\n\n","category":"function"},{"location":"api/#AbstractGPs.TestUtils.test_finitegp_primary_and_secondary_public_interface","page":"The Main APIs","title":"AbstractGPs.TestUtils.test_finitegp_primary_and_secondary_public_interface","text":"test_finitegp_primary_and_secondary_public_interface(\n rng::AbstractRNG, fx::FiniteGP; atol::Real=1e-12\n)\n\nBasic consistency tests for both the Primary and Secondary Public FiniteGP APIs. Runs test_finitegp_primary_public_interface as part of these tests. You should run these tests if you implement both the primary and secondary public APIs – see API section of the docs for more information about these APIs.\n\nThese are consistency checks, not correctness tests in the absolute sense. For example, these tests ensure that samples generated by rand are of the correct size, but does not check that they come from the intended distribution.\n\n\n\n\n\n","category":"function"},{"location":"api/#AbstractGPs.TestUtils.test_internal_abstractgps_interface","page":"The Main APIs","title":"AbstractGPs.TestUtils.test_internal_abstractgps_interface","text":"test_internal_abstractgps_interface(\n rng::AbstractRNG,\n f::AbstractGP,\n x::AbstractVector,\n z::AbstractVector;\n atol=1e-12,\n σ²::Real=1e-1,\n jitter::Real=AbstractGPs.default_σ²,\n)\n\nBasic consistency tests for the Internal AbstractGPs API. Runs test_finitegp_primary_and_secondary_public_interface as part of these tests. Run these tests if you implement the Internal AbstractGPs API – see the API section of the docs for more information about this API.\n\nThese are consistency checks, not correctness tests in the absolute sense. For example, these tests ensure that samples generated by rand are of the correct size, but does not check that they come from the intended distribution.\n\nkwargs\n\natol: the minimum eigenvalue of cov(f, x) must be greater than this.\nσ²: the observation noise under which things are observed.\njitter: constant added to diagonal of cov(f, x) and cov(f, z) before inversion.\n\n\n\n\n\n","category":"function"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"EditURL = \"../../../../examples/3-parametric-heteroscedastic/script.jl\"","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/master/examples/3-parametric-heteroscedastic/script.jl\"","category":"page"},{"location":"examples/3-parametric-heteroscedastic/#Parametric-Heteroscedastic-Model","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"","category":"section"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"(Image: )","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"This example is a small extension of the standard GP regression problem, in which the observation noise variance is a function of the input. It is assumed to be a simple quadratic form, with a single unknown scaling parameter, in addition to the usual lengthscale and variance parameters of the GP. A point estimate of all parameters is obtained using type-II maximum likelihood, as per usual.","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"using AbstractGPs\nusing AbstractGPsMakie\nusing CairoMakie\nusing KernelFunctions\nusing Optim\nusing ParameterHandling\nusing Zygote\n\nusing LinearAlgebra\nusing Random\nRandom.seed!(42) # setting the seed for reproducibility of this notebook","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"In this example we work with a simple GP with a Gaussian kernel and heteroscedastic observation variance.","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"observation_variance(θ, x::AbstractVector{<:Real}) = Diagonal(0.01 .+ θ.σ² .* x .^ 2)\nfunction build_gpx(θ, x::AbstractVector{<:Real})\n Σ = observation_variance(θ, x)\n return GP(0, θ.s * with_lengthscale(SEKernel(), θ.l))(x, Σ)\nend;","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"We specify the following hyperparameters:","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"const flat_θ, unflatten = ParameterHandling.value_flatten((\n s=positive(1.0), l=positive(3.0), σ²=positive(0.1)\n));\nθ = unflatten(flat_θ);","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"We generate some observations:","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"const x = 0.0:0.1:10.0\nconst y = rand(build_gpx(θ, x));","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"We specify the objective function:","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"function objective(flat_θ)\n θ = unflatten(flat_θ)\n fx = build_gpx(θ, x)\n return -logpdf(fx, y)\nend;","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"We use L-BFGS for optimising the objective function. It is a first-order method and hence requires computing the gradient of the objective function. We do not derive and implement the gradient function manually here but instead use reverse-mode automatic differentiation with Zygote. When computing gradients with Zygote, the objective function is evaluated as well. We can exploit this and avoid re-evaluating the objective function in such cases.","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"function objective_and_gradient(F, G, flat_θ)\n if G !== nothing\n val_grad = Zygote.withgradient(objective, flat_θ)\n copyto!(G, only(val_grad.grad))\n if F !== nothing\n return val_grad.val\n end\n end\n if F !== nothing\n return objective(flat_θ)\n end\n return nothing\nend;","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"We optimise the hyperparameters using initializations close to the values that the observations were generated with.","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"flat_θ_init = flat_θ + 0.01 * randn(length(flat_θ))\nresult = optimize(\n Optim.only_fg!(objective_and_gradient),\n flat_θ_init,\n LBFGS(;\n alphaguess=Optim.LineSearches.InitialStatic(; scaled=true),\n linesearch=Optim.LineSearches.BackTracking(),\n ),\n Optim.Options(; show_every=100),\n)","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":" * Status: success\n\n * Candidate solution\n Final objective value: 1.576205e+02\n\n * Found with\n Algorithm: L-BFGS\n\n * Convergence measures\n |x - x'| = 3.97e-08 ≰ 0.0e+00\n |x - x'|/|x'| = 1.58e-08 ≰ 0.0e+00\n |f(x) - f(x')| = 0.00e+00 ≤ 0.0e+00\n |f(x) - f(x')|/|f(x')| = 0.00e+00 ≤ 0.0e+00\n |g(x)| = 6.34e-09 ≤ 1.0e-08\n\n * Work counters\n Seconds run: 1 (vs limit Inf)\n Iterations: 10\n f(x) calls: 13\n ∇f(x) calls: 11\n","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"The optimal model parameters are:","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"θ_final = unflatten(result.minimizer)","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"(s = 0.385143986830958, l = 2.3342748073768, σ² = 0.08082582089918736)","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"We compute the posterior GP with these optimal model parameters:","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"fx_final = build_gpx(θ_final, x)\nf_post = posterior(fx_final, y);","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"We visualize the results with AbstractGPsMakie:","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"using CairoMakie.Makie.ColorSchemes: Set1_4\n\nwith_theme(\n Theme(;\n palette=(color=Set1_4,),\n patchcolor=(Set1_4[2], 0.2),\n Axis=(limits=((0, 10), nothing),),\n ),\n) do\n plot(\n x,\n f_post(x, observation_variance(θ_final, x));\n bandscale=3,\n label=\"posterior + noise\",\n color=(:orange, 0.3),\n )\n plot!(x, f_post; bandscale=3, label=\"posterior\")\n gpsample!(x, f_post; samples=10, color=Set1_4[3])\n scatter!(x, y; label=\"y\")\n axislegend()\n current_figure()\nend","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"(Image: )","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"
    \n
    Package and system information
    \n
    \nPackage information (click to expand)\n
    \nStatus `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/3-parametric-heteroscedastic/Project.toml`\n  [99985d1d] AbstractGPs v0.5.20 `/home/runner/work/AbstractGPs.jl/AbstractGPs.jl#07d5f00`\n  [7834405d] AbstractGPsMakie v0.2.6\n⌃ [13f3f980] CairoMakie v0.10.12\n  [ec8451be] KernelFunctions v0.10.63\n  [98b081ad] Literate v2.16.1\n  [429524aa] Optim v1.9.2\n⌅ [2412ca09] ParameterHandling v0.4.10\n  [e88e6eb3] Zygote v0.6.69\n  [37e2e46d] LinearAlgebra\n  [9a3f8284] Random\nInfo Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated`\n
    \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
    \n
    \nSystem information (click to expand)\n
    \nJulia Version 1.10.2\nCommit bd47eca2c8a (2024-03-01 10:14 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 4 × AMD EPYC 7763 64-Core Processor\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\nThreads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
    \n
    ","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"","category":"page"},{"location":"examples/3-parametric-heteroscedastic/","page":"Parametric Heteroscedastic Model","title":"Parametric Heteroscedastic Model","text":"This page was generated using Literate.jl.","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"EditURL = \"../../../../examples/2-deep-kernel-learning/script.jl\"","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/master/examples/2-deep-kernel-learning/script.jl\"","category":"page"},{"location":"examples/2-deep-kernel-learning/#Deep-Kernel-Learning-with-Flux","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"","category":"section"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"(Image: )","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"# Background","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"This example trains a GP whose inputs are passed through a neural network. This kind of model has been considered previously [Calandra] [Wilson], although it has been shown that some care is needed to avoid substantial overfitting [Ober]. In this example we make use of the FunctionTransform from KernelFunctions.jl to put a simple Multi-Layer Perceptron built using Flux.jl inside a standard kernel.","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"[Calandra]: Calandra, R., Peters, J., Rasmussen, C. E., & Deisenroth, M. P. (2016, July). Manifold Gaussian processes for regression. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 3338-3345). IEEE.","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"[Wilson]: Wilson, A. G., Hu, Z., Salakhutdinov, R. R., & Xing, E. P. (2016). Stochastic variational deep kernel learning. Advances in Neural Information Processing Systems, 29.","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"[Ober]: Ober, S. W., Rasmussen, C. E., & van der Wilk, M. (2021, December). The promises and pitfalls of deep kernel learning. In Uncertainty in Artificial Intelligence (pp. 1206-1216). PMLR.","category":"page"},{"location":"examples/2-deep-kernel-learning/#Package-loading","page":"Deep Kernel Learning with Flux","title":"Package loading","text":"","category":"section"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"We use a couple of useful packages to plot and optimize the different hyper-parameters","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"using AbstractGPs\nusing Distributions\nusing Flux\nusing KernelFunctions\nusing LinearAlgebra\nusing Plots\ndefault(; legendfontsize=15.0, linewidth=3.0);","category":"page"},{"location":"examples/2-deep-kernel-learning/#Data-creation","page":"Deep Kernel Learning with Flux","title":"Data creation","text":"","category":"section"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"We create a simple 1D Problem with very different variations","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"xmin, xmax = (-3, 3) # Limits\nN = 150\nnoise_std = 0.01\nx_train_vec = rand(Uniform(xmin, xmax), N) # Training dataset\nx_train = collect(eachrow(x_train_vec)) # vector-of-vectors for Flux compatibility\ntarget_f(x) = sinc(abs(x)^abs(x)) # We use sinc with a highly varying value\ny_train = target_f.(x_train_vec) + randn(N) * noise_std\nx_test_vec = range(xmin, xmax; length=200) # Testing dataset\nx_test = collect(eachrow(x_test_vec)) # vector-of-vectors for Flux compatibility\n\nplot(xmin:0.01:xmax, target_f; label=\"ground truth\")\nscatter!(x_train_vec, y_train; label=\"training data\")","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/2-deep-kernel-learning/#Model-definition","page":"Deep Kernel Learning with Flux","title":"Model definition","text":"","category":"section"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"We create a neural net with 2 layers and 10 units each. The data is passed through the NN before being used in the kernel.","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"neuralnet = Chain(Dense(1, 20), Dense(20, 30), Dense(30, 5))","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"Chain(\n Dense(1 => 20), # 40 parameters\n Dense(20 => 30), # 630 parameters\n Dense(30 => 5), # 155 parameters\n) # Total: 6 arrays, 825 parameters, 3.598 KiB.","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"We use the Squared Exponential Kernel:","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"k = SqExponentialKernel() ∘ FunctionTransform(neuralnet)","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"Squared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t- Function Transform: Chain(Dense(1 => 20), Dense(20 => 30), Dense(30 => 5))","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"We now define our model:","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"gpprior = GP(k) # GP Prior\nfx = AbstractGPs.FiniteGP(gpprior, x_train, noise_std^2) # Prior at the observations\nfp = posterior(fx, y_train) # Posterior of f given the observations","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"AbstractGPs.PosteriorGP{AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.FunctionTransform{Flux.Chain{Tuple{Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}}}}, @NamedTuple{α::Vector{Float64}, C::LinearAlgebra.Cholesky{Float64, Matrix{Float64}}, x::Vector{SubArray{Float64, 1, Matrix{Float64}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}, δ::Vector{Float64}}}(AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.FunctionTransform{Flux.Chain{Tuple{Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}}}}(AbstractGPs.ZeroMean{Float64}(), Squared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t- Function Transform: Chain(Dense(1 => 20), Dense(20 => 30), Dense(30 => 5))), (α = [-701.1388050972998, 484.7628436219038, -1469.7818185173296, 598.4896099252254, -28.37330126200084, -1204.9104474162616, -368.92333277448313, -526.4255972350581, -199.92519155937478, -1626.7497780328176, 542.7341811143751, 504.21553444458897, -70.6248456998835, 73.75787171813063, -637.6002584964182, 110.33892243470423, -474.18996320833907, -660.5467484683887, -659.7326121860807, 672.2091629543114, 665.6601342518854, 1120.5438249474564, -546.7134141035283, 1245.7237350856647, -227.70765217332402, 879.7242710791257, 876.3055702798916, -218.70441049135363, -152.06133901326265, 848.9544310441407, -313.1315330357074, -754.0189412136584, 188.96327955336375, -827.7453089403782, 263.2940773111813, -1215.9123838858677, 569.7609342101207, -374.2744442832052, 745.8508168662794, 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-0.18582961703438408, 0.004374204772543916, 0.10783157043411237, -0.05704431445175237]))","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"This computes the negative log evidence of y (the negative log marginal likelihood of the neural network parameters), which is going to be used as the objective:","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"loss(y) = -logpdf(fx, y)\n\n@info \"Initial loss = $(loss(y_train))\"","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"┌ Warning: Layer with Float32 parameters got Float64 input.\n│ The input will be converted, but any earlier layers may be very slow.\n│ layer = Dense(1 => 20) # 40 parameters\n│ summary(x) = \"1-element view(::Matrix{Float64}, 1, :) with eltype Float64\"\n└ @ Flux ~/.julia/packages/Flux/qZO6T/src/layers/stateless.jl:60\n[ Info: Initial loss = 4090.717331659456\n","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"Flux will automatically extract all the parameters of the kernel","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"ps = Flux.params(k)","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"Params([Float32[-0.267783; -0.45613554; -0.51431525; 0.36201957; -0.06702539; 0.31383085; -0.016083641; -0.3159858; -0.28528228; 0.43957305; 0.45529974; -0.50481147; -0.23413388; -0.41008896; 0.39742747; 0.1611269; -0.19932841; -0.35804477; 0.10675026; -0.08516879;;], Float32[0.0, 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0.0]])","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"We show the initial prediction with the untrained model","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"p_init = plot(; title=\"Loss = $(round(loss(y_train); sigdigits=6))\")\nplot!(vcat(x_test...), target_f; label=\"true f\")\nscatter!(vcat(x_train...), y_train; label=\"data\")\npred_init = marginals(fp(x_test))\nplot!(vcat(x_test...), mean.(pred_init); ribbon=std.(pred_init), label=\"Prediction\")","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/2-deep-kernel-learning/#Training","page":"Deep Kernel Learning with Flux","title":"Training","text":"","category":"section"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"nmax = 200\nopt = Flux.ADAM(0.1)\n\nanim = Animation()\nfor i in 1:nmax\n grads = gradient(ps) do\n loss(y_train)\n end\n Flux.Optimise.update!(opt, ps, grads)\n\n if i % 10 == 0\n L = loss(y_train)\n @info \"iteration $i/$nmax: loss = $L\"\n\n p = plot(; title=\"Loss[$i/$nmax] = $(round(L; sigdigits=6))\")\n plot!(vcat(x_test...), target_f; label=\"true f\")\n scatter!(vcat(x_train...), y_train; label=\"data\")\n pred = marginals(posterior(fx, y_train)(x_test))\n plot!(vcat(x_test...), mean.(pred); ribbon=std.(pred), label=\"Prediction\")\n frame(anim)\n display(p)\n end\nend\ngif(anim, \"train-dkl.gif\"; fps=3)","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"┌ Warning: Layer with Float32 parameters got Float64 input.\n│ The input will be converted, but any earlier layers may be very slow.\n│ layer = Dense(1 => 20) # 40 parameters\n│ summary(x) = \"1-element view(::Matrix{Float64}, 1, :) with eltype Float64\"\n└ @ Flux ~/.julia/packages/Flux/qZO6T/src/layers/stateless.jl:60\n[ Info: iteration 10/200: loss = 76.0354504227719\n[ Info: iteration 20/200: loss = -0.9394704761251926\n[ Info: iteration 30/200: loss = 32.64950825879501\n[ Info: iteration 40/200: loss = 51.34619725383423\n[ Info: iteration 50/200: loss = -186.9573985312025\n[ Info: iteration 60/200: loss = 90.20641080950064\n[ Info: iteration 70/200: loss = 125.57395643728562\n[ Info: iteration 80/200: loss = 128.52814346820105\n[ Info: iteration 90/200: loss = 128.96777494909188\n[ Info: iteration 100/200: loss = 128.69756159631837\n[ Info: iteration 110/200: loss = 128.11952169143362\n[ Info: iteration 120/200: loss = 127.33786275210127\n[ Info: iteration 130/200: loss = 126.35728321434976\n[ Info: iteration 140/200: loss = 125.1327243923329\n[ Info: iteration 150/200: loss = 123.56936409299618\n[ Info: iteration 160/200: loss = 121.48655719765259\n[ Info: iteration 170/200: loss = 118.51590542776988\n[ Info: iteration 180/200: loss = 113.74951112525171\n[ Info: iteration 190/200: loss = 104.23131367025238\n[ Info: iteration 200/200: loss = 71.02513873978248\n[ Info: Saved animation to /home/runner/work/AbstractGPs.jl/AbstractGPs.jl/docs/src/examples/2-deep-kernel-learning/train-dkl.gif\n","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"(Image: )","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"
    \n
    Package and system information
    \n
    \nPackage information (click to expand)\n
    \nStatus `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/2-deep-kernel-learning/Project.toml`\n  [99985d1d] AbstractGPs v0.5.20 `/home/runner/work/AbstractGPs.jl/AbstractGPs.jl#07d5f00`\n  [31c24e10] Distributions v0.25.107\n  [587475ba] Flux v0.14.13\n  [ec8451be] KernelFunctions v0.10.63\n  [98b081ad] Literate v2.16.1\n  [cc2ba9b6] MLDataUtils v0.5.4\n  [91a5bcdd] Plots v1.40.2\n  [e88e6eb3] Zygote v0.6.69\n  [37e2e46d] LinearAlgebra\n
    \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
    \n
    \nSystem information (click to expand)\n
    \nJulia Version 1.10.2\nCommit bd47eca2c8a (2024-03-01 10:14 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 4 × AMD EPYC 7763 64-Core Processor\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\nThreads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
    \n
    ","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"","category":"page"},{"location":"examples/2-deep-kernel-learning/","page":"Deep Kernel Learning with Flux","title":"Deep Kernel Learning with Flux","text":"This page was generated using Literate.jl.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"EditURL = \"../../../../examples/0-intro-1d/script.jl\"","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/master/examples/0-intro-1d/script.jl\"","category":"page"},{"location":"examples/0-intro-1d/#Intro-to-AbstractGPs:-one-dimensional-regression","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"","category":"section"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"(Image: )","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"","category":"page"},{"location":"examples/0-intro-1d/#Setup","page":"Intro to AbstractGPs: one-dimensional regression","title":"Setup","text":"","category":"section"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"Loading the necessary packages.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"using AbstractGPs\nusing Distributions\nusing FillArrays\nusing StatsFuns\n\nusing Plots\ndefault(; legend=:outertopright, size=(700, 400))\n\nusing Random\nRandom.seed!(42) # setting the seed for reproducibility of this notebook","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"Load toy regression dataset taken from GPflow examples.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"x = [\n 0.8658165855998895,\n 0.6661700880180962,\n 0.8049218148148531,\n 0.7714303440386239,\n 0.14790478354654835,\n 0.8666105548197428,\n 0.007044577166530286,\n 0.026331737288148638,\n 0.17188596617099916,\n 0.8897812990554013,\n 0.24323574561119998,\n 0.028590102134105955,\n]\ny = [\n 1.5255314337144372,\n 3.6434202968230003,\n 3.010885733911661,\n 3.774442382979625,\n 3.3687639483798324,\n 1.5506452040608503,\n 3.790447985799683,\n 3.8689707574953,\n 3.4933565751758713,\n 1.4284538820635841,\n 3.8715350915692364,\n 3.7045949061144983,\n]\nscatter(x, y; xlabel=\"x\", ylabel=\"y\", legend=false)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We split the observations into train and test data.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"x_train = x[1:8]\ny_train = y[1:8]\nx_test = x[9:end]\ny_test = y[9:end]","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We instantiate a Gaussian process with a Matern kernel. The kernel has fixed variance and length scale parameters of default value 1.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"f = GP(Matern52Kernel())","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We create a finite dimensional projection at the inputs of the training dataset observed under Gaussian noise with variance noise_var = 01, and compute the log-likelihood of the outputs of the training dataset.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"noise_var = 0.1\nfx = f(x_train, noise_var)\nlogpdf(fx, y_train)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"-25.53057444906228","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We compute the posterior Gaussian process given the training data, and calculate the log-likelihood of the test dataset.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"p_fx = posterior(fx, y_train)\nlogpdf(p_fx(x_test, noise_var), y_test)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"-2.878533694097381","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We plot the posterior Gaussian process (its mean and a ribbon of 2 standard deviations around it) on a grid along with the observations.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"scatter(\n x_train,\n y_train;\n xlim=(0, 1),\n xlabel=\"x\",\n ylabel=\"y\",\n title=\"posterior (default parameters)\",\n label=\"Train Data\",\n)\nscatter!(x_test, y_test; label=\"Test Data\")\nplot!(0:0.001:1, p_fx; label=false, ribbon_scale=2)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/0-intro-1d/#Markov-Chain-Monte-Carlo","page":"Intro to AbstractGPs: one-dimensional regression","title":"Markov Chain Monte Carlo","text":"","category":"section"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"Previously we computed the log likelihood of the untuned kernel parameters of the GP. We now also perform approximate inference over said kernel parameters using different Markov chain Monte Carlo (MCMC) methods. I.e., we approximate the posterior distribution of the kernel parameters with samples from a Markov chain.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We define a function which returns the log-likelihood of the data for different variance and inverse lengthscale parameters of the Matern kernel. We ensure that these parameters are positive with the softplus function","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"f(x) = log (1 + exp x)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"function gp_loglikelihood(x, y)\n function loglikelihood(params)\n kernel =\n softplus(params[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(params[2])))\n f = GP(kernel)\n fx = f(x, noise_var)\n return logpdf(fx, y)\n end\n return loglikelihood\nend\n\nconst loglik_train = gp_loglikelihood(x_train, y_train)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We define a Gaussian prior for the joint distribution of the two transformed kernel parameters. We assume that both parameters are independent with mean 0 and variance 1.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"logprior(params) = logpdf(MvNormal(Eye(2)), params)","category":"page"},{"location":"examples/0-intro-1d/#Hamiltonian-Monte-Carlo","page":"Intro to AbstractGPs: one-dimensional regression","title":"Hamiltonian Monte Carlo","text":"","category":"section"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We start with a Hamiltonian Monte Carlo (HMC) sampler. More precisely, we use the No-U-Turn sampler (NUTS), which is provided by the Julia packages AdvancedHMC.jl and DynamicHMC.jl.","category":"page"},{"location":"examples/0-intro-1d/#AdvancedHMC","page":"Intro to AbstractGPs: one-dimensional regression","title":"AdvancedHMC","text":"","category":"section"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We start with performing inference with AdvancedHMC.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"using AdvancedHMC\nusing ForwardDiff","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"Set the number of samples to draw and warmup iterations.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"n_samples = 2_000\nn_adapts = 1_000","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"AdvancedHMC and DynamicHMC below require us to implement the LogDensityProblems interface for the log joint probability.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"using LogDensityProblems\n\nstruct LogJointTrain end\n\n# Log joint density\nLogDensityProblems.logdensity(::LogJointTrain, θ) = loglik_train(θ) + logprior(θ)\n\n# The parameter space is two-dimensional\nLogDensityProblems.dimension(::LogJointTrain) = 2\n\n# `LogJointTrain` does not allow to evaluate derivatives of the log density function\nfunction LogDensityProblems.capabilities(::Type{LogJointTrain})\n return LogDensityProblems.LogDensityOrder{0}()\nend","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We use ForwardDiff.jl to compute the derivatives of the log joint density with automatic differentiation.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"using LogDensityProblemsAD\n\nconst logjoint_train = ADgradient(Val(:ForwardDiff), LogJointTrain())","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We define an Hamiltonian system of the log joint probability.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"metric = DiagEuclideanMetric(2)\nhamiltonian = Hamiltonian(metric, logjoint_train)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"Define a leapfrog solver, with initial step size chosen heuristically.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"initial_params = rand(2)\ninitial_ϵ = find_good_stepsize(hamiltonian, initial_params)\nintegrator = Leapfrog(initial_ϵ)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"Define an HMC sampler, with the following components:","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"multinomial sampling scheme,\ngeneralised No-U-Turn criteria, and\nwindowed adaption for step-size and diagonal mass matrix","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"proposal = HMCKernel(Trajectory{MultinomialTS}(integrator, GeneralisedNoUTurn()))\nadaptor = StanHMCAdaptor(MassMatrixAdaptor(metric), StepSizeAdaptor(0.8, integrator))","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We draw samples from the posterior distribution of kernel parameters. These samples are in the unconstrained space mathbbR^2.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"samples, _ = sample(\n hamiltonian, proposal, initial_params, n_samples, adaptor, n_adapts; progress=false\n)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"┌ Info: Finished 1000 adapation steps\n│ adaptor =\n│ StanHMCAdaptor(\n│ pc=WelfordVar,\n│ ssa=NesterovDualAveraging(γ=0.05, t_0=10.0, κ=0.75, δ=0.8, state.ϵ=0.729267980509611),\n│ init_buffer=75, term_buffer=50, window_size=25,\n│ state=window(76, 950), window_splits(100, 150, 250, 450, 950)\n│ )\n│ κ.τ.integrator = Leapfrog(ϵ=0.729)\n└ h.metric = DiagEuclideanMetric([0.44910857845910196, 0.511 ...])\n┌ Info: Finished 2000 sampling steps for 1 chains in 1.592507863 (s)\n│ h = Hamiltonian(metric=DiagEuclideanMetric([0.44910857845910196, 0.511 ...]), kinetic=AdvancedHMC.GaussianKinetic())\n│ κ = AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}(AdvancedHMC.FullMomentumRefreshment(), Trajectory{AdvancedHMC.MultinomialTS}(integrator=Leapfrog(ϵ=0.729), tc=AdvancedHMC.GeneralisedNoUTurn{Float64}(10, 1000.0)))\n│ EBFMI_est = 1.073491835278073\n└ average_acceptance_rate = 0.8645436053764034\n","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We transform the samples back to the constrained space and compute the mean of both parameters:","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"samples_constrained = [map(softplus, p) for p in samples]\nmean_samples = mean(samples_constrained)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"2-element Vector{Float64}:\n 2.281171532295106\n 2.2420363237188656","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We plot a histogram of the samples for the two parameters. The vertical line in each graph indicates the mean of the samples.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"histogram(\n reduce(hcat, samples_constrained)';\n xlabel=\"sample\",\n ylabel=\"counts\",\n layout=2,\n title=[\"variance\" \"inverse length scale\"],\n legend=false,\n)\nvline!(mean_samples'; linewidth=2)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We approximate the log-likelihood of the test data using the posterior Gaussian processes for kernels with the sampled kernel parameters. We can observe that there is a significant improvement over the log-likelihood of the test data with respect to the posterior Gaussian process with default kernel parameters of value 1.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"function gp_posterior(x, y, p)\n kernel = softplus(p[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(p[2])))\n f = GP(kernel)\n return posterior(f(x, noise_var), y)\nend\n\nmean(logpdf(gp_posterior(x_train, y_train, p)(x_test, noise_var), y_test) for p in samples)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"-0.9982165090824527","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We sample 5 functions from each posterior GP given by the final 100 samples of kernel parameters.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"plt = plot(; xlim=(0, 1), xlabel=\"x\", ylabel=\"y\", title=\"posterior (AdvancedHMC)\")\nfor (i, p) in enumerate(samples[(end - 100):end])\n sampleplot!(\n plt,\n 0:0.02:1,\n gp_posterior(x_train, y_train, p);\n samples=5,\n seriescolor=\"red\",\n label=(i == 1 ? \"samples\" : nothing),\n )\nend\nscatter!(plt, x_train, y_train; label=\"Train Data\", markercolor=1)\nscatter!(plt, x_test, y_test; label=\"Test Data\", markercolor=2)\nplt","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/0-intro-1d/#DynamicHMC","page":"Intro to AbstractGPs: one-dimensional regression","title":"DynamicHMC","text":"","category":"section"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We repeat the inference with DynamicHMC.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"using DynamicHMC\n\nsamples =\n mcmc_with_warmup(\n Random.GLOBAL_RNG, logjoint_train, n_samples; reporter=NoProgressReport()\n ).posterior_matrix","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We transform the samples back to the constrained space and compute the mean of both parameters:","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"samples_constrained = map(softplus, samples)\nmean_samples = vec(mean(samples_constrained; dims=2))","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"2-element Vector{Float64}:\n 2.2964420066634905\n 2.279998092861483","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We plot a histogram of the samples for the two parameters. The vertical line in each graph indicates the mean of the samples.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"histogram(\n samples_constrained';\n xlabel=\"sample\",\n ylabel=\"counts\",\n layout=2,\n title=[\"variance\" \"inverse length scale\"],\n legend=false,\n)\nvline!(mean_samples'; linewidth=2)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"Again we can observe that there is a significant improvement over the log-likelihood of the test data with respect to the posterior Gaussian process with default kernel parameters.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"mean(logpdf(gp_posterior(x_train, y_train, p)(x_test), y_test) for p in eachcol(samples))","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"-8.367931279409353","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We sample a function from the posterior GP for the final 100 samples of kernel parameters.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"plt = plot(; xlim=(0, 1), xlabel=\"x\", ylabel=\"y\", title=\"posterior (DynamicHMC)\")\nscatter!(plt, x_train, y_train; label=\"Train Data\")\nscatter!(plt, x_test, y_test; label=\"Test Data\")\nfor i in (n_samples - 100):n_samples\n p = @view samples[:, i]\n sampleplot!(plt, 0:0.02:1, gp_posterior(x_train, y_train, p); seriescolor=\"red\")\nend\nplt","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/0-intro-1d/#Elliptical-slice-sampling","page":"Intro to AbstractGPs: one-dimensional regression","title":"Elliptical slice sampling","text":"","category":"section"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"Instead of HMC, we use elliptical slice sampling which is provided by the Julia package EllipticalSliceSampling.jl.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"using EllipticalSliceSampling","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We draw 2000 samples from the posterior distribution of kernel parameters.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"samples = sample(ESSModel(\n MvNormal(Eye(2)), # Gaussian prior\n loglik_train,\n), ESS(), n_samples; progress=false)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We transform the samples back to the constrained space and compute the mean of both parameters:","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"samples_constrained = [map(softplus, p) for p in samples]\nmean_samples = mean(samples_constrained)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"2-element Vector{Float64}:\n 2.323481323947446\n 2.3243119513909822","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We plot a histogram of the samples for the two parameters. The vertical line in each graph indicates the mean of the samples.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"histogram(\n reduce(hcat, samples_constrained)';\n xlabel=\"sample\",\n ylabel=\"counts\",\n layout=2,\n title=[\"variance\" \"inverse length scale\"],\n)\nvline!(mean_samples'; layout=2, labels=\"mean\")","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"Again we can observe that there is a significant improvement over the log-likelihood of the test data with respect to the posterior Gaussian process with default kernel parameters.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"mean(logpdf(gp_posterior(x_train, y_train, p)(x_test), y_test) for p in samples)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"-31.841050394184858","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We sample a function from the posterior GP for the final 100 samples of kernel parameters.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"plt = plot(;\n xlim=(0, 1), xlabel=\"x\", ylabel=\"y\", title=\"posterior (EllipticalSliceSampling)\"\n)\nscatter!(plt, x_train, y_train; label=\"Train Data\")\nscatter!(plt, x_test, y_test; label=\"Test Data\")\nfor p in samples[(end - 100):end]\n sampleplot!(plt, 0:0.02:1, gp_posterior(x_train, y_train, p); seriescolor=\"red\")\nend\nplt","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/0-intro-1d/#Variational-Inference","page":"Intro to AbstractGPs: one-dimensional regression","title":"Variational Inference","text":"","category":"section"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"Sanity check for the Evidence Lower BOund (ELBO) implemented according to M. K. Titsias's Variational learning of inducing variables in sparse Gaussian processes.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"elbo(VFE(f(rand(5))), fx, y_train)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"-25.927861625387596","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We use the LBFGS algorithm to maximize the given ELBO. It is provided by the Julia package Optim.jl.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"using Optim","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We define a function which returns the negative ELBO for different variance and inverse lengthscale parameters of the Matern kernel and different pseudo-points. We ensure that the kernel parameters are positive with the softplus function","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"f(x) = log (1 + exp x)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"and that the pseudo-points are in the unit interval 01 with the logistic function","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"f(x) = frac11 + exp(-x)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"jitter = 1e-6 # \"observing\" the latent process with some (small) amount of jitter improves numerical stability\n\nfunction objective_function(x, y)\n function negative_elbo(params)\n kernel =\n softplus(params[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(params[2])))\n f = GP(kernel)\n fx = f(x, noise_var)\n z = logistic.(params[3:end])\n approx = VFE(f(z, jitter))\n return -elbo(approx, fx, y)\n end\n return negative_elbo\nend","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We randomly initialize the kernel parameters and 5 pseudo points, and minimize the negative ELBO with the LBFGS algorithm and obtain the following optimal parameters:","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"x0 = rand(7)\nopt = optimize(objective_function(x_train, y_train), x0, LBFGS())","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":" * Status: success\n\n * Candidate solution\n Final objective value: 1.086925e+01\n\n * Found with\n Algorithm: L-BFGS\n\n * Convergence measures\n |x - x'| = 1.38e-08 ≰ 0.0e+00\n |x - x'|/|x'| = 1.65e-09 ≰ 0.0e+00\n |f(x) - f(x')| = 0.00e+00 ≤ 0.0e+00\n |f(x) - f(x')|/|f(x')| = 0.00e+00 ≤ 0.0e+00\n |g(x)| = 9.39e-09 ≤ 1.0e-08\n\n * Work counters\n Seconds run: 0 (vs limit Inf)\n Iterations: 33\n f(x) calls: 90\n ∇f(x) calls: 90\n","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"opt.minimizer","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"7-element Vector{Float64}:\n 8.379380163263326\n 3.932737537022372\n 1.2763097368903071\n 1.8479571188160673\n -1.7583267679064027\n -4.133679971062581\n 0.6917102791672016","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"The optimized value of the variance is","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"softplus(opt.minimizer[1])","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"8.3796096891065","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"and of the inverse lengthscale is","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"softplus(opt.minimizer[2])","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"3.952138094098159","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We compute the log-likelihood of the test data for the resulting approximate posterior. We can observe that there is a significant improvement over the log-likelihood with the default kernel parameters of value 1.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"opt_kernel =\n softplus(opt.minimizer[1]) *\n (Matern52Kernel() ∘ ScaleTransform(softplus(opt.minimizer[2])))\nopt_f = GP(opt_kernel)\nopt_fx = opt_f(x_train, noise_var)\nap = posterior(VFE(opt_f(logistic.(opt.minimizer[3:end]), jitter)), opt_fx, y_train)\nlogpdf(ap(x_test, noise_var), y_test)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"-2.068599765069256","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We visualize the approximate posterior with optimized parameters.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"scatter(\n x_train,\n y_train;\n xlim=(0, 1),\n xlabel=\"x\",\n ylabel=\"y\",\n title=\"posterior (VI with sparse grid)\",\n label=\"Train Data\",\n)\nscatter!(x_test, y_test; label=\"Test Data\")\nplot!(0:0.001:1, ap; label=false, ribbon_scale=2)\nvline!(logistic.(opt.minimizer[3:end]); label=\"Pseudo-points\")","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/0-intro-1d/#Exact-Gaussian-Process-Inference","page":"Intro to AbstractGPs: one-dimensional regression","title":"Exact Gaussian Process Inference","text":"","category":"section"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"Here we use Type-II MLE to train the hyperparameters of the Gaussian process. This means that our loss function is the negative log marginal likelihood.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We re-calculate the log-likelihood of the test dataset with the default kernel parameters of value 1 for the sake of comparison.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"logpdf(p_fx(x_test), y_test)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"-232.51565975779937","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We define a function which returns the negative log marginal likelihood for different variance and inverse lengthscale parameters of the Matern kernel and different pseudo-points. We ensure that the kernel parameters are positive with the softplus function f(x) = log (1 + exp x).","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"function loss_function(x, y)\n function negativelogmarginallikelihood(params)\n kernel =\n softplus(params[1]) * (Matern52Kernel() ∘ ScaleTransform(softplus(params[2])))\n f = GP(kernel)\n fx = f(x, noise_var)\n return -logpdf(fx, y)\n end\n return negativelogmarginallikelihood\nend\n","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We randomly initialize the kernel parameters, and minimize the negative log marginal likelihood with the LBFGS algorithm and obtain the following optimal parameters:","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"θ0 = randn(2)\nopt = Optim.optimize(loss_function(x_train, y_train), θ0, LBFGS())","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":" * Status: success (objective increased between iterations)\n\n * Candidate solution\n Final objective value: 1.085252e+01\n\n * Found with\n Algorithm: L-BFGS\n\n * Convergence measures\n |x - x'| = 5.44e-08 ≰ 0.0e+00\n |x - x'|/|x'| = 6.48e-09 ≰ 0.0e+00\n |f(x) - f(x')| = 7.64e-14 ≰ 0.0e+00\n |f(x) - f(x')|/|f(x')| = 7.04e-15 ≰ 0.0e+00\n |g(x)| = 1.11e-10 ≤ 1.0e-08\n\n * Work counters\n Seconds run: 0 (vs limit Inf)\n Iterations: 15\n f(x) calls: 51\n ∇f(x) calls: 51\n","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"opt.minimizer","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"2-element Vector{Float64}:\n 8.385520525672359\n 3.968794221481056","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"The optimized value of the variance is","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"softplus(opt.minimizer[1])","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"8.385748646622107","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"and of the inverse lengthscale is","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"softplus(opt.minimizer[2])","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"3.987514109926022","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We compute the log-likelihood of the test data for the resulting optimized posterior. We can observe that there is a significant improvement over the log-likelihood with the default kernel parameters of value 1.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"opt_kernel =\n softplus(opt.minimizer[1]) *\n (Matern52Kernel() ∘ ScaleTransform(softplus(opt.minimizer[2])))\n\nopt_f = GP(opt_kernel)\nopt_fx = opt_f(x_train, noise_var)\nopt_p_fx = posterior(opt_fx, y_train)\nlogpdf(opt_p_fx(x_test, noise_var), y_test)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"-2.092647856958113","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"We visualize the posterior with optimized parameters.","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"scatter(\n x_train,\n y_train;\n xlim=(0, 1),\n xlabel=\"x\",\n ylabel=\"y\",\n title=\"posterior (optimized parameters)\",\n label=\"Train Data\",\n)\nscatter!(x_test, y_test; label=\"Test Data\")\nplot!(0:0.001:1, opt_p_fx; label=false, ribbon_scale=2)","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"
    \n
    Package and system information
    \n
    \nPackage information (click to expand)\n
    \nStatus `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/0-intro-1d/Project.toml`\n  [99985d1d] AbstractGPs v0.5.20 `/home/runner/work/AbstractGPs.jl/AbstractGPs.jl#07d5f00`\n  [0bf59076] AdvancedHMC v0.6.1\n  [31c24e10] Distributions v0.25.107\n  [bbc10e6e] DynamicHMC v3.4.7\n⌅ [cad2338a] EllipticalSliceSampling v1.1.0\n  [1a297f60] FillArrays v1.9.3\n  [f6369f11] ForwardDiff v0.10.36\n  [98b081ad] Literate v2.16.1\n  [6fdf6af0] LogDensityProblems v2.1.1\n  [996a588d] LogDensityProblemsAD v1.7.0\n  [429524aa] Optim v1.9.2\n  [91a5bcdd] Plots v1.40.2\n  [4c63d2b9] StatsFuns v1.3.1\n  [9a3f8284] Random\nInfo Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated`\n
    \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
    \n
    \nSystem information (click to expand)\n
    \nJulia Version 1.10.2\nCommit bd47eca2c8a (2024-03-01 10:14 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 4 × AMD EPYC 7763 64-Core Processor\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\nThreads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
    \n
    ","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"","category":"page"},{"location":"examples/0-intro-1d/","page":"Intro to AbstractGPs: one-dimensional regression","title":"Intro to AbstractGPs: one-dimensional regression","text":"This page was generated using Literate.jl.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"EditURL = \"../../../../examples/1-mauna-loa/script.jl\"","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"EditURL = \"https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/master/examples/1-mauna-loa/script.jl\"","category":"page"},{"location":"examples/1-mauna-loa/#Mauna-Loa-time-series-example","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"","category":"section"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"(Image: )","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"In this notebook, we apply Gaussian process regression to the Mauna Loa CO₂ dataset. This showcases a rich combination of kernels, and how to handle and optimize all their parameters.","category":"page"},{"location":"examples/1-mauna-loa/#Setup","page":"Mauna Loa time series example","title":"Setup","text":"","category":"section"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"We make use of the following packages:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"using CSV, DataFrames # data loading\nusing AbstractGPs # exact GP regression\nusing ParameterHandling # for nested and constrained parameters\nusing Optim # optimization\nusing Zygote # auto-diff gradient computation\nusing Plots # visualisation","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"Let's load and visualize the dataset.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"tip: Tip\nThe let block creates a new scope, so any utility variables we define in here won't leak outside. This is particularly helpful to keep notebooks tidy! The return value of the block is given by its last expression.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"(xtrain, ytrain), (xtest, ytest) = let\n data = CSV.read(joinpath(\"/home/runner/work/AbstractGPs.jl/AbstractGPs.jl/examples/1-mauna-loa\", \"CO2_data.csv\"), Tables.matrix; header=0)\n year = data[:, 1]\n co2 = data[:, 2]\n\n # We split the data into training and testing set:\n idx_train = year .< 2004\n idx_test = .!idx_train\n\n (year[idx_train], co2[idx_train]), (year[idx_test], co2[idx_test]) # block's return value\nend\n# The utility variables such as `idx_train` and `idx_test` are not available outside the `let` scope\n\nfunction plotdata()\n plot(; xlabel=\"year\", ylabel=\"CO₂ [ppm]\", legend=:bottomright)\n scatter!(xtrain, ytrain; label=\"training data\", ms=2, markerstrokewidth=0)\n return scatter!(xtest, ytest; label=\"test data\", ms=2, markerstrokewidth=0)\nend\n\nplotdata()","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/1-mauna-loa/#Prior","page":"Mauna Loa time series example","title":"Prior","text":"","category":"section"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"We will model this dataset using a sum of several kernels which describe","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"smooth trend: squared exponential kernel with long lengthscale;\nseasonal component: periodic covariance function with period of one year, multiplied with a squared exponential kernel to allow decay away from exact periodicity;\nmedium-term irregularities: rational quadratic kernel;\nnoise terms: squared exponential kernel with short lengthscale and uncorrelated observation noise.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"For more details, see Rasmussen & Williams (2005), chapter 5.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"We will use ParameterHandling.jl for handling the (hyper)parameters of our model. It provides functions such as positive with which we can put constraints on the hyperparameters, and allows us to represent all required parameters as a nested NamedTuple:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"# initial values to match http://stor-i.github.io/GaussianProcesses.jl/latest/mauna_loa/\nθ_init = (;\n se_long = (;\n σ = positive(exp(4.0)),\n ℓ = positive(exp(4.0)),\n ),\n seasonal = (;\n # product kernels only need a single overall signal variance\n per = (;\n ℓ = positive(exp(0.0)), # relative to period!\n p = fixed(1.0), # 1 year, do not optimize over\n ),\n se = (;\n σ = positive(exp(1.0)),\n ℓ = positive(exp(4.0)),\n ),\n ),\n rq = (;\n σ = positive(exp(0.0)),\n ℓ = positive(exp(0.0)),\n α = positive(exp(-1.0)),\n ),\n se_short = (;\n σ = positive(exp(-2.0)),\n ℓ = positive(exp(-2.0)),\n ),\n noise_scale = positive(exp(-2.0)),\n)","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"We define a couple of helper functions to simplify the kernel construction:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"SE(θ) = θ.σ^2 * with_lengthscale(SqExponentialKernel(), θ.ℓ)\nPer(θ) = with_lengthscale(PeriodicKernel(; r=[θ.ℓ / 2]), θ.p) # NOTE- discrepancy with GaussianProcesses.jl\nRQ(θ) = θ.σ^2 * with_lengthscale(RationalQuadraticKernel(; α=θ.α), θ.ℓ)","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"This allows us to write a function that, given the nested tuple of parameter values, constructs the GP prior:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"function build_gp_prior(θ)\n k_smooth_trend = SE(θ.se_long)\n k_seasonality = Per(θ.seasonal.per) * SE(θ.seasonal.se)\n k_medium_term_irregularities = RQ(θ.rq)\n k_noise_terms = SE(θ.se_short) + θ.noise_scale^2 * WhiteKernel()\n kernel = k_smooth_trend + k_seasonality + k_medium_term_irregularities + k_noise_terms\n return GP(kernel) # [`ZeroMean`](@ref) mean function by default\nend","category":"page"},{"location":"examples/1-mauna-loa/#Posterior","page":"Mauna Loa time series example","title":"Posterior","text":"","category":"section"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"To construct the posterior, we need to first build a FiniteGP, which represents the infinite-dimensional GP at a finite number of input features:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"function build_finite_gp(θ)\n f = build_gp_prior(θ)\n return f(xtrain)\nend","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"info: `WhiteKernel` vs `FiniteGP` observation noise\nIn this notebook, we already included observation noise through the WhiteKernel as part of the GP prior covariance in build_gp_prior. We therefore call f(xtrain) which implies zero (additional) observation noise.Alternatively, we could have omitted the θ.noise_scale^2 * WhiteKernel() term and instead passed the noise variance as a second argument to the GP call in build_finite_gp, f(xtrain, θ.noise_scale^2).These two approaches have slightly different semantics: In the first one, the WhiteKernel contributes non-zero variance to the [i, j] element of the covariance matrix of the FiniteGP if xtrain[i] == xtrain[j] (based on the values of the features). In the second one, the observation noise variance passed to FiniteGP only contributes to the diagonal elements of the covariance matrix, i.e. for i == j.Moreover, the variance (uncertainty) of the posterior predictions includes the variance from the WhiteKernel, but does not include the variance of the observation noise passed to the FiniteGP. To include the observation noise in posterior predictions from the second approach, call fpost_opt(xtest, noise_scale^2).","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"tip: Tip\nFor most use-cases and if in any doubt, we recommend that you pass in observation noise to the FiniteGP, and omit the explicit WhiteKernel. This is slightly faster (no need to check xtrain[i] == xtrain[j] for all pairs i, j), and WhiteKernel will not give stable gradients if you wish to compute the gradient of the log marginal likelihood logpdf(f(x), y) w.r.t. x.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"We obtain the posterior, conditioned on the (finite) observations, by calling posterior:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"function build_posterior_gp(θ)\n fx = build_finite_gp(θ)\n return posterior(fx, ytrain)\nend","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"Now we can put it all together to obtain a PosteriorGP. The call to ParameterHandling.value is required to replace the constraints (such as positive in our case) with concrete numbers:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"fpost_init = build_posterior_gp(ParameterHandling.value(θ_init))","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"AbstractGPs.PosteriorGP{AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.TransformedKernel{KernelFunctions.PeriodicKernel{Float64}, KernelFunctions.ScaleTransform{Float64}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}, @NamedTuple{α::Vector{Float64}, C::LinearAlgebra.Cholesky{Float64, Matrix{Float64}}, x::Vector{Float64}, δ::Vector{Float64}}}(AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.TransformedKernel{KernelFunctions.PeriodicKernel{Float64}, KernelFunctions.ScaleTransform{Float64}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}(AbstractGPs.ZeroMean{Float64}(), Sum of 5 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.018315638888734182)\n\t\t\t- σ² = 2980.957987041728\n\tProduct of 2 kernels:\n\t\t\tPeriodic Kernel, length(r) = 1\n\t\t\t\t\t- Scale Transform (s = 1.0)\n\t\t\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t\t\t- Scale Transform (s = 0.018315638888734182)\n\t\t\t\t\t- σ² = 7.3890560989306495\n\tRational Quadratic Kernel (α = 0.36787944117144233, metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 1.0)\n\t\t\t- σ² = 1.0\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 7.3890560989306495)\n\t\t\t- σ² = 0.018315638888734182\n\tWhite Kernel\n\t\t\t- σ² = 0.018315638888734182), (α = [-9.508930877725136, 19.389883608061695, -7.197900325224828, -4.730615302853674, -15.23471364637827, 18.62620628181198, 7.526138666092433, -0.03155846836128887, -13.788654737243691, 1.6524756606264315, 0.8879541948939996, 10.099173485471407, -2.386212779947056, -5.988194191445047, -4.048795928866536, 13.138399647575577, -9.170412574810548, -11.408070387850291, 15.064400955731097, -9.597695546280644, 12.86030013862022, 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2001.5416666666667, 2001.625, 2001.7083333333333, 2001.7916666666667, 2001.875, 2001.9583333333333, 2002.0416666666667, 2002.125, 2002.2083333333333, 2002.2916666666667, 2002.375, 2002.4583333333333, 2002.5416666666667, 2002.625, 2002.7083333333333, 2002.7916666666667, 2002.875, 2002.9583333333333, 2003.0416666666667, 2003.125, 2003.2083333333333, 2003.2916666666667, 2003.375, 2003.4583333333333, 2003.5416666666667, 2003.625, 2003.7083333333333, 2003.7916666666667, 2003.875, 2003.9583333333333], δ = [315.71, 317.45, 317.5, 317.1, 315.86, 314.93, 313.2, 312.66, 313.33, 314.67, 315.62, 316.38, 316.71, 317.72, 318.29, 318.15, 316.54, 314.8, 313.84, 313.26, 314.8, 315.58, 316.43, 316.97, 317.58, 319.02, 320.03, 319.59, 318.18, 315.91, 314.16, 313.83, 315.0, 316.19, 316.93, 317.7, 318.54, 319.48, 320.58, 319.77, 318.57, 316.79, 314.8, 315.38, 316.1, 317.01, 317.94, 318.56, 319.68, 320.63, 321.01, 320.55, 319.58, 317.4, 316.26, 315.42, 316.69, 317.69, 318.74, 319.08, 319.86, 321.39, 322.25, 321.47, 319.74, 317.77, 316.21, 315.99, 317.12, 318.31, 319.57, 320.07, 320.73, 321.77, 322.25, 321.89, 320.44, 318.7, 316.7, 316.79, 317.79, 318.71, 319.44, 320.44, 320.89, 322.13, 322.16, 321.87, 321.39, 318.81, 317.81, 317.3, 318.87, 319.42, 320.62, 321.59, 322.39, 323.87, 324.01, 323.75, 322.39, 320.37, 318.64, 318.1, 319.79, 321.08, 322.07, 322.5, 323.04, 324.42, 325.0, 324.09, 322.55, 320.92, 319.31, 319.31, 320.72, 321.96, 322.57, 323.15, 323.89, 325.02, 325.57, 325.36, 324.14, 322.03, 320.41, 320.25, 321.31, 322.84, 324.0, 324.42, 325.64, 326.66, 327.34, 326.76, 325.88, 323.67, 322.38, 321.78, 322.85, 324.11, 325.03, 325.99, 326.87, 328.13, 328.07, 327.66, 326.35, 324.69, 323.1, 323.16, 323.98, 325.13, 326.17, 326.68, 327.18, 327.78, 328.92, 328.57, 327.34, 325.46, 323.36, 323.57, 324.8, 326.01, 326.77, 327.63, 327.75, 329.72, 330.07, 329.09, 328.05, 326.32, 324.93, 325.06, 326.5, 327.55, 328.54, 329.56, 330.3, 331.5, 332.48, 332.07, 330.87, 329.31, 327.51, 327.18, 328.16, 328.64, 329.35, 330.71, 331.48, 332.65, 333.19, 332.12, 330.99, 329.17, 327.41, 327.21, 328.34, 329.5, 330.68, 331.41, 331.85, 333.29, 333.91, 333.4, 331.74, 329.88, 328.57, 328.36, 329.33, 330.59, 331.66, 332.75, 333.46, 334.78, 334.78, 334.06, 332.95, 330.64, 328.96, 328.77, 330.18, 331.65, 332.69, 333.23, 334.97, 336.03, 336.82, 336.1, 334.79, 332.53, 331.19, 331.21, 332.35, 333.47, 335.1, 335.26, 336.61, 337.77, 338.01, 337.98, 336.48, 334.37, 332.33, 332.41, 333.76, 334.83, 336.21, 336.65, 338.13, 338.94, 339.0, 339.2, 337.6, 335.56, 333.93, 334.12, 335.26, 336.78, 337.8, 338.28, 340.04, 340.86, 341.47, 341.26, 339.34, 337.45, 336.1, 336.05, 337.21, 338.29, 339.36, 340.51, 341.57, 342.56, 343.01, 342.49, 340.68, 338.49, 336.92, 337.12, 338.59, 339.9, 340.92, 341.69, 342.85, 343.92, 344.67, 343.78, 342.23, 340.11, 338.32, 338.39, 339.48, 340.88, 341.64, 342.87, 343.59, 345.25, 345.96, 345.52, 344.15, 342.25, 340.17, 340.3, 341.53, 343.07, 344.05, 344.77, 345.46, 346.77, 347.55, 346.98, 345.55, 343.2, 341.35, 341.68, 343.06, 344.54, 345.25, 346.06, 347.66, 348.2, 348.92, 348.4, 346.66, 344.85, 343.2, 343.08, 344.4, 345.82, 346.54, 347.13, 348.05, 349.77, 350.53, 349.9, 348.11, 346.09, 345.01, 344.47, 345.86, 347.15, 348.38, 348.7, 349.72, 351.32, 352.14, 351.61, 349.91, 347.84, 346.52, 346.65, 347.96, 349.18, 350.38, 351.68, 352.24, 353.66, 354.18, 353.68, 352.58, 350.66, 349.03, 349.08, 350.15, 351.44, 352.89, 353.24, 353.8, 355.59, 355.89, 355.3, 353.98, 351.53, 350.02, 350.29, 351.44, 352.84, 353.79, 354.88, 355.65, 356.27, 357.29, 356.32, 354.88, 352.89, 351.28, 351.59, 353.05, 354.27, 354.87, 355.68, 357.06, 358.51, 359.09, 358.1, 356.12, 353.89, 352.3, 352.32, 353.79, 355.07, 356.17, 356.93, 357.82, 359.0, 359.55, 359.32, 356.85, 354.91, 352.93, 353.31, 354.27, 355.53, 356.86, 357.27, 358.36, 359.27, 360.19, 359.52, 357.42, 355.46, 354.1, 354.12, 355.4, 356.84, 358.22, 358.98, 359.91, 361.32, 361.68, 360.8, 359.39, 357.42, 355.63, 356.09, 357.56, 358.87, 359.87, 360.79, 361.77, 363.23, 363.77, 363.22, 361.7, 359.11, 358.11, 357.97, 359.4, 360.61, 362.04, 363.17, 364.17, 364.51, 365.16, 364.93, 363.53, 361.38, 359.6, 359.54, 360.84, 362.18, 363.04, 364.09, 364.47, 366.25, 366.69, 365.59, 364.34, 362.2, 360.31, 360.71, 362.44, 364.33, 365.18, 365.98, 367.13, 368.61, 369.49, 368.95, 367.74, 365.79, 364.01, 364.35, 365.52, 367.08, 368.12, 368.98, 369.6, 370.96, 370.77, 370.33, 369.28, 366.86, 364.94, 365.35, 366.68, 368.04, 369.25, 369.5, 370.56, 371.82, 371.51, 371.71, 369.85, 368.2, 366.91, 366.99, 368.33, 369.67, 370.52, 371.49, 372.53, 373.37, 373.82, 373.18, 371.57, 369.63, 368.16, 368.42, 369.69, 371.18, 372.45, 373.14, 373.93, 375.0, 375.65, 375.5, 374.0, 371.83, 370.66, 370.51, 372.2, 373.71, 374.87, 375.62, 376.48, 377.74, 378.5, 378.18, 376.72, 374.31, 373.2, 373.1, 374.64, 375.93]))","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"Let's visualize what the GP fitted to the data looks like, for the initial choice of kernel hyperparameters.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"We use the following function to plot a GP f on a specific range, using the AbstractGPs plotting recipes. By setting ribbon_scale=2 we visualize the uncertainty band with pm 2 (instead of the default pm 1) standard deviations.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"plot_gp!(f; label) = plot!(f(1920:0.2:2030); ribbon_scale=2, linewidth=1, label)\n\nplotdata()\nplot_gp!(fpost_init; label=\"posterior f(⋅)\")","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"A reasonable fit to the data, but poor extrapolation away from the observations!","category":"page"},{"location":"examples/1-mauna-loa/#Hyperparameter-Optimization","page":"Mauna Loa time series example","title":"Hyperparameter Optimization","text":"","category":"section"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"To improve the fit, we want to maximize the (log) marginal likelihood with respect to the hyperparameters. Optim.jl expects to minimize a loss, so we define it as the negative log marginal likelihood:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"function loss(θ)\n fx = build_finite_gp(θ)\n lml = logpdf(fx, ytrain) # this computes the log marginal likelihood\n return -lml\nend","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"note: Work-in-progress\nIn the future, we are planning to provide the optimize_loss utility function as part of JuliaGaussianProcesses – for now, we just define it inline.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"The L-BFGS parameters were chosen because they seem to work well empirically. You could also try with the defaults.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"default_optimizer = LBFGS(;\n alphaguess=Optim.LineSearches.InitialStatic(; scaled=true),\n linesearch=Optim.LineSearches.BackTracking(),\n)\n\nfunction optimize_loss(loss, θ_init; optimizer=default_optimizer, maxiter=1_000)\n options = Optim.Options(; iterations=maxiter, show_trace=true)\n\n θ_flat_init, unflatten = ParameterHandling.value_flatten(θ_init)\n loss_packed = loss ∘ unflatten\n\n # https://julianlsolvers.github.io/Optim.jl/stable/#user/tipsandtricks/#avoid-repeating-computations\n function fg!(F, G, x)\n if F !== nothing && G !== nothing\n val, grad = Zygote.withgradient(loss_packed, x)\n G .= only(grad)\n return val\n elseif G !== nothing\n grad = Zygote.gradient(loss_packed, x)\n G .= only(grad)\n return nothing\n elseif F !== nothing\n return loss_packed(x)\n end\n end\n\n result = optimize(Optim.only_fg!(fg!), θ_flat_init, optimizer, options; inplace=false)\n\n return unflatten(result.minimizer), result\nend","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"We now run the optimization:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"θ_opt, opt_result = optimize_loss(loss, θ_init)\nopt_result","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":" * Status: success\n\n * Candidate solution\n Final objective value: 1.152862e+02\n\n * Found with\n Algorithm: L-BFGS\n\n * Convergence measures\n |x - x'| = 0.00e+00 ≤ 0.0e+00\n |x - x'|/|x'| = 0.00e+00 ≤ 0.0e+00\n |f(x) - f(x')| = 0.00e+00 ≤ 0.0e+00\n |f(x) - f(x')|/|f(x')| = 0.00e+00 ≤ 0.0e+00\n |g(x)| = 3.78e-02 ≰ 1.0e-08\n\n * Work counters\n Seconds run: 28 (vs limit Inf)\n Iterations: 61\n f(x) calls: 95\n ∇f(x) calls: 61\n","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"The final value of the log marginal likelihood is:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"-opt_result.minimum","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"-115.28622096603124","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"warning: Warning\nTo avoid bad local optima, we could (and should) have carried out several random restarts with different initial values for the hyperparameters, and then picked the result with the highest marginal likelihood. We omit this for simplicity. For more details on how to fit GPs in practice, check out A Practical Guide to Gaussian Processes.","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"Let's construct the posterior GP with the optimized hyperparameters:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"fpost_opt = build_posterior_gp(ParameterHandling.value(θ_opt))","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"This is the kernel with the point-estimated hyperparameters:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"fpost_opt.prior.kernel","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"Sum of 5 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.006456502042458794)\n\t\t\t- σ² = 191273.47839039602\n\tProduct of 2 kernels:\n\t\t\tPeriodic Kernel, length(r) = 1\n\t\t\t\t\t- Scale Transform (s = 1.0)\n\t\t\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t\t\t- Scale Transform (s = 0.012139119857609751)\n\t\t\t\t\t- σ² = 5.99003503589132\n\tRational Quadratic Kernel (α = 0.0007411653885252467, metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.04622167413324385)\n\t\t\t- σ² = 178.92175101210103\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 8.752621585494843)\n\t\t\t- σ² = 0.028315070269881937\n\tWhite Kernel\n\t\t\t- σ² = 0.03576077215040964","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"Let's print the optimized values of the hyperparameters in a more helpful format:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"note: Work-in-progress\nThis is another utility function we would eventually like to move out of this notebook:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"using Printf\n\nshow_params(nt::Union{Dict,NamedTuple}) = String(take!(show_params(IOBuffer(), nt)))\nfunction show_params(io, nt::Union{Dict,NamedTuple}, indent::Int=0)\n for (s, v) in pairs(nt)\n if v isa Union{Dict,NamedTuple}\n println(io, \" \"^indent, s, \":\")\n show_params(io, v, indent + 4)\n else\n println(io, \" \"^indent, s, \" = \", @sprintf(\"%.3f\", v))\n end\n end\n return io\nend\n\nprint(show_params(ParameterHandling.value(θ_opt)))","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"se_long:\n σ = 437.348\n ℓ = 154.883\nseasonal:\n per:\n ℓ = 1.456\n p = 1.000\n se:\n σ = 2.447\n ℓ = 82.378\nrq:\n σ = 13.376\n ℓ = 21.635\n α = 0.001\nse_short:\n σ = 0.168\n ℓ = 0.114\nnoise_scale = 0.189\n","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"And, finally, we can visualize our optimized posterior GP:","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"plotdata()\nplot_gp!(fpost_opt; label=\"optimized posterior f(⋅)\")","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"
    \n
    Package and system information
    \n
    \nPackage information (click to expand)\n
    \nStatus `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/1-mauna-loa/Project.toml`\n  [99985d1d] AbstractGPs v0.5.20 `/home/runner/work/AbstractGPs.jl/AbstractGPs.jl#07d5f00`\n  [336ed68f] CSV v0.10.13\n  [a93c6f00] DataFrames v1.6.1\n  [98b081ad] Literate v2.16.1\n  [429524aa] Optim v1.9.2\n⌅ [2412ca09] ParameterHandling v0.4.10\n  [91a5bcdd] Plots v1.40.2\n  [e88e6eb3] Zygote v0.6.69\nInfo Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated`\n
    \nTo reproduce this notebook's package environment, you can\n\ndownload the full Manifest.toml.\n
    \n
    \nSystem information (click to expand)\n
    \nJulia Version 1.10.2\nCommit bd47eca2c8a (2024-03-01 10:14 UTC)\nBuild Info:\n  Official https://julialang.org/ release\nPlatform Info:\n  OS: Linux (x86_64-linux-gnu)\n  CPU: 4 × AMD EPYC 7763 64-Core Processor\n  WORD_SIZE: 64\n  LIBM: libopenlibm\n  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)\nThreads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)\nEnvironment:\n  JULIA_DEBUG = Documenter\n  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src\n
    \n
    ","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"","category":"page"},{"location":"examples/1-mauna-loa/","page":"Mauna Loa time series example","title":"Mauna Loa time series example","text":"This page was generated using Literate.jl.","category":"page"},{"location":"concrete_features/#Features","page":"Concrete Features","title":"Features","text":"","category":"section"},{"location":"concrete_features/#Setup","page":"Concrete Features","title":"Setup","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"using AbstractGPs, Random\nrng = MersenneTwister(0)\n\n# Construct a zero-mean Gaussian process with a matern-3/2 kernel.\nf = GP(Matern32Kernel())\n\n# Specify some input and target locations.\nx = randn(rng, 10)\ny = randn(rng, 10)","category":"page"},{"location":"concrete_features/#Finite-dimensional-projection","page":"Concrete Features","title":"Finite dimensional projection","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"Look at the finite-dimensional projection of f at x, under zero-mean observation noise with variance 0.1.","category":"page"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"fx = f(x, 0.1)","category":"page"},{"location":"concrete_features/#Sample-from-GP-from-the-prior-at-x-under-noise.","page":"Concrete Features","title":"Sample from GP from the prior at x under noise.","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"y_sampled = rand(rng, fx)","category":"page"},{"location":"concrete_features/#Compute-the-log-marginal-probability-of-y.","page":"Concrete Features","title":"Compute the log marginal probability of y.","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"logpdf(fx, y)","category":"page"},{"location":"concrete_features/#Construct-the-posterior-process-implied-by-conditioning-f-at-x-on-y.","page":"Concrete Features","title":"Construct the posterior process implied by conditioning f at x on y.","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"f_posterior = posterior(fx, y)","category":"page"},{"location":"concrete_features/#A-posterior-process-follows-the-AbstractGP-interface,-so-the-same-functions-which-work-on-the-posterior-as-on-the-prior.","page":"Concrete Features","title":"A posterior process follows the AbstractGP interface, so the same functions which work on the posterior as on the prior.","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"rand(rng, f_posterior(x))\nlogpdf(f_posterior(x), y)","category":"page"},{"location":"concrete_features/#Compute-the-VFE-approximation-to-the-log-marginal-probability-of-y.","page":"Concrete Features","title":"Compute the VFE approximation to the log marginal probability of y.","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"Here, z is a set of pseudo-points.","category":"page"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"z = randn(rng, 4)","category":"page"},{"location":"concrete_features/#Evidence-Lower-BOund-(ELBO)","page":"Concrete Features","title":"Evidence Lower BOund (ELBO)","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"We provide a ready implentation of elbo w.r.t to the pseudo points. We can perform Variational Inference on pseudo-points by maximizing the ELBO term w.r.t pseudo-points z and any kernel parameters. For more information, see examples.","category":"page"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"elbo(VFE(f(z)), fx, y)","category":"page"},{"location":"concrete_features/#Construct-the-approximate-posterior-process-implied-by-the-VFE-approximation.","page":"Concrete Features","title":"Construct the approximate posterior process implied by the VFE approximation.","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"The optimal pseudo-points obtained above can be used to create a approximate/sparse posterior. This can be used like a regular posterior in many cases.","category":"page"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"f_approx_posterior = posterior(VFE(f(z)), fx, y)","category":"page"},{"location":"concrete_features/#An-approximate-posterior-process-is-yet-another-AbstractGP,-so-you-can-do-things-with-it-like","page":"Concrete Features","title":"An approximate posterior process is yet another AbstractGP, so you can do things with it like","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"marginals(f_approx_posterior(x))","category":"page"},{"location":"concrete_features/#Sequential-Conditioning","page":"Concrete Features","title":"Sequential Conditioning","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"Sequential conditioning allows you to compute your posterior in an online fashion. We do this in an efficient manner by updating the cholesky factorisation of the covariance matrix and avoiding recomputing it from original covariance matrix.","category":"page"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"# Define GP prior\nf = GP(SqExponentialKernel())","category":"page"},{"location":"concrete_features/#Exact-Posterior","page":"Concrete Features","title":"Exact Posterior","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"Generate posterior with the first batch of data by conditioning the prior on them:","category":"page"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"p_fx = posterior(f(x[1:3], 0.1), y[1:3])","category":"page"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"Generate posterior with the second batch of data, considering the previous posterior p_fx as the prior:","category":"page"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"p_p_fx = posterior(p_fx(x[4:10], 0.1), y[4:10])","category":"page"},{"location":"concrete_features/#Approximate-Posterior","page":"Concrete Features","title":"Approximate Posterior","text":"","category":"section"},{"location":"concrete_features/#Adding-observations-in-a-sequential-fashion","page":"Concrete Features","title":"Adding observations in a sequential fashion","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"Z1 = rand(rng, 4)\nZ2 = rand(rng, 3)\nZ = vcat(Z1, Z2)\np_fx1 = posterior(VFE(f(Z)), f(x[1:7], 0.1), y[1:7])\nu_p_fx1 = update_posterior(p_fx1, f(x[8:10], 0.1), y[8:10])","category":"page"},{"location":"concrete_features/#Adding-pseudo-points-in-a-sequential-fashion","page":"Concrete Features","title":"Adding pseudo-points in a sequential fashion","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"p_fx2 = posterior(VFE(f(Z1)), f(x, 0.1), y)\nu_p_fx2 = update_posterior(p_fx2, f(Z2))","category":"page"},{"location":"concrete_features/#Plotting","page":"Concrete Features","title":"Plotting","text":"","category":"section"},{"location":"concrete_features/#Plots.jl","page":"Concrete Features","title":"Plots.jl","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"We provide functions for plotting samples and predictions of Gaussian processes with Plots.jl. You can see some examples in the One-dimensional regression tutorial.","category":"page"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"AbstractGPs.RecipesBase.plot(::AbstractVector, ::AbstractGPs.FiniteGP)\nAbstractGPs.RecipesBase.plot(::AbstractGPs.FiniteGP)\nAbstractGPs.RecipesBase.plot(::AbstractVector, ::AbstractGPs.AbstractGP)\nsampleplot","category":"page"},{"location":"concrete_features/#RecipesBase.plot-Tuple{AbstractVector, AbstractGPs.FiniteGP}","page":"Concrete Features","title":"RecipesBase.plot","text":"plot(x::AbstractVector, f::FiniteGP; ribbon_scale=1, kwargs...)\nplot!([plot, ]x::AbstractVector, f::FiniteGP; ribbon_scale=1, kwargs...)\n\nPlot the predictive mean for the projection f of a Gaussian process and a ribbon of ribbon_scale standard deviations around it versus x.\n\nnote: Note\nMake sure to load Plots.jl before you use this function.\n\nExamples\n\nPlot the mean and a ribbon of 3 standard deviations:\n\nusing Plots\n\ngp = GP(SqExponentialKernel())\nplot(gp(rand(5)); ribbon_scale=3)\n\n\n\n\n\n","category":"method"},{"location":"concrete_features/#RecipesBase.plot-Tuple{AbstractGPs.FiniteGP}","page":"Concrete Features","title":"RecipesBase.plot","text":"plot(f::FiniteGP; kwargs...)\nplot!([plot, ]f::FiniteGP; kwargs...)\n\nPlot the predictive mean and a ribbon around it for the projection f of a Gaussian process versus f.x.\n\n\n\n\n\n","category":"method"},{"location":"concrete_features/#RecipesBase.plot-Tuple{AbstractVector, AbstractGPs.AbstractGP}","page":"Concrete Features","title":"RecipesBase.plot","text":"plot(x::AbstractVector, gp::AbstractGP; kwargs...)\nplot!([plot, ]x::AbstractVector, gp::AbstractGP; kwargs...)\n\nPlot the predictive mean and a ribbon around it for the projection gp(x) of the Gaussian process gp.\n\n\n\n\n\n","category":"method"},{"location":"concrete_features/#AbstractGPs.sampleplot","page":"Concrete Features","title":"AbstractGPs.sampleplot","text":"sampleplot([x::AbstractVector=f.x, ]f::FiniteGP; samples=1, kwargs...)\n\nPlot samples from the projection f of a Gaussian process versus x.\n\nnote: Note\nMake sure to load Plots.jl before you use this function.\n\nWhen plotting multiple samples, these are treated as a single series (i.e., only a single entry will be added to the legend when providing a label).\n\nExample\n\nusing Plots\n\ngp = GP(SqExponentialKernel())\nsampleplot(gp(rand(5)); samples=10, linealpha=1.0)\n\nThe given example plots 10 samples from the projection of the GP gp. The linealpha is modified from default of 0.35 to 1.\n\n\n\nsampleplot(x::AbstractVector, gp::AbstractGP; samples=1, kwargs...)\n\nPlot samples from the finite projection gp(x, 1e-9) versus x.\n\n\n\n\n\n","category":"function"},{"location":"concrete_features/#Makie.jl","page":"Concrete Features","title":"Makie.jl","text":"","category":"section"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"You can use the Julia package AbstractGPsMakie.jl to plot Gaussian processes with Makie.jl.","category":"page"},{"location":"concrete_features/","page":"Concrete Features","title":"Concrete Features","text":"(Image: posterior animation)","category":"page"},{"location":"#AbstractGPs.jl","page":"Home","title":"AbstractGPs.jl","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"Abstract types and methods for Gaussian Processes.","category":"page"},{"location":"","page":"Home","title":"Home","text":"AbstractGPs.jl is a package that defines a low-level API for working with Gaussian processes (GPs), and basic functionality for working with them in the simplest cases. While it is aimed more at developers and researchers who are interested in using it as a building block than end-users of GPs, much can be achieved with it as an end-user.","category":"page"}] +} diff --git a/previews/PR387/siteinfo.js b/previews/PR387/siteinfo.js new file mode 100644 index 00000000..ba99fad8 --- /dev/null +++ b/previews/PR387/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "previews/PR387";
    GitHub

    Parametric Heteroscedastic Model

    You are seeing the HTML output generated by Documenter.jl and Literate.jl from the Julia source file. The corresponding notebook can be viewed in nbviewer.

    This example is a small extension of the standard GP regression problem, in which the observation noise variance is a function of the input. It is assumed to be a simple quadratic form, with a single unknown scaling parameter, in addition to the usual lengthscale and variance parameters of the GP. A point estimate of all parameters is obtained using type-II maximum likelihood, as per usual.

    using AbstractGPs
+using AbstractGPsMakie
+using CairoMakie
+using KernelFunctions
+using Optim
+using ParameterHandling
+using Zygote
+
+using LinearAlgebra
+using Random
+Random.seed!(42)  # setting the seed for reproducibility of this notebook

    In this example we work with a simple GP with a Gaussian kernel and heteroscedastic observation variance.

    observation_variance(θ, x::AbstractVector{<:Real}) = Diagonal(0.01 .+ θ.σ² .* x .^ 2)
+function build_gpx(θ, x::AbstractVector{<:Real})
+    Σ = observation_variance(θ, x)
+    return GP(0, θ.s * with_lengthscale(SEKernel(), θ.l))(x, Σ)
+end;

    We specify the following hyperparameters:

    const flat_θ, unflatten = ParameterHandling.value_flatten((
+    s=positive(1.0), l=positive(3.0), σ²=positive(0.1)
+));
+θ = unflatten(flat_θ);

    We generate some observations:

    const x = 0.0:0.1:10.0
+const y = rand(build_gpx(θ, x));

    We specify the objective function:

    function objective(flat_θ)
+    θ = unflatten(flat_θ)
+    fx = build_gpx(θ, x)
+    return -logpdf(fx, y)
+end;

    We use L-BFGS for optimising the objective function. It is a first-order method and hence requires computing the gradient of the objective function. We do not derive and implement the gradient function manually here but instead use reverse-mode automatic differentiation with Zygote. When computing gradients with Zygote, the objective function is evaluated as well. We can exploit this and avoid re-evaluating the objective function in such cases.

    function objective_and_gradient(F, G, flat_θ)
+    if G !== nothing
+        val_grad = Zygote.withgradient(objective, flat_θ)
+        copyto!(G, only(val_grad.grad))
+        if F !== nothing
+            return val_grad.val
+        end
+    end
+    if F !== nothing
+        return objective(flat_θ)
+    end
+    return nothing
+end;

    We optimise the hyperparameters using initializations close to the values that the observations were generated with.

    flat_θ_init = flat_θ + 0.01 * randn(length(flat_θ))
+result = optimize(
+    Optim.only_fg!(objective_and_gradient),
+    flat_θ_init,
+    LBFGS(;
+        alphaguess=Optim.LineSearches.InitialStatic(; scaled=true),
+        linesearch=Optim.LineSearches.BackTracking(),
+    ),
+    Optim.Options(; show_every=100),
+)
     * Status: success
+
+ * Candidate solution
+    Final objective value:     1.576205e+02
+
+ * Found with
+    Algorithm:     L-BFGS
+
+ * Convergence measures
+    |x - x'|               = 3.97e-08 ≰ 0.0e+00
+    |x - x'|/|x'|          = 1.58e-08 ≰ 0.0e+00
+    |f(x) - f(x')|         = 0.00e+00 ≤ 0.0e+00
+    |f(x) - f(x')|/|f(x')| = 0.00e+00 ≤ 0.0e+00
+    |g(x)|                 = 6.34e-09 ≤ 1.0e-08
+
+ * Work counters
+    Seconds run:   1  (vs limit Inf)
+    Iterations:    10
+    f(x) calls:    13
+    ∇f(x) calls:   11
+

    The optimal model parameters are:

    θ_final = unflatten(result.minimizer)
    (s = 0.385143986830958, l = 2.3342748073768, σ² = 0.08082582089918736)

    We compute the posterior GP with these optimal model parameters:

    fx_final = build_gpx(θ_final, x)
+f_post = posterior(fx_final, y);

    We visualize the results with AbstractGPsMakie:

    using CairoMakie.Makie.ColorSchemes: Set1_4
+
+with_theme(
+    Theme(;
+        palette=(color=Set1_4,),
+        patchcolor=(Set1_4[2], 0.2),
+        Axis=(limits=((0, 10), nothing),),
+    ),
+) do
+    plot(
+        x,
+        f_post(x, observation_variance(θ_final, x));
+        bandscale=3,
+        label="posterior + noise",
+        color=(:orange, 0.3),
+    )
+    plot!(x, f_post; bandscale=3, label="posterior")
+    gpsample!(x, f_post; samples=10, color=Set1_4[3])
+    scatter!(x, y; label="y")
+    axislegend()
+    current_figure()
+end

    +
    Package and system information
    +
    +Package information (click to expand) +
    +Status `~/work/AbstractGPs.jl/AbstractGPs.jl/examples/3-parametric-heteroscedastic/Project.toml`
+  [99985d1d] AbstractGPs v0.5.20 `/home/runner/work/AbstractGPs.jl/AbstractGPs.jl#07d5f00`
+  [7834405d] AbstractGPsMakie v0.2.6
+⌃ [13f3f980] CairoMakie v0.10.12
+  [ec8451be] KernelFunctions v0.10.63
+  [98b081ad] Literate v2.16.1
+  [429524aa] Optim v1.9.2
+⌅ [2412ca09] ParameterHandling v0.4.10
+  [e88e6eb3] Zygote v0.6.69
+  [37e2e46d] LinearAlgebra
+  [9a3f8284] Random
+Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated`
+
    +To reproduce this notebook's package environment, you can + +download the full Manifest.toml. +
    +
    +System information (click to expand) +
    +Julia Version 1.10.2
+Commit bd47eca2c8a (2024-03-01 10:14 UTC)
+Build Info:
+  Official https://julialang.org/ release
+Platform Info:
+  OS: Linux (x86_64-linux-gnu)
+  CPU: 4 × AMD EPYC 7763 64-Core Processor
+  WORD_SIZE: 64
+  LIBM: libopenlibm
+  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
+Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)
+Environment:
+  JULIA_DEBUG = Documenter
+  JULIA_LOAD_PATH = :/home/runner/.julia/packages/JuliaGPsDocs/7M86H/src
+
    +

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