Ran JuliaFormatter

JuliaGaussianProcesses · Jul 30, 2021 · be96722 · be96722
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commit be96722
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Showing 9 changed files with 125 additions and 148 deletions.
diff --git a/examples/classification.jl b/examples/classification.jl
@@ -12,7 +12,7 @@ using DelimitedFiles
 using IterTools
 
 using Plots
-default(; legend=:outertopright, size=(700, 400))
+default(; legend = :outertopright, size = (700, 400))
 
 using Random
 Random.seed!(1234)
@@ -40,45 +40,33 @@ fx = f(x)
 # %%
 # Then, plot some samples from the prior underlying GP
 x_plot = 0:0.02:6
-prior_f_samples = rand(f.f(x_plot, 1e-6),20)
+prior_f_samples = rand(f.f(x_plot, 1e-6), 20)
 
-plt = plot(
-    x_plot,
-    prior_f_samples;
-    seriescolor="red",
-    linealpha=0.2,
-    label=""
-)
-scatter!(plt, x, y; seriescolor="blue", label="Data points")
+plt = plot(x_plot, prior_f_samples; seriescolor = "red", linealpha = 0.2, label = "")
+scatter!(plt, x, y; seriescolor = "blue", label = "Data points")
 
 
 # %%
 # Plot the same samples, but pushed through a logistic sigmoid to constrain
 # them in (0, 1).
 prior_y_samples = mean.(f.lik.(prior_f_samples))
 
-plt = plot(
-    x_plot,
-    prior_y_samples;
-    seriescolor="red",
-    linealpha=0.2,
-    label=""
-)
-scatter!(plt, x, y; seriescolor="blue", label="Data points")
+plt = plot(x_plot, prior_y_samples; seriescolor = "red", linealpha = 0.2, label = "")
+scatter!(plt, x, y; seriescolor = "blue", label = "Data points")
 
 
 # %%
 # A simple Flux model
 using Flux
 
 struct SVGPModel
-    k # kernel parameters
-    m # variational mean
-    A # variational covariance
-    z # inducing points
+    k::Any # kernel parameters
+    m::Any # variational mean
+    A::Any # variational covariance
+    z::Any # inducing points
 end
 
-@Flux.functor SVGPModel (k, m, A,) # Don't train the inducing inputs
+Flux.@functor SVGPModel (k, m, A) # Don't train the inducing inputs
 
 lik = BernoulliLikelihood()
 jitter = 1e-4
@@ -92,9 +80,9 @@ function (m::SVGPModel)(x)
     return fx, fu, q
 end
 
-function flux_loss(x, y; n_data=length(y))
+function flux_loss(x, y; n_data = length(y))
     fx, fu, q = model(x)
-    return -SparseGPs.elbo(fx, y, fu, q; n_data, method=MonteCarlo())
+    return -SparseGPs.elbo(fx, y, fu, q; n_data, method = MonteCarlo())
 end
 
 # %%
@@ -121,7 +109,7 @@ Flux.train!(
     (x, y) -> flux_loss(x, y),
     parameters,
     ncycle([(x, y)], 2000), # Train for 1000 epochs
-    opt
+    opt,
 )
 
 # %%
@@ -136,13 +124,7 @@ l_post = LatentGP(post, BernoulliLikelihood(), jitter)
 
 post_f_samples = rand(l_post.f(x_plot, 1e-6), 20)
 
-plt = plot(
-    x_plot,
-    post_f_samples;
-    seriescolor="red",
-    linealpha=0.2,
-    legend=false
-)
+plt = plot(x_plot, post_f_samples; seriescolor = "red", linealpha = 0.2, legend = false)
 
 # %%
 # As above, push these samples through a logistic sigmoid to get posterior predictions.
@@ -151,10 +133,10 @@ post_y_samples = mean.(l_post.lik.(post_f_samples))
 plt = plot(
     x_plot,
     post_y_samples;
-    seriescolor="red",
-    linealpha=0.2,
+    seriescolor = "red",
+    linealpha = 0.2,
     # legend=false,
-    label=""
+    label = "",
 )
-scatter!(plt, x, y; seriescolor="blue", label="Data points")
-vline!(z; label="Pseudo-points")
+scatter!(plt, x, y; seriescolor = "blue", label = "Data points")
+vline!(z; label = "Pseudo-points")
diff --git a/examples/regression.jl b/examples/regression.jl
@@ -8,7 +8,7 @@ using Optim
 using IterTools
 
 using Plots
-default(; legend=:outertopright, size=(700, 400))
+default(; legend = :outertopright, size = (700, 400))
 
 using Random
 Random.seed!(1234)
@@ -23,7 +23,7 @@ N = 10000 # Number of training points
 x = rand(Uniform(-1, 1), N)
 y = g.(x) + 0.3 * randn(N)
 
-scatter(x, y; xlabel="x", ylabel="y", legend=false)
+scatter(x, y; xlabel = "x", ylabel = "y", legend = false)
 
 
 # %%
@@ -34,13 +34,13 @@ lik_noise = 0.3
 jitter = 1e-5
 
 struct SVGPModel
-    k # kernel parameters
-    m # variational mean
-    A # variational covariance
-    z # inducing points
+    k::Any # kernel parameters
+    m::Any # variational mean
+    A::Any # variational covariance
+    z::Any # inducing points
 end
 
-@Flux.functor SVGPModel (k, m, A,) # Don't train the inducing inputs
+Flux.@functor SVGPModel (k, m, A) # Don't train the inducing inputs
 
 function make_kernel(k)
     return softplus(k[1]) * (SqExponentialKernel() ∘ ScaleTransform(softplus(k[2])))
@@ -68,7 +68,7 @@ function posterior(m::SVGPModel)
 end
 
 # Return the loss given data - in this case the negative ELBO.
-function flux_loss(x, y; n_data=length(y))
+function flux_loss(x, y; n_data = length(y))
     fx, fu, q = model(x)
     return -SparseGPs.elbo(fx, y, fu, q; n_data)
 end
@@ -90,7 +90,7 @@ model = SVGPModel(k, m, A, z)
 b = 100 # minibatch size
 opt = ADAM(0.001)
 parameters = Flux.params(model)
-data_loader = Flux.Data.DataLoader((x, y), batchsize=b)
+data_loader = Flux.Data.DataLoader((x, y), batchsize = b)
 
 # %%
 # Negative ELBO before training
@@ -99,10 +99,10 @@ println(flux_loss(x, y))
 # %%
 # Train the model
 Flux.train!(
-    (x, y) -> flux_loss(x, y; n_data=N),
+    (x, y) -> flux_loss(x, y; n_data = N),
     parameters,
     ncycle(data_loader, 300), # Train for 300 epochs
-    opt
+    opt,
 )
 
 # %%
@@ -116,16 +116,16 @@ post = posterior(model)
 scatter(
     x,
     y;
-    markershape=:xcross,
-    markeralpha=0.1,
-    xlim=(-1, 1),
-    xlabel="x",
-    ylabel="y",
-    title="posterior (VI with sparse grid)",
-    label="Train Data",
+    markershape = :xcross,
+    markeralpha = 0.1,
+    xlim = (-1, 1),
+    xlabel = "x",
+    ylabel = "y",
+    title = "posterior (VI with sparse grid)",
+    label = "Train Data",
 )
-plot!(-1:0.001:1, post; label="Posterior")
-vline!(z; label="Pseudo-points")
+plot!(-1:0.001:1, post; label = "Posterior")
+vline!(z; label = "Pseudo-points")
 
 
 # %% There is a closed form optimal solution for the variational posterior q(u)
@@ -137,8 +137,8 @@ function exact_q(fu, fx, y)
     σ² = fx.Σy[1]
     Kuf = cov(fu, fx)
     Kuu = Symmetric(cov(fu))
-    Σ = (Symmetric(cov(fu) + (1/σ²) * Kuf * Kuf'))
-    m = ((1/σ²)*Kuu* (Σ\Kuf)) * y
+    Σ = (Symmetric(cov(fu) + (1 / σ²) * Kuf * Kuf'))
+    m = ((1 / σ²) * Kuu * (Σ \ Kuf)) * y
     S = Symmetric(Kuu * (Σ \ Kuu))
     return MvNormal(m, S)
 end
@@ -164,15 +164,14 @@ AbstractGPs.elbo(fx, y, fu)
 scatter(
     x,
     y;
-    markershape=:xcross,
-    markeralpha=0.1,
-    xlim=(-1, 1),
-    xlabel="x",
-    ylabel="y",
-    title="posterior (VI with sparse grid)",
-    label="Train Data",
+    markershape = :xcross,
+    markeralpha = 0.1,
+    xlim = (-1, 1),
+    xlabel = "x",
+    ylabel = "y",
+    title = "posterior (VI with sparse grid)",
+    label = "Train Data",
 )
-plot!(-1:0.001:1, ap_ex; label="SVGP posterior")
-plot!(-1:0.001:1, ap_tits; label="Titsias posterior")
-vline!(z; label="Pseudo-points")
-
+plot!(-1:0.001:1, ap_ex; label = "SVGP posterior")
+plot!(-1:0.001:1, ap_tits; label = "Titsias posterior")
+vline!(z; label = "Pseudo-points")
diff --git a/src/SparseGPs.jl b/src/SparseGPs.jl
@@ -13,19 +13,9 @@ using FillArrays
 using KLDivergences
 
 using AbstractGPs:
-    AbstractGP,
-    FiniteGP,
-    LatentFiniteGP,
-    ApproxPosteriorGP,
-    At_A,
-    diag_At_A,
-    Xt_invA_X
+    AbstractGP, FiniteGP, LatentFiniteGP, ApproxPosteriorGP, At_A, diag_At_A
 
-export SVGP,
-    Default,
-    Analytic,
-    Quadrature,
-    MonteCarlo
+export SVGP, Default, Analytic, Quadrature, MonteCarlo
 
 include("elbo.jl")
 include("svgp.jl")