Merge pull request #115 from JuliaGaussianProcesses/fix-CI

Fix CI

Author: simsurace

Project.toml

@@ -17,13 +17,13 @@ StructArrays = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-AbstractGPs = "0.5.15"
+AbstractGPs = "0.5.17"
 Bessels = "0.2.8"
 BlockDiagonals = "0.1.7"
 ChainRulesCore = "1"
 FillArrays = "0.13.0 - 0.13.7, 1"
 KernelFunctions = "0.9, 0.10.1"
 StaticArrays = "1"
 StructArrays = "0.5, 0.6"
-Zygote = "0.6"
+Zygote = "0.6.65"
 julia = "1.6"

README.md

@@ -8,10 +8,6 @@ TemporalGPs.jl is a tool to make Gaussian processes (GPs) defined using [Abstrac
 
 [JuliaCon 2020 Talk](https://www.youtube.com/watch?v=dysmEpX1QoE)
 
-# Dependency Status
-
-In the interest of managing expectations, please note that TemporalGPs does not currently operate with the most current version of AbstractGPs / Zygote / ChainRules. I (Will) am aware of this problem, and will sort it out as soon as I have the time!
-
 # Installation
 
 TemporalGPs.jl is registered, so simply type the following at the REPL:

examples/approx_space_time_inference.jl

@@ -61,6 +61,6 @@ if get(ENV, "TESTING", "FALSE") == "FALSE"
             heatmap(reshape(σ_post_marginals, N_pr, T));
             layout=(1, 2),
         ),
-        "posterior.png",
+        "approx_space_time_inference.png",
     );
 end

examples/approx_space_time_learning.jl

@@ -100,6 +100,6 @@ if get(ENV, "TESTING", "FALSE") == "FALSE"
             heatmap(reshape(σ_post_marginals, N_pr, T));
             layout=(1, 2),
         ),
-        "posterior.png",
+        "approx_space_time_learning.png",
     );
 end

examples/augmented_inference.jl

@@ -1,6 +1,6 @@
 using AbstractGPs
 using TemporalGPs
-using Distributions
+using Distributions: Bernoulli
 using StatsFuns: logistic
 
 # In this example we are showing how to work with non-Gaussian likelihoods,
@@ -73,5 +73,5 @@ if get(ENV, "TESTING", "FALSE") == "FALSE"
     plot!(plt, x_pr, f_post_samples; color=:red, alpha=0.3, label="");
     plot!(plt, x, f_true; label="", lw=2.0, color=:blue); # Plot the true latent GP on top 
     scatter!(plt, x, y; label="", markersize=1.0, alpha=1.0); # Plot the data
-    savefig(plt, "posterior.png");
+    savefig(plt, "augmented_inference.png");
 end

examples/exact_space_time_inference.jl

@@ -59,6 +59,6 @@ if get(ENV, "TESTING", "FALSE") == "FALSE"
             heatmap(reshape(σ_post_marginals, N, T_pr));
             layout=(1, 2),
         ),
-        "posterior.png",
+        "exact_space_time_inference.png",
     );
 end

examples/exact_space_time_learning.jl

@@ -39,7 +39,7 @@ end
 # Exact inference only works for such grids.
 # Times must be increasing, points in space can be anywhere.
 N = 50;
-T = 1_000;
+T = 500;
 points_in_space = collect(range(-3.0, 3.0; length=N));
 points_in_time = RegularSpacing(0.0, 0.01, T);
 x = RectilinearGrid(points_in_space, points_in_time);
@@ -73,7 +73,7 @@ final_params = unpack(training_results.minimizer)
 f_post = posterior(build_gp(final_params)(x, final_params.var_noise), y);
 
 # Specify some locations at which to make predictions.
-T_pr = 1200;
+T_pr = 600;
 points_in_time_pr = RegularSpacing(0.0, 0.01, T_pr);
 x_pr = RectilinearGrid(points_in_space, points_in_time_pr);
 
@@ -93,6 +93,6 @@ if get(ENV, "TESTING", "FALSE") == "FALSE"
             heatmap(reshape(σ_post_marginals, N, T_pr));
             layout=(1, 2),
         ),
-        "posterior.png",
+        "exact_space_time_learning.png",
     );
 end

examples/exact_time_inference.jl

@@ -48,5 +48,5 @@ if get(ENV, "TESTING", "FALSE") == "FALSE"
     scatter!(plt, x, y; label="", markersize=0.1, alpha=0.1);
     plot!(plt, f_post(x_pr); ribbon_scale=3.0, label="");
     plot!(x_pr, f_post_samples; color=:red, label="");
-    savefig(plt, "posterior.png");
+    savefig(plt, "exact_time_inference.png");
 end

examples/exact_time_learning.jl

@@ -86,5 +86,5 @@ if get(ENV, "TESTING", "FALSE") == "FALSE"
     scatter!(plt, x, y; label="", markersize=0.1, alpha=0.1);
     plot!(plt, f_post(x_pr); ribbon_scale=3.0, label="");
     plot!(plt, x_pr, f_post_samples; color=:red, label="");
-    savefig(plt, "posterior.png");
+    savefig(plt, "exact_time_learning.png");
 end

src/models/linear_gaussian_conditionals.jl

@@ -46,6 +46,7 @@ be equivalent to
 function predict(x::Gaussian, f::AbstractLGC)
     A, a, Q = get_fields(f)
     m, P = get_fields(x)
+    
     # Symmetric wrapper needed for numerical stability. Do not unwrap.
     return Gaussian(A * m + a, (A * symmetric(P)) * A' + Q)
 end