Resolve merge

examples/approx_space_time_learning.jl

@@ -54,61 +54,12 @@ y = sin.(first.(xs)) .+ cos.(last.(xs)) + sqrt.(params.var_noise) .* randn(lengt
 # Spatial pseudo-point inputs.
 z_r = collect(range(-3.0, 3.0; length=5));
 
-# # Specify an objective function for Optim to minimise in terms of x and y.
-# # We choose the usual negative log marginal likelihood (NLML).
-# function make_objective(unpack, x, y, z_r)
-#     function objective(flat_params)
-#         params = unpack(flat_params)
-#         f = build_gp(params)
-#         return elbo(f(x, params.var_noise), y, z_r)
-#     end
-#     return objective
-# end
-# objective = make_objective(unpack, x, y, z_r)
-
 function objective(flat_params)
     params = unpack(flat_params)
     f = build_gp(params)
     return -elbo(f(x, params.var_noise), y, z_r)
 end
 
-# using Random
-# # y = y
-# # z_r = z_r
-# # fx = build_gp(unpack(flat_initial_params))(x, params.var_noise)
-# # fx_dtc = TemporalGPs.dtcify(z_r, fx)
-# # lgssm = TemporalGPs.build_lgssm(fx_dtc)
-# # Σs = lgssm.emissions.fan_out.Q
-# # marg_diags = TemporalGPs.marginals_diag(lgssm)
-
-# # k = fx_dtc.f.f.kernel
-# # Cf_diags = TemporalGPs.kernel_diagonals(k, fx_dtc.x)
-
-# # # Transform a vector into a vector-of-vectors.
-# # y_vecs = TemporalGPs.restructure(y, lgssm.emissions)
-
-# # tmp = TemporalGPs.zygote_friendly_map(
-# #     ((Σ, Cf_diag, marg_diag, yn), ) -> begin
-# #         Σ_, _ = TemporalGPs.fill_in_missings(Σ, yn)
-# #         return sum(TemporalGPs.diag(Σ_ \ (Cf_diag - marg_diag.P))) -
-# #             count(ismissing, yn) + size(Σ_, 1)
-# #     end,
-# #     zip(Σs, Cf_diags, marg_diags, y_vecs),
-# # )
-
-# # logpdf(lgssm, y_vecs) # this is the failing thing
-
-# # for _ in 1:10
-# #     Tapir.TestUtils.test_rule(
-# #         Xoshiro(123456), objective, flat_initial_params;
-# #         perf_flag=:none,
-# #         interp=Tapir.TapirInterpreter(),
-# #         interface_only=false,
-# #         is_primitive=false,
-# #         safety_on=false,
-# #     )
-# # end
-
 # Optimise using Optim.
 function objective_grad(rule, flat_params)
     return Mooncake.value_and_gradient!!(rule, objective, flat_params)[2][2]