From ce49649c799b6ae0dc5a71da3739a49d2952c855 Mon Sep 17 00:00:00 2001 From: Nikos Gianniotis Date: Mon, 19 Aug 2024 14:14:44 +0200 Subject: [PATCH] commented code --- src/gplvmplus/unpack_gplvmplus.jl | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/src/gplvmplus/unpack_gplvmplus.jl b/src/gplvmplus/unpack_gplvmplus.jl index 9d3ebee..ef3ab8c 100644 --- a/src/gplvmplus/unpack_gplvmplus.jl +++ b/src/gplvmplus/unpack_gplvmplus.jl @@ -6,26 +6,26 @@ function unpack_gplvmplus(p, D, N, net, Q) MARK = 0 - Z = reshape(p[MARK+1:MARK+Q*N], Q, N); MARK += Q*N + Z = reshape(p[MARK+1:MARK+Q*N], Q, N); MARK += Q*N # latent coordinates - θ = log1pexp(p[MARK+1]); MARK += 1 + θ = log1pexp(p[MARK+1]); MARK += 1 # GP lengthscale - β = log1pexp(p[MARK+1]) + 1; MARK += 1 + β = log1pexp(p[MARK+1]) + 1; MARK += 1 # inverse noise - w = p[MARK+1:MARK+nwts]; MARK += nwts + w = p[MARK+1:MARK+nwts]; MARK += nwts # neural network weights - Λroot = Diagonal((p[MARK+1:MARK+N])); MARK += N + Λroot = Diagonal((p[MARK+1:MARK+N])); MARK += N # diagonal for parametrising covariance of posterior function values - α = log1pexp(p[MARK+1]); MARK += 1 + α = log1pexp(p[MARK+1]); MARK += 1 # scaling coefficient inside exp(⋅) non-linearity - b = p[MARK+1]; MARK += 1 + b = p[MARK+1]; MARK += 1 # shift coefficient inside exp(⋅) non-linearity - c = log1pexp.(p[MARK+1:MARK+N]); MARK += N + c = log1pexp.(p[MARK+1:MARK+N]); MARK += N # individual scaling coefficients @assert(MARK == length(p)) - μ = net(w, Z) + μ = net(w, Z) # posterior mean of latent function values parametrised by neural network - return Z, [1.0;θ], β, μ, Λroot, w, α, b, c + return Z, [1.0;θ], β, μ, Λroot, w, α, b, c # global amplitude fixed to 1.0 without loss of generalisation end \ No newline at end of file