theogf

using Distributions
using Bijectors
using Makie, Colors
using Animations

p0 = MvNormal(ones(2))

samples = rand(p0, 1)

abslim = -7

theogf

using Plots; pyplot()

N = 100
x = range(-3, 3, length=N)
f1(x, y) = begin z = Complex(x, y); abs(z^z); end
f2(x, y) = begin z = Complex(x, y); angle(z^z); end
surface(x, x, f1.(x, x'), fill_z = f2.(x, x'), clims = (-pi, pi), color = :colorwheel)

theogf

using Plots, Colors, Measures
using AugmentedGaussianProcesses
pyplot()

julia_blue = RGB(0.251, 0.388, 0.847)
julia_green = RGB(0.22, 0.596, 0.149)
julia_purple = RGB(0.584, 0.345, 0.698)
julia_red = RGB(0.796, 0.235, 0.2)

x = [0.1, 0.2, 0.3]

theogf

using KernelFunctions
using Stheno
using Stheno: pw
using BenchmarkTools
using Zygote

# Ds = [1,2,5,10,20,50,100,200,500,1000]
Ds = [1,10,100,1000]
timestheno = zeros(Float64,length(Ds)); memstheno = similar(timestheno)
timekf = similar(timestheno); memkf = similar(timestheno)

theogf

using Makie
N_grid = 100
x_grid = range(0,1,length=N_grid)

scene = Makie.surface(x_grid,x_grid,rand(N_grid,N_grid))
push!(scene[end][1],scene[end][1][].+ 1.0f0)

theogf

using Turing
using MLKernels
using LinearAlgebra

N=100
X = sort(rand(N,1),1) #Create some data
K = kernelmatrix(SquaredExponentialKernel(100.0),X) #Kernel matrix
y = rand(MvNormal(zeros(N),K+1e-1I)) #Sample from the GP prior with some noise

# Model representing Student-T Likelihood => Normal(y|f,omega)IG(omega|nu/2,nu/2)