theogf

module TestInterface

using Random
using Test

function test_interface(
rng::AbstractRNG, lik, x::AbstractVector; functor_args=()
)
gp = GP(kernel)
lgp = LatentGP(gp, lik, 1e-5)

theogf

using Tullio
using Distances
using LinearAlgebra
using BenchmarkTools
using CUDA, CUDAKernels, KernelAbstractions
using Functors
using KernelFunctions
using Test
using Functors

theogf

[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
CUDAKernels = "72cfdca4-0801-4ab0-bf6a-d52aa10adc57"
Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
KernelFunctions = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Tullio = "bc48ee85-29a4-5162-ae0b-a64e1601d4bc"

theogf

using CairoMakie
# using Animations
using Distributions
using Random
using AbstractTrees

CairoMakie.activate!()
mutable struct Partition
    x
    y

theogf

using StatsFuns

function h(x::AbstractVector, v::AbstractVector, y::AbstractVector, ν::AbstractVector, ϵ::Real, c)
    dot(v, ν) - ϵ * logsumexp((v - c.(Ref(x), y)) ./ ϵ .+ log.(ν)) - ϵ
end

function h(x::AbstractVector, v::AbstractVector, y::AbstractVector, ν::AbstractVector, ϵ::Int, c)
    ϵ == 0 || error("ϵ has to be 0")
    dot(v,ν) + mininum(c.(Ref(x), y) .- v)
end

theogf

using Makie
using AbstractPlotting.MakieLayout
using OnlineStats
t = Node(0)

function foo()
    scene, layout = layoutscene()
    axis = layout[1,1] = LAxis(scene)
    t = Node(0)
    it = lift(t, init = MovingWindow(10, Float32)) do t

theogf

using BlockDiagonals
using StatsBase
using BenchmarkTools
using Test
D = 2000
N = 100
K = 20
G = rand(D, N)
X = rand(D, N)
indices = vcat(1, sort(sample(2:D-1, K-1, replace = false)), D+1)

theogf

using HTTP
using Dates
using Printf
using JSON
using DataFrames
using FileIO
using CSV

work_location = (52.516747,13.323553)

theogf

using Makie
using MakieLayout
using LinearAlgebra, Distances
using Distributions



α = MixtureModel([Normal(0.2, 0.05), Normal(0.6, 0.06), Normal(0.8, 0.04)], [0.3, 0.5, 0.2])
# β = Normal(0.3, 0.09)
β = Laplace(0.5, 0.1)

theogf

using Distributions
using Plots; pyplot()
using ForwardDiff
using LaTeXStrings

λ = 3.0
μ = 2.0
s = 0.8
# α = Exponential(λ) # Measure alpha the starting point
α = Laplace(μ, s + 1.0)