From 2abf3020737508d747803402b588743e77a34a63 Mon Sep 17 00:00:00 2001
From: Stephan Hilb <stephan@ecshi.net>
Date: Wed, 2 Dec 2020 23:20:33 +0100
Subject: [PATCH] cleanup
---
src/DualTVDD.jl | 182 -----------------------------------------------
src/surrogate.jl | 1 -
2 files changed, 183 deletions(-)
diff --git a/src/DualTVDD.jl b/src/DualTVDD.jl
index 5cfbd96..bb18d01 100644
--- a/src/DualTVDD.jl
+++ b/src/DualTVDD.jl
@@ -13,186 +13,4 @@ include("projgrad.jl")
include("surrogate.jl")
#include("tvnewton.jl")
-#using Plots: heatmap
-#
-#plotimage(u) = heatmap(u, c=:grayC, legend=:none, framestyle=:none, aspect_ratio=1)
-#
-#function alg_energy(alg, niter)
-# print("run ...")
-# res = Float64[]
-# (p, ctx) = iterate(alg)
-# push!(res, energy(ctx))
-# for i in 1:niter
-# (p, ctx) = iterate(alg, ctx)
-# push!(res, energy(ctx))
-# end
-# println(" finished")
-# return res
-#end
-#
-#function alg_error(alg, pmin, niter, ninner=1)
-# print("run ...")
-# res = Float64[]
-# ctx = init(alg)
-# push!(res, error(fetch(ctx), pmin, ctx.algorithm.problem))
-# for i in 1:niter
-# for j in 1:ninner
-# step!(ctx)
-# end
-# push!(res, error(fetch(ctx), pmin, ctx.algorithm.problem))
-# end
-# println(" finished")
-# return res
-#end
-#
-#function calc_energy(prob, niter)
-# alg_ref = DualTVDD.ChambolleAlgorithm(prob)
-# ctx = init(alg_ref)
-# for i in 1:niter
-# ctx = step!(ctx)
-# end
-# return energy(ctx), fetch(ctx)
-#end
-#
-#function rundd()
-# λ = 2.5
-# β = 0
-# #f = zeros(100,100)
-# #f[1] = 1
-# #f = [0. 2; 1 0.]
-#
-#
-# #A = 0. * rand(length(f), length(f))
-# #A .+= diagm(ones(length(f)))
-# #B = inv(A'*A + β*I)
-#
-# #g = similar(f)
-# #vec(g) .= A' * vec(f)
-#
-# g = rand(50, 50)
-#
-# prob = DualTVDD.DualTVL1ROFOpProblem(g, I, λ)
-#
-# alg_ref = DualTVDD.ChambolleAlgorithm(prob)
-# alg_dd = DualTVDD.DualTVDDAlgorithm(prob, M=(2,2), overlap=(4,4))
-# alg_dd2 = DualTVDD.DualTVDDAlgorithm(prob, M=(2,2), overlap=(4,4), parallel=false)
-#
-# n = 1000
-#
-# ref_energy, pmin = calc_energy(prob, 100000)
-#
-# lognan(x) = x > 0 ? x : NaN
-#
-# #y = [
-# # lognan.(alg_energy(alg_ref, n) .- ref_energy),
-# # lognan.(alg_energy(alg_dd, n) .- ref_energy),
-# # lognan.(alg_energy(alg_dd2, n) .- ref_energy),
-# # ]
-# y = [
-# lognan.(alg_error(alg_ref, pmin, n, 10)),
-# lognan.(alg_error(alg_dd, pmin, n)),
-# lognan.(alg_error(alg_dd2, pmin, n)),
-# ]
-#
-# plt = plot(y, xaxis=:log, yaxis=:log)
-# display(plt)
-#
-# #display(energy(ctx))
-# #display(ctx.p)
-# #display(recover_u(p, prob))
-#
-# #println(energy(ctx))
-# #println(energy(ctx2))
-#end
-#
-#function run3()
-# f = rand(20,20)
-# A = 0.1*rand(length(f), length(f))
-# A .+= diagm(ones(length(f)))
-#
-# g = reshape(A'*vec(f), size(f))
-#
-# β = 0
-# B = inv(A'*A + β*I)
-# println(norm(A))
-#
-# λ = 0.1
-#
-# # Chambolle
-# md = DualTVDD.DualTVL1ROFOpProblem(g, B, λ)
-# alg = DualTVDD.ChambolleAlgorithm()
-# ctx = DualTVDD.init(md, alg)
-#
-# # Projected Gradient
-# md = DualTVDD.DualTVL1ROFOpProblem(f, A, λ, 0., 0.)
-# alg = DualTVDD.ProjGradAlgorithm(λ = 1/norm(A)^2)
-# ctx2 = DualTVDD.init(md, alg)
-#
-# for i in 1:100000
-# step!(ctx)
-# step!(ctx2)
-# end
-#
-# #display(ctx.p)
-# #display(ctx2.p)
-# display(recover_u!(ctx))
-# display(recover_u!(ctx2))
-#
-# println(energy(ctx))
-# println(energy(ctx2))
-#
-# ctx, ctx2
-#end
-#
-#function energy(ctx::Union{DualTVDDState,ProjGradState,ChambolleState})
-# return energy(ctx.p, ctx.algorithm.problem)
-#end
-#
-#function error(p, pmin, prob::DualTVL1ROFOpProblem)
-# return energy_norm(p .- pmin, prob) / energy(pmin, prob)
-#end
-#
-#function energy(p, prob::DualTVL1ROFOpProblem)
-# d = ndims(p)
-#
-# @inline kfΛ(w) = @inbounds divergence(w)
-# kΛ = Kernel{ntuple(_->-1:1, d)}(kfΛ)
-#
-# v = similar(prob.g)
-#
-# # v = div(p) + g
-# map!(kΛ, v, extend(p, StaticKernels.ExtensionNothing()))
-# v .+= prob.g
-# #display(v)
-#
-# # |v|_B^2 / 2
-# u = prob.B * vec(v)
-# return sum(u .* vec(v)) / 2
-#end
-#
-#function energy_norm(p, prob::DualTVL1ROFOpProblem)
-# d = ndims(p)
-#
-# @inline kfΛ(w) = @inbounds divergence(w)
-# kΛ = Kernel{ntuple(_->-1:1, d)}(kfΛ)
-#
-# v = similar(prob.g)
-#
-# # v = div(p)
-# map!(kΛ, v, extend(p, StaticKernels.ExtensionNothing()))
-# #display(v)
-#
-# # |v|_B^2 / 2
-# u = prob.B * vec(v)
-# return sum(u .* vec(v)) / 2
-#end
-#
-#
-#function energy(md::DualTVL1ROFOpProblem, u::AbstractMatrix)
-# @inline kf(w) = @inbounds 1/2 * (w[0,0] - md.g[w.position])^2 +
-# md.λ * sqrt((w[1,0] - w[0,0])^2 + (w[0,1] - w[0,0])^2)
-# k = Kernel{(0:1, 0:1)}(kf, StaticKernels.ExtensionReplicate())
-# return sum(k, u)
-#end
-
end # module
diff --git a/src/surrogate.jl b/src/surrogate.jl
index 9914b99..a4bcb48 100644
--- a/src/surrogate.jl
+++ b/src/surrogate.jl
@@ -40,7 +40,6 @@ function init(alg::SurrogateAlgorithm)
r = reshape(rv, size(g))
s = reshape(sv, size(g))
- #s = extend(reshape(sv, size(g)), StaticKernels.ExtensionReplicate())
@inline kf1(pw) = @inbounds -divergence(pw)
k1 = Kernel{ntuple(_->-1:1, d)}(kf1)
--
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