cleanup and bump version

Author: BatyLeo

Project.toml

@@ -1,7 +1,7 @@
 name = "DecisionFocusedLearningBenchmarks"
 uuid = "2fbe496a-299b-4c81-bab5-c44dfc55cf20"
 authors = ["Members of JuliaDecisionFocusedLearning"]
-version = "0.2.3"
+version = "0.2.4"
 
 [deps]
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"

src/Argmax2D/polytope.jl

@@ -51,75 +51,3 @@ function plot_maximizer!(pl, θ, instance, maximizer)
         label=L"f(\theta)",
     )
 end;
-
-# function get_angle(v)
-#     @assert !(norm(v) ≈ 0)
-#     v = v ./ norm(v)
-#     if v[2] >= 0
-#         return acos(v[1])
-#     else
-#         return π + acos(-v[1])
-#     end
-# end;
-
-# function plot_distribution!(pl, probadist)
-#     A = probadist.atoms
-#     As = sort(A; by=get_angle)
-#     p = probadist.weights
-#     Plots.plot!(
-#         pl,
-#         vcat(map(first, As), first(As[1])),
-#         vcat(map(last, As), last(As[1]));
-#         fillrange=0,
-#         fillcolor=:blue,
-#         fillalpha=0.1,
-#         linestyle=:dash,
-#         linecolor=Colors.JULIA_LOGO_COLORS.blue,
-#         label=L"\mathrm{conv}(\hat{p}(\theta))",
-#     )
-#     return Plots.scatter!(
-#         pl,
-#         map(first, A),
-#         map(last, A);
-#         markersize=25 .* p .^ 0.5,
-#         markercolor=Colors.JULIA_LOGO_COLORS.blue,
-#         markerstrokewidth=0,
-#         markeralpha=0.4,
-#         label=L"\hat{p}(\theta)",
-#     )
-# end;
-
-# function plot_expectation!(pl, probadist)
-#     ŷΩ = compute_expectation(probadist)
-#     return scatter!(
-#         pl,
-#         [ŷΩ[1]],
-#         [ŷΩ[2]];
-#         color=Colors.JULIA_LOGO_COLORS.blue,
-#         markersize=6,
-#         markershape=:hexagon,
-#         label=L"\hat{f}(\theta)",
-#     )
-# end;
-
-# function compress_distribution!(
-#     probadist::DifferentiableExpectations.FixedAtomsProbabilityDistribution{A,W}; atol=0
-# ) where {A,W}
-#     (; atoms, weights) = probadist
-#     to_delete = Int[]
-#     for i in length(probadist):-1:1
-#         ai = atoms[i]
-#         for j in 1:(i - 1)
-#             aj = atoms[j]
-#             if isapprox(ai, aj; atol=atol)
-#                 weights[j] += weights[i]
-#                 push!(to_delete, i)
-#                 break
-#             end
-#         end
-#     end
-#     sort!(to_delete)
-#     deleteat!(atoms, to_delete)
-#     deleteat!(weights, to_delete)
-#     return probadist
-# end;