feat: Indexing reward computed for ixs

An alternative formulation of the indexing reward for use with Pairwise
Greedy Optimisation.

src/domain.jl

@@ -473,7 +473,47 @@ function newtokenissuance(n::FlexTable, config::Dict)
 end
 
 """
-    function indexingreward(
+    indexingreward(
+        ixs::AbstractVector{Integer},
+        x::AbstractVector{Real},
+        Ω::AbstractVector{Real},
+        ψ::AbstractVector{Real},
+        Φ::Real,
+        Ψ::Real
+    )
+
+The indexing rewards for the allocation vector `x` given signals `ψ`, the existing
+allocations on subgraphs `Ω`, token issuance `Φ`, and total signal `Ψ`. Here `ixs`
+is a vector of indices `Ω`, and `ψ`. `x` will be filtered by `SemioticOpt`, so we
+don't do this here.
+
+
+```julia
+julia> using AllocationOpt
+julia> using AllocationOpt
+julia> ixs = Int32[2]
+julia> ψ = [0.0, 1.0]
+julia> Ω = [1.0, 1.0]
+julia> Φ = 1.0
+julia> Ψ = 2.0
+julia> x = [0.0, 1.0]
+julia> AllocationOpt.indexingreward(ixs, x, Ω, ψ, Φ, Ψ)
+0.25
+````
+"""
+function indexingreward(
+    ixs::AbstractVector{I},
+    x::AbstractVector{T},
+    Ω::AbstractVector{T},
+    ψ::AbstractVector{T},
+    Φ::Real,
+    Ψ::Real,
+) where {T<:Real,I<:Integer}
+    return indexingreward(x, Ω[ixs], ψ[ixs], Φ, Ψ)
+end
+
+"""
+    indexingreward(
         x::AbstractVector{Real},
         Ω::AbstractVector{Real},
         ψ::AbstractVector{Real},

test/domain.jl

@@ -59,11 +59,7 @@
 
     @testset "availablestake" begin
         x = flextable([
-            Dict(
-                "stakedTokens" => 10,
-                "delegatedTokens" => 20,
-                "lockedTokens" => 5,
-            ),
+            Dict("stakedTokens" => 10, "delegatedTokens" => 20, "lockedTokens" => 5)
         ])
         @test AllocationOpt.availablestake(Val(:indexer), x) == 25
     end
@@ -258,6 +254,24 @@
             x = [1.0, 1.0]
             @test AllocationOpt.indexingreward(x, Ω, ψ, Φ, Ψ) == 0.5
         end
+
+        @testset "specifying ixs" begin
+            ixs = Int32[2]
+            ψ = [0.0, 1.0]
+            Ω = [1.0, 1.0]
+            Φ = 1.0
+            Ψ = 2.0
+            x = [0.0, 1.0]
+            @test AllocationOpt.indexingreward(ixs, x, Ω, ψ, Φ, Ψ) == 0.25
+
+            ixs = Int32[1]
+            ψ = [0.0, 1.0]
+            Ω = [1.0, 1.0]
+            Φ = 1.0
+            Ψ = 2.0
+            x = [0.0, 1.0]
+            @test AllocationOpt.indexingreward(ixs, x, Ω, ψ, Φ, Ψ) == 0.0
+        end
     end
 
     @testset "profit" begin