More efficient credit market loop (#41)

src/markets/search_and_matching_credit.jl

@@ -18,11 +18,14 @@ function search_and_matching_credit(firms::AbstractFirms, model)
     E_k, zeta, zeta_LTV = model.bank.E_k, model.prop.zeta, model.prop.zeta_LTV
 
     DL_i = zeros(size(DL_d_i))
+    sum_DL_i = sum(DL_i)
     I_FG = findall(DL_d_i .> 0)
     shuffle!(I_FG)
     s_L_e_i = sum(L_e_i)
     for i in I_FG
-        DL_i[i] = max(0.0, min(DL_d_i[i], zeta_LTV * K_e_i[i] - L_e_i[i], E_k / zeta - s_L_e_i - sum(DL_i)))
+        DL_i_p = DL_i[i]
+        DL_i[i] = max(0.0, min(DL_d_i[i], zeta_LTV * K_e_i[i] - L_e_i[i], E_k / zeta - s_L_e_i - sum_DL_i))
+        sum_DL_i += (DL_i[i] - DL_i_p)
     end
     return DL_i
 end

test/deterministic/make_model_deterministic.jl

@@ -7,20 +7,18 @@ import Random: shuffle!, rand, randn
 import StatsBase: wsample
 using Distributions
 
-function allinds(s::DynamicSampler)
-    return sort!(reduce(vcat, s.level_buckets))
+function allinds(sp::DynamicSampler)
+    inds = collect(Iterators.Flatten((Iterators.map(x -> x[1], b) for b in sp.level_buckets)))
+    return sort!(inds)
 end
 
 function randn()
     return 0.0
 end
 
-function rand(s::DynamicSampler)
-    idx = minimum(minimum.(s.level_buckets; init=typemax(Int)))
-    weight = s.weights[idx]
-    level = ceil(Int, log2(weight)) - s.info.level_min + 1
-    idx_in_level = findfirst(x -> x == idx, s.level_buckets[level])
-    return idx
+function rand(sp::DynamicSampler)
+    inds = collect(Iterators.Flatten((Iterators.map(x -> x[1], b) for b in sp.level_buckets)))
+    return minimum(inds)
 end
 function rand(n::UnitRange)
     return 1