make named tuple outputs consistent

itsdfish · Aug 27, 2024 · 003199a · 003199a
1 parent c5196e5
commit 003199a
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Showing 4 changed files with 13 additions and 13 deletions.
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "SequentialSamplingModels"
 uuid = "0e71a2a6-2b30-4447-8742-d083a85e82d1"
 authors = ["itsdfish"]
-version = "0.11.7"
+version = "0.11.8"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

diff --git a/src/ClassicMDFT.jl b/src/ClassicMDFT.jl
@@ -121,14 +121,14 @@ function rand(
     n_alternatives = size(M, 1)
     x = fill(0.0, n_alternatives)
     Δμ = fill(0.0, n_alternatives)
-    choices = fill(0, n_sim)
-    rts = fill(0.0, n_sim)
+    choice = fill(0, n_sim)
+    rt = fill(0.0, n_sim)
     CM = dist.C * M
     for i ∈ 1:n_sim
-        choices[i], rts[i] = _rand(rng, dist, x, Δμ, CM)
+        choice[i], rt[i] = _rand(rng, dist, x, Δμ, CM)
         x .= 0.0
     end
-    return (; choices, rts)
+    return (; choice, rt)
 end
 
 rand(dist::ClassicMDFT, M::AbstractArray;) = rand(Random.default_rng(), dist, M)

diff --git a/src/LCA.jl b/src/LCA.jl
@@ -101,13 +101,13 @@ function rand(rng::AbstractRNG, dist::AbstractLCA, n_sim::Int; Δt = 0.001)
     n = length(dist.ν)
     x = fill(0.0, n)
     Δμ = fill(0.0, n)
-    choices = fill(0, n_sim)
-    rts = fill(0.0, n_sim)
+    choice = fill(0, n_sim)
+    rt = fill(0.0, n_sim)
     for i ∈ 1:n_sim
-        choices[i], rts[i] = _rand(rng, dist, x, Δμ; Δt)
+        choice[i], rt[i] = _rand(rng, dist, x, Δμ; Δt)
         x .= 0.0
     end
-    return (; choices, rts)
+    return (; choice, rt)
 end
 
 function _rand(rng::AbstractRNG, dist::AbstractLCA, x, Δμ; Δt = 0.001)

diff --git a/src/MDFT.jl b/src/MDFT.jl
@@ -153,16 +153,16 @@ function rand(
     x = fill(0.0, n_options)
     Δμ = fill(0.0, n_options)
     ϵ = fill(0.0, n_options)
-    choices = fill(0, n_sim)
-    rts = fill(0.0, n_sim)
+    choice = fill(0, n_sim)
+    rt = fill(0.0, n_sim)
     dist._CM = dist.C * M * dist.γ
     distances = compute_distances(dist, M)
     dist.S = compute_feedback_matrix(dist, distances)
     for i ∈ 1:n_sim
-        choices[i], rts[i] = _rand(rng, dist, x, Δμ; Δt)
+        choice[i], rt[i] = _rand(rng, dist, x, Δμ; Δt)
         x .= 0.0
     end
-    return (; choices, rts)
+    return (; choice, rt)
 end
 
 rand(dist::MDFT, M::AbstractArray; Δt = 0.001) = rand(Random.default_rng(), dist, M; Δt)