update simulate for lba, lnr, update doc strings

Author: itsdfish

Project.toml

@@ -1,7 +1,7 @@
 name = "SequentialSamplingModels"
 uuid = "0e71a2a6-2b30-4447-8742-d083a85e82d1"
 authors = ["itsdfish"]
-version = "0.11.13"
+version = "0.12.0"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

src/LBA.jl

@@ -229,17 +229,17 @@ represent samples of evidence per time step and columns represent different accu
 
 # Keywords 
 
-- `n_steps=100`: number of time steps at which evidence is recorded
+- `Δt = 0.001`: the time step
 """
-function simulate(rng::AbstractRNG, model::AbstractLBA; Δt = 0.01, _...)
-    (; τ, A, k, ν, σ) = model
+function simulate(rng::AbstractRNG, model::AbstractLBA; Δt = 0.001, _...)
+    (; A, k, ν, σ) = model
     b = A + k
     n = length(ν)
     νs = sample_drift_rates(rng, ν, σ)
-    a = rand(rng,Uniform(0, A), n)
+    a = rand(rng, Uniform(0, A), n)
     dt = @. (b - a) / νs
-    choice, t = select_winner(dt)
-    evidence = collect.(range.(a, a + νs * t, step = Δt))
-    time_steps = range(0, t, length=length(evidence[1]))
+    _, t = select_winner(dt)
+    time_steps = range(0, t, step = Δt)  # Define time steps first with Δt
+    evidence = collect.(range.(a, a + νs * t, length = length(time_steps)))  # Match evidence to time steps
     return time_steps, hcat(evidence...)
 end

src/LNR.jl

@@ -122,16 +122,15 @@ represent samples of evidence per time step and columns represent different accu
 
 # Keywords 
 
-- `n_steps=100`: number of time steps at which evidence is recorded
+- `Δt = 0.001`: the time step
 """
-function simulate(rng::AbstractRNG, model::AbstractLNR; n_steps = 100, _...)
-    (; τ, ν, σ) = model
-    n = length(ν)
+function simulate(rng::AbstractRNG, model::AbstractLNR; Δt = 0.001, _...)
+    (; ν, σ) = model
     νs = @. rand(rng, Normal(ν, σ))
     βs = @. exp(νs)
     _, choice = findmax(βs)
     t = 1 / βs[choice]
-    evidence = collect.(range.(0, βs * t, length = 100))
-    time_steps = range(0, t, length = n_steps)
+    time_steps = range(0, t, step = Δt)
+    evidence = collect.(range.(0, βs * t, length = length(time_steps)))
     return time_steps, hcat(evidence...)
 end

test/lba_tests.jl

@@ -102,7 +102,7 @@
         α = A + k
         dist = LBA(; A, k, ν = [2, 1])
 
-        time_steps, evidence = simulate(dist; n_steps = 100)
+        time_steps, evidence = simulate(dist; Δt = 0.001)
 
         @test time_steps[1] ≈ 0
         @test length(time_steps) == size(evidence, 1)