Automatic build\nPublished by build of: SciML/SciMLBenchmarks.jl@3acd389

Author: JuliaLangBuildkite

markdown/BayesianInference/DiffEqBayesFitzHughNagumo.md

@@ -2,56 +2,61 @@
 using DiffEqBayes, BenchmarkTools
 
 
-using OrdinaryDiffEq, RecursiveArrayTools, Distributions, ParameterizedFunctions, StanSample, DynamicHMC
+using OrdinaryDiffEq, RecursiveArrayTools, Distributions, ParameterizedFunctions,
+      StanSample, DynamicHMC
 using Plots, StaticArrays, Turing, LinearAlgebra
 
 
-gr(fmt=:png)
+gr(fmt = :png)
 
 
 fitz = @ode_def FitzhughNagumo begin
-  dv = v - 0.33*v^3 -w + l
-  dw = τinv*(v +  a - b*w)
+    dv = v - 0.33*v^3 - w + l
+    dw = τinv*(v + a - b*w)
 end a b τinv l
 
 
-prob_ode_fitzhughnagumo = ODEProblem(fitz, [1.0,1.0], (0.0,10.0), [0.7,0.8,1/12.5,0.5])
+prob_ode_fitzhughnagumo = ODEProblem(fitz, [1.0, 1.0], (0.0, 10.0), [0.7, 0.8, 1/12.5, 0.5])
 sol = solve(prob_ode_fitzhughnagumo, Tsit5())
 
 
-sprob_ode_fitzhughnagumo = ODEProblem{false,SciMLBase.FullSpecialize}(fitz, SA[1.0,1.0], (0.0,10.0), SA[0.7,0.8,1/12.5,0.5])
+sprob_ode_fitzhughnagumo = ODEProblem{false, SciMLBase.FullSpecialize}(
+    fitz, SA[1.0, 1.0], (0.0, 10.0), SA[0.7, 0.8, 1 / 12.5, 0.5])
 sol = solve(sprob_ode_fitzhughnagumo, Tsit5())
 
 
-t = collect(range(1,stop=10,length=10))
+t = collect(range(1, stop = 10, length = 10))
 sig = 0.20
 data = convert(Array, VectorOfArray([(sol(t[i]) + sig*randn(2)) for i in 1:length(t)]))
 
 
-scatter(t, data[1,:])
-scatter!(t, data[2,:])
+scatter(t, data[1, :])
+scatter!(t, data[2, :])
 plot!(sol)
 
 
-priors = [truncated(Normal(1.0,0.5),0,1.5), truncated(Normal(1.0,0.5),0,1.5), truncated(Normal(0.0,0.5),0.0,0.5), truncated(Normal(0.5,0.5),0,1)]
+priors = [truncated(Normal(1.0, 0.5), 0, 1.5), truncated(Normal(1.0, 0.5), 0, 1.5),
+    truncated(Normal(0.0, 0.5), 0.0, 0.5), truncated(Normal(0.5, 0.5), 0, 1)]
 
 
-@time bayesian_result_stan = stan_inference(prob_ode_fitzhughnagumo,t,data,priors; delta = 0.65, num_samples = 10_000, print_summary=false, vars=(DiffEqBayes.StanODEData(), InverseGamma(2, 3)))
+@time bayesian_result_stan = stan_inference(
+    prob_ode_fitzhughnagumo, t, data, priors; delta = 0.65, num_samples = 10_000,
+    print_summary = false, vars = (DiffEqBayes.StanODEData(), InverseGamma(2, 3)))
 
 
 @model function fitlv(data, prob)
 
     # Prior distributions.
     σ ~ InverseGamma(2, 3)
-    a ~ truncated(Normal(1.0,0.5),0,1.5)
-    b ~ truncated(Normal(1.0,0.5),0,1.5)
-    τinv ~ truncated(Normal(0.0,0.5),0.0,0.5)
-    l ~ truncated(Normal(0.5,0.5),0,1)
+    a ~ truncated(Normal(1.0, 0.5), 0, 1.5)
+    b ~ truncated(Normal(1.0, 0.5), 0, 1.5)
+    τinv ~ truncated(Normal(0.0, 0.5), 0.0, 0.5)
+    l ~ truncated(Normal(0.5, 0.5), 0, 1)
 
     # Simulate Lotka-Volterra model. 
-    p = SA[a,b,τinv,l]
+    p = SA[a, b, τinv, l]
     _prob = remake(prob, p = p)
-    predicted = solve(_prob, Tsit5(); saveat=t)
+    predicted = solve(_prob, Tsit5(); saveat = t)
 
     # Observations.
     for i in 1:length(predicted)
@@ -63,12 +68,13 @@ end
 
 model = fitlv(data, sprob_ode_fitzhughnagumo)
 
-@time chain = sample(model, Turing.NUTS(0.65), 10000; progress=false)
+@time chain = sample(model, Turing.NUTS(0.65), 10000; progress = false)
 
 
-@time bayesian_result_turing = turing_inference(prob_ode_fitzhughnagumo,Tsit5(),t,data,priors;num_samples = 10_000)
+@time bayesian_result_turing = turing_inference(
+    prob_ode_fitzhughnagumo, Tsit5(), t, data, priors; num_samples = 10_000)
 
 
 using SciMLBenchmarks
-SciMLBenchmarks.bench_footer(WEAVE_ARGS[:folder],WEAVE_ARGS[:file])
+SciMLBenchmarks.bench_footer(WEAVE_ARGS[:folder], WEAVE_ARGS[:file])