update BPINN_tests.jl

test/BPINN_tests.jl

@@ -363,113 +363,4 @@ end
     bitvec = abs.(p .- sol_pestim1.estimated_de_params) .>
              abs.(p .- sol_pestim2.estimated_de_params)
     @test_broken bitvec == ones(size(bitvec))
-end
-
-using MCMCChains, Distributions, OrdinaryDiffEq, OptimizationOptimisers, Lux,
-      AdvancedHMC, Statistics, Random, Functors, ComponentArrays, MonteCarloMeasurements
-import Flux
-using NeuralPDE
-
-Random.seed!(100)
-
-function lotka_volterra(u, p, t)
-    # Model parameters.
-    α, β, γ, δ = p
-    # Current state.
-    x, y = u
-
-    # Evaluate differential equations.
-    dx = (α - β * y) * x # prey
-    dy = (δ * x - γ) * y # predator
-
-    return [dx, dy]
-end
-
-# initial-value problem.
-u0 = [1.0, 1.0]
-p = [1.5, 1.0, 3.0, 1.0]
-tspan = (0.0, 4.0)
-prob = ODEProblem(lotka_volterra, u0, tspan, p)
-
-# Solve using OrdinaryDiffEq.jl solver
-dt = 0.2
-solution = solve(prob, Tsit5(); saveat = dt)
-
-times = solution.t
-u = hcat(solution.u...)
-x = u[1, :] + (0.8 .* randn(length(u[1, :])))
-y = u[2, :] + (0.8 .* randn(length(u[2, :])))
-dataset = [x, y, times]
-
-chain = Chain(Dense(1, 6, tanh), Dense(6, 6, tanh), Dense(6, 2))
-
-alg1 = BNNODE(chain; dataset = dataset, draw_samples = 1000,
-    l2std = [0.2, 0.2], phystd = [0.1, 0.1], priorsNNw = (0.0, 1.0),
-    param = [Normal(2, 0.5), Normal(2, 0.5), Normal(2, 0.5), Normal(2, 0.5)], progress = true, verbose = true)
-
-alg2 = BNNODE(chain; dataset = dataset, draw_samples = 1000,
-    l2std = [0.2, 0.2], phystd = [0.1, 0.1], phynewstd = [0.1, 0.1], priorsNNw = (0.0, 1.0),
-    param = [Normal(2, 0.5), Normal(2, 0.5), Normal(2, 0.5), Normal(2, 0.5)],
-    estim_collocate = true, progress = true, verbose = true)
-
-@time sol_pestim1 = solve(prob, alg1; saveat = dt)
-@time sol_pestim2 = solve(prob, alg2; saveat = dt)
-
-unsafe_comparisons(true)
-bitvec = abs.(p .- sol_pestim1.estimated_de_params) .>
-         abs.(p .- sol_pestim2.estimated_de_params)
-bitvec == ones(size(bitvec))
-
-using MCMCChains, Distributions, OrdinaryDiffEq, OptimizationOptimisers, Lux,
-      AdvancedHMC, Statistics, Random, Functors, ComponentArrays, MonteCarloMeasurements
-using NeuralPDE
-import Flux
-
-Random.seed!(100)
-
-linear_analytic = (u0, p, t) -> u0 + sin(2 * π * t) / (2 * π)
-linear = (u, p, t) -> cos(2 * π * t)
-tspan = (0.0, 2.0)
-u0 = 0.0
-prob = ODEProblem(ODEFunction(linear, analytic = linear_analytic), u0, tspan)
-p = prob.p
-
-# Numerical and Analytical Solutions: testing ahmc_bayesian_pinn_ode()
-ta = range(tspan[1], tspan[2], length = 300)
-u = [linear_analytic(u0, nothing, ti) for ti in ta]
-x̂ = collect(Float64, Array(u) + 0.02 * randn(size(u)))
-time = vec(collect(Float64, ta))
-physsol1 = [linear_analytic(prob.u0, p, time[i]) for i in eachindex(time)]
-
-# testing points for solve() call must match saveat(1/50.0) arg
-ta0 = range(tspan[1], tspan[2], length = 101)
-u1 = [linear_analytic(u0, nothing, ti) for ti in ta0]
-x̂1 = collect(Float64, Array(u1) + 0.02 * randn(size(u1)))
-time1 = vec(collect(Float64, ta0))
-physsol0_1 = [linear_analytic(prob.u0, p, time1[i]) for i in eachindex(time1)]
-
-chainlux = Chain(Dense(1, 7, tanh), Dense(7, 1))
-θinit, st = Lux.setup(Random.default_rng(), chainlux)
-
-fh_mcmc_chain, fhsamples, fhstats = ahmc_bayesian_pinn_ode(
-    prob, chainlux, draw_samples = 2500, progress = true,
-    estim_collocate = true, verbose = true)
-
-alg = BNNODE(chainlux, draw_samples = 2500)
-sol1lux = solve(prob, alg)
-
-# testing points
-t = time
-# Mean of last 500 sampled parameter's curves[Ensemble predictions]
-θ = [vector_to_parameters(fhsamples[i], θinit) for i in 2000:length(fhsamples)]
-luxar = [chainlux(t', θ[i], st)[1] for i in eachindex(θ)]
-luxmean = [mean(vcat(luxar...)[:, i]) for i in eachindex(t)]
-meanscurve = prob.u0 .+ (t .- prob.tspan[1]) .* luxmean
-
-# --------------------- ahmc_bayesian_pinn_ode() call
-@test mean(abs.(x̂ .- meanscurve)) < 0.05
-@test mean(abs.(physsol1 .- meanscurve)) < 0.005
-
-#--------------------- solve() call
-@test mean(abs.(x̂1 .- pmean(sol1lux.ensemblesol[1]))) < 0.025
-@test mean(abs.(physsol0_1 .- pmean(sol1lux.ensemblesol[1]))) < 0.025
+end