changes from reviews-1

.github/workflows/CI.yml

@@ -14,7 +14,7 @@ jobs:
  matrix:
  group:
  #fixes 682
- -ODEBPINN
+ - ODEBPINN
 
  - NNPDE1
  - NNPDE2

src/advancedHMC_MCMC.jl

@@ -134,7 +134,6 @@ end
 function generate_Tar(chain::Flux.Chain, init_params::Nothing)
  θ, re = Flux.destructure(chain)
  # find_good_stepsize,phasepoint takes only float64
- θ = collect(Float64, θ)
  return θ, re, nothing
 end
 
@@ -193,9 +192,10 @@ function physloglikelihood(Tar::LogTargetDensity, θ)
  p = Tar.prob.p
  dt = Tar.physdt
  if isempty(Tar.dataset[end])
- t = collect(Float64, Tar.prob.tspan[1]:dt:Tar.prob.tspan[2])
+ t = collect(eltype(dt), Tar.prob.tspan[1]:dt:Tar.prob.tspan[2])
  else
- t = vcat(collect(Float64, Tar.prob.tspan[1]:dt:Tar.prob.tspan[2]), Tar.dataset[end])
+ t = vcat(collect(eltype(dt), Tar.prob.tspan[1]:dt:Tar.prob.tspan[2]),
+ Tar.dataset[end])
  end
 
  # parameter estimation chosen or not
@@ -309,12 +309,11 @@ end
 
 function ahmc_bayesian_pinn_ode(prob::DiffEqBase.DEProblem, chain;
  dataset = [[]],
- # dataset::Vector{Vector{Float64}}
  init_params = nothing, nchains = 1,
  draw_samples = 1000, l2std = [0.05],
  phystd = [0.05], priorsNNw = (0.0, 2.0),
  param = [],
- autodiff = false, physdt = 1 / 20.0f0,
+ autodiff = false, physdt = 1 / 20.0,
  Proposal = StaticTrajectory,
  Adaptor = StanHMCAdaptor, targetacceptancerate = 0.8,
  Integrator = Leapfrog,
@@ -338,12 +337,13 @@ function ahmc_bayesian_pinn_ode(prob::DiffEqBase.DEProblem, chain;
  throw(error("number of chains must be greater than 1"))
  end
 
- # 
+ # eltype(physdt) cause needs Float64 for find_good_stepsize
  if chain isa Lux.AbstractExplicitLayer
- # Lux chain(using component array later as vector_to_parameter need namedtuple,AHMC uses Float64)
- initial_θ = collect(Float64, vcat(ComponentArrays.ComponentArray(initial_nnθ)))
+ # Lux chain(using component array later as vector_to_parameter need namedtuple)
+ initial_θ = collect(eltype(physdt),
+ vcat(ComponentArrays.ComponentArray(initial_nnθ)))
  else
- initial_θ = initial_nnθ
+ initial_θ = collect(eltype(physdt), initial_nnθ)
  end
 
  # adding ode parameter estimation