ContinuousNormalizingFlows.jl

using Pkg Pkg.add("ContinuousNormalizingFlows")

# Enable Logging using Logging, TerminalLoggers global_logger(TerminalLogger()) # Parameters nvars = 1 naugs = nvars # n_in = nvars # without augmentation n_in = nvars + naugs # with augmentation n = 1024 # Model using ContinuousNormalizingFlows, Lux, ADTypes, Enzyme #, CUDA, ComputationalResources nn = Chain(Dense(n_in => 3 * n_in, tanh), Dense(3 * n_in => n_in, tanh)) # icnf = construct(RNODE, nn, nvars) # use defaults icnf = construct( RNODE, nn, nvars, # number of variables naugs; # number of augmented dimensions compute_mode = DIJacVecMatrixMode(AutoEnzyme(; function_annotation = Enzyme.Const)), # process data in batches tspan = (0.0f0, 13.0f0), # have bigger time span steer_rate = 1.0f-1, # add random noise to end of the time span # resource = CUDALibs(), # process data by GPU # inplace = true, # use the inplace version of functions ) # Data using Distributions data_dist = Beta{Float32}(2.0f0, 4.0f0) r = rand(data_dist, nvars, n) r = convert.(Float32, r) # Fit It using DataFrames, MLJBase #, ForwardDiff, ADTypes, OptimizationOptimisers df = DataFrame(transpose(r), :auto) # model = ICNFModel(icnf) # use defaults model = ICNFModel( icnf; batch_size = 256, # have bigger batchs # n_epochs = 100, # have less epochs # optimizers = (Adam(),), # use a different optimizer # adtype = AutoForwardDiff(), # use ForwardDiff ) mach = machine(model, df) fit!(mach) ps, st = fitted_params(mach) # Store It using JLD2, UnPack jldsave("fitted.jld2"; ps, st) # save @unpack ps, st = load("fitted.jld2") # load # Use It d = ICNFDist(icnf, TestMode(), ps, st) # direct way # d = ICNFDist(mach, TestMode()) # alternative way actual_pdf = pdf.(data_dist, vec(r)) estimated_pdf = pdf(d, r) new_data = rand(d, n) # Evaluate It using Distances mad_ = meanad(estimated_pdf, actual_pdf) msd_ = msd(estimated_pdf, actual_pdf) tv_dis = totalvariation(estimated_pdf, actual_pdf) / n res_df = DataFrame(; mad_, msd_, tv_dis) display(res_df) # Plot It using CairoMakie f = Figure() ax = Makie.Axis(f[1, 1]; title = "Result") lines!(ax, 0.0f0 .. 1.0f0, x -> pdf(data_dist, x); label = "actual") lines!(ax, 0.0f0 .. 1.0f0, x -> pdf(d, vcat(x)); label = "estimated") axislegend(ax) save("result-fig.svg", f) save("result-fig.png", f)