implement get_model and refactor

Author: foldfelis

example/DoublePendulum/notebook/data.jl

@@ -14,7 +14,7 @@ begin
 end
 
 # ╔═╡ 5268feee-bda2-4612-9d4c-a1db424a11c7
-data = get_double_pendulum_chaotic_data(i=0, n=-1)
+data = get_data(i=0, n=-1)
 
 # ╔═╡ 9c8b3f8a-1b85-4c32-a416-ead51b244b94
 begin

example/DoublePendulum/src/DoublePendulum.jl

@@ -59,4 +59,12 @@ function train(; loss_bounds=[0.05])
     Flux.@epochs 50 @time(Flux.train!(loss, params(m), data, opt, cb=call_back))
 end
 
+function get_model()
+    f = jldopen(joinpath(@__DIR__, "../model/model.jld2"))
+    model = f["model"]
+    close(f)
+
+    return model
+end
+
 end

example/DoublePendulum/src/data.jl

@@ -2,8 +2,6 @@ using DataDeps
 using CSV
 using DataFrames
 
-export get_double_pendulum_chaotic_data
-
 function register_double_pendulum_chaotic()
     register(DataDep(
         "DoublePendulumChaotic",
@@ -26,7 +24,7 @@ function register_double_pendulum_chaotic()
     ))
 end
 
-function get_double_pendulum_chaotic_data(; i=0, n=-1)
+function get_data(; i=0, n=-1)
     data_path = joinpath(datadep"DoublePendulumChaotic", "original", "dpc_dataset_csv")
     df = CSV.read(
         joinpath(data_path, "$i.csv"),
@@ -41,7 +39,7 @@ function get_double_pendulum_chaotic_data(; i=0, n=-1)
 end
 
 function get_dataloader(; i=0, n_train=15001, n_test=1501, Δn=1024, batchsize=100)
-    x = reshape(get_double_pendulum_chaotic_data(; i=i, n=-1), :)
+    x = reshape(get_data(; i=i, n=-1), :)
     𝐱 = reshape(vcat([x[i:(i+6Δn-1)] for i in 1:6:(length(x)-6(Δn-1))]...), 1, 6, 1024, :)
 
     𝐱_train, 𝐲_train = 𝐱[:, :, :, 1:(n_train-1)], 𝐱[:, :, :, 2:n_train]

example/DoublePendulum/test/data.jl

@@ -1,5 +1,5 @@
 @testset "double pendulum" begin
-    xs = get_double_pendulum_chaotic_data(i=0, n=100)
+    xs = DoublePendulum.get_data(i=0, n=100)
 
     @test size(xs) == (6, 100)
 end