Format .jl files (#161)

Co-authored-by: prbzrg <[email protected]>

README.md

@@ -1,31 +1,31 @@
-# ICNF.jl
-
-[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://impICNF.github.io/ICNF.jl/stable)
-[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://impICNF.github.io/ICNF.jl/dev)
-[![Build Status](https://github.com/impICNF/ICNF.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/impICNF/ICNF.jl/actions/workflows/CI.yml?query=branch%3Amain)
-[![Coverage](https://codecov.io/gh/impICNF/ICNF.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/impICNF/ICNF.jl)
-[![Coverage](https://coveralls.io/repos/github/impICNF/ICNF.jl/badge.svg?branch=main)](https://coveralls.io/github/impICNF/ICNF.jl?branch=main)
-[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
-[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
-
-Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
-
-## Citing
-
-See [`CITATION.bib`](CITATION.bib) for the relevant reference(s).
-
-## Usage
-
-To add this package, we can do it by
-
-```julia
+# ICNF.jl
+
+[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://impICNF.github.io/ICNF.jl/stable)
+[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://impICNF.github.io/ICNF.jl/dev)
+[![Build Status](https://github.com/impICNF/ICNF.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/impICNF/ICNF.jl/actions/workflows/CI.yml?query=branch%3Amain)
+[![Coverage](https://codecov.io/gh/impICNF/ICNF.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/impICNF/ICNF.jl)
+[![Coverage](https://coveralls.io/repos/github/impICNF/ICNF.jl/badge.svg?branch=main)](https://coveralls.io/github/impICNF/ICNF.jl?branch=main)
+[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
+[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
+
+Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
+
+## Citing
+
+See [`CITATION.bib`](CITATION.bib) for the relevant reference(s).
+
+## Usage
+
+To add this package, we can do it by
+
+```julia
 using Pkg
 Pkg.add(; url = "https://github.com/impICNF/ICNF.jl")
-```
-
-To use this package, here is an example:
-
-```julia
+```
+
+To use this package, here is an example:
+
+```julia
 using ICNF
 using Distributions, Lux
 using DifferentialEquations, SciMLSensitivity
@@ -61,4 +61,4 @@ using Distances
 mad_ = meanad(estimated_pdf, actual_pdf)
 msd_ = msd(estimated_pdf, actual_pdf)
 tv_dis = totalvariation(estimated_pdf, actual_pdf) / n
-```
+```

src/core_cond_icnf.jl

@@ -35,11 +35,23 @@ function CondICNFModel(
     optimizer::Any = Optimisers.Adam(),
     n_epochs::Integer = 128,
     adtype::SciMLBase.AbstractADType = Optimization.AutoZygote(),
-    use_batch::Bool=true,
+    use_batch::Bool = true,
     batch_size::Integer = 128,
     resource::AbstractResource = CPU1(),
 ) where {T <: AbstractFloat, AT <: AbstractArray, CM <: ComputeMode}
-    CondICNFModel(m, loss, optimizer, n_epochs, adtype, use_batch, batch_size, resource, T, AT, CM)
+    CondICNFModel(
+        m,
+        loss,
+        optimizer,
+        n_epochs,
+        adtype,
+        use_batch,
+        batch_size,
+        resource,
+        T,
+        AT,
+        CM,
+    )
 end
 
 function MLJModelInterface.fit(model::CondICNFModel, verbosity, XY)

src/core_icnf.jl

@@ -35,11 +35,23 @@ function ICNFModel(
     optimizer::Any = Optimisers.Adam(),
     n_epochs::Integer = 128,
     adtype::SciMLBase.AbstractADType = Optimization.AutoZygote(),
-    use_batch::Bool=true,
+    use_batch::Bool = true,
     batch_size::Integer = 128,
     resource::AbstractResource = CPU1(),
 ) where {T <: AbstractFloat, AT <: AbstractArray, CM <: ComputeMode}
-    ICNFModel(m, loss, optimizer, n_epochs, adtype, use_batch, batch_size, resource, T, AT, CM)
+    ICNFModel(
+        m,
+        loss,
+        optimizer,
+        n_epochs,
+        adtype,
+        use_batch,
+        batch_size,
+        resource,
+        T,
+        AT,
+        CM,
+    )
 end
 
 function MLJModelInterface.fit(model::ICNFModel, verbosity, X)

src/flux_compat.jl

@@ -1,19 +1,19 @@
-export MyFluxLayer
-
-struct MyFluxLayer{L, RE, I} <: LuxCore.AbstractExplicitLayer
-    layer::L
-    re::RE
-    init_parameters::I
-end
-
-function MyFluxLayer(l)
-    p, re = Optimisers.destructure(l)
-    p_ = copy(p)
-    return MyFluxLayer(l, re, () -> p_)
-end
-
-Lux.initialparameters(::AbstractRNG, l::MyFluxLayer) = l.init_parameters()
-
-(l::MyFluxLayer)(x, ps, st) = l.re(ps)(x), st
-
-Base.show(io::IO, l::MyFluxLayer) = print(io, "MyFluxLayer($(l.layer))")
+export MyFluxLayer
+
+struct MyFluxLayer{L, RE, I} <: LuxCore.AbstractExplicitLayer
+    layer::L
+    re::RE
+    init_parameters::I
+end
+
+function MyFluxLayer(l)
+    p, re = Optimisers.destructure(l)
+    p_ = copy(p)
+    return MyFluxLayer(l, re, () -> p_)
+end
+
+Lux.initialparameters(::AbstractRNG, l::MyFluxLayer) = l.init_parameters()
+
+(l::MyFluxLayer)(x, ps, st) = l.re(ps)(x), st
+
+Base.show(io::IO, l::MyFluxLayer) = print(io, "MyFluxLayer($(l.layer))")