Added formatting (#260)

* addd formatting settings

* formatting

* aded formatting to workflows

Author: torfjelde

.JuliaFormatter.toml

@@ -0,0 +1,2 @@
+style="blue"
+format_markdown=true

.github/workflows/Format.yml

@@ -0,0 +1,38 @@
+name: Format
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    branches:
+      - master
+  merge_group:
+    types: [checks_requested]
+
+concurrency:
+  # Skip intermediate builds: always.
+  # Cancel intermediate builds: only if it is a pull request build.
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
+
+jobs:
+  format:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: 1
+      - name: Format code
+        run: |
+          using Pkg
+          Pkg.add(; name="JuliaFormatter", uuid="98e50ef6-434e-11e9-1051-2b60c6c9e899")
+          using JuliaFormatter
+          format("."; verbose=true)
+        shell: julia --color=yes {0}
+      - uses: reviewdog/action-suggester@v1
+        if: github.event_name == 'pull_request'
+        with:
+          tool_name: JuliaFormatter
+          fail_on_error: true

docs/make.jl

@@ -4,13 +4,18 @@ using Bijectors
 # Doctest setup
 DocMeta.setdocmeta!(Bijectors, :DocTestSetup, :(using Bijectors); recursive=true)
 
-makedocs(
-    sitename = "Bijectors",
-    format = Documenter.HTML(),
-    modules = [Bijectors],
-    pages = ["Home" => "index.md", "Transforms" => "transforms.md", "Distributions.jl integration" => "distributions.md", "Examples" => "examples.md"],
+makedocs(;
+    sitename="Bijectors",
+    format=Documenter.HTML(),
+    modules=[Bijectors],
+    pages=[
+        "Home" => "index.md",
+        "Transforms" => "transforms.md",
+        "Distributions.jl integration" => "distributions.md",
+        "Examples" => "examples.md",
+    ],
     strict=false,
     checkdocs=:exports,
 )
 
-deploydocs(repo = "github.com/TuringLang/Bijectors.jl.git", push_preview=true)
+deploydocs(; repo="github.com/TuringLang/Bijectors.jl.git", push_preview=true)

docs/src/distributions.md

@@ -1,10 +1,13 @@
 ## Basic usage
+
 Other than the `logpdf_with_trans` methods, the package also provides a more composable interface through the `Bijector` types. Consider for example the one from above with `Beta(2, 2)`.
 
 ```julia
-julia> using Random; Random.seed!(42);
+julia> using Random;
+       Random.seed!(42);
 
-julia> using Bijectors; using Bijectors: Logit
+julia> using Bijectors;
+       using Bijectors: Logit;
 
 julia> dist = Beta(2, 2)
 Beta{Float64}(α=2.0, β=2.0)

docs/src/examples.md

@@ -3,9 +3,11 @@ using Bijectors
 ```
 
 ## Univariate ADVI example
+
 But the real utility of `TransformedDistribution` becomes more apparent when using `transformed(dist, b)` for any bijector `b`. To get the transformed distribution corresponding to the `Beta(2, 2)`, we called `transformed(dist)` before. This is simply an alias for `transformed(dist, bijector(dist))`. Remember `bijector(dist)` returns the constrained-to-constrained bijector for that particular `Distribution`. But we can of course construct a `TransformedDistribution` using different bijectors with the same `dist`. This is particularly useful in something called _Automatic Differentiation Variational Inference (ADVI)_.[2] An important part of ADVI is to approximate a constrained distribution, e.g. `Beta`, as follows:
-1. Sample `x` from a `Normal` with parameters `μ` and `σ`, i.e. `x ~ Normal(μ, σ)`.
-2. Transform `x` to `y` s.t. `y ∈ support(Beta)`, with the transform being a differentiable bijection with a differentiable inverse (a "bijector")
+
+ 1. Sample `x` from a `Normal` with parameters `μ` and `σ`, i.e. `x ~ Normal(μ, σ)`.
+ 2. Transform `x` to `y` s.t. `y ∈ support(Beta)`, with the transform being a differentiable bijection with a differentiable inverse (a "bijector")
 
 This then defines a probability density with same _support_ as `Beta`! Of course, it's unlikely that it will be the same density, but it's an _approximation_. Creating such a distribution becomes trivial with `Bijector` and `TransformedDistribution`:
 
@@ -16,7 +18,7 @@ dist = Beta(2, 2)
 b = bijector(dist)              # (0, 1) → ℝ
 b⁻¹ = inverse(b)                # ℝ → (0, 1)
 td = transformed(Normal(), b⁻¹) # x ∼ 𝓝(0, 1) then b(x) ∈ (0, 1)
- x = rand(rng, td)                   # ∈ (0, 1)
+x = rand(rng, td)                   # ∈ (0, 1)
 ```
 
 It's worth noting that `support(Beta)` is the _closed_ interval `[0, 1]`, while the constrained-to-unconstrained bijection, `Logit` in this case, is only well-defined as a map `(0, 1) → ℝ` for the _open_ interval `(0, 1)`. This is of course not an implementation detail. `ℝ` is itself open, thus no continuous bijection exists from a _closed_ interval to `ℝ`. But since the boundaries of a closed interval has what's known as measure zero, this doesn't end up affecting the resulting density with support on the entire real line. In practice, this means that
@@ -29,25 +31,22 @@ inverse(td.transform)(rand(rng, td))
 will never result in `0` or `1` though any sample arbitrarily close to either `0` or `1` is possible. _Disclaimer: numerical accuracy is limited, so you might still see `0` and `1` if you're lucky._
 
 ## Multivariate ADVI example
+
 We can also do _multivariate_ ADVI using the `Stacked` bijector. `Stacked` gives us a way to combine univariate and/or multivariate bijectors into a singe multivariate bijector. Say you have a vector `x` of length 2 and you want to transform the first entry using `Exp` and the second entry using `Log`. `Stacked` gives you an easy and efficient way of representing such a bijector.
 
 ```@repl advi
 using Bijectors: SimplexBijector
 
 # Original distributions
-dists = (
-    Beta(),
-    InverseGamma(),
-    Dirichlet(2, 3)
-);
+dists = (Beta(), InverseGamma(), Dirichlet(2, 3));
 
 # Construct the corresponding ranges
 ranges = [];
 idx = 1;
 
-for i = 1:length(dists)
+for i in 1:length(dists)
     d = dists[i]
-    push!(ranges, idx:idx + length(d) - 1)
+    push!(ranges, idx:(idx + length(d) - 1))
 
     global idx
     idx += length(d)
@@ -74,6 +73,7 @@ sum(y[3:4]) ≈ 1.0
 ```
 
 ## Normalizing flows
+
 A very interesting application is that of _normalizing flows_.[1] Usually this is done by sampling from a multivariate normal distribution, and then transforming this to a target distribution using invertible neural networks. Currently there are two such transforms available in Bijectors.jl: `PlanarLayer` and `RadialLayer`. Let's create a flow with a single `PlanarLayer`:
 
 ```@setup normalizing-flows
@@ -144,7 +144,7 @@ f = NLLObjective(reconstruct, MvNormal(2, 1), xs);
 
 # Train using gradient descent.
 ε = 1e-3;
-for i = 1:100
+for i in 1:100
     ∇s = Zygote.gradient(f, θs...)
     θs = map(θs, ∇s) do θ, ∇
         θ - ε .* ∇

docs/src/index.md

@@ -1,31 +1,32 @@
 # Bijectors.jl
 
 This package implements a set of functions for transforming constrained random variables (e.g. simplexes, intervals) to Euclidean space. The 3 main functions implemented in this package are the `link`, `invlink` and `logpdf_with_trans` for a number of distributions. The distributions supported are:
-1. `RealDistribution`: `Union{Cauchy, Gumbel, Laplace, Logistic, NoncentralT, Normal, NormalCanon, TDist}`,
-2. `PositiveDistribution`: `Union{BetaPrime, Chi, Chisq, Erlang, Exponential, FDist, Frechet, Gamma, InverseGamma, InverseGaussian, Kolmogorov, LogNormal, NoncentralChisq, NoncentralF, Rayleigh, Weibull}`,
-3. `UnitDistribution`: `Union{Beta, KSOneSided, NoncentralBeta}`,
-4. `SimplexDistribution`: `Union{Dirichlet}`,
-5. `PDMatDistribution`: `Union{InverseWishart, Wishart}`, and
-6. `TransformDistribution`: `Union{T, Truncated{T}} where T<:ContinuousUnivariateDistribution`.
+
+ 1. `RealDistribution`: `Union{Cauchy, Gumbel, Laplace, Logistic, NoncentralT, Normal, NormalCanon, TDist}`,
+ 2. `PositiveDistribution`: `Union{BetaPrime, Chi, Chisq, Erlang, Exponential, FDist, Frechet, Gamma, InverseGamma, InverseGaussian, Kolmogorov, LogNormal, NoncentralChisq, NoncentralF, Rayleigh, Weibull}`,
+ 3. `UnitDistribution`: `Union{Beta, KSOneSided, NoncentralBeta}`,
+ 4. `SimplexDistribution`: `Union{Dirichlet}`,
+ 5. `PDMatDistribution`: `Union{InverseWishart, Wishart}`, and
+ 6. `TransformDistribution`: `Union{T, Truncated{T}} where T<:ContinuousUnivariateDistribution`.
 
 All exported names from the [Distributions.jl](https://github.com/TuringLang/Bijectors.jl) package are reexported from `Bijectors`.
 
 Bijectors.jl also provides a nice interface for working with these maps: composition, inversion, etc.
 The following table lists mathematical operations for a bijector and the corresponding code in Bijectors.jl.
 
-| Operation                          | Method          | Automatic |
-|:------------------------------------:|:-----------------:|:-----------:|
-| `b ↦ b⁻¹`                                      | `inverse(b)`                | ✓         |
-| `(b₁, b₂) ↦ (b₁ ∘ b₂)`                         | `b₁ ∘ b₂`               | ✓         |
-| `(b₁, b₂) ↦ [b₁, b₂]`                          | `stack(b₁, b₂)`         | ✓         |
-| `x ↦ b(x)`                                     | `b(x)`                  | ×         |
-| `y ↦ b⁻¹(y)`                                   | `inverse(b)(y)`             | ×         |
-| `x ↦ log｜det J(b, x)｜`                       | `logabsdetjac(b, x)`    | AD        |
-| `x ↦ b(x), log｜det J(b, x)｜`                 | `with_logabsdet_jacobian(b, x)`         | ✓         |
-| `p ↦ q := b_* p`                                | `q = transformed(p, b)` | ✓         |
-| `y ∼ q`                                        | `y = rand(q)`           | ✓         |
-| `p ↦ b` such that `support(b_* p) = ℝᵈ`               | `bijector(p)`           | ✓         |
-| `(x ∼ p, b(x), log｜det J(b, x)｜, log q(y))` | `forward(q)`            | ✓         |
+| Operation                                   | Method                          | Automatic |
+|:-------------------------------------------:|:-------------------------------:|:---------:|
+| `b ↦ b⁻¹`                                   | `inverse(b)`                    | ✓         |
+| `(b₁, b₂) ↦ (b₁ ∘ b₂)`                      | `b₁ ∘ b₂`                       | ✓         |
+| `(b₁, b₂) ↦ [b₁, b₂]`                       | `stack(b₁, b₂)`                 | ✓         |
+| `x ↦ b(x)`                                  | `b(x)`                          | ×         |
+| `y ↦ b⁻¹(y)`                                | `inverse(b)(y)`                 | ×         |
+| `x ↦ log｜det J(b, x)｜`                      | `logabsdetjac(b, x)`            | AD        |
+| `x ↦ b(x), log｜det J(b, x)｜`                | `with_logabsdet_jacobian(b, x)` | ✓         |
+| `p ↦ q := b_* p`                            | `q = transformed(p, b)`         | ✓         |
+| `y ∼ q`                                     | `y = rand(q)`                   | ✓         |
+| `p ↦ b` such that `support(b_* p) = ℝᵈ`     | `bijector(p)`                   | ✓         |
+| `(x ∼ p, b(x), log｜det J(b, x)｜, log q(y))` | `forward(q)`                    | ✓         |
 
 In this table, `b` denotes a `Bijector`, `J(b, x)` denotes the Jacobian of `b` evaluated at `x`, `b_*` denotes the [push-forward](https://www.wikiwand.com/en/Pushforward_measure) of `p` by `b`, and `x ∼ p` denotes `x` sampled from the distribution with density `p`.
 

docs/src/transforms.md

@@ -1,8 +1,9 @@
 ## Usage
 
 A very simple example of a "bijector"/diffeomorphism, i.e. a differentiable transformation with a differentiable inverse, is the `exp` function:
-- The inverse of `exp` is `log`.
-- The derivative of `exp` at an input `x` is simply `exp(x)`, hence `logabsdetjac` is simply `x`.
+
+  - The inverse of `exp` is `log`.
+  - The derivative of `exp` at an input `x` is simply `exp(x)`, hence `logabsdetjac` is simply `x`.
 
 ```@repl usage
 using Bijectors
@@ -100,4 +101,3 @@ Bijectors.OrderedBijector
 Bijectors.NamedTransform
 Bijectors.NamedCoupling
 ```
-