Auto-format Julia code with JuliaFormatter

Author: github-actions[bot]

src/mcmc/external_sampler.jl

@@ -28,7 +28,7 @@ struct ExternalSampler{S<:AbstractSampler,AD<:ADTypes.AbstractADType,Unconstrain
     function ExternalSampler(
         sampler::AbstractSampler,
         adtype::ADTypes.AbstractADType,
-        ::Val{unconstrained}=Val(true),
+        (::Val{unconstrained})=Val(true),
     ) where {unconstrained}
         if !(unconstrained isa Bool)
             throw(
@@ -44,9 +44,11 @@ end
 
 Return `true` if the sampler requires unconstrained space, and `false` otherwise.
 """
-requires_unconstrained_space(
+function requires_unconstrained_space(
     ::ExternalSampler{<:Any,<:Any,Unconstrained}
-) where {Unconstrained} = Unconstrained
+) where {Unconstrained}
+    Unconstrained
+end
 
 """
     externalsampler(sampler::AbstractSampler; adtype=AutoForwardDiff(), unconstrained=true)

test/mcmc/Inference.jl

@@ -297,7 +297,7 @@ using Turing
         chain = sample(StableRNG(seed), gauss(x), PG(10), 10)
         chain = sample(StableRNG(seed), gauss(x), SMC(), 10)
 
-        @model function gauss2(::Type{TV}=Vector{Float64}; x) where {TV}
+        @model function gauss2((::Type{TV})=Vector{Float64}; x) where {TV}
             priors = TV(undef, 2)
             priors[1] ~ InverseGamma(2, 3)         # s
             priors[2] ~ Normal(0, sqrt(priors[1])) # m
@@ -321,7 +321,7 @@ using Turing
             StableRNG(seed), gauss2(DynamicPPL.TypeWrap{Vector{Float64}}(); x=x), SMC(), 10
         )
 
-        @model function gauss3(x, ::Type{TV}=Vector{Float64}) where {TV}
+        @model function gauss3(x, (::Type{TV})=Vector{Float64}) where {TV}
             priors = TV(undef, 2)
             priors[1] ~ InverseGamma(2, 3)         # s
             priors[2] ~ Normal(0, sqrt(priors[1])) # m
@@ -548,7 +548,7 @@ using Turing
         N = 10
         alg = HMC(0.01, 5)
         x = randn(1000)
-        @model function vdemo1(::Type{T}=Float64) where {T}
+        @model function vdemo1((::Type{T})=Float64) where {T}
             x = Vector{T}(undef, N)
             for i in 1:N
                 x[i] ~ Normal(0, sqrt(4))
@@ -563,7 +563,7 @@ using Turing
         vdemo1kw(; T) = vdemo1(T)
         sample(StableRNG(seed), vdemo1kw(; T=DynamicPPL.TypeWrap{Float64}()), alg, 10)
 
-        @model function vdemo2(::Type{T}=Float64) where {T<:Real}
+        @model function vdemo2((::Type{T})=Float64) where {T<:Real}
             x = Vector{T}(undef, N)
             @. x ~ Normal(0, 2)
         end
@@ -574,7 +574,7 @@ using Turing
         vdemo2kw(; T) = vdemo2(T)
         sample(StableRNG(seed), vdemo2kw(; T=DynamicPPL.TypeWrap{Float64}()), alg, 10)
 
-        @model function vdemo3(::Type{TV}=Vector{Float64}) where {TV<:AbstractVector}
+        @model function vdemo3((::Type{TV})=Vector{Float64}) where {TV<:AbstractVector}
             x = TV(undef, N)
             @. x ~ InverseGamma(2, 3)
         end

test/mcmc/gibbs.jl

@@ -141,8 +141,9 @@ end
         inner::Alg
     end
 
-    unwrap_sampler(sampler::DynamicPPL.Sampler{<:AlgWrapper}) =
-        DynamicPPL.Sampler(sampler.alg.inner)
+    unwrap_sampler(sampler::DynamicPPL.Sampler{<:AlgWrapper}) = DynamicPPL.Sampler(
+        sampler.alg.inner
+    )
 
     # Methods we need to define to be able to use AlgWrapper instead of an actual algorithm.
     # They all just propagate the call to the inner algorithm.
@@ -201,7 +202,7 @@ end
     end
 
     # A test model that includes several different kinds of tilde syntax.
-    @model function test_model(val, ::Type{M}=Vector{Float64}) where {M}
+    @model function test_model(val, (::Type{M})=Vector{Float64}) where {M}
         s ~ Normal(0.1, 0.2)
         m ~ Poisson()
         val ~ Normal(s, 1)
@@ -508,7 +509,7 @@ end
     end
 
     @testset "dynamic model" begin
-        @model function imm(y, alpha, ::Type{M}=Vector{Float64}) where {M}
+        @model function imm(y, alpha, (::Type{M})=Vector{Float64}) where {M}
             N = length(y)
             rpm = DirichletProcess(alpha)
 
@@ -574,7 +575,7 @@ end
 
     @testset "dynamic model with dot tilde" begin
         @model function dynamic_model_with_dot_tilde(
-            num_zs=10, ::Type{M}=Vector{Float64}
+            num_zs=10, (::Type{M})=Vector{Float64}
         ) where {M}
             z = Vector{Int}(undef, num_zs)
             z .~ Poisson(1.0)
@@ -720,7 +721,7 @@ end
         struct Wrap{T}
             a::T
         end
-        @model function model1(::Type{T}=Float64) where {T}
+        @model function model1((::Type{T})=Float64) where {T}
             x = Vector{T}(undef, 1)
             x[1] ~ Normal()
             y = Wrap{T}(0.0)

test/mcmc/hmc.jl

@@ -107,9 +107,8 @@ using Turing
             w13 ~ MvNormal([0.0; 0.0], [var_prior 0.0; 0.0 var_prior])
             bo ~ Normal(0, var_prior)
 
-            wo ~ MvNormal(
-                [0.0; 0; 0], [var_prior 0.0 0.0; 0.0 var_prior 0.0; 0.0 0.0 var_prior]
-            )
+            wo ~
+            MvNormal([0.0; 0; 0], [var_prior 0.0 0.0; 0.0 var_prior 0.0; 0.0 0.0 var_prior])
             for i in rand(1:N, 10)
                 y = nn(xs[i], b1, w11, w12, w13, bo, wo)
                 ts[i] ~ Bernoulli(y)
@@ -215,7 +214,7 @@ using Turing
     end
 
     @testset "(partially) issue: #2095" begin
-        @model function vector_of_dirichlet(::Type{TV}=Vector{Float64}) where {TV}
+        @model function vector_of_dirichlet((::Type{TV})=Vector{Float64}) where {TV}
             xs = Vector{TV}(undef, 2)
             xs[1] ~ Dirichlet(ones(5))
             return xs[2] ~ Dirichlet(ones(5))

test/optimisation/Optimisation.jl

@@ -33,8 +33,9 @@ using Turing
         end
         DynamicPPL.NodeTrait(::OverrideContext) = DynamicPPL.IsParent()
         DynamicPPL.childcontext(parent::OverrideContext) = parent.context
-        DynamicPPL.setchildcontext(parent::OverrideContext, child) =
-            OverrideContext(child, parent.logprior_weight, parent.loglikelihood_weight)
+        DynamicPPL.setchildcontext(parent::OverrideContext, child) = OverrideContext(
+            child, parent.logprior_weight, parent.loglikelihood_weight
+        )
 
         # Only implement what we need for the models above.
         function DynamicPPL.tilde_assume(context::OverrideContext, right, vn, vi)
@@ -215,8 +216,9 @@ using Turing
             Random.seed!(222)
             true_value = [0.0625, 1.75]
             true_logp = loglikelihood(gdemo_default, (s=true_value[1], m=true_value[2]))
-            check_success(result, check_retcode=true) =
-                check_optimisation_result(result, true_value, true_logp, check_retcode)
+            check_success(result, check_retcode=true) = check_optimisation_result(
+                result, true_value, true_logp, check_retcode
+            )
 
             lb = [0.0, 0.0]
             ub = [2.0, 2.0]
@@ -275,8 +277,9 @@ using Turing
             Random.seed!(222)
             true_value = [49 / 54, 7 / 6]
             true_logp = logjoint(gdemo_default, (s=true_value[1], m=true_value[2]))
-            check_success(result, check_retcode=true) =
-                check_optimisation_result(result, true_value, true_logp, check_retcode)
+            check_success(result, check_retcode=true) = check_optimisation_result(
+                result, true_value, true_logp, check_retcode
+            )
 
             lb = [0.0, 0.0]
             ub = [2.0, 2.0]
@@ -336,8 +339,9 @@ using Turing
             Random.seed!(222)
             true_value = [0.0625, 1.75]
             true_logp = loglikelihood(gdemo_default, (s=true_value[1], m=true_value[2]))
-            check_success(result, check_retcode=true) =
-                check_optimisation_result(result, true_value, true_logp, check_retcode)
+            check_success(result, check_retcode=true) = check_optimisation_result(
+                result, true_value, true_logp, check_retcode
+            )
 
             # Set two constraints: The first parameter must be non-negative, and the L2 norm
             # of the parameters must be between 0.5 and 2.
@@ -389,8 +393,9 @@ using Turing
             Random.seed!(222)
             true_value = [49 / 54, 7 / 6]
             true_logp = logjoint(gdemo_default, (s=true_value[1], m=true_value[2]))
-            check_success(result, check_retcode=true) =
-                check_optimisation_result(result, true_value, true_logp, check_retcode)
+            check_success(result, check_retcode=true) = check_optimisation_result(
+                result, true_value, true_logp, check_retcode
+            )
 
             # Set two constraints: The first parameter must be non-negative, and the L2 norm
             # of the parameters must be between 0.5 and 2.