Merge pull request #204 from ReactiveBayes/dev_fix_format

Fix format in repo

Author: albertpod

src/common.jl

@@ -45,4 +45,4 @@ function binomial_prod(n, p, x)
     end
 end
 
-mvdigamma(η,p) = sum( digamma(η + (one(d) - d)/2) for d=1:p)
+mvdigamma(η, p) = sum(digamma(η + (one(d) - d) / 2) for d in 1:p)

src/distributions/beta.jl

@@ -69,7 +69,7 @@ getgradlogpartition(::NaturalParametersSpace, ::Type{Beta}) = (η) -> begin
     ηsum = η₁p + η₂p
     dig = digamma(ηsum)
 
-    return SA[digamma(η₁p) - dig, digamma(η₂p) - dig]
+    return SA[digamma(η₁p)-dig, digamma(η₂p)-dig]
 end
 
 getfisherinformation(::NaturalParametersSpace, ::Type{Beta}) = (η) -> begin

src/distributions/binomial.jl

@@ -95,7 +95,7 @@ end
 
 getgradlogpartition(::NaturalParametersSpace, ::Type{Binomial}, ntrials) = (η) -> begin
     (η₁,) = unpack_parameters(Binomial, η)
-    return SA[ntrials*exp(η₁) / (one(η₁) + exp(η₁))]
+    return SA[ntrials*exp(η₁)/(one(η₁)+exp(η₁))]
 end
 
 getfisherinformation(::NaturalParametersSpace, ::Type{Binomial}, ntrials) = (η) -> begin

src/distributions/categorical.jl

@@ -28,7 +28,8 @@ end
 # The default implementation via `@generated` function fails to infer this
 exponential_family_typetag(::Categorical) = Categorical
 
-isproper(::NaturalParametersSpace, ::Type{Categorical}, η, conditioner) = isinteger(conditioner) && (conditioner === length(η)) && (length(η) >= 2) && (η[end] ≈ 0)
+isproper(::NaturalParametersSpace, ::Type{Categorical}, η, conditioner) =
+    isinteger(conditioner) && (conditioner === length(η)) && (length(η) >= 2) && (η[end] ≈ 0)
 isproper(::MeanParametersSpace, ::Type{Categorical}, θ, conditioner) =
     isinteger(conditioner) && (conditioner === length(θ)) && (length(θ) >= 2) && all(>(0), θ) && isapprox(sum(θ), 1)
 
@@ -51,14 +52,14 @@ function (::MeanToNatural{Categorical})(tuple_of_θ::Tuple{Any}, _)
     return (LoopVectorization.vmap(pᵢ -> log(pᵢ / pₖ), p),)
 end
 
-function (::NaturalToMean{Categorical})(tuple_of_η::Tuple{V}, _) where { V <: Vector }
+function (::NaturalToMean{Categorical})(tuple_of_η::Tuple{V}, _) where {V <: Vector}
     (η,) = tuple_of_η
     return (softmax(η),)
 end
 
 # We use `Categorical` from `Distributions.jl` for the `MeanParametersSpace` 
 # and their implementation supports only `Vector`s
-function (::NaturalToMean{Categorical})(tuple_of_η::Tuple{V}, _) where { V <: AbstractVector }
+function (::NaturalToMean{Categorical})(tuple_of_η::Tuple{V}, _) where {V <: AbstractVector}
     (η,) = tuple_of_η
     return (softmax(convert(Vector, η)),)
 end
@@ -87,7 +88,7 @@ getlogpartition(::NaturalParametersSpace, ::Type{Categorical}, conditioner) =
         return logsumexp(η)
     end
 
-getgradlogpartition(::NaturalParametersSpace, ::Type{Categorical}, conditioner) = 
+getgradlogpartition(::NaturalParametersSpace, ::Type{Categorical}, conditioner) =
     (η) -> begin
         if (conditioner !== length(η))
             throw(
@@ -97,7 +98,7 @@ getgradlogpartition(::NaturalParametersSpace, ::Type{Categorical}, conditioner)
             )
         end
         sumη = vmapreduce(exp, +, η)
-        return vmap(d->exp(d)/sumη ,η)
+        return vmap(d -> exp(d) / sumη, η)
     end
 
 getfisherinformation(::NaturalParametersSpace, ::Type{Categorical}, conditioner) =

src/distributions/chi_squared.jl

@@ -56,7 +56,7 @@ unpack_parameters(::Type{Chisq}, packed) = (first(packed),)
 isbasemeasureconstant(::Type{Chisq}) = NonConstantBaseMeasure()
 
 getbasemeasure(::Type{Chisq}) = (x) -> exp(-x / 2)
-getlogbasemeasure(::Type{Chisq}) = (x) -> -x/2
+getlogbasemeasure(::Type{Chisq}) = (x) -> -x / 2
 getsufficientstatistics(::Type{Chisq}) = (log,)
 
 getlogpartition(::NaturalParametersSpace, ::Type{Chisq}) = (η) -> begin
@@ -67,7 +67,7 @@ end
 
 getgradlogpartition(::NaturalParametersSpace, ::Type{Chisq}) = (η) -> begin
     (η1,) = unpack_parameters(Chisq, η)
-    return SA[digamma(η1 + one(η1)) + logtwo]
+    return SA[digamma(η1 + one(η1))+logtwo]
 end
 
 getfisherinformation(::NaturalParametersSpace, ::Type{Chisq}) = (η) -> begin

src/distributions/dirichlet.jl

@@ -92,4 +92,4 @@ end
 getgradlogpartition(::MeanParametersSpace, ::Type{Dirichlet}) = (θ) -> begin
     (α,) = unpack_parameters(Dirichlet, θ)
     return digamma.(α) .- digamma(sum(α))
-end
+end

src/distributions/erlang.jl

@@ -60,7 +60,7 @@ end
 getgradlogpartition(::NaturalParametersSpace, ::Type{Erlang}) = (η) -> begin
     (η1, η2) = unpack_parameters(Erlang, η)
     dη1 = digamma(η1 + 1) - log(-η2)
-    dη2 = - (η1 + one(η1))*inv(η2)
+    dη2 = -(η1 + one(η1)) * inv(η2)
     return SA[dη1, dη2]
 end
 

src/distributions/gamma_family/gamma_family.jl

@@ -102,7 +102,7 @@ end
 
 getgradlogpartition(::NaturalParametersSpace, ::Type{Gamma}) = (η) -> begin
     (η₁, η₂) = unpack_parameters(Gamma, η)
-    return SA[digamma(η₁ + one(η₁)) - log(-η₂), - (η₁ + one(η₁)) / η₂]
+    return SA[digamma(η₁ + one(η₁))-log(-η₂), -(η₁ + one(η₁))/η₂]
 end
 
 # Mean parametrization
@@ -122,5 +122,5 @@ end
 
 getgradlogpartition(::MeanParametersSpace, ::Type{Gamma}) = (θ) -> begin
     (shape, scale) = unpack_parameters(Gamma, θ)
-    return SA[digamma(shape) - log(scale), - shape / scale]
-end
+    return SA[digamma(shape)-log(scale), -shape/scale]
+end

src/distributions/gamma_inverse.jl

@@ -66,7 +66,7 @@ end
 getgradlogpartition(::NaturalParametersSpace, ::Type{GammaInverse}) = (η) -> begin
     (η₁, η₂) = unpack_parameters(GammaInverse, η)
     dη1 = -digamma(-η₁ - one(η₁)) + log(-η₂)
-    dη2 = - (-η₁ - one(η₁))/η₂
+    dη2 = -(-η₁ - one(η₁)) / η₂
     return SA[dη1, dη2]
 end
 

src/distributions/geometric.jl

@@ -69,4 +69,4 @@ end
 getgradlogpartition(::MeanParametersSpace, ::Type{Geometric}) = (θ) -> begin
     (p,) = unpack_parameters(Geometric, θ)
     return SA[one(p) / (p^2 - p);]
-end
+end