improve formatting

TuringLang · Sep 16, 2019 · ef2db2f · ef2db2f
1 parent 0297f47
commit ef2db2f
Showing 1 changed file with 22 additions and 6 deletions.
diff --git a/src/adaptation/precond.jl b/src/adaptation/precond.jl
@@ -141,7 +141,6 @@ UnitPreconditioner(::Type{T}=Float64) where {T} = UnitPreconditioner{T}()
 string(::UnitPreconditioner) = "I"
 reset!(::UnitPreconditioner) = nothing
 getM⁻¹(dpc::UnitPreconditioner{T}) where {T<:AbstractFloat} = UniformScaling{T}(one(T))
-
 adapt!(
     ::UnitPreconditioner,
     ::AbstractVector{<:Real},
@@ -150,7 +149,11 @@ adapt!(
 ) = nothing
 
 
-mutable struct DiagPreconditioner{T<:Real, AT<:AbstractVector{T}, TEst <: VarEstimator{T}} <: AbstractPreconditioner
+mutable struct DiagPreconditioner{
+    T<:Real, 
+    AT<:AbstractVector{T}, 
+    TEst<:VarEstimator{T}
+} <: AbstractPreconditioner
     n_min   :: Int
     ve      :: TEst
     var     :: AT
@@ -159,7 +162,12 @@ Base.show(io::IO, ::DiagPreconditioner) = print(io, "DiagPreconditioner")
 
 # Diagonal
 DiagPreconditioner(d::Int, n_min::Int=10) = DiagPreconditioner(Float64, d, n_min)
-function DiagPreconditioner(::Type{T}, d::Int; n_min::Int=10, var=Vector(ones(T, d))) where {T}
+function DiagPreconditioner(
+    ::Type{T}, 
+    d::Int; 
+    n_min::Int=10, 
+    var=Vector(ones(T, d))
+) where {T}
     ve = WelfordVar(T, d)
     return DiagPreconditioner(n_min, ve, var)
 end
@@ -183,15 +191,23 @@ function adapt!(
 end
 
 # Dense
-mutable struct DensePreconditioner{T<:AbstractFloat, TEst <: CovEstimator{T}} <: AbstractPreconditioner
+mutable struct DensePreconditioner{
+    T<:AbstractFloat, 
+    TEst<:CovEstimator{T}
+} <: AbstractPreconditioner
     n_min :: Int
     ce    :: TEst
     covar :: Matrix{T}
 end
 Base.show(io::IO, ::DensePreconditioner) = print(io, "DensePreconditioner")
 
 DensePreconditioner(d::Int, n_min::Int=10) = DensePreconditioner(Float64, d, n_min)
-function DensePreconditioner(::Type{T}, d::Int; n_min::Int=10, covar=LinearAlgebra.diagm(0 => ones(T, d))) where {T}
+function DensePreconditioner(
+    ::Type{T}, 
+    d::Int; 
+    n_min::Int=10, 
+    covar=LinearAlgebra.diagm(0 => ones(T, d))
+) where {T}
     ce = WelfordCov(T, d)
     # TODO: take use of the line below when we have an interface to set which covariance estimator to use
     # ce = NaiveCov(T)
@@ -363,4 +379,4 @@ Preconditioner(m::DiagEuclideanMetric{T}) where {T} = DiagPreconditioner(T, m.di
 Preconditioner(m::DenseEuclideanMetric{T}) where {T} = DensePreconditioner(T, m.dim; covar=m.M⁻¹)
 
 Preconditioner(m::Type{TM}, dim::Int=2) where {TM<:AbstractMetric} = Preconditioner(Float64, m, dim)
-Preconditioner(::Type{T}, ::Type{TM}, dim::Int=2) where {T<:AbstractFloat,TM<:AbstractMetric} = Preconditioner(TM(T, dim))
+Preconditioner(::Type{T}, ::Type{TM}, dim::Int=2) where {T<:AbstractFloat, TM<:AbstractMetric} = Preconditioner(TM(T, dim))