Fix loess dispatch to avoid infinite recursion

src/Loess.jl

@@ -21,34 +21,14 @@ modelmatrix(model::LoessModel) = model.xs
 
 response(model::LoessModel) = model.ys
 
-"""
-    loess(xs, ys; normalize=true, span=0.75, degree=2)
-
-Fit a loess model.
-
-Args:
-  - `xs`: A `n` by `m` matrix with `n` observations from `m` independent predictors
-  - `ys`: A length `n` response vector.
-  - `normalize`: Normalize the scale of each predicitor. (default true when `m > 1`)
-  - `span`: The degree of smoothing, typically in [0,1]. Smaller values result in smaller
-      local context in fitting.
-  - `degree`: Polynomial degree.
-  - `cell`: Control parameter for bucket size. Internal interpolation nodes will be
-    added to the K-D tree until the number of bucket element is below `n * cell * span`.
-
-Returns:
-  A fit `LoessModel`.
-
-"""
-function loess(
+function _loess(
     xs::AbstractMatrix{T},
     ys::AbstractVector{T};
     normalize::Bool = true,
     span::AbstractFloat = 0.75,
     degree::Integer = 2,
     cell::AbstractFloat = 0.2
 ) where T<:AbstractFloat
-
     Base.require_one_based_indexing(xs)
     Base.require_one_based_indexing(ys)
 
@@ -146,12 +126,43 @@ function loess(
     LoessModel(convert(Matrix{T}, xs), convert(Vector{T}, ys), predictions_and_gradients, kdtree)
 end
 
-loess(xs::AbstractVector{T}, ys::AbstractVector{T}; kwargs...) where {T<:AbstractFloat} =
+"""
+    loess(xs, ys; normalize=true, span=0.75, degree=2)
+
+Fit a loess model.
+
+Args:
+  - `xs`: A `n` by `m` matrix with `n` observations from `m` independent predictors
+  - `ys`: A length `n` response vector.
+  - `normalize`: Normalize the scale of each predicitor. (default true when `m > 1`)
+  - `span`: The degree of smoothing, typically in [0,1]. Smaller values result in smaller
+      local context in fitting.
+  - `degree`: Polynomial degree.
+  - `cell`: Control parameter for bucket size. Internal interpolation nodes will be
+    added to the K-D tree until the number of bucket element is below `n * cell * span`.
+
+Returns:
+  A fit `LoessModel`.
+
+"""
+function loess(
+    xs::AbstractMatrix{T},
+    ys::AbstractVector{T};
+    normalize::Bool = true,
+    span::AbstractFloat = 0.75,
+    degree::Integer = 2,
+    cell::AbstractFloat = 0.2
+) where T<:AbstractFloat
+    _loess(xs, ys; normalize, span, degree, cell)
+end
+
+loess(xs::AbstractVector{T}, ys::AbstractVector{S}; kwargs...) where {T,S} =
     loess(reshape(xs, (length(xs), 1)), ys; kwargs...)
 
-function loess(xs::AbstractArray{T,N}, ys::AbstractVector{S}; kwargs...) where {T,N,S}
+function loess(xs::AbstractMatrix{T}, ys::AbstractVector{S}; kwargs...) where {T,S}
     R = float(promote_type(T, S))
-    loess(convert(AbstractArray{R,N}, xs), convert(AbstractVector{R}, ys); kwargs...)
+    # Dispatch to another function here to avoid potential infinite recursion
+    _loess(convert(AbstractMatrix{R}, xs), convert(AbstractVector{R}, ys); kwargs...)
 end
 
 

test/runtests.jl

@@ -113,3 +113,9 @@ end
     @test predict(ft, 1.0) == 1.0
     @test_throws ArgumentError loess(Float64[], Float64[])
 end
+
+@testset "infinite recursion. Issue 60" begin
+    x = collect(1.0:10.0)
+    y = convert(Vector{Union{Nothing, Float64}}, x)
+    @test_throws MethodError loess(x, y)
+end