Skip to content

explicitly restrict method params to avoid ambiguities in julia 1.2 #21

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
May 15, 2019
Merged

explicitly restrict method params to avoid ambiguities in julia 1.2 #21

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
5619670
explicitly restrict method params to avoid ambiguities in julia 1.2
JeffBezanson May 14, 2019
b731df6
Update linear_discretizer.jl
tawheeler May 15, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
22 changes: 11 additions & 11 deletions src/linear_discretizer.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@ function supports_encoding(ld::LinearDiscretizer{N,D}, x::N) where {N<:Real,D<:I
end
supports_decoding(ld::LinearDiscretizer{N,D}, d::D) where {N<:Real,D<:Integer} = 1 ≤ d ≤ ld.nbins

function encode(ld::LinearDiscretizer{N,D}, x::N) where {N,D<:Integer}
function encode(ld::LinearDiscretizer{N,D}, x::N) where {N<:Real,D<:Integer}
!isnan(x) || error("cannot encode NaN values")

if x < ld.binedges[1]
Expand All @@ -105,8 +105,8 @@ function encode(ld::LinearDiscretizer{N,D}, x::N) where {N,D<:Integer}
return ld.i2d[a]
end
end
encode(ld::LinearDiscretizer{N,D}, x) where {N,D} = encode(ld, convert(N, x))::D
function encode(ld::LinearDiscretizer{N,D}, data::AbstractArray) where {N,D<:Integer}
encode(ld::LinearDiscretizer{N,D}, x) where {N<:Real,D<:Integer} = encode(ld, convert(N, x))::D
function encode(ld::LinearDiscretizer{N,D}, data::AbstractArray) where {N<:Real,D<:Integer}
arr = [encode(ld, x) for x in data]
reshape(arr, size(data))
end
Expand Down Expand Up @@ -152,7 +152,7 @@ decode(ld::LinearDiscretizer{N,D}, d::D) where {N<:Integer,D<:Integer} = decode(
decode(ld::LinearDiscretizer{N,D}, d::I, method::AbstractSampleMethod=SAMPLE_UNIFORM) where {N<:Real,D<:Integer,I<:Integer} =
decode(ld, convert(D,d), method)

function decode(ld::LinearDiscretizer{N,D}, data::AbstractArray{D}, ::AbstractSampleMethod=SAMPLE_UNIFORM) where {N,D<:Integer}
function decode(ld::LinearDiscretizer{N,D}, data::AbstractArray{D}, ::AbstractSampleMethod=SAMPLE_UNIFORM) where {N<:Real,D<:Integer}
arr = Vector{N}(undef, length(data))
for (i,d) in enumerate(data)
arr[i] = decode(ld, d)
Expand All @@ -162,18 +162,18 @@ end

Base.max(ld::LinearDiscretizer) = ld.binedges[end]
Base.min(ld::LinearDiscretizer) = ld.binedges[1]
function Base.extrema(ld::LinearDiscretizer{N,D}) where {N,D}
function Base.extrema(ld::LinearDiscretizer{N,D}) where {N<:Real,D<:Integer}
lo = ld.binedges[1]
hi = ld.binedges[end]
return (lo, hi)
end
function Base.extrema(ld::LinearDiscretizer{N,D}, d::D) where {N<:AbstractFloat,D}
function Base.extrema(ld::LinearDiscretizer{N,D}, d::D) where {N<:AbstractFloat,D<:Integer}
ind = ld.d2i[d]
lo = ld.binedges[ind]
hi = ld.binedges[ind+1]
return (lo, hi)
end
function Base.extrema(ld::LinearDiscretizer{N,D}, d::D) where {N<:Integer,D}
function Base.extrema(ld::LinearDiscretizer{N,D}, d::D) where {N<:Integer,D<:Integer}
ind = ld.d2i[d]
lo = ld.binedges[ind]
hi = ld.binedges[ind+1]
Expand All @@ -190,14 +190,14 @@ end

nlabels(ld::LinearDiscretizer) = ld.nbins
binedges(ld::LinearDiscretizer) = ld.binedges
bincenters(ld::LinearDiscretizer{N,D}) where {N<:AbstractFloat,D} = (0.5*(ld.binedges[1:ld.nbins] + ld.binedges[2:end]))::Vector{Float64}
function bincenters(ld::LinearDiscretizer{N,D}) where {N<:Integer,D}
bincenters(ld::LinearDiscretizer{N,D}) where {N<:AbstractFloat,D<:Integer} = (0.5*(ld.binedges[1:ld.nbins] + ld.binedges[2:end]))::Vector{Float64}
function bincenters(ld::LinearDiscretizer{N,D}) where {N<:Integer,D<:Integer}
retval = Vector{Float64}(undef, ld.nbins)
for i = 1 : length(retval)-1
retval[i] = 0.5(ld.binedges[i+1]-1 + ld.binedges[i])
end
retval[end] = 0.5(ld.binedges[end] + ld.binedges[end-1])
retval
end
binwidth(ld::LinearDiscretizer{N,D}, d::D) where {N<:AbstractFloat,D} = ld.binedges[d+1] - ld.binedges[d]
binwidths(ld::LinearDiscretizer{N,D}) where {N<:AbstractFloat,D} = ld.binedges[2:end] - ld.binedges[1:end-1]
binwidth(ld::LinearDiscretizer{N,D}, d::D) where {N<:Real,D<:Integer} = ld.binedges[d+1] - ld.binedges[d]
binwidths(ld::LinearDiscretizer{N,D}) where {N<:Real,D<:Integer} = ld.binedges[2:end] - ld.binedges[1:end-1]