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Use IEEE comparison in binindex #1010

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31 changes: 7 additions & 24 deletions src/hist.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -197,14 +197,6 @@ mutable struct Histogram{T<:Real,N,E} <: AbstractHistogram{T,N,E}
closed == :right || closed == :left || error("closed must :left or :right")
isdensity && !(T <: AbstractFloat) && error("Density histogram must have float-type weights")
_edges_nbins(edges) == size(weights) || error("Histogram edge vectors must be 1 longer than corresponding weight dimensions")
# We do not handle -0.0 in ranges correctly in `binindex` for performance
# Constructing ranges starting or ending with -0.0 is very hard,
# and ranges containing -0.0 elsewhere virtually impossible,
# but check this just in case as it is cheap
foreach(edges) do e
e isa AbstractRange && any(isequal(-0.0), e) &&
throw(ArgumentError("ranges containing -0.0 not allowed in edges"))
end
new{T,N,E}(edges,weights,closed,isdensity)
end
end
Expand Down Expand Up @@ -240,25 +232,16 @@ binindex(h::AbstractHistogram{T,1}, x::Real) where {T} = binindex(h, (x,))[1]
binindex(h::Histogram{T,N}, xs::NTuple{N,Real}) where {T,N} =
map((edge, x) -> _edge_binindex(edge, h.closed, x), h.edges, xs)

_normalize_zero(x::AbstractFloat) = isequal(x, -0.0) ? zero(x) : x
_normalize_zero(x::Any) = x

# Always treat -0.0 like 0.0
# Compare with `<` rather than the default `isless`: `isless(-0.0, 0.0)` is true, so -0.0
# would be binned differently from 0.0, whereas `-0.0 < 0.0` is false and the two are
# treated as equal. NaN compares false with everything under `<`, so it ends up outside
# the edges (bin index 0 or `length(edge)`) and is dropped by `push!` as before. `<` is also
# cheaper than `isless` and keeps the arithmetic fast path for ranges.
@inline function _edge_binindex(edge::AbstractVector, closed::Symbol, x::Real)
if closed === :right
return searchsortedfirst(edge, _normalize_zero(x), by=_normalize_zero) - 1
else
return searchsortedlast(edge, _normalize_zero(x), by=_normalize_zero)
end
end
# Passing by=_normalize_zero for ranges would have a large performance hit
# as it would force using the AbstractVector fallback
# This is not worth it given that it is very difficult to construct a range containing -0.0
@inline function _edge_binindex(edge::AbstractRange, closed::Symbol, x::Real)
if closed === :right
return searchsortedfirst(edge, _normalize_zero(x)) - 1
return searchsortedfirst(edge, x, lt = <) - 1
else
return searchsortedlast(edge, _normalize_zero(x))
return searchsortedlast(edge, x, lt = <)
end
end

Expand Down
16 changes: 14 additions & 2 deletions test/hist.jl
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Expand Up @@ -295,8 +295,20 @@ end
fit(Histogram, [0.0, 1.0], -0.5:0.5:0.0, closed=:right) ==
fit(Histogram, [-0.0, 1.0], -0.5:0.5:0.0, closed=:right)

@test_throws ArgumentError fit(Histogram, [-0.5], LinRange(-1.0, -0.0, 3))
@test_throws ArgumentError fit(Histogram, [-0.5], UnitRange(-0.0, 1.0))
# ranges containing -0.0 behave like the corresponding ranges with 0.0
@test fit(Histogram, [-0.5, 0.0], LinRange(-1.0, -0.0, 3)) ==
fit(Histogram, [-0.5, 0.0], LinRange(-1.0, 0.0, 3))
@test fit(Histogram, [-0.5, 0.0], LinRange(-1.0, -0.0, 3), closed=:right) ==
fit(Histogram, [-0.5, -0.0], LinRange(-1.0, 0.0, 3), closed=:right)
@test fit(Histogram, [0.0, 0.5], UnitRange(-0.0, 1.0)) ==
fit(Histogram, [-0.0, 0.5], UnitRange(0.0, 1.0))
@test fit(Histogram, [0.0, 0.5], UnitRange(-0.0, 1.0), closed=:right) ==
fit(Histogram, [-0.0, 0.5], UnitRange(0.0, 1.0), closed=:right)
# NaN is dropped
@test fit(Histogram, [NaN, 0.5], 0.0:0.5:1.0).weights == [0, 1]
@test fit(Histogram, [NaN, 0.5], 0.0:0.5:1.0, closed=:right).weights == [1, 0]
@test fit(Histogram, [NaN, 0.5], [0.0, 0.5, 1.0]).weights == [0, 1]
@test fit(Histogram, [NaN, 0.5], [0.0, 0.5, 1.0], closed=:right).weights == [1, 0]
end

end # @testset "StatsBase.Histogram"