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Revert "Prepare standalone package, step 2 (#128)"
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This reverts commit 14438ab.
`mean` will not be moved to Base as Statistics will remain an (upgradable)
stdlib.

Keep version at 1.11.0 though.
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@nalimilan
nalimilan committed Aug 25, 2023
1 parent f54010d commit f91df38
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2 changes: 2 additions & 0 deletions docs/src/index.md
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Expand Up @@ -9,6 +9,8 @@ var
varm
cor
cov
mean!
mean
median!
median
middle
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318 changes: 158 additions & 160 deletions src/Statistics.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -16,189 +16,189 @@ export cor, cov, std, stdm, var, varm, mean!, mean,

##### mean #####

if !isdefined(Base, :mean)
"""
mean(itr)
Compute the mean of all elements in a collection.
!!! note
If `itr` contains `NaN` or [`missing`](@ref) values, the result is also
`NaN` or `missing` (`missing` takes precedence if array contains both).
Use the [`skipmissing`](@ref) function to omit `missing` entries and compute the
mean of non-missing values.
# Examples
```jldoctest
julia> using Statistics
julia> mean(1:20)
10.5
julia> mean([1, missing, 3])
missing
julia> mean(skipmissing([1, missing, 3]))
2.0
```
"""
mean(itr) = mean(identity, itr)

"""
mean(f, itr)
Apply the function `f` to each element of collection `itr` and take the mean.
```jldoctest
julia> using Statistics
julia> mean(√, [1, 2, 3])
1.3820881233139908
julia> mean([√1, √2, √3])
1.3820881233139908
```
"""
function mean(f, itr)
y = iterate(itr)
if y === nothing
return Base.mapreduce_empty_iter(f, +, itr,
Base.IteratorEltype(itr)) / 0
end
count = 1
"""
mean(itr)
Compute the mean of all elements in a collection.
!!! note
If `itr` contains `NaN` or [`missing`](@ref) values, the result is also
`NaN` or `missing` (`missing` takes precedence if array contains both).
Use the [`skipmissing`](@ref) function to omit `missing` entries and compute the
mean of non-missing values.
# Examples
```jldoctest
julia> using Statistics
julia> mean(1:20)
10.5
julia> mean([1, missing, 3])
missing
julia> mean(skipmissing([1, missing, 3]))
2.0
```
"""
mean(itr) = mean(identity, itr)

"""
mean(f, itr)
Apply the function `f` to each element of collection `itr` and take the mean.
```jldoctest
julia> using Statistics
julia> mean(√, [1, 2, 3])
1.3820881233139908
julia> mean([√1, √2, √3])
1.3820881233139908
```
"""
function mean(f, itr)
y = iterate(itr)
if y === nothing
return Base.mapreduce_empty_iter(f, +, itr,
Base.IteratorEltype(itr)) / 0
end
count = 1
value, state = y
f_value = f(value)/1
total = Base.reduce_first(+, f_value)
y = iterate(itr, state)
while y !== nothing
value, state = y
f_value = f(value)/1
total = Base.reduce_first(+, f_value)
total += _mean_promote(total, f(value))
count += 1
y = iterate(itr, state)
while y !== nothing
value, state = y
total += _mean_promote(total, f(value))
count += 1
y = iterate(itr, state)
end
return total/count
end
return total/count
end

"""
mean(f, A::AbstractArray; dims)
Apply the function `f` to each element of array `A` and take the mean over dimensions `dims`.
"""
mean(f, A::AbstractArray; dims)
!!! compat "Julia 1.3"
This method requires at least Julia 1.3.
Apply the function `f` to each element of array `A` and take the mean over dimensions `dims`.
```jldoctest
julia> using Statistics
!!! compat "Julia 1.3"
This method requires at least Julia 1.3.
julia> mean(√, [1, 2, 3])
1.3820881233139908
```jldoctest
julia> using Statistics
julia> mean([√1, √2, √3])
1.3820881233139908
julia> mean(√, [1, 2, 3])
1.3820881233139908
julia> mean(√, [1 2 3; 4 5 6], dims=2)
2×1 Matrix{Float64}:
1.3820881233139908
2.2285192400943226
```
"""
mean(f, A::AbstractArray; dims=:) = _mean(f, A, dims)
julia> mean([√1, √2, √3])
1.3820881233139908
function mean(f::Number, itr::Number)
f_value = try
f(itr)
catch MethodError
rethrow(ArgumentError("""mean(f, itr) requires a function and an iterable.
Perhaps you meant middle(x, y)?""",))
end
Base.reduce_first(+, f_value)/1
julia> mean(√, [1 2 3; 4 5 6], dims=2)
2×1 Matrix{Float64}:
1.3820881233139908
2.2285192400943226
```
"""
mean(f, A::AbstractArray; dims=:) = _mean(f, A, dims)

function mean(f::Number, itr::Number)
f_value = try
f(itr)
catch MethodError
rethrow(ArgumentError("""mean(f, itr) requires a function and an iterable.
Perhaps you meant middle(x, y)?""",))
end
Base.reduce_first(+, f_value)/1
end

"""
mean!(r, v)
Compute the mean of `v` over the singleton dimensions of `r`, and write results to `r`.
# Examples
```jldoctest
julia> using Statistics
julia> v = [1 2; 3 4]
2×2 Matrix{Int64}:
1 2
3 4
julia> mean!([1., 1.], v)
2-element Vector{Float64}:
1.5
3.5
julia> mean!([1. 1.], v)
1×2 Matrix{Float64}:
2.0 3.0
```
"""
function mean!(R::AbstractArray, A::AbstractArray)
sum!(R, A; init=true)
x = max(1, length(R)) // length(A)
R .= R .* x
return R
end
"""
mean!(r, v)
"""
mean(A::AbstractArray; dims)
Compute the mean of `v` over the singleton dimensions of `r`, and write results to `r`.
Compute the mean of an array over the given dimensions.
# Examples
```jldoctest
julia> using Statistics
!!! compat "Julia 1.1"
`mean` for empty arrays requires at least Julia 1.1.
julia> v = [1 2; 3 4]
2×2 Matrix{Int64}:
1 2
3 4
# Examples
```jldoctest
julia> using Statistics
julia> mean!([1., 1.], v)
2-element Vector{Float64}:
1.5
3.5
julia> A = [1 2; 3 4]
2×2 Matrix{Int64}:
1 2
3 4
julia> mean!([1. 1.], v)
1×2 Matrix{Float64}:
2.0 3.0
```
"""
function mean!(R::AbstractArray, A::AbstractArray)
sum!(R, A; init=true)
x = max(1, length(R)) // length(A)
R .= R .* x
return R
end

julia> mean(A, dims=1)
1×2 Matrix{Float64}:
2.0 3.0
"""
mean(A::AbstractArray; dims)
julia> mean(A, dims=2)
2×1 Matrix{Float64}:
1.5
3.5
```
"""
mean(A::AbstractArray; dims=:) = _mean(identity, A, dims)
Compute the mean of an array over the given dimensions.
_mean_promote(x::T, y::S) where {T,S} = convert(promote_type(T, S), y)
!!! compat "Julia 1.1"
`mean` for empty arrays requires at least Julia 1.1.
# ::Dims is there to force specializing on Colon (as it is a Function)
function _mean(f, A::AbstractArray, dims::Dims=:) where Dims
isempty(A) && return sum(f, A, dims=dims)/0
if dims === (:)
n = length(A)
else
n = mapreduce(i -> size(A, i), *, unique(dims); init=1)
end
x1 = f(first(A)) / 1
result = sum(x -> _mean_promote(x1, f(x)), A, dims=dims)
if dims === (:)
return result / n
else
return result ./= n
end
end
# Examples
```jldoctest
julia> using Statistics
julia> A = [1 2; 3 4]
2×2 Matrix{Int64}:
1 2
3 4
julia> mean(A, dims=1)
1×2 Matrix{Float64}:
2.0 3.0
julia> mean(A, dims=2)
2×1 Matrix{Float64}:
1.5
3.5
```
"""
mean(A::AbstractArray; dims=:) = _mean(identity, A, dims)

function mean(r::AbstractRange{<:Real})
isempty(r) && return oftype((first(r) + last(r)) / 2, NaN)
(first(r) + last(r)) / 2
_mean_promote(x::T, y::S) where {T,S} = convert(promote_type(T, S), y)

# ::Dims is there to force specializing on Colon (as it is a Function)
function _mean(f, A::AbstractArray, dims::Dims=:) where Dims
isempty(A) && return sum(f, A, dims=dims)/0
if dims === (:)
n = length(A)
else
n = mapreduce(i -> size(A, i), *, unique(dims); init=1)
end
x1 = f(first(A)) / 1
result = sum(x -> _mean_promote(x1, f(x)), A, dims=dims)
if dims === (:)
return result / n
else
return result ./= n
end
end

function mean(r::AbstractRange{<:Real})
isempty(r) && return oftype((first(r) + last(r)) / 2, NaN)
(first(r) + last(r)) / 2
end

median(r::AbstractRange{<:Real}) = mean(r)

##### variances #####

# faster computation of real(conj(x)*y)
Expand Down Expand Up @@ -877,8 +877,6 @@ _median(v::AbstractArray, dims) = mapslices(median!, v, dims = dims)

_median(v::AbstractArray{T}, ::Colon) where {T} = median!(copyto!(Array{T,1}(undef, length(v)), v))

median(r::AbstractRange{<:Real}) = mean(r)

"""
quantile!([q::AbstractArray, ] v::AbstractVector, p; sorted=false, alpha::Real=1.0, beta::Real=alpha)
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