# 通用的距离函数
function Minkowski(
x::Array{T,1},
y::Array{T,1},
p::Int
) where T <: Number
return sum(abs.(x - y).^p)^(1/p)
end
# 欧式距离-1
Euclidean(x::Array{T,1}, y::Array{T,1}) where T <: Number = Minkowski(x, y, 2)
# 欧式距离-2
function Euclidean2(
x::Array{T,1},
y::Array{T,1}
) where T <: Number
return sqrt(sum(abs2.(x - y)))
end
测试代码
using BenchmarkTools
rng = 10^5
x = rand(-rng:rng, rng)
y = rand(-rng:rng, rng)
help?> mapreduce
search: mapreduce
mapreduce(f, op, itr; [init])
Apply function f to each element in itr, and then reduce the result using the binary function op. If provided, init must be a neutral element for op that will be returne
for empty collections. It is unspecified whether init is used for non-empty collections. In general, it will be necessary to provide init to work with empty collections.
mapreduce is functionally equivalent to calling reduce(op, map(f, itr); init=init), but will in general execute faster since no intermediate collection needs to be
created. See documentation for reduce and map.
划重点：
but will in general execute faster since no intermediate collection needs to be created.
试一下：
julia> using BenchmarkTools
julia> x = randn(10^5);
julia> y = randn(10^5);
julia> function f(x,y)
dist = 0
for i in 1:length(x)
dist += (x[i]-y[i])^2
end
dist = sqrt(dist)
return dist
end
julia> @btime f(x,y)
121.136 μs (1 allocation: 16 bytes)
448.6707620555013
julia> @btime sqrt(reduce(+, map((a, b) -> abs2(a-b), x, y)))
225.889 μs (6 allocations: 781.41 KiB)
448.6707620554991
julia> @btime sqrt(mapreduce(((a, b),) -> abs2(a-b), +, zip(x, y)))
105.932 μs (10 allocations: 208 bytes)
448.6707620555013
