如何在Julia中重复字符串中的单个字符

string 函数运行得更快。以下是我的基准:

julia> n = 2;

julia> s = "ABCDEFGHIJKLMNOPQRSTUVWXYZ";

julia> string((c^n for c in s)...) # proof that it works
"AABBCCDDEEFFGGHHIIJJKKLLMMNNOOPPQQRRSSTTUUVVWWXXYYZZ"

julia> n = 26000;

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  36
  --------------
  minimum time:     390.616 μs (0.00% GC)
  median time:      425.861 μs (0.00% GC)
  mean time:        484.638 μs (6.54% GC)
  maximum time:     45.006 ms (98.99% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark string((c^n for c in s)...)
BenchmarkTools.Trial:
  memory estimate:  1.29 MiB
  allocs estimate:  31
  --------------
  minimum time:     77.480 μs (0.00% GC)
  median time:      101.667 μs (0.00% GC)
  mean time:        126.455 μs (0.00% GC)
  maximum time:     832.524 μs (0.00% GC)
  --------------
  samples:          10000
  evals/sample:     1

正如你所看到的,它比 join @Julia Learner提出的解决方案。 我在0.7上测试了上述内容,但没有任何弃用警告,所以我假设它在1.0上也可以正常工作。偶数TIO says so .

julia> VERSION
v"1.0.0"

julia> s = "123abc"
"123abc"

# n is number of times to repeat each character.
julia> n = 3
3

# Using a Julia comprehension with [...]
julia> join([c^n for c in s])
"111222333aaabbbccc"

# Using a Julia generator without the [...]
julia> join(c^n for c in s)
"111222333aaabbbccc"

对于小弦,速度上应该没有什么实际差别。

编辑

热释光;DR:一般来说,生成器比理解要快一些。但是,请参见案例3了解相反的情况。记忆评估结果非常相似。

@rickhg12hs建议最好有基准。

n = the number of times to repeat each character

s = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" in each case

在每种情况下,首先列出理解的中间时间C,然后列出生成器的中间时间G。时间被四舍五入似乎是适当的,原始数字是下面的编号摘要。当然,越小越好。

记忆的估计并没有很大的不同。

1n=26,C=3.8 vs.G=2.8 s,G更快

julia> using BenchmarkTools

julia> n = 26;

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  3.55 KiB
  allocs estimate:  39
  --------------
  minimum time:     3.688 μs (0.00% GC)
  median time:      3.849 μs (0.00% GC)
  mean time:        4.956 μs (16.27% GC)
  maximum time:     5.211 ms (99.85% GC)
  --------------
  samples:          10000
  evals/sample:     8

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  3.19 KiB
  allocs estimate:  36
  --------------
  minimum time:     2.661 μs (0.00% GC)
  median time:      2.756 μs (0.00% GC)
  mean time:        3.622 μs (19.94% GC)
  maximum time:     4.638 ms (99.89% GC)
  --------------
  samples:          10000
  evals/sample:     9

julia> n = 260;

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  19.23 KiB
  allocs estimate:  39
  --------------
  minimum time:     8.125 μs (0.00% GC)
  median time:      10.691 μs (0.00% GC)
  mean time:        18.559 μs (35.36% GC)
  maximum time:     43.930 ms (99.92% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  18.88 KiB
  allocs estimate:  36
  --------------
  minimum time:     7.270 μs (0.00% GC)
  median time:      8.126 μs (0.00% GC)
  mean time:        10.872 μs (18.04% GC)
  maximum time:     10.592 ms (99.87% GC)
  --------------
  samples:          10000
  evals/sample:     4

julia> n = 2600; 

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  150.16 KiB
  allocs estimate:  39
  --------------
  minimum time:     51.746 μs (0.00% GC)
  median time:      63.293 μs (0.00% GC)
  mean time:        77.315 μs (2.79% GC)
  maximum time:     3.721 ms (96.85% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  149.80 KiB
  allocs estimate:  36
  --------------
  minimum time:     47.897 μs (0.00% GC)
  median time:      63.720 μs (0.00% GC)
  mean time:        88.716 μs (17.58% GC)
  maximum time:     42.457 ms (99.83% GC)
  --------------
  samples:          10000
  evals/sample:     1

4n=26000,C=667 vs.G=516 s,G更快

julia> n = 26000; 

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  39
  --------------
  minimum time:     457.589 μs (0.00% GC)
  median time:      666.710 μs (0.00% GC)
  mean time:        729.592 μs (10.91% GC)
  maximum time:     42.673 ms (98.76% GC)
  --------------
  samples:          6659
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  36
  --------------
  minimum time:     475.977 μs (0.00% GC)
  median time:      516.176 μs (0.00% GC)
  mean time:        659.001 μs (10.36% GC)
  maximum time:     42.268 ms (98.41% GC)
  --------------
  samples:          7548
  evals/sample:     1

在中测试的代码 Version 1.0.0 (2018-08-08)

当我试着写作的时候 map(x -> x^3, "123abc") ,我出错了。

julia> map(x -> x^3, "123abc")
ERROR: ArgumentError: map(f, s::AbstractString) requires f to return AbstractChar; try map(f, collect(s)) or a comprehension instead

所以,还有另一种方法。

julia> map(x -> x^3, collect("123abc"))
6-element Array{String,1}:
 "111"
 "222"
 "333"
 "aaa"
 "bbb"
 "ccc"

julia> join(map(x -> x^3, collect("123abc")))
"111222333aaabbbccc"

也许吧 repeat 更方便。

julia> repeat(collect("123abc"), inner=3)
18-element Array{Char,1}:
 '1'
 '1'
 '1'
 '2'
 '2'
 '2'
 '3'
 '3'
 '3'
 'a'
 'a'
 'a'
 'b'
 'b'
 'b'
 'c'
 'c'
 'c'
julia> join(repeat(collect("123abc"), inner=3))
"111222333aaabbbccc"