在数组中查找和乘以重复值的最快方法

Fastest way of finding and multiplying repeated values in array

在数组中查找重复值并将它们相乘的最快方法是什么?

示例:

a = [ 2 2 3 5 11 11 17 ]

结果:

a = [ 4 3 5 121 17 ]

我可以想到迭代方法(通过查找历史记录、遍历 bins,...),但是有 vectorized/fast 方法吗?

前瞻性方法和解决代码

似乎发布的问题很适合 accumarray -

%// Starting indices of each "group"
start_ind = find(diff([0 ; a(:)]))

%// Setup IDs for each group
id = zeros(1,numel(a)) %// Or id(numel(a))=0 for faster pre-allocation
id(start_ind) = 1

%// Use accumarray to get the products of elements within the same group
out = accumarray(cumsum(id(:)),a(:),[],@prod)

对于非单调递增的输入,需要多加两行代码-

[~,sorted_idx] = ismember(sort(start_ind),start_ind)
out = out(sorted_idx)

样本运行-

>> a
a =
     2     2     3     5    11    11    17     4     4     1     1     1     7     7
>> out.'
ans =
     4     3     5   121    17    16     1    49

Tweaky-Squeaky

现在,可以使用 logical indexing 删除 find,也可以使用 更快的预分配方案,为所提出的方法提供 超级提升 并为我们提供 调整后的代码 -

id(numel(a))=0;
id([true ; diff(a(:))~=0])=1;
out = accumarray(cumsum(id(:)),a(:),[],@prod);

基准测试

这是基准测试代码,它比较了迄今为止针对所述问题发布的所有提议方法 运行 次 -

%// Setup huge random input array
maxn = 10000;
N = 100000000;
a = sort(randi(maxn,1,N));

%// Warm up tic/toc.
for k = 1:100000
    tic(); elapsed = toc();
end

disp('------------------------- With UNIQUE')
tic
ua = unique(a);
out = ua.^histc(a,ua);
toc, clear ua out

disp('------------------------- With ACCUMARRAY')
tic
id(numel(a))=0;
id([true ; diff(a(:))~=0])=1;
out = accumarray(cumsum(id(:)),a(:),[],@prod);
toc, clear out id

disp('------------------------- With FOR-LOOP')
tic
b = a(1);
for k = 2:numel(a)
    if a(k)==a(k-1)
        b(end) = b(end)*a(k);
    else
        b(end+1) = a(k);
    end
end
toc

运行时间

------------------------- With UNIQUE
Elapsed time is 3.050523 seconds.
------------------------- With ACCUMARRAY
Elapsed time is 1.710499 seconds.
------------------------- With FOR-LOOP
Elapsed time is 1.811323 seconds.

结论: 看起来 运行 次,支持 accumarray 的想法优于其他两种方法!

使用histc and unique

ua = unique(a)
out = ua.^histc(a,ua)

out =

     4     3     5   121    17

考虑到向量 a 非单调递增 的情况,它变得有点复杂:

%// non monotonically increasing vector
a = [ 2 2 3 5 11 11 17 4 4 1 1 1 7 7]

[ua, ia] = unique(a)             %// get unique values and sort as required for histc  
[~, idx] = ismember(sort(ia),ia) %// get original order
hc = histc(a,ua)                 %// count occurences
prods = ua.^hc                   %// calculate products
out = prods(idx)                 %// reorder to original order

或:

ua = unique(a,'stable')          %// get unique values in original order
uas = unique(a)                  %// get unique values sorted as required for histc  
[~,idx] = ismember(ua,uas)       %// get indices of original order
hc = histc(a,uas)                %// count occurences
out = ua.^hc(idx)                %// calculate products and reorder 

out =

     4     3     5   121    17    16     1    49

似乎仍然是一个很好的解决方案,因为 accumarray 也是

您可能会惊讶于一个简单的 for 循环在速度方面的比较:

b = a(1);
for k = 2:numel(a)
    if a(k)==a(k-1)
        b(end) = b(end)*a(k);
    else
        b(end+1) = a(k);
    end
end

即使不进行任何预分配,它的性能也与 accumarray 解决方案相当。