在Python中，利用递归的优势和所有事情都是通过引用完成的事实。对于非常大的集合，这将占用大量内存，但其优点是初始集合可以是一个复杂的对象。它只会找到唯一的组合。

import copy

def find_combinations( length, set, combinations = None, candidate = None ):
    # recursive function to calculate all unique combinations of unique values
    # from [set], given combinations of [length].  The result is populated
    # into the 'combinations' list.
    #
    if combinations == None:
        combinations = []
    if candidate == None:
        candidate = []

    for item in set:
        if item in candidate:
            # this item already appears in the current combination somewhere.
            # skip it
            continue

        attempt = copy.deepcopy(candidate)
        attempt.append(item)
        # sorting the subset is what gives us completely unique combinations,
        # so that [1, 2, 3] and [1, 3, 2] will be treated as equals
        attempt.sort()

        if len(attempt) < length:
            # the current attempt at finding a new combination is still too
            # short, so add another item to the end of the set
            # yay recursion!
            find_combinations( length, set, combinations, attempt )
        else:
            # the current combination attempt is the right length.  If it
            # already appears in the list of found combinations then we'll
            # skip it.
            if attempt in combinations:
                continue
            else:
                # otherwise, we append it to the list of found combinations
                # and move on.
                combinations.append(attempt)
                continue
    return len(combinations)

你可以这样使用它。传递'result'是可选的，所以你可以用它来获取可能组合的数量…尽管这样做效率很低(最好通过计算来完成)。

size = 3
set = [1, 2, 3, 4, 5]
result = []

num = find_combinations( size, set, result ) 
print "size %d results in %d sets" % (size, num)
print "result: %s" % (result,)

您应该从测试数据中得到以下输出:

size 3 results in 10 sets
result: [[1, 2, 3], [1, 2, 4], [1, 2, 5], [1, 3, 4], [1, 3, 5], [1, 4, 5], [2, 3, 4], [2, 3, 5], [2, 4, 5], [3, 4, 5]]

如果你的集合是这样的，它也会工作得很好:

set = [
    [ 'vanilla', 'cupcake' ],
    [ 'chocolate', 'pudding' ],
    [ 'vanilla', 'pudding' ],
    [ 'chocolate', 'cookie' ],
    [ 'mint', 'cookie' ]
]

递归，一个很简单的答案，combo，在Free Pascal中。

    procedure combinata (n, k :integer; producer :oneintproc);

        procedure combo (ndx, nbr, len, lnd :integer);
        begin
            for nbr := nbr to len do begin
                productarray[ndx] := nbr;
                if len < lnd then
                    combo(ndx+1,nbr+1,len+1,lnd)
                else
                    producer(k);
            end;
        end;

    begin
        combo (0, 0, n-k, n-1);
    end;

“producer”处理为每个组合生成的产品数组。

Haskell中的简单递归算法

import Data.List

combinations 0 lst = [[]]
combinations n lst = do
    (x:xs) <- tails lst
    rest   <- combinations (n-1) xs
    return $ x : rest

我们首先定义特殊情况，即选择零元素。它产生一个单一的结果，这是一个空列表(即一个包含空列表的列表)。

对于n> 0, x遍历列表中的每一个元素xs是x之后的每一个元素。

Rest通过对组合的递归调用从xs中选取n - 1个元素。该函数的最终结果是一个列表，其中每个元素都是x: rest(即对于x和rest的每个不同值，x为头部，rest为尾部的列表)。

> combinations 3 "abcde"
["abc","abd","abe","acd","ace","ade","bcd","bce","bde","cde"]

当然，由于Haskell是懒惰的，列表是根据需要逐渐生成的，因此您可以部分计算指数级的大组合。

> let c = combinations 8 "abcdefghijklmnopqrstuvwxyz"
> take 10 c
["abcdefgh","abcdefgi","abcdefgj","abcdefgk","abcdefgl","abcdefgm","abcdefgn",
 "abcdefgo","abcdefgp","abcdefgq"]

《计算机程序设计艺术》第4卷第3册有大量这样的内容，它们可能比我描述的更适合你的特定情况。

格雷码

你会遇到的一个问题当然是内存，很快，你会在你的集合中遇到20个元素的问题——20C3 = 1140。如果你想遍历这个集合，最好使用修改过的灰码算法，这样你就不会把它们都保存在内存中。这将从前一个组合中生成下一个组合并避免重复。有很多不同的用途。我们想要最大化连续组合之间的差异吗?最小化?等等。

一些描述灰色代码的原始论文:

Hamilton路径与最小变化算法 相邻交换组合生成算法

以下是涉及该主题的其他一些论文:

Eades、Hickey、Read相邻交换组合生成算法的高效实现(PDF, Pascal代码) 结合发电机 组合灰色编码综述(PostScript) 灰色编码的一种算法

Chase's Twiddle(算法)

菲利普·J·蔡斯，《算法382:N个对象中M个对象的组合》(1970)

该算法在C…

按字典顺序排列的组合索引(Buckles算法515)

还可以通过索引(按字典顺序)引用组合。意识到索引应该是基于索引从右到左的一些变化，我们可以构造一些应该恢复组合的东西。

So, we have a set {1,2,3,4,5,6}... and we want three elements. Let's say {1,2,3} we can say that the difference between the elements is one and in order and minimal. {1,2,4} has one change and is lexicographically number 2. So the number of 'changes' in the last place accounts for one change in the lexicographical ordering. The second place, with one change {1,3,4} has one change but accounts for more change since it's in the second place (proportional to the number of elements in the original set).

我所描述的方法是一种解构，从集合到索引，我们需要做相反的事情——这要复杂得多。这就是巴克尔斯解决问题的方法。我写了一些C来计算它们，做了一些小改动——我使用集合的索引而不是一个数字范围来表示集合，所以我们总是从0…n开始工作。 注意:

由于组合是无序的，{1,3,2}={1,2,3}——我们将它们按字典顺序排列。 该方法有一个隐式的0来开始第一个差值集。

词典顺序组合索引(麦卡弗里)

还有另一种方法:，它的概念更容易掌握和编程，但它没有Buckles的优化。幸运的是，它也不会产生重复的组合:

最大化的集合，其中。

例如:27 = C (6, 4) + C (5,3) + C (2, 2) + C(1, 1)。那么，第27个单词的字典组合是{1,2,5,6}，它们是你想要查找的任何集合的索引。下面的例子(OCaml)，需要选择函数，留给读者:

(* this will find the [x] combination of a [set] list when taking [k] elements *)
let combination_maccaffery set k x =
    (* maximize function -- maximize a that is aCb              *)
    (* return largest c where c < i and choose(c,i) <= z        *)
    let rec maximize a b x =
        if (choose a b ) <= x then a else maximize (a-1) b x
    in
    let rec iterate n x i = match i with
        | 0 -> []
        | i ->
            let max = maximize n i x in
            max :: iterate n (x - (choose max i)) (i-1)
    in
    if x < 0 then failwith "errors" else
    let idxs =  iterate (List.length set) x k in
    List.map (List.nth set) (List.sort (-) idxs)

一个小而简单的组合迭代器

为了教学目的，提供了以下两个算法。它们实现了一个迭代器和(更通用的)文件夹整体组合。 它们尽可能快，复杂度为O(nCk)。内存消耗受k约束。

我们将从迭代器开始，它将为每个组合调用用户提供的函数

let iter_combs n k f =
  let rec iter v s j =
    if j = k then f v
    else for i = s to n - 1 do iter (i::v) (i+1) (j+1) done in
  iter [] 0 0

更通用的版本将从初始状态开始调用用户提供的函数和状态变量。因为我们需要在不同的状态之间传递状态，所以我们不使用for循环，而是使用递归，

let fold_combs n k f x =
  let rec loop i s c x =
    if i < n then
      loop (i+1) s c @@
      let c = i::c and s = s + 1 and i = i + 1 in
      if s < k then loop i s c x else f c x
    else x in
  loop 0 0 [] x

这是我用c++写的命题

我尽可能少地限制迭代器类型，所以这个解决方案假设只有前向迭代器，它可以是const_iterator。这应该适用于任何标准容器。在参数没有意义的情况下，它抛出std:: invalid_argument

#include <vector>
#include <stdexcept>

template <typename Fci> // Fci - forward const iterator
std::vector<std::vector<Fci> >
enumerate_combinations(Fci begin, Fci end, unsigned int combination_size)
{
    if(begin == end && combination_size > 0u)
        throw std::invalid_argument("empty set and positive combination size!");
    std::vector<std::vector<Fci> > result; // empty set of combinations
    if(combination_size == 0u) return result; // there is exactly one combination of
                                              // size 0 - emty set
    std::vector<Fci> current_combination;
    current_combination.reserve(combination_size + 1u); // I reserve one aditional slot
                                                        // in my vector to store
                                                        // the end sentinel there.
                                                        // The code is cleaner thanks to that
    for(unsigned int i = 0u; i < combination_size && begin != end; ++i, ++begin)
    {
        current_combination.push_back(begin); // Construction of the first combination
    }
    // Since I assume the itarators support only incrementing, I have to iterate over
    // the set to get its size, which is expensive. Here I had to itrate anyway to  
    // produce the first cobination, so I use the loop to also check the size.
    if(current_combination.size() < combination_size)
        throw std::invalid_argument("combination size > set size!");
    result.push_back(current_combination); // Store the first combination in the results set
    current_combination.push_back(end); // Here I add mentioned earlier sentinel to
                                        // simplyfy rest of the code. If I did it 
                                        // earlier, previous statement would get ugly.
    while(true)
    {
        unsigned int i = combination_size;
        Fci tmp;                            // Thanks to the sentinel I can find first
        do                                  // iterator to change, simply by scaning
        {                                   // from right to left and looking for the
            tmp = current_combination[--i]; // first "bubble". The fact, that it's 
            ++tmp;                          // a forward iterator makes it ugly but I
        }                                   // can't help it.
        while(i > 0u && tmp == current_combination[i + 1u]);

        // Here is probably my most obfuscated expression.
        // Loop above looks for a "bubble". If there is no "bubble", that means, that
        // current_combination is the last combination, Expression in the if statement
        // below evaluates to true and the function exits returning result.
        // If the "bubble" is found however, the ststement below has a sideeffect of 
        // incrementing the first iterator to the left of the "bubble".
        if(++current_combination[i] == current_combination[i + 1u])
            return result;
        // Rest of the code sets posiotons of the rest of the iterstors
        // (if there are any), that are to the right of the incremented one,
        // to form next combination

        while(++i < combination_size)
        {
            current_combination[i] = current_combination[i - 1u];
            ++current_combination[i];
        }
        // Below is the ugly side of using the sentinel. Well it had to haave some 
        // disadvantage. Try without it.
        result.push_back(std::vector<Fci>(current_combination.begin(),
                                          current_combination.end() - 1));
    }
}

c#简单算法。 (我发布它是因为我试图使用你们上传的那个，但由于某种原因我无法编译它——扩展一个类?所以我自己写了一个，以防别人遇到和我一样的问题)。 顺便说一下，除了基本的编程，我对c#没什么兴趣，但是这个工作得很好。

public static List<List<int>> GetSubsetsOfSizeK(List<int> lInputSet, int k)
        {
            List<List<int>> lSubsets = new List<List<int>>();
            GetSubsetsOfSizeK_rec(lInputSet, k, 0, new List<int>(), lSubsets);
            return lSubsets;
        }

public static void GetSubsetsOfSizeK_rec(List<int> lInputSet, int k, int i, List<int> lCurrSet, List<List<int>> lSubsets)
        {
            if (lCurrSet.Count == k)
            {
                lSubsets.Add(lCurrSet);
                return;
            }

            if (i >= lInputSet.Count)
                return;

            List<int> lWith = new List<int>(lCurrSet);
            List<int> lWithout = new List<int>(lCurrSet);
            lWith.Add(lInputSet[i++]);

            GetSubsetsOfSizeK_rec(lInputSet, k, i, lWith, lSubsets);
            GetSubsetsOfSizeK_rec(lInputSet, k, i, lWithout, lSubsets);
        }

GetSubsetsOfSizeK(set of type List<int>， integer k)

您可以修改它以遍历您正在处理的任何内容。

好运！