这是一个简单的JS解决方案:

function getAllCombinations(n, k, f1) { indexes = Array(k); for (let i =0; i< k; i++) { indexes[i] = i; } var total = 1; f1(indexes); while (indexes[0] !== n-k) { total++; getNext(n, indexes); f1(indexes); } return {total}; } function getNext(n, vec) { const k = vec.length; vec[k-1]++; for (var i=0; i<k; i++) { var currentIndex = k-i-1; if (vec[currentIndex] === n - i) { var nextIndex = k-i-2; vec[nextIndex]++; vec[currentIndex] = vec[nextIndex] + 1; } } for (var i=1; i<k; i++) { if (vec[i] === n - (k-i - 1)) { vec[i] = vec[i-1] + 1; } } return vec; } let start = new Date(); let result = getAllCombinations(10, 3, indexes => console.log(indexes)); let runTime = new Date() - start; console.log({ result, runTime });

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));
    }
}

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' ]
]

下面是Clojure版本，它使用了我在OCaml实现答案中描述的相同算法:

(defn select
  ([items]
     (select items 0 (inc (count items))))
  ([items n1 n2]
     (reduce concat
             (map #(select % items)
                  (range n1 (inc n2)))))
  ([n items]
     (let [
           lmul (fn [a list-of-lists-of-bs]
                     (map #(cons a %) list-of-lists-of-bs))
           ]
       (if (= n (count items))
         (list items)
         (if (empty? items)
           items
           (concat
            (select n (rest items))
            (lmul (first items) (select (dec n) (rest items))))))))) 

它提供了三种调用方法:

(a)按问题要求，选出n项:

  user=> (count (select 3 "abcdefgh"))
  56

(b) n1至n2个选定项目:

user=> (select '(1 2 3 4) 2 3)
((3 4) (2 4) (2 3) (1 4) (1 3) (1 2) (2 3 4) (1 3 4) (1 2 4) (1 2 3))

(c)在0至所选项目的集合大小之间:

user=> (select '(1 2 3))
(() (3) (2) (1) (2 3) (1 3) (1 2) (1 2 3))

我已经编写了一个类来处理处理二项式系数的常见函数，这是您的问题属于的问题类型。它执行以下任务:

Outputs all the K-indexes in a nice format for any N choose K to a file. The K-indexes can be substituted with more descriptive strings or letters. This method makes solving this type of problem quite trivial. Converts the K-indexes to the proper index of an entry in the sorted binomial coefficient table. This technique is much faster than older published techniques that rely on iteration. It does this by using a mathematical property inherent in Pascal's Triangle. My paper talks about this. I believe I am the first to discover and publish this technique, but I could be wrong. Converts the index in a sorted binomial coefficient table to the corresponding K-indexes. Uses Mark Dominus method to calculate the binomial coefficient, which is much less likely to overflow and works with larger numbers. The class is written in .NET C# and provides a way to manage the objects related to the problem (if any) by using a generic list. The constructor of this class takes a bool value called InitTable that when true will create a generic list to hold the objects to be managed. If this value is false, then it will not create the table. The table does not need to be created in order to perform the 4 above methods. Accessor methods are provided to access the table. There is an associated test class which shows how to use the class and its methods. It has been extensively tested with 2 cases and there are no known bugs.

要了解这个类并下载代码，请参见将二项式系数表化。

将这个类转换为c++应该不难。

像Andrea Ambu一样用Python写的，但不是硬编码来选择三个。

def combinations(list, k):
    """Choose combinations of list, choosing k elements(no repeats)"""
    if len(list) < k:
        return []
    else:
        seq = [i for i in range(k)]
        while seq:
            print [list[index] for index in seq]
            seq = get_next_combination(len(list), k, seq)

def get_next_combination(num_elements, k, seq):
        index_to_move = find_index_to_move(num_elements, seq)
        if index_to_move == None:
            return None
        else:
            seq[index_to_move] += 1

            #for every element past this sequence, move it down
            for i, elem in enumerate(seq[(index_to_move+1):]):
                seq[i + 1 + index_to_move] = seq[index_to_move] + i + 1

            return seq

def find_index_to_move(num_elements, seq):
        """Tells which index should be moved"""
        for rev_index, elem in enumerate(reversed(seq)):
            if elem < (num_elements - rev_index - 1):
                return len(seq) - rev_index - 1
        return None