如果你可以使用SQL语法——比如，如果你使用LINQ访问一个结构或数组的字段，或者直接访问一个数据库，其中有一个名为“Alphabet”的表，只有一个字符字段“Letter”，你可以适应以下代码:

SELECT A.Letter, B.Letter, C.Letter
FROM Alphabet AS A, Alphabet AS B, Alphabet AS C
WHERE A.Letter<>B.Letter AND A.Letter<>C.Letter AND B.Letter<>C.Letter
AND A.Letter<B.Letter AND B.Letter<C.Letter

这将返回所有3个字母的组合，不管你在表格“字母表”中有多少个字母(它可以是3,8,10,27等)。

如果你想要的是所有的排列，而不是组合(也就是说，你想要“ACB”和“ABC”被视为不同的，而不是只出现一次)，只需删除最后一行(and一行)，就完成了。

Post-Edit:重新阅读问题后，我意识到需要的是通用算法，而不仅仅是选择3个项目的特定算法。Adam Hughes的答案是完整的，不幸的是我还不能投票。这个答案很简单，但只适用于你想要三样东西的时候。

一个简洁的Javascript解决方案:

Array.prototype.combine=function combine(k){    
    var toCombine=this;
    var last;
    function combi(n,comb){             
        var combs=[];
        for ( var x=0,y=comb.length;x<y;x++){
            for ( var l=0,m=toCombine.length;l<m;l++){      
                combs.push(comb[x]+toCombine[l]);           
            }
        }
        if (n<k-1){
            n++;
            combi(n,combs);
        } else{last=combs;}
    }
    combi(1,toCombine);
    return last;
}
// Example:
// var toCombine=['a','b','c'];
// var results=toCombine.combine(4);

void combine(char a[], int N, int M, int m, int start, char result[]) {
    if (0 == m) {
        for (int i = M - 1; i >= 0; i--)
            std::cout << result[i];
        std::cout << std::endl;
        return;
    }
    for (int i = start; i < (N - m + 1); i++) {
        result[m - 1] = a[i];
        combine(a, N, M, m-1, i+1, result);
    }
}

void combine(char a[], int N, int M) {
    char *result = new char[M];
    combine(a, N, M, M, 0, result);
    delete[] result;
}

在第一个函数中，m表示还需要选择多少个，start表示必须从数组中的哪个位置开始选择。

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。

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' ]
]

我有一个用于project euler的排列算法，用python编写:

def missing(miss,src):
    "Returns the list of items in src not present in miss"
    return [i for i in src if i not in miss]


def permutation_gen(n,l):
    "Generates all the permutations of n items of the l list"
    for i in l:
        if n<=1: yield [i]
        r = [i]
        for j in permutation_gen(n-1,missing([i],l)):  yield r+j

If

n<len(l) 

你应该有所有你需要的组合，没有重复，你需要吗?

它是一个生成器，所以你可以这样使用它:

for comb in permutation_gen(3,list("ABCDEFGH")):
    print comb