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"]

另一种python递归解决方案。

def combination_indicies(n, k, j = 0, stack = []):   
    if len(stack) == k:            
        yield list(stack)
        return
        
    for i in range(j, n):
        stack.append(i)
        for x in combination_indicies(n, k, i + 1, stack):            
            yield x
        stack.pop()  
        
list(combination_indicies(5, 3))

输出:

[[0, 1, 2],
 [0, 1, 3],
 [0, 1, 4],
 [0, 2, 3],
 [0, 2, 4],
 [0, 3, 4],
 [1, 2, 3],
 [1, 2, 4],
 [1, 3, 4],
 [2, 3, 4]]

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   

现在又出现了祖辈COBOL，一种饱受诟病的语言。

让我们假设一个包含34个元素的数组，每个元素8个字节(完全是任意选择)。其思想是枚举所有可能的4元素组合，并将它们加载到一个数组中。

我们使用4个指标，每个指标代表4个组中的每个位置

数组是这样处理的:

    idx1 = 1
    idx2 = 2
    idx3 = 3
    idx4 = 4

我们把idx4从4变到最后。对于每个idx4，我们得到一个唯一的组合 四人一组。当idx4到达数组的末尾时，我们将idx3增加1，并将idx4设置为idx3+1。然后再次运行idx4到最后。我们以这种方式继续，分别增加idx3、idx2和idx1，直到idx1的位置距离数组末端小于4。算法就完成了。

1          --- pos.1
2          --- pos 2
3          --- pos 3
4          --- pos 4
5
6
7
etc.

第一次迭代:

1234
1235
1236
1237
1245
1246
1247
1256
1257
1267
etc.

一个COBOL的例子:

01  DATA_ARAY.
    05  FILLER     PIC X(8)    VALUE  "VALUE_01".
    05  FILLER     PIC X(8)    VALUE  "VALUE_02".
  etc.
01  ARAY_DATA    OCCURS 34.
    05  ARAY_ITEM       PIC X(8).

01  OUTPUT_ARAY   OCCURS  50000   PIC X(32).

01   MAX_NUM   PIC 99 COMP VALUE 34.

01  INDEXXES  COMP.
    05  IDX1            PIC 99.
    05  IDX2            PIC 99.
    05  IDX3            PIC 99.
    05  IDX4            PIC 99.
    05  OUT_IDX   PIC 9(9).

01  WHERE_TO_STOP_SEARCH          PIC 99  COMP.

* Stop the search when IDX1 is on the third last array element:

COMPUTE WHERE_TO_STOP_SEARCH = MAX_VALUE - 3     

MOVE 1 TO IDX1

PERFORM UNTIL IDX1 > WHERE_TO_STOP_SEARCH
   COMPUTE IDX2 = IDX1 + 1
   PERFORM UNTIL IDX2 > MAX_NUM
      COMPUTE IDX3 = IDX2 + 1
      PERFORM UNTIL IDX3 > MAX_NUM
         COMPUTE IDX4 = IDX3 + 1
         PERFORM UNTIL IDX4 > MAX_NUM
            ADD 1 TO OUT_IDX
            STRING  ARAY_ITEM(IDX1)
                    ARAY_ITEM(IDX2)
                    ARAY_ITEM(IDX3)
                    ARAY_ITEM(IDX4)
                    INTO OUTPUT_ARAY(OUT_IDX)
            ADD 1 TO IDX4
         END-PERFORM
         ADD 1 TO IDX3
      END-PERFORM
      ADD 1 TO IDX2
   END_PERFORM
   ADD 1 TO IDX1
END-PERFORM.

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' ]
]

短快C实现

#include <stdio.h>

void main(int argc, char *argv[]) {
  const int n = 6; /* The size of the set; for {1, 2, 3, 4} it's 4 */
  const int p = 4; /* The size of the subsets; for {1, 2}, {1, 3}, ... it's 2 */
  int comb[40] = {0}; /* comb[i] is the index of the i-th element in the combination */

  int i = 0;
  for (int j = 0; j <= n; j++) comb[j] = 0;
  while (i >= 0) {
    if (comb[i] < n + i - p + 1) {
       comb[i]++;
       if (i == p - 1) { for (int j = 0; j < p; j++) printf("%d ", comb[j]); printf("\n"); }
       else            { comb[++i] = comb[i - 1]; }
    } else i--; }
}

要查看它有多快，请使用这段代码并测试它

#include <time.h>
#include <stdio.h>

void main(int argc, char *argv[]) {
  const int n = 32; /* The size of the set; for {1, 2, 3, 4} it's 4 */
  const int p = 16; /* The size of the subsets; for {1, 2}, {1, 3}, ... it's 2 */
  int comb[40] = {0}; /* comb[i] is the index of the i-th element in the combination */

  int c = 0; int i = 0;
  for (int j = 0; j <= n; j++) comb[j] = 0;
  while (i >= 0) {
    if (comb[i] < n + i - p + 1) {
       comb[i]++;
       /* if (i == p - 1) { for (int j = 0; j < p; j++) printf("%d ", comb[j]); printf("\n"); } */
       if (i == p - 1) c++;
       else            { comb[++i] = comb[i - 1]; }
    } else i--; }
  printf("%d!%d == %d combination(s) in %15.3f second(s)\n ", p, n, c, clock()/1000.0);
}

使用cmd.exe (windows)测试:

Microsoft Windows XP [Version 5.1.2600]
(C) Copyright 1985-2001 Microsoft Corp.

c:\Program Files\lcc\projects>combination
16!32 == 601080390 combination(s) in          5.781 second(s)

c:\Program Files\lcc\projects>

祝你有愉快的一天。