Huffman Codes
作者：互联网
Huffman Codes
In 1953, David A. Huffman published his paper "A Method for the Construction of Minimum-Redundancy Codes", and hence printed his name in the history of computer science. As a professor who gives the final exam problem on Huffman codes, I am encountering a big problem: the Huffman codes are NOT unique. For example, given a string "aaaxuaxz", we can observe that the frequencies of the characters 'a', 'x', 'u' and 'z' are 4, 2, 1 and 1, respectively. We may either encode the symbols as {'a'=0, 'x'=10, 'u'=110, 'z'=111}, or in another way as {'a'=1, 'x'=01, 'u'=001, 'z'=000}, both compress the string into 14 bits. Another set of code can be given as {'a'=0, 'x'=11, 'u'=100, 'z'=101}, but {'a'=0, 'x'=01, 'u'=011, 'z'=001} is NOT correct since "aaaxuaxz" and "aazuaxax" can both be decoded from the code 00001011001001. The students are submitting all kinds of codes, and I need a computer program to help me determine which ones are correct and which ones are not.
Input Specification:
Each input file contains one test case. For each case, the first line gives an integer N (2), then followed by a line that contains all the N distinct characters and their frequencies in the following format:
c[1] f[1] c[2] f[2] ... c[N] f[N]
where c[i] is a character chosen from {'0' - '9', 'a' - 'z', 'A' - 'Z', '_'}, and f[i] is the frequency of c[i] and is an integer no more than 1000. The next line gives a positive integer M (≤), then followed by M student submissions. Each student submission consists of N lines, each in the format:
c[i] code[i]
where c[i] is the i-th character and code[i] is an non-empty string of no more than 63 '0's and '1's.
Output Specification:
For each test case, print in each line either "Yes" if the student's submission is correct, or "No" if not.
Note: The optimal solution is not necessarily generated by Huffman algorithm. Any prefix code with code length being optimal is considered correct.
Sample Input:
7
A 1 B 1 C 1 D 3 E 3 F 6 G 6
4
A 00000
B 00001
C 0001
D 001
E 01
F 10
G 11
A 01010
B 01011
C 0100
D 011
E 10
F 11
G 00
A 000
B 001
C 010
D 011
E 100
F 101
G 110
A 00000
B 00001
C 0001
D 001
E 00
F 10
G 11
Sample Output:
Yes
Yes
No
No
解题思路
要判断编码是否为最优编码，需要对编码进行两个方面的检验：对每一组编码来判断WPL是否为最小的，以及是否为前缀码。
下面给出第一种思路，需要构建Huffman Tree的方法，同时也是用堆来实现的。
树节点的定义如下：
1 struct Data {
2 char letter;
3 int freq;
4 };
5
6 struct TNode {
7 Data data;
8 TNode *left, *right;
9 };
首先我们要根据给出的字符频率来构造出一颗对应的Huffman Tree，同时计算出WPL。
又因为我们是用堆来实现的，所以要构造一颗Huffman Tree我们先要把给定的频率来构建一个最小堆。
这里我们需要对堆进行定义，同时还要定义堆的相关操作。这里就直接上代码，不解释了。
1 struct Heap {
2 TNode *H; // 堆的每一个元素的数据类型是树节点TNode
3 int size;
4 int capacity;
5 };
6
7 Heap *createMinHeap(int n) {
8 Heap *minHeap = new Heap;
9 minHeap->size = 0;
10 minHeap->capacity = n;
11 minHeap->H = new TNode[n + 1];
12 minHeap->H[0].data.freq = -1;
13
14 for (int i = 0; i < minHeap->capacity; i++) {
15 TNode *tmp = new TNode;
16 tmp->left = tmp->right = NULL;
17 getchar();
18 scanf("%c %d", &tmp->data.letter, &tmp->data.freq);
19 insertHeap(minHeap, tmp);
20 }
21
22 return minHeap;
23 }
24
25 void insertHeap(Heap *minHeap, TNode *treeNode) {
26 int pos = ++minHeap->size;
27 for ( ; treeNode->data.freq < minHeap->H[pos / 2].data.freq; pos /= 2) {
28 minHeap->H[pos] = minHeap->H[pos / 2];
29 }
30 minHeap->H[pos] = *treeNode;
31 }
32
33 TNode *deleteMin(Heap *minHeap) {
34 TNode *minTreeNode = new TNode;
35 *minTreeNode = minHeap->H[1];
36 TNode tmp = minHeap->H[minHeap->size--];
37
38 int parent = 1, child;
39 for ( ; parent * 2 <= minHeap->size; parent = child) {
40 child = parent * 2;
41 if (child != minHeap->size && minHeap->H[child].data.freq > minHeap->H[child + 1].data.freq) child++;
42 if (tmp.data.freq < minHeap->H[child].data.freq) break;
43 else minHeap->H[parent] = minHeap->H[child];
44 }
45 minHeap->H[parent] = tmp;
46
47 return minTreeNode;
48 }
构建好最小堆后，下一步我们需要通过这个堆来构造出对应的Huffman Tree。
就是每次从堆中弹出最小频率的那两个节点，然后把这两个节点分别插在新节点的左右边，作为左右孩子。再把新节点压入堆中。如此循环n-1次后（其中n代表节点的个数），堆中就只剩下一个元素，那个元素就是Huffman Tree的根节点，我们直接返回即可。
按照上面构造Huffman Tree的思路，相应的代码如下：
1 TNode *createHuffmanTree(Heap *minHeap) {
2 int n = minHeap->size - 1;
3 while (n--) {
4 TNode *tmp = new TNode;
5 tmp->left = deleteMin(minHeap);
6 tmp->right = deleteMin(minHeap);
7 tmp->data.freq = tmp->left->data.freq + tmp->right->data.freq;
8
9 insertHeap(minHeap, tmp);
10 }
11
12 return deleteMin(minHeap);
13 }
然后，我们要根据这颗Huffman Tree来计算WPL。我们用递归来实现计算WPL。
如果这个节点是叶子节点，那么就用当前深度乘以对应的频率，然后返回。如果不是叶子节点，就递归来计算左右子树的WPL，相加后返回。
1 int WPL(TNode *T, int depth) {
2 if (T->left == NULL && T->right == NULL) return depth * T->data.freq; // 叶子节点直接返回结果
3 else return WPL(T->left, depth + 1) + WPL(T->right, depth + 1); // 不是叶子节点，计算左右子树的WPL并返回，同时由于左右子树的深度加深一层，记得depth+1
4 }
好了，折腾了这么久，终于计算出给定频率的WPL了。
接下来我们先对每一组编码来检测其WPL是不是最小的，也就是每组编码的WPL是否与给定频率的WPL相等。
计算方法很简单，每一组编码的WPL计算公式为：
再判断codeLen是否与上面求出的给定频率的WPL相等，如果不相等，就说明这个编码不是最优编码，就不需要再判断是否为前缀码了。如果相等再去判断是否为前缀码。
这里还有个陷阱。首先我们要知道，一个最优编码的长度是不会超过n-1的。所以如果某个编码的长度大于n-1也说明该编码不是最优编码。
这里同时给出计算编码长度和判断是否是前缀码的函数：
1 bool check(TNode *huffmanTree, int n) {
2 int wpl = WPL(huffmanTree, 0); // 计算给定频率构成的Huuffman Tree的WPL
3
4 std::string code[n]; // 存放每一个字符的编码
5 int codeLen = 0;
6 bool ret = true; // 用来标记该组编码是否为最优编码
7
8 for (int i = 0; i < n; i++) {
9 char letter;
10 getchar();
11 scanf("%c", &letter);
12 getchar();
13 std::cin >> code[i];
14
15 if (ret) { // 如果已经知道该组编码不是最优编码就不需要再计算编码长度了，但仍要继续输入
16 if (code[i].size() > n - 1) ret = false; // 如果某个字符的编码长度大于n-1，说明该组编码不是最优编码
17 codeLen += code[i].size() * findFreq(huffmanTree, letter); // 计算编码长度
18 }
19 }
20
21 if (ret && codeLen == wpl) { // 如果ret == true并且编码长度与WPL相同，接着判断该组编码是否为前缀码
22 TNode *T = new TNode; // 为这组编码构造一颗Huffman Tree，初始化Huffman Tree的根节点
23 T->data.freq = 0;
24 T->left = T->right = NULL;
25
26 for (int i = 0; i < n; i++) { // 有n个节点，需要判断n次
27 TNode *pre = T; // 每次判断一个字符都从根节点开始
28
29 for (std::string::iterator it = code[i].begin(); it != code[i].end(); it++) { // 对该字符的每一个编码进行判断
30 if (*it == '0') { // 如果编码是0
31 if (pre->left == NULL) { // 如果当前节点的左子树为空
32 TNode *tmp = new TNode; // 就为当前节点生成一颗左子树
33 tmp->data.freq = 0; // 该节点的频率标记为0，表示该节点还没有字符占用
34 tmp->left = tmp->right = NULL;
35 pre->left = tmp;
36 }
37 pre = pre->left; // pre指针指向左子树
38 }
39 else { // 如果编码是1
40 if (pre->right == NULL) { // 如果当前节点的右子树为空
41 TNode *tmp = new TNode; // 就为当前节点生成一颗右子树
42 tmp->data.freq = 0; // 该节点的频率标记为0，表示该节点还没有字符占用
43 tmp->left = tmp->right = NULL;
44 pre->right = tmp;
45 }
46 pre = pre->right; // pre指针指向左子树
47 }
48 }
49
50 // 读完了字符的编码后，pre指针就指向这个字符应该占用的位置
51 // 这时需要判断pre指向的这个节点是否为叶子节点，并且该节点有没有被其他字符占用
52 if (pre->left == NULL && pre->right == NULL && pre->data.freq == 0) {
53 pre->data.freq = 1; // 如果是叶子节点并且没有被占用，该字符就占用了这个节点，并把这个节点的频率标记为1
54 }
55 else { // 否则，如果这些条件中有一个不满足
56 ret = false; // 就说明该组字符不满足前缀码的要求，ret赋值为false
57 break; // 后面的字符不需要判断了，直接退出退出判断前缀码的循环
58 }
59 }
60 }
61 else { // 如果ret == false并且编码长度不等于WPL，就说明该组编码不是最优编码
62 ret = false;
63 }
64
65 return ret;
66 }
这里是通过构造一颗Huffman Tree来判断该组编码是否符合前缀码。
判断的过程如下：
有一个指向Huffman Tree根节点的指针。
- 如果编码是'0'，先判断当前节点的左子树是否存在，如果不存在先生成左子树，再让指针移到左子树的节点。如果存在那么直接让指针移到左子树的节点即可。
- 如果编码是'1'，先判断当前节点的右子树是否存在，如果不存在先生成右子树，再让指针移到右子树的节点。如果存在那么直接让指针移到右子树的节点即可。
读完该字符的编码后，那么此时字符应该放入这个指针指向的节点。这个节点要满足两个条件才可以放入：
- 该节点的左右孩子都为空，也就是该节点为叶子节点。
- 该节点每有被标记过，也就是说该节点没有存放其他的字符。
如果有一个条件不满足，就说明该组编码不是前缀码。
最后，给出这种方法的完整AC代码，代码量有点多。
#include <cstdio>
#include <iostream>
#include <string>
struct Data {
char letter;
int freq;
};
struct TNode {
Data data;
TNode *left, *right;
};
struct Heap {
TNode *H;
int size;
int capacity;
};
Heap *createMinHeap(int n);
void insertHeap(Heap *minHeap, TNode *treeNode);
TNode *deleteHeap(Heap *minHeap);
TNode *createHuffmanTree(Heap *minHeap);
bool check(TNode *huffmanTree, int n);
int WPL(TNode *T, int depth);
int findFreq(TNode *huffmanTree, char letter);
int main() {
int n;
scanf("%d", &n);
Heap *minHeap = createMinHeap(n);
TNode *huffmanTree = createHuffmanTree(minHeap);
int m;
scanf("%d", &m);
for (int i = 0; i < m; i++) {
bool ret = check(huffmanTree, n);
printf("%s\n", ret ? "Yes" : "No");
}
return 0;
}
Heap *createMinHeap(int n) {
Heap *minHeap = new Heap;
minHeap->size = 0;
minHeap->capacity = n;
minHeap->H = new TNode[n + 1];
minHeap->H[0].data.freq = -1;
for (int i = 0; i < minHeap->capacity; i++) {
TNode *tmp = new TNode;
tmp->left = tmp->right = NULL;
getchar();
scanf("%c %d", &tmp->data.letter, &tmp->data.freq);
insertHeap(minHeap, tmp);
}
return minHeap;
}
void insertHeap(Heap *minHeap, TNode *treeNode) {
int pos = ++minHeap->size;
for ( ; treeNode->data.freq < minHeap->H[pos / 2].data.freq; pos /= 2) {
minHeap->H[pos] = minHeap->H[pos / 2];
}
minHeap->H[pos] = *treeNode;
}
TNode *deleteHeap(Heap *minHeap) {
TNode *minTreeNode = new TNode;
*minTreeNode = minHeap->H[1];
TNode tmp = minHeap->H[minHeap->size--];
int parent = 1, child;
for ( ; parent * 2 <= minHeap->size; parent = child) {
child = parent * 2;
if (child != minHeap->size && minHeap->H[child].data.freq > minHeap->H[child + 1].data.freq) child++;
if (tmp.data.freq < minHeap->H[child].data.freq) break;
else minHeap->H[parent] = minHeap->H[child];
}
minHeap->H[parent] = tmp;
return minTreeNode;
}
TNode *createHuffmanTree(Heap *minHeap) {
int n = minHeap->size - 1;
while (n--) {
TNode *tmp = new TNode;
tmp->left = deleteHeap(minHeap);
tmp->right = deleteHeap(minHeap);
tmp->data.freq = tmp->left->data.freq + tmp->right->data.freq;
insertHeap(minHeap, tmp);
}
return deleteHeap(minHeap);
}
bool check(TNode *huffmanTree, int n) {
int wpl = WPL(huffmanTree, 0);
std::string code[n];
int codeLen = 0;
bool ret = true;
for (int i = 0; i < n; i++) {
char letter;
getchar();
scanf("%c", &letter);
getchar();
std::cin >> code[i];
if (ret) {
if (code[i].size() > n - 1) ret = false;
codeLen += code[i].size() * findFreq(huffmanTree, letter);
}
}
if (ret && codeLen == wpl) {
TNode *T = new TNode;
T->data.freq = 0;
T->left = T->right = NULL;
for (int i = 0; i < n; i++) {
TNode *pre = T;
for (std::string::iterator it = code[i].begin(); it != code[i].end(); it++) {
if (*it == '0') {
if (pre->left == NULL) {
TNode *tmp = new TNode;
tmp->data.freq = 0;
tmp->left = tmp->right = NULL;
pre->left = tmp;
}
pre = pre->left;
}
else {
if (pre->right == NULL) {
TNode *tmp = new TNode;
tmp->data.freq = 0;
tmp->left = tmp->right = NULL;
pre->right = tmp;
}
pre = pre->right;
}
}
if (pre->left == NULL && pre->right == NULL && pre->data.freq == 0) {
pre->data.freq = 1;
}
else {
ret = false;
break;
}
}
}
else {
ret = false;
}
return ret;
}
int WPL(TNode *T, int depth) {
if (T->left == NULL && T->right == NULL) return depth * T->data.freq;
else return WPL(T->left, depth + 1) + WPL(T->right, depth + 1);
}
int findFreq(TNode *huffmanTree, char letter) {
int ret = 0;
if (huffmanTree) {
if (huffmanTree->left == NULL && huffmanTree->right == NULL && huffmanTree->data.letter == letter) ret = huffmanTree->data.freq;
if (ret == 0) ret = findFreq(huffmanTree->left, letter);
if (ret == 0) ret = findFreq(huffmanTree->right, letter);
}
return ret;
}
AC Code1
然后，我们来对判定前缀码的代码进行改进，下面给出判定前缀码的另外一种思路，这个方法不需要构造Huffman Tree。
首先，假设现在有两个编码，如果这两个编码不满足前缀码的话，比如"110"和"1101"，那么其中一个编码会与另外一个编码前的m个位置的相同（其中m是指这两个编码长度中最小的那个长度）。也就是说"110"，与"1101"的前3个位置的"110"相同，就说明"110"和"1101"不满足前缀码。
我们需要对同组编码的每两个字符进行比较，需要比较的次数为 C(n, 2) = n * (n - 1) / 2 。
check函数改进的代码如下：
1 bool check(TNode *huffmanTree, int n) {
2 int wpl = WPL(huffmanTree, 0);
3
4 std::string code[n];
5 int codeLen = 0;
6 bool ret = true;
7
8 for (int i = 0; i < n; i++) {
9 char letter;
10 getchar();
11 scanf("%c", &letter);
12 getchar();
13 std::cin >> code[i];
14
15 if (ret) {
16 if (code[i].size() > n - 1) ret = false;
17 codeLen += code[i].size() * findFreq(huffmanTree, letter);
18 }
19 }
20
21 if (ret && codeLen == wpl) { // 一样的，如果ret == true并且编码长度与WPL相同，才判断该组编码是否为前缀码
22 for (int i = 0; i < n; i++) { // 每个字符都跟它之后的字符进行判断是否满足前缀码的要求
23 for (int j = i + 1; j < n; j++) {
24 // 判断某个编码是否与另外一个编码前m个位置的相同，详细请看图片
25 if (code[i].substr(0, code[j].size()) == code[j].substr(0, code[i].size())) {
26 ret = false; // 只要有一对编码的前缀相同，就说明这组的编码不满足前缀码
27 break; // 后面的字符不需要判断了，直接退出退出判断前缀码的循环
28 }
29 }
30 if (ret == false) break;
31 }
32 }
33 else {
34 ret = false;
35 }
36
37 return ret;
38 }
code[i].substr(0, code[j].size()) == code[j].substr(0, code[i].size()) ，这么做始终能够保证取到两个编码中，长度最小那个编码的全部，以及另外一个编码的前面同样长度的部分，来进行判断是否满足前缀码。
这个方法也可以通过，下面给出完整的AC代码，其中改动的部分就是check部分，其他的不变。
#include <cstdio>
#include <iostream>
#include <string>
struct Data {
char letter;
int freq;
};
struct TNode {
Data data;
TNode *left, *right;
};
struct Heap {
TNode *H;
int size;
int capacity;
};
Heap *createMinHeap(int n);
void insertHeap(Heap *minHeap, TNode *treeNode);
TNode *deleteHeap(Heap *minHeap);
TNode *createHuffmanTree(Heap *minHeap);
bool check(TNode *huffmanTree, int n);
int WPL(TNode *T, int depth);
int findFreq(TNode *huffmanTree, char letter);
int main() {
int n;
scanf("%d", &n);
Heap *minHeap = createMinHeap(n);
TNode *huffmanTree = createHuffmanTree(minHeap);
int m;
scanf("%d", &m);
for (int i = 0; i < m; i++) {
bool ret = check(huffmanTree, n);
printf("%s\n", ret ? "Yes" : "No");
}
return 0;
}
Heap *createMinHeap(int n) {
Heap *minHeap = new Heap;
minHeap->size = 0;
minHeap->capacity = n;
minHeap->H = new TNode[n + 1];
minHeap->H[0].data.freq = -1;
for (int i = 0; i < minHeap->capacity; i++) {
TNode *tmp = new TNode;
tmp->left = tmp->right = NULL;
getchar();
scanf("%c %d", &tmp->data.letter, &tmp->data.freq);
insertHeap(minHeap, tmp);
}
return minHeap;
}
void insertHeap(Heap *minHeap, TNode *treeNode) {
int pos = ++minHeap->size;
for ( ; treeNode->data.freq < minHeap->H[pos / 2].data.freq; pos /= 2) {
minHeap->H[pos] = minHeap->H[pos / 2];
}
minHeap->H[pos] = *treeNode;
}
TNode *deleteHeap(Heap *minHeap) {
TNode *minTreeNode = new TNode;
*minTreeNode = minHeap->H[1];
TNode tmp = minHeap->H[minHeap->size--];
int parent = 1, child;
for ( ; parent * 2 <= minHeap->size; parent = child) {
child = parent * 2;
if (child != minHeap->size && minHeap->H[child].data.freq > minHeap->H[child + 1].data.freq) child++;
if (tmp.data.freq < minHeap->H[child].data.freq) break;
else minHeap->H[parent] = minHeap->H[child];
}
minHeap->H[parent] = tmp;
return minTreeNode;
}
TNode *createHuffmanTree(Heap *minHeap) {
int n = minHeap->size - 1;
while (n--) {
TNode *tmp = new TNode;
tmp->left = deleteHeap(minHeap);
tmp->right = deleteHeap(minHeap);
tmp->data.freq = tmp->left->data.freq + tmp->right->data.freq;
insertHeap(minHeap, tmp);
}
return deleteHeap(minHeap);
}
bool check(TNode *huffmanTree, int n) {
int wpl = WPL(huffmanTree, 0);
std::string code[n];
int codeLen = 0;
bool ret = true;
for (int i = 0; i < n; i++) {
char letter;
getchar();
scanf("%c", &letter);
getchar();
std::cin >> code[i];
if (ret) {
if (code[i].size() > n - 1) ret = false;
codeLen += code[i].size() * findFreq(huffmanTree, letter);
}
}
if (ret && codeLen == wpl) {
for (int i = 0; i < n; i++) {
for (int j = i + 1; j < n; j++) {
if (code[i].substr(0, code[j].size()) == code[j].substr(0, code[i].size())) {
ret = false;
break;
}
}
if (ret == false) break;
}
}
else {
ret = false;
}
return ret;
}
int WPL(TNode *T, int depth) {
if (T->left == NULL && T->right == NULL) return depth * T->data.freq;
else return WPL(T->left, depth + 1) + WPL(T->right, depth + 1);
}
int findFreq(TNode *huffmanTree, char letter) {
int ret = 0;
if (huffmanTree) {
if (huffmanTree->left == NULL && huffmanTree->right == NULL && huffmanTree->data.letter == letter) ret = huffmanTree->data.freq;
if (ret == 0) ret = findFreq(huffmanTree->left, letter);
if (ret == 0) ret = findFreq(huffmanTree->right, letter);
}
return ret;
}
AC Code2
还可以再改进！我们不用堆，而改用优先队列，同时不需要构造任何的Huffman Tree，甚至不需要定义树节点，也可以计算出给定频率的WPL！并且代码长度也缩短许多。
这里主要说明如何通过不构造Huffman Tree来计算给定频率的WPL。其实计算WPL不一定要用深度乘以频率再求和来得到。另外一种方法是把Huffman Tree中度为2的节点存放的频率都相加起来，最后得到的结果也是WPL。这是因为叶子节点被重复计算，和用深度乘以频率的原理基本一样。
就拿题目给的测试样例来举例：
我们往优先队列里面压的就是每个字符对应的频率，而不是树节点。代码实现的过程是：我们要有一个变量来累加如上图度为2节点存放频率。每次从优先队列里弹出两个频率，这两个频率是优先队列中所包含频率里面最小的那两个，然后把这两个频率相加，相加的结果其实就对应上图度为2节点存放的频率，也就是红色的数字。然后把相加的结果累加到那个变量，同时把相加的结果压入优先队列中。其实这个累加的过程就是累加上图红色的那些数字。一直重复，直到优先队列为空，那么那个变量最后累加的结果就是我们要计算的WPL。
AC代码如下：
1 #include <cstdio>
2 #include <iostream>
3 #include <string>
4 #include <vector>
5 #include <queue>
6 #include <map>
7 using namespace std;
8
9 void readLetterFreq(map<char, int> &letterFreq, priority_queue< int, vector<int>, greater<int> > &pq, int n);
10 void checkOptimalCode(map<char, int> &letterFreq, priority_queue< int, vector<int>, greater<int> > &pq, int n);
11 int getWPL(priority_queue< int, vector<int>, greater<int> > &pq);
12
13 int main() {
14 map<char, int> letterFreq; // 用map来存储字符和对应的频率，字符映射为对应的频率
15 priority_queue< int, vector<int>, greater<int> > pq;
16
17 int n;
18 cin >> n;
19 readLetterFreq(letterFreq, pq, n);
20 checkOptimalCode(letterFreq, pq, n);
21
22 return 0;
23 }
24
25 void readLetterFreq(map<char, int> &letterFreq, priority_queue< int, vector<int>, greater<int> > &pq, int n) {
26 for (int i = 0; i < n; i++) {
27 char letter;
28 getchar();
29 cin >> letter; // 读入字符
30 getchar();
31 cin >> letterFreq[letter]; // 读入频率，为字符的映射
32
33 pq.push(letterFreq[letter]);// 把读入的频率压入到优先队列中
34 }
35 }
36
37 void checkOptimalCode(map<char, int> &letterFreq, priority_queue< int, vector<int>, greater<int> > &pq, int n) {
38 int wpl = getWPL(pq); // 用不构造Huffman Tree的方法来计算WPL
39
40 int m;
41 cin >> m;
42 for (int i = 0; i < m; i++) {
43 string code[n];
44 int codeLen = 0;
45 bool ret = true;
46
47 for (int i = 0; i < n; i++) {
48 char letter;
49 getchar();
50 cin >> letter >> code[i];
51
52 if (ret) {
53 if (code[i].size() > n - 1) ret = false;
54 codeLen += code[i].size() * letterFreq[letter];
55 }
56 }
57
58 if (ret && codeLen == wpl) {
59 for (int i = 0; i < n; i++) {
60 for (int j = i + 1; j < n; j++) {
61 if (code[i].substr(0, code[j].size()) == code[j].substr(0, code[i].size())) {
62 ret = false;
63 break;
64 }
65 }
66 if (ret == false) break;
67 }
68 }
69 else {
70 ret = false;
71 }
72
73 cout << (ret ? "Yes\n" : "No\n");
74 }
75 }
76
77 int getWPL(priority_queue< int, vector<int>, greater<int> > &pq) {
78 int wpl = 0; // 用来保存累加的结果
79 while (!pq.empty()) { // 当优先队列不为空
80 int tmp = pq.top(); // 从优先队列弹出一个元素，这个元素就是最小频率
81 pq.pop();
82
83 if (pq.empty()) break; // 如果弹出那个频元素优先队列就为空了，退出循环
84
85 tmp += pq.top(); // 如果优先队列不为空，再弹出一个元素，同时把两个频率进行相加
86 pq.pop();
87 pq.push(tmp); // 把两个频率相加的结果压入优先队列中
88
89 wpl += tmp; // 同时，把这个相加结果进行累加，对应着累加度为2节点存放的频率
90 }
91
92 return wpl;
93 }
参考资料
浙江大学——数据结构：https://www.icourse163.org/course/ZJU-93001?tid=1461682474
priority_queue的用法：https://www.cnblogs.com/Deribs4/p/5657746.html
pta5-9 Huffman Codes (30分)：https://www.cnblogs.com/Deribs4/p/4801656.html
标签：tmp,Codes,int,data,TNode,ret,minHeap,Huffman 来源： https://www.cnblogs.com/onlyblues/p/14628257.html