【转】常用算法复习及实现（C++版）
一、霍夫曼树实现
给定n个权值作为n个叶子结点，构造一棵二叉树，若带权路径长度达到最小，称这样的二叉树为最优二叉树，也称为哈夫曼树(Huffman tree)。哈夫曼树是带权路径长度最短的树，权值较大的结点离根较近。
#include"iostream"
#include "queue"
#include "map"
#include "string"
#include "iterator"
#include "vector"
#include "algorithm"
using namespace std;
#define NChar 8 //suppose use 8 bits to describe all symbols
#define Nsymbols 1<<NChar //can describe 256 symbols totally (include a-z, A-Z)
typedef vector<bool> Huff_code;//8 bit code of one char
map<char,Huff_code> Huff_Dic; //huffman coding dictionary
/************************************************************************/
/* Tree Class elements:
*2 child trees
*character and frequency of current node
*/
/************************************************************************/
class HTree
{
public :
HTree* left;
HTree* right;
char ch;
int weight;
HTree(){left = right = NULL; weight=0;ch ='\0';}
HTree(HTree* l,HTree* r,int w,char c){left = l; right = r; weight=w; ch=c;}
~HTree(){delete left; delete right;}
bool Isleaf(){return !left && !right; }
};
/************************************************************************/
/* prepare for pointer sorting*/
/*because we cannot use overloading in class HTree directly*/
/************************************************************************/
class Compare_tree
{
public:
bool operator () (HTree* t1, HTree* t2)
{
return t1->weight> t2->weight;
}
};
/************************************************************************/
/* use priority queue to build huffman tree*/
/************************************************************************/
HTree* BuildTree(int *frequency)
{
priority_queue<HTree*,vector<HTree*>,Compare_tree> QTree;
//1st level add characters
for (int i=0;i<Nsymbols;i++)
{
if(frequency[i])
QTree.push(new HTree(NULL,NULL,frequency[i],(char)i));
}
//build
while (QTree.size()>1)
{
HTree* lc = QTree.top();
QTree.pop();
HTree* rc = QTree.top();
QTree.pop();
HTree* parent = new HTree(lc,rc,lc->weight+rc->weight,(char)256);
QTree.push(parent);
}
//return tree root
return QTree.top();
}
/************************************************************************/
/* Give Huffman Coding to the Huffman Tree*/
/************************************************************************/
void Huffman_Coding(HTree* root, Huff_code& curcode)
{
if(root->Isleaf())
{
Huff_Dic[root->ch] = curcode;
return;
}
Huff_code lcode = curcode;
Huff_code rcode = curcode;
lcode.push_back(false);
rcode.push_back(true);
Huffman_Coding(root->left,lcode);
Huffman_Coding(root->right,rcode);
}
int main()
{
int freq[Nsymbols] = {0};
char *str = "this is the string need to be compressed";
//statistic character frequency
while (*str!='\0')
freq[*str++]++;
//build tree
HTree* r = BuildTree(freq);
Huff_code nullcode;
nullcode.clear();
Huffman_Coding(r,nullcode);
for(map<char,Huff_code>::iterator it = Huff_Dic.begin(); it != Huff_Dic.end(); it++)
{
cout<<(*it).first<<'\t';
std::copy(it->second.begin(),it->second.end(),std::ostream_iterator<bool>(cout));
cout<<endl;
}
}
二、LZW算法
LZW就是通过建立一个字符串表，用较短的代码来表示较长的字符串来实现压缩。LZW压缩算法基本原理：提取原始文本文件数据中的不同字符，基于这些字符创建一个编译表，然后用编译表中的字符的索引来替代原始文本文件数据中的相应字符，减少原始数据大小。
/************************************************************************/
/* File Name: LZW.cpp
* @Function: Lossless Compression
@Author: Sophia Zhang
@Create Time: 2012-9-19 10:00
@Last Modify: 2012-9-19 11:10
*/
/************************************************************************/
#include"iostream"
#include "map"
#include "string"
#include "iterator"
#include "vector"
using namespace std;
/************************************************************************/
/* Compress Module
* input:
str - the string need to be compressed
result - compress result
*/
/************************************************************************/
template<typename TypeIterator>
TypeIterator Compress(string str, TypeIterator result)
{
//Build the dictionary
map<string,int>dictionary;
int Dictsize=256;
for(int i=0;i<Dictsize;i++)
dictionary[string(1,i)]=i;
char z;
string S;
for(string::const_iterator it = str.begin(); it!=str.end(); it++)
{
z = *it;
if(dictionary.count(S+z))//can find S
S+=z;
else//S is not in dictionary D
{
*result++ = dictionary[S]; //output pointer (S,D)
dictionary[S+z] = Dictsize++; //add to dictionary
S = z;
}
}
if(!S.empty())
*result++ = dictionary[S];
return result;
}
/************************************************************************/
/* Decompress Module
* input:
TypeIterator result - compression result, to be decompressed
*/
/************************************************************************/
template<typename TypeIterator>
string Decompress(TypeIterator result)
{
map<int,string>inv_dictionary;
int Dictsize=256;
for(int i=0;i<Dictsize;i++)
inv_dictionary[i] = string(1,i);
char z;
string S;
string entry;
string res;
Dictsize--;//because the first "Dictsize++" make no sense, it has only one char in [0,255]
for(TypeIterator::iterator it = result.begin(); it!=result.end(); it++)
{
int k = *it;
if(inv_dictionary.count(k))
entry = inv_dictionary[k];
else if(k==Dictsize)
entry = S+ S[0];//???
else
throw "Bad compression code";
res += entry;
inv_dictionary[Dictsize++] = S + entry[0];
S = entry;
}
return res;
}
int main()
{
typedef vector<int> TypeIterator;
TypeIterator compress_res;
string S = "the/rain/in/Spain/falls/mainly/on/the/plain";
Compress(S,std::back_inserter(compress_res));
// copy(compress_res.begin(),compress_res.end(),std::ostream_iterator<int>(std::cout,","));
// std::cout<<std::endl;
//output the compressed result
for( TypeIterator::iterator it= compress_res.begin(); it!=compress_res.end(); it++)
cout<<(*it)<<endl;
//decompress the compressed result
string decompress = Decompress(compress_res);
cout<<decompress<<endl;
}
三、二叉堆算法
二叉堆是一种特殊的堆，二叉堆是完全二元树（二叉树）或者是近似完全二元树（二叉树）。二叉堆有两种：最大堆和最小堆。最大堆：父结点的键值总是大于或等于任何一个子节点的键值；最小堆：父结点的键值总是小于或等于任何一个子节点的键值。
#include<iostream>
#include<cstdio>
#include<cstring>
#include<cstdlib>
#include<climits>
using namespace std;
typedef struct BinHeapNode BinNode;
typedef struct BinHeapNode * BinHeap;
typedef struct BinHeapNode * Position;
//结点ADT
struct BinHeapNode {
int key;
int degree;
Position parent;
Position leftChild;
Position sibling;
};
//用数组内的值建堆
BinHeap MakeBinHeapWithArray(int keys[], int n);
//两个堆合并
BinHeap BinHeapUnion(BinHeap &H1, BinHeap &H2);
//将H1, H2的根表合并成一个按度数的单调递增次序排列的链表
BinHeap BinHeapMerge(BinHeap &H1, BinHeap &H2);
//使H2成为H1的父节点
void BinLink(BinHeap &H1, BinHeap &H2);
//返回最小根节点的指针
BinHeap BinHeapMin(BinHeap heap);
//减少关键字的值
void BinHeapDecreaseKey(BinHeap heap, BinHeap x, int key);
//删除一个关键字
BinHeap BinHeapDelete(BinHeap &heap, int key);
//找出一个关键字
BinHeap BinHeapFind(BinHeap &heap, int key);
//打印输出堆结构
void PrintBinHeap(BinHeap heap);
//销毁堆
void DestroyBinHeap(BinHeap &heap);
//用数组内的值建堆
BinHeap MakeBinHeapWithArray(int keys[], int n) {
BinHeap heap = NULL, newHeap = NULL;
for (int i = 0; i < n; i++) {
newHeap = (BinHeap) malloc(sizeof(BinNode));
if (newHeap == NULL) {
puts("Out of the Space");
exit(1);
}
memset(newHeap, 0, sizeof(BinNode));
newHeap->key = keys[i];
if (NULL == heap) {
heap = newHeap;
} else {
heap = BinHeapUnion(heap, newHeap);
newHeap = NULL;
}
}
return heap;
}
//两个堆合并
BinHeap BinHeapUnion(BinHeap &H1, BinHeap &H2) {
Position heap = NULL, pre_x = NULL, x = NULL, next_x = NULL;
heap = BinHeapMerge(H1, H2);
if (heap == NULL) {
return heap;
}
pre_x = NULL;
x = heap;
next_x = x->sibling;
while (next_x != NULL) {
if ((x->degree != next_x->degree) ||//Cases 1 and 2
((next_x->sibling != NULL) && (next_x->degree == next_x->sibling->degree))) {
pre_x = x;
x = next_x;
} else if (x->key <= next_x->key) {//Cases 3
x->sibling = next_x->sibling;
BinLink(next_x, x);
} else {//Cases 4
if (pre_x == NULL) {
heap = next_x;
} else {
pre_x->sibling = next_x;
}
BinLink(x, next_x);
x = next_x;
}
next_x = x->sibling;
}
return heap;
}
//将H1, H2的根表合并成一个按度数的单调递增次序排列的链表
BinHeap BinHeapMerge(BinHeap &H1, BinHeap &H2) {
//heap->堆的首地址，H3为指向新堆根结点
BinHeap heap = NULL, firstHeap = NULL, secondHeap = NULL,
pre_H3 = NULL, H3 = NULL;
if (H1 != NULL && H2 != NULL){
firstHeap = H1;
secondHeap = H2;
//整个while，firstHeap, secondHeap, pre_H3, H3都在往后顺移
while (firstHeap != NULL && secondHeap != NULL) {
if (firstHeap->degree <= secondHeap->degree) {
H3 = firstHeap;
firstHeap = firstHeap->sibling;
} else {
H3 = secondHeap;
secondHeap = secondHeap->sibling;
}
if (pre_H3 == NULL) {
pre_H3 = H3;
heap = H3;
} else {
pre_H3->sibling = H3;
pre_H3 = H3;
}
if (firstHeap != NULL) {
H3->sibling = firstHeap;
} else {
H3->sibling = secondHeap;
}
}//while
} else if (H1 != NULL) {
heap = H1;
} else {
heap = H2;
}
H1 = H2 = NULL;
return heap;
}
//使H2成为H1的父节点
void BinLink(BinHeap &H1, BinHeap &H2) {
H1->parent = H2;
H1->sibling = H2->leftChild;
H2->leftChild = H1;
H2->degree++;
}
//返回最小根节点的指针
BinHeap BinHeapMin(BinHeap heap) {
Position y = NULL, x = heap;
int min = INT_MAX;
while (x != NULL) {
if (x->key < min) {
min = x->key;
y = x;
}
x = x->sibling;
}
return y;
}
//抽取有最小关键字的结点
BinHeap BinHeapExtractMin(BinHeap &heap) {
BinHeap pre_y = NULL, y = NULL, x = heap;
int min = INT_MAX;
while (x != NULL) {
if (x->key < min) {
min = x->key;
pre_y = y;
y = x;
}
x = x->sibling;
}
if (y == NULL) {
return NULL;
}
if (pre_y == NULL) {
heap = heap->sibling;
} else {
pre_y->sibling = y->sibling;
}
//将y的子结点指针reverse
BinHeap H2 = NULL, p = NULL;
x = y->leftChild;
while (x != NULL) {
p = x;
x = x->sibling;
p->sibling = H2;
H2 = p;
p->parent = NULL;
}
heap = BinHeapUnion(heap, H2);
return y;
}
//减少关键字的值
void BinHeapDecreaseKey(BinHeap heap, BinHeap x, int key) {
if(key > x->key) {
puts("new key is greaer than current key");
exit(1); //不为降键
}
x->key = key;
BinHeap z = NULL, y = NULL;
y = x;
z = x->parent;
while(z != NULL && z->key > y->key) {
swap(z->key, y->key);
y = z;
z = y->parent;
}
}
//删除一个关键字
BinHeap BinHeapDelete(BinHeap &heap, int key) {
BinHeap x = NULL;
x = BinHeapFind(heap, key);
if (x != NULL) {
BinHeapDecreaseKey(heap, x, INT_MIN);
return BinHeapExtractMin(heap);
}
return x;
}
//找出一个关键字
BinHeap BinHeapFind(BinHeap &heap, int key) {
Position p = NULL, x = NULL;
p = heap;
while (p != NULL) {
if (p->key == key) {
return p;
} else {
if((x =BinHeapFind(p->leftChild, key)) != NULL) {
return x;
}
p = p->sibling;
}
}
return NULL;
}
//打印输出堆结构
void PrintBinHeap(BinHeap heap) {
if (NULL == heap) {
return ;
}
Position p = heap;
while (p != NULL) {
printf(" (");
printf("%d", p->key);
//显示其孩子
if(NULL != p->leftChild) {
PrintBinHeap(p->leftChild);
}
printf(") ");
p = p->sibling;
}
}
int kp1[8] = {12,
7, 25,
15, 28, 33, 41};
int kp2[20] = {18,
3, 37,
6, 8, 29, 10, 44, 30, 23, 2, 48, 31, 17, 45, 32, 24, 50, 55};
int kp4[23] = {37, 41,
10, 28, 13, 77,
1, 6, 16, 12, 25, 8, 14, 29, 26, 23, 18, 11, 17, 38, 42, 27};
int main() {
BinHeap H1 = NULL;
H1 = MakeBinHeapWithArray(kp1, 7);
puts("第一个二叉堆H1:");
PrintBinHeap(H1);
BinHeap H2 = NULL;
H2 = MakeBinHeapWithArray(kp2, 19);
puts("\n\n第二个二叉堆H2:");
PrintBinHeap(H2);
BinHeap H3 = NULL;
H3 = BinHeapUnion(H1, H2);
puts("\n\n合并H1,H2后，得到H3:");
PrintBinHeap(H3);
BinHeap H4 = NULL;
H4 = MakeBinHeapWithArray(kp4, 22);
puts("\n\n用于测试提取和删除的二叉堆H4:");
PrintBinHeap(H4);
BinHeap extractNode = BinHeapExtractMin(H4);
if (extractNode != NULL) {
printf("\n\n抽取最小的值%d后：\n", extractNode->key);
PrintBinHeap(H4);
}
extractNode = BinHeapExtractMin(H4);
if (extractNode != NULL) {
printf("\n\n抽取最小的值%d后：\n", extractNode->key);
PrintBinHeap(H4);
}
extractNode = BinHeapExtractMin(H4);
if (extractNode != NULL) {
printf("\n\n抽取最小的值%d后：\n", extractNode->key);
PrintBinHeap(H4);
}
BinHeapDelete(H4, 12);
puts("\n\n删除12后：");
PrintBinHeap(H4);
return 0;
}
四、pair heap实现
#include <stdlib.h>
typedef struct PairingHeapNode
{
int key;
struct PairingHeapNode* child;
struct PairingHeapNode* sibling;
struct PairingHeapNode* prev;
}PairHeap;
static PairHeap* merge_subheaps(PairHeap *p, PairHeap *q);
static PairHeap* combine_siblings(PairHeap *p);
PairHeap* PairHeap_insert(PairHeap *p, int key)
{
PairHeap *node;
node = (PairHeap*)malloc(sizeof(*node));
if(node == NULL)
return NULL;
node->key = key;
node->child = node->sibling = node->prev = NULL;
if(p == NULL)
return node;
else
return merge_subheaps(p, node);
}
PairHeap* PairHeap_DecreaseKey(PairHeap *p, PairHeap *pos, int d)
{
if(d < 0)
return p;
pos->key = pos->key - d;
if(p == pos)
return p;
if(pos->sibling != NULL)
pos->sibling->prev = pos->prev;
if(pos->prev->child = pos)
pos->prev->child = pos->sibling;
else
pos->prev->sibling = p->sibling;
p->sibling = NULL;
return merge_subheaps(p, pos);
}
PairHeap* PairHeap_DeleteMin(int *key, PairHeap *p)
{
PairHeap *new_root;
if(p == NULL)
return NULL;
else
{
*key = p->key;
if(p->child != NULL)
new_root = combine_siblings(p->child);
free(p);
}
return new_root;
}
static PairHeap* combine_siblings(PairHeap *p)
{
PairHeap *tree_array[1024];
int i, count;
if(p->sibling == NULL)
return p;
for(count = 0; p != NULL; count++)
{
tree_array[count] = p;
p->prev->sibling = NULL;
p = p->sibling;
}
tree_array[count] = NULL;
for(i = 1; i < count; i++)
tree_array[i] = merge_subheaps(tree_array[i-1], tree_array[i]);
return tree_array[count-1];
}
static PairHeap* merge_subheaps(PairHeap *p, PairHeap *q)
{
if(q == NULL)
return p;
else if(p->key <= q->key)
{
q->prev = p;
p->sibling = q->sibling;
if(p->sibling != NULL)
p->sibling->prev = p;
q->sibling = p->child;
if(q->sibling != NULL)
q->sibling->prev = q;
p->child = q;
return p;
}
else
{
q->prev = p->prev;
p->prev = q;
p->sibling = q->child;
if(p->sibling != NULL)
p->sibling->prev = p;
q->child = p;
return q;
}
}
五、B-Tree实现
B-tree（多路搜索树，并不是二叉的）是一种常见的数据结构。使用B-tree结构可以显著减少定位记录时所经历的中间过程，从而加快存取速度。按照翻译，B 通常认为是Balance的简称。这个数据结构一般用于数据库的索引，综合效率较高。
//实现对order序(阶)的B-TREE结构基本操作的封装。
//查找：search，插入：insert，删除：remove。
//创建：create，销毁：destory，打印：print。
#ifndef BTREE_H
#define BTREE_H
#ifdef __cplusplus
extern "C" {
#endif
////* 定义m序(阶)B 树的最小度数BTree_D=ceil(m)*/
/// 在这里定义每个节点中关键字的最大数目为:2 * BTree_D - 1，即序(阶)：2 * BTree_D.
#define BTree_D 2
#define ORDER (BTree_D * 2) //定义为4阶B-tree,2-3-4树。最简单为3阶B-tree,2-3树。
//#define ORDER (BTree_T * 2-1) //最简单为3阶B-tree,2-3树。
typedef int KeyType;
typedef struct BTNode{
int keynum; /// 结点中关键字的个数，keynum <= BTree_N
KeyType key[ORDER-1]; /// 关键字向量为key[0..keynum - 1]
struct BTNode* child[ORDER]; /// 孩子指针向量为child[0..keynum]
bool isLeaf; /// 是否是叶子节点的标志
}BTNode;
typedef BTNode* BTree; ///定义BTree
///给定数据集data,创建BTree。
void BTree_create(BTree* tree, const KeyType* data, int length);
///销毁BTree，释放内存空间。
void BTree_destroy(BTree* tree);
///在BTree中插入关键字key。
void BTree_insert(BTree* tree, KeyType key);
///在BTree中移除关键字key。
void BTree_remove(BTree* tree, KeyType key);
///深度遍历BTree打印各层结点信息。
void BTree_print(const BTree tree, int layer=1);
/// 在BTree中查找关键字 key，
/// 成功时返回找到的节点的地址及 key 在其中的位置 *pos
/// 失败时返回 NULL 及查找失败时扫描到的节点位置 *pos
BTNode* BTree_search(const BTree tree, int key, int* pos);
#ifdef __cplusplus
}
#endif
#endif
六、Fibonacci 堆
斐波那契堆(Fibonacci heap)是计算机科学中最小堆有序树的集合。斐波那契堆的特点：不涉及删除元素的操作有O(1)的平摊时间。 Extract-Min和Delete的数目和其它相比较小时效率更佳。
用途：稠密图每次Decrease－key只要O(1)的平摊时间，和二项堆的O(lgn)相比是巨大的改进。
斐波那契堆的结构较二项堆更松散。因此对结构的维护可以到方便时再做。斐波那契堆中的树是有根而无序的。每个节点包含一个指向其父节点的指针p[x]，以及一个指向其任一子女的指针child[x]（指向子女双链表）。
//说明：
//代码中Fibonacci Heap 用变量heap表示
//结点通常用x，y等表示
#include<iostream>
#include<cstdio>
#include<cstdlib>
#include<cmath>
#include<climits>
using namespace std;
//斐波那契结点ADT
struct FibonacciHeapNode {
int key; //结点
int degree; //度
FibonacciHeapNode * left; //左兄弟
FibonacciHeapNode * right; //右兄弟
FibonacciHeapNode * parent; //父结点
FibonacciHeapNode * child; //第一个孩子结点
bool marked; //是否被删除第1个孩子
};
typedef FibonacciHeapNode FibNode;
//斐波那契堆ADT
struct FibonacciHeap {
int keyNum; //堆中结点个数
FibonacciHeapNode * min;//最小堆，根结点
int maxNumOfDegree; //最大度
FibonacciHeapNode * * cons;//指向最大度的内存区域
};
typedef FibonacciHeap FibHeap;
/*****************函数申明*************************/
//将x从双链表移除
inline void FibNodeRemove(FibNode * x);
//将x堆结点加入y结点之前(循环链表中)
void FibNodeAdd(FibNode * x, FibNode * y);
//初始化一个空的Fibonacci Heap
FibHeap * FibHeapMake() ;
//初始化结点x
FibNode * FibHeapNodeMake();
//堆结点x插入fibonacci heap中
void FibHeapInsert(FibHeap * heap, FibNode * x);
//将数组内的值插入Fibonacci Heap
void FibHeapInsertKeys(FibHeap * heap, int keys[], int keyNum);
//将值插入Fibonacci Heap
static void FibHeapInsertKey(FibHeap * heap, int key);
//抽取最小结点
FibNode * FibHeapExtractMin(FibHeap * heap);
//合并左右相同度数的二项树
void FibHeapConsolidate(FibHeap * heap);
//将x根结点链接到y根结点
void FibHeapLink(FibHeap * heap, FibNode * x, FibNode *y);
//开辟FibHeapConsolidate函数哈希所用空间
static void FibHeapConsMake(FibHeap * heap);
//将堆的最小结点移出，并指向其有兄弟
static FibNode *FibHeapMinRemove(FibHeap * heap);
//减小一个关键字
void FibHeapDecrease(FibHeap * heap, FibNode * x, int key);
//切断x与父节点y之间的链接，使x成为一个根
static void FibHeapCut(FibHeap * heap, FibNode * x, FibNode * y);
//级联剪切
static void FibHeapCascadingCut(FibHeap * heap, FibNode * y);
//修改度数
void renewDegree(FibNode * parent, int degree);
//删除结点
void FibHeapDelete(FibHeap * heap, FibNode * x);
//堆内搜索关键字
FibNode * FibHeapSearch(FibHeap * heap, int key);
//被FibHeapSearch调用
static FibNode * FibNodeSearch(FibNode * x, int key);
//销毁堆
void FibHeapDestory(FibHeap * heap);
//被FibHeapDestory调用
static void FibNodeDestory(FibNode * x);
//输出打印堆
static void FibHeapPrint(FibHeap * heap);
//被FibHeapPrint调用
static void FibNodePrint(FibNode * x);
/************************************************/
//将x从双链表移除
inline void FibNodeRemove(FibNode * x) {
x->left->right = x->right;
x->right->left = x->left;
}
/*
将x堆结点加入y结点之前(循环链表中)
a …… y
a …… x …… y
*/
inline void FibNodeAdd(FibNode * x, FibNode * y) {
x->left = y->left;
y->left->right = x;
x->right = y;
y->left = x;
}
//初始化一个空的Fibonacci Heap
FibHeap * FibHeapMake() {
FibHeap * heap = NULL;
heap = (FibHeap *) malloc(sizeof(FibHeap));
if (NULL == heap) {
puts("Out of Space!!");
exit(1);
}
memset(heap, 0, sizeof(FibHeap));
return heap;
}
//初始化结点x
FibNode * FibHeapNodeMake() {
FibNode * x = NULL;
x = (FibNode *) malloc(sizeof(FibNode));
if (NULL == x) {
puts("Out of Space!!");
exit(1);
}
memset(x, 0, sizeof(FibNode));
x->left = x->right = x;
return x;
}
//堆结点x插入fibonacci heap中
void FibHeapInsert(FibHeap * heap, FibNode * x) {
if (0 == heap->keyNum) {
heap->min = x;
} else {
FibNodeAdd(x, heap->min);
x->parent = NULL;
if (x->key < heap->min->key) {
heap->min = x;
}
}
heap->keyNum++;
}
//将数组内的值插入Fibonacci Heap
void FibHeapInsertKeys(FibHeap * heap, int keys[], int keyNum) {
for (int i = 0; i < keyNum; i++) {
FibHeapInsertKey(heap, keys[i]);
}
}
//将值插入Fibonacci Heap
static void FibHeapInsertKey(FibHeap * heap, int key) {
FibNode * x = NULL;
x = FibHeapNodeMake();
x->key = key;
FibHeapInsert(heap, x);
}
//抽取最小结点
FibNode * FibHeapExtractMin(FibHeap * heap) {
FibNode * x = NULL, * z = heap->min;
if (z != NULL) {
//删除z的每一个孩子
while (NULL != z->child) {
x = z->child;
FibNodeRemove(x);
if (x->right == x) {
z->child = NULL;
} else {
z->child = x->right;
}
FibNodeAdd(x, z);//add x to the root list heap
x->parent = NULL;
}
FibNodeRemove(z);
if (z->right == z) {
heap->min = NULL;
} else {
heap->min = z->right;
FibHeapConsolidate(heap);
}
heap->keyNum--;
}
return z;
}
//合并左右相同度数的二项树
void FibHeapConsolidate(FibHeap * heap) {
int D, d;
FibNode * w = heap->min, * x = NULL, * y = NULL;
FibHeapConsMake(heap);//开辟哈希所用空间
D = heap->maxNumOfDegree + 1;
for (int i = 0; i < D; i++) {
*(heap->cons + i) = NULL;
}
//合并相同度的根节点，使每个度数的二项树唯一
while (NULL != heap->min) {
x = FibHeapMinRemove(heap);
d = x->degree;
while (NULL != *(heap->cons + d)) {
y = *(heap->cons + d);
if (x->key > y->key) {//根结点key最小
swap(x, y);
}
FibHeapLink(heap, y, x);
*(heap->cons + d) = NULL;
d++;
}
*(heap->cons + d) = x;
}
heap->min = NULL;//原有根表清除
//将heap->cons中结点都重新加到根表中，且找出最小根
for (int i = 0; i < D; i++) {
if (*(heap->cons + i) != NULL) {
if (NULL == heap->min) {
heap->min = *(heap->cons + i);
} else {
FibNodeAdd(*(heap->cons + i), heap->min);
if ((*(heap->cons + i))->key < heap->min->key) {
heap->min = *(heap->cons + i);
}//if(<)
}//if-else(==)
}//if(!=)
}//for(i)
}
//将x根结点链接到y根结点
void FibHeapLink(FibHeap * heap, FibNode * x, FibNode *y) {
FibNodeRemove(x);
if (NULL == y->child) {
y->child = x;
} else {
FibNodeAdd(x, y->child);
}
x->parent = y;
y->degree++;
x->marked = false;
}
//开辟FibHeapConsolidate函数哈希所用空间
static void FibHeapConsMake(FibHeap * heap) {
int old = heap->maxNumOfDegree;
//最大度的计算
heap->maxNumOfDegree = int(log(heap->keyNum * 1.0) / log(2.0)) + 1;
if (old < heap->maxNumOfDegree) {
//因为度为heap->maxNumOfDegree可能被合并,所以要maxNumOfDegree + 1
heap->cons = (FibNode **) realloc(heap->cons,
sizeof(FibHeap *) * (heap->maxNumOfDegree + 1));
if (NULL == heap->cons) {
puts("Out of Space!");
exit(1);
}
}
}
//将堆的最小结点移出，并指向其有兄弟
static FibNode *FibHeapMinRemove(FibHeap * heap) {
FibNode *min = heap->min;
if (heap->min == min->right) {
heap->min = NULL;
} else {
FibNodeRemove(min);
heap->min = min->right;
}
min->left = min->right = min;
return min;
}
//减小一个关键字
void FibHeapDecrease(FibHeap * heap, FibNode * x, int key) {
FibNode * y = x->parent;
if (x->key < key) {
puts("new key is greater than current key!");
exit(1);
}
x->key = key;
if (NULL != y && x->key < y->key) {
//破坏了最小堆性质，需要进行级联剪切操作
FibHeapCut(heap, x, y);
FibHeapCascadingCut(heap, y);
}
if (x->key < heap->min->key) {
heap->min = x;
}
}
//切断x与父节点y之间的链接，使x成为一个根
static void FibHeapCut(FibHeap * heap, FibNode * x, FibNode * y) {
FibNodeRemove(x);
renewDegree(y, x->degree);
if (x == x->right) {
y->child = NULL;
} else {
y->child = x->right;
}
x->parent = NULL;
x->left = x->right = x;
x->marked = false;
FibNodeAdd(x, heap->min);
}
//级联剪切
static void FibHeapCascadingCut(FibHeap * heap, FibNode * y) {
FibNode * z = y->parent;
if (NULL != z) {
if (y->marked == false) {
y->marked = true;
} else {
FibHeapCut(heap, y, z);
FibHeapCascadingCut(heap, z);
}
}
}
//修改度数
void renewDegree(FibNode * parent, int degree) {
parent->degree -= degree;
if (parent-> parent != NULL) {
renewDegree(parent->parent, degree);
}
}
//删除结点
void FibHeapDelete(FibHeap * heap, FibNode * x) {
FibHeapDecrease(heap, x, INT_MIN);
FibHeapExtractMin(heap);
}
//堆内搜索关键字
FibNode * FibHeapSearch(FibHeap * heap, int key) {
return FibNodeSearch(heap->min, key);
}
//被FibHeapSearch调用
static FibNode * FibNodeSearch(FibNode * x, int key) {
FibNode * w = x, * y = NULL;
if (x != NULL) {
do {
if (w->key == key) {
y = w;
break;
} else if (NULL != (y = FibNodeSearch(w->child, key))) {
break;
}
w = w->right;
} while (w != x);
}
return y;
}
//销毁堆
void FibHeapDestory(FibHeap * heap) {
FibNodeDestory(heap->min);
free(heap);
heap = NULL;
}
//被FibHeapDestory调用
static void FibNodeDestory(FibNode * x) {
FibNode * p = x, *q = NULL;
while (p != NULL) {
FibNodeDestory(p->child);
q = p;
if (p -> left == x) {
p = NULL;
} else {
p = p->left;
}
free(q->right);
}
}
//输出打印堆
static void FibHeapPrint(FibHeap * heap) {
printf("The keyNum = %d\n", heap->keyNum);
FibNodePrint(heap->min);
puts("\n");
};
//被FibHeapPrint调用
static void FibNodePrint(FibNode * x) {
FibNode * p = NULL;
if (NULL == x) {
return ;
}
p = x;
do {
printf(" (");
printf("%d", p->key);
if (p->child != NULL) {
FibNodePrint(p->child);
}
printf(") ");
p = p->left;
}while (x != p);
}
int keys[10] = {1, 2, 3, 4, 5, 6, 7, 9, 10, 11};
int main() {
FibHeap * heap = NULL;
FibNode * x = NULL;
heap = FibHeapMake();
FibHeapInsertKeys(heap, keys, 10);
FibHeapPrint(heap);
x = FibHeapExtractMin(heap);
printf("抽取最小值%d之后：\n", x->key);
FibHeapPrint(heap);
x = FibHeapSearch(heap, 11);
if (NULL != x) {
printf("查找%d成功,", x->key);
FibHeapDecrease(heap, x, 8);
printf("减小到%d后：\n", x->key);
FibHeapPrint(heap);
}
x = FibHeapSearch(heap, 7);
if (NULL != x) {
printf("删除%d成功:\n", x->key);
FibHeapDelete(heap, x);
FibHeapPrint(heap);
}
FibHeapDestory(heap);
return 0;
}
