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算法导论第六章优先队列(二)

时间:2015-09-20 17:50:25      阅读:330      评论:0      收藏:0      [点我收藏+]

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优先队列可以说是堆的一个非常重要的应用,和堆对应,优先队列也分最小优先队列和最大优先队列。

优先队列是一种用来维护由一组元素构成的集合S的数据结构,其中每一个元素都有一个关键字(key),关键字赋予了一个元素的优先级,故名为优先队列。之所以用堆来实现优先队列,我想最大的原因是堆很容易对元素按关键字进行排序。

优先队列的应用:

最大优先队列:其中最为典型的就是“共享计算机系统的作业调度”,通过记录各个作业的优先级,来调度一个作业的执行、删除和插入等操作。

最小优先队列:可以被用于“基于事件驱动的模拟器”,队列中保存要模拟的事件,每个事件都有一个发生时间作为关键字。事件必须按发生的时间顺序进行模拟,因为某一事件的模拟结果可能会触发对其他事件的模拟。

优先队列的实现:

为了实现优先队列,需要实现这样的几个过程:

1)Insert(S, x):插入x到集合S中;

2)Maximum(S):返回S中具有最大关键字的元素;

3)Extract_Max(S):去掉并返回集合S中具有最大关键字的元素;

4)Increase_Key(S, x, key):将元素x的关键字增加到key。

我们暂且不用管这些奇怪的函数为什么要这么定义,因为这是前人的成功经验总结,肯定是在实际应用中这几个函数用得是最多的,总之,知道这样的四个函数就行了,等用到的时候就知道它们的好处了。

首先,基于堆,Maximum可以在O(1)的时间内完成,如:

1 int PriorityQueue::HeapMaximum()    //return the maximum key from priority queue;
2 {
3     return GetQueueElement(0);        //the first element is max;
4 }

其次,Extract_Max(S)和Maximum()的区别就在于,得到了之后还要删除,在不改变结构的情况下,最好的删除方法就是用最后一个元素来代替第一个元素,但是要注意,删除之后,有可能堆的性质就不能保证了,所以,这个时候调用Max_Heapify()调整一下。如下:

 1 int PriorityQueue::HeapExtractMax()        //delete and return the maximum key from queue;
 2 {
 3     if (IsEmptyHeap())
 4         throw "heap underflow";
 5 
 6     int heap_size = GetHeapSize();
 7     int maxNum = GetQueueElement(0); 
 8     SetQueueElement(0, GetQueueElement(heap_size-1)); //A[1] = A[heap_size]
 9     SetHeapSizeMinus1();
10     MaxHeapify(0);    //Max_Heapify()
11     return maxNum;
12 }

对于Increase_Key(S, x, key),我们直接用Key替换X,但是替换之后堆的性质也可能改变了,可以知道的是,X后面的元素仍然满足堆的性质,因为Key>X,这时就之用维护X前面的元素,一层层往上调用Max_Heapify()即可,如:

 1 void PriorityQueue::HeapIncreaseKey(int srcIndex, int dstKey)    //increasing the srcKey to dstKey
 2 {
 3     if (dstKey < GetQueueElement(srcIndex))
 4         throw "new key is smaller than current key!";
 5     SetQueueElement(srcIndex, dstKey); //x = key
 6     while(srcIndex > 0 && GetQueueElement(getParent(srcIndex)) < GetQueueElement(srcIndex)) {
 7         Swap(srcIndex, getParent(srcIndex));
 8         srcIndex = getParent(srcIndex); //get parent
 9     }
10 }

最后,Insert(S, x)操作,我们可以在最后增加一个-∞的元素,然后将其转换成将-∞增加到x,即转换成Increase_Key(S, -∞, x)。如:

1 void PriorityQueue::HeapInsert(int key)    //insert key to the priority queue;
2 {
3     AddQueueElement(INT_MIN);            //add the INT_MIN to the end of heapQueue;
4     int heap_size = GetHeapSize();
5     HeapIncreaseKey(heap_size-1, key);
6 }

除此之外,习题6.5-8要求在O(lgn)的时间内实现一个Delete(S, i)操作。很容易想到相当于对A[i]为根的堆进行Extract_Max()操作。如:

1 Heap-Delete(A, i)
2     A[i] = A[A.heap-size]
3     A.heap-size = A.heap-size - 1
4     Heapify(A, i)

以上所有的操作,除了Maximum()之外,其余所有的函数的时间复杂度皆为O(lgn),所以,这也是为什么用堆来实现优先队列一个非常重要的原因。下面给出一个非常简单的优先队列的实现(元素的值就是Key)。

技术分享
 1 //PriorityQueue.h
 2 
 3 
 4 #ifndef _PRIORITY_QUEUE_H_
 5 #define _PRIORITY_QUEUE_H_
 6 
 7 /************************************************************************/
 8 /*    priority queue                                                                   
 9 /************************************************************************/
10 class PriorityQueue {
11 public:
12     PriorityQueue() { m_heapSize = 0; m_length = 0; }
13     ~PriorityQueue() {}
14     
15     //inline
16     int getParent(int index) { return (index-1)/2; }
17     int getLeft(int index) { return 2*index + 1; }
18     int getRight(int index) { return 2*index + 2; }
19 
20     //heap operation
21     void BuildMaxHeap();        //build the max heap
22     void MaxHeapify(int index);    //protect the max heap
23     void HeapSort();            //heap sort
24     
25     //priority queue operation
26     void HeapInsert(int key);    //insert key to the priority queue;
27     int HeapMaximum();            //return the maximum key from priority queue;
28     int HeapExtractMax();        //delete and return the maximum key from queue;
29     void HeapIncreaseKey(int srcIndex, int dstKey);    //increasing the srcKey to dstKey
30     
31     void HeapDelete(int key);    //delete key 习题6.5-8
32     
33     //other assist functions
34     void AddQueueElement(int key);
35     void DisplayQueue();
36     void DisplayHeapQueue();
37 
38 public:
39     
40     
41 private:
42     int GetQueueElement(int index) { return m_vecQueue[index]; }
43     void SetQueueElement(int index, int key) { m_vecQueue[index] = key; }
44     void Swap(int i, int j) { 
45         int temp = m_vecQueue[i]; 
46         m_vecQueue[i] = m_vecQueue[j]; 
47         m_vecQueue[j] = temp; 
48     }
49     int GetHeapSize() { return m_heapSize; }
50     int GetArrayLength() { return m_length; }
51     void SetHeapSizeMinus1() { m_heapSize = m_heapSize - 1; }
52     void SetHeapSizePlus1() { m_heapSize = m_heapSize + 1; }
53 
54     bool IsEmptyHeap() { return (m_heapSize > 1 ? false : true); }
55 
56 private:
57     vector<int>        m_vecQueue;
58     int                m_heapSize;    //堆元素个数
59     int                m_length;    //数组元素个数
60     
61 };
62 
63 
64 #endif//_PRIORITY_QUEUE_H_
View Code
技术分享
  1 //PriorityQueue.cpp
  2 
  3 
  4 #include <iostream>
  5 #include <vector>
  6 using namespace std;
  7 
  8 #include "PriorityQueue.h"
  9 
 10 //heap operation
 11 void PriorityQueue::BuildMaxHeap()        //build the max heap
 12 {
 13     int heap_size = GetHeapSize();
 14     for (int i = (heap_size-1)/2; i >= 0; i --)
 15         MaxHeapify(i);
 16 }
 17 
 18 void PriorityQueue::MaxHeapify(int index)    //protect the max heap
 19 {
 20     if (index < 0 || index >= GetHeapSize())
 21         return;
 22 
 23     int heap_size = GetHeapSize();
 24     bool isHeapify = true; 
 25 
 26     int largest = -1;
 27     while (isHeapify && index < heap_size) {
 28         int left = getLeft(index);
 29         int right = getRight(index);
 30 
 31         if (left < heap_size && GetQueueElement(left) > GetQueueElement(index) )
 32             largest = left;
 33         else
 34             largest = index;
 35         if (right < heap_size && GetQueueElement(right) > GetQueueElement(largest) )
 36             largest = right;
 37 
 38         if (largest != index) {
 39             Swap(index, largest);
 40             index = largest;
 41         }
 42         else
 43             isHeapify = false;
 44     }
 45 
 46 }
 47 
 48 void PriorityQueue::HeapSort()            //heap sort
 49 {
 50     BuildMaxHeap();
 51     int heap_size = GetHeapSize();
 52     for (int i = heap_size-1; i >= 1; i --) {
 53         Swap(0, i);
 54         SetHeapSizeMinus1(); //heap_size--;
 55         MaxHeapify(0);
 56     }
 57 }
 58 
 59 //priority queue operation
 60 void PriorityQueue::HeapInsert(int key)    //insert key to the priority queue;
 61 {
 62 //     SetHeapSizePlus1();
 63 //     int heap_size = GetHeapSize();
 64     AddQueueElement(INT_MIN);            //add the INT_MIN to the end of heapQueue;
 65     int heap_size = GetHeapSize();
 66     HeapIncreaseKey(heap_size-1, key);
 67 }
 68 
 69 int PriorityQueue::HeapMaximum()    //return the maximum key from priority queue;
 70 {
 71     return GetQueueElement(0);        //the first element is max;
 72 }
 73 
 74 int PriorityQueue::HeapExtractMax()        //delete and return the maximum key from queue;
 75 {
 76     if (IsEmptyHeap())
 77         throw "heap underflow";
 78 
 79     int heap_size = GetHeapSize();
 80     int maxNum = GetQueueElement(0); 
 81     SetQueueElement(0, GetQueueElement(heap_size-1)); //A[1] = A[heap_size]
 82     SetHeapSizeMinus1();
 83     MaxHeapify(0);    //Max_Heapify()
 84     return maxNum;
 85 }
 86 
 87 void PriorityQueue::HeapIncreaseKey(int srcIndex, int dstKey)    //increasing the srcKey to dstKey
 88 {
 89     if (dstKey < GetQueueElement(srcIndex))
 90         throw "new key is smaller than current key!";
 91     SetQueueElement(srcIndex, dstKey); //x = key
 92     while(srcIndex > 0 && GetQueueElement(getParent(srcIndex)) < GetQueueElement(srcIndex)) {
 93         Swap(srcIndex, getParent(srcIndex));
 94         srcIndex = getParent(srcIndex); //get parent
 95     }
 96 }
 97 
 98 void PriorityQueue::HeapDelete(int key)    //delete key 习题6.5-8
 99 {
100     int heap_size = GetHeapSize();
101     int index = 0;
102     for (;index < heap_size; index ++) {
103         if (GetQueueElement(index) == key)
104             break;
105     }
106     SetQueueElement(index, GetQueueElement(heap_size-1));
107     SetHeapSizeMinus1();
108     MaxHeapify(index);
109 }
110 
111 //other assist functions
112 void PriorityQueue::DisplayQueue()
113 {
114     int nLen = GetArrayLength();
115     cout << "------------------------" << endl;
116     for (int i = 0; i < nLen; i ++)
117         cout << GetQueueElement(i) << " ";
118     cout << endl;
119 }
120 
121 void PriorityQueue::DisplayHeapQueue()
122 {
123     int heap_size = GetHeapSize();
124     cout << "------------------------" << endl;
125     for (int i = 0; i < heap_size; i ++)
126         cout << GetQueueElement(i) << " ";
127     cout << endl;
128 }
129 
130 void PriorityQueue::AddQueueElement(int key)
131 {
132     m_vecQueue.push_back(key);
133     m_heapSize ++;
134     m_length ++;
135 }
136 
137 
138 // int main()
139 // {
140 //     //int key, num;
141 //     PriorityQueue PQ;
142 // 
143 // //     cout << "the number:" << endl;
144 // //     cin >> num;
145 //     int arr[] = {10,8,7,16,14,9,3,2,4,1};
146 //     cout << "the key:" << endl;
147 //     for (int i = 0; i < 10; i ++) {
148 //         PQ.AddQueueElement(arr[i]);
149 //     }
150 // 
151 //     PQ.BuildMaxHeap();
152 //     PQ.DisplayQueue();
153 // 
154 //     //Max
155 //     cout << "Max:" << PQ.HeapMaximum() << endl;
156 // 
157 //     //IncreaseKey
158 //     cout << "IncreaseKey:" << endl;
159 //     PQ.HeapIncreaseKey(0, 18);
160 //     PQ.DisplayHeapQueue();
161 //     
162 //     //InsertKey
163 //     cout << "InsertKey:" << endl;
164 //     PQ.HeapInsert(20);
165 //     PQ.DisplayHeapQueue();
166 // 
167 //     //Extract_Max
168 //     cout << "Extract_Max:" << PQ.HeapExtractMax() << endl;
169 //     PQ.DisplayHeapQueue();
170 // 
171 //     //Extract_Max
172 //     cout << "Extract_Max:" << PQ.HeapExtractMax() << endl;
173 //     PQ.DisplayHeapQueue();
174 // 
175 //     return 0;
176 // }
View Code

 

习题精讲:

优先队列的讲述,基本就是这样,其实主要的设计思想还是堆。下面看两道有意思的习题:

1)习题6.5-6:在Heap_Increase_Key的第5行操作中,一般需要通过三次赋值来完成。想一想如何利用Insertion_Sort内循环部分的思想,只用一次赋值就完成这一交换操作?

分析:这是一种非常好的出题思路,能够打开我们的思维,让人有一种眼前一亮的感觉。我们可以先向下移动小于他的祖先,直到没有小于他的祖先后放在空出的位置上。如:

1 Heap-Increase-Key(A, i, key)
2     if A[i] < key
3         error "new key is smaller than original"
4     while i > 1 and A[Parent(i)] < key
5         A[i] = A[Parent(i)]
6         i = Parent(i)
7     A[i] = key

2)习题6.5-9:请设计一个能够在O(nlgk)的算法,它能够将k个有序链表合并成一个有序链表,这里n是所有输入链表包含的总的元素个数。(提示:使用最小堆来完成k路归并)。

这个题首先想到2路归并排序,但此处是k路,乍一看没什么思路,看了提示后,仍然没有什么头绪,看了网友Anker的思路后,发现可以这样来做(太水了):创建一个大小为k的数组,将k个链表中的第一个元素依次存放到数组中,然后将数组调整为最小堆,这样保证数组的第一个元素是最小的,假设为min,将min从最小堆取出并存放到最终结果的链表中,此时将min所在链表的下一个元素到插入的最小堆中,继续上面的操作,直到堆中没有元素为止。举个例子如下图所示(只给出不部分操作):

技术分享

 

技术分享

我们采用C++语言,借助STL实现此过程,链表采用vector,最小堆中存放的是vector的迭代器,表示vector中元素的位置。完整程序如下:

技术分享
  1 //MinHeap_KMerge.cpp
  2 
  3 #include <iostream>
  4 #include <vector>
  5 
  6 using namespace std;
  7 
  8 #include "MinHeap.h"
  9 
 10 //merge the k list to a heap;
 11 template<class T>
 12 MinHeap<T>::MinHeap(size_t kSize) 
 13 {
 14     if (!m_minHeap)
 15         delete []m_minHeap;
 16     m_minHeap = new T[kSize+1];
 17     m_heapSize = 0;
 18 }
 19 
 20 //adjust the min heap;
 21 template<class T>
 22 void MinHeap<T>::MinHeapify(size_t index)
 23 {
 24     //assert
 25     size_t heap_size = GetHeapSize();
 26 
 27     while (true) {
 28         size_t left = LEFT(index);
 29         size_t right = RIGHT(index);
 30         
 31         size_t smallest;
 32         if (left < heap_size && HeapCompare(left, index))
 33             smallest = left;
 34         else smallest = index;
 35         if (right < heap_size && HeapCompare(right, smallest))
 36             smallest = right;
 37 
 38         if (smallest != index) {
 39             Swap(index, smallest);
 40             index = smallest;
 41         }
 42         else break;
 43     }
 44 }
 45 
 46 //insert element
 47 template<class T>
 48 void MinHeap<T>::HeapInsert(const T &element)
 49 {
 50     m_minHeap[m_heapSize] = element;
 51     m_heapSize += 1;
 52 
 53     size_t index = m_heapSize-1;
 54 
 55     while (index > 0 && HeapCompare(index, PARENT(index))) {
 56         Swap(index, PARENT(index));
 57         index = PARENT(index);
 58     }
 59 }
 60 
 61 //return and delete the min element
 62 template<class T>
 63 T MinHeap<T>::HeapExtractMin()
 64 {
 65     if (IsEmptyHeap())
 66         throw "Heap is Empty!";
 67     T minElement = HeapMin();
 68 
 69     int heap_size = GetHeapSize();
 70     m_minHeap[0] = m_minHeap[heap_size-1];
 71     m_heapSize -= 1;
 72     MinHeapify(0);
 73     return minElement;
 74 }
 75 
 76 //return min element;
 77 template<class T>
 78 T    MinHeap<T>::HeapMin()  const
 79 {
 80     return m_minHeap[0];
 81 }
 82 
 83 int main()
 84 {
 85     const size_t k = 3;
 86     vector<int> vecList[k];
 87     vector<int>::iterator iterList[k];
 88     vector<int> vecSort;
 89     vector<int>::iterator iterS;
 90     
 91     vector<int>::iterator it;
 92 
 93     MinHeap<vector<int>::iterator> minHeap(k);
 94     //first list
 95     vecList[0].push_back(12);
 96     vecList[0].push_back(24);
 97     vecList[0].push_back(52);
 98     cout << "first list:" << endl;
 99     for ( it = vecList[0].begin();it != vecList[0].end(); ++it) 
100         cout << *it << "->";
101     cout << "NULL" << endl;
102 
103     vecList[1].push_back(9);
104     vecList[1].push_back(32);
105     
106     cout << "second list:" << endl;
107     for ( it = vecList[1].begin();it != vecList[1].end(); ++it) 
108         cout << *it << "->";
109     cout << "NULL" << endl;
110 
111     vecList[2].push_back(34);
112     vecList[2].push_back(42);
113     vecList[2].push_back(78);
114     cout << "third list:" << endl;
115     for ( it = vecList[2].begin();it != vecList[2].end(); ++it) 
116         cout << *it << "->";
117     cout << "NULL" << endl;
118     
119     iterList[0] = vecList[0].begin();
120     iterList[1] = vecList[1].begin();
121     iterList[2] = vecList[2].begin();
122 
123 
124     minHeap.HeapInsert(iterList[0]);
125     minHeap.HeapInsert(iterList[1]);
126     minHeap.HeapInsert(iterList[2]);
127 
128     while(minHeap.GetHeapSize()) {
129         it = minHeap.HeapExtractMin();
130         vecSort.push_back(*it);
131         ++it;
132         if (it != vecList[0].end() && it != vecList[1].end() && it != vecList[2].end()) { //!!!!Expression vector iterators incompatible
133             minHeap.HeapInsert(it);
134         }
135     }
136 
137     cout << "merge:" << endl;
138     for (iterS = vecSort.begin(); iterS != vecSort.end(); ++ iterS)
139         cout << *iterS << "->";
140     cout << "NULL" << endl;
141 
142     return 0;
143 }
View Code

 

算法导论第六章优先队列(二)

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原文地址:http://www.cnblogs.com/bakari/p/4823753.html

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