本题要求实现一个函数,将两个链表表示的递增整数序列合并为一个递增的整数序列。
函数接口定义:
List Merge( List L1, List L2 );
其中List
结构定义如下:
typedef struct Node *PtrToNode;
struct Node {
ElementType Data; /* 存储结点数据 */
PtrToNode Next; /* 指向下一个结点的指针 */
};
typedef PtrToNode List; /* 定义单链表类型 */
L1
和L2
是给定的带头结点的单链表,其结点存储的数据是递增有序的;函数Merge
要将L1
和L2
合并为一个递增的整数序列。应直接使用原序列中的结点,返回归并后的链表头指针。
裁判测试程序样例:
#include <stdio.h>
#include <stdlib.h>
typedef int ElementType;
typedef struct Node *PtrToNode;
struct Node {
ElementType Data;
PtrToNode Next;
};
typedef PtrToNode List;
List Read(); /* 细节在此不表 */
void Print( List L ); /* 细节在此不表;空链表将输出NULL */
List Merge( List L1, List L2 );
int main()
{
List L1, L2, L;
L1 = Read();
L2 = Read();
L = Merge(L1, L2);
Print(L);
Print(L1);
Print(L2);
return 0;
}
/* 你的代码将被嵌在这里 */
输入样例:
3
1 3 5
5
2 4 6 8 10
输出样例:
1 2 3 4 5 6 8 10
NULL
NULL
=============================================
第一次code:
1 #include <stdio.h> 2 #include <stdlib.h> 3 4 typedef int ElementType; 5 typedef struct Node *PtrToNode; 6 struct Node { 7 ElementType Data; 8 PtrToNode Next; 9 }; 10 typedef PtrToNode List; 11 12 List Read(); /* 细节在此不表 */ 13 void Print( List L); /* 细节在此不表;空链表将输出NULL */ 14 15 List Merge( List L1, List L2 ); 16 17 int main() 18 { 19 List L1, L2, L; 20 L1 = Read(); 21 L2 = Read(); 22 L = Merge(L1, L2); 23 Print(L); 24 Print(L1); 25 Print(L2); 26 return 0; 27 } 28 List Read() 29 { 30 int i,n; 31 scanf("%d",&n); 32 List p,q,L; 33 L=(List)malloc(sizeof(PtrToNode)); 34 L->Next=NULL;//建立头结点 35 q=L; 36 for (i=1;i<=n;i++) 37 { 38 p=(List)malloc(sizeof(PtrToNode)); 39 scanf("%d",&p->Data); 40 q->Next=p; 41 q=q->Next;//q后移 42 } 43 p->Next=NULL;//最后一个节点是尾结点 44 return L; 45 } 46 void Print( List L) 47 { 48 List p=L->Next;//指向第一个结点 49 if(p==NULL) 50 { 51 printf("NULL"); 52 printf("\n"); 53 return; 54 } 55 while(p) 56 { 57 printf("%d ",p->Data); 58 p=p->Next; 59 } 60 printf("\n"); 61 } 62 List Merge( List L1, List L2) 63 { 64 List L, p1, p2,tail; 65 L=(List)malloc(sizeof(PtrToNode)); 66 p1=L1->Next; 67 p2=L2->Next; 68 tail=L; 69 while(p1 && p2) 70 { 71 if(p1->Data < p2->Data)//链上data值小的 72 { 73 tail->Next=p1; 74 tail=p1; 75 p1=p1->Next; 76 L1->Next=NULL; 77 L2->Next=NULL; 78 } 79 else 80 { 81 tail->Next=p2; 82 tail=p2; 83 p2=p2->Next; 84 L1->Next=NULL; 85 L2->Next=NULL; 86 } 87 } 88 if(p1) 89 { 90 tail->Next=p1; 91 } 92 else 93 { 94 tail->Next=p2; 95 } 96 return L; 97 }
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)