【项目2 - 建立链栈算法库】

代码：

<pre name="code" class="cpp">//main函数

#include <iostream>#include "LiStack.h"
using namespace std;int main()
{int a=1,b=2,c=3,d=4,e=5;LiStack *s;printf("初始化栈\n");InitStack(s);printf("栈为:%s\n",(StackEmpty(s)?"空":"非空"));printf("依次进栈元素为：%d %d %d %d %d\n",a,b,c,d,e);Push(s,a);Push(s,b);Push(s,c);Push(s,d);Push(s,e);printf("栈为:%s\n",(StackEmpty(s)?"空":"非空"));printf("链栈长度为：%d\n",StackLength(s));printf("从栈顶到栈底元素:\n");DispStack(s);printf("出栈序列：\n");while(!StackEmpty(s)){Pop(s,e);printf("%d\n",e);}printf("栈为:%s\n",(StackEmpty(s)?"空":"非空"));printf("释放链栈\n");DestroyStack(s);return 0;
}

//头文件

#ifndef LISTACK_H_INCLUDED
#define LISTACK_H_INCLUDED
#include <iostream>
#include <stdio.h>
#include <malloc.h>
typedef int ElemType ;
typedef struct linknode
{ElemType data;struct linknode *next;
}LiStack;
void InitStack(LiStack *&s);    //初始化栈
void DestroyStack(LiStack *&s);  //销毁栈
bool StackEmpty(LiStack *s);     //栈是否为空
int StackLength(LiStack *s);  //返回栈中元素个数——栈长度
void Push(LiStack *&s,ElemType e); //入栈
bool Pop(LiStack *&s,ElemType &e); //出栈
bool GetTop(LiStack *s,ElemType &e); //取栈顶数据元素
void DispStack(LiStack *s);  //输出栈#endif // LISTACK_H_INCLUDED

//源文件

<pre name="code" class="cpp">#include "LiStack.h"void InitStack(LiStack *&s)    //初始化栈
{s=(LiStack *)malloc(sizeof(LiStack));s->next=NULL;
}
void DestroyStack(LiStack *&s)  //销毁栈
{LiStack *p=s->next;while(p!=NULL){free(s);s=p;p=p->next;}free(s);
}
bool StackEmpty(LiStack *s)     //栈是否为空
{return(s->next==NULL);
}
int StackLength(LiStack *s)  //返回栈中元素个数——栈长度
{int i=0;LiStack *p=s->next;while(p!=NULL){i++;p=p->next;}return i;
}
void Push(LiStack *&s,ElemType e) //入栈
{LiStack *p;p=(LiStack *)malloc(sizeof(LiStack));p->data=e;p->next=s->next;s->next=p;
}
bool Pop(LiStack *&s,ElemType &e) //出栈
{LiStack *p;if(s->next==NULL)return false;p=s->next;e=p->data;s->next=p->next;free(p);return true;
}
bool GetTop(LiStack *s,ElemType &e) //取栈顶数据元素
{if(s->next==NULL)return false;e=s->next->data;return true;
}
void DispStack(LiStack *s)   //输出栈
{LiStack *p=s->next;if(s->next==NULL)printf("栈为空\n");while(p!=NULL){printf("%d\n",p->data);p=p->next;}
}

运行结果：

<img 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" alt="" />

心得：

    通过自己一行行的敲出链栈算法库的代码，也在一遍遍的加深自己的理解与记忆，算法库建立起来也简化了以后写代码的流程，节省了时间。

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