1 链表、栈、队列、递归、哈希
1.1 链表
1.1.1 单向链表
单向链表的节点结构(可以实现成泛型) :
public class Node {
public int value;
public Node next;
public Node(int data) {
value = data;
}
}
1.1.2 双向链表
双向链表的节点结构(可以实现成功泛型):
public static class DoubleNode {
public int value;
public DoubleNode last;
public DoubleNode next;
public DoubleNode(int data) {
value = data;
}
}
1.1.3 单双链表简单练习
- 单链表和双链表如何反转
1 -> 2 -> 3 转换为 3 -> 2 -> 1
package class02;
import java.util.ArrayList;
public class Code01_ReverseList {
public static class Node {
public int value;
public Node next;
public Node(int data) {
value = data;
}
}
public static class DoubleNode {
public int value;
public DoubleNode last;
public DoubleNode next;
public DoubleNode(int data) {
value = data;
}
}
// 翻转单向链表,传入头结点
public static Node reverseLinkedList(Node head) {
Node pre = null;
Node next = null;
while (head != null) {
next = head.next;
head.next = pre;
pre = head;
head = next;
}
return pre;
}
// 翻转双向链表,传入头结点
public static DoubleNode reverseDoubleList(DoubleNode head) {
DoubleNode pre = null;
DoubleNode next = null;
while (head != null) {
next = head.next;
head.next = pre;
head.last = next;
pre = head;
head = next;
}
return pre;
}
public static Node testReverseLinkedList(Node head) {
if (head == null) {
return null;
}
ArrayList<Node> list = new ArrayList<>();
while (head != null) {
list.add(head);
head = head.next;
}
list.get(0).next = null;
int N = list.size();
for (int i = 1; i < N; i++) {
list.get(i).next = list.get(i - 1);
}
return list.get(N - 1);
}
public static DoubleNode testReverseDoubleList(DoubleNode head) {
if (head == null) {
return null;
}
ArrayList<DoubleNode> list = new ArrayList<>();
while (head != null) {
list.add(head);
head = head.next;
}
list.get(0).next = null;
DoubleNode pre = list.get(0);
int N = list.size();
for (int i = 1; i < N; i++) {
DoubleNode cur = list.get(i);
cur.last = null;
cur.next = pre;
pre.last = cur;
pre = cur;
}
return list.get(N - 1);
}
public static Node generateRandomLinkedList(int len, int value) {
int size = (int) (Math.random() * (len + 1));
if (size == 0) {
return null;
}
size--;
Node head = new Node((int) (Math.random() * (value + 1)));
Node pre = head;
while (size != 0) {
Node cur = new Node((int) (Math.random() * (value + 1)));
pre.next = cur;
pre = cur;
size--;
}
return head;
}
public static DoubleNode generateRandomDoubleList(int len, int value) {
int size = (int) (Math.random() * (len + 1));
if (size == 0) {
return null;
}
size--;
DoubleNode head = new DoubleNode((int) (Math.random() * (value + 1)));
DoubleNode pre = head;
while (size != 0) {
DoubleNode cur = new DoubleNode((int) (Math.random() * (value + 1)));
pre.next = cur;
cur.last = pre;
pre = cur;
size--;
}
return head;
}
// 要求无环,有环别用这个函数
public static boolean checkLinkedListEqual(Node head1, Node head2) {
while (head1 != null && head2 != null) {
if (head1.value != head2.value) {
return false;
}
head1 = head1.next;
head2 = head2.next;
}
return head1 == null && head2 == null;
}
// 要求无环,有环别用这个函数
public static boolean checkDoubleListEqual(DoubleNode head1, DoubleNode head2) {
boolean null1 = head1 == null;
boolean null2 = head2 == null;
if (null1 && null2) {
return true;
}
if (null1 ^ null2) {
return false;
}
if (head1.last != null || head2.last != null) {
return false;
}
DoubleNode end1 = null;
DoubleNode end2 = null;
while (head1 != null && head2 != null) {
if (head1.value != head2.value) {
return false;
}
end1 = head1;
end2 = head2;
head1 = head1.next;
head2 = head2.next;
}
if (head1 != null || head2 != null) {
return false;
}
while (end1 != null && end2 != null) {
if (end1.value != end2.value) {
return false;
}
end1 = end1.last;
end2 = end2.last;
}
return end1 == null && end2 == null;
}
public static void main(String[] args) {
int len = 50;
int value = 100;
int testTime = 100000;
for (int i = 0; i < testTime; i++) {
Node node1 = generateRandomLinkedList(len, value);
Node reverse1 = reverseLinkedList(node1);
Node back1 = testReverseLinkedList(reverse1);
if (!checkLinkedListEqual(node1, back1)) {
System.out.println("oops!");
break;
}
DoubleNode node2 = generateRandomDoubleList(len, value);
DoubleNode reverse2 = reverseDoubleList(node2);
DoubleNode back2 = testReverseDoubleList(reverse2);
if (!checkDoubleListEqual(node2, back2)) {
System.out.println("oops!");
break;
}
}
System.out.println("finish!");
}
}
- 把给定的值都删除
比如给定一个链表头结点,删除该节点上值为3的节点,那么可能头结点就是3,存在删头部的情况,这里最终返回应该是删除所有值为3的节点之后的新的头部
package class02;
public class Code02_DeleteGivenValue {
public static class Node {
public int value;
public Node next;
public Node(int data) {
this.value = data;
}
}
// 先检查头部,寻找第一个不等于需要删除的值的节点,就是新的头部
public static Node removeValue(Node head, int num) {
while (head != null) {
if (head.value != num) {
break;
}
head = head.next;
}
// head来到 第一个不需要删的位置
Node pre = head;
Node cur = head;
while (cur != null) {
if (cur.value == num) {
pre.next = cur.next;
} else {
pre = cur;
}
cur = cur.next;
}
return head;
}
}
Tips: Java中也有可能产生内存泄漏,与CPP不同,CPP的内存泄漏有可能是我们开辟了内存空间忘记释放。而Java的内存泄漏大可能是程序中的变量的生存周期引起的,如果该程序是一个类似定时任务的7*24小时不间断运行,那么申请的变量(数据结构)就有可能不会被及时释放。如果不注意往里面添加一些不必要的变量,这些变量就是内存泄漏
1.2 栈、队列
- 逻辑概念
栈:数据先进后出,犹如弹夹
队列: 数据先进先出,排队
- 底层实现方式
双向链表实现
package class02;
import java.util.LinkedList;
import java.util.Queue;
import java.util.Stack;
public class Code03_DoubleEndsQueueToStackAndQueue {
public static class Node<T> {
public T value;
public Node<T> last;
public Node<T> next;
public Node(T data) {
value = data;
}
}
public static class DoubleEndsQueue<T> {
public Node<T> head;
public Node<T> tail;
// 从头部加节点
public void addFromHead(T value) {
Node<T> cur = new Node<T>(value);
if (head == null) {
head = cur;
tail = cur;
} else {
cur.next = head;
head.last = cur;
head = cur;
}
}
// 从尾部加节点
public void addFromBottom(T value) {
Node<T> cur = new Node<T>(value);
if (head == null) {
head = cur;
tail = cur;
} else {
cur.last = tail;
tail.next = cur;
tail = cur;
}
}
// 从头部弹出节点
public T popFromHead() {
if (head == null) {
return null;
}
Node<T> cur = head;
if (head == tail) {
head = null;
tail = null;
} else {
head = head.next;
cur.next = null;
head.last = null;
}
return cur.value;
}
// 从尾部弹出节点
public T popFromBottom() {
if (head == null) {
return null;
}
Node<T> cur = tail;
if (head == tail) {
head = null;
tail = null;
} else {
tail = tail.last;
tail.next = null;
cur.last = null;
}
return cur.value;
}
// 该双向链表结构是否为空
public boolean isEmpty() {
return head == null;
}
}
// 用上述双向链表结构实现栈
public static class MyStack<T> {
private DoubleEndsQueue<T> queue;
public MyStack() {
queue = new DoubleEndsQueue<T>();
}
public void push(T value) {
queue.addFromHead(value);
}
public T pop() {
return queue.popFromHead();
}
public boolean isEmpty() {
return queue.isEmpty();
}
}
// 用上述双向链表结构实现队列
public static class MyQueue<T> {
private DoubleEndsQueue<T> queue;
public MyQueue() {
queue = new DoubleEndsQueue<T>();
}
public void push(T value) {
queue.addFromHead(value);
}
public T poll() {
return queue.popFromBottom();
}
public boolean isEmpty() {
return queue.isEmpty();
}
}
public static boolean isEqual(Integer o1, Integer o2) {
if (o1 == null && o2 != null) {
return false;
}
if (o1 != null && o2 == null) {
return false;
}
if (o1 == null && o2 == null) {
return true;
}
return o1.equals(o2);
}
public static void main(String[] args) {
int oneTestDataNum = 100;
int value = 10000;
int testTimes = 100000;
for (int i = 0; i < testTimes; i++) {
MyStack<Integer> myStack = new MyStack<>();
MyQueue<Integer> myQueue = new MyQueue<>();
Stack<Integer> stack = new Stack<>();
Queue<Integer> queue = new LinkedList<>();
for (int j = 0; j < oneTestDataNum; j++) {
int nums = (int) (Math.random() * value);
if (stack.isEmpty()) {
myStack.push(nums);
stack.push(nums);
} else {
if (Math.random() < 0.5) {
myStack.push(nums);
stack.push(nums);
} else {
if (!isEqual(myStack.pop(), stack.pop())) {
System.out.println("oops!");
}
}
}
int numq = (int) (Math.random() * value);
if (stack.isEmpty()) {
myQueue.push(numq);
queue.offer(numq);
} else {
if (Math.random() < 0.5) {
myQueue.push(numq);
queue.offer(numq);
} else {
if (!isEqual(myQueue.poll(), queue.poll())) {
System.out.println("oops!");
}
}
}
}
}
System.out.println("finish!");
}
}
数组实现,对于栈特别简单,对于队列,如下
package class02;
public class Code04_RingArray {
public static class MyQueue {
// 数组结构
private int[] arr;
// 往当前队列添加数的下标位置
private int pushi;
// 当前队列需要出队列的位置
private int polli;
// 当前队列使用的空间大小
private int size;
// 数组最大大小,用户传入
private final int limit;
public MyQueue(int limit) {
arr = new int[limit];
pushi = 0;
polli = 0;
size = 0;
this.limit = limit;
}
public void push(int value) {
if (size == limit) {
throw new RuntimeException("栈满了,不能再加了");
}
size++;
arr[pushi] = value;
pushi = nextIndex(pushi);
}
public int pop() {
if (size == 0) {
throw new RuntimeException("栈空了,不能再拿了");
}
size--;
int ans = arr[polli];
polli = nextIndex(polli);
return ans;
}
public boolean isEmpty() {
return size == 0;
}
// 如果现在的下标是i,返回下一个位置,该实现可以实现环形的ringbuffer
private int nextIndex(int i) {
return i < limit - 1 ? i + 1 : 0;
}
}
}
1.3 栈、队列常见面试题
一、实现一个特殊的栈,在基本功能的基础上,再实现返回栈中最小元素的功能更
1、pop、push、getMin操作的时间复杂度都是O(1)
2、设计的栈类型可以使用现成的栈结构
思路:准备两个栈,一个data栈,一个min栈。数据压data栈,min栈对比min栈顶元素,谁小加谁。这样的话data栈和min栈是同步上升的,元素个数一样多,且min栈的栈顶,是data栈所有元素中最小的那个。数据弹出data栈,我们同步弹出min栈,保证个数相等,切min栈弹出的就是最小值
package class02;
import java.util.Stack;
public class Code05_GetMinStack {
public static class MyStack1 {
private Stack<Integer> stackData;
private Stack<Integer> stackMin;
public MyStack1() {
this.stackData = new Stack<Integer>();
this.stackMin = new Stack<Integer>();
}
public void push(int newNum) {
// 当前最小栈为空,直接压入
if (this.stackMin.isEmpty()) {
this.stackMin.push(newNum);
// 当前元素小于最小栈的栈顶,压入当前值
} else if (newNum <= this.getmin()) {
this.stackMin.push(newNum);
}
// 往数据栈中压入当前元素
this.stackData.push(newNum);
}
public int pop() {
if (this.stackData.isEmpty()) {
throw new RuntimeException("Your stack is empty.");
}
int value = this.stackData.pop();
if (value == this.getmin()) {
this.stackMin.pop();
}
return value;
}
public int getmin() {
if (this.stackMin.isEmpty()) {
throw new RuntimeException("Your stack is empty.");
}
return this.stackMin.peek();
}
}
public static class MyStack2 {
private Stack<Integer> stackData;
private Stack<Integer> stackMin;
public MyStack2() {
this.stackData = new Stack<Integer>();
this.stackMin = new Stack<Integer>();
}
public void push(int newNum) {
if (this.stackMin.isEmpty()) {
this.stackMin.push(newNum);
} else if (newNum < this.getmin()) {
this.stackMin.push(newNum);
} else {
int newMin = this.stackMin.peek();
this.stackMin.push(newMin);
}
this.stackData.push(newNum);
}
public int pop() {
if (this.stackData.isEmpty()) {
throw new RuntimeException("Your stack is empty.");
}
// 弹出操作,同步弹出,保证大小一致,只返回给用户data栈中的内容即可
this.stackMin.pop();
return this.stackData.pop();
}
public int getmin() {
if (this.stackMin.isEmpty()) {
throw new RuntimeException("Your stack is empty.");
}
return this.stackMin.peek();
}
}
public static void main(String[] args) {
MyStack1 stack1 = new MyStack1();
stack1.push(3);
System.out.println(stack1.getmin());
stack1.push(4);
System.out.println(stack1.getmin());
stack1.push(1);
System.out.println(stack1.getmin());
System.out.println(stack1.pop());
System.out.println(stack1.getmin());
System.out.println("=============");
MyStack1 stack2 = new MyStack1();
stack2.push(3);
System.out.println(stack2.getmin());
stack2.push(4);
System.out.println(stack2.getmin());
stack2.push(1);
System.out.println(stack2.getmin());
System.out.println(stack2.pop());
System.out.println(stack2.getmin());
}
}
二、如何用栈结构实现队列结构,如何用队列结构实现栈结构
这两种结构的应用实在太多,刷题时会大量见到
/**
* 两个栈实现队列
**/
package class02;
import java.util.Stack;
public class Code06_TwoStacksImplementQueue {
public static class TwoStacksQueue {
public Stack<Integer> stackPush;
public Stack<Integer> stackPop;
public TwoStacksQueue() {
stackPush = new Stack<Integer>();
stackPop = new Stack<Integer>();
}
// push栈向pop栈倒入数据
private void pushToPop() {
if (stackPop.empty()) {
while (!stackPush.empty()) {
stackPop.push(stackPush.pop());
}
}
}
public void add(int pushInt) {
stackPush.push(pushInt);
pushToPop();
}
public int poll() {
if (stackPop.empty() && stackPush.empty()) {
throw new RuntimeException("Queue is empty!");
}
pushToPop();
return stackPop.pop();
}
public int peek() {
if (stackPop.empty() && stackPush.empty()) {
throw new RuntimeException("Queue is empty!");
}
pushToPop();
return stackPop.peek();
}
}
public static void main(String[] args) {
TwoStacksQueue test = new TwoStacksQueue();
test.add(1);
test.add(2);
test.add(3);
System.out.println(test.peek());
System.out.println(test.poll());
System.out.println(test.peek());
System.out.println(test.poll());
System.out.println(test.peek());
System.out.println(test.poll());
}
}
/**
* 两个队列实现栈
**/
package class02;
import java.util.LinkedList;
import java.util.Queue;
import java.util.Stack;
public class Code07_TwoQueueImplementStack {
public static class TwoQueueStack<T> {
public Queue<T> queue;
public Queue<T> help;
public TwoQueueStack() {
queue = new LinkedList<>();
help = new LinkedList<>();
}
public void push(T value) {
queue.offer(value);
}
public T poll() {
while (queue.size() > 1) {
help.offer(queue.poll());
}
T ans = queue.poll();
Queue<T> tmp = queue;
queue = help;
help = tmp;
return ans;
}
public T peek() {
while (queue.size() > 1) {
help.offer(queue.poll());
}
T ans = queue.poll();
help.offer(ans);
Queue<T> tmp = queue;
queue = help;
help = tmp;
return ans;
}
public boolean isEmpty() {
return queue.isEmpty();
}
}
public static void main(String[] args) {
System.out.println("test begin");
TwoQueueStack<Integer> myStack = new TwoQueueStack<>();
Stack<Integer> test = new Stack<>();
int testTime = 1000000;
int max = 1000000;
for (int i = 0; i < testTime; i++) {
if (myStack.isEmpty()) {
if (!test.isEmpty()) {
System.out.println("Oops");
}
int num = (int) (Math.random() * max);
myStack.push(num);
test.push(num);
} else {
if (Math.random() < 0.25) {
int num = (int) (Math.random() * max);
myStack.push(num);
test.push(num);
} else if (Math.random() < 0.5) {
if (!myStack.peek().equals(test.peek())) {
System.out.println("Oops");
}
} else if (Math.random() < 0.75) {
if (!myStack.poll().equals(test.pop())) {
System.out.println("Oops");
}
} else {
if (myStack.isEmpty() != test.isEmpty()) {
System.out.println("Oops");
}
}
}
}
System.out.println("test finish!");
}
}
1.4 递归
1、从思想上理解递归
2、从实现角度出发理解递归
例子:
求数组arr[L…R]中的最大值,怎么用递归方法实现
1、 将[L…R]范围分成左右两半。左[L…Mid],右[Mid+1…R] 2、 左部分求最大值,右部分求最大值 3、[L…R]范围上的最大值,就是max{左部分最大值,右部分最大值}
==2步骤是个递归过程,当范围上只有一个数,就可以不用再递归了==
package class02;
public class Code08_GetMax {
// 求arr中的最大值
public static int getMax(int[] arr) {
return process(arr, 0, arr.length - 1);
}
// arr[L..R]范围上求最大值 L ... R N
public static int process(int[] arr, int L, int R) {
if (L == R) { // arr[L..R]范围上只有一个数,直接返回,base case
return arr[L];
}
int mid = L + ((R - L) >> 1); // 中点
// 左部分最大值
int leftMax = process(arr, L, mid);
// 右部分最大值
int rightMax = process(arr, mid + 1, R);
return Math.max(leftMax, rightMax);
}
}
递归在系统中是怎么实现的?递归实际上利用的是系统栈来实现的。保存当前调用现场,去执行子问题,子问题的返回作为现场的需要的参数填充,最终构建还原栈顶的现场,返回。所以递归行为不是玄学,任何递归都可以改为非递归实现,我们自己压栈用迭代等实现就行
1.4.1 递归行为的时间复杂度
对于满足
T(N) = aT(N/b) + O(N^d)
其中: a,b,d为常数
公式表示,子问题的规模是一致的,该子问题调用了a次,N/b代表子问题的规模,O(N^d)为除去递归调用剩余的时间复杂度。
比如上述问题的递归,[L…R]上有N个数,第一个子问题的规模是N/2,第二个子问题的规模也是N/2。子问题调用了2次。额为复杂度为O(1),那么公式为:
T(N) = 2T(N/2) + O(N^0)
结论:如果我们的递归满足这种公式,那么该递归的时间复杂度(Master公式)为
logb^a > d => O(N ^ (logb^a))
logb^a < d => O(N^d)
logb^a == d => O(N^d * logN)
那么上述问题的a=2, b=2,d=0,满足第一条,递归时间复杂度为:O(N)
1.5 哈希表HashMap、HashSet
Hash表的增删改查,在使用的时候,一律认为时间复杂度是O(1)的
在Java中,int double float基础类型,按值传递; Integer, Double, Float按引用传递的,比较包装类型的值是否相等,使用equals方法。
==注意:在Java底层,包装类如果范围比较小,底层仍然采用值传递,比如Integer如果范围在-128~127之间,是按值传递的==
==但是在Hash表中,即使是包装类型的key,我们也一律按值传递,例如Hash<Integer,String>如果我们put相同的key的值,那么不会产生两个值相等的key而是覆盖操作。但是Hash表并不是一直是按值传递的,只是针对包装类型,如果是我们自定义的引用类型,那么仍然按引用传递==
1.6 顺序表 TreeMap、TreeSet
顺序表比哈希表功能多,但是顺序表的很多操作时间复杂度是O(logN)
有序表的底层可以有很多结构实现,比如AVL树,SB树,红黑树,跳表。其中AVL,SB,红黑都是具备各自平衡性的搜索二叉树
由于平衡二叉树每时每刻都会维持自身的平衡,所以操作为O(logN)。暂时理解,后面会单独整理
由于满足去重排序功能来维持底层树的平衡,所以如果是基础类型和包装类型的key直接按值来做比较,但是如果我们的key是自己定义的类型,那么我们要自己制定比较规则(比较器),用来让底层的树保持比较后的平衡
package class02;
import java.util.HashMap;
import java.util.HashSet;
import java.util.TreeMap;
public class HashMapAndSortedMap {
public static class Node{
public int value;
public Node(int v) {
value = v;
}
}
public static void main(String[] args) {
// UnSortedMap
HashMap<Integer, String> map = new HashMap<>();
map.put(1000000, "我是1000000");
map.put(2, "我是2");
map.put(3, "我是3");
map.put(4, "我是4");
map.put(5, "我是5");
map.put(6, "我是6");
map.put(1000000, "我是1000001");
System.out.println(map.containsKey(1));
System.out.println(map.containsKey(10));
System.out.println(map.get(4));
System.out.println(map.get(10));
map.put(4, "他是4");
System.out.println(map.get(4));
map.remove(4);
System.out.println(map.get(4));
// key
HashSet<String> set = new HashSet<>();
set.add("abc");
set.contains("abc");
set.remove("abc");
// 哈希表,增、删、改、查,在使用时,O(1)
System.out.println("=====================");
int a = 100000;
int b = 100000;
System.out.println(a == b);
Integer c = 100000;
Integer d = 100000;
System.out.println(c.equals(d));
Integer e = 127; // - 128 ~ 127
Integer f = 127;
System.out.println(e == f);
HashMap<Node, String> map2 = new HashMap<>();
Node node1 = new Node(1);
Node node2 = node1;
map2.put(node1, "我是node1");
map2.put(node2, "我是node1");
System.out.println(map2.size());
System.out.println("======================");
TreeMap<Integer, String> treeMap = new TreeMap<>();
treeMap.put(3, "我是3");
treeMap.put(4, "我是4");
treeMap.put(8, "我是8");
treeMap.put(5, "我是5");
treeMap.put(7, "我是7");
treeMap.put(1, "我是1");
treeMap.put(2, "我是2");
System.out.println(treeMap.containsKey(1));
System.out.println(treeMap.containsKey(10));
System.out.println(treeMap.get(4));
System.out.println(treeMap.get(10));
treeMap.put(4, "他是4");
System.out.println(treeMap.get(4));
treeMap.remove(4);
System.out.println(treeMap.get(4));
System.out.println(treeMap.firstKey());
System.out.println(treeMap.lastKey());
// <= 4
System.out.println(treeMap.floorKey(4));
// >= 4
System.out.println(treeMap.ceilingKey(4));
// O(logN)
}
}