我的解法
/**
* 2ms 21.07
* 41M 33.17
* 其实是广度优先搜索
* 切入点是一层一层看,根据当前层构建下一层。
* */
public static int maxDepth(TreeNode root) {
if (null == root) {
return 0;
}
int deep = 1;
List<TreeNode> curLevel = new ArrayList<>();
curLevel.add(root);
List<TreeNode> nextLevel = null;
while (hasNextLevel(curLevel)) {
nextLevel = new ArrayList<>();
for (TreeNode cur : curLevel) {
if (null != cur) {
nextLevel.add(cur.left);
nextLevel.add(cur.right);
}
}
curLevel = nextLevel;
deep ++;
}
return deep;
}
官方-广度优先搜索
/**
* 官方的广度优先搜索
* 运用了Queue @see MyQueue
* */
public static int maxDepth_l1(TreeNode root) {
if (null == root) {
return 0;
}
Queue<TreeNode> curLevel = new LinkedList<>();
curLevel.offer(root);
int deep = 0;
while (!curLevel.isEmpty()) {
int size = curLevel.size();//TODO 固定poll次数
while (size > 0) {
TreeNode curNode = curLevel.poll();
if (curNode == null) {
continue;
}
if (curNode.left != null) {
curLevel.offer(curNode.left);
}
if (curNode.right != null) {
curLevel.offer(curNode.right);
}
size --;
}
// Queue<TreeNode> curLevelTemp = curLevel;
// for (TreeNode cur : curLevelTemp) {java.util.ConcurrentModificationException
// if (cur == null) {
// continue;
// }
// if (cur.left != null) {
// curLevel.offer(cur.left);
// }
// if (cur.right != null) {
// curLevel.offer(cur.right);
// }
// curLevel.poll();
// }
deep ++;
}
return deep;
}
官方-深度优先搜索
/**
* 官方深度优先搜索
* 运用递归
* 如果我们知道了左子树和右子树的最大深度 l 和 r,那么该二叉树的最大深度即为
*
* max(l,r) + 1
*
* 而左子树和右子树的最大深度又可以以同样的方式进行计算。
* */
public static int maxDepth_l2(TreeNode root) {
// 3
// / \
// 9 20
// / \
// 15 7
if (root == null) {
return 0;
} else {
int maxLeft = maxDepth_l2(root.left);//1
int maxRight = maxDepth_l2(root.right);
return Math.max(maxLeft, maxRight) + 1;
}
}
拓展-DFS&BFS
深度优先搜索算法DFS
1、算法步骤:
——访问顶点v;
——依次从v的未被访问的邻接点出发,对图进行深度优先遍历;直至图中和v有路径相通的顶点都被访问;
——若此时图中尚有顶点未被访问,则从一个未被访问的顶点出发,重新进行深度优先遍历,直到图中所有顶点均被访问过为止
2、DFS属于盲目搜索。深度优先搜索是图论中的经典算法,利用深度优先搜索算法可以产生目标图的相应拓扑排序表,利用拓扑排序表可以方便的解决很多相关的图论问题,如最大路径问题等等。一般用堆数据结构来辅助实现DFS算法。
广度优先搜索算法BFS
1、算法步骤:
——首先将根节点放入队列中;
——从队列中取出第一个节点,并检验它是否为目标。如果找到目标,则结束搜寻并回传结果。否则将它所有尚未检验过的直接子节点加入队列中;
——若队列为空,表示整张图都检查过了——亦即图中没有欲搜寻的目标。结束搜寻并回传“找不到目标”;
——重复步骤2
2、BFS同样属于盲目搜索。简单的说,BFS是从根节点开始,沿着树(图)的宽度遍历树(图)的节点。如果所有节点均被访问,则算法中止。一般用队列数据结构来辅助实现BFS算法。
拓展API-Queue
特性
1、线性表/线性队列
2、FIFO
3、表头出,表尾加
implements
Queue<String> queue_ArrayBlockingQueue = new ArrayBlockingQueue<>(10);
Queue<String> queue_ArrayDeque = new ArrayDeque<>();
Queue<String> queue_ConcurrentLinkedDeque = new ConcurrentLinkedDeque<>();
Queue<String> queue_LinkedBlockingDeque = new LinkedBlockingDeque<>();
Queue<String> queue_LinkedList = new LinkedList<>();
methods
method | 特性 |
---|---|
add | 队列尾插入,队列满时抛出IllegalStateException |
offer | 队列尾插入,队列满时返回false |
remove | 移除并返回头部元素,队列空时,抛出NoSuchElementException |
pool | 移除并返回头部元素,队列空时,返回null |
element | 查看队列头元素,队列空时,抛出NoSuchElementException |
peek | 查看队列头元素,队列空时,返回null |
code
public static void main(String[] args) {
Queue<String> queue_ArrayBlockingQueue = new ArrayBlockingQueue<>(10);
Queue<String> queue_ArrayDeque = new ArrayDeque<>();
Queue<String> queue_ConcurrentLinkedDeque = new ConcurrentLinkedDeque<>();
Queue<String> queue_LinkedBlockingDeque = new LinkedBlockingDeque<>();
Queue<String> queue_LinkedList = new LinkedList<>();
System.out.println("++++++++++++++++++++++++++");
//add 表尾插 ——>[1,2,3]
//队列满时,抛出IllegalStateException
Queue<Integer> queue_add = new ArrayBlockingQueue<>(2);
queue_add.add(1);
queue_add.add(2);
try {
queue_add.add(3);
} catch (Exception e) {
System.out.println(e);
}
for (Integer q : queue_add) {
System.out.println(q);
}
try {
Queue<Integer> queue_LinkedList_add = new LinkedList<>();
for (int i = 0; i < 1000000; i++) {
queue_LinkedList_add.add(i);
}
System.out.println(queue_LinkedList_add.size());
} catch (Exception e) {
System.out.println(e);
}
System.out.println("++++++++++++++++++++++++++");
//offer 表尾插 ——>[1,2,3,4,5,6]
//队列满时,返回false
Queue<Integer> queue_offer = new ArrayBlockingQueue<>(4);
System.out.println(queue_offer.offer(1));
System.out.println(queue_offer.offer(2));
System.out.println(queue_offer.offer(3));
System.out.println(queue_offer.offer(4));
try {
System.out.println(queue_offer.offer(5));
} catch (Exception e) {
System.out.println(e);
}
for (Integer q : queue_offer) {
System.out.println(q);
}
try {
Queue<Integer> queue_LinkedList_offer = new LinkedList<>();
for (int i = 0; i < 1000000; i++) {
if (!queue_LinkedList_offer.add(i)) {
System.out.println("offer false");
}
}
System.out.println(queue_LinkedList_offer.size());
} catch (Exception e) {
System.out.println(e);
}
System.out.println("++++++++++++++++++++++++++");
//remove 移除并返回队列头部元素
//队列空时,抛出NoSuchElementException
Queue<Integer> queue_LinkedList_remove = new LinkedList<>();
for (int i = 0; i < 6; i++) {
queue_LinkedList_remove.offer(i);
}
for (int i = 0; i < 7; i++) {
try {
System.out.println(queue_LinkedList_remove.remove());
} catch (Exception e) {
System.out.println(i + " e = " + e);
}
}
System.out.println("++++++++++++++++++++++++++");
//poll 移除并返回队列头部元素
//队列空时,返回null
Queue<Integer> queue_poll = new LinkedList<>();
for (int i = 0; i < 10; i++) {
queue_poll.offer(i);
}
for (int i = 0; i < 11; i++) {
try {
System.out.println(queue_poll.poll());
} catch (Exception e) {
System.out.println(i + " e = " + e);
}
}
System.out.println("++++++++++++++++++++++++++");
//element 返回队列头部元素
//队列空时,抛出NoSuchElementException
Queue<Integer> queue_element = new LinkedList<>();
queue_element.offer(1);
queue_element.offer(2);
System.out.println(queue_element.element());
System.out.println(queue_element.poll());
System.out.println(queue_element.element());
System.out.println(queue_element.poll());
try {
System.out.println(queue_element.element());
} catch (Exception e) {
System.out.println(e);
}
System.out.println("++++++++++++++++++++++++++");
//peek 返回队列头部元素
//队列空时,返回null
Queue<Integer> queue_peek = new LinkedList<>();
for (int i = 0; i < 3; i++) {
queue_peek.offer(i);
}
for (int i = 0; i < 4; i++) {
try {
System.out.println(queue_peek.peek());
System.out.println(queue_peek.poll());
} catch (Exception e) {
System.out.println(i + " e = " + e);
}
}
System.out.println("++++++++++++++++++++++++++");
}
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