本文图片来源:代码随想录
这一部分有10道题,全部可以套用相同的层序遍历方法,但是需要在每一层进行处理或者修改。
层序遍历一个二叉树。就是从左到右一层一层的去遍历二叉树。这种遍历的方式和我们之前讲过的都不太一样。
需要借用一个辅助数据结构即队列来实现,队列先进先出,符合一层一层遍历的逻辑,而用栈先进后出适合模拟深度优先遍历也就是递归的逻辑。
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def levelOrder(self, root):
"""
:type root: TreeNode
:rtype: List[List[int]]
"""
# 判断是不是空二叉树
if not root:
return []
# 先把根节点放入队列
queue = collections.deque([root])
res = []
# 然后逐层放入队列再弹出
while queue:
# 每层储存在level数组里
level = []
for _ in range(len(queue)):
# 弹出根节点,存值
cur = queue.popleft()
level.append(cur.val)
# 队列添加左右节点
if cur.left:
queue.append(cur.left)
if cur.right:
queue.append(cur.right)
# 每层走完
res.append(level)
return res
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def levelOrder(self, root):
"""
:type root: TreeNode
:rtype: List[List[int]]
"""
# 递归法
levels = []
self.helper(root, 0, levels)
return levels
def helper(self, node, level, levels):
# 退出条件
if not node:
return
# 当层数和levels的长度相等的时候,也就是满二叉树的时候,需要再加上一个新的层
if len(levels) == level:
levels.append([])
levels[level].append(node.val)
self.helper(node.left, level + 1, levels)
self.helper(node.right, level + 1, levels)
只需要倒序输出即可,代码和上面102保持一致。
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def levelOrderBottom(self, root):
"""
:type root: TreeNode
:rtype: List[List[int]]
"""
if not root:
return []
queue = collections.deque([root])
res = []
while queue:
levels = []
for _ in range(len(queue)):
cur = queue.popleft()
levels.append(cur.val)
if cur.left:
queue.append(cur.left)
if cur.right:
queue.append(cur.right)
res.append(levels)
# 反向输出即可
return res[::-1]
只需要每次把levels最后一个也就是最右侧的节点赋值给res即可。
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def rightSideView(self, root):
"""
:type root: TreeNode
:rtype: List[int]
"""
if not root:
return []
res = []
queue = collections.deque([root])
while queue:
levels = []
for _ in range(len(queue)):
cur = queue.popleft()
levels.append(cur.val)
if cur.left:
queue.append(cur.left)
if cur.right:
queue.append(cur.right)
# 只需要每次把levels最后一个也就是左右侧的节点赋值给res即可
res.append(levels[-1])
return res
只需要在求出每层的基础上求平均即可。
注意作除法之前需要类型转换为float。
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def averageOfLevels(self, root):
"""
:type root: TreeNode
:rtype: List[float]
"""
if not root:
return []
res = []
queue = collections.deque([root])
while queue:
levels = []
for _ in range(len(queue)):
cur = queue.popleft()
levels.append(cur.val)
if cur.left:
queue.append(cur.left)
if cur.right:
queue.append(cur.right)
# 需要强制类型转换为浮点数
sum_ = float(sum(levels))
len_ = float(len(levels))
avg = sum_ / len_
res.append(avg)
return res
只需要把children列表里的元素按照顺序取出即可。
"""
# Definition for a Node.
class Node(object):
def __init__(self, val=None, children=None):
self.val = val
self.children = children
"""
class Solution(object):
def levelOrder(self, root):
"""
:type root: Node
:rtype: List[List[int]]
"""
if not root:
return []
res = []
queue = collections.deque([root])
while queue:
levels = []
for _ in range(len(queue)):
cur = queue.popleft()
levels.append(cur.val)
# children按列表储存child
for child in cur.children:
queue.append(child)
res.append(levels)
return res
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def largestValues(self, root):
"""
:type root: TreeNode
:rtype: List[int]
"""
if not root:
return []
res = []
queue = collections.deque([root])
while queue:
level = []
for _ in range(len(queue)):
cur = queue.popleft()
level.append(cur.val)
if cur.left:
queue.append(cur.left)
if cur.right:
queue.append(cur.right)
# 每层最大值赋值给res
res.append(max(level))
return res
"""
# Definition for a Node.
class Node(object):
def __init__(self, val=0, left=None, right=None, next=None):
self.val = val
self.left = left
self.right = right
self.next = next
"""
class Solution(object):
def connect(self, root):
"""
:type root: Node
:rtype: Node
"""
if not root:
return root # 注意这一题的返回值是根节点地址,相当于只在原链表的基础上做修改
queue = collections.deque([root])
while queue:
level = []
for _ in range(len(queue)):
cur = queue.popleft()
# queue的下一个值其实就是next需要指向的地址
# if _ < len(queue) - 1:
# cur.next = queue[0]
level.append(cur)
if cur.left:
queue.append(cur.left)
if cur.right:
queue.append(cur.right)
# 在每一层遍历
for i in range(len(level) - 1):
level[i].next = level[i + 1]
return root
"""
# Definition for a Node.
class Node(object):
def __init__(self, val=0, left=None, right=None, next=None):
self.val = val
self.left = left
self.right = right
self.next = next
"""
class Solution(object):
def connect(self, root):
"""
:type root: Node
:rtype: Node
"""
if not root:
return root # 注意这一题的返回值是根节点地址,相当于只在原链表的基础上做修改
queue = collections.deque([root])
while queue:
level = []
for _ in range(len(queue)):
cur = queue.popleft()
# queue的下一个值其实就是next需要指向的地址
# if _ < len(queue) - 1:
# cur.next = queue[0]
level.append(cur)
if cur.left:
queue.append(cur.left)
if cur.right:
queue.append(cur.right)
# 在每一层遍历
for i in range(len(level) - 1):
level[i].next = level[i + 1]
return root
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def maxDepth(self, root):
"""
:type root: TreeNode
:rtype: int
"""
# 广度优先
if not root:
return 0
cnt = 0
queue = collections.deque([root])
while queue:
# 构造一层的队列
for _ in range(len(queue)):
cur = queue.popleft()
if cur.left:
queue.append(cur.left)
if cur.right:
queue.append(cur.right)
# 构造完加一
cnt += 1
return cnt
# 递归法
if not root:
return 0
else:
left_height = self.maxDepth(root.left)
right_height = self.maxDepth(root.right)
return max(left_height, right_height) + 1
# 精简递归
return 0 if not root else max(self.maxDepth(root.left), self.maxDepth(root.right)) + 1
找到第一个叶子节点就退出while。这里用到了flag标记。
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def minDepth(self, root):
"""
:type root: TreeNode
:rtype: int
"""
# 广度优先
if not root:
return 0
cnt = 0
queue = collections.deque([root])
flag = 0
while queue:
# 构造一层的队列
for _ in range(len(queue)):
cur = queue.popleft()
if cur.left:
queue.append(cur.left)
if cur.right:
queue.append(cur.right)
if not (cur.left or cur.right):
flag = 1
cnt += 1
# 构造完加一
if flag:
break
return cnt
omg!baseline只需要上面的套模板即可实现!!这不神奇吗??!!
根据顺序做具体的微调
只需要在原来代码的基础上加上子节点的交换过程即可。
递归法里的写法:
root.left, root.right = root.right, root.left
?迭代法里的写法:
node = stack.pop()
node.left, node.right = node.right, node.left
需要注意中序在执行完交换之后原来的左节点是右节点,但是right指针指向的是原left节点,所以需要在两次递归的时候都要指向left。
self.invertTree(root.left)
root.left, root.right = root.right, root.left
selfinvertTree(root.left)
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def invertTree(self, root):
"""
:type root: TreeNode
:rtype: TreeNode
"""
# 层序遍历
if not root:
return root
queue = collections.deque([root])
while queue:
for _ in range(len(queue)):
node = queue.popleft()
# 对于取出队列里的每一个节点交换左右子节点
node.left, node.right = node.right, node.left
if node.left:
queue.append(node.left)
if node.right:
queue.append(node.right)
return root
?思路是分别比较两个子树的内侧和外侧,然后将结果取交集返回中间节点。所以确定便利的顺序一定是后序遍历(最后一个确定中间节点)。
因为要遍历两棵树而且要比较内侧和外侧节点,所以准确的来说是一个树的遍历顺序是左右中,一个树的遍历顺序是右左中。
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def isSymmetric(self, root):
"""
:type root: TreeNode
:rtype: bool
"""
# 递归法
if not root:
return True
return self.compare(root.left, root.right)
def compare(self, left, right):
"""
左----右----result
空 空 True
空 有 False
有 空 False
有 != 有 False
有 == 有 True
"""
if left == None and right == None:
return True
elif left == None and right != None:
return False
elif left != None and right == None:
return False
elif left.val != right.val:
return False
else: # 此时已经说明left == right,需要再判断他们的子节点
outside = self.compare(left.left, right.right) # 外侧子节点
inside = self.compare(left.right, right.left) # 内侧子节点
is_same = outside and inside # 求交集
return is_same
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def isSymmetric(self, root):
"""
:type root: TreeNode
:rtype: bool
"""
# 层次法
queue = collections.deque([root])
while queue:
level_val = [] # 只需要保存值
for _ in range(len(queue)):
node = queue.popleft()
if node:
level_val.append(node.val)
queue.append(node.left)
queue.append(node.right)
else: # 空节点赋值为None
level_val.append(None)
if level_val != level_val[::-1]: # 倒序比较数组
return False
return True
关键:成对取出外侧和内侧节点进行比较。
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def isSymmetric(self, root):
"""
:type root: TreeNode
:rtype: bool
"""
# 栈或队列(队列只要把栈改成队列,pop搞成popleft就可以了)
if not root:
return True
stack = []
stack.append(root.left)
stack.append(root.right)
while stack:
left = stack.pop()
right = stack.pop()
if left == None and right == None:
continue # 注意在while里需要continue
if left == None or right == None or left.val != right.val:
return False
stack.append(left.left)
stack.append(right.right)
stack.append(left.right)
stack.append(right.left)
return True
两个树,如果采用层序遍历的方法最好把重复部分封装成一个整体的函数。
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def isSameTree(self, p, q):
"""
:type p: TreeNode
:type q: TreeNode
:rtype: bool
"""
# 层次法
queue1 = collections.deque([p])
queue2 = collections.deque([q])
while queue1 and queue2:
if len(queue1) != len(queue2):
return False
level1 = self.get_level(queue1)
level2 = self.get_level(queue2)
if level1 != level2:
return False
return True
def get_level(self, queue):
level_val = []
for _ in range(len(queue)):
node = queue.popleft()
if node:
level_val.append(node.val)
queue.append(node.left)
queue.append(node.right)
else:
level_val.append(None)
return level_val
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def isSubtree(self, root, subRoot):
"""
:type root: TreeNode
:type subRoot: TreeNode
:rtype: bool
"""
# 深度遍历+递归
if not root:
return False
return self.check_is_same(root, subRoot) or self.isSubtree(root.left, subRoot) or self.isSubtree(root.right, subRoot)
def check_is_same(self, root, subroot):
if root == None and subroot == None:
return True
elif root == None or subroot == None or root.val != subroot.val:
return False
return self.check_is_same(root.left, subroot.left) and self.check_is_same(root.right, subroot.right)
第15天完结🎉