一、获取二叉树的深度
就是二叉树最后的层次,如下图:
实现代码:
代码如下:
def getheight(self):
''' 获取二叉树深度 '''
return self.__get_tree_height(self.root)
def __get_tree_height(self, root):
if root is 0:
return 0
if root.left is 0 and root.right is 0:
return 1
else:
left = self.__get_tree_height(root.left)
right = self.__get_tree_height(root.right)
if left return right + 1
else:
return left + 1
二、叶子的统计
叶子就是二叉树的节点的 left 指针和 right 指针分别指向空的节点
代码如下:
def getleafcount(self):
''' 获取二叉树叶子数 '''
return self.__count_leaf_node(self.root)
def __count_leaf_node(self, root):
res = 0
if root is 0:
return res
if root.left is 0 and root.right is 0:
res += 1
return res
if root.left is not 0:
res += self.__count_leaf_node(root.left)
if root.right is not 0:
res += self.__count_leaf_node(root.right)
return res
三、统计叶子的分支节点
与叶子节点相对的其他节点 left 和 right 的指针指向其他节点
代码如下:
def getbranchcount(self):
''' 获取二叉树分支节点数 '''
return self.__get_branch_node(self.root)
def __get_branch_node(self, root):
if root is 0:
return 0
if root.left is 0 and root.right is 0:
return 0
else:
return 1 + self.__get_branch_node(root.left) + self.__get_branch_node(root.right)
四、二叉树左右树互换
代码如下:
def replacelem(self):
''' 二叉树所有结点的左右子树相互交换 '''
self.__replace_element(self.root)
def __replace_element(self, root):
if root is 0:
return
root.left, root.right = root.right, root.left
self.__replace_element(root.left)
self.__replace_element(root.right)
这些方法和操作,都是运用递归。其实二叉树的定义也是一种递归。附上最后的完整代码:
代码如下:
# -*- coding: utf - 8 - *-
class TreeNode(object):
def __init__(self, left=0, right=0, data=0):
self.left = left
self.right = right
self.data = data
class BinaryTree(object):
def __init__(self, root=0):
self.root = root
def is_empty(self):
if self.root is 0:
return True
else:
return False
def create(self):
temp = input('enter a value:')
if temp is '#':
return 0
treenode = TreeNode(data=temp)
if self.root is 0:
self.root = treenode
treenode.left = self.create()
treenode.right = self.create()
def preorder(self, treenode):
'前序(pre-order,NLR)遍历'
if treenode is 0:
return
print treenode.data
self.preorder(treenode.left)
self.preorder(treenode.right)
def inorder(self, treenode):
'中序(in-order,LNR'
if treenode is 0:
return
self.inorder(treenode.left)
print treenode.data
self.inorder(treenode.right)
def postorder(self, treenode):
'后序(post-order,LRN)遍历'
if treenode is 0:
return
self.postorder(treenode.left)
self.postorder(treenode.right)
print treenode.data
def preorders(self, treenode):
'前序(pre-order,NLR)非递归遍历'
stack = []
while treenode or stack:
if treenode is not 0:
print treenode.data
stack.append(treenode)
treenode = treenode.left
else:
treenode = stack.pop()
treenode = treenode.right
def inorders(self, treenode):
'中序(in-order,LNR) 非递归遍历'
stack = []
while treenode or stack:
if treenode:
stack.append(treenode)
treenode = treenode.left
else:
treenode = stack.pop()
print treenode.data
treenode = treenode.right
def postorders(self, treenode):
'后序(post-order,LRN)非递归遍历'
stack = []
pre = 0
while treenode or stack:
if treenode:
stack.append(treenode)
treenode = treenode.left
elif stack[-1].right != pre:
treenode = stack[-1].right
pre = 0
else:
pre = stack.pop()
print pre.data
# def postorders(self, treenode):
# '后序(post-order,LRN)非递归遍历'
# stack = []
# queue = []
# queue.append(treenode)
# while queue:
# treenode = queue.pop()
# if treenode.left:
# queue.append(treenode.left)
# if treenode.right:
# queue.append(treenode.right)
# stack.append(treenode)
# while stack:
# print stack.pop().data
def levelorders(self, treenode):
'层序(post-order,LRN)非递归遍历'
from collections import deque
if not treenode:
return
q = deque([treenode])
while q:
treenode = q.popleft()
print treenode.data
if treenode.left:
q.append(treenode.left)
if treenode.right:
q.append(treenode.right)
def getheight(self):
''' 获取二叉树深度 '''
return self.__get_tree_height(self.root)
def __get_tree_height(self, root):
if root is 0:
return 0
if root.left is 0 and root.right is 0:
return 1
else:
left = self.__get_tree_height(root.left)
right = self.__get_tree_height(root.right)
if left return right + 1
else:
return left + 1
def getleafcount(self):
''' 获取二叉树叶子数 '''
return self.__count_leaf_node(self.root)
def __count_leaf_node(self, root):
res = 0
if root is 0:
return res
if root.left is 0 and root.right is 0:
res += 1
return res
if root.left is not 0:
res += self.__count_leaf_node(root.left)
if root.right is not 0:
res += self.__count_leaf_node(root.right)
return res
def getbranchcount(self):
''' 获取二叉树分支节点数 '''
return self.__get_branch_node(self.root)
def __get_branch_node(self, root):
if root is 0:
return 0
if root.left is 0 and root.right is 0:
return 0
else:
return 1 + self.__get_branch_node(root.left) + self.__get_branch_node(root.right)
def replacelem(self):
''' 二叉树所有结点的左右子树相互交换 '''
self.__replace_element(self.root)
def __replace_element(self, root):
if root is 0:
return
root.left, root.right = root.right, root.left
self.__replace_element(root.left)
self.__replace_element(root.right)
node1 = TreeNode(data=1)
node2 = TreeNode(node1, 0, 2)
node3 = TreeNode(data=3)
node4 = TreeNode(data=4)
node5 = TreeNode(node3, node4, 5)
node6 = TreeNode(node2, node5, 6)
node7 = TreeNode(node6, 0, 7)
node8 = TreeNode(data=8)
root = TreeNode(node7, node8, 'root')
bt = BinaryTree(root)
print u'''
生成的二叉树
------------------------
root
7 8
6
2 5
1 3 4
-------------------------
'''

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

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Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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