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Format python codes with black. (#453)

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Yudong Jin 2023-04-09 05:05:35 +08:00 committed by GitHub
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43
codes/python/chapter_array_and_linkedlist/array.py
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@ -6,18 +6,20 @@ Author: Krahets (krahets@163.com)
import random
def random_access(nums: list[int]) -> int:
""" 随机访问元素 """
"""随机访问元素"""
# 在区间 [0, len(nums)-1] 中随机抽取一个数字
random_index = random.randint(0, len(nums) - 1)
# 获取并返回随机元素
random_num = nums[random_index]
return random_num
# 请注意，Python 的 list 是动态数组，可以直接扩展
# 为了方便学习，本函数将 list 看作是长度不可变的数组
def extend(nums: list[int], enlarge: int) -> list[int]:
""" 扩展数组长度 """
"""扩展数组长度"""
# 初始化一个扩展长度后的数组
res = [0] * (len(nums) + enlarge)
# 将原数组中的所有元素复制到新数组
@ -26,22 +28,25 @@ def extend(nums: list[int], enlarge: int) -> list[int]:
# 返回扩展后的新数组
return res
def insert(nums: list[int], num: int, index: int) -> None:
""" 在数组的索引 index 处插入元素 num """
"""在数组的索引 index 处插入元素 num"""
# 把索引 index 以及之后的所有元素向后移动一位
for i in range(len(nums) - 1, index, -1):
nums[i] = nums[i - 1]
# 将 num 赋给 index 处元素
nums[index] = num
def remove(nums: list[int], index: int) -> None:
""" 删除索引 index 处元素 """
"""删除索引 index 处元素"""
# 把索引 index 之后的所有元素向前移动一位
for i in range(index, len(nums) - 1):
nums[i] = nums[i + 1]
def traverse(nums: list[int]) -> None:
""" 遍历数组 """
"""遍历数组"""
count = 0
# 通过索引遍历数组
for i in range(len(nums)):
@ -50,40 +55,42 @@ def traverse(nums: list[int]) -> None:
for num in nums:
count += 1
def find(nums: list[int], target: int) -> int:
""" 在数组中查找指定元素 """
"""在数组中查找指定元素"""
for i in range(len(nums)):
if nums[i] == target:
return i
return -1
""" Driver Code """
if __name__ == "__main__":
""" 初始化数组 """
# 初始化数组
arr: list[int] = [0] * 5
print("数组 arr =", arr)
nums: list[int] = [1, 3, 2, 5, 4]
print("数组 nums =", nums)
""" 随机访问 """
# 随机访问
random_num: int = random_access(nums)
print("在 nums 中获取随机元素", random_num)
""" 长度扩展 """
# 长度扩展
nums: list[int] = extend(nums, 3)
print("将数组长度扩展至 8 ，得到 nums =", nums)
""" 插入元素 """
# 插入元素
insert(nums, 6, 3)
print("在索引 3 处插入数字 6 ，得到 nums =", nums)
""" 删除元素 """
# 删除元素
remove(nums, 2)
print("删除索引 2 处的元素，得到 nums =", nums)
""" 遍历数组 """
# 遍历数组
traverse(nums)
""" 查找元素 """
# 查找元素
index: int = find(nums, 3)
print("在 nums 中查找元素 3 ，得到索引 =", index)

25
codes/python/chapter_array_and_linkedlist/linked_list.py
View File

@ -5,17 +5,20 @@ Author: Krahets (krahets@163.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
def insert(n0: ListNode, P: ListNode) -> None:
""" 在链表的节点 n0 之后插入节点 P """
"""在链表的节点 n0 之后插入节点 P"""
n1 = n0.next
P.next = n1
n0.next = P
def remove(n0: ListNode) -> None:
""" 删除链表的节点 n0 之后的首个节点 """
"""删除链表的节点 n0 之后的首个节点"""
if not n0.next:
return
# n0 -> P -> n1
@ -23,16 +26,18 @@ def remove(n0: ListNode) -> None:
n1 = P.next
n0.next = n1
def access(head: ListNode, index: int) -> ListNode | None:
""" 访问链表中索引为 index 的节点 """
"""访问链表中索引为 index 的节点"""
for _ in range(index):
if not head:
return None
head = head.next
return head
def find(head: ListNode, target: int) -> int:
""" 在链表中查找值为 target 的首个节点 """
"""在链表中查找值为 target 的首个节点"""
index = 0
while head:
if head.val == target:
@ -44,8 +49,8 @@ def find(head: ListNode, target: int) -> int:
""" Driver Code """
if __name__ == "__main__":
""" 初始化链表 """
# 初始化各个节点
# 初始化链表
# 初始化各个节点
n0 = ListNode(1)
n1 = ListNode(3)
n2 = ListNode(2)
@ -59,20 +64,20 @@ if __name__ == "__main__":
print("初始化的链表为")
print_linked_list(n0)
""" 插入节点 """
# 插入节点
insert(n0, ListNode(0))
print("插入节点后的链表为")
print_linked_list(n0)
""" 删除节点 """
# 删除节点
remove(n0)
print("删除节点后的链表为")
print_linked_list(n0)
""" 访问节点 """
# 访问节点
node: ListNode = access(n0, 3)
print("链表中索引 3 处的节点的值 = {}".format(node.val))
""" 查找节点 """
# 查找节点
index: int = find(n0, 2)
print("链表中值为 2 的节点的索引 = {}".format(index))

22
codes/python/chapter_array_and_linkedlist/list.py
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@ -6,23 +6,23 @@ Author: Krahets (krahets@163.com)
""" Driver Code """
if __name__ == "__main__":
""" 初始化列表 """
# 初始化列表
arr: list[int] = [1, 3, 2, 5, 4]
print("列表 arr =", arr)
""" 访问元素 """
# 访问元素
num: int = arr[1]
print("访问索引 1 处的元素，得到 num =", num)
""" 更新元素 """
# 更新元素
arr[1] = 0
print("将索引 1 处的元素更新为 0 ，得到 arr =", arr)
""" 清空列表 """
# 清空列表
arr.clear()
print("清空列表后 arr =", arr)
""" 尾部添加元素 """
# 尾部添加元素
arr.append(1)
arr.append(3)
arr.append(2)
@ -30,29 +30,29 @@ if __name__ == "__main__":
arr.append(4)
print("添加元素后 arr =", arr)
""" 中间插入元素 """
# 中间插入元素
arr.insert(3, 6)
print("在索引 3 处插入数字 6 ，得到 arr =", arr)
""" 删除元素 """
# 删除元素
arr.pop(3)
print("删除索引 3 处的元素，得到 arr =", arr)
""" 通过索引遍历列表 """
# 通过索引遍历列表
count: int = 0
for i in range(len(arr)):
count += 1
""" 直接遍历列表元素 """
# 直接遍历列表元素
count: int = 0
for n in arr:
count += 1
""" 拼接两个列表 """
# 拼接两个列表
arr1: list[int] = [6, 8, 7, 10, 9]
arr += arr1
print("将列表 arr1 拼接到 arr 之后，得到 arr =", arr)
""" 排序列表 """
# 排序列表
arr.sort()
print("排序列表后 arr =", arr)

64
codes/python/chapter_array_and_linkedlist/my_list.py
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@ -4,36 +4,38 @@ Created Time: 2022-11-25
Author: Krahets (krahets@163.com)
"""
class MyList:
""" 列表类简易实现 """
"""列表类简易实现"""
def __init__(self):
""" 构造方法 """
self.__capacity: int = 10 # 列表容量
"""构造方法"""
self.__capacity: int = 10 # 列表容量
self.__nums: my_list[int] = [0] * self.__capacity # 数组（存储列表元素）
self.__size: int = 0 # 列表长度（即当前元素数量）
self.__extend_ratio: int = 2 # 每次列表扩容的倍数
self.__size: int = 0 # 列表长度（即当前元素数量）
self.__extend_ratio: int = 2 # 每次列表扩容的倍数
def size(self) -> int:
""" 获取列表长度（即当前元素数量） """
"""获取列表长度（即当前元素数量）"""
return self.__size
def capacity(self) -> int:
""" 获取列表容量 """
"""获取列表容量"""
return self.__capacity
def get(self, index: int) -> int:
""" 访问元素 """
"""访问元素"""
# 索引如果越界则抛出异常，下同
assert index >= 0 and index < self.__size, "索引越界"
return self.__nums[index]
def set(self, num: int, index: int) -> None:
""" 更新元素 """
"""更新元素"""
assert index >= 0 and index < self.__size, "索引越界"
self.__nums[index] = num
def add(self, num: int) -> None:
""" 尾部添加元素 """
"""尾部添加元素"""
# 元素数量超出容量时，触发扩容机制
if self.size() == self.capacity():
self.extend_capacity()
@ -41,7 +43,7 @@ class MyList:
self.__size += 1
def insert(self, num: int, index: int) -> None:
""" 中间插入元素 """
"""中间插入元素"""
assert index >= 0 and index < self.__size, "索引越界"
# 元素数量超出容量时，触发扩容机制
if self.__size == self.capacity():
@ -54,7 +56,7 @@ class MyList:
self.__size += 1
def remove(self, index: int) -> int:
""" 删除元素 """
"""删除元素"""
assert index >= 0 and index < self.__size, "索引越界"
num = self.__nums[index]
# 索引 i 之后的元素都向前移动一位
@ -66,49 +68,51 @@ class MyList:
return num
def extend_capacity(self) -> None:
""" 列表扩容 """
"""列表扩容"""
# 新建一个长度为 self.__size 的数组，并将原数组拷贝到新数组
self.__nums = self.__nums + [0] * self.capacity() * (self.__extend_ratio - 1)
# 更新列表容量
self.__capacity = len(self.__nums)
def to_array(self) -> list[int]:
""" 返回有效长度的列表 """
return self.__nums[:self.__size]
"""返回有效长度的列表"""
return self.__nums[: self.__size]
""" Driver Code """
if __name__ == "__main__":
""" 初始化列表 """
# 初始化列表
my_list = MyList()
""" 尾部添加元素 """
# 尾部添加元素
my_list.add(1)
my_list.add(3)
my_list.add(2)
my_list.add(5)
my_list.add(4)
print("列表 my_list = {} ，容量 = {} ，长度 = {}"
.format(my_list.to_array(), my_list.capacity(), my_list.size()))
print(
f"列表 my_list = {my_list.to_array()} ，容量 = {my_list.capacity()} ，长度 = {my_list.size()}"
)
""" 中间插入元素 """
# 中间插入元素
my_list.insert(6, index=3)
print("在索引 3 处插入数字 6 ，得到 my_list =", my_list.to_array())
""" 删除元素 """
# 删除元素
my_list.remove(3)
print("删除索引 3 处的元素，得到 my_list =", my_list.to_array())
""" 访问元素 """
# 访问元素
num = my_list.get(1)
print("访问索引 1 处的元素，得到 num =", num)
""" 更新元素 """
# 更新元素
my_list.set(0, 1)
print("将索引 1 处的元素更新为 0 ，得到 my_list =", my_list.to_array())
""" 测试扩容机制 """
# 测试扩容机制
for i in range(10):
# 在 i = 5 时，列表长度将超出列表容量，此时触发扩容机制
my_list.add(i)
print("扩容后的列表 my_list = {} ，容量 = {} ，长度 = {}"
.format(my_list.to_array(), my_list.capacity(), my_list.size()))
print(
"扩容后的列表 {my_list.to_array()} ，容量 = {my_list.capacity()} ，长度 = {my_list.size()}"
)

12
codes/python/chapter_computational_complexity/leetcode_two_sum.py
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@ -4,8 +4,9 @@ Created Time: 2022-11-25
Author: Krahets (krahets@163.com)
"""
def two_sum_brute_force(nums: list[int], target: int) -> list[int]:
""" 方法一：暴力枚举 """
"""方法一：暴力枚举"""
# 两层循环，时间复杂度 O(n^2)
for i in range(len(nums) - 1):
for j in range(i + 1, len(nums)):
@ -13,8 +14,9 @@ def two_sum_brute_force(nums: list[int], target: int) -> list[int]:
return [i, j]
return []
def two_sum_hash_table(nums: list[int], target: int) -> list[int]:
""" 方法二：辅助哈希表 """
"""方法二：辅助哈希表"""
# 辅助哈希表，空间复杂度 O(n)
dic = {}
# 单层循环，时间复杂度 O(n)
@ -26,11 +28,11 @@ def two_sum_hash_table(nums: list[int], target: int) -> list[int]:
""" Driver Code """
if __name__ == '__main__':
if __name__ == "__main__":
# ======= Test Case =======
nums = [2,7,11,15]
nums = [2, 7, 11, 15]
target = 9
# ====== Driver Code ======
# 方法一
res: list[int] = two_sum_brute_force(nums, target)

31
codes/python/chapter_computational_complexity/space_complexity.py
View File

@ -5,16 +5,19 @@ Author: Krahets (krahets@163.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
def function() -> int:
""" 函数 """
"""函数"""
# do something
return 0
def constant(n: int) -> None:
""" 常数阶 """
"""常数阶"""
# 常量、变量、对象占用 O(1) 空间
a: int = 0
nums: list[int] = [0] * 10000
@ -26,8 +29,9 @@ def constant(n: int) -> None:
for _ in range(n):
function()
def linear(n: int) -> None:
""" 线性阶 """
"""线性阶"""
# 长度为 n 的列表占用 O(n) 空间
nums: list[int] = [0] * n
# 长度为 n 的哈希表占用 O(n) 空间
@ -35,27 +39,34 @@ def linear(n: int) -> None:
for i in range(n):
mapp[i] = str(i)
def linear_recur(n: int) -> None:
""" 线性阶（递归实现） """
"""线性阶（递归实现）"""
print("递归 n =", n)
if n == 1: return
if n == 1:
return
linear_recur(n - 1)
def quadratic(n: int) -> None:
""" 平方阶 """
"""平方阶"""
# 二维列表占用 O(n^2) 空间
num_matrix: list[list[int]] = [[0] * n for _ in range(n)]
def quadratic_recur(n: int) -> int:
""" 平方阶（递归实现） """
if n <= 0: return 0
"""平方阶（递归实现）"""
if n <= 0:
return 0
# 数组 nums 长度为 n, n-1, ..., 2, 1
nums: list[int] = [0] * n
return quadratic_recur(n - 1)
def build_tree(n: int) -> TreeNode | None:
""" 指数阶（建立满二叉树） """
if n == 0: return None
"""指数阶（建立满二叉树）"""
if n == 0:
return None
root = TreeNode(0)
root.left = build_tree(n - 1)
root.right = build_tree(n - 1)

48
codes/python/chapter_computational_complexity/time_complexity.py
View File

@ -4,31 +4,35 @@ Created Time: 2022-11-25
Author: Krahets (krahets@163.com)
"""
def constant(n: int) -> int:
""" 常数阶 """
"""常数阶"""
count: int = 0
size: int = 100000
for _ in range(size):
count += 1
return count
def linear(n: int) -> int:
""" 线性阶 """
"""线性阶"""
count: int = 0
for _ in range(n):
count += 1
return count
def array_traversal(nums: list[int]) -> int:
""" 线性阶（遍历数组）"""
"""线性阶（遍历数组）"""
count: int = 0
# 循环次数与数组长度成正比
for num in nums:
count += 1
return count
def quadratic(n: int) -> int:
""" 平方阶 """
"""平方阶"""
count: int = 0
# 循环次数与数组长度成平方关系
for i in range(n):
@ -36,8 +40,9 @@ def quadratic(n: int) -> int:
count += 1
return count
def bubble_sort(nums: list[int]) -> int:
""" 平方阶（冒泡排序）"""
"""平方阶（冒泡排序）"""
count: int = 0 # 计数器
# 外循环：待排序元素数量为 n-1, n-2, ..., 1
for i in range(len(nums) - 1, 0, -1):
@ -51,8 +56,9 @@ def bubble_sort(nums: list[int]) -> int:
count += 3 # 元素交换包含 3 个单元操作
return count
def exponential(n: int) -> int:
""" 指数阶（循环实现）"""
"""指数阶（循环实现）"""
count: int = 0
base: int = 1
# cell 每轮一分为二，形成数列 1, 2, 4, 8, ..., 2^(n-1)
@ -63,36 +69,44 @@ def exponential(n: int) -> int:
# count = 1 + 2 + 4 + 8 + .. + 2^(n-1) = 2^n - 1
return count
def exp_recur(n: int) -> int:
""" 指数阶（递归实现）"""
if n == 1: return 1
"""指数阶（递归实现）"""
if n == 1:
return 1
return exp_recur(n - 1) + exp_recur(n - 1) + 1
def logarithmic(n: float) -> int:
""" 对数阶（循环实现）"""
"""对数阶（循环实现）"""
count: int = 0
while n > 1:
n = n / 2
count += 1
return count
def log_recur(n: float) -> int:
""" 对数阶（递归实现）"""
if n <= 1: return 0
"""对数阶（递归实现）"""
if n <= 1:
return 0
return log_recur(n / 2) + 1
def linear_log_recur(n: float) -> int:
""" 线性对数阶 """
if n <= 1: return 1
count: int = linear_log_recur(n // 2) + \
linear_log_recur(n // 2)
"""线性对数阶"""
if n <= 1:
return 1
count: int = linear_log_recur(n // 2) + linear_log_recur(n // 2)
for _ in range(n):
count += 1
return count
def factorial_recur(n: int) -> int:
""" 阶乘阶（递归实现）"""
if n == 0: return 1
"""阶乘阶（递归实现）"""
if n == 0:
return 1
count: int = 0
# 从 1 个分裂出 n 个
for _ in range(n):

6
codes/python/chapter_computational_complexity/worst_best_time_complexity.py
View File

@ -6,16 +6,18 @@ Author: Krahets (krahets@163.com)
import random
def random_numbers(n: int) -> list[int]:
""" 生成一个数组，元素为: 1, 2, ..., n ，顺序被打乱 """
"""生成一个数组，元素为: 1, 2, ..., n ，顺序被打乱"""
# 生成数组 nums =: 1, 2, 3, ..., n
nums: list[int] = [i for i in range(1, n + 1)]
# 随机打乱数组元素
random.shuffle(nums)
return nums
def find_one(nums: list[int]) -> int:
""" 查找数组 nums 中数字 1 所在索引 """
"""查找数组 nums 中数字 1 所在索引"""
for i in range(len(nums)):
# 当元素 1 在数组头部时，达到最佳时间复杂度 O(1)
# 当元素 1 在数组尾部时，达到最差时间复杂度 O(n)

39
codes/python/chapter_graph/graph_adjacency_list.py
View File

@ -5,13 +5,16 @@ Author: Krahets (krahets@163.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
class GraphAdjList:
""" 基于邻接表实现的无向图类 """
"""基于邻接表实现的无向图类"""
def __init__(self, edges: list[list[Vertex]]) -> None:
""" 构造方法 """
"""构造方法"""
# 邻接表，key: 顶点，value：该顶点的所有邻接顶点
self.adj_list = dict[Vertex, Vertex]()
# 添加所有顶点和边
@ -21,11 +24,11 @@ class GraphAdjList:
self.add_edge(edge[0], edge[1])
def size(self) -> int:
""" 获取顶点数量 """
"""获取顶点数量"""
return len(self.adj_list)
def add_edge(self, vet1: Vertex, vet2: Vertex) -> None:
""" 添加边 """
"""添加边"""
if vet1 not in self.adj_list or vet2 not in self.adj_list or vet1 == vet2:
raise ValueError
# 添加边 vet1 - vet2
@ -33,7 +36,7 @@ class GraphAdjList:
self.adj_list[vet2].append(vet1)
def remove_edge(self, vet1: Vertex, vet2: Vertex) -> None:
""" 删除边 """
"""删除边"""
if vet1 not in self.adj_list or vet2 not in self.adj_list or vet1 == vet2:
raise ValueError
# 删除边 vet1 - vet2
@ -41,14 +44,14 @@ class GraphAdjList:
self.adj_list[vet2].remove(vet1)
def add_vertex(self, vet: Vertex) -> None:
""" 添加顶点 """
"""添加顶点"""
if vet in self.adj_list:
return
# 在邻接表中添加一个新链表
self.adj_list[vet] = []
def remove_vertex(self, vet: Vertex) -> None:
""" 删除顶点 """
"""删除顶点"""
if vet not in self.adj_list:
raise ValueError
# 在邻接表中删除顶点 vet 对应的链表
@ -59,7 +62,7 @@ class GraphAdjList:
self.adj_list[vertex].remove(vet)
def print(self) -> None:
""" 打印邻接表 """
"""打印邻接表"""
print("邻接表 =")
for vertex in self.adj_list:
tmp = [v.val for v in self.adj_list[vertex]]
@ -68,33 +71,39 @@ class GraphAdjList:
""" Driver Code """
if __name__ == "__main__":
""" 初始化无向图 """
# 初始化无向图
v = vals_to_vets([1, 3, 2, 5, 4])
edges = [[v[0], v[1]], [v[0], v[3]], [v[1], v[2]],
[v[2], v[3]], [v[2], v[4]], [v[3], v[4]]]
edges = [
[v[0], v[1]],
[v[0], v[3]],
[v[1], v[2]],
[v[2], v[3]],
[v[2], v[4]],
[v[3], v[4]],
]
graph = GraphAdjList(edges)
print("\n初始化后，图为")
graph.print()
""" 添加边 """
# 添加边
# 顶点 1, 2 即 v[0], v[2]
graph.add_edge(v[0], v[2])
print("\n添加边 1-2 后，图为")
graph.print()
""" 删除边 """
# 删除边
# 顶点 1, 3 即 v[0], v[1]
graph.remove_edge(v[0], v[1])
print("\n删除边 1-3 后，图为")
graph.print()
""" 添加顶点 """
# 添加顶点
v5 = Vertex(6)
graph.add_vertex(v5)
print("\n添加顶点 6 后，图为")
graph.print()
""" 删除顶点 """
# 删除顶点
# 顶点 3 即 v[1]
graph.remove_vertex(v[1])
print("\n删除顶点 3 后，图为")

29
codes/python/chapter_graph/graph_adjacency_matrix.py
View File

@ -5,18 +5,21 @@ Author: Krahets (krahets@163.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
class GraphAdjMat:
""" 基于邻接矩阵实现的无向图类 """
"""基于邻接矩阵实现的无向图类"""
# 顶点列表，元素代表“顶点值”，索引代表“顶点索引”
vertices: list[int] = []
# 邻接矩阵，行列索引对应“顶点索引”
adj_mat: list[list[int]] = []
def __init__(self, vertices: list[int], edges: list[list[int]]) -> None:
""" 构造方法 """
"""构造方法"""
self.vertices: list[int] = []
self.adj_mat: list[list[int]] = []
# 添加顶点
@ -28,11 +31,11 @@ class GraphAdjMat:
self.add_edge(e[0], e[1])
def size(self) -> int:
""" 获取顶点数量 """
"""获取顶点数量"""
return len(self.vertices)
def add_vertex(self, val: int) -> None:
""" 添加顶点 """
"""添加顶点"""
n = self.size()
# 向顶点列表中添加新顶点的值
self.vertices.append(val)
@ -44,7 +47,7 @@ class GraphAdjMat:
row.append(0)
def remove_vertex(self, index: int) -> None:
""" 删除顶点 """
"""删除顶点"""
if index >= self.size():
raise IndexError()
# 在顶点列表中移除索引 index 的顶点
@ -56,7 +59,7 @@ class GraphAdjMat:
row.pop(index)
def add_edge(self, i: int, j: int) -> None:
""" 添加边 """
"""添加边"""
# 参数 i, j 对应 vertices 元素索引
# 索引越界与相等处理
if i < 0 or j < 0 or i >= self.size() or j >= self.size() or i == j:
@ -66,7 +69,7 @@ class GraphAdjMat:
self.adj_mat[j][i] = 1
def remove_edge(self, i: int, j: int) -> None:
""" 删除边 """
"""删除边"""
# 参数 i, j 对应 vertices 元素索引
# 索引越界与相等处理
if i < 0 or j < 0 or i >= self.size() or j >= self.size() or i == j:
@ -75,7 +78,7 @@ class GraphAdjMat:
self.adj_mat[j][i] = 0
def print(self) -> None:
""" 打印邻接矩阵 """
"""打印邻接矩阵"""
print("顶点列表 =", self.vertices)
print("邻接矩阵 =")
print_matrix(self.adj_mat)
@ -83,7 +86,7 @@ class GraphAdjMat:
""" Driver Code """
if __name__ == "__main__":
""" 初始化无向图 """
# 初始化无向图
# 请注意，edges 元素代表顶点索引，即对应 vertices 元素索引
vertices: list[int] = [1, 3, 2, 5, 4]
edges: list[list[int]] = [[0, 1], [0, 3], [1, 2], [2, 3], [2, 4], [3, 4]]
@ -91,24 +94,24 @@ if __name__ == "__main__":
print("\n初始化后，图为")
graph.print()
""" 添加边 """
# 添加边
# 顶点 1, 2 的索引分别为 0, 2
graph.add_edge(0, 2)
print("\n添加边 1-2 后，图为")
graph.print()
""" 删除边 """
# 删除边
# 顶点 1, 3 的索引分别为 0, 1
graph.remove_edge(0, 1)
print("\n删除边 1-3 后，图为")
graph.print()
""" 添加顶点 """
# 添加顶点
graph.add_vertex(6)
print("\n添加顶点 6 后，图为")
graph.print()
""" 删除顶点 """
# 删除顶点
# 顶点 3 的索引为 1
graph.remove_vertex(1)
print("\n删除顶点 3 后，图为")

31
codes/python/chapter_graph/graph_bfs.py
View File

@ -5,13 +5,15 @@ Author: Krahets (krahets@163.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
from collections import deque
from graph_adjacency_list import GraphAdjList
def graph_bfs(graph: GraphAdjList, start_vet: Vertex) -> list[Vertex]:
""" 广度优先遍历 BFS """
"""广度优先遍历 BFS"""
# 使用邻接表来表示图，以便获取指定顶点的所有邻接顶点
# 顶点遍历序列
res = []
@ -22,29 +24,40 @@ def graph_bfs(graph: GraphAdjList, start_vet: Vertex) -> list[Vertex]:
# 以顶点 vet 为起点，循环直至访问完所有顶点
while len(que) > 0:
vet = que.popleft() # 队首顶点出队
res.append(vet) # 记录访问顶点
res.append(vet) # 记录访问顶点
# 遍历该顶点的所有邻接顶点
for adj_vet in graph.adj_list[vet]:
if adj_vet in visited:
continue # 跳过已被访问过的顶点
continue # 跳过已被访问过的顶点
que.append(adj_vet) # 只入队未访问的顶点
visited.add(adj_vet) # 标记该顶点已被访问
visited.add(adj_vet) # 标记该顶点已被访问
# 返回顶点遍历序列
return res
""" Driver Code """
if __name__ == "__main__":
"""初始化无向图"""
# 初始化无向图
v = vals_to_vets([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
edges = [[v[0], v[1]], [v[0], v[3]], [v[1], v[2]], [v[1], v[4]],
[v[2], v[5]], [v[3], v[4]], [v[3], v[6]], [v[4], v[5]],
[v[4], v[7]], [v[5], v[8]], [v[6], v[7]], [v[7], v[8]]]
edges = [
[v[0], v[1]],
[v[0], v[3]],
[v[1], v[2]],
[v[1], v[4]],
[v[2], v[5]],
[v[3], v[4]],
[v[3], v[6]],
[v[4], v[5]],
[v[4], v[7]],
[v[5], v[8]],
[v[6], v[7]],
[v[7], v[8]],
]
graph = GraphAdjList(edges)
print("\n初始化后，图为")
graph.print()
"""广度优先遍历 BFS"""
# 广度优先遍历 BFS
res = graph_bfs(graph, v[0])
print("\n广度优先遍历（BFS）顶点序列为")
print(vets_to_vals(res))

25
codes/python/chapter_graph/graph_dfs.py
View File

@ -5,24 +5,27 @@ Author: Krahets (krahets@163.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
from graph_adjacency_list import GraphAdjList
def dfs(graph: GraphAdjList, visited: set[Vertex], res: list[Vertex], vet: Vertex):
""" 深度优先遍历 DFS 辅助函数 """
res.append(vet) # 记录访问顶点
visited.add(vet) # 标记该顶点已被访问
"""深度优先遍历 DFS 辅助函数"""
res.append(vet) # 记录访问顶点
visited.add(vet) # 标记该顶点已被访问
# 遍历该顶点的所有邻接顶点
for adjVet in graph.adj_list[vet]:
if adjVet in visited:
continue # 跳过已被访问过的顶点
continue # 跳过已被访问过的顶点
# 递归访问邻接顶点
dfs(graph, visited, res, adjVet)
# 使用邻接表来表示图，以便获取指定顶点的所有邻接顶点
def graph_dfs(graph: GraphAdjList, start_vet: Vertex) -> list[Vertex]:
""" 深度优先遍历 DFS """
"""深度优先遍历 DFS"""
# 顶点遍历序列
res = []
# 哈希表，用于记录已被访问过的顶点
@ -35,12 +38,18 @@ def graph_dfs(graph: GraphAdjList, start_vet: Vertex) -> list[Vertex]:
if __name__ == "__main__":
# 初始化无向图
v = vals_to_vets([0, 1, 2, 3, 4, 5, 6])
edges = [[v[0], v[1]], [v[0], v[3]], [v[1], v[2]],
[v[2], v[5]], [v[4], v[5]], [v[5], v[6]]]
edges = [
[v[0], v[1]],
[v[0], v[3]],
[v[1], v[2]],
[v[2], v[5]],
[v[4], v[5]],
[v[5], v[6]],
]
graph = GraphAdjList(edges)
print("\n初始化后，图为")
graph.print()
# 深度优先遍历 DFS
res = graph_dfs(graph, v[0])
print("\n深度优先遍历（DFS）顶点序列为")

36
codes/python/chapter_hashing/array_hash_map.py
View File

@ -4,26 +4,30 @@ Created Time: 2022-12-14
Author: msk397 (machangxinq@gmail.com)
"""
class Entry:
""" 键值对 int->String """
"""键值对 int->String"""
def __init__(self, key: int, val: str):
self.key = key
self.val = val
class ArrayHashMap:
""" 基于数组简易实现的哈希表 """
"""基于数组简易实现的哈希表"""
def __init__(self):
""" 构造方法 """
"""构造方法"""
# 初始化数组，包含 100 个桶
self.buckets: list[Entry | None] = [None] * 100
def hash_func(self, key: int) -> int:
""" 哈希函数 """
"""哈希函数"""
index: int = key % 100
return index
def get(self, key: int) -> str:
""" 查询操作 """
"""查询操作"""
index: int = self.hash_func(key)
pair: Entry = self.buckets[index]
if pair is None:
@ -31,19 +35,19 @@ class ArrayHashMap:
return pair.val
def put(self, key: int, val: str) -> None:
""" 添加操作 """
"""添加操作"""
pair = Entry(key, val)
index: int = self.hash_func(key)
self.buckets[index] = pair
def remove(self, key: int) -> None:
""" 删除操作 """
"""删除操作"""
index: int = self.hash_func(key)
# 置为 None ，代表删除
self.buckets[index] = None
def entry_set(self) -> list[Entry]:
""" 获取所有键值对 """
"""获取所有键值对"""
result: list[Entry] = []
for pair in self.buckets:
if pair is not None:
@ -51,7 +55,7 @@ class ArrayHashMap:
return result
def key_set(self) -> list[int]:
""" 获取所有键 """
"""获取所有键"""
result: list[int] = []
for pair in self.buckets:
if pair is not None:
@ -59,7 +63,7 @@ class ArrayHashMap:
return result
def value_set(self) -> list[str]:
""" 获取所有值 """
"""获取所有值"""
result: list[str] = []
for pair in self.buckets:
if pair is not None:
@ -67,7 +71,7 @@ class ArrayHashMap:
return result
def print(self) -> None:
""" 打印哈希表 """
"""打印哈希表"""
for pair in self.buckets:
if pair is not None:
print(pair.key, "->", pair.val)
@ -75,10 +79,10 @@ class ArrayHashMap:
""" Driver Code """
if __name__ == "__main__":
""" 初始化哈希表 """
# 初始化哈希表
mapp = ArrayHashMap()
""" 添加操作 """
# 添加操作
# 在哈希表中添加键值对 (key, value)
mapp.put(12836, "小哈")
mapp.put(15937, "小啰")
@ -88,18 +92,18 @@ if __name__ == "__main__":
print("\n添加完成后，哈希表为\nKey -> Value")
mapp.print()
""" 查询操作 """
# 查询操作
# 向哈希表输入键 key ，得到值 value
name = mapp.get(15937)
print("\n输入学号 15937 ，查询到姓名 " + name)
""" 删除操作 """
# 删除操作
# 在哈希表中删除键值对 (key, value)
mapp.remove(10583)
print("\n删除 10583 后，哈希表为\nKey -> Value")
mapp.print()
""" 遍历哈希表 """
# 遍历哈希表
print("\n遍历键值对 Key->Value")
for pair in mapp.entry_set():
print(pair.key, "->", pair.val)

11
codes/python/chapter_hashing/hash_map.py
View File

@ -5,15 +5,16 @@ Author: msk397 (machangxinq@gmail.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
""" Driver Code """
if __name__ == "__main__":
""" 初始化哈希表 """
# 初始化哈希表
mapp = dict[int, str]()
""" 添加操作 """
# 添加操作
# 在哈希表中添加键值对 (key, value)
mapp[12836] = "小哈"
mapp[15937] = "小啰"
@ -23,18 +24,18 @@ if __name__ == "__main__":
print("\n添加完成后，哈希表为\nKey -> Value")
print_dict(mapp)
""" 查询操作 """
# 查询操作
# 向哈希表输入键 key ，得到值 value
name: str = mapp[15937]
print("\n输入学号 15937 ，查询到姓名 " + name)
""" 删除操作 """
# 删除操作
# 在哈希表中删除键值对 (key, value)
mapp.pop(10583)
print("\n删除 10583 后，哈希表为\nKey -> Value")
print_dict(mapp)
""" 遍历哈希表 """
# 遍历哈希表
print("\n遍历键值对 Key->Value")
for key, value in mapp.items():
print(key, "->", value)

3
codes/python/chapter_heap/heap.py
View File

@ -5,6 +5,7 @@ Author: Krahets (krahets@163.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
@ -16,11 +17,13 @@ def test_push(heap: list, val: int, flag: int = 1) -> None:
print(f"\n元素 {val} 入堆后")
print_heap([flag * val for val in heap])
def test_pop(heap: list, flag: int = 1) -> None:
val = flag * heapq.heappop(heap) # 堆顶元素出堆
print(f"\n堆顶元素 {val} 出堆后")
print_heap([flag * val for val in heap])
""" Driver Code """
if __name__ == "__main__":
# 初始化小顶堆

31
codes/python/chapter_heap/my_heap.py
View File

@ -5,13 +5,16 @@ Author: Krahets (krahets@163.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
class MaxHeap:
""" 大顶堆 """
"""大顶堆"""
def __init__(self, nums: list[int]):
""" 构造方法 """
"""构造方法"""
# 将列表元素原封不动添加进堆
self.max_heap = nums
# 堆化除叶节点以外的其他所有节点
@ -19,43 +22,43 @@ class MaxHeap:
self.sift_down(i)
def left(self, i: int) -> int:
""" 获取左子节点索引 """
"""获取左子节点索引"""
return 2 * i + 1
def right(self, i: int) -> int:
""" 获取右子节点索引 """
"""获取右子节点索引"""
return 2 * i + 2
def parent(self, i: int) -> int:
""" 获取父节点索引 """
"""获取父节点索引"""
return (i - 1) // 2 # 向下整除
def swap(self, i: int, j: int):
""" 交换元素 """
"""交换元素"""
a, b = self.max_heap[i], self.max_heap[j]
self.max_heap[i], self.max_heap[j] = b, a
def size(self) -> int:
""" 获取堆大小 """
"""获取堆大小"""
return len(self.max_heap)
def is_empty(self) -> bool:
""" 判断堆是否为空 """
"""判断堆是否为空"""
return self.size() == 0
def peek(self) -> int:
""" 访问堆顶元素 """
"""访问堆顶元素"""
return self.max_heap[0]
def push(self, val: int):
""" 元素入堆 """
"""元素入堆"""
# 添加节点
self.max_heap.append(val)
# 从底至顶堆化
self.sift_up(self.size() - 1)
def sift_up(self, i: int):
""" 从节点 i 开始，从底至顶堆化 """
"""从节点 i 开始，从底至顶堆化"""
while True:
# 获取节点 i 的父节点
p = self.parent(i)
@ -68,7 +71,7 @@ class MaxHeap:
i = p
def pop(self) -> int:
""" 元素出堆 """
"""元素出堆"""
# 判空处理
assert not self.is_empty()
# 交换根节点与最右叶节点（即交换首元素与尾元素）
@ -81,7 +84,7 @@ class MaxHeap:
return val
def sift_down(self, i: int):
""" 从节点 i 开始，从顶至底堆化 """
"""从节点 i 开始，从顶至底堆化"""
while True:
# 判断节点 i, l, r 中值最大的节点，记为 ma
l, r, ma = self.left(i), self.right(i), i
@ -98,7 +101,7 @@ class MaxHeap:
i = ma
def print(self):
""" 打印堆（二叉树） """
"""打印堆（二叉树）"""
print_heap(self.max_heap)

25
codes/python/chapter_searching/binary_search.py
View File

@ -4,46 +4,47 @@ Created Time: 2022-11-26
Author: timi (xisunyy@163.com)
"""
def binary_search(nums: list[int], target: int) -> int:
""" 二分查找（双闭区间） """
"""二分查找（双闭区间）"""
# 初始化双闭区间 [0, n-1] ，即 i, j 分别指向数组首元素、尾元素
i, j = 0, len(nums) - 1
while i <= j:
m = (i + j) // 2 # 计算中点索引 m
if nums[m] < target: # 此情况说明 target 在区间 [m+1, j] 中
m = (i + j) // 2 # 计算中点索引 m
if nums[m] < target: # 此情况说明 target 在区间 [m+1, j] 中
i = m + 1
elif nums[m] > target: # 此情况说明 target 在区间 [i, m-1] 中
j = m - 1
else:
return m # 找到目标元素，返回其索引
return -1 # 未找到目标元素，返回 -1
return m # 找到目标元素，返回其索引
return -1 # 未找到目标元素，返回 -1
def binary_search1(nums: list[int], target: int) -> int:
""" 二分查找（左闭右开） """
"""二分查找（左闭右开）"""
# 初始化左闭右开 [0, n) ，即 i, j 分别指向数组首元素、尾元素+1
i, j = 0, len(nums)
# 循环，当搜索区间为空时跳出（当 i = j 时为空）
while i < j:
m = (i + j) // 2 # 计算中点索引 m
if nums[m] < target: # 此情况说明 target 在区间 [m+1, j) 中
m = (i + j) // 2 # 计算中点索引 m
if nums[m] < target: # 此情况说明 target 在区间 [m+1, j) 中
i = m + 1
elif nums[m] > target: # 此情况说明 target 在区间 [i, m) 中
j = m
else: # 找到目标元素，返回其索引
else: # 找到目标元素，返回其索引
return m
return -1 # 未找到目标元素，返回 -1
return -1 # 未找到目标元素，返回 -1
""" Driver Code """
if __name__ == '__main__':
if __name__ == "__main__":
target: int = 6
nums: list[int] = [1, 3, 6, 8, 12, 15, 23, 67, 70, 92]
# 二分查找（双闭区间）
index: int = binary_search(nums, target)
print("目标元素 6 的索引 = ", index)
# 二分查找（左闭右开）
index: int = binary_search1(nums, target)
print("目标元素 6 的索引 = ", index)

15
codes/python/chapter_searching/hashing_search.py
View File

@ -5,26 +5,31 @@ Author: timi (xisunyy@163.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
def hashing_search_array(mapp: dict[int, int], target: int) -> int:
""" 哈希查找（数组） """
"""哈希查找（数组）"""
# 哈希表的 key: 目标元素，value: 索引
# 若哈希表中无此 key ，返回 -1
return mapp.get(target, -1)
def hashing_search_linkedlist(mapp: dict[int, ListNode], target: int) -> ListNode | None:
""" 哈希查找（链表） """
def hashing_search_linkedlist(
mapp: dict[int, ListNode], target: int
) -> ListNode | None:
"""哈希查找（链表）"""
# 哈希表的 key: 目标元素，value: 节点对象
# 若哈希表中无此 key ，返回 None
return mapp.get(target, None)
""" Driver Code """
if __name__ == '__main__':
if __name__ == "__main__":
target: int = 3
# 哈希查找（数组）
nums: list[int] = [1, 5, 3, 2, 4, 7, 5, 9, 10, 8]
# 初始化哈希表

17
codes/python/chapter_searching/linear_search.py
View File

@ -5,31 +5,34 @@ Author: timi (xisunyy@163.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
def linear_search_array(nums: list[int], target: int) -> int:
""" 线性查找（数组） """
"""线性查找（数组）"""
# 遍历数组
for i in range(len(nums)):
if nums[i] == target: # 找到目标元素，返回其索引
return i
return -1 # 未找到目标元素，返回 -1
return -1 # 未找到目标元素，返回 -1
def linear_search_linkedlist(head: ListNode, target: int) -> ListNode | None:
""" 线性查找（链表） """
"""线性查找（链表）"""
# 遍历链表
while head:
if head.val == target: # 找到目标节点，返回之
if head.val == target: # 找到目标节点，返回之
return head
head = head.next
return None # 未找到目标节点，返回 None
return None # 未找到目标节点，返回 None
""" Driver Code """
if __name__ == '__main__':
if __name__ == "__main__":
target: int = 3
# 在数组中执行线性查找
nums: list[int] = [1, 5, 3, 2, 4, 7, 5, 9, 10, 8]
index: int = linear_search_array(nums, target)

10
codes/python/chapter_sorting/bubble_sort.py
View File

@ -4,8 +4,9 @@ Created Time: 2022-11-25
Author: timi (xisunyy@163.com)
"""
def bubble_sort(nums: list[int]) -> None:
""" 冒泡排序 """
"""冒泡排序"""
n: int = len(nums)
# 外循环：待排序元素数量为 n-1, n-2, ..., 1
for i in range(n - 1, 0, -1):
@ -15,8 +16,9 @@ def bubble_sort(nums: list[int]) -> None:
# 交换 nums[j] 与 nums[j + 1]
nums[j], nums[j + 1] = nums[j + 1], nums[j]
def bubble_sort_with_flag(nums: list[int]) -> None:
""" 冒泡排序（标志优化） """
"""冒泡排序（标志优化）"""
n: int = len(nums)
# 外循环：待排序元素数量为 n-1, n-2, ..., 1
for i in range(n - 1, 0, -1):
@ -28,11 +30,11 @@ def bubble_sort_with_flag(nums: list[int]) -> None:
nums[j], nums[j + 1] = nums[j + 1], nums[j]
flag = True # 记录交换元素
if not flag:
break # 此轮冒泡未交换任何元素，直接跳出
break # 此轮冒泡未交换任何元素，直接跳出
""" Driver Code """
if __name__ == '__main__':
if __name__ == "__main__":
nums: list[int] = [4, 1, 3, 1, 5, 2]
bubble_sort(nums)
print("排序后数组 nums =", nums)

3
codes/python/chapter_sorting/bucket_sort.py
View File

@ -4,6 +4,7 @@ Created Time: 2023-03-30
Author: Krahets (krahets@163.com)
"""
def bucket_sort(nums: list[float]) -> None:
# 初始化 k = n/2 个桶，预期向每个桶分配 2 个元素
k = len(nums) // 2
@ -26,7 +27,7 @@ def bucket_sort(nums: list[float]) -> None:
i += 1
if __name__ == '__main__':
if __name__ == "__main__":
# 设输入数据为浮点数，范围为 [0, 1)
nums = [0.49, 0.96, 0.82, 0.09, 0.57, 0.43, 0.91, 0.75, 0.15, 0.37]
bucket_sort(nums)

7
codes/python/chapter_sorting/counting_sort.py
View File

@ -4,8 +4,9 @@ Created Time: 2023-03-21
Author: Krahets (krahets@163.com)
"""
def counting_sort_naive(nums: list[int]) -> None:
""" 计数排序 """
"""计数排序"""
# 简单实现，无法用于排序对象
# 1. 统计数组最大元素 m
m = 0
@ -23,8 +24,9 @@ def counting_sort_naive(nums: list[int]) -> None:
nums[i] = num
i += 1
def counting_sort(nums: list[int]) -> None:
""" 计数排序 """
"""计数排序"""
# 完整实现，可排序对象，并且是稳定排序
# 1. 统计数组最大元素 m
m = max(nums)
@ -49,6 +51,7 @@ def counting_sort(nums: list[int]) -> None:
for i in range(n):
nums[i] = res[i]
""" Driver Code """
if __name__ == "__main__":
nums = [1, 0, 1, 2, 0, 4, 0, 2, 2, 4]

9
codes/python/chapter_sorting/insertion_sort.py
View File

@ -4,9 +4,10 @@ Created Time: 2022-11-25
Author: timi (xisunyy@163.com)
"""
def insertion_sort(nums: list[int]) -> None:
""" 插入排序 """
# 外循环：base = nums[1], nums[2], ..., nums[n-1]
"""插入排序"""
# 外循环：base = nums[1], nums[2], ..., nums[n-1]
for i in range(1, len(nums)):
base: int = nums[i]
j: int = i - 1
@ -14,11 +15,11 @@ def insertion_sort(nums: list[int]) -> None:
while j >= 0 and nums[j] > base:
nums[j + 1] = nums[j] # 1. 将 nums[j] 向右移动一位
j -= 1
nums[j + 1] = base # 2. 将 base 赋值到正确位置
nums[j + 1] = base # 2. 将 base 赋值到正确位置
""" Driver Code """
if __name__ == '__main__':
if __name__ == "__main__":
nums: list[int] = [4, 1, 3, 1, 5, 2]
insertion_sort(nums)
print("排序后数组 nums =", nums)

18
codes/python/chapter_sorting/merge_sort.py
View File

@ -4,12 +4,13 @@ Created Time: 2022-11-25
Author: timi (xisunyy@163.com)
"""
def merge(nums: list[int], left: int, mid: int, right: int) -> None:
""" 合并左子数组和右子数组 """
"""合并左子数组和右子数组"""
# 左子数组区间 [left, mid]
# 右子数组区间 [mid + 1, right]
# 初始化辅助数组
tmp: list[int] = list(nums[left:right + 1])
tmp: list[int] = list(nums[left : right + 1])
# 左子数组的起始索引和结束索引
left_start: int = 0
left_end: int = mid - left
@ -34,21 +35,22 @@ def merge(nums: list[int], left: int, mid: int, right: int) -> None:
nums[k] = tmp[j]
j += 1
def merge_sort(nums: list[int], left: int, right: int) -> None:
""" 归并排序 """
"""归并排序"""
# 终止条件
if left >= right:
return # 当子数组长度为 1 时终止递归
return # 当子数组长度为 1 时终止递归
# 划分阶段
mid: int = (left + right) // 2 # 计算中点
merge_sort(nums, left, mid) # 递归左子数组
mid: int = (left + right) // 2 # 计算中点
merge_sort(nums, left, mid) # 递归左子数组
merge_sort(nums, mid + 1, right) # 递归右子数组
# 合并阶段
merge(nums, left, mid, right)
""" Driver Code """
if __name__ == '__main__':
nums: list[int] = [ 7, 3, 2, 6, 0, 1, 5, 4 ]
if __name__ == "__main__":
nums: list[int] = [7, 3, 2, 6, 0, 1, 5, 4]
merge_sort(nums, 0, len(nums) - 1)
print("归并排序完成后 nums =", nums)

34
codes/python/chapter_sorting/quick_sort.py
View File

@ -4,10 +4,12 @@ Created Time: 2022-11-25
Author: timi (xisunyy@163.com)
"""
class QuickSort:
""" 快速排序类 """
"""快速排序类"""
def partition(self, nums: list[int], left: int, right: int) -> int:
""" 哨兵划分 """
"""哨兵划分"""
# 以 nums[left] 作为基准数
i, j = left, right
while i < j:
@ -22,7 +24,7 @@ class QuickSort:
return i # 返回基准数的索引
def quick_sort(self, nums: list[int], left: int, right: int) -> None:
""" 快速排序 """
"""快速排序"""
# 子数组长度为 1 时终止递归
if left >= right:
return
@ -32,10 +34,12 @@ class QuickSort:
self.quick_sort(nums, left, pivot - 1)
self.quick_sort(nums, pivot + 1, right)
class QuickSortMedian:
""" 快速排序类（中位基准数优化）"""
"""快速排序类（中位基准数优化）"""
def median_three(self, nums: list[int], left: int, mid: int, right: int) -> int:
""" 选取三个元素的中位数 """
"""选取三个元素的中位数"""
# 此处使用异或运算来简化代码
# 异或规则为 0 ^ 0 = 1 ^ 1 = 0, 0 ^ 1 = 1 ^ 0 = 1
if (nums[left] < nums[mid]) ^ (nums[left] < nums[right]):
@ -45,7 +49,7 @@ class QuickSortMedian:
return right
def partition(self, nums: list[int], left: int, right: int) -> int:
""" 哨兵划分（三数取中值） """
"""哨兵划分（三数取中值）"""
# 以 nums[left] 作为基准数
med: int = self.median_three(nums, left, (left + right) // 2, right)
# 将中位数交换至数组最左端
@ -64,7 +68,7 @@ class QuickSortMedian:
return i # 返回基准数的索引
def quick_sort(self, nums: list[int], left: int, right: int) -> None:
""" 快速排序 """
"""快速排序"""
# 子数组长度为 1 时终止递归
if left >= right:
return
@ -74,10 +78,12 @@ class QuickSortMedian:
self.quick_sort(nums, left, pivot - 1)
self.quick_sort(nums, pivot + 1, right)
class QuickSortTailCall:
""" 快速排序类（尾递归优化） """
"""快速排序类（尾递归优化）"""
def partition(self, nums: list[int], left: int, right: int) -> int:
""" 哨兵划分 """
"""哨兵划分"""
# 以 nums[left] 作为基准数
i, j = left, right
while i < j:
@ -92,7 +98,7 @@ class QuickSortTailCall:
return i # 返回基准数的索引
def quick_sort(self, nums: list[int], left: int, right: int) -> None:
""" 快速排序（尾递归优化） """
"""快速排序（尾递归优化）"""
# 子数组长度为 1 时终止
while left < right:
# 哨兵划分操作
@ -100,15 +106,15 @@ class QuickSortTailCall:
# 对两个子数组中较短的那个执行快排
if pivot - left < right - pivot:
self.quick_sort(nums, left, pivot - 1) # 递归排序左子数组
left = pivot + 1 # 剩余待排序区间为 [pivot + 1, right]
left = pivot + 1 # 剩余待排序区间为 [pivot + 1, right]
else:
self.quick_sort(nums, pivot + 1, right) # 递归排序右子数组
right = pivot - 1 # 剩余待排序区间为 [left, pivot - 1]
right = pivot - 1 # 剩余待排序区间为 [left, pivot - 1]
""" Driver Code """
if __name__ == '__main__':
# 快速排序
if __name__ == "__main__":
# 快速排序
nums: list[int] = [2, 4, 1, 0, 3, 5]
QuickSort().quick_sort(nums, 0, len(nums) - 1)
print("快速排序完成后 nums =", nums)

31
codes/python/chapter_sorting/radix_sort.py
View File

@ -6,19 +6,20 @@ Author: Krahets (krahets@163.com)
def digit(num: int, exp: int) -> int:
""" 获取元素 num 的第 k 位，其中 exp = 10^(k-1) """
"""获取元素 num 的第 k 位，其中 exp = 10^(k-1)"""
# 传入 exp 而非 k 可以避免在此重复执行昂贵的次方计算
return (num // exp) % 10
def counting_sort_digit(nums: list[int], exp: int) -> None:
""" 计数排序（根据 nums 第 k 位排序） """
"""计数排序（根据 nums 第 k 位排序）"""
# 十进制的位范围为 0~9 ，因此需要长度为 10 的桶
counter = [0] * 10
n = len(nums)
# 统计 0~9 各数字的出现次数
for i in range(n):
d = digit(nums[i], exp) # 获取 nums[i] 第 k 位，记为 d
counter[d] += 1 # 统计数字 d 的出现次数
counter[d] += 1 # 统计数字 d 的出现次数
# 求前缀和，将“出现个数”转换为“数组索引”
for i in range(1, 10):
counter[i] += counter[i - 1]
@ -27,14 +28,15 @@ def counting_sort_digit(nums: list[int], exp: int) -> None:
for i in range(n - 1, -1, -1):
d = digit(nums[i], exp)
j = counter[d] - 1 # 获取 d 在数组中的索引 j
res[j] = nums[i] # 将当前元素填入索引 j
counter[d] -= 1 # 将 d 的数量减 1
res[j] = nums[i] # 将当前元素填入索引 j
counter[d] -= 1 # 将 d 的数量减 1
# 使用结果覆盖原数组 nums
for i in range(n):
nums[i] = res[i]
def radix_sort(nums: list[int]) -> None:
""" 基数排序 """
"""基数排序"""
# 获取数组的最大元素，用于判断最大位数
m = max(nums)
# 按照从低位到高位的顺序遍历
@ -47,10 +49,21 @@ def radix_sort(nums: list[int]) -> None:
counting_sort_digit(nums, exp)
exp *= 10
""" Driver Code """
if __name__ == '__main__':
if __name__ == "__main__":
# 基数排序
nums = [10546151, 35663510, 42865989, 34862445, 81883077,
88906420, 72429244, 30524779, 82060337, 63832996]
nums = [
10546151,
35663510,
42865989,
34862445,
81883077,
88906420,
72429244,
30524779,
82060337,
63832996,
]
radix_sort(nums)
print("基数排序完成后 nums =", nums)

40
codes/python/chapter_stack_and_queue/array_deque.py
View File

@ -4,35 +4,37 @@ Created Time: 2023-03-01
Author: Krahets (krahets@163.com)
"""
class ArrayDeque:
""" 基于环形数组实现的双向队列 """
"""基于环形数组实现的双向队列"""
def __init__(self, capacity: int) -> None:
""" 构造方法 """
"""构造方法"""
self.__nums: list[int] = [0] * capacity
self.__front: int = 0
self.__size: int = 0
def capacity(self) -> int:
""" 获取双向队列的容量 """
"""获取双向队列的容量"""
return len(self.__nums)
def size(self) -> int:
""" 获取双向队列的长度 """
"""获取双向队列的长度"""
return self.__size
def is_empty(self) -> bool:
""" 判断双向队列是否为空 """
"""判断双向队列是否为空"""
return self.__size == 0
def index(self, i: int) -> int:
""" 计算环形数组索引 """
"""计算环形数组索引"""
# 通过取余操作实现数组首尾相连
# 当 i 越过数组尾部后，回到头部
# 当 i 越过数组头部后，回到尾部
return (i + self.capacity()) % self.capacity()
def push_first(self, num: int) -> None:
""" 队首入队 """
"""队首入队"""
if self.__size == self.capacity():
print("双向队列已满")
return
@ -44,7 +46,7 @@ class ArrayDeque:
self.__size += 1
def push_last(self, num: int) -> None:
""" 队尾入队 """
"""队尾入队"""
if self.__size == self.capacity():
print("双向队列已满")
return
@ -55,7 +57,7 @@ class ArrayDeque:
self.__size += 1
def pop_first(self) -> int:
""" 队首出队 """
"""队首出队"""
num = self.peek_first()
# 队首指针向后移动一位
self.__front = self.index(self.__front + 1)
@ -63,25 +65,25 @@ class ArrayDeque:
return num
def pop_last(self) -> int:
""" 队尾出队 """
"""队尾出队"""
num = self.peek_last()
self.__size -= 1
return num
def peek_first(self) -> int:
""" 访问队首元素 """
"""访问队首元素"""
assert not self.is_empty(), "双向队列为空"
return self.__nums[self.__front]
def peek_last(self) -> int:
""" 访问队尾元素 """
"""访问队尾元素"""
assert not self.is_empty(), "双向队列为空"
# 计算尾元素索引
last = self.index(self.__front + self.__size - 1)
return self.__nums[last]
def to_array(self) -> list[int]:
""" 返回数组用于打印 """
"""返回数组用于打印"""
# 仅转换有效长度范围内的列表元素
res = []
for i in range(self.__size):
@ -91,35 +93,35 @@ class ArrayDeque:
""" Driver Code """
if __name__ == "__main__":
""" 初始化双向队列 """
# 初始化双向队列
deque = ArrayDeque(10)
deque.push_last(3)
deque.push_last(2)
deque.push_last(5)
print("双向队列 deque =", deque.to_array())
""" 访问元素 """
# 访问元素
peek_first: int = deque.peek_first()
print("队首元素 peek_first =", peek_first)
peek_last: int = deque.peek_last()
print("队尾元素 peek_last =", peek_last)
""" 元素入队 """
# 元素入队
deque.push_last(4)
print("元素 4 队尾入队后 deque =", deque.to_array())
deque.push_first(1)
print("元素 1 队首入队后 deque =", deque.to_array())
""" 元素出队 """
# 元素出队
pop_last: int = deque.pop_last()
print("队尾出队元素 =", pop_last, "，队尾出队后 deque =", deque.to_array())
pop_first: int = deque.pop_first()
print("队首出队元素 =", pop_first, "，队首出队后 deque =", deque.to_array())
""" 获取双向队列的长度 """
# 获取双向队列的长度
size: int = deque.size()
print("双向队列长度 size =", size)
""" 判断双向队列是否为空 """
# 判断双向队列是否为空
is_empty: bool = deque.is_empty()
print("双向队列是否为空 =", is_empty)

38
codes/python/chapter_stack_and_queue/array_queue.py
View File

@ -4,28 +4,30 @@ Created Time: 2022-12-01
Author: Peng Chen (pengchzn@gmail.com)
"""
class ArrayQueue:
""" 基于环形数组实现的队列 """
"""基于环形数组实现的队列"""
def __init__(self, size: int) -> None:
""" 构造方法 """
"""构造方法"""
self.__nums: list[int] = [0] * size # 用于存储队列元素的数组
self.__front: int = 0 # 队首指针，指向队首元素
self.__size: int = 0 # 队列长度
self.__front: int = 0 # 队首指针，指向队首元素
self.__size: int = 0 # 队列长度
def capacity(self) -> int:
""" 获取队列的容量 """
"""获取队列的容量"""
return len(self.__nums)
def size(self) -> int:
""" 获取队列的长度 """
"""获取队列的长度"""
return self.__size
def is_empty(self) -> bool:
""" 判断队列是否为空 """
"""判断队列是否为空"""
return self.__size == 0
def push(self, num: int) -> None:
""" 入队 """
"""入队"""
assert self.__size < self.capacity(), "队列已满"
# 计算尾指针，指向队尾索引 + 1
# 通过取余操作，实现 rear 越过数组尾部后回到头部
@ -35,7 +37,7 @@ class ArrayQueue:
self.__size += 1
def pop(self) -> int:
""" 出队 """
"""出队"""
num: int = self.peek()
# 队首指针向后移动一位，若越过尾部则返回到数组头部
self.__front = (self.__front + 1) % self.capacity()
@ -43,12 +45,12 @@ class ArrayQueue:
return num
def peek(self) -> int:
""" 访问队首元素 """
"""访问队首元素"""
assert not self.is_empty(), "队列为空"
return self.__nums[self.__front]
def to_list(self) -> list[int]:
""" 返回列表用于打印 """
"""返回列表用于打印"""
res: list[int] = [0] * self.size()
j: int = self.__front
for i in range(self.size()):
@ -59,10 +61,10 @@ class ArrayQueue:
""" Driver Code """
if __name__ == "__main__":
""" 初始化队列 """
# 初始化队列
queue = ArrayQueue(10)
""" 元素入队 """
# 元素入队
queue.push(1)
queue.push(3)
queue.push(2)
@ -70,24 +72,24 @@ if __name__ == "__main__":
queue.push(4)
print("队列 queue =", queue.to_list())
""" 访问队首元素 """
# 访问队首元素
peek: int = queue.peek()
print("队首元素 peek =", peek)
""" 元素出队 """
# 元素出队
pop: int = queue.pop()
print("出队元素 pop =", pop)
print("出队后 queue =", queue.to_list())
""" 获取队列的长度 """
# 获取队列的长度
size: int = queue.size()
print("队列长度 size =", size)
""" 判断队列是否为空 """
# 判断队列是否为空
is_empty: bool = queue.is_empty()
print("队列是否为空 =", is_empty)
""" 测试环形数组 """
# 测试环形数组
for i in range(10):
queue.push(i)
queue.pop()

32
codes/python/chapter_stack_and_queue/array_stack.py
View File

@ -4,45 +4,47 @@ Created Time: 2022-11-29
Author: Peng Chen (pengchzn@gmail.com)
"""
class ArrayStack:
""" 基于数组实现的栈 """
"""基于数组实现的栈"""
def __init__(self) -> None:
""" 构造方法 """
"""构造方法"""
self.__stack: list[int] = []
def size(self) -> int:
""" 获取栈的长度 """
"""获取栈的长度"""
return len(self.__stack)
def is_empty(self) -> bool:
""" 判断栈是否为空 """
"""判断栈是否为空"""
return self.__stack == []
def push(self, item: int) -> None:
""" 入栈 """
"""入栈"""
self.__stack.append(item)
def pop(self) -> int:
""" 出栈 """
"""出栈"""
assert not self.is_empty(), "栈为空"
return self.__stack.pop()
def peek(self) -> int:
""" 访问栈顶元素 """
"""访问栈顶元素"""
assert not self.is_empty(), "栈为空"
return self.__stack[-1]
def to_list(self) -> list[int]:
""" 返回列表用于打印 """
"""返回列表用于打印"""
return self.__stack
""" Driver Code """
if __name__ == "__main__":
""" 初始化栈 """
# 初始化栈
stack = ArrayStack()
""" 元素入栈 """
# 元素入栈
stack.push(1)
stack.push(3)
stack.push(2)
@ -50,19 +52,19 @@ if __name__ == "__main__":
stack.push(4)
print("栈 stack =", stack.to_list())
""" 访问栈顶元素 """
# 访问栈顶元素
peek: int = stack.peek()
print("栈顶元素 peek =", peek)
""" 元素出栈 """
# 元素出栈
pop: int = stack.pop()
print("出栈元素 pop =", pop)
print("出栈后 stack =", stack.to_list())
""" 获取栈的长度 """
# 获取栈的长度
size: int = stack.size()
print("栈的长度 size =", size)
""" 判断是否为空 """
# 判断是否为空
is_empty: bool = stack.is_empty()
print("栈是否为空 =", is_empty)

16
codes/python/chapter_stack_and_queue/deque.py
View File

@ -8,35 +8,35 @@ from collections import deque
""" Driver Code """
if __name__ == "__main__":
""" 初始化双向队列 """
# 初始化双向队列
deq: deque[int] = deque()
""" 元素入队 """
deq.append(2) # 添加至队尾
# 元素入队
deq.append(2) # 添加至队尾
deq.append(5)
deq.append(4)
deq.appendleft(3) # 添加至队首
deq.appendleft(1)
print("双向队列 deque =", deq)
""" 访问元素 """
# 访问元素
front: int = deq[0] # 队首元素
print("队首元素 front =", front)
rear: int = deq[-1] # 队尾元素
print("队尾元素 rear =", rear)
""" 元素出队 """
# 元素出队
pop_front: int = deq.popleft() # 队首元素出队
print("队首出队元素 pop_front =", pop_front)
print("队首出队后 deque =", deq)
pop_rear: int = deq.pop() # 队尾元素出队
pop_rear: int = deq.pop() # 队尾元素出队
print("队尾出队元素 pop_rear =", pop_rear)
print("队尾出队后 deque =", deq)
""" 获取双向队列的长度 """
# 获取双向队列的长度
size: int = len(deq)
print("双向队列长度 size =", size)
""" 判断双向队列是否为空 """
# 判断双向队列是否为空
is_empty: bool = len(deq) == 0
print("双向队列是否为空 =", is_empty)

50
codes/python/chapter_stack_and_queue/linkedlist_deque.py
View File

@ -4,32 +4,36 @@ Created Time: 2023-03-01
Author: Krahets (krahets@163.com)
"""
class ListNode:
""" 双向链表节点 """
"""双向链表节点"""
def __init__(self, val: int) -> None:
""" 构造方法 """
"""构造方法"""
self.val: int = val
self.next: ListNode | None = None # 后继节点引用（指针）
self.prev: ListNode | None = None # 前驱节点引用（指针）
class LinkedListDeque:
""" 基于双向链表实现的双向队列 """
"""基于双向链表实现的双向队列"""
def __init__(self) -> None:
""" 构造方法 """
self.front: ListNode | None = None # 头节点 front
"""构造方法"""
self.front: ListNode | None = None # 头节点 front
self.rear: ListNode | None = None # 尾节点 rear
self.__size: int = 0 # 双向队列的长度
self.__size: int = 0 # 双向队列的长度
def size(self) -> int:
""" 获取双向队列的长度 """
"""获取双向队列的长度"""
return self.__size
def is_empty(self) -> bool:
""" 判断双向队列是否为空 """
"""判断双向队列是否为空"""
return self.size() == 0
def push(self, num: int, is_front: bool) -> None:
""" 入队操作 """
"""入队操作"""
node = ListNode(num)
# 若链表为空，则令 front, rear 都指向 node
if self.is_empty():
@ -49,15 +53,15 @@ class LinkedListDeque:
self.__size += 1 # 更新队列长度
def push_first(self, num: int) -> None:
""" 队首入队 """
"""队首入队"""
self.push(num, True)
def push_last(self, num: int) -> None:
""" 队尾入队 """
"""队尾入队"""
self.push(num, False)
def pop(self, is_front: bool) -> int:
""" 出队操作 """
"""出队操作"""
# 若队列为空，直接返回 None
if self.is_empty():
return None
@ -83,23 +87,23 @@ class LinkedListDeque:
return val
def pop_first(self) -> int:
""" 队首出队 """
"""队首出队"""
return self.pop(True)
def pop_last(self) -> int:
""" 队尾出队 """
"""队尾出队"""
return self.pop(False)
def peek_first(self) -> int:
""" 访问队首元素 """
"""访问队首元素"""
return None if self.is_empty() else self.front.val
def peek_last(self) -> int:
""" 访问队尾元素 """
"""访问队尾元素"""
return None if self.is_empty() else self.rear.val
def to_array(self) -> list[int]:
""" 返回数组用于打印 """
"""返回数组用于打印"""
node: ListNode | None = self.front
res: list[int] = [0] * self.size()
for i in range(self.size()):
@ -110,35 +114,35 @@ class LinkedListDeque:
""" Driver Code """
if __name__ == "__main__":
""" 初始化双向队列 """
# 初始化双向队列
deque = LinkedListDeque()
deque.push_last(3)
deque.push_last(2)
deque.push_last(5)
print("双向队列 deque =", deque.to_array())
""" 访问元素 """
# 访问元素
peek_first: int = deque.peek_first()
print("队首元素 peek_first =", peek_first)
peek_last: int = deque.peek_last()
print("队尾元素 peek_last =", peek_last)
""" 元素入队 """
# 元素入队
deque.push_last(4)
print("元素 4 队尾入队后 deque =", deque.to_array())
deque.push_first(1)
print("元素 1 队首入队后 deque =", deque.to_array())
""" 元素出队 """
# 元素出队
pop_last: int = deque.pop_last()
print("队尾出队元素 =", pop_last, "，队尾出队后 deque =", deque.to_array())
pop_first: int = deque.pop_first()
print("队首出队元素 =", pop_first, "，队首出队后 deque =", deque.to_array())
""" 获取双向队列的长度 """
# 获取双向队列的长度
size: int = deque.size()
print("双向队列长度 size =", size)
""" 判断双向队列是否为空 """
# 判断双向队列是否为空
is_empty: bool = deque.is_empty()
print("双向队列是否为空 =", is_empty)

33
codes/python/chapter_stack_and_queue/linkedlist_queue.py
View File

@ -5,27 +5,30 @@ Author: Peng Chen (pengchzn@gmail.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
class LinkedListQueue:
""" 基于链表实现的队列 """
"""基于链表实现的队列"""
def __init__(self):
""" 构造方法 """
"""构造方法"""
self.__front: ListNode | None = None # 头节点 front
self.__rear: ListNode | None = None # 尾节点 rear
self.__rear: ListNode | None = None # 尾节点 rear
self.__size: int = 0
def size(self) -> int:
""" 获取队列的长度 """
"""获取队列的长度"""
return self.__size
def is_empty(self) -> bool:
""" 判断队列是否为空 """
"""判断队列是否为空"""
return not self.__front
def push(self, num: int) -> None:
""" 入队 """
"""入队"""
# 尾节点后添加 num
node = ListNode(num)
# 如果队列为空，则令头、尾节点都指向该节点
@ -39,7 +42,7 @@ class LinkedListQueue:
self.__size += 1
def pop(self) -> int:
""" 出队 """
"""出队"""
num = self.peek()
# 删除头节点
self.__front = self.__front.next
@ -47,14 +50,14 @@ class LinkedListQueue:
return num
def peek(self) -> int:
""" 访问队首元素 """
"""访问队首元素"""
if self.size() == 0:
print("队列为空")
return False
return self.__front.val
def to_list(self) -> list[int]:
""" 转化为列表用于打印 """
"""转化为列表用于打印"""
queue = []
temp = self.__front
while temp:
@ -65,10 +68,10 @@ class LinkedListQueue:
""" Driver Code """
if __name__ == "__main__":
""" 初始化队列 """
# 初始化队列
queue = LinkedListQueue()
""" 元素入队 """
# 元素入队
queue.push(1)
queue.push(3)
queue.push(2)
@ -76,19 +79,19 @@ if __name__ == "__main__":
queue.push(4)
print("队列 queue =", queue.to_list())
""" 访问队首元素 """
# 访问队首元素
peek: int = queue.peek()
print("队首元素 front =", peek)
""" 元素出队 """
# 元素出队
pop_front: int = queue.pop()
print("出队元素 pop =", pop_front)
print("出队后 queue =", queue.to_list())
""" 获取队列的长度 """
# 获取队列的长度
size: int = queue.size()
print("队列长度 size =", size)
""" 判断队列是否为空 """
# 判断队列是否为空
is_empty: bool = queue.is_empty()
print("队列是否为空 =", is_empty)

34
codes/python/chapter_stack_and_queue/linkedlist_stack.py
View File

@ -5,46 +5,50 @@ Author: Peng Chen (pengchzn@gmail.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
class LinkedListStack:
""" 基于链表实现的栈 """
"""基于链表实现的栈"""
def __init__(self):
""" 构造方法 """
"""构造方法"""
self.__peek: ListNode | None = None
self.__size: int = 0
def size(self) -> int:
""" 获取栈的长度 """
"""获取栈的长度"""
return self.__size
def is_empty(self) -> bool:
""" 判断栈是否为空 """
"""判断栈是否为空"""
return not self.__peek
def push(self, val: int) -> None:
""" 入栈 """
"""入栈"""
node = ListNode(val)
node.next = self.__peek
self.__peek = node
self.__size += 1
def pop(self) -> int:
""" 出栈 """
"""出栈"""
num: int = self.peek()
self.__peek = self.__peek.next
self.__size -= 1
return num
def peek(self) -> int:
""" 访问栈顶元素 """
"""访问栈顶元素"""
# 判空处理
if not self.__peek: return None
if not self.__peek:
return None
return self.__peek.val
def to_list(self) -> list[int]:
""" 转化为列表用于打印 """
"""转化为列表用于打印"""
arr: list[int] = []
node = self.__peek
while node:
@ -56,10 +60,10 @@ class LinkedListStack:
""" Driver Code """
if __name__ == "__main__":
""" 初始化栈 """
# 初始化栈
stack = LinkedListStack()
""" 元素入栈 """
# 元素入栈
stack.push(1)
stack.push(3)
stack.push(2)
@ -67,19 +71,19 @@ if __name__ == "__main__":
stack.push(4)
print("栈 stack =", stack.to_list())
""" 访问栈顶元素 """
# 访问栈顶元素
peek: int = stack.peek()
print("栈顶元素 peek =", peek)
""" 元素出栈 """
# 元素出栈
pop: int = stack.pop()
print("出栈元素 pop =", pop)
print("出栈后 stack =", stack.to_list())
""" 获取栈的长度 """
# 获取栈的长度
size: int = stack.size()
print("栈的长度 size =", size)
""" 判断是否为空 """
# 判断是否为空
is_empty: bool = stack.is_empty()
print("栈是否为空 =", is_empty)

13
codes/python/chapter_stack_and_queue/queue.py
View File

@ -8,13 +8,12 @@ from collections import deque
""" Driver Code """
if __name__ == "__main__":
""" 初始化队列 """
# 初始化队列
# 在 Python 中，我们一般将双向队列类 deque 看作队列使用
# 虽然 queue.Queue() 是纯正的队列类，但不太好用
que: deque[int] = deque()
""" 元素入队 """
# 元素入队
que.append(1)
que.append(3)
que.append(2)
@ -22,19 +21,19 @@ if __name__ == "__main__":
que.append(4)
print("队列 que =", que)
""" 访问队首元素 """
# 访问队首元素
front: int = que[0]
print("队首元素 front =", front)
""" 元素出队 """
# 元素出队
pop: int = que.popleft()
print("出队元素 pop =", pop)
print("出队后 que =", que)
""" 获取队列的长度 """
# 获取队列的长度
size: int = len(que)
print("队列长度 size =", size)
""" 判断队列是否为空 """
# 判断队列是否为空
is_empty: bool = len(que) == 0
print("队列是否为空 =", is_empty)

14
codes/python/chapter_stack_and_queue/stack.py
View File

@ -6,11 +6,11 @@ Author: Peng Chen (pengchzn@gmail.com)
""" Driver Code """
if __name__ == "__main__":
""" 初始化栈 """
# Python 没有内置的栈类，可以把 list 当作栈来使用
# 初始化栈
# Python 没有内置的栈类，可以把 list 当作栈来使用
stack: list[int] = []
""" 元素入栈 """
# 元素入栈
stack.append(1)
stack.append(3)
stack.append(2)
@ -18,19 +18,19 @@ if __name__ == "__main__":
stack.append(4)
print("栈 stack =", stack)
""" 访问栈顶元素 """
# 访问栈顶元素
peek: int = stack[-1]
print("栈顶元素 peek =", peek)
""" 元素出栈 """
# 元素出栈
pop: int = stack.pop()
print("出栈元素 pop =", pop)
print("出栈后 stack =", stack)
""" 获取栈的长度 """
# 获取栈的长度
size: int = len(stack)
print("栈的长度 size =", size)
""" 判断是否为空 """
# 判断是否为空
is_empty: bool = len(stack) == 0
print("栈是否为空 =", is_empty)

34
codes/python/chapter_tree/avl_tree.py
View File

@ -5,13 +5,16 @@ Author: a16su (lpluls001@gmail.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
class AVLTree:
""" AVL 树 """
"""AVL 树"""
def __init__(self, root: TreeNode | None = None):
""" 构造方法 """
"""构造方法"""
self.__root = root
@property
@ -19,19 +22,19 @@ class AVLTree:
return self.__root
def height(self, node: TreeNode | None) -> int:
""" 获取节点高度 """
"""获取节点高度"""
# 空节点高度为 -1 ，叶节点高度为 0
if node is not None:
return node.height
return -1
def __update_height(self, node: TreeNode | None):
""" 更新节点高度 """
"""更新节点高度"""
# 节点高度等于最高子树高度 + 1
node.height = max([self.height(node.left), self.height(node.right)]) + 1
def balance_factor(self, node: TreeNode | None) -> int:
""" 获取平衡因子 """
"""获取平衡因子"""
# 空节点平衡因子为 0
if node is None:
return 0
@ -39,7 +42,7 @@ class AVLTree:
return self.height(node.left) - self.height(node.right)
def __right_rotate(self, node: TreeNode | None) -> TreeNode | None:
""" 右旋操作 """
"""右旋操作"""
child = node.left
grand_child = child.right
# 以 child 为原点，将 node 向右旋转
@ -52,7 +55,7 @@ class AVLTree:
return child
def __left_rotate(self, node: TreeNode | None) -> TreeNode | None:
""" 左旋操作 """
"""左旋操作"""
child = node.right
grand_child = child.left
# 以 child 为原点，将 node 向左旋转
@ -65,7 +68,7 @@ class AVLTree:
return child
def __rotate(self, node: TreeNode | None) -> TreeNode | None:
""" 执行旋转操作，使该子树重新恢复平衡 """
"""执行旋转操作，使该子树重新恢复平衡"""
# 获取节点 node 的平衡因子
balance_factor = self.balance_factor(node)
# 左偏树
@ -90,12 +93,12 @@ class AVLTree:
return node
def insert(self, val) -> TreeNode:
""" 插入节点 """
"""插入节点"""
self.__root = self.__insert_helper(self.__root, val)
return self.__root
def __insert_helper(self, node: TreeNode | None, val: int) -> TreeNode:
""" 递归插入节点（辅助方法）"""
"""递归插入节点（辅助方法）"""
if node is None:
return TreeNode(val)
# 1. 查找插入位置，并插入节点
@ -112,12 +115,12 @@ class AVLTree:
return self.__rotate(node)
def remove(self, val: int) -> TreeNode | None:
""" 删除节点 """
"""删除节点"""
self.__root = self.__remove_helper(self.__root, val)
return self.__root
return self.__root
def __remove_helper(self, node: TreeNode | None, val: int) -> TreeNode | None:
""" 递归删除节点（辅助方法） """
"""递归删除节点（辅助方法）"""
if node is None:
return None
# 1. 查找节点，并删除之
@ -144,7 +147,7 @@ class AVLTree:
return self.__rotate(node)
def __get_inorder_next(self, node: TreeNode | None) -> TreeNode | None:
""" 获取中序遍历中的下一个节点（仅适用于 root 有左子节点的情况） """
"""获取中序遍历中的下一个节点（仅适用于 root 有左子节点的情况）"""
if node is None:
return None
# 循环访问左子节点，直到叶节点时为最小节点，跳出
@ -153,7 +156,7 @@ class AVLTree:
return node
def search(self, val: int) -> TreeNode | None:
""" 查找节点 """
"""查找节点"""
cur = self.__root
# 循环查找，越过叶节点后跳出
while cur is not None:
@ -172,6 +175,7 @@ class AVLTree:
""" Driver Code """
if __name__ == "__main__":
def test_insert(tree: AVLTree, val: int):
tree.insert(val)
print("\n插入节点 {} 后，AVL 树为".format(val))

32
codes/python/chapter_tree/binary_search_tree.py
View File

@ -5,19 +5,23 @@ Author: a16su (lpluls001@gmail.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
class BinarySearchTree:
""" 二叉搜索树 """
"""二叉搜索树"""
def __init__(self, nums: list[int]) -> None:
""" 构造方法 """
"""构造方法"""
nums.sort()
self.__root = self.build_tree(nums, 0, len(nums) - 1)
def build_tree(self, nums: list[int], start_index: int, end_index: int) -> TreeNode | None:
""" 构建二叉搜索树 """
def build_tree(
self, nums: list[int], start_index: int, end_index: int
) -> TreeNode | None:
"""构建二叉搜索树"""
if start_index > end_index:
return None
@ -25,8 +29,12 @@ class BinarySearchTree:
mid: int = (start_index + end_index) // 2
root = TreeNode(nums[mid])
# 递归建立左子树和右子树
root.left = self.build_tree(nums=nums, start_index=start_index, end_index=mid - 1)
root.right = self.build_tree(nums=nums, start_index=mid + 1, end_index=end_index)
root.left = self.build_tree(
nums=nums, start_index=start_index, end_index=mid - 1
)
root.right = self.build_tree(
nums=nums, start_index=mid + 1, end_index=end_index
)
return root
@property
@ -34,7 +42,7 @@ class BinarySearchTree:
return self.__root
def search(self, num: int) -> TreeNode | None:
""" 查找节点 """
"""查找节点"""
cur: TreeNode | None = self.__root
# 循环查找，越过叶节点后跳出
while cur is not None:
@ -50,11 +58,11 @@ class BinarySearchTree:
return cur
def insert(self, num: int) -> TreeNode | None:
""" 插入节点 """
"""插入节点"""
# 若树为空，直接提前返回
if self.__root is None:
return None
# 循环查找，越过叶节点后跳出
cur, pre = self.__root, None
while cur is not None:
@ -78,7 +86,7 @@ class BinarySearchTree:
return node
def remove(self, num: int) -> TreeNode | None:
""" 删除节点 """
"""删除节点"""
# 若树为空，直接提前返回
if self.__root is None:
return None
@ -119,7 +127,7 @@ class BinarySearchTree:
return cur
def get_inorder_next(self, root: TreeNode | None) -> TreeNode | None:
""" 获取中序遍历中的下一个节点（仅适用于 root 有左子节点的情况） """
"""获取中序遍历中的下一个节点（仅适用于 root 有左子节点的情况）"""
if root is None:
return root
# 循环访问左子节点，直到叶节点时为最小节点，跳出
@ -131,7 +139,7 @@ class BinarySearchTree:
""" Driver Code """
if __name__ == "__main__":
# 初始化二叉搜索树
nums = list(range(1, 16)) # [1, 2, ..., 15]
nums = list(range(1, 16)) # [1, 2, ..., 15]
bst = BinarySearchTree(nums=nums)
print("\n初始化的二叉树为\n")
print_tree(bst.root)

4
codes/python/chapter_tree/binary_tree.py
View File

@ -12,7 +12,7 @@ from modules import *
""" Driver Code """
if __name__ == "__main__":
""" 初始化二叉树 """
# 初始化二叉树
# 初始化节点
n1 = TreeNode(val=1)
n2 = TreeNode(val=2)
@ -27,7 +27,7 @@ if __name__ == "__main__":
print("\n初始化二叉树\n")
print_tree(n1)
""" 插入与删除节点 """
# 插入与删除节点
P = TreeNode(0)
# 在 n1 -> n2 中间插入节点 P
n1.left = P

12
codes/python/chapter_tree/binary_tree_bfs.py
View File

@ -5,24 +5,26 @@ Author: a16su (lpluls001@gmail.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
from collections import deque
def level_order(root: TreeNode | None) -> list[int]:
""" 层序遍历 """
"""层序遍历"""
# 初始化队列，加入根节点
queue: deque[TreeNode] = deque()
queue.append(root)
# 初始化一个列表，用于保存遍历序列
res: list[int] = []
while queue:
node: TreeNode = queue.popleft() # 队列出队
res.append(node.val) # 保存节点值
node: TreeNode = queue.popleft() # 队列出队
res.append(node.val) # 保存节点值
if node.left is not None:
queue.append(node.left) # 左子节点入队
queue.append(node.left) # 左子节点入队
if node.right is not None:
queue.append(node.right) # 右子节点入队
queue.append(node.right) # 右子节点入队
return res

10
codes/python/chapter_tree/binary_tree_dfs.py
View File

@ -5,11 +5,13 @@ Author: a16su (lpluls001@gmail.com)
"""
import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from modules import *
def pre_order(root: TreeNode | None) -> None:
""" 前序遍历 """
"""前序遍历"""
if root is None:
return
# 访问优先级：根节点 -> 左子树 -> 右子树
@ -17,8 +19,9 @@ def pre_order(root: TreeNode | None) -> None:
pre_order(root=root.left)
pre_order(root=root.right)
def in_order(root: TreeNode | None) -> None:
""" 中序遍历 """
"""中序遍历"""
if root is None:
return
# 访问优先级：左子树 -> 根节点 -> 右子树
@ -26,8 +29,9 @@ def in_order(root: TreeNode | None) -> None:
res.append(root.val)
in_order(root=root.right)
def post_order(root: TreeNode | None) -> None:
""" 后序遍历 """
"""后序遍历"""
if root is None:
return
# 访问优先级：左子树 -> 右子树 -> 根节点

16
codes/python/modules/__init__.py
View File

@ -1,8 +1,20 @@
# Follow the PEP 585 – Type Hinting Generics In Standard Collections
# https://peps.python.org/pep-0585/
from __future__ import annotations
# Import common libs here to simplify the codes by `from module import *`
from .linked_list import ListNode, list_to_linked_list, linked_list_to_list, get_list_node
from .linked_list import (
ListNode,
list_to_linked_list,
linked_list_to_list,
get_list_node,
)
from .binary_tree import TreeNode, list_to_tree, tree_to_list, get_tree_node
from .vertex import Vertex, vals_to_vets, vets_to_vals
from .print_util import print_matrix, print_linked_list, print_tree, print_dict, print_heap
from .print_util import (
print_matrix,
print_linked_list,
print_tree,
print_dict,
print_heap,
)

35
codes/python/modules/binary_tree.py
View File

@ -6,40 +6,47 @@ Author: Krahets (krahets@163.com)
from collections import deque
class TreeNode:
""" Definition for a binary tree node """
"""Definition for a binary tree node"""
def __init__(self, val: int = 0):
self.val: int = val # 节点值
self.height: int = 0 # 节点高度
self.val: int = val # 节点值
self.height: int = 0 # 节点高度
self.left: TreeNode | None = None # 左子节点引用
self.right: TreeNode | None = None # 右子节点引用
self.right: TreeNode | None = None # 右子节点引用
def list_to_tree(arr: list[int]) -> TreeNode | None:
""" Generate a binary tree with a list """
"""Generate a binary tree with a list"""
if not arr:
return None
i: int = 0
root = TreeNode(arr[0])
queue: deque[TreeNode] = deque([root])
while queue:
node: TreeNode = queue.popleft()
i += 1
if i >= len(arr): break
if i >= len(arr):
break
if arr[i] != None:
node.left = TreeNode(arr[i])
queue.append(node.left)
i += 1
if i >= len(arr): break
if i >= len(arr):
break
if arr[i] != None:
node.right = TreeNode(arr[i])
queue.append(node.right)
return root
def tree_to_list(root: TreeNode | None) -> list[int]:
""" Serialize a tree into an array """
if not root: return []
"""Serialize a tree into an array"""
if not root:
return []
queue: deque[TreeNode] = deque()
queue.append(root)
res: list[int] = []
@ -49,11 +56,13 @@ def tree_to_list(root: TreeNode | None) -> list[int]:
res.append(node.val)
queue.append(node.left)
queue.append(node.right)
else: res.append(None)
else:
res.append(None)
return res
def get_tree_node(root: TreeNode | None, val: int) -> TreeNode | None:
""" Get a tree node with specific value in a binary tree """
"""Get a tree node with specific value in a binary tree"""
if not root:
return
if root.val == val:

17
codes/python/modules/linked_list.py
View File

@ -4,14 +4,17 @@ Created Time: 2021-12-11
Author: Krahets (krahets@163.com)
"""
class ListNode:
""" Definition for a singly-linked list node """
"""Definition for a singly-linked list node"""
def __init__(self, val: int):
self.val: int = val # 节点值
self.next: ListNode | None = None # 后继节点引用
self.val: int = val # 节点值
self.next: ListNode | None = None # 后继节点引用
def list_to_linked_list(arr: list[int]) -> ListNode | None:
""" Generate a linked list with a list """
"""Generate a linked list with a list"""
dum = head = ListNode(0)
for a in arr:
node = ListNode(a)
@ -19,16 +22,18 @@ def list_to_linked_list(arr: list[int]) -> ListNode | None:
head = head.next
return dum.next
def linked_list_to_list(head: ListNode | None) -> list[int]:
""" Serialize a linked list into an array """
"""Serialize a linked list into an array"""
arr: list[int] = []
while head:
arr.append(head.val)
head = head.next
return arr
def get_list_node(head: ListNode | None, val: int) -> ListNode | None:
""" Get a list node with specific value from a linked list """
"""Get a list node with specific value from a linked list"""
while head and head.val != val:
head = head.next
return head

43
codes/python/modules/print_util.py
View File

@ -7,31 +7,38 @@ Author: Krahets (krahets@163.com), msk397 (machangxinq@gmail.com)
from .binary_tree import TreeNode, list_to_tree
from .linked_list import ListNode, linked_list_to_list
def print_matrix(mat: list[list[int]]) -> None:
""" Print a matrix """
"""Print a matrix"""
s: list[str] = []
for arr in mat:
s.append(' ' + str(arr))
s.append(" " + str(arr))
print("[\n" + ",\n".join(s) + "\n]")
print('[\n' + ',\n'.join(s) + '\n]')
def print_linked_list(head: ListNode | None) -> None:
""" Print a linked list """
"""Print a linked list"""
arr: list[int] = linked_list_to_list(head)
print(' -> '.join([str(a) for a in arr]))
print(" -> ".join([str(a) for a in arr]))
class Trunk:
def __init__(self, prev, string: str | None = None) -> None:
self.prev = prev
self.str = string
def show_trunks(p: Trunk | None) -> None:
if p is None:
return
show_trunks(p.prev)
print(p.str, end='')
print(p.str, end="")
def print_tree(root: TreeNode | None, prev: Trunk | None = None, is_left: bool = False) -> None:
def print_tree(
root: TreeNode | None, prev: Trunk | None = None, is_left: bool = False
) -> None:
"""
Print a binary tree
This tree printer is borrowed from TECHIE DELIGHT
@ -40,33 +47,35 @@ def print_tree(root: TreeNode | None, prev: Trunk | None = None, is_left: bool =
if root is None:
return
prev_str: str = ' '
prev_str: str = " "
trunk = Trunk(prev, prev_str)
print_tree(root.right, trunk, True)
if prev is None:
trunk.str = '———'
trunk.str = "———"
elif is_left:
trunk.str = '/———'
prev_str = ' |'
trunk.str = "/———"
prev_str = " |"
else:
trunk.str = '\———'
trunk.str = "\———"
prev.str = prev_str
show_trunks(trunk)
print(' ' + str(root.val))
print(" " + str(root.val))
if prev:
prev.str = prev_str
trunk.str = ' |'
trunk.str = " |"
print_tree(root.left, trunk, False)
def print_dict(mapp: dict) -> None:
""" Print a dict """
"""Print a dict"""
for key, value in mapp.items():
print(key, '->', value)
print(key, "->", value)
def print_heap(heap: list[int]) -> None:
""" Print a heap both in array and tree representations """
"""Print a heap both in array and tree representations"""
print("堆的数组表示：", heap)
print("堆的树状表示：")
root: TreeNode | None = list_to_tree(heap)

14
codes/python/modules/vertex.py
View File

@ -2,15 +2,19 @@
# Created Time: 2023-02-23
# Author: Krahets (krahets@163.com)
class Vertex:
""" 顶点类 """
"""顶点类"""
def __init__(self, val: int) -> None:
self.val = val
def vals_to_vets(vals: list[int]) -> list['Vertex']:
""" 输入值列表 vals ，返回顶点列表 vets """
def vals_to_vets(vals: list[int]) -> list["Vertex"]:
"""输入值列表 vals ，返回顶点列表 vets"""
return [Vertex(val) for val in vals]
def vets_to_vals(vets: list['Vertex']) -> list[int]:
""" 输入顶点列表 vets ，返回值列表 vals """
def vets_to_vals(vets: list["Vertex"]) -> list[int]:
"""输入顶点列表 vets ，返回值列表 vals"""
return [vet.val for vet in vets]