build

chapter_computational_complexity/space_complexity.md

@@ -629,7 +629,7 @@ $$
         """ 常数阶 """
         # 常量、变量、对象占用 O(1) 空间
         a: int = 0
-        nums: List[int] = [0] * 10000
+        nums: list[int] = [0] * 10000
         node = ListNode(0)
         # 循环中的变量占用 O(1) 空间
         for _ in range(n):
@@ -833,9 +833,9 @@ $$
     def linear(n: int) -> None:
         """ 线性阶 """
         # 长度为 n 的列表占用 O(n) 空间
-        nums: List[int] = [0] * n
+        nums: list[int] = [0] * n
         # 长度为 n 的哈希表占用 O(n) 空间
-        mapp: Dict = {}
+        mapp = dict[int, str]()
         for i in range(n):
             mapp[i] = str(i)
     ```
@@ -1131,7 +1131,7 @@ $$
     def quadratic(n: int) -> None:
         """ 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)]
+        num_matrix: list[list[int]] = [[0] * n for _ in range(n)]
     ```
 
 === "Go"
@@ -1280,7 +1280,7 @@ $$
         """ 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
+        nums: list[int] = [0] * n
         return quadratic_recur(n - 1)
     ```
 
@@ -1406,7 +1406,7 @@ $$
 === "Python"
 
     ```python title="space_complexity.py"
-    def build_tree(n: int) -> Optional[TreeNode]:
+    def build_tree(n: int) -> TreeNode | None:
         """ 指数阶（建立满二叉树） """
         if n == 0: return None
         root = TreeNode(0)

chapter_computational_complexity/space_time_tradeoff.md

@@ -65,7 +65,7 @@ comments: true
 === "Python"
 
     ```python title="leetcode_two_sum.py"
-    def two_sum_brute_force(nums: List[int], target: int) -> List[int]:
+    def two_sum_brute_force(nums: list[int], target: int) -> list[int]:
         """ 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):
@@ -242,7 +242,7 @@ comments: true
 === "Python"
 
     ```python title="leetcode_two_sum.py"
-    def two_sum_hash_table(nums: List[int], target: int) -> List[int]:
+    def two_sum_hash_table(nums: list[int], target: int) -> list[int]:
         """ 方法二：辅助哈希表 """
         # 辅助哈希表，空间复杂度 O(n)
         dic = {}

chapter_computational_complexity/time_complexity.md

@@ -1075,7 +1075,7 @@ $$
 === "Python"
 
     ```python title="time_complexity.py"
-    def array_traversal(nums: List[int]) -> int:
+    def array_traversal(nums: list[int]) -> int:
         """ 线性阶（遍历数组）"""
         count: int = 0
         # 循环次数与数组长度成正比
@@ -1391,7 +1391,7 @@ $$
 === "Python"
 
     ```python title="time_complexity.py"
-    def bubble_sort(nums: List[int]) -> int:
+    def bubble_sort(nums: list[int]) -> int:
         """ 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
@@ -2481,15 +2481,15 @@ $$
 === "Python"
 
     ```python title="worst_best_time_complexity.py"
-    def random_numbers(n: int) -> List[int]:
+    def random_numbers(n: int) -> list[int]:
         """ 生成一个数组，元素为: 1, 2, ..., n ，顺序被打乱 """
         # 生成数组 nums =: 1, 2, 3, ..., n
-        nums: List[int] = [i for i in range(1, n + 1)]
+        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:
+    def find_one(nums: list[int]) -> int:
         """ 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)