mirror of
https://github.com/krahets/hello-algo.git
synced 2024-12-24 10:16:28 +08:00
Simplify the declarations of the Python code.
This commit is contained in:
krahets
parent
081b76d620
commit
e196962d0a
27 changed files with 88 additions and 87 deletions
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@ -70,9 +70,9 @@ def find(nums: list[int], target: int) -> int:
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"""Driver Code"""
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if __name__ == "__main__":
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# 初始化数组
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arr: list[int] = [0] * 5
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arr = [0] * 5
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print("数组 arr =", arr)
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nums: list[int] = [1, 3, 2, 5, 4]
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nums = [1, 3, 2, 5, 4]
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print("数组 nums =", nums)
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# 随机访问
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@ -7,7 +7,7 @@ Author: Krahets (krahets@163.com)
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"""Driver Code"""
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if __name__ == "__main__":
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# 初始化列表
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arr: list[int] = [1, 3, 2, 5, 4]
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arr = [1, 3, 2, 5, 4]
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print("列表 arr =", arr)
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# 访问元素
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@ -39,17 +39,17 @@ if __name__ == "__main__":
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print("删除索引 3 处的元素，得到 arr =", arr)
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# 通过索引遍历列表
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count: int = 0
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count = 0
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for i in range(len(arr)):
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count += 1
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# 直接遍历列表元素
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count: int = 0
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count = 0
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for n in arr:
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count += 1
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# 拼接两个列表
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arr1: list[int] = [6, 8, 7, 10, 9]
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arr1 = [6, 8, 7, 10, 9]
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arr += arr1
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print("将列表 arr1 拼接到 arr 之后，得到 arr =", arr)
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@ -35,7 +35,9 @@ def undo_choice(state: list[TreeNode], choice: TreeNode):
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state.pop()
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def backtrack(state: list[TreeNode], choices: list[TreeNode], res: list[list[TreeNode]]):
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def backtrack(
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state: list[TreeNode], choices: list[TreeNode], res: list[list[TreeNode]]
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):
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"""回溯算法：例题三"""
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# 检查是否为解
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if is_solution(state):
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@ -59,7 +61,7 @@ if __name__ == "__main__":
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root = list_to_tree([1, 7, 3, 4, 5, 6, 7])
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print("\n初始化二叉树")
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print_tree(root)
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# 回溯算法
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res = []
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backtrack(state=[], choices=[root], res=res)
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@ -19,12 +19,12 @@ def function() -> int:
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def constant(n: int) -> None:
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"""常数阶"""
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# 常量、变量、对象占用 O(1) 空间
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a: int = 0
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nums: list[int] = [0] * 10000
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a = 0
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nums = [0] * 10000
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node = ListNode(0)
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# 循环中的变量占用 O(1) 空间
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for _ in range(n):
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c: int = 0
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c = 0
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# 循环中的函数占用 O(1) 空间
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for _ in range(n):
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function()
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@ -33,7 +33,7 @@ def constant(n: int) -> None:
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def linear(n: int) -> None:
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"""线性阶"""
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# 长度为 n 的列表占用 O(n) 空间
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nums: list[int] = [0] * n
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nums = [0] * n
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# 长度为 n 的哈希表占用 O(n) 空间
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mapp = dict[int, str]()
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for i in range(n):
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@ -51,7 +51,7 @@ def linear_recur(n: int) -> None:
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def quadratic(n: int) -> None:
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"""平方阶"""
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# 二维列表占用 O(n^2) 空间
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num_matrix: list[list[int]] = [[0] * n for _ in range(n)]
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num_matrix = [[0] * n for _ in range(n)]
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def quadratic_recur(n: int) -> int:
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@ -59,7 +59,7 @@ def quadratic_recur(n: int) -> int:
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if n <= 0:
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return 0
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# 数组 nums 长度为 n, n-1, ..., 2, 1
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nums: list[int] = [0] * n
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nums = [0] * n
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return quadratic_recur(n - 1)
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@ -7,8 +7,8 @@ Author: Krahets (krahets@163.com)
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def constant(n: int) -> int:
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"""常数阶"""
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count: int = 0
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size: int = 100000
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count = 0
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size = 100000
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for _ in range(size):
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count += 1
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return count
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@ -16,7 +16,7 @@ def constant(n: int) -> int:
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def linear(n: int) -> int:
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"""线性阶"""
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count: int = 0
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count = 0
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for _ in range(n):
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count += 1
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return count
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@ -24,7 +24,7 @@ def linear(n: int) -> int:
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def array_traversal(nums: list[int]) -> int:
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"""线性阶（遍历数组）"""
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count: int = 0
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count = 0
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# 循环次数与数组长度成正比
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for num in nums:
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count += 1
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@ -33,7 +33,7 @@ def array_traversal(nums: list[int]) -> int:
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def quadratic(n: int) -> int:
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"""平方阶"""
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count: int = 0
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count = 0
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# 循环次数与数组长度成平方关系
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for i in range(n):
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for j in range(n):
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@ -43,7 +43,7 @@ def quadratic(n: int) -> int:
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def bubble_sort(nums: list[int]) -> int:
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"""平方阶（冒泡排序）"""
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count: int = 0 # 计数器
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count = 0 # 计数器
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# 外循环：待排序元素数量为 n-1, n-2, ..., 1
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for i in range(len(nums) - 1, 0, -1):
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# 内循环：冒泡操作
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@ -59,8 +59,8 @@ def bubble_sort(nums: list[int]) -> int:
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def exponential(n: int) -> int:
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"""指数阶（循环实现）"""
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count: int = 0
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base: int = 1
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count = 0
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base = 1
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# cell 每轮一分为二，形成数列 1, 2, 4, 8, ..., 2^(n-1)
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for _ in range(n):
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for _ in range(base):
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@ -79,7 +79,7 @@ def exp_recur(n: int) -> int:
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def logarithmic(n: float) -> int:
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"""对数阶（循环实现）"""
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count: int = 0
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count = 0
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while n > 1:
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n = n / 2
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count += 1
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@ -107,7 +107,7 @@ def factorial_recur(n: int) -> int:
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"""阶乘阶（递归实现）"""
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if n == 0:
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return 1
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count: int = 0
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count = 0
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# 从 1 个分裂出 n 个
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for _ in range(n):
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count += factorial_recur(n - 1)
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@ -130,7 +130,7 @@ if __name__ == "__main__":
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count: int = quadratic(n)
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print("平方阶的计算操作数量 =", count)
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nums: list[int] = [i for i in range(n, 0, -1)] # [n,n-1,...,2,1]
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nums = [i for i in range(n, 0, -1)] # [n, n-1, ..., 2, 1]
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count: int = bubble_sort(nums)
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print("平方阶（冒泡排序）的计算操作数量 =", count)
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@ -10,7 +10,7 @@ import random
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def random_numbers(n: int) -> list[int]:
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"""生成一个数组，元素为: 1, 2, ..., n ，顺序被打乱"""
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# 生成数组 nums =: 1, 2, 3, ..., n
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nums: list[int] = [i for i in range(1, n + 1)]
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nums = [i for i in range(1, n + 1)]
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# 随机打乱数组元素
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random.shuffle(nums)
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return nums
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@ -29,7 +29,7 @@ def find_one(nums: list[int]) -> int:
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"""Driver Code"""
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if __name__ == "__main__":
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for i in range(10):
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n: int = 100
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n = 100
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nums: list[int] = random_numbers(n)
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index: int = find_one(nums)
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print("\n数组 [ 1, 2, ..., n ] 被打乱后 =", nums)
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@ -88,8 +88,8 @@ class GraphAdjMat:
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if __name__ == "__main__":
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# 初始化无向图
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# 请注意，edges 元素代表顶点索引，即对应 vertices 元素索引
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vertices: list[int] = [1, 3, 2, 5, 4]
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edges: list[list[int]] = [[0, 1], [0, 3], [1, 2], [2, 3], [2, 4], [3, 4]]
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vertices = [1, 3, 2, 5, 4]
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edges = [[0, 1], [0, 3], [1, 2], [2, 3], [2, 4], [3, 4]]
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graph = GraphAdjMat(vertices, edges)
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print("\n初始化后，图为")
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graph.print()
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@ -23,7 +23,7 @@ class ArrayHashMap:
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def hash_func(self, key: int) -> int:
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"""哈希函数"""
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index: int = key % 100
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index = key % 100
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return index
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def get(self, key: int) -> str:
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@ -56,7 +56,7 @@ class ArrayHashMap:
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def key_set(self) -> list[int]:
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"""获取所有键"""
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result: list[int] = []
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result = []
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for pair in self.buckets:
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if pair is not None:
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result.append(pair.key)
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@ -64,7 +64,7 @@ class ArrayHashMap:
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def value_set(self) -> list[str]:
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"""获取所有值"""
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result: list[str] = []
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result = []
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for pair in self.buckets:
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if pair is not None:
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result.append(pair.val)
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@ -64,7 +64,7 @@ if __name__ == "__main__":
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# 输入列表并建堆
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# 时间复杂度为 O(n) ，而非 O(nlogn)
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min_heap: list[int] = [1, 3, 2, 5, 4]
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min_heap = [1, 3, 2, 5, 4]
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heapq.heapify(min_heap)
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print("\n输入列表并建立小顶堆后")
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print_heap(min_heap)
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@ -40,8 +40,8 @@ def binary_search_lcro(nums: list[int], target: int) -> int:
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"""Driver Code"""
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if __name__ == "__main__":
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target: int = 6
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nums: list[int] = [1, 3, 6, 8, 12, 15, 23, 26, 31, 35]
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target = 6
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nums = [1, 3, 6, 8, 12, 15, 23, 26, 31, 35]
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# 二分查找（双闭区间）
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index: int = binary_search(nums, target)
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@ -41,8 +41,8 @@ def binary_search_right_edge(nums: list[int], target: int) -> int:
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"""Driver Code"""
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if __name__ == "__main__":
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target: int = 6
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nums: list[int] = [1, 3, 6, 6, 6, 6, 6, 10, 12, 15]
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target = 6
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nums = [1, 3, 6, 6, 6, 6, 6, 10, 12, 15]
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# 二分查找最左一个元素
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index_left = binary_search_left_edge(nums, target)
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@ -28,10 +28,10 @@ def hashing_search_linkedlist(
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"""Driver Code"""
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if __name__ == "__main__":
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target: int = 3
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target = 3
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# 哈希查找（数组）
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nums: list[int] = [1, 5, 3, 2, 4, 7, 5, 9, 10, 8]
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nums = [1, 5, 3, 2, 4, 7, 5, 9, 10, 8]
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# 初始化哈希表
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map0 = dict[int, int]()
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for i in range(len(nums)):
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@ -31,10 +31,10 @@ def linear_search_linkedlist(head: ListNode, target: int) -> ListNode | None:
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"""Driver Code"""
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if __name__ == "__main__":
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target: int = 3
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target = 3
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# 在数组中执行线性查找
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nums: list[int] = [1, 5, 3, 2, 4, 7, 5, 9, 10, 8]
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nums = [1, 5, 3, 2, 4, 7, 5, 9, 10, 8]
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index: int = linear_search_array(nums, target)
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print("目标元素 3 的索引 =", index)
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@ -7,7 +7,7 @@ Author: timi (xisunyy@163.com)
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def bubble_sort(nums: list[int]) -> None:
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"""冒泡排序"""
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n: int = len(nums)
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n = len(nums)
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# 外循环：待排序元素数量为 n-1, n-2, ..., 1
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for i in range(n - 1, 0, -1):
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# 内循环：冒泡操作
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@ -19,10 +19,10 @@ def bubble_sort(nums: list[int]) -> None:
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def bubble_sort_with_flag(nums: list[int]) -> None:
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"""冒泡排序（标志优化）"""
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n: int = len(nums)
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n = len(nums)
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# 外循环：待排序元素数量为 n-1, n-2, ..., 1
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for i in range(n - 1, 0, -1):
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flag: bool = False # 初始化标志位
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flag = False # 初始化标志位
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# 内循环：冒泡操作
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for j in range(i):
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if nums[j] > nums[j + 1]:
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@ -35,10 +35,10 @@ def bubble_sort_with_flag(nums: list[int]) -> None:
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"""Driver Code"""
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if __name__ == "__main__":
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nums: list[int] = [4, 1, 3, 1, 5, 2]
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nums = [4, 1, 3, 1, 5, 2]
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bubble_sort(nums)
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print("排序后数组 nums =", nums)
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nums1: list[int] = [4, 1, 3, 1, 5, 2]
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nums1 = [4, 1, 3, 1, 5, 2]
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bubble_sort_with_flag(nums1)
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print("排序后数组 nums =", nums1)
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@ -9,8 +9,8 @@ def insertion_sort(nums: list[int]) -> None:
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"""插入排序"""
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# 外循环：base = nums[1], nums[2], ..., nums[n-1]
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for i in range(1, len(nums)):
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base: int = nums[i]
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j: int = i - 1
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base = nums[i]
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j = i - 1
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# 内循环：将 base 插入到左边的正确位置
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while j >= 0 and nums[j] > base:
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nums[j + 1] = nums[j] # 1. 将 nums[j] 向右移动一位
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@ -20,6 +20,6 @@ def insertion_sort(nums: list[int]) -> None:
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"""Driver Code"""
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if __name__ == "__main__":
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nums: list[int] = [4, 1, 3, 1, 5, 2]
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nums = [4, 1, 3, 1, 5, 2]
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insertion_sort(nums)
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print("排序后数组 nums =", nums)
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@ -10,16 +10,16 @@ def merge(nums: list[int], left: int, mid: int, right: int) -> None:
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# 左子数组区间 [left, mid]
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# 右子数组区间 [mid + 1, right]
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# 初始化辅助数组
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tmp: list[int] = list(nums[left : right + 1])
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tmp = list(nums[left : right + 1])
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# 左子数组的起始索引和结束索引
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left_start: int = 0
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left_end: int = mid - left
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left_start = 0
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left_end = mid - left
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# 右子数组的起始索引和结束索引
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right_start: int = mid + 1 - left
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right_end: int = right - left
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right_start = mid + 1 - left
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right_end = right - left
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# i, j 分别指向左子数组、右子数组的首元素
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i: int = left_start
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j: int = right_start
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i = left_start
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j = right_start
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# 通过覆盖原数组 nums 来合并左子数组和右子数组
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for k in range(left, right + 1):
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# 若“左子数组已全部合并完”，则选取右子数组元素，并且 j++
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@ -42,7 +42,7 @@ def merge_sort(nums: list[int], left: int, right: int) -> None:
|
|||
if left >= right:
|
||||
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
|
||||
mid = (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
|
||||
|
|
@ -51,6 +51,6 @@ def merge_sort(nums: list[int], left: int, right: int) -> None:
|
|||
|
||||
"""Driver Code"""
|
||||
if __name__ == "__main__":
|
||||
nums: list[int] = [7, 3, 2, 6, 0, 1, 5, 4]
|
||||
nums = [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)
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ class QuickSort:
|
|||
if left >= right:
|
||||
return
|
||||
# 哨兵划分
|
||||
pivot: int = self.partition(nums, left, right)
|
||||
pivot = self.partition(nums, left, right)
|
||||
# 递归左子数组、右子数组
|
||||
self.quick_sort(nums, left, pivot - 1)
|
||||
self.quick_sort(nums, pivot + 1, right)
|
||||
|
|
@ -51,7 +51,7 @@ class QuickSortMedian:
|
|||
def partition(self, nums: list[int], left: int, right: int) -> int:
|
||||
"""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)
|
||||
med = self.median_three(nums, left, (left + right) // 2, right)
|
||||
# 将中位数交换至数组最左端
|
||||
nums[left], nums[med] = nums[med], nums[left]
|
||||
# 以 nums[left] 作为基准数
|
||||
|
|
@ -73,7 +73,7 @@ class QuickSortMedian:
|
|||
if left >= right:
|
||||
return
|
||||
# 哨兵划分
|
||||
pivot: int = self.partition(nums, left, right)
|
||||
pivot = self.partition(nums, left, right)
|
||||
# 递归左子数组、右子数组
|
||||
self.quick_sort(nums, left, pivot - 1)
|
||||
self.quick_sort(nums, pivot + 1, right)
|
||||
|
|
@ -102,7 +102,7 @@ class QuickSortTailCall:
|
|||
# 子数组长度为 1 时终止
|
||||
while left < right:
|
||||
# 哨兵划分操作
|
||||
pivot: int = self.partition(nums, left, right)
|
||||
pivot = self.partition(nums, left, right)
|
||||
# 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
|
||||
|
|
@ -115,16 +115,16 @@ class QuickSortTailCall:
|
|||
"""Driver Code"""
|
||||
if __name__ == "__main__":
|
||||
# 快速排序
|
||||
nums: list[int] = [2, 4, 1, 0, 3, 5]
|
||||
nums = [2, 4, 1, 0, 3, 5]
|
||||
QuickSort().quick_sort(nums, 0, len(nums) - 1)
|
||||
print("快速排序完成后 nums =", nums)
|
||||
|
||||
# 快速排序（中位基准数优化）
|
||||
nums1: list[int] = [2, 4, 1, 0, 3, 5]
|
||||
nums1 = [2, 4, 1, 0, 3, 5]
|
||||
QuickSortMedian().quick_sort(nums1, 0, len(nums1) - 1)
|
||||
print("快速排序（中位基准数优化）完成后 nums =", nums1)
|
||||
|
||||
# 快速排序（尾递归优化）
|
||||
nums2: list[int] = [2, 4, 1, 0, 3, 5]
|
||||
nums2= [2, 4, 1, 0, 3, 5]
|
||||
QuickSortTailCall().quick_sort(nums2, 0, len(nums2) - 1)
|
||||
print("快速排序（尾递归优化）完成后 nums =", nums2)
|
||||
|
|
|
|||
|
|
@ -53,7 +53,7 @@ class ArrayQueue:
|
|||
|
||||
def to_list(self) -> list[int]:
|
||||
"""返回列表用于打印"""
|
||||
res: list[int] = [0] * self.size()
|
||||
res = [0] * self.size()
|
||||
j: int = self.__front
|
||||
for i in range(self.size()):
|
||||
res[i] = self.__nums[(j % self.capacity())]
|
||||
|
|
|
|||
|
|
@ -104,8 +104,8 @@ class LinkedListDeque:
|
|||
|
||||
def to_array(self) -> list[int]:
|
||||
"""返回数组用于打印"""
|
||||
node: ListNode | None = self.front
|
||||
res: list[int] = [0] * self.size()
|
||||
node = self.front
|
||||
res = [0] * self.size()
|
||||
for i in range(self.size()):
|
||||
res[i] = node.val
|
||||
node = node.next
|
||||
|
|
|
|||
|
|
@ -49,7 +49,7 @@ class LinkedListStack:
|
|||
|
||||
def to_list(self) -> list[int]:
|
||||
"""转化为列表用于打印"""
|
||||
arr: list[int] = []
|
||||
arr = []
|
||||
node = self.__peek
|
||||
while node:
|
||||
arr.append(node.val)
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ class BinarySearchTree:
|
|||
return None
|
||||
|
||||
# 将数组中间节点作为根节点
|
||||
mid: int = (start_index + end_index) // 2
|
||||
mid = (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(
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ def level_order(root: TreeNode | None) -> list[int]:
|
|||
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] = []
|
||||
res = []
|
||||
while queue:
|
||||
node: TreeNode = queue.popleft() # 队列出队
|
||||
res.append(node.val) # 保存节点值
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ def list_to_tree(arr: list[int]) -> TreeNode | None:
|
|||
if not arr:
|
||||
return None
|
||||
|
||||
i: int = 0
|
||||
i = 0
|
||||
root = TreeNode(arr[0])
|
||||
queue: deque[TreeNode] = deque([root])
|
||||
while queue:
|
||||
|
|
|
|||
|
|
@ -10,10 +10,9 @@ from .linked_list import ListNode, linked_list_to_list
|
|||
|
||||
def print_matrix(mat: list[list[int]]) -> None:
|
||||
"""Print a matrix"""
|
||||
s: list[str] = []
|
||||
s = []
|
||||
for arr in mat:
|
||||
s.append(" " + str(arr))
|
||||
|
||||
print("[\n" + ",\n".join(s) + "\n]")
|
||||
|
||||
|
||||
|
|
@ -47,7 +46,7 @@ def print_tree(
|
|||
if root is None:
|
||||
return
|
||||
|
||||
prev_str: str = " "
|
||||
prev_str = " "
|
||||
trunk = Trunk(prev, prev_str)
|
||||
print_tree(root.right, trunk, True)
|
||||
|
||||
|
|
|
|||
|
|
@ -430,12 +430,12 @@
|
|||
|
||||
```python title="list.py"
|
||||
# 通过索引遍历列表
|
||||
count: int = 0
|
||||
count = 0
|
||||
for i in range(len(list)):
|
||||
count += 1
|
||||
|
||||
# 直接遍历列表元素
|
||||
count: int = 0
|
||||
count = 0
|
||||
for n in list:
|
||||
count += 1
|
||||
```
|
||||
|
|
|
|||
|
|
@ -87,10 +87,10 @@
|
|||
return 0
|
||||
|
||||
def algorithm(n) -> int: # 输入数据
|
||||
A: int = 0 # 暂存数据（常量，一般用大写字母表示）
|
||||
b: int = 0 # 暂存数据（变量）
|
||||
A = 0 # 暂存数据（常量，一般用大写字母表示）
|
||||
b = 0 # 暂存数据（变量）
|
||||
node = Node(0) # 暂存数据（对象）
|
||||
c: int = function() # 栈帧空间（调用函数）
|
||||
c = function() # 栈帧空间（调用函数）
|
||||
return A + b + c # 输出数据
|
||||
```
|
||||
|
||||
|
|
@ -293,10 +293,10 @@
|
|||
|
||||
```python title=""
|
||||
def algorithm(n: int) -> None:
|
||||
a: int = 0 # O(1)
|
||||
b: List[int] = [0] * 10000 # O(1)
|
||||
a = 0 # O(1)
|
||||
b = [0] * 10000 # O(1)
|
||||
if n > 10:
|
||||
nums: List[int] = [0] * n # O(n)
|
||||
nums = [0] * n # O(n)
|
||||
```
|
||||
|
||||
=== "Go"
|
||||
|
|
|
|||
|
|
@ -415,7 +415,7 @@ $$
|
|||
|
||||
```python title=""
|
||||
def algorithm(n: int) -> None:
|
||||
a: int = 1 # +1
|
||||
a = 1 # +1
|
||||
a = a + 1 # +1
|
||||
a = a * 2 # +1
|
||||
# 循环 n 次
|
||||
|
|
@ -604,8 +604,8 @@ $$
|
|||
|
||||
```python title=""
|
||||
def algorithm(n: int) -> None:
|
||||
a: int = 1 # +0（技巧 1）
|
||||
a = a + n # +0（技巧 1）
|
||||
a = 1 # +0（技巧 1）
|
||||
a = a + n # +0（技巧 1）
|
||||
# +n（技巧 2）
|
||||
for i in range(5 * n + 1):
|
||||
print(0)
|
||||
|
|
@ -619,7 +619,7 @@ $$
|
|||
|
||||
```go title=""
|
||||
func algorithm(n int) {
|
||||
a := 1 // +0（技巧 1）
|
||||
a := 1 // +0（技巧 1）
|
||||
a = a + n // +0（技巧 1）
|
||||
// +n（技巧 2）
|
||||
for i := 0; i < 5 * n + 1; i++ {
|
||||
|
|
|
|||
Loading…
Reference in a new issue