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Add python codes and for the chapter of

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computational complexity.
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Yudong Jin 2022-11-25 20:12:20 +08:00
parent 83629f3d2c
commit daf25d5e64
17 changed files with 571 additions and 82 deletions
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2
README.md
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@ -48,7 +48,7 @@
## To-Dos
- [ ] [代码翻译](https://github.com/krahets/hello-algo/issues/15)（JavaScript, TypeScript, C, C#, ... 请求大佬帮助）
- [x] [代码翻译](https://github.com/krahets/hello-algo/issues/15)（JavaScript, TypeScript, C, C#, ... 请求大佬帮助）
- [ ] 数据结构：散列表、堆（优先队列）、图
- [ ] 算法：搜索与回溯、选择 / 堆排序、动态规划、贪心、分治

3
codes/go/chapter_computational_complexity/leetcode_two_sum.go
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@ -7,6 +7,7 @@ package chapter_computational_complexity
// twoSumBruteForce
func twoSumBruteForce(nums []int, target int) []int {
size := len(nums)
// 两层循环，时间复杂度 O(n^2)
for i := 0; i < size-1; i++ {
for j := i + 1; i < size; j++ {
if nums[i]+nums[j] == target {
@ -19,7 +20,9 @@ func twoSumBruteForce(nums []int, target int) []int {
// twoSumHashTable
func twoSumHashTable(nums []int, target int) []int {
// 辅助哈希表，空间复杂度 O(n)
hashTable := map[int]int{}
// 单层循环，时间复杂度 O(n)
for idx, val := range nums {
if preIdx, ok := hashTable[target-val]; ok {
return []int{preIdx, idx}

11
codes/java/chapter_computational_complexity/leetcode_two_sum.java
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@ -8,9 +8,10 @@ package chapter_computational_complexity;
import java.util.*;
class solution_brute_force {
class SolutionBruteForce {
public int[] twoSum(int[] nums, int target) {
int size = nums.length;
// 两层循环，时间复杂度 O(n^2)
for (int i = 0; i < size - 1; i++) {
for (int j = i + 1; j < size; j++) {
if (nums[i] + nums[j] == target)
@ -21,10 +22,12 @@ class solution_brute_force {
}
}
class solution_hash_map {
class SolutionHashMap {
public int[] twoSum(int[] nums, int target) {
int size = nums.length;
// 辅助哈希表，空间复杂度 O(n)
Map<Integer, Integer> dic = new HashMap<>();
// 单层循环，时间复杂度 O(n)
for (int i = 0; i < size; i++) {
if (dic.containsKey(target - nums[i])) {
return new int[] { dic.get(target - nums[i]), i };
@ -43,11 +46,11 @@ public class leetcode_two_sum {
// ====== Driver Code ======
// 方法一
solution_brute_force slt1 = new solution_brute_force();
SolutionBruteForce slt1 = new SolutionBruteForce();
int[] res = slt1.twoSum(nums, target);
System.out.println(Arrays.toString(res));
// 方法二
solution_hash_map slt2 = new solution_hash_map();
SolutionHashMap slt2 = new SolutionHashMap();
res = slt2.twoSum(nums, target);
System.out.println(Arrays.toString(res));
}

4
codes/java/chapter_computational_complexity/space_complexity_types.java
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@ -100,7 +100,7 @@ public class space_complexity_types {
quadratic(n);
quadraticRecur(n);
// 指数阶
TreeNode tree = buildTree(n);
PrintUtil.printTree(tree);
TreeNode root = buildTree(n);
PrintUtil.printTree(root);
}
}

21
codes/java/chapter_computational_complexity/time_complexity_types.java
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@ -29,7 +29,6 @@ public class time_complexity_types {
int count = 0;
// 循环次数与数组长度成正比
for (int num : nums) {
// System.out.println(num);
count++;
}
return count;
@ -38,6 +37,7 @@ public class time_complexity_types {
/* 平方阶 */
static int quadratic(int n) {
int count = 0;
// 循环次数与数组长度成平方关系
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
count++;
@ -47,18 +47,22 @@ public class time_complexity_types {
}
/* 平方阶（冒泡排序） */
static void bubbleSort(int[] nums) {
int n = nums.length;
for (int i = 0; i < n - 1; i++) {
for (int j = 0; j < n - 1 - i; j++) {
static int bubbleSort(int[] nums) {
int count = 0; // 计数器
// 外循环：待排序元素数量为 n-1, n-2, ..., 1
for (int i = nums.length - 1; i > 0; i--) {
// 内循环：冒泡操作
for (int j = 0; j < i; j++) {
if (nums[j] > nums[j + 1]) {
// 交换 nums[j] 和 nums[j + 1]
// 交换 nums[j] 与 nums[j + 1]
int tmp = nums[j];
nums[j] = nums[j + 1];
nums[j + 1] = tmp;
count += 3; // 元素交换包含 3 个单元操作
}
}
}
return count;
}
/* 指数阶（循环实现） */
@ -135,6 +139,11 @@ public class time_complexity_types {
count = quadratic(n);
System.out.println("平方阶的计算操作数量 = " + count);
int[] nums = new int[n];
for (int i = 0; i < n; i++)
nums[i] = n - i; // [n,n-1,...,2,1]
count = bubbleSort(nums);
System.out.println("平方阶（冒泡排序）的计算操作数量 = " + count);
count = exponential(n);
System.out.println("指数阶（循环实现）的计算操作数量 = " + count);

1
codes/python/chapter_array_and_linkedlist/array.py
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@ -57,6 +57,7 @@ def find(nums, target):
return i
return -1
""" Driver Code """
if __name__ == "__main__":
""" 初始化数组 """

5
codes/python/chapter_array_and_linkedlist/linked_list.py
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@ -41,9 +41,8 @@ def find(head, target):
index += 1
return -1
"""
Driver Code
"""
""" Driver Code """
if __name__ == "__main__":
""" 初始化链表 """
# 初始化各个结点

5
codes/python/chapter_array_and_linkedlist/list.py
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@ -8,9 +8,8 @@ import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from include import *
"""
Driver Code
"""
""" Driver Code """
if __name__ == "__main__":
""" 初始化列表 """
list = [1, 3, 2, 5, 4]

5
codes/python/chapter_array_and_linkedlist/my_list.py
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@ -8,7 +8,6 @@ import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from include import *
""" 列表类简易实现 """
class MyList:
""" 构造函数 """
@ -71,9 +70,7 @@ class MyList:
return self._nums[:self._size]
"""
Driver Code
"""
""" Driver Code """
if __name__ == "__main__":
""" 初始化列表 """
list = MyList()

31
codes/python/chapter_computational_complexity/leetcode_two_sum.py
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@ -8,3 +8,34 @@ import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from include import *
class SolutionBruteForce:
def twoSum(self, nums: List[int], target: int) -> List[int]:
for i in range(len(nums) - 1):
for j in range(i + 1, len(nums)):
if nums[i] + nums[j] == target:
return i, j
return []
class SolutionHashMap:
def twoSum(self, nums: List[int], target: int) -> List[int]:
dic = {}
for i in range(len(nums)):
if target - nums[i] in dic:
return dic[target - nums[i]], i
dic[nums[i]] = i
return []
""" Driver Code """
if __name__ == '__main__':
# ======= Test Case =======
nums = [ 2,7,11,15 ];
target = 9;
# ====== Driver Code ======
# 方法一
res = SolutionBruteForce().twoSum(nums, target);
print(res)
# 方法二
res = SolutionHashMap().twoSum(nums, target);
print(res)

68
codes/python/chapter_computational_complexity/space_complexity_types.py
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@ -8,3 +8,71 @@ import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from include import *
""" 函数 """
def function():
# do something
return 0
""" 常数阶 """
def constant(n):
# 常量、变量、对象占用 O(1) 空间
a = 0
nums = [0] * 10000
node = ListNode(0)
# 循环中的变量占用 O(1) 空间
for _ in range(n):
c = 0
# 循环中的函数占用 O(1) 空间
for _ in range(n):
function()
""" 线性阶 """
def linear(n):
# 长度为 n 的列表占用 O(n) 空间
nums = [0] * n
# 长度为 n 的哈希表占用 O(n) 空间
mapp = {}
for i in range(n):
mapp[i] = str(i)
""" 线性阶（递归实现） """
def linearRecur(n):
print("递归 n = ", n)
if n == 1: return
linearRecur(n - 1)
""" 平方阶 """
def quadratic(n):
# 二维列表占用 O(n^2) 空间
num_matrix = [[0] * n for _ in range(n)]
""" 平方阶（递归实现） """
def quadratic_recur(n):
if n <= 0: return 0
nums = [0] * n
print("递归 n = {} 中的 nums 长度 = {}".format(n, len(nums)))
return quadratic_recur(n - 1)
""" 指数阶（建立满二叉树） """
def build_tree(n):
if n == 0: return None
root = TreeNode(0)
root.left = build_tree(n - 1)
root.right = build_tree(n - 1)
return root
""" Driver Code """
if __name__ == "__main__":
n = 5
# 常数阶
constant(n)
# 线性阶
linear(n)
linearRecur(n)
# 平方阶
quadratic(n)
quadratic_recur(n)
# 指数阶
root = build_tree(n)
print_tree(root)

130
codes/python/chapter_computational_complexity/time_complexity_types.py
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@ -8,3 +8,133 @@ import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from include import *
""" 常数阶 """
def constant(n):
count = 0
size = 100000
for _ in range(size):
count += 1
return count
""" 线性阶 """
def linear(n):
count = 0
for _ in range(n):
count += 1
return count
""" 线性阶（遍历数组）"""
def array_traversal(nums):
count = 0
# 循环次数与数组长度成正比
for num in nums:
count += 1
return count
""" 平方阶 """
def quadratic(n):
count = 0
# 循环次数与数组长度成平方关系
for i in range(n):
for j in range(n):
count += 1
return count
""" 平方阶（冒泡排序）"""
def bubble_sort(nums):
count = 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):
# 内循环：冒泡操作
for j in range(i):
if nums[j] > nums[j + 1]:
# 交换 nums[j] 与 nums[j + 1]
tmp = nums[j]
nums[j] = nums[j + 1]
nums[j + 1] = tmp
count += 3 # 元素交换包含 3 个单元操作
return count
""" 指数阶（循环实现）"""
def exponential(n):
count, base = 0, 1
# cell 每轮一分为二，形成数列 1, 2, 4, 8, ..., 2^(n-1)
for _ in range(n):
for _ in range(base):
count += 1
base *= 2
# count = 1 + 2 + 4 + 8 + .. + 2^(n-1) = 2^n - 1
return count
""" 指数阶（递归实现）"""
def exp_recur(n):
if n == 1: return 1
return exp_recur(n - 1) + exp_recur(n - 1) + 1
""" 对数阶（循环实现）"""
def logarithmic(n):
count = 0
while n > 1:
n = n / 2
count += 1
return count
""" 对数阶（递归实现）"""
def log_recur(n):
if n <= 1: return 0
return log_recur(n / 2) + 1
""" 线性对数阶 """
def linear_log_recur(n):
if n <= 1: return 1
count = linear_log_recur(n // 2) + \
linear_log_recur(n // 2)
for _ in range(n):
count += 1
return count
""" 阶乘阶（递归实现）"""
def factorial_recur(n):
if n == 0: return 1
count = 0
# 从 1 个分裂出 n 个
for _ in range(n):
count += factorial_recur(n - 1)
return count
""" Driver Code """
if __name__ == "__main__":
# 可以修改 n 运行，体会一下各种复杂度的操作数量变化趋势
n = 8
print("输入数据大小 n =", n)
count = constant(n)
print("常数阶的计算操作数量 =", count)
count = linear(n)
print("线性阶的计算操作数量 =", count)
count = array_traversal([0] * n)
print("线性阶（遍历数组）的计算操作数量 =", count)
count = quadratic(n)
print("平方阶的计算操作数量 =", count)
nums = [i for i in range(n, 0, -1)] # [n,n-1,...,2,1]
count = bubble_sort(nums)
print("平方阶（冒泡排序）的计算操作数量 =", count)
count = exponential(n)
print("指数阶（循环实现）的计算操作数量 =", count)
count = exp_recur(n)
print("指数阶（递归实现）的计算操作数量 =", count)
count = logarithmic(n)
print("对数阶（循环实现）的计算操作数量 =", count)
count = log_recur(n)
print("对数阶（递归实现）的计算操作数量 =", count)
count = linear_log_recur(n)
print("线性对数阶（递归实现）的计算操作数量 =", count)
count = factorial_recur(n)
print("阶乘阶（递归实现）的计算操作数量 =", count)

24
codes/python/chapter_computational_complexity/worst_best_time_complexity.py
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@ -8,3 +8,27 @@ import sys, os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from include import *
""" 生成一个数组，元素为: 1, 2, ..., n ，顺序被打乱 """
def random_numbers(n):
# 生成数组 nums =: 1, 2, 3, ..., n
nums = [i for i in range(1, n + 1)]
# 随机打乱数组元素
random.shuffle(nums)
return nums
""" 查找数组 nums 中数字 1 所在索引 """
def find_one(nums):
for i in range(len(nums)):
if nums[i] == 1:
return i
return -1
""" Driver Code """
if __name__ == "__main__":
for i in range(10):
n = 100
nums = random_numbers(n)
index = find_one(nums)
print("\n数组 [ 1, 2, ..., n ] 被打乱后 =", nums)
print("数字 1 的索引为", index)

88
docs/chapter_computational_complexity/space_complexity.md
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@ -62,7 +62,23 @@ comments: true
=== "Python"
```python title=""
""" 类 """
class Node:
def __init__(self, x):
self.val = x # 结点值
self.next = None # 指向下一结点的指针（引用）
""" 函数（或称方法） """
def function():
# do something...
return 0
def algorithm(n): # 输入数据
a = 0 # 暂存数据（常量）
b = 0 # 暂存数据（变量）
node = Node(0) # 暂存数据（对象）
c = function() # 栈帧空间（调用函数）
return a + b + c # 输出数据
```
## 推算方法
@ -94,7 +110,11 @@ comments: true
=== "Python"
```python title=""
def algorithm(n):
a = 0 # O(1)
b = [0] * 10000 # O(1)
if n > 10:
nums = [0] * n # O(n)
```
**在递归函数中，需要注意统计栈帧空间。** 例如函数 `loop()`，在循环中调用了 $n$ 次 `function()` ，每轮中的 `function()` 都返回并释放了栈帧空间，因此空间复杂度仍为 $O(1)$ 。而递归函数 `recur()` 在运行中会同时存在 $n$ 个未返回的 `recur()` ，从而使用 $O(n)$ 的栈帧空间。
@ -106,13 +126,13 @@ comments: true
// do something
return 0;
}
/* 循环 */
/* 循环 O(1) */
void loop(int n) {
for (int i = 0; i < n; i++) {
function();
}
}
/* 递归 */
/* 递归 O(n) */
void recur(int n) {
if (n == 1) return;
return recur(n - 1);
@ -128,7 +148,19 @@ comments: true
=== "Python"
```python title=""
def function():
# do something
return 0
""" 循环 O(1) """
def loop(n):
for _ in range(n):
function()
""" 递归 O(n) """
def recur(n):
if n == 1: return
return recur(n - 1)
```
## 常见类型
@ -186,7 +218,18 @@ $$
=== "Python"
```python title="space_complexity_types.py"
""" 常数阶 """
def constant(n):
# 常量、变量、对象占用 O(1) 空间
a = 0
nums = [0] * 10000
node = ListNode(0)
# 循环中的变量占用 O(1) 空间
for _ in range(n):
c = 0
# 循环中的函数占用 O(1) 空间
for _ in range(n):
function()
```
### 线性阶 $O(n)$
@ -222,7 +265,14 @@ $$
=== "Python"
```python title="space_complexity_types.py"
""" 线性阶 """
def linear(n):
# 长度为 n 的列表占用 O(n) 空间
nums = [0] * n
# 长度为 n 的哈希表占用 O(n) 空间
mapp = {}
for i in range(n):
mapp[i] = str(i)
```
以下递归函数会同时存在 $n$ 个未返回的 `algorithm()` 函数，使用 $O(n)$ 大小的栈帧空间。
@ -247,7 +297,11 @@ $$
=== "Python"
```python title="space_complexity_types.py"
""" 线性阶（递归实现） """
def linearRecur(n):
print("递归 n = ", n)
if n == 1: return
linearRecur(n - 1)
```
![space_complexity_recursive_linear](space_complexity.assets/space_complexity_recursive_linear.png)
@ -286,7 +340,10 @@ $$
=== "Python"
```python title="space_complexity_types.py"
""" 平方阶 """
def quadratic(n):
# 二维列表占用 O(n^2) 空间
num_matrix = [[0] * n for _ in range(n)]
```
在以下递归函数中，同时存在 $n$ 个未返回的 `algorihtm()` ，并且每个函数中都初始化了一个数组，长度分别为 $n, n-1, n-2, ..., 2, 1$ ，平均长度为 $\frac{n}{2}$ ，因此总体使用 $O(n^2)$ 空间。
@ -297,8 +354,8 @@ $$
/* 平方阶（递归实现） */
int quadraticRecur(int n) {
if (n <= 0) return 0;
// 数组 nums 长度为 n, n-1, ..., 2, 1
int[] nums = new int[n];
System.out.println("递归 n = " + n + " 中的 nums 长度 = " + nums.length);
return quadraticRecur(n - 1);
}
```
@ -312,7 +369,12 @@ $$
=== "Python"
```python title="space_complexity_types.py"
""" 平方阶（递归实现） """
def quadratic_recur(n):
if n <= 0: return 0
# 数组 nums 长度为 n, n-1, ..., 2, 1
nums = [0] * n
return quadratic_recur(n - 1)
```
![space_complexity_recursive_quadratic](space_complexity.assets/space_complexity_recursive_quadratic.png)
@ -345,7 +407,13 @@ $$
=== "Python"
```python title="space_complexity_types.py"
""" 指数阶（建立满二叉树） """
def build_tree(n):
if n == 0: return None
root = TreeNode(0)
root.left = build_tree(n - 1)
root.right = build_tree(n - 1)
return root
```
![space_complexity_exponential](space_complexity.assets/space_complexity_exponential.png)

33
docs/chapter_computational_complexity/space_time_tradeoff.md
View File

@ -17,9 +17,10 @@
=== "Java"
```java title="" title="leetcode_two_sum.java"
class SolutionBruteForce {
public int[] twoSum(int[] nums, int target) {
int size = nums.length;
// 外层 * 内层循环，时间复杂度为 O(n)
// 两层循环，时间复杂度 O(n^2)
for (int i = 0; i < size - 1; i++) {
for (int j = i + 1; j < size; j++) {
if (nums[i] + nums[j] == target)
@ -28,6 +29,7 @@
}
return new int[0];
}
}
```
=== "C++"
@ -39,14 +41,22 @@
=== "Python"
```python title="leetcode_two_sum.py"
class SolutionBruteForce:
def twoSum(self, nums: List[int], target: int) -> List[int]:
# 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)):
if nums[i] + nums[j] == target:
return i, j
return []
```
=== "Go"
```go title="leetcode_two_sum.go"
func twoSum(nums []int, target int) []int {
func twoSumBruteForce(nums []int, target int) []int {
size := len(nums)
// 两层循环，时间复杂度 O(n^2)
for i := 0; i < size-1; i++ {
for j := i + 1; i < size; j++ {
if nums[i]+nums[j] == target {
@ -58,8 +68,6 @@
}
```
### 方法二：辅助哈希表
时间复杂度 $O(N)$ ，空间复杂度 $O(N)$ ，属于「空间换时间」。
@ -69,6 +77,7 @@
=== "Java"
```java title="" title="leetcode_two_sum.java"
class SolutionHashMap {
public int[] twoSum(int[] nums, int target) {
int size = nums.length;
// 辅助哈希表，空间复杂度 O(n)
@ -82,6 +91,7 @@
}
return new int[0];
}
}
```
=== "C++"
@ -93,14 +103,25 @@
=== "Python"
```python title="leetcode_two_sum.py"
class SolutionHashMap:
def twoSum(self, nums: List[int], target: int) -> List[int]:
# 辅助哈希表，空间复杂度 O(n)
dic = {}
# 单层循环，时间复杂度 O(n)
for i in range(len(nums)):
if target - nums[i] in dic:
return dic[target - nums[i]], i
dic[nums[i]] = i
return []
```
=== "Go"
```go title="leetcode_two_sum.go"
func twoSumHashTable(nums []int, target int) []int {
// 辅助哈希表，空间复杂度 O(n)
hashTable := map[int]int{}
// 单层循环，时间复杂度 O(n)
for idx, val := range nums {
if preIdx, ok := hashTable[target-val]; ok {
return []int{preIdx, idx}

178
docs/chapter_computational_complexity/time_complexity.md
View File

@ -42,7 +42,14 @@ $$
=== "Python"
```python title=""
# 在某运行平台下
def algorithm(n):
a = 2 # 1 ns
a = a + 1 # 1 ns
a = a * 2 # 10 ns
# 循环 n 次
for _ in range(n): # 1 ns
print(0) # 5 ns
```
但实际上， **统计算法的运行时间既不合理也不现实。** 首先，我们不希望预估时间和运行平台绑定，毕竟算法需要跑在各式各样的平台之上。其次，我们很难获知每一种操作的运行时间，这为预估过程带来了极大的难度。
@ -87,7 +94,17 @@ $$
=== "Python"
```python title=""
# 算法 A 时间复杂度：常数阶
def algorithm_A(n):
print(0)
# 算法 B 时间复杂度：线性阶
def algorithm_B(n):
for _ in range(n):
print(0)
# 算法 C 时间复杂度：常数阶
def algorithm_C(n):
for _ in range(1000000):
print(0)
```
![time_complexity_first_example](time_complexity.assets/time_complexity_first_example.png)
@ -105,9 +122,11 @@ $$
## 函数渐进上界
设算法「计算操作数量」为 $T(n)$ ，其是一个关于输入数据大小 $n$ 的函数。例如，以下算法的操作数量为
$$
T(n) = 3 + 2n
$$
=== "Java"
```java title=""
@ -131,7 +150,14 @@ $$
=== "Python"
```python title=""
def algorithm(n):
a = 1 # +1
a = a + 1 # +1
a = a * 2 # +1
# 循环 n 次
for i in range(n): # +1
print(0) # +1
}
```
$T(n)$ 是个一次函数，说明时间增长趋势是线性的，因此易得时间复杂度是线性阶。
@ -174,6 +200,7 @@ $T(n)$ 是个一次函数，说明时间增长趋势是线性的，因此易得
3. **循环嵌套时使用乘法。** 总操作数量等于外层循环和内层循环操作数量之积，每一层循环依然可以分别套用上述 `1.` 和 `2.` 技巧。
根据以下示例，使用上述技巧前、后的统计结果分别为
$$
\begin{aligned}
T(n) & = 2n(n + 1) + (5n + 1) + 2 & \text{完整统计 (-.-|||)} \newline
@ -181,6 +208,7 @@ T(n) & = 2n(n + 1) + (5n + 1) + 2 & \text{完整统计 (-.-|||)} \newline
T(n) & = n^2 + n & \text{偷懒统计 (o.O)}
\end{aligned}
$$
最终，两者都能推出相同的时间复杂度结果，即 $O(n^2)$ 。
=== "Java"
@ -211,7 +239,16 @@ $$
=== "Python"
```python title=""
def algorithm(n):
a = 1 # +0（技巧 1）
a = a + n # +0（技巧 1）
# +n（技巧 2）
for i in range(5 * n + 1):
print(0)
# +n*n（技巧 3）
for i in range(2 * n):
for j in range(n + 1):
print(0)
```
### 2. 判断渐进上界
@ -279,7 +316,13 @@ $$
=== "Python"
```python title="time_complexity_types.py"
""" 常数阶 """
def constant(n):
count = 0
size = 100000
for _ in range(size):
count += 1
return count
```
### 线性阶 $O(n)$
@ -307,7 +350,12 @@ $$
=== "Python"
```python title="time_complexity_types.py"
""" 线性阶 """
def linear(n):
count = 0
for _ in range(n):
count += 1
return count
```
「遍历数组」和「遍历链表」等操作，时间复杂度都为 $O(n)$ ，其中 $n$ 为数组或链表的长度。
@ -339,7 +387,13 @@ $$
=== "Python"
```python title="time_complexity_types.py"
""" 线性阶（遍历数组）"""
def array_traversal(nums):
count = 0
# 循环次数与数组长度成正比
for num in nums:
count += 1
return count
```
### 平方阶 $O(n^2)$
@ -352,6 +406,7 @@ $$
/* 平方阶 */
int quadratic(int n) {
int count = 0;
// 循环次数与数组长度成平方关系
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
count++;
@ -370,7 +425,14 @@ $$
=== "Python"
```python title="time_complexity_types.py"
""" 平方阶 """
def quadratic(n):
count = 0
# 循环次数与数组长度成平方关系
for i in range(n):
for j in range(n):
count += 1
return count
```
![time_complexity_constant_linear_quadratic](time_complexity.assets/time_complexity_constant_linear_quadratic.png)
@ -387,18 +449,22 @@ $$
```java title="" title="time_complexity_types.java"
/* 平方阶（冒泡排序） */
void bubbleSort(int[] nums) {
int n = nums.length;
for (int i = 0; i < n - 1; i++) {
for (int j = 0; j < n - 1 - i; j++) {
int bubbleSort(int[] nums) {
int count = 0; // 计数器
// 外循环：待排序元素数量为 n-1, n-2, ..., 1
for (int i = nums.length - 1; i > 0; i--) {
// 内循环：冒泡操作
for (int j = 0; j < i; j++) {
if (nums[j] > nums[j + 1]) {
// 交换 nums[j] 和 nums[j + 1]
// 交换 nums[j] 与 nums[j + 1]
int tmp = nums[j];
nums[j] = nums[j + 1];
nums[j + 1] = tmp;
count += 3; // 元素交换包含 3 个单元操作
}
}
}
return count;
}
```
@ -411,7 +477,20 @@ $$
=== "Python"
```python title="time_complexity_types.py"
""" 平方阶（冒泡排序）"""
def bubble_sort(nums):
count = 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):
# 内循环：冒泡操作
for j in range(i):
if nums[j] > nums[j + 1]:
# 交换 nums[j] 与 nums[j + 1]
tmp = nums[j]
nums[j] = nums[j + 1]
nums[j + 1] = tmp
count += 3 # 元素交换包含 3 个单元操作
return count
```
### 指数阶 $O(2^n)$
@ -425,7 +504,7 @@ $$
=== "Java"
```java title="" title="time_complexity_types.java"
/* 指数阶（遍历实现） */
/* 指数阶（循环实现） */
int exponential(int n) {
int count = 0, base = 1;
// cell 每轮一分为二，形成数列 1, 2, 4, 8, ..., 2^(n-1)
@ -449,7 +528,16 @@ $$
=== "Python"
```python title="time_complexity_types.py"
""" 指数阶（循环实现）"""
def exponential(n):
count, base = 0, 1
# cell 每轮一分为二，形成数列 1, 2, 4, 8, ..., 2^(n-1)
for _ in range(n):
for _ in range(base):
count += 1
base *= 2
# count = 1 + 2 + 4 + 8 + .. + 2^(n-1) = 2^n - 1
return count
```
![time_complexity_exponential](time_complexity.assets/time_complexity_exponential.png)
@ -477,7 +565,10 @@ $$
=== "Python"
```python title="time_complexity_types.py"
""" 指数阶（递归实现）"""
def exp_recur(n):
if n == 1: return 1
return exp_recur(n - 1) + exp_recur(n - 1) + 1
```
### 对数阶 $O(\log n)$
@ -511,7 +602,13 @@ $$
=== "Python"
```python title="time_complexity_types.py"
""" 对数阶（循环实现）"""
def logarithmic(n):
count = 0
while n > 1:
n = n / 2
count += 1
return count
```
![time_complexity_logarithmic](time_complexity.assets/time_complexity_logarithmic.png)
@ -539,7 +636,10 @@ $$
=== "Python"
```python title="time_complexity_types.py"
""" 对数阶（递归实现）"""
def log_recur(n):
if n <= 1: return 0
return log_recur(n / 2) + 1
```
### 线性对数阶 $O(n \log n)$
@ -572,7 +672,14 @@ $$
=== "Python"
```python title="time_complexity_types.py"
""" 线性对数阶 """
def linear_log_recur(n):
if n <= 1: return 1
count = linear_log_recur(n // 2) + \
linear_log_recur(n // 2)
for _ in range(n):
count += 1
return count
```
![time_complexity_logarithmic_linear](time_complexity.assets/time_complexity_logarithmic_linear.png)
@ -613,7 +720,14 @@ $$
=== "Python"
```python title="time_complexity_types.py"
""" 阶乘阶（递归实现）"""
def factorial_recur(n):
if n == 0: return 1
count = 0
# 从 1 个分裂出 n 个
for _ in range(n):
count += factorial_recur(n - 1)
return count
```
![time_complexity_factorial](time_complexity.assets/time_complexity_factorial.png)
@ -681,7 +795,29 @@ $$
=== "Python"
```python title="worst_best_time_complexity.py"
""" 生成一个数组，元素为: 1, 2, ..., n ，顺序被打乱 """
def random_numbers(n):
# 生成数组 nums =: 1, 2, 3, ..., n
nums = [i for i in range(1, n + 1)]
# 随机打乱数组元素
random.shuffle(nums)
return nums
""" 查找数组 nums 中数字 1 所在索引 """
def find_one(nums):
for i in range(len(nums)):
if nums[i] == 1:
return i
return -1
""" Driver Code """
if __name__ == "__main__":
for i in range(10):
n = 100
nums = random_numbers(n)
index = find_one(nums)
print("\n数组 [ 1, 2, ..., n ] 被打乱后 =", nums)
print("数字 1 的索引为", index)
```
!!! tip

2
docs/chapter_preface/index.md
View File

@ -111,7 +111,7 @@ comments: true
## 致谢
感谢本开源书的每一位撰稿人，是他们的无私奉献让这本书变得更好，他们的 GitHub ID（按首次提交时间排序）为：krahets, *（等待下一位创作者）*
感谢本开源书的每一位撰稿人，是他们的无私奉献让这本书变得更好，他们的 GitHub ID（按首次提交时间排序）为：krahets, Reanon.
本书的成书过程中，我获得了许多人的帮助，包括但不限于：