Add python codes and for the chapter of

computational complexity.
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codes/java/chapter_computational_complexity/leetcode_two_sum.java

@@ -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));
     }

codes/java/chapter_computational_complexity/space_complexity_types.java

@@ -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);
     }
 }

codes/java/chapter_computational_complexity/time_complexity_types.java

@@ -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);