数组是一种连续存储线性结构,元素范例相同,巨细相称,数组是多维的,通过使用整型索引值来访问他们的元素,数组尺寸不能改变。
# 知识点
数组的优点:
数组的缺点:
- 事先必须知道数组的长度
- 插入删除元素很慢
- 空间通常是有限制的
- 必要大块连续的内存块
- 插入删除元素的效率很低
# 数组与矩阵相关题目
把数组中的 0 移到末尾
283. Move Zeroes (Easy)在新窗口打开
- For example, given nums = [0, 1, 0, 3, 12], after calling your function, nums should be [1, 3, 12, 0, 0].
复制代码- public void moveZeroes(int[] nums) {
- int idx = 0;
- for (int num : nums) {
- if (num != 0) {
- nums[idx++] = num;
- }
- }
- while (idx < nums.length) {
- nums[idx++] = 0;
- }
- }
复制代码 改变矩阵维度
566. Reshape the Matrix (Easy)在新窗口打开
- Input:
- nums =
- [[1,2],
- [3,4]]
- r = 1, c = 4
- Output:
- [[1,2,3,4]]
- Explanation:
- The row-traversing of nums is [1,2,3,4]. The new reshaped matrix is a 1 * 4 matrix, fill it row by row by using the previous list.
复制代码- public int[][] matrixReshape(int[][] nums, int r, int c) {
- int m = nums.length, n = nums[0].length;
- if (m * n != r * c) {
- return nums;
- }
- int[][] reshapedNums = new int[r][c];
- int index = 0;
- for (int i = 0; i < r; i++) {
- for (int j = 0; j < c; j++) {
- reshapedNums[i][j] = nums[index / n][index % n];
- index++;
- }
- }
- return reshapedNums;
- }
复制代码 找出数组中最长的连续 1
485. Max Consecutive Ones (Easy)在新窗口打开
- public int findMaxConsecutiveOnes(int[] nums) {
- int max = 0, cur = 0;
- for (int x : nums) {
- cur = x == 0 ? 0 : cur + 1;
- max = Math.max(max, cur);
- }
- return max;
- }
复制代码 有序矩阵查找
240. Search a 2D Matrix II (Medium)在新窗口打开
- [
- [ 1, 5, 9],
- [10, 11, 13],
- [12, 13, 15]
- ]
复制代码- public boolean searchMatrix(int[][] matrix, int target) {
- if (matrix == null || matrix.length == 0 || matrix[0].length == 0) return false;
- int m = matrix.length, n = matrix[0].length;
- int row = 0, col = n - 1;
- while (row < m && col >= 0) {
- if (target == matrix[row][col]) return true;
- else if (target < matrix[row][col]) col--;
- else row++;
- }
- return false;
- }
复制代码 有序矩阵的 Kth Element
378. Kth Smallest Element in a Sorted Matrix ((Medium))在新窗口打开
- matrix = [
- [ 1, 5, 9],
- [10, 11, 13],
- [12, 13, 15]
- ],
- k = 8,
- return 13.
复制代码 解题参考: Share my thoughts and Clean Java Code在新窗口打开
二分查找解法:
- public int kthSmallest(int[][] matrix, int k) {
- int m = matrix.length, n = matrix[0].length;
- int lo = matrix[0][0], hi = matrix[m - 1][n - 1];
- while (lo <= hi) {
- int mid = lo + (hi - lo) / 2;
- int cnt = 0;
- for (int i = 0; i < m; i++) {
- for (int j = 0; j < n && matrix[i][j] <= mid; j++) {
- cnt++;
- }
- }
- if (cnt < k) lo = mid + 1;
- else hi = mid - 1;
- }
- return lo;
- }
复制代码 堆解法:
- public int kthSmallest(int[][] matrix, int k) {
- int m = matrix.length, n = matrix[0].length;
- PriorityQueue<Tuple> pq = new PriorityQueue<Tuple>();
- for(int j = 0; j < n; j++) pq.offer(new Tuple(0, j, matrix[0][j]));
- for(int i = 0; i < k - 1; i++) { // 小根堆,去掉 k - 1 个堆顶元素,此时堆顶元素就是第 k 的数
- Tuple t = pq.poll();
- if(t.x == m - 1) continue;
- pq.offer(new Tuple(t.x + 1, t.y, matrix[t.x + 1][t.y]));
- }
- return pq.poll().val;
- }
- class Tuple implements Comparable<Tuple> {
- int x, y, val;
- public Tuple(int x, int y, int val) {
- this.x = x; this.y = y; this.val = val;
- }
- @Override
- public int compareTo(Tuple that) {
- return this.val - that.val;
- }
- }
复制代码 一个数组元素在 [1, n] 之间,此中一个数被替换为另一个数,找出重复的数和丢失的数
645. Set Mismatch (Easy)在新窗口打开
- Input: nums = [1,2,2,4]
- Output: [2,3]
复制代码- Input: nums = [1,2,2,4]
- Output: [2,3]
复制代码 最直接的方法是先对数组举行排序,这种方法时间复杂度为 O(NlogN)。本题可以以 O(N) 的时间复杂度、O(1) 空间复杂度来求解。
重要头脑是通过交换数组元素,使得数组上的元素在精确的位置上。
- public int[] findErrorNums(int[] nums) {
- for (int i = 0; i < nums.length; i++) {
- while (nums[i] != i + 1 && nums[nums[i] - 1] != nums[i]) {
- swap(nums, i, nums[i] - 1);
- }
- }
- for (int i = 0; i < nums.length; i++) {
- if (nums[i] != i + 1) {
- return new int[]{nums[i], i + 1};
- }
- }
- return null;
- }
- private void swap(int[] nums, int i, int j) {
- int tmp = nums[i];
- nums[i] = nums[j];
- nums[j] = tmp;
- }
复制代码 类似题目:
- 448. Find All Numbers Disappeared in an Array (Easy)在新窗口打开,探求所有丢失的元素
- 442. Find All Duplicates in an Array (Medium)在新窗口打开,探求所有重复的元素。
找出数组中重复的数,数组值在 [1, n] 之间
287. Find the Duplicate Number (Medium)在新窗口打开
要求不能修改数组,也不能使用额外的空间。
二分查找解法:
- public int findDuplicate(int[] nums) {
- int l = 1, h = nums.length - 1;
- while (l <= h) {
- int mid = l + (h - l) / 2;
- int cnt = 0;
- for (int i = 0; i < nums.length; i++) {
- if (nums[i] <= mid) cnt++;
- }
- if (cnt > mid) h = mid - 1;
- else l = mid + 1;
- }
- return l;
- }
复制代码 双指针解法,类似于有环链表中找出环的入口:
- public int findDuplicate(int[] nums) {
- int slow = nums[0], fast = nums[nums[0]];
- while (slow != fast) {
- slow = nums[slow];
- fast = nums[nums[fast]];
- }
- fast = 0;
- while (slow != fast) {
- slow = nums[slow];
- fast = nums[fast];
- }
- return slow;
- }
复制代码 数组相邻差值的个数
667. Beautiful Arrangement II (Medium)在新窗口打开
- Input: n = 3, k = 2
- Output: [1, 3, 2]
- Explanation: The [1, 3, 2] has three different positive integers ranging from 1 to 3, and the [2, 1] has exactly 2 distinct integers: 1 and 2.
复制代码 题目形貌: 数组元素为 1~n 的整数,要求构建数组,使得相邻元素的差值不相同的个数为 k。
让前 k+1 个元素构建出 k 个不相同的差值,序列为: 1 k+1 2 k 3 k-1 ... k/2 k/2+1.
- public int[] constructArray(int n, int k) {
- int[] ret = new int[n];
- ret[0] = 1;
- for (int i = 1, interval = k; i <= k; i++, interval--) {
- ret[i] = i % 2 == 1 ? ret[i - 1] + interval : ret[i - 1] - interval;
- }
- for (int i = k + 1; i < n; i++) {
- ret[i] = i + 1;
- }
- return ret;
- }
复制代码 数组的度
697. Degree of an Array (Easy)在新窗口打开
- Input: [1,2,2,3,1,4,2]
- Output: 6
复制代码 题目形貌: 数组的度定义为元素出现的最高频率,比方上面的数组度为 3。要求找到一个最小的子数组,这个子数组的度和原数组一样。
- public int findShortestSubArray(int[] nums) {
- Map<Integer, Integer> numsCnt = new HashMap<>();
- Map<Integer, Integer> numsLastIndex = new HashMap<>();
- Map<Integer, Integer> numsFirstIndex = new HashMap<>();
- for (int i = 0; i < nums.length; i++) {
- int num = nums[i];
- numsCnt.put(num, numsCnt.getOrDefault(num, 0) + 1);
- numsLastIndex.put(num, i);
- if (!numsFirstIndex.containsKey(num)) {
- numsFirstIndex.put(num, i);
- }
- }
- int maxCnt = 0;
- for (int num : nums) {
- maxCnt = Math.max(maxCnt, numsCnt.get(num));
- }
- int ret = nums.length;
- for (int i = 0; i < nums.length; i++) {
- int num = nums[i];
- int cnt = numsCnt.get(num);
- if (cnt != maxCnt) continue;
- ret = Math.min(ret, numsLastIndex.get(num) - numsFirstIndex.get(num) + 1);
- }
- return ret;
- }
复制代码 对角元素相称的矩阵
766. Toeplitz Matrix (Easy)在新窗口打开
- 1234
- 5123
- 9512
- In the above grid, the diagonals are "[9]", "[5, 5]", "[1, 1, 1]", "[2, 2, 2]", "[3, 3]", "[4]", and in each diagonal all elements are the same, so the answer is True.
复制代码- public boolean isToeplitzMatrix(int[][] matrix) {
- for (int i = 0; i < matrix[0].length; i++) {
- if (!check(matrix, matrix[0][i], 0, i)) {
- return false;
- }
- }
- for (int i = 0; i < matrix.length; i++) {
- if (!check(matrix, matrix[i][0], i, 0)) {
- return false;
- }
- }
- return true;
- }
- private boolean check(int[][] matrix, int expectValue, int row, int col) {
- if (row >= matrix.length || col >= matrix[0].length) {
- return true;
- }
- if (matrix[row][col] != expectValue) {
- return false;
- }
- return check(matrix, expectValue, row + 1, col + 1);
- }
复制代码 嵌套数组
565. Array Nesting (Medium)在新窗口打开
- Input: A = [5,4,0,3,1,6,2]
- Output: 4
- Explanation:
- A[0] = 5, A[1] = 4, A[2] = 0, A[3] = 3, A[4] = 1, A[5] = 6, A[6] = 2.
- One of the longest S[K]:
- S[0] = {A[0], A[5], A[6], A[2]} = {5, 6, 2, 0}
复制代码 题目形貌: S 表示一个聚集,聚集的第一个元素是 A,第二个元素是 A[A],云云嵌套下去。求最大的 S。
- public int arrayNesting(int[] nums) {
- int max = 0;
- for (int i = 0; i < nums.length; i++) {
- int cnt = 0;
- for (int j = i; nums[j] != -1; ) {
- cnt++;
- int t = nums[j];
- nums[j] = -1; // 标记该位置已经被访问
- j = t;
- }
- max = Math.max(max, cnt);
- }
- return max;
- }
复制代码 分隔数组
769. Max Chunks To Make Sorted (Medium)在新窗口打开
- Input: arr = [1,0,2,3,4]
- Output: 4
- Explanation:
- We can split into two chunks, such as [1, 0], [2, 3, 4].
- However, splitting into [1, 0], [2], [3], [4] is the highest number of chunks possible.
复制代码 题目形貌: 分隔数组,使得对每部门排序后数组就为有序。
- public int maxChunksToSorted(int[] arr) {
- if (arr == null) return 0;
- int ret = 0;
- int right = arr[0];
- for (int i = 0; i < arr.length; i++) {
- right = Math.max(right, arr[i]);
- if (right == i) ret++;
- }
- return ret;
- }
复制代码
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