leetcode第三十六题java解法——有效的数独

判断一个 9x9 的数独是否有效。只需要根据以下规则,验证已经填入的数字是否有效即可。

数字 1-9 在每一行只能出现一次。
数字 1-9 在每一列只能出现一次。
数字 1-9 在每一个以粗实线分隔的 3x3 宫内只能出现一次。

上图是一个部分填充的有效的数独。

数独部分空格内已填入了数字,空白格用 ‘.’ 表示。

示例 1:

输入:
[
  ["5","3",".",".","7",".",".",".","."],
  ["6",".",".","1","9","5",".",".","."],
  [".","9","8",".",".",".",".","6","."],
  ["8",".",".",".","6",".",".",".","3"],
  ["4",".",".","8",".","3",".",".","1"],
  ["7",".",".",".","2",".",".",".","6"],
  [".","6",".",".",".",".","2","8","."],
  [".",".",".","4","1","9",".",".","5"],
  [".",".",".",".","8",".",".","7","9"]
]
输出: true

示例 2:

输入:
[
  ["8","3",".",".","7",".",".",".","."],
  ["6",".",".","1","9","5",".",".","."],
  [".","9","8",".",".",".",".","6","."],
  ["8",".",".",".","6",".",".",".","3"],
  ["4",".",".","8",".","3",".",".","1"],
  ["7",".",".",".","2",".",".",".","6"],
  [".","6",".",".",".",".","2","8","."],
  [".",".",".","4","1","9",".",".","5"],
  [".",".",".",".","8",".",".","7","9"]
]
输出: false
解释: 除了第一行的第一个数字从 5 改为 8 以外,空格内其他数字均与 示例1 相同。
     但由于位于左上角的 3x3 宫内有两个 8 存在, 因此这个数独是无效的。

说明:

一个有效的数独(部分已被填充)不一定是可解的。
只需要根据以上规则,验证已经填入的数字是否有效即可。
给定数独序列只包含数字 1-9 和字符 '.' 。
给定数独永远是 9x9 形式的。

思路:建立三个hashset,分别对于行列和3*3宫格进行扫描。hashset的add方法返回的是boolean类型,有相同元素返回false。代码应该很容易懂。

class Solution {
    public boolean isValidSudoku(char[][] board) {
       
        for(int i=0;i<9;i++)
        {
            HashSet<Character> row=new HashSet<>();//写在for循环里,每次初始化
            HashSet<Character> column=new HashSet<>();
            HashSet<Character> cube=new HashSet<>();
            for(int j=0;j<9;j++)
            {
                if(board[i][j]!='.'&&!row.add(board[i][j]))//有相同元素返回false,所以为!
                    return false;
                if(board[j][i]!='.'&&!column.add(board[j][i]))
                    return false;
                int r=3*(i/3)+j/3;
                int c=3*(i%3)+j%3;
                if(board[r][c]!='.'&&!cube.add(board[r][c]))
                    return false;
            }
        }
        return true;
    }
}
### 不同版本YOLO模型的主要特性 #### YOLOv8 特性 YOLOv8引入了一系列改进,旨在提高速度和准确性的同时保持简单易用的特点。为了验证YOLOv8模型的效果,可以采用验证模式来评估模型的表现并识别需要改进的地方[^3]。 #### YOLOv7 特性 YOLOv7专注于提供更快的速度以及更高的精度,在资源受限设备上也能高效运行。该版本强调了无需额外训练数据即可超越许多现有模型的能力,并且特别优化了对于移动平台的支持。 #### YOLOv6 特性 YOLOv6继续沿用了前几代的设计理念——快速而精确的目标检测算法;同时针对实际应用场景做了进一步调整,比如更好的支持多尺度预测等功能,使得模型更加灵活适用不同类型的片尺寸。 #### YOLOv5 特性 作为较早的一个版本,YOLOv5已经具备相当成熟的技术框架,实现了端到端的一次性推理过程,能够处理多种复杂环境下的目标定位任务;此外还增加了对自定义配置文件的支持以便于用户根据具体需求定制化修改网络结构参数等设置. ```python import torch from ultralytics import YOLO model_v5 = YOLO('yolov5s.pt') model_v6 = YOLO('yolov6s.pt') model_v7 = YOLO('yolov7s.pt') model_v8 = YOLO('yolov8s.pt') results_v5 = model_v5(imgsz=640) results_v6 = model_v6(imgsz=640) results_v7 = model_v7(imgsz=640) results_v8 = model_v8(imgsz=640) print(f'YOLOv5 Results: {results_v5}') print(f'YOLOv6 Results: {results_v6}') print(f'YOLOv7 Results: {results_v7}') print(f'YOLOv8 Results: {results_v8}') ```
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