在一个由 '0' 和 '1' 组成的二维矩阵内,找到只包含 '1' 的最大正方形,并返回其面积。
示例 1:
输入:matrix = [["1","0","1","0","0"],["1","0","1","1","1"],["1","1","1","1","1"],["1","0","0","1","0"]]
输出:4
示例 2:
输入:matrix = [["0","1"],["1","0"]]
输出:1
示例 3:
输入:matrix = [["0"]]
输出:0初始化一个二维数组,值默认填充为0,长和宽均比当前矩阵加一,令dp[i + 1][j + 1]对应matrix[i][j] 以覆盖边界
遍历二维数组,当元素值为1时,填充dp数组,正方形需要满足,上,左,坐上均为1,所以木桶效应找最短的那条边 + 1;
找到最长的边,相乘得到最后结果
/**
* @param {character[][]} matrix
* @return {number}
*/
var maximalSquare = function(matrix) {
let m = matrix.length;
let n = matrix[0].length;
//宽高多设置一圈
let dp = new Array(m + 1).fill().map(() => {
return new Array(n + 1).fill(0)
})
let max = 0;
for (let i = 0; i < m; i++) {
for (let j = 0; j < n; j++) {
if (matrix[i][j] == '1') {
//此时的dp[i + 1][j + 1]实际为matrix[i][j]所在元素
dp[i + 1][j + 1] = Math.min(dp[i][j + 1], dp[i + 1][j], dp[i][j]) + 1;
max = Math.max(max, dp[i + 1][j + 1])
}
}
}
return max * max;
};