You are given a 0-indexed m x n binary matrix matrix and an integer numSelect, which denotes the number of distinct columns you must select from matrix.
Let us consider s = {c1, c2, ...., cnumSelect} as the set of columns selected by you. A row row is covered by s if:
matrix[row][col] (0 <= col <= n - 1) where matrix[row][col] == 1, col is present in s or,row has a value of 1.You need to choose numSelect columns such that the number of rows that are covered is maximized.
Return the maximum number of rows that can be covered by a set of numSelect columns.
\n
Example 1:
\n![\"\"](\"https://assets.leetcode.com/uploads/2022/07/14/rowscovered.png\")
\n\nInput: matrix = [[0,0,0],[1,0,1],[0,1,1],[0,0,1]], numSelect = 2\nOutput: 3\nExplanation: One possible way to cover 3 rows is shown in the diagram above.\nWe choose s = {0, 2}.\n- Row 0 is covered because it has no occurrences of 1.\n- Row 1 is covered because the columns with value 1, i.e. 0 and 2 are present in s.\n- Row 2 is not covered because matrix[2][1] == 1 but 1 is not present in s.\n- Row 3 is covered because matrix[2][2] == 1 and 2 is present in s.\nThus, we can cover three rows.\nNote that s = {1, 2} will also cover 3 rows, but it can be shown that no more than three rows can be covered.\n\n\nExample 2:
\n![\"\"](\"https://assets.leetcode.com/uploads/2022/07/14/rowscovered2.png\")
\n\nInput: matrix = [[1],[0]], numSelect = 1\nOutput: 2\nExplanation: Selecting the only column will result in both rows being covered since the entire matrix is selected.\nTherefore, we return 2.\n\n\n
\n
Constraints:
\n\nm == matrix.lengthn == matrix[i].length1 <= m, n <= 12matrix[i][j] is either 0 or 1.1 <= numSelect <= ncols columns.",
"For each valid set, check how many rows are covered, and return the maximum."
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