{ "data": { "question": { "questionId": "566", "questionFrontendId": "566", "categoryTitle": "Algorithms", "boundTopicId": 1293, "title": "Reshape the Matrix", "titleSlug": "reshape-the-matrix", "content": "

In MATLAB, there is a handy function called reshape which can reshape an m x n matrix into a new one with a different size r x c keeping its original data.

\n\n

You are given an m x n matrix mat and two integers r and c representing the number of rows and the number of columns of the wanted reshaped matrix.

\n\n

The reshaped matrix should be filled with all the elements of the original matrix in the same row-traversing order as they were.

\n\n

If the reshape operation with given parameters is possible and legal, output the new reshaped matrix; Otherwise, output the original matrix.

\n\n

 

\n

Example 1:

\n\"\"\n
\nInput: mat = [[1,2],[3,4]], r = 1, c = 4\nOutput: [[1,2,3,4]]\n
\n\n

Example 2:

\n\"\"\n
\nInput: mat = [[1,2],[3,4]], r = 2, c = 4\nOutput: [[1,2],[3,4]]\n
\n\n

 

\n

Constraints:

\n\n\n", "translatedTitle": "重塑矩阵", "translatedContent": "

在 MATLAB 中,有一个非常有用的函数 reshape ,它可以将一个 m x n 矩阵重塑为另一个大小不同(r x c)的新矩阵,但保留其原始数据。

\n\n

给你一个由二维数组 mat 表示的 m x n 矩阵,以及两个正整数 rc ,分别表示想要的重构的矩阵的行数和列数。

\n\n

重构后的矩阵需要将原始矩阵的所有元素以相同的 行遍历顺序 填充。

\n\n

如果具有给定参数的 reshape 操作是可行且合理的,则输出新的重塑矩阵;否则,输出原始矩阵。

\n\n

 

\n\n

示例 1:

\n\"\"\n
\n输入:mat = [[1,2],[3,4]], r = 1, c = 4\n输出:[[1,2,3,4]]\n
\n\n

示例 2:

\n\"\"\n
\n输入:mat = [[1,2],[3,4]], r = 2, c = 4\n输出:[[1,2],[3,4]]\n
\n\n

 

\n\n

提示:

\n\n\n", "isPaidOnly": false, "difficulty": "Easy", "likes": 431, "dislikes": 0, "isLiked": null, "similarQuestions": "[{\"title\": \"Convert 1D Array Into 2D Array\", \"titleSlug\": \"convert-1d-array-into-2d-array\", \"difficulty\": \"Easy\", \"translatedTitle\": \"\\u5c06\\u4e00\\u7ef4\\u6570\\u7ec4\\u8f6c\\u53d8\\u6210\\u4e8c\\u7ef4\\u6570\\u7ec4\", \"isPaidOnly\": false}]", "contributors": [], "langToValidPlayground": "{\"cpp\": false, \"java\": true, \"python\": true, \"python3\": false, \"mysql\": false, \"mssql\": false, \"oraclesql\": false, \"c\": false, \"csharp\": false, \"javascript\": false, \"typescript\": false, \"bash\": false, \"php\": false, \"swift\": false, \"kotlin\": false, \"dart\": false, \"golang\": false, \"ruby\": false, \"scala\": false, \"html\": false, \"pythonml\": false, \"rust\": false, \"racket\": false, \"erlang\": false, \"elixir\": false, \"pythondata\": false, \"react\": false, \"vanillajs\": false, \"postgresql\": false, \"cangjie\": false}", "topicTags": [ { "name": "Array", "slug": "array", "translatedName": "数组", "__typename": "TopicTagNode" }, { "name": "Matrix", "slug": "matrix", "translatedName": "矩阵", "__typename": "TopicTagNode" }, { "name": "Simulation", "slug": "simulation", "translatedName": "模拟", "__typename": "TopicTagNode" } ], "companyTagStats": null, "codeSnippets": [ { "lang": "C++", "langSlug": "cpp", "code": "class Solution {\npublic:\n vector> matrixReshape(vector>& mat, int r, int c) {\n \n }\n};", "__typename": "CodeSnippetNode" }, { "lang": "Java", "langSlug": "java", "code": "class Solution {\n public int[][] matrixReshape(int[][] mat, int r, int c) {\n \n }\n}", "__typename": "CodeSnippetNode" }, { "lang": "Python", "langSlug": "python", "code": "class Solution(object):\n def matrixReshape(self, mat, r, c):\n \"\"\"\n :type mat: List[List[int]]\n :type r: int\n :type c: int\n :rtype: List[List[int]]\n \"\"\"\n ", "__typename": "CodeSnippetNode" }, { "lang": "Python3", "langSlug": "python3", "code": "class Solution:\n def matrixReshape(self, mat: List[List[int]], r: int, c: int) -> List[List[int]]:\n ", "__typename": "CodeSnippetNode" }, { "lang": "C", "langSlug": "c", "code": "/**\n * Return an array of arrays of size *returnSize.\n * The sizes of the arrays are returned as *returnColumnSizes array.\n * Note: Both returned array and *columnSizes array must be malloced, assume caller calls free().\n */\nint** matrixReshape(int** mat, int matSize, int* matColSize, int r, int c, int* returnSize, int** returnColumnSizes) {\n \n}", "__typename": "CodeSnippetNode" }, { "lang": "C#", "langSlug": "csharp", "code": "public class Solution {\n public int[][] MatrixReshape(int[][] mat, int r, int c) {\n \n }\n}", "__typename": "CodeSnippetNode" }, { "lang": "JavaScript", "langSlug": "javascript", "code": "/**\n * @param {number[][]} mat\n * @param {number} r\n * @param {number} c\n * @return {number[][]}\n */\nvar matrixReshape = function(mat, r, c) {\n \n};", "__typename": "CodeSnippetNode" }, { "lang": "TypeScript", "langSlug": "typescript", "code": "function matrixReshape(mat: number[][], r: number, c: number): number[][] {\n \n};", "__typename": "CodeSnippetNode" }, { "lang": "PHP", "langSlug": "php", "code": "class Solution {\n\n /**\n * @param Integer[][] $mat\n * @param Integer $r\n * @param Integer $c\n * @return Integer[][]\n */\n function matrixReshape($mat, $r, $c) {\n \n }\n}", "__typename": "CodeSnippetNode" }, { "lang": "Swift", "langSlug": "swift", "code": "class Solution {\n func matrixReshape(_ mat: [[Int]], _ r: Int, _ c: Int) -> [[Int]] {\n \n }\n}", "__typename": "CodeSnippetNode" }, { "lang": "Kotlin", "langSlug": "kotlin", "code": "class Solution {\n fun matrixReshape(mat: Array, r: Int, c: Int): Array {\n \n }\n}", "__typename": "CodeSnippetNode" }, { "lang": "Dart", "langSlug": "dart", "code": "class Solution {\n List> matrixReshape(List> mat, int r, int c) {\n \n }\n}", "__typename": "CodeSnippetNode" }, { "lang": "Go", "langSlug": "golang", "code": "func matrixReshape(mat [][]int, r int, c int) [][]int {\n \n}", "__typename": "CodeSnippetNode" }, { "lang": "Ruby", "langSlug": "ruby", "code": "# @param {Integer[][]} mat\n# @param {Integer} r\n# @param {Integer} c\n# @return {Integer[][]}\ndef matrix_reshape(mat, r, c)\n \nend", "__typename": "CodeSnippetNode" }, { "lang": "Scala", "langSlug": "scala", "code": "object Solution {\n def matrixReshape(mat: Array[Array[Int]], r: Int, c: Int): Array[Array[Int]] = {\n \n }\n}", "__typename": "CodeSnippetNode" }, { "lang": "Rust", "langSlug": "rust", "code": "impl Solution {\n pub fn matrix_reshape(mat: Vec>, r: i32, c: i32) -> Vec> {\n \n }\n}", "__typename": "CodeSnippetNode" }, { "lang": "Racket", "langSlug": "racket", "code": "(define/contract (matrix-reshape mat r c)\n (-> (listof (listof exact-integer?)) exact-integer? exact-integer? (listof (listof exact-integer?)))\n )", "__typename": "CodeSnippetNode" }, { "lang": "Erlang", "langSlug": "erlang", "code": "-spec matrix_reshape(Mat :: [[integer()]], R :: integer(), C :: integer()) -> [[integer()]].\nmatrix_reshape(Mat, R, C) ->\n .", "__typename": "CodeSnippetNode" }, { "lang": "Elixir", "langSlug": "elixir", "code": "defmodule Solution do\n @spec matrix_reshape(mat :: [[integer]], r :: integer, c :: integer) :: [[integer]]\n def matrix_reshape(mat, r, c) do\n \n end\nend", "__typename": "CodeSnippetNode" }, { "lang": "Cangjie", "langSlug": "cangjie", "code": "class Solution {\n func matrixReshape(mat: Array>, r: Int64, c: Int64): Array> {\n\n }\n}", "__typename": "CodeSnippetNode" } ], "stats": "{\"totalAccepted\": \"161.8K\", \"totalSubmission\": \"250.3K\", \"totalAcceptedRaw\": 161788, \"totalSubmissionRaw\": 250314, \"acRate\": \"64.6%\"}", "hints": [ "Do you know how 2d matrix is stored in 1d memory? Try to map 2-dimensions into one.", "M[i][j]=M[n*i+j] , where n is the number of cols. \r\nThis is the one way of converting 2-d indices into one 1-d index. \r\nNow, how will you convert 1-d index into 2-d indices?", "Try to use division and modulus to convert 1-d index into 2-d indices.", "M[i] => M[i/n][i%n] Will it result in right mapping? Take some example and check this formula." ], "solution": null, "status": null, "sampleTestCase": "[[1,2],[3,4]]\n1\n4", "metaData": "{\n \"name\": \"matrixReshape\",\n \"params\": [\n {\n \"name\": \"mat\",\n \"type\": \"integer[][]\"\n },\n {\n \"name\": \"r\",\n \"type\": \"integer\"\n },\n {\n \"name\": \"c\",\n \"type\": \"integer\"\n }\n ],\n \"return\": {\n \"type\": \"integer[][]\"\n }\n}", "judgerAvailable": true, "judgeType": "large", "mysqlSchemas": [], "enableRunCode": true, "envInfo": "{\"cpp\":[\"C++\",\"

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