reshape

Reshape array by rearranging existing elements

Syntax

B = reshape(A,sz)

B = reshape(A,sz1,...,szN)

Description

B = reshape(A,sz) reshapes A using the size vector, sz, to define size(B). For example, reshape(A,[2,3]) reshapes A into a 2-by-3 matrix. sz must contain at least 2 elements, and prod(sz) must be the same as numel(A).

B = reshape(A,sz1,...,szN) reshapes A into a sz1-by-...-by-szN array where sz1,...,szN indicates the size of each dimension. You can specify a single dimension size of [] to have the dimension size automatically calculated, such that the number of elements in B matches the number of elements in A. For example, if A is a 10-by-10 matrix, then reshape(A,2,2,[]) reshapes the 100 elements of A into a 2-by-2-by-25 array.

Examples

Reshape Vector into Matrix

Reshape a 1-by-10 vector into a 5-by-2 matrix.

A = 1:10;
B = reshape(A,[5,2])

B = 5×2

     1     6
     2     7
     3     8
     4     9
     5    10

Reshape Matrix to Have Specified Number of Columns

Reshape a 4-by-4 square matrix into a matrix that has 2 columns. Specify [] for the first dimension to let reshape automatically calculate the appropriate number of rows.

A = magic(4)

A = 4×4

    16     2     3    13
     5    11    10     8
     9     7     6    12
     4    14    15     1

B = reshape(A,[],2)

B = 8×2

    16     3
     5    10
     9     6
     4    15
     2    13
    11     8
     7    12
    14     1

The result is an 8-by-2 matrix, which maintains the same number of elements as the original matrix. The elements in B also maintain their columnwise order from A.

Reshape Multidimensional Array into Matrix

Reshape a 2-by-2-by-3 array of random values into a 6-by-2 matrix.

rng default
A = rand(2,2,3)

A = 
A(:,:,1) =

    0.8147    0.1270
    0.9058    0.9134


A(:,:,2) =

    0.6324    0.2785
    0.0975    0.5469


A(:,:,3) =

    0.9575    0.1576
    0.9649    0.9706

B = reshape(A,6,2)

B = 6×2

    0.8147    0.2785
    0.9058    0.5469
    0.1270    0.9575
    0.9134    0.9649
    0.6324    0.1576
    0.0975    0.9706

Input Arguments

A — Input array

vector | matrix | multidimensional array

Input array, specified as a vector, matrix, or multidimensional array.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string | cell | struct | datetime | duration | calendarDuration
Complex Number Support: Yes

sz — Output size

row vector of integers

Output size, specified as a row vector of integers. Each element of sz indicates the size of the corresponding dimension in B. You must specify sz so that the number of elements in A and B are the same. That is, prod(sz) must be the same as numel(A).

Beyond the second dimension, the output, B, does not reflect trailing dimensions with a size of 1. For example, reshape(A,[3,2,1,1]) produces a 3-by-2 matrix.

Example: reshape(A,[3,2])

Example: reshape(A,[6,4,10])

Example: reshape(A,[5,5,5,5])

sz1,...,szN — Size of each dimension

two or more integers | [] (optional)

Size of each dimension, specified as two or more integers with at most one [] (optional). You must specify at least 2 dimension sizes, and at most one dimension size can be specified as [], which automatically calculates the size of that dimension to ensure that numel(B) matches numel(A). When you use [] to automatically calculate a dimension size, the dimensions that you do explicitly specify must divide evenly into the number of elements in the input matrix, numel(A).

Beyond the second dimension, the output, B, does not reflect trailing dimensions with a size of 1. For example, reshape(A,3,2,1,1) produces a 3-by-2 matrix.

Example: reshape(A,3,2)

Example: reshape(A,6,[],10)

Example: reshape(A,2,5,3,[])

Example: reshape(A,5,5,5,5)

Output Arguments

B — Reshaped array

vector | matrix | multidimensional array | cell array

Reshaped array, returned as a vector, matrix, multidimensional array, or cell array. The data type and number of elements in B are the same as the data type and number of elements in A. The elements in B preserve their columnwise ordering from A.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string | cell | datetime | duration | calendarDuration

Tips

• The reshape function rearranges existing elements in the input data. To add or remove elements, use the resize function.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

This function supports tall arrays with the limitations:

• Reshaping the tall dimension (dimension one) is not supported. The first dimension input should always be empty, such as reshape(X,[],M,N,...).

For more information, see Tall Arrays for Out-of-Memory Data.

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• If the input is a compile-time empty cell array, then the size arguments must be constants.

• Size arguments must have a fixed size.

• See Variable-Sizing Restrictions for Code Generation of Toolbox Functions (MATLAB Coder).

• For sparse matrices, the reshape function does not support trailing ones as inputs after the first two dimensions.

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

• If the input is a compile-time empty cell array, then the size arguments must be constants.

• Size arguments must have a fixed size.

• For sparse matrices, the reshape function does not support trailing ones as inputs after the first two dimensions.

HDL Code Generation
Generate VHDL, Verilog and SystemVerilog code for FPGA and ASIC designs using HDL Coder™.

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The reshape function supports GPU array input with these usage notes and limitations:

• Input array A must be nonsparse.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced before R2006a