ndims
Number of array dimensions
Syntax
Description
Examples
Find Dimensions of Vector
Create a row vector.
A = 1:5;
Find the number of dimensions in the vector.
ndims(A)
ans = 2
The result is 2
because the vector has a size of 1-by-5.
Find Dimensions of Cell Array
Create a cell array of character vectors.
A{1,1,1} = 'cell_1'; A{1,1,2} = 'cell_2'; A{1,1,3} = 'cell_3'
A = 1x1x3 cell array
A(:,:,1) =
{'cell_1'}
A(:,:,2) =
{'cell_2'}
A(:,:,3) =
{'cell_3'}
Find the number of dimensions of the cell array.
ndims(A)
ans = 3
The result is 3
because the cell array has a size of 1-by-1-by-3.
Input Arguments
A
— Input array
scalar | vector | matrix | multidimensional array | table | timetable
Input array, specified as a scalar, vector, matrix, multidimensional array, table, or timetable.
Data Types: double
| single
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
| char
| string
| struct
| table
| timetable
| cell
| categorical
| datetime
| duration
| calendarDuration
Algorithms
The number of dimensions in an array is the same as the length
of the size vector of the array. In other words, ndims(A)
= length(size(A))
.
Extended Capabilities
Tall Arrays
Calculate with arrays that have more rows than fit in memory.
The
ndims
function fully supports tall arrays. For more information,
see Tall Arrays.
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
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 ndims
function
fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray
(Parallel Computing Toolbox). 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
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