ipermute

Inverse permute array dimensions

Syntax

A = ipermute(B,dimorder)

Description

A = ipermute(B,dimorder) rearranges the dimensions of an array B in the order specified by the vector dimorder such that B = permute(A,dimorder).

Examples

3-D Array

Create a 4-by-3-by-2 array B, and compute its inverse permutation according to the dimension order [3 1 2].

rng default
B = rand(4,3,2)

B = 
B(:,:,1) =

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


B(:,:,2) =

    0.9572    0.4218    0.6557
    0.4854    0.9157    0.0357
    0.8003    0.7922    0.8491
    0.1419    0.9595    0.9340

A = ipermute(B,[3 1 2])

A = 
A(:,:,1) =

    0.8147    0.9572
    0.6324    0.4218
    0.9575    0.6557


A(:,:,2) =

    0.9058    0.4854
    0.0975    0.9157
    0.9649    0.0357


A(:,:,3) =

    0.1270    0.8003
    0.2785    0.7922
    0.1576    0.8491


A(:,:,4) =

    0.9134    0.1419
    0.5469    0.9595
    0.9706    0.9340

The inverse permutation A is the array such that, when you permute it using the same dimension order, the result is equal to the original array B.

C = permute(A,[3 1 2])

C = 
C(:,:,1) =

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


C(:,:,2) =

    0.9572    0.4218    0.6557
    0.4854    0.9157    0.0357
    0.8003    0.7922    0.8491
    0.1419    0.9595    0.9340

Input Arguments

B — Input array

vector | matrix | multidimensional array

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

dimorder — Dimension order

row vector

Dimension order, specified as a row vector with unique, positive integer elements representing the dimensions of the input array.

Extended Capabilities

Tall Arrays

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

This function supports tall arrays with the limitation:

Permuting the tall dimension (dimension one) is not supported.

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:

• Does not support cell arrays for the first argument.

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

GPU Arrays

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

This function fully supports GPU arrays. 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).