So, there will be 1 4x2 (4x2x1) matrix(itself!). The input and output dimensions of the model array are not counted as array dimensions for this operation. * G = permute(A,) % this makes no difference, using to show the reasoningĤx2x1 ( row(1) dimension of A = 4, column(2) dimension of A = 2, page(3) dimension of A = 1 4 is row dimension, 2 is column dimension and 1 is page dimension for the generated G) Syntax newarray permute (sysarray,order) Description example newarray permute (sysarray,order) rearranges the array dimensions of a model array so that the dimensions are in the specified order. * F = permute(A,) % this is transpose and same as Ģx4x1 ( column(2) dimension of A = 2, row(1) dimension of A = 4, page(3) dimension of A = 1 2 is row dimension, 4 is column dimension and 1 is page dimension for the generated F) So, there will be 4 2x1 (2x1x4) column matrixes. As in: ans(:,:,1) =Ģx1x4 ( column(2) dimension of A = 2, page(3) dimension of A = 1, row(1) dimension of A = 4 2 is row dimension, 1 is column dimension and 4 is page dimension for the generated E) So, there will be 2 4x1 (4x1x2) column matrixes. As in: ans(:,:,1) =Ĥx1x2 ( row(1) dimension of A = 4, page(3) dimension of A = 1, column(2) dimension of A = 2 4 is row dimension, 1 is column dimension and 2 is page dimension for the generated D) So, there will be 2 1x4 (1x4x2) row matrixes. As in: ans(:,:,1) =ġx4x2 ( page(3) dimension of A = 1, row(1) dimension of A = 4, column(2) dimension of A = 2 1 is row dimension, 4 is column dimension and 2 is page dimension for the generated C) For example, permute (A, 2 1) switches the row and column dimensions of a matrix A. So, there will be 4 1x2 (1x2x4) row matrixes. Syntax B permute (A,dimorder) Description example B permute (A,dimorder) rearranges the dimensions of an array in the order specified by the vector dimorder. G = permute(A,) % means ġx2x4 ( page(3) dimension of A = 1, column(2) dimension of A = 2, row(1) dimension of A = 4 1 is row dimension, 2 is column dimension and 4 is page dimension for the generated B. To be concrete, an example: Theme Copy A rand (220,1) Large vector, with one dimension a power of 2 A A. % 3 = page, 2 = column and 1 = row dimensions):ī = permute(A,) % means Ĭ = permute(A,) % means ĭ = permute(A,) % means Į = permute(A,) % means į = permute(A,) % means Accepted Answer: Matt J I'm trying to optimize a very specific vector operation, namely taking a large (220 x 1) vector, reshaping it, permuting the indices, and reshaping once more. % (numbers in the order argument of permute function indicates dimensions, Now let's move to the examples, Finally: % A has 4 rows, 2 columns and 1 page

Order argument passed to permute swap these dimensions in the matrix and produce an awkward combination of arrays, I think permute is a misnomer for this effect. B=zeros(10,3) has 10 rows, 3 columns and 1 page, this order is important!) And if you don't specify a dimension, its default count is set to 1. Here are some examples to prevent you from suffering a similar excruciating pain:įirst, let's remember the dimensions' names of matrix in matlab: A = zeros(4,5,7), matrix A has 4 rows, 5 columns and 7 pages. Therefore, I used the F*ck word many times during ' my journey of understanding the permute function'. Then I get the same array of matrices in lnA for both the MATLAB and R implementations.Wow, this is one of the hardest functions to figure out among all the different SDKs I have used up to now. If I add lnA = cell(T, NumModalities) to the MATLAB script before your final for loop and then modify the inside of the loop to be lnA(outcomes(modal,tau),:,:,:,:,:),) R does that by default unless you specify drop = FALSE when you subset an array, e.g., lnA] <- a],drop = FALSE] In the MATLAB script, permute appears to be simply dropping excess dimensions. I don't think you actually need an equivalent for permute. I believe I successfully replicated the MATLAB script in R.

Permute matlab