#RANDOMLY PERMUTE VECTOR MATLAB FULL#

To permute both rows and columns I think you either have to run it twice, or pull some ugly shenanigans with numpy.unravel_index that hurts my head to think about. PVec0, pVec1 = calcMyPermutationVectors()ДЄrr.take(pVec0, axis=0, out=arr, mode='clip')ДЄrr.take(pVec1, axis=1, out=arr, mode='clip') So in total you call should look something like the following: #Inplace Rearrange On a final note, take seems to be a array method, so instead of np.take(i, rr, axis=0) If you don't set the mode it will make a copy to restore the array state on a Python exception (see here). To do it in-place, all you need to do is specify the 'out' parameter to be the same as the input array AND you have to set the mode='clip' or mode='wrap'. Here is an example of doing a random permutation of an identity matrix's rows: import numpy as npДЄrray(,

You can do this in-place with numpy's take() function, but it requires a bit of hoop jumping.

#RANDOMLY PERMUTE VECTOR MATLAB FULL#

Warning: The below example works properly, but using the full set of parameters suggested at the post end exposes a bug, or at least an 'undocumented feature' in the numpy.take() function. I currently copy matrices this large unnecessarily, but I would hope this could be easily avoided for permutation. In Matlab, we use an array which can collect numbers and can be accessed using an index. Each element is an array that has an index number and indexing starts from 0 th position and can be referred to as the first element in an array. Actually I would like to use matrices as large as possible, but I don't want to deal with the inconvenience of not being able to hold them in RAM, and I do O(N^3) LAPACK operations on the matrix which would also limit the practical matrix size. An array is a collection of numbers or string of characters stored in the memory. The matrix is small enough to fit into desktop RAM but big enough that I do not want to copy it thoughtlessly. Or they mean counting or enumerating all possible permutations. Sometimes when people talk about permutations, they only mean the sampling of random permutations, for example as part of a procedure to obtain p-values in statistics. Also for some simple situations it may be sufficient to just separately track an index permutation, but this is not convenient in my case. There are various syntax which is used in Matlab like: Rreshape (X, size) This function is used to reshape the original matrix that is X into R with the size defined in the vector вЂsizeвЂ™. Something like this might be possible with numpy's 'advanced indexing' but my understanding is that such a solution would not be in-place. Reshape function in Matlab is used to modify the original or existing array into a different array with different dimensions or sequence. Maybe I am just failing at searching the internet.

Right now I am manually swapping rows and columns, but I would have expected numpy to have a nice function f(M, v) where M has n rows and columns, and v has n entries, so that f(M, v) updates M according to the index permutation v. Mathematically this corresponds to pre-multiplying the matrix by the permutation matrix P and post-multiplying it by P^-1 = P^T, but this is not a computationally reasonable solution. I want to modify a dense square transition matrix in-place by changing the order of several of its rows and columns, using python's numpy library.

Randomly permute vector matlab