I've used a different example array in this case - your version will yield an identical output after performing the row/column swaps which makes it difficult to understand what's going on.
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c = np.arange(25).reshape(5, 5)Ĭ, :] = c, :] # swap row 0 with row 4.Ĭ] = c] #. press bc button on the indicator stalk to enter reset procedure bmw idrive reboot restart fix f series 0i, i have the usb port in the glove box, but there is no software update option anywhere in the idrive menu from holding volume and eject for 25 seconds on earlier models to holding just volume on newer ones avery storti wakefield whether. You can use the same indexing approach to swap columns. In this particular case you could avoid the copy by using slice indexing, which returns a view rather than a copy: b = b # invert the row order Note that array indexing always returns a copy rather than a view - there's no way to swap arbitrary rows/columns of an array without generating a copy. Python indexing starts at 0 rather than 1) You can perform the swap in a one-liner using integer array indexing: a = np.array(,
Furthermore, I need to do an arbitrary number of permutations (more than one). That doesn't work for me because the matrices are adjacency matrices (representing graphs), and I need to do the permutations which will give me a graph which is isomorphic with the original graph. A.row perm A.row A.col perm A. Mathematically this corresponds to pre-multiplying the matrix by the permutation matrix P and post-multiplying it by P-1 PT, but this is not a computationally reasonable solution. numpy.shuffle and numpy.permutation seem to permute only the rows of the matrix (not the columns at the same time). If you have a sparse matrix stored in COO format, the following might be helpful.
Now, an incredibly naive (and memory costly) way of doing so might be: a2 = deepcopy(a1)īut, I would like to know if there is something more efficient that does this. Assuming that I have the following matrix/array: array(,Īnd I want to apply the following permutation: 1 -> 5
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