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. I.e., the value of pixels 3, 5, 0 will be the the (3, 5) pixel, and the red component of that pixel. You can use the same indexing approach to swap columns. When I load an image with PIL and convert it into a NumPy array: image Image.open ('myimage.png') pixels np.asarray (image) The data is stored as x y channel. 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 To convert a 1-D array into a 2-D column vector, an additional dimension must be added, e.g., np.atleast2d (a).T achieves this, as does a :, np.newaxis. 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) According to the docs, there is no in-place permutation method in numpy, something like ndarray.sort. You can perform the swap in a one-liner using integer array indexing: a = np.array(, In general, the ith dimension of the output array is the dimension dimorder (i) from the input array. For example, permute (A, 2 1) switches the row and column dimensions of a matrix A. Furthermore, I need to do an arbitrary number of permutations (more than one). B permute (A,dimorder) rearranges the dimensions of an array in the order specified by the vector dimorder. 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. By default, reverse the dimensions, otherwise permute the axes according to the values given. numpy.shuffle and numpy.permutation seem to permute only the rows of the matrix (not the columns at the same time). anspose¶ anspose(a, axesNone) source ¶ Permute the dimensions of an array. I would like to create a new array that contains the n possible arrays of permutations of 0-k. 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. Get all permutations of a numpy array Ask Question Asked 6 years, 7 months ago Modified 3 years, 2 months ago Viewed 56k times 24 I have a numpy array 0, 1, 1, 2, 2, 0, 1. Assuming that I have the following matrix/array: array(,Īnd I want to apply the following permutation: 1 -> 5
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