![]() ![]() 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. You can use the same indexing approach to swap columns. getObjVal()) def createinitialtour(n): Returns a permuted trivial. 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 TSP example using numpy functions (for efficiency) (C) Fair Isaac Corp. ![]() 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) 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. You can perform the swap in a one-liner using integer array indexing: a = np.array(, For a 1-D array, this returns an unchanged view of the original array, as a transposed vector is simply the same vector. My permutations are not random, but they are independant, different from each other. In the example: 21 (1 permutation for first dimension, 4 for second, 16 for third - generalised: sized for d in range(dim) permutations). Furthermore, I need to do an arbitrary number of permutations (more than one). My need: transform M, by moving its elements according to as many permutations as I need. 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. 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. 31 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. The anspose () function can be useful in various applications such as image processing, signal processing, and numerical analysis. The NumPy library is the core library for scientific computing in. It returns a view of the original array with the axes transposed. image cannot do this: AttributeError: Image object has no attribute permute. numpy.shuffle and numpy.permutation seem to permute only the rows of the matrix (not the columns at the same time). The anspose () function is used to reverse or permute the dimensions of an array. It provides tools for converting ROS messages to and from numpy arrays. 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. The anspose() function changes the row elements. Assuming that I have the following matrix/array: array(,Īnd I want to apply the following permutation: 1 -> 5 This function permutes or reserves the dimension of the given array and returns the modified array. ![]()
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