This is just a minor improvement over Divakar’s answer. It is a little faster because it replaces a 3D-array permute with a 2D-array permute: B = bsxfun(@times, permute(A, [1 3 2]), permute(A, [3 1 2]));
You could also do this completely vectorized by using mat2cell, cellfun, then cell2mat. Assuming our matrix is stored in A, try: numBlocks = size(A,1) / 3; B = mat2cell(A, 3*ones(1,numBlocks), 3); C = cellfun(@(x) x – x([1 1 1], :), B, ‘UniformOutput’, false); D = cell2mat(C); %//Output The first line figures out how many 3 … Read more
Introduction & Test Setup To perform memory tests to inquire about the points raised in the question, let’s define the inputs A and B: A = rand(M,N) B = rand(1,N) Here, M and N are the size parameters and are kept as really large numbers. I would be using repmat for comparisons as that seems … Read more
You could do something like this – # Reshape input array to a 2D array with rows being kept as with original array. # Then, get idnices of max values along the columns. max_idx = A.reshape(A.shape[0],-1).argmax(1) # Get unravel indices corresponding to original shape of A maxpos_vect = np.column_stack(np.unravel_index(max_idx, A[0,:,:].shape)) Sample run – In [214]: … Read more
What you want to do is compile different object files for each instruction set you are targeting. Then create a cpu dispatcher which asks CPUID for the available instruction set and then jumps to the appropriate version of the function. I already described this in several different questions and answers disable-avx2-functions-on-non-haswell-processors do-i-need-to-make-multiple-executables-for-targetting-different-instruction-set how-to-check-with-intel-intrinsics-if-avx-extensions-is-supported-by-the-cpu cpu-dispatcher-for-visual-studio-for-avx-and-sse create-separate-object-files-from-the-same-source-code-and-link-to-an-executable
Combining some comments into an answer: Other than performance, they have identical behaviour (I think even with a memory argument: same lack of alignment requirements for all AVX instructions). On Nehalem to Broadwell, (V)PXOR can run on any of the 3 ALU execution ports, p0/p1/p5. (V)XORPS/D can only run on p5. Some CPUs have a … Read more
Here’s one vectorized way to get the random indices per row, with a as the 2D array of probabilities – (a.cumsum(1) > np.random.rand(a.shape[0])[:,None]).argmax(1) Generalizing to cover both along the rows and columns for 2D array – def random_choice_prob_index(a, axis=1): r = np.expand_dims(np.random.rand(a.shape[1-axis]), axis=axis) return (a.cumsum(axis=axis) > r).argmax(axis=axis) Let’s verify with the given sample by running … Read more
You can use the rolling window technique as explained here, here and here, but for 2D array. The source code for 2D rolling window in NumPy: # Rolling window for 2D arrays in NumPy import numpy as np def rolling_window(a, shape): # rolling window for 2D array s = (a.shape[0] – shape[0] + 1,) + … Read more
Here are the original input variables: A = np.array([[1,1,1,1],[2,2,2,2]]) B = np.array([[1,2,3,4],[1,1,1,1],[1,2,1,9]]) A # array([[1, 1, 1, 1], # [2, 2, 2, 2]]) B # array([[1, 2, 3, 4], # [1, 1, 1, 1], # [1, 2, 1, 9]]) A is a 2×4 array. B is a 3×4 array. We want to compute the Euclidean … Read more
You can compute the norm of each column or row of a matrix yourself by using element-wise arithmetic operators and functions defined to operate over given matrix dimensions (like SUM and MAX). Here’s how you could compute some column-wise norms for a matrix M: twoNorm = sqrt(sum(abs(M).^2,1)); %# The two-norm of each column pNorm = … Read more