vectorization

问题 I have a matrix Y of shape (n, d). I already calculated the pairwise row-differences in the following way: I, J = np.triu_indices(Y.shape[0], 0) rowDiffs = (Y[I, :] - Y[J, :]) No i want to create a 3d-array, containing the differences of the rows i and j of Y at position (i,j, :). How would you do it? The aim of it is to replace this inefficient loop: for i in range(Y.shape[0]): for j in range(Y.shape[0]): C[i,:] = C[i,:] + W[i, j] * (Y[i, :]-Y[j, :]) 回答1: I have found some success with this:

问题 This question already has answers here : Collapsing matrix into columns (8 answers) Closed 3 years ago . Let's say I have A = [1:8; 11:18; 21:28; 31:38; 41:48] Now I would like to move everything from column 4 onward to the row position. How do I achieve this? A = 1 2 3 4 5 6 7 8 11 12 13 14 15 16 17 18 21 22 23 24 25 26 27 28 31 32 33 34 35 36 37 38 41 42 43 44 45 46 47 48 to A2 = 1 2 3 4 11 12 13 14 21 22 23 24 31 32 33 34 41 42 43 44 5 6 7 8 15 16 17 18 35 36 37 38 45 46 47 48 reshape

问题 In a route planning algorithm, I'm trying to perform a filter on a list of nodes based on distance to another node. I'm actually pulling the lists from a crude scene graph. I use the term "cell" to refer to a volume within a simple scenegraph from which we've fetched a list of nodes that are close to each other. Right now, I'm implementing this as: # SSCCE version of the core function def nodes_in_range(src, cell, maxDist): srcX, srcY, srcZ = src.x, src.y, src.z maxDistSq = maxDist ** 2 for

问题 I try to vectorize a CBRNG which uses 64bit widening multiplication. static __inline__ uint64_t mulhilo64(uint64_t a, uint64_t b, uint64_t* hip) { __uint128_t product = ((__uint128_t)a)*((__uint128_t)b); *hip = product>>64; return (uint64_t)product; } Is such a multiplication exists in a vectorized form in AVX2? 回答1: No. There's no 64 x 64 -> 128 bit arithmetic as a vector instruction. Nor is there a vector mulhi type instruction (high word result of multiply). [V]PMULUDQ can do 32 x 32 -> 64

问题 I'm fairly new to MATLAB. Normal matrix multiplication of a M x K matrix by an K x N matrix -- C = A * B -- has c_ij = sum(a_ik * b_kj, k = 1:K) . What if I want this to be instead c_ij = sum(op(a_ik, b_kj), k = 1:K) for some simple binary operation op ? Is there any nice way to vectorize this in MATLAB (or maybe even a built-in function)? EDIT: This is currently the best I can do. % A is M x K, B is K x N % op is min C = zeros(M, N); for i = 1:M: C(i, :) = sum(bsxfun(@min, A(i, :)', B)); end

问题 Given that I have a square matrix of indices, such as: idxs = np.array([[1, 1], [0, 1]]) and an array of square matrices of the same size as each other (not necessarily the same size as idxs ): mats = array([[[ 0. , 0. ], [ 0. , 0.5]], [[ 1. , 0.3], [ 1. , 1. ]]]) I'd like to replace each index in idxs with the corresponding matrix in mats , to obtain: array([[ 1. , 0.3, 1. , 0.3], [ 1. , 1. , 1. , 1. ], [ 0. , 0. , 1. , 0.3], [ 0. , 0.5, 1. , 1. ]]) mats[idxs] gives me a nested version of

问题 I'd like to compare every entry in array b with its respective column to find how many entries (from that column) are larger than the reference. My code seems embarrassingly slow and I suspect it is due to for loops rather than vector operations. Can we 'vectorize' the following code? import numpy as np n = 1000 m = 200 b = np.random.rand(n,m) p = np.zeros((n,m)) for i in range(0,n): #rows for j in range(0,m): # cols r = b[i,j] p[i,j] = ( ( b[:,j] > r).sum() ) / (n) After some more thought, I

问题 Given an nxn array A of real positive numbers, I'm trying to find the minimum of the maximum of the element-wise minimum of all combinations of three rows of the 2-d array. Using for-loops, that comes out to something like this: import numpy as np n = 100 np.random.seed(2) A = np.random.rand(n,n) global_best = np.inf for i in range(n-2): for j in range(i+1, n-1): for k in range(j+1, n): # find the maximum of the element-wise minimum of the three vectors local_best = np.amax(np.array([A[i,:],

问题 I am just learning MATLAB and I find it hard to understand the performance factors of loops vs vectorized functions. In my previous question: Nested for loops extremely slow in MATLAB (preallocated) I realized that using a vectorized function vs. 4 nested loops made a 7x times difference in running time . In that example instead of looping through all dimensions of a 4 dimensional array and calculating median for each vector, it was much cleaner and faster to just call median(stack, n) where

问题 So, I've got X, a 300-by-1 vector and I'd like [1, X, X*X, X*X*X, ... , X*X*...*X], a 300-by-twenty matrix. How should I do this? X=[2;1] [X,X.*X,X.*X.*X] ans = 2 4 8 1 1 1 That works, but I can't face typing out the whole thing. Surely I don't have to write a for loop? 回答1: If you want to minimize the number of operations : cumprod(repmat(X(:),1,20),2) %// replace "20" by the maximum exponent you want Benchmarking : for X of size 300x1, maximum exponent 20. I measure time with tic , toc ,