dot-product

问题 This question already has answers here : Vectorized way of calculating row-wise dot product two matrices with Scipy (5 answers) Closed 4 years ago . We are currently working on a python project and have to vectorize a lot due to performance constraints. We end up with the following calculation: We have two numpy arrays of shape (20,6) and want to calculate the pairwise dot product of the rows, i.e. we should obtain a (20,1) matrix in the end, where each row is the scalar obtained by the

问题 I have 2 arrays x and y with shapes (2, 3, 3) , respectively, (3, 3) . I want to compute the dot product z with shape (2, 3) in the following way: x = np.array([[[a111, a121, a131], [a211, a221, a231], [a311, a321, a331]], [[a112, a122, a132], [a212, a222, a232], [a312, a322, a332]]]) y = np.array([[b11, b12, b13], [b21, b22, b23], [b31, b32, b33]]) z = np.array([[a111*b11+a121*b12+a131*b13, a211*b21+a221*b22+a231*b23, a311*b31+a321*b32+a331*b33], [a112*b11+a122*b12+a132*b13, a212*b21+a222

问题 I'm trying to take a tensor dot product in numpy using tensordot , but I'm not sure how I should reshape my arrays to achieve my computation. (I'm still new to the mathematics of tensors, in general.) I have arr = np.array([[[1, 1, 1], [0, 0, 0], [2, 2, 2]], [[0, 0, 0], [4, 4, 4], [0, 0, 0]]]) w = [1, 1, 1] And I want to take a dot product along axis=2 , such that I have the matrix array([[3, 0, 6], [0, 12, 0]]) What's the proper numpy syntax for this? np.tensordot(arr, [1, 1, 1], axes=2)

问题 I'm trying to take a tensor dot product in numpy using tensordot , but I'm not sure how I should reshape my arrays to achieve my computation. (I'm still new to the mathematics of tensors, in general.) I have arr = np.array([[[1, 1, 1], [0, 0, 0], [2, 2, 2]], [[0, 0, 0], [4, 4, 4], [0, 0, 0]]]) w = [1, 1, 1] And I want to take a dot product along axis=2 , such that I have the matrix array([[3, 0, 6], [0, 12, 0]]) What's the proper numpy syntax for this? np.tensordot(arr, [1, 1, 1], axes=2)

问题 If I see the following line in a python code where numpy is imported: c = a * b What is the easiest and most practical way to determine if this operation is executed as a Hadamard (elementwise) or dot product (pointwise) operation? Is it right that for a Hadamard product the column and row size of A and B must be the same. For a dot product only the column size of A must be the same as the row size of B, correct? So I can lookup the shape of both and find out which operation is used? 回答1:

问题 I am generating a matrix in R using following, ncolumns = 3 nrows = 10 my.mat <- matrix(runif(ncolumns*nrows), ncol=ncolumns) This matrix indicates the co-ordinates of a point in 3D. How to calculate following in R? sum of x(i)*y(i) e.g. if the matrix is, x y z 1 2 3 4 5 6 then output = 1*2 + 4*5 I'm trying to learn R. So any help will be really appreciated. Thanks 回答1: You're looking for the %*% function. ncolumns = 3 nrows = 10 my.mat <- matrix(runif(ncolumns*nrows), ncol=ncolumns) (my

问题 Question What numpy function to use for mathematical dot product in the case below? Backpropagation for a Linear Layer 回答1: Define sample (2,3) array: In [299]: dldx = np.arange(6).reshape(2,3) In [300]: w Out[300]: array([[0.1, 0.2, 0.3], [0. , 0. , 0. ]]) Element wise multiplication: In [301]: dldx*w Out[301]: array([[0. , 0.2, 0.6], [0. , 0. , 0. ]]) and summing on the last axis (size 3) produces a 2 element array: In [302]: (dldx*w).sum(axis=1) Out[302]: array([0.8, 0. ]) Your (6) is the

来源： https://stackoverflow.com/questions/64668780/basic-neural-network-without-numpy-understanding-the-iteration-steps

问题 Using C#'s Vector<T> , how can we most efficiently vectorize the operation of finding the index of a particular element in a set? As constraints, the set will always be a Span<T> of an integer primitive, and it will contain at most 1 matching element. I have come up with a solution that seems alright, but I'm curious if we can do better. Here is the approach: Create a Vector<T> consisting only of the target element, in each slot. Use Vector.Equals() between the input set vector and the vector

问题 I will preface this by saying that I am a complete beginner at SIMD intrinsics. Essentially, I have a CPU which supports the AVX2 instrinsic ( Intel(R) Core(TM) i5-7500T CPU @ 2.70GHz ). I would like to know the fastest way to compute the dot product of two std::vector<float> of size 512 . I have done some digging online and found this and this, and this stack overflow question suggests using the following function __m256 _mm256_dp_ps(__m256 m1, __m256 m2, const int mask); , However, these