array-broadcasting

问题 Problem: When using broadcasting, rather than broadcasting scalars to match the arrays, the vectorized function is instead, for some reason, shrinking the arrays to scalars. MWE: Below is a MWE. It contains a double for loop. I am having trouble writing faster code that does not use the for loops, but instead, uses broadcasting/vectorized numpy. import numpy as np def OneD(x, y, z): ret = np.exp(x)**(y+1) / (z+1) return ret def ThreeD(a,b,c): value = OneD(a[0],b[0], c) value *= OneD(a[1],b[1]

问题 Problem: When using broadcasting, rather than broadcasting scalars to match the arrays, the vectorized function is instead, for some reason, shrinking the arrays to scalars. MWE: Below is a MWE. It contains a double for loop. I am having trouble writing faster code that does not use the for loops, but instead, uses broadcasting/vectorized numpy. import numpy as np def OneD(x, y, z): ret = np.exp(x)**(y+1) / (z+1) return ret def ThreeD(a,b,c): value = OneD(a[0],b[0], c) value *= OneD(a[1],b[1]

问题 Purpose I have turned a double for loop into a single for loop using vectorization . I would like to now get rid of the last loop . I want to slice an Nx3 array of coordinates and calculate distances between the sliced portion and the remaining portion without using a for loop . Two cases (1) the slice is always 3x3 . (2) the slice is variable i.e., Mx3 where M is always significantly smaller than N Vectorizing the interaction of 1 row of the slice interacting with the remainder is

问题 I would like to construct an array with the following structure: A[i,j,i,j,] = B[i,j] with all other entries 0: A[i,j,l,k]=0 # (i,j) =\= (l,k) I.e. if I have the B matrix constructed how can I create the matrix A , preferably in a vectorized manner. Explicitly, let B = [[1,2],[3,4]] Then: A[1,1,:,:]=[[1,0],[0,0]] A[1,2,:,:]=[[0,2],[0,0]] A[2,1,:,:]=[[0,0],[3,0]] A[2,2,:,:]=[[0,0],[0,4]] 回答1: We can use an open grid to assign to A broadcasting the indexing arrays across the axes: B = np.array(

问题 I would like to construct an array with the following structure: A[i,j,i,j,] = B[i,j] with all other entries 0: A[i,j,l,k]=0 # (i,j) =\= (l,k) I.e. if I have the B matrix constructed how can I create the matrix A , preferably in a vectorized manner. Explicitly, let B = [[1,2],[3,4]] Then: A[1,1,:,:]=[[1,0],[0,0]] A[1,2,:,:]=[[0,2],[0,0]] A[2,1,:,:]=[[0,0],[3,0]] A[2,2,:,:]=[[0,0],[0,4]] 回答1: We can use an open grid to assign to A broadcasting the indexing arrays across the axes: B = np.array(

问题 Introduction I have a function func which is vectorizable, and I vectorize it using np.frompyfunc . Rather than using a nested for loop, I want to call it only once, and thus I'll need to pad the inputs with np.newaxis 's. My goal is to get rid of the two nested for loops and use the numpy.array broadcasting feature instead. Here is the MWE for loops (I want to get rid of the for loops and instead pad the variables c_0 , c_1 , rn_1 , rn_2 , and factor when calling myfunc . MWE of the problem

问题 I want to perform some numpy methods on items of structured array instead of numbers. So, for example, while working with array of integers of shape (4, 3), I need to convert it to array of items of length 3 and perform some operations as it were a single one dimensional array of shape (4,). These conversions itself, unfortunately, looks really complicated for me. Let's take another example: n, m, r = 2, 3, 4 array = np.arange(n*m).reshape((n,m)) dt = np.dtype(','.join('i'*m)) arr1 = np.array

问题 I am trying creating an NumPy array filled with an object, and I was wondering if there was a way I could broadcast to the entire array for each object to do something. Code: class player: def __init__(self,num = 5): self.num = num def printnum(): print(self.num) ... objs = np.array([player(5),player(6)],dtype=Object) objs.printnum() As it stands this returns an error. I have tried changing the dtype to: _object as per the manual, but nothing seems to work. 回答1: A numpy array of objects does

问题 The arrays are of following dimensions: dists : (500,5000) train : (5000,) test :(500,) Why does the first two statements throw an error whereas the third one works fine? dists += train + test Error: ValueError: operands could not be broadcast together with shapes (5000,) (500,) dists += train.reshape(-1,1) + test.reshape(-1,1) Error: ValueError: operands could not be broadcast together with shapes (5000,1) (500,1) dists += train + test.reshape(-1,1) This works fine! Why does this happen? 回答1

来源： https://stackoverflow.com/questions/62994636/numpy-stop-numpy-array-from-trying-to-reconcile-elements-create-ndarry-from