numpy-broadcasting

I have this piece of code in Python for i in range(len(ax)): for j in range(len(rx)): x = ax[i] + rx[j] y = ay[i] + ry[j] A[x,y] = A[x,y] + 1 where A.shape = (N,M) ax.shape = ay.shape = (L) rx.shape = ry.shape = (K) I wanted to vectorize or otherwise make it more efficient, i.e. faster, and if possible more economical in memory consumption. Here, my ax and ay refer to the absolute elements of an array A, while rx and ay are relative coordinates. So, I'm updating the counter array A. My table A can be 1000x1000, while ax,ay are 100x1 and cx,cy are 300x1. The whole thing's inside the loop,

I have a 3d numpy array ( n_samples x num_components x 2 ) in the example below n_samples = 5 and num_components = 7. I have another array ( indices ) which is the selected component for each sample which is of shape ( n_samples ,). I want to select from the data array given the indices so that the resulting array is n_samples x 2 . The code is below: import numpy as np np.random.seed(77) data=np.random.randint(low=0, high=10, size=(5, 7, 2)) indices = np.array([0, 1, 6, 4, 5]) #how can I select indices from the data array? For example for data 0, the selected component should be the 0th and

I am trying to multiply a 3D array by a 1D array, such that each 2D array along the 3rd (depth: d) dimension is calculated like: 1D_array[d]*2D_array And I end up with an array that looks like, say: [[ [1,1] [1,1]] [ [2,2] [2,2]] [ [3,3] [3,3]]] Which would be the result of correctly multiplying np.ones((3,2,2)) with [1,2,3]. I've been trying for some time now and whatever I seem to do I can't end up with this result, just variations on the theme. How do I correctly go about doing this? Thanks for any help. 来源： https://stackoverflow.com/questions/14513222/multiplying-numpy-3d-arrays-by-1d

I have an ndarray, and I want to replace every value in the array with the mean of its adjacent elements. The code below can do the job, but it is super slow when I have 700 arrays all with shape (7000, 7000) , so I wonder if there are better ways to do it. Thanks! a = np.array(([1,2,3,4,5,6,7,8,9],[4,5,6,7,8,9,10,11,12],[3,4,5,6,7,8,9,10,11])) row,col = a.shape new_arr = np.ndarray(a.shape) for x in xrange(row): for y in xrange(col): min_x = max(0, x-1) min_y = max(0, y-1) new_arr[x][y] = a[min_x:(x+2),min_y:(y+2)].mean() print new_arr Well, that's a smoothing operation in image processing ,

In python, suppose I have a square numpy matrix X , of size n x n and I have a numpy vector a of size n . Very simply, I want to perform a broadcasting subtraction of X - a , but I want to be able to specify along which dimension, so that I can specify for the subtraction to be either along axis 0 or axis 1. How can I specify the axis? Let's generate arrays with random elems Inputs : In [62]: X Out[62]: array([[ 0.32322974, 0.50491961, 0.40854442, 0.36908488], [ 0.58840196, 0.1696713 , 0.75428203, 0.01445901], [ 0.27728281, 0.33722084, 0.64187916, 0.51361972], [ 0.39151808, 0.6883594 , 0