Myujth

i have to write this function:

in which x is a vector with dimensions [150,2] and c is [N,2] (lets suppose N=20). From each component xi (i=1,2) I have to subtract the components of c in this way ([x11-c11,x12-c12])...([x11-cN1, x12-cN2])for all the 150 sample.
I've trasformed them in a way I have the same dimensions and I can subtract them, but the result of the function should be a vector. Maybe How can I write this in numpy?
Thank you
Ok, lets suppose x=(5,2) and c=(3,2)

this is what I have obtained transforming dimensions of the two arrays. the problem is that, I have to do this but with a iteration "for loop" because the exp function should give me as a result a vector. so I have to obtain a sort of matrix divided in N blocks.

From what I understand of the issue, the problem seems to be in the way you are calculating the vector norm, not in the subtraction. Using your example, but calculating exp(-||x-c||), try:

x = np.linspace(8,17,10).reshape((5,2))
c = np.linspace(1,6,6).reshape((3,2))
sub = np.linalg.norm(x[:,None] - c, axis=-1)
np.exp(-sub)

array([[ 5.02000299e-05, 8.49325705e-04, 1.43695961e-02],
 [ 2.96711024e-06, 5.02000299e-05, 8.49325705e-04],
 [ 1.75373266e-07, 2.96711024e-06, 5.02000299e-05],
 [ 1.03655678e-08, 1.75373266e-07, 2.96711024e-06],
 [ 6.12664624e-10, 1.03655678e-08, 1.75373266e-07]])

np.exp(-sub).shape
(5, 3)

numpy.linalg.norm will try to return some kind of matrix norm across all the dimensions of its input unless you tell it explicitly which axis represents the vector components.

I I understand, try if this give the expected result, but there is still the problem that the result has the same shape of x:

import numpy as np

x = np.arange(10).reshape(5,2)
c = np.arange(6).reshape(3,2)

c_col_sum = np.sum(c, axis=0)

for (h,k), value in np.ndenumerate(x):
 x[h,k] = c.shape[0] * x[h,k] - c_col_sum[k]

Initially x is:

[[0 1]
 [2 3]
 [4 5]
 [6 7]
 [8 9]]

And c is:

[[0 1]
 [2 3]
 [4 5]]

After the function x becomes:

[[-6 -6]
 [ 0 0]
 [ 6 6]
 [12 12]
 [18 18]]