I've got an array, called X, where every element is a 2d-vector itself. The diagonal of this array is filled with nothing but zero-vectors. Now I need to normalize every vector in this array, without changing the structure of it.
First I tried to calculate the norm of every vector and put it in an array, called N. After that I wanted to divide every element of X by every element of N. Two problems occured to me:
1) Many entries of N are zero, which is obviously a problem when I try to divide by them.
2) The shapes of the arrays don't match, so np.divide() doesn't work as expected.
Beyond that I don't think, that it's a good idea to calculate N like this, because later on I want to be able to do the same with more than two vectors.
import numpy as np
# Example array
X = np.array([[[0, 0], [1, -1]], [[-1, 1], [0, 0]]])
# Array containing the norms
N = np.vstack((np.linalg.norm(X[0], axis=1), np.linalg.norm(X[1],
axis=1)))
R = np.divide(X, N)
I want the output to look like this:
R = np.array([[[0, 0], [0.70710678, -0.70710678]], [[-0.70710678, 0.70710678], [0, 0]]])
You do not need to use sklearn. Just define a function and then use list comprehension:
Assuming that the 0th dimension of the X is equal to the number of 2D arrays that you have, use this:
import numpy as np
# Example array
X = np.array([[[0, 0], [1, -1]], [[-1, 1], [0, 0]]])
def stdmtx(X):
X= X - X.mean(axis =1)[:, np.newaxis]
X= X / X.std(axis= 1, ddof=1)[:, np.newaxis]
return np.nan_to_num(X)
R = np.array([stdmtx(X[i,:,:]) for i in range(X.shape[0])])
The desired output R:
array([[[ 0. , 0. ],
[ 0.70710678, -0.70710678]],
[[-0.70710678, 0.70710678],
[ 0. , 0. ]]])