Take for example:
In [2]: numpy.array([1, 2, 3])[[0, 2, 1]]
Out[2]: array([1, 3, 2])
https://scipy-cookbook.readthedocs.io/items/ViewsVsCopies.ht...
You can verify there's a copy by changing the new array after putting the result in a new variable (see above link for why this makes a difference) and verifying the old one is unchanged:
>>> import numpy as np
>>> x = np.array([1, 2, 3])
>>> y = x[[0, 2, 1]]
>>> y[0] = 3
>>> y
array([3, 3, 2])
>>> x
array([1, 2, 3])
But a view can be based on a slice that includes a skip parameter, and in fact you even slice in multiple dimensions and it will still be a view. That is worth discussing in the article:
>>> x = np.array([np.arange(7), np.arange(7)+1]*3)
>>> y = x[4:1:-2, 1:5:2]
>>> y
array([[1, 3],
[1, 3]])
>>> y[0,0] = 99
>>> x
array([[ 0, 1, 2, 3, 4, 5, 6],
[ 1, 2, 3, 4, 5, 6, 7],
[ 0, 1, 2, 3, 4, 5, 6],
[ 1, 2, 3, 4, 5, 6, 7],
[ 0, 99, 2, 3, 4, 5, 6],
[ 1, 2, 3, 4, 5, 6, 7]]) >>> a = np.arange(9).reshape(3,3) # a matrix
>>> a[0:3,0:3] # ranges are treated independently
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> a[[0,1,2],[0,1,2]] # but arrays are treated at once
array([0, 4, 8])A better way would be to track memory use. A copy being created by either `y = x[[0, 2, 1]]` or `y[0] = 3` would show as a memory increase.
https://alysivji.github.io/python-matrix-multiplication-oper...
# element at the top right. i.e. (1, 2) aka (0, 1) in python
A[0, 0] * B[0, 1] + A[0, 1] * B[1, 1]Also, nitpick but I can't hold it: Why isn't the MSE np.mean(np.square(predictions - labels)? That's even breez-ier!
https://jalammar.github.io/gentle-visual-intro-to-data-analy...