jjrbfi I used image_int[::8]
That works but I have to do it in a for loop
wanted to ask here before opening an issue.
Is there a numpy function to get the
Pooled Standard Deviation?
as shown here
Hi friend, I'm trying to fit postgresql max CUBE by reducing numpy nd array, but the results are not as expected
the code for reducing dimension :
pca = PCA(1) feature = feature.reshape(-1, 1) feature = pca.fit_transform(feature)
i think the code still wrong and do not reduce the "feature" dimension
some_numpy_array[:] = some_iterableand
some_numpy_array[...] = some_iterable, for a 1D array?
[...]refers to "all axes" while
[:]means the first axis, which is the same in this case :)
axis=(-2, -1). :facepalm: