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When using numpy, we call np.split(x,2),np.hsplit(x,2),np.vsplit(x,2), x is a numpy array. But the command didn't output anything or report any mistakes
#35
However when I replaced np.hsplit(x,2) with np.hsplit(x,[2]), it can work . So I wonder how can I use np.hsplit(x,2)
The text was updated successfully, but these errors were encountered:
xie233
changed the title
When Using numpy, we call np.split(x,2),np.hsplit(x,2),np.hsplit(x,2), x is a numpy array. But the command didn't output anything or report any mistakes
When Using numpy, we call np.split(x,2),np.hsplit(x,2),np.vsplit(x,2), x is a numpy array. But the command didn't output anything or report any mistakes
Jul 15, 2019
xie233
changed the title
When Using numpy, we call np.split(x,2),np.hsplit(x,2),np.vsplit(x,2), x is a numpy array. But the command didn't output anything or report any mistakes
When using numpy, we call np.split(x,2),np.hsplit(x,2),np.vsplit(x,2), x is a numpy array. But the command didn't output anything or report any mistakes
Jul 15, 2019
It looks like you hit the limits of current numpy support. Actually it's good that you found such a concise workaround. I wasn't aware of this specific limit but note that JyNI is still experimental and numpy support is incomplete in manifold ways (e.g. buffer protocol, random module). Use with care!
As of this writing I cannot tell when I will be able to resume work on JyNI, so your best bet is just to stick with your workaround using [2] in stead of 2. You are lucky that such an easy workaround does the trick.
However when I replaced np.hsplit(x,2) with np.hsplit(x,[2]), it can work . So I wonder how can I use np.hsplit(x,2)
The text was updated successfully, but these errors were encountered: