On Wed, Jan 26, 2011 at 6:41 PM, Jonathan Rocher <jrocher@enthought.com>wrote:
Nice function, and wonderful that it speeds some tasks up.
some feedback: the following notation is a little counter intuitive to me: >>> np.einsum('i...->', a) array([50, 55, 60, 65, 70]) >>> np.sum(a, axis=0) array([50, 55, 60, 65, 70]) Since there is nothing after the ->, I expected a scalar not a vector. I might suggest 'i...->...'
Hmm, the dimension that's left is a a broadcast dimension, and the dimension labeled 'i' did go away. I suppose disallowing the empty output string and forcing a '...' is reasonable. Would disallowing broadcasting by default be a good approach? Then, einsum('ii->i', a) would only except two dimensional inputs, and you would have to specify einsum('...ii->...i', a) to get the current default behavior for it. Just noticed also a typo in the doc:
order : 'C', 'F', 'A', or 'K' Controls the memory layout of the output. 'C' means it should be Fortran contiguous. 'F' means it should be Fortran contiguous, should be changed to order : 'C', 'F', 'A', or 'K' Controls the memory layout of the output. 'C' means it should be C contiguous. 'F' means it should be Fortran contiguous,
Thanks, Mark