,
Invalid __array_interface__ value, must be a dict”);
}
Py_DECREF(iface);
return NULL;
}
Thoughts?
Cheers,
-Daniel Smith
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solution becomes large.
Do not worry about it.
Thank you for your time,
-Daniel Smith
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not sure if this is possible for the faster greedy path algorithm
without large changes.
Overall this sounds great. If anyone has a suggestion of where this should go I
can start working on a PR and we can work out the remaining issues there?
-Daniel Smith
On Jan 3, 2015, at 10:57 AM, Nathaniel
Hello everyone,
I have been working on a chunk of code that basically sets out to provide a
single function that can take an arbitrary einsum expression and computes it in
the most optimal way. While np.einsum can compute arbitrary expressions, there
are two drawbacks to using pure einsum:
Hello all,
I was noticing that `np.triu_indices` took quite awhile and discovered it
creates an upper triu array and then uses `np.where`. This seems quite
inefficient and I was curious if something like the following would be better:
def fast_triu_indices(dim,k=0):
tmp_range =
When calling the average() or mean() functions on a small array (3
numbers), I am seeing significant numerical errors (on the order of 1%
with data to 8 significant digits). The code I am using is essentially:
A = zeros(3)
A[i] = X
B = average(A)
Is there something else I need to call to get
