On Sep 5, 2008, at 8:52 AM, Keith Goodman wrote:
Here's another difference:
a = np.random.randn(10)
timeit np.sum(a[np.where(a0)])
100 loops, best of 3: 3.44 ms per loop
timeit a[a 0].sum()
100 loops, best of 3: 2.21 ms per loop
Here is an even faster method (but much more ugly!):
On Sep 5, 2008, at 8:52 AM, Keith Goodman wrote:
Here's another difference:
a = np.random.randn(10)
timeit np.sum(a[np.where(a0)])
100 loops, best of 3: 3.44 ms per loop
timeit a[a 0].sum()
100 loops, best of 3: 2.21 ms per loop
Here is an even faster method (but much more ugly!):
Hi there,
The pdist function computes pairwise distances between vectors in a
single collection, storing the distances in a condensed distance matrix.
This is not exactly what you want--you want to compute distance
between two collections of vectors.
Suppose XA is a m_A by n array and XB is
Excellent.
David said that distance computation will be moved in a
separate package soon. I guess that your implementation
will be the suitable one for this package. Am I wrong?
Thanks again,
Emanuele
Damian Eads wrote:
Hi there,
The pdist function computes pairwise distances between
On Sun, Sep 7, 2008 at 4:07 PM, Emanuele Olivetti
[EMAIL PROTECTED] wrote:
David said that distance computation will be moved in a
separate package soon. I guess that your implementation
will be the suitable one for this package. Am I wrong?
Yes, that is correct. David was talking about
