Hi,
I want to compute the following dot product:
P = np.array( [[ p11, p12 ], [p21, p22]] )
C = np.array( [c1, c2] )
where c1 and c2 are m*m matrices, so that
C.shape = (2,m,m)
I want to compute:
A = np.array([a1, a2])
where a1 and a2 are two matrices m*m, from the dot product of P and
Ben's suggestion is identical to:
A = numpy.tensordot(P, C, axes=(1, 0))
Yes, that does the trick! Thank, very good idea.
Since i've build atlas with threading support, in the computation
of the dot product all four cpus go to 100%, which makes it quite fast.
I'm starting to love numpy
Hi all,
What is the fastest and lowest memory consumption way to compute this?
y = np.arange(2**24)
bases = y[1:] + y[:-1]
Actually it is already quite fast, but i'm not sure whether it is occupying
some temporary memory
is the summation. Any help is appreciated.
Cheers
Davide
Well, actually np.arange(2**24) was just to test the following line ;). I'm
particularly concerned about memory consumption rather than speed.
On 16 May 2010 22:53, Brent Pedersen bpede...@gmail.com wrote:
On Sun, May 16, 2010 at 12:14 PM, Davide Lasagna
lasagnadav...@gmail.com wrote:
Hi all
If your x data are equispaced I would do something like this
def derive( func, x):
Approximate the first derivative of function func at points x.
# compute the values of y = func(x)
y = func(x)
# compute the step
dx = x[1] - x[0]
# kernel array for second order accuracy centered
Hi all,
I noticed some performance problems with np.mean and np.std functions.
Here is the console output in ipython:
# make some test data
: a = np.arange(80*64, dtype=np.float64).reshape(80, 64)
: c = np.tile( a, [1, 1, 1])
: timeit np.mean(c, axis=0)
1 loops, best of 3: 2.09 s per loop
