Hey, Am 27.03.2017 um 16:09 schrieb Sebastian Berg: > On Mon, 2017-03-27 at 13:06 +0200, Florian Lindner wrote: >> Hey, >> >> I've timed the two versions, one basisfunction being a function: >> >> 1 loop, best of 3: 17.3 s per loop >> >> the other one, basisfunction being a list of functions: >> >> 1 loop, best of 3: 33.5 s per loop >> >>> To be honest, I am a bit surprised that its a problem, since "basis >>> function" sounds a bit like you have to do this once and then use >>> the >>> result many times. >> >> It's part of a radial basis function interpolation algorithm. Yes, in >> practice the matrix is filled only once and reused >> a couple of times, but in my case, which is exploration of parameters >> for the algorithm, I call eval_BF many times. >> >>> You can get rid of the `row` loop though in case row if an >>> individual >>> row is a pretty small array. >> >> Would you elaborate on that? Do you mean that the inner col loop >> produces an array which is then assigned to the row. >> But I think it stell need to row loop there. > > Well, I like to not serve the result, but if you exchange the loops: > > A = np.empty((len(meshA), len(meshB))) > for j, col in enumerate(meshB): > for i, row in enumerate(meshA): > A[i, j] = self.basisfunction[j](row - col) > > Then you can see that there is broadcasting magic similar (do not want > to use too many brain cells now) to: > > A = np.empty((len(meshA), len(meshB))) > for j, col in enumerate(meshB): > # possibly insert np.newaxis/None or a reshape in [??] > A[:, j] = self.basisfunction[j](meshA[??] - col)
I have it like that now:
A = np.empty((len(meshA), len(meshB)))
for j, col in enumerate(meshB):
A[:,j] = self.basisfunction[j](meshA - col)
which has improved my speeds by a factor of 36.
Thanks!
Florian
>
> - Sebastian
>
>>
>> Best,
>> Florian
>>
>> Am 25.03.2017 um 22:31 schrieb Sebastian Berg:
>>> On Sat, 2017-03-25 at 18:46 +0100, Florian Lindner wrote:
>>>> Hello,
>>>>
>>>> I have this function:
>>>>
>>>> def eval_BF(self, meshA, meshB):
>>>> """ Evaluates single BF or list of BFs on the meshes. """
>>>> if type(self.basisfunction) is list:
>>>> A = np.empty((len(meshA), len(meshB)))
>>>> for i, row in enumerate(meshA):
>>>> for j, col in enumerate(meshB):
>>>> A[i, j] = self.basisfunction[j](row - col)
>>>> else:
>>>> mgrid = np.meshgrid(meshB, meshA)
>>>> A = self.basisfunction( np.abs(mgrid[0] - mgrid[1]) )
>>>> return A
>>>>
>>>>
>>>> meshA and meshB are 1-dimensional numpy arrays.
>>>> self.basisfunction is
>>>> e.g.
>>>>
>>>> def Gaussian(radius, shape):
>>>> """ Gaussian Basis Function """
>>>> return np.exp( -np.power(shape*abs(radius), 2))
>>>>
>>>>
>>>> or a list of partial instantations of such functions (from
>>>> functools.partial).
>>>>
>>>> How can I optimize eval_BF? Esp. in the case of basisfunction
>>>> being a
>>>> list.
>>>>
>>>
>>> Are you sure you need to optimize it? If they have a couple of
>>> hundred
>>> elements or so for each row, the math is probably the problem and
>>> most
>>> of that might be the `exp`.
>>> You can get rid of the `row` loop though in case row if an
>>> individual
>>> row is a pretty small array.
>>>
>>> To be honest, I am a bit surprised that its a problem, since "basis
>>> function" sounds a bit like you have to do this once and then use
>>> the
>>> result many times.
>>>
>>> - Sebastian
>>>
>>>
>>>> Thanks!
>>>> Florian
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