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. 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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
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