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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