[Numpy-discussion] Optimize evaluation of function on matrix

[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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