thanks. I hadn't seen it.
anyway, from very rough benchmarks I did, the quickest and easiest way of
computing the euclidean norm of a 1D array is:
n = sqrt(dot(x,x.conj()))
much faster than:
n = sqrt(sum(abs(x)**2))
and much much faster than:
n = scipy.linalg.norm(x)
regards,
lorenzo.
On 3/14/
There is numpy.linalg.norm.
Here's what it does:
def norm(x, ord=None):
x = asarray(x)
nd = len(x.shape)
if ord is None: # check the default case first and handle it immediately
return sqrt(add.reduce((x.conj() * x).ravel().real))
if nd == 1:
if ord == Inf:
Hi all,
just a quick (and easy?) question.
what is the best (fastest) way to implement the euclidean norm of a vector,
i.e. the function:
import scipy as S
def norm(x):
"""normalize a vector."""
return S.sqrt(S.sum(S.absolute(x)**2))
?
thanks in advance,
Lorenzo.
___
