for the sum function part, It's not a good way to fix but... if you want more accuracy
x=(np.array([1]*10000 + [1e4], dtype=np.float32)) np.sum(x*x) 1.0001e+08 You can sort x from small numbers to bigger numbers before you call sum. -Kibeom Kim On Sat, Mar 5, 2011 at 6:27 PM, Xavier Gnata <xavier.gn...@gmail.com> wrote: > Hi, > > I got this problem in a real life code and it took me some time to > figure out that np.linalg.norm has a terrible numerical behavior. > The problem is nicely described here > http://fseoane.net/blog/2011/computing-the-vector-norm/ > > numpy/linalg/linalg.py claims to be a "high-level Python interface to > the LAPACK library". > That's great but the low level norm function is coded in pure python : > x = asarray(x) > if ord is None: # check the default case first and handle it > immediately > return sqrt(add.reduce((x.conj() * x).ravel().real)) > > Is there a way to use *by default* the blas function to compute the norm? > > ok....looks like sum as the same terrible numerical behavior > x=(np.array([1e4] + [1]*10000, dtype=np.float32)) > np.sum(x*x) > returns 1e+08 instead of 1.0001e+08 > > Moreover, np.linalg.norm is slow compare to blas. > > Is there a way/plan to fix it? > > Xavier > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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