Hi Warren, Yes you are absolutely right. I had some values close to log(x), where x is almost 0. That caused the problem.
Thanks, Tom On Sat, Aug 24, 2013 at 12:39 PM, Warren Weckesser < [email protected]> wrote: > On 8/24/13, Warren Weckesser <[email protected]> wrote: > > On 8/24/13, Tom Bennett <[email protected]> wrote: > >> Hi All, > >> > >> I have two arrays, A and B.A is 3 x 100,000 and B is 100,000. If I do > >> np.dot(A,B), I get [nan, nan, nan]. > >> > >> However, np.any(np.isnan(A))==False and np.any(no.isnan(B))==False. And > >> also np.seterr(all='print') does not print anything. > >> > >> I am not wondering what is going on and how to avoid. > >> > >> In case it is important, A and B are from the normal equation of doing > >> regression. I am regressing 100,000 observations on 3 100,000 long > >> factors. > >> > >> Thanks, > >> Tom > >> > > > > What are the data types of the arrays, and what are the typical sizes > > of the values in these arrays? I can get all nans from np.dot if the > > values are huge floating point values: > > > > ``` > > In [79]: x = 1e160*np.random.randn(3, 10) > > > > In [80]: y = 1e160*np.random.randn(10) > > > > In [81]: np.dot(x, y) > > Out[81]: array([ nan, nan, nan]) > > ``` > > ...and that happens because some intermediate terms overflow to inf or > -inf, and adding these gives nan: > > ``` > In [89]: x = np.array([1e300]) > > In [90]: y = np.array([1e10]) > > In [91]: np.dot(x,y) > Out[91]: inf > > In [92]: x2 = np.array([1e300, 1e300]) > > In [93]: y2 = np.array([1e10,-1e10]) > > In [94]: np.dot(x2, y2) > Out[94]: nan > ``` > > Warren > > > > > > Warren > > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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