I saw the earlier discussion on the handling of NaN on numpy, and I can see that currently it is ignored when you use pure numpy min:
In [25]: a Out[25]: array([ 0., NaN, 0.]) In [26]: a.min() Out[26]: 0.0 In [27]: a.argmin() Out[27]: 0 However, somehow the pycuda drv.out() leaves the array in such a state that a.min() returns NaN while a[a.argmin()] returns something else. Not sure exactly what causes this, as it only happens sometimes. When I have seen this bug, it's on a large unwieldy dataset that's hard to debug. The workaround seems to be to just use a[a.argmin()]... -- Benjamin P. Horstman Delta Upsilon International Fraternity President, Gamers Anonymous CWRU EECS BS/MS 2009
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