(sorry for starting a new thread...I wasn't subscribed yet) Stéfan van der Walt wrote the following on 09/19/2008 02:10 AM: > > So am I. In all my use cases, NaNs indicate trouble. >
I can provide a use case where NaNs do not indicate trouble. In fact, they need to be treated as 0. For example, As x->0 in y(x) = x log x, it is traditional (eg in information theory) to take y(0) = 0. So if one is multiplying arrays and 0 * -inf is encountered, the desirable behavior is that we get 0. Because you're always working with probabilities, there is almost always no ambiguity...whenever NaN is encounter, 0 is what is desired. Perhaps numpy can have some method by which a user can specify how NaNis treated (in addition to ignore, raise, etc). Good idea? Bad idea?
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