On 31-Mar-10, at 6:15 PM, T J wrote:
In : np.logaddexp2(-1.5849625007211563, -53.584962500721154) Out: -1.5849625007211561
In : np.logaddexp2(-0.5849625007211563, -53.584962500721154) Out: nan
In : np.log2(np.exp2(-0.5849625007211563) + np.exp2(-53.584962500721154)) Out: -0.58496250072115608
Shouldn't the result at least behave nicely and just return the larger value?
Unfortunately there's no good way of getting around order-of- operations-related rounding error using the reduce() machinery, that I know of.
I'd like to see a 'logsumexp' and 'logsumexp2' in NumPy instead, using the generalized ufunc architecture, to do it over an arbitrary dimension of an array, rather than needing to invoke 'reduce' on logaddexp. I tried my hand at writing one but I couldn't manage to get it working for some reason, and I haven't had time to revisit it: http://mail.scipy.org/pipermail/numpy-discussion/2010-January/048067.html