[Numpy-discussion] wrong casting of augmented assignment statements
Christopher Barker
Chris.Barker@noaa....
Tue Jan 12 13:34:11 CST 2010
Sebastian Walter wrote:
>>> However, this particular problem occurs when you try to automatically
>>> differentiate an algorithm by using an Algorithmic Differentiation
>>> (AD) tool.
>>> E.g. given a function
>>>
>>> x = numpy.ones(2)
>>> def f(x):
>>> a = numpy.ones(2)
>>> a *= x
>>> return numpy.sum(a)
I don't know anything about AD, but in general, when I write a function
that requires a given numpy array type as input, I'll do something like:
def f(x):
x = np.asarray(a, dtype=np.float)
a = np.ones(2)
a *= x
return np.sum(a)
That makes the casting explicit, and forces it to happen at the top of
the function, where the error will be more obvious. asarray will just
pass through a conforming array, so little performance penalty when you
do give it the right type.
-Chris
--
Christopher Barker, Ph.D.
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Chris.Barker@noaa.gov
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