[Numpy-discussion] matrix multiplication
Fri Jun 5 18:00:10 CDT 2009
On Fri, Jun 5, 2009 at 17:54, Keith Goodman <firstname.lastname@example.org> wrote:
> On Fri, Jun 5, 2009 at 3:02 PM, Alan G Isaac <email@example.com> wrote:
>> I think something close to this would be possible:
>> add dot as an array method.
>> A .dot(B) .dot(C)
>> is not as pretty as
>> A * B * C
>> but it is much better than
> I've noticed that x.sum() is faster than sum(x)
>>> x = np.array([1,2,3])
>>> timeit x.sum()
> 100000 loops, best of 3: 3.01 µs per loop
>>> from numpy import sum
>>> timeit sum(x)
> 100000 loops, best of 3: 4.84 µs per loop
> Would the same be true of dot? That is, would x.dot(y) be faster than
> dot(x,y)? Or is it just that np.sum() has to go through some extra
> python code before it hits the C code?
No and yes, respectively.
> In general, if I'm trying to speed up an inner loop, I try to replace
> func(x) with x.func(). But I don't really understand the general
> principle at work here.
Most of the functions that mirror methods, including numpy.sum(), are
Python functions that have a little of bit of code to convert the
input to an array if necessary and to dispatch to the method.
numpy.dot() is already implemented in C.
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
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