Hi Marc,
ufuncs are quite tricky to compile. Part of your problem is that, I
think, you started a bit too high up: `divmod` is also a binary
operation, so that part you do not need at all. It may be an idea to
start instead with a PR that implemented a new ufunc, e.g.,
https://github.com/numpy/numpy/pull/8795, so that you can see what is
involved.
All the best,
Marten
On Thu, May 18, 2017 at 9:04 AM, marc
Dear Numpy developers,
I'm trying to add a routine to calculate the sum of a product of two arrays (a dot product). But that would not increase the memory (from what I saw np.dot is increasing the memory while it should not be necessary). The idea is to avoid the use of the temporary array in the calculation of the variance ( numpy/numpy/core/_methods.py line 112).
The routine that I want to implement look like this in python,
arr = np.random.rand(100000) mean = arr.mean() var = 0.0 for ai in arr: var += (ai-mean)**2
I would like to implement it in the umath module. As a first step, I tried to reproduce the divmod function of umath, but I did not manage to do it, you can find my fork here (the branch with the changes is call looking_around). During compilation I get the following error,
gcc: numpy/core/src/multiarray/number.c In file included from numpy/core/src/multiarray/number.c:17:0: numpy/core/src/multiarray/number.c: In function ‘array_sum_multiply’: numpy/core/src/private/binop_override.h:176:39: error: ‘PyNumberMethods {aka struct <anonymous>}’ has no member named ‘nb_sum_multiply’ (void*)(Py_TYPE(m2)->tp_as_number->SLOT_NAME) != (void*)(test_func)) ^ numpy/core/src/private/binop_override.h:180:13: note: in expansion of macro ‘BINOP_IS_FORWARD’ if (BINOP_IS_FORWARD(m1, m2, slot_expr, test_func) && \ ^ numpy/core/src/multiarray/number.c:363:5: note: in expansion of macro ‘BINOP_GIVE_UP_IF_NEEDED’ BINOP_GIVE_UP_IF_NEEDED(m1, m2, nb_sum_multiply, array_sum_multiply);
Sorry if my question seems basic, but I'm new in Numpy development. Any help?
Thank you in advance,
Marc Barbry
PS: I opened an issues as well on the github repository https://github.com/numpy/numpy/issues/9130
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