blas

I'm writing some moderately performance critical code in numpy. This code will be in the inner most loop, of a computation that's run time is measured in hours. A quick calculation suggest that this code will be executed up something like 10^12 times, in some variations of the calculation. So the function is to calculate sigmoid(X) and another to calculate its derivative (gradient). Sigmoid has the property that for y=sigmoid(x), dy/dx= y(1-y) In python for numpy this looks like: sigmoid = vectorize(lambda(x): 1.0/(1.0+exp(-x))) grad_sigmoid = vectorize(lambda (x): sigmoid(x)*(1-sigmoid(x)))

I am builing my numpy/scipy environment based on blas and lapack more or less based on this walk through. When I am done, how can I check, that my numpy/scipy functions really do use the previously built blas/lapack functionalities? rabra What you are searching for is this: system info I compiled numpy/scipy with atlas and i can check this with: import numpy.distutils.system_info as sysinfo sysinfo.get_info('atlas') Check the documentation for more commands. The method numpy.__config__.show() outputs information about linkage gathered at build time. My output looks like this. I think it means

I'm trying to use dot products, matrix inversion and other basic linear algebra operations that are available in numpy from Cython. Functions like numpy.linalg.inv (inversion), numpy.dot (dot product), X.t (transpose of matrix/array). There's a large overhead to calling numpy.* from Cython functions and the rest of the function is written in Cython, so I'd like to avoid this. If I assume users have numpy installed, is there a way to do something like: #include "numpy/npy_math.h" as an extern , and call these functions? Or alternatively call BLAS directly (or whatever it is that numpy calls for