I have a Cython function that takes a list of weights/probabilities (
double) and returns a random index into the list.
choose_one(np.array([0.1, 0.4, 0.2, 0.3]), 4)
0 with probability
1 with probability
The Cython code is:
cdef extern from "stdlib.h": double drand48() void srand48(long int seedval) @cython.boundscheck(False) @cython.wraparound(False) @cython.nonecheck(False) def choose_one(np.ndarray[double, ndim=1] weights, Py_ssize_t length): cdef Py_ssize_t idx, i cdef double cs cdef double random random = drand48() cs = 0.0 i = 0 while cs < random and i < length: cs += weights[i] i += 1 return i - 1
I profiled some complex simulation code and found that about 50% of the execution time is spent in this function which is called approximately 3.5 million times. Because it is such a time hog, and such an important utility, I'd like to speed it up if possible.
One of the problems is that the input arrays are often very large with thousands of entries.
I tried various methods from Numpy in Cython and none of them come close to as fast as what I have written in Cython. I am a Cython newbie and am curious if there is something else I need to do to speed this up.