Numpy Pure Functions for performance, caching

Question

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 ca

Answer 1

Mostly I agree with Warren Weckesser and his answer above. But for derivative of sigmoid the following can be used:

In [002]: def sg(x):
     ...: s = scipy.special.expit(x)
     ...: return s * (1.0 - s) 

Timings:

In [003]: %timeit y = logistic._pdf(x)
10000 loops, best of 3: 45 µs per loop

In [004]: %timeit y = sg(x)
10000 loops, best of 3: 20.4 µs per loop

The only problem is accuracy:

In [005]: sg(37)
Out[005]: 0.0

In [006]: logistic._pdf(37)
Out[006]: 8.5330476257440658e-17