Arshpreet Singh wrote:
> Hope this is good place to ask question about Cython as well.
> Following code of mine is taking 2.5 times more time than Native Python
> code:
>
> %%cython
> import numpy as np
> a = np.array([])
> def large_sum2(int num_range):
> np.append(a,[i for i in xrange(num_range)])
> return a.sum
>
> %timeit large_sum2(100000)
> 10 loops, best of 3: 19 ms per loop
>
> on the other side python takes much less time:
>
> def large_sum(num_range):
> return sum([i for i in xrange(num_range)])
>
> %timeit large_sum(100000)
> 100 loops, best of 3: 7 ms per loop
But the two versions aren't the same!
If you compare similar code:
In [2]: %%cython
def sigma(n): return sum(i for i in range(n))
...:
In [3]: def tau(n): return sum(i for i in range(n))
In [4]: %timeit sigma(100000)
100 loops, best of 3: 8.34 ms per loop
In [5]: %timeit tau(100000)
100 loops, best of 3: 12.7 ms per loop
And if you decide to go lowlevel (there may be smarter ways, but I'm a
complete amateur with Cython):
In [7]: %%cython
def omega(int n):
cdef long i
cdef long result = 0
for i in range(n): result += i
return result
...:
In [8]: %timeit omega(100000)
10000 loops, best of 3: 91.6 µs per loop
In [9]: assert omega(100000) == tau(100000) == sigma(100000)
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