nearest neighbor in 2D

Jim Richardson warlock at eskimo.com
Wed Nov 5 02:53:27 CET 2003


On Tue, 04 Nov 2003 17:13:58 +0100,
 Peter Otten <__peter__ at web.de> wrote:
> Alex Martelli wrote:
>
>> Andrew Dalke wrote:
>> 
>>> Ron Adam
>>>> for point in p:
>>>>     distance = math.sqrt((new_point[0]-point[0])**2 \
>>>>                          +(new_point[1]-point[1])**2)
>>> 
>>>> I really don't know how you can make this faster.  There might be a
>> 
>> Hmmm, that's what math.hypot is for, isn't it...?
>> 
>> [alex at lancelot Lib]$ timeit.py -c -s'import math; p=1.6,2.5; np=2.4,1.3'
>> 'math.sqrt((np[0]-p[0])**2 + (np[1]-p[1])**2)'
>> 100000 loops, best of 3: 3 usec per loop
>> 
>> [alex at lancelot Lib]$ timeit.py -c -s'import math; p=1.6,2.5; np=2.4,1.3'
>> 'math.hypot(np[0]-p[0], np[1]-p[1])'
>> 100000 loops, best of 3: 1.9 usec per loop
>> 
>> 
>>>> library that has a distance between two points function that could
>>>> speed it up.
>>> 
>>> An easy way is to move the math.sqrt call outside the loop, since
>>>  sqrt(d1) < sqrt(d2) iff d1 < d2 (when d1,d2>=0)
>> 
>> Yes, omitting the math.sqrt gives the same speed as calling math.hypot,
>> and it's the classic solution to speed up minimum-distance problems.
>> 
>> I vaguely suspect you could shave some further fraction of a microsecond
>> by saving those differences as dx and dy and then computing dx*dx+dy*dy --
>> since another classic tip is that a**2 is slower than a*2.  Let's see...:
>> 
>> [alex at lancelot Lib]$ timeit.py -c -s'import math; p=1.6,2.5; np=2.4,1.3'
>> 'dx=np[0]-p[0]; dy=np[1]-p[1]; disq=dx*dx+dy*dy'
>> 1000000 loops, best of 3: 1.39 usec per loop
>> 
>> ...yep, another small enhancement.  Ain't measuring _FUN_?-)
>
> Finally found an application for complex numbers:
>
> ...> timeit.py -s"p= 1.6+2.5j; np=2.4+1.3j" "d=abs(p-np)"
> 1000000 loops, best of 3: 0.436 usec per loop
>
> ...> timeit.py -s"p= 1.6,2.5; np=2.4,1.3" "dx=np[0]-p[0];
> dy=np[1]-p[1];d=dx*dx+dy*dy"
> 1000000 loops, best of 3: 1.15 usec per loop
>
> This is of course all premature optimization as the most promising approach
> is to try hard to reduce the number of candidate points, as David Eppstein
> seems to have done. But then, he could use complex numbers, too.
>
> Peter
>


I am new to timeit.py, but this is odd. 

jim at grendel:~$ /usr/lib/python2.3/timeit.py -c ' p=1.6+2.5j;np=2.4+1.3j; d=abs(p-np)'
100000 loops, best of 3: 3.1 usec per loop

vs

jim at grendel:~$ /usr/lib/python2.3/timeit.py -c -s'import math; p=1.6+2.5j;np=2.4+1.3j; d=abs(p-np)'
10000000 loops, best of 3: 0.141 usec per loop

Is it because the builtin math functions are much slower? 


-- 
Jim Richardson     http://www.eskimo.com/~warlock
Televangelists: The Pro Wrestlers of Religion




More information about the Python-list mailing list