On 7/20/07, Kevin Jacobs <[EMAIL PROTECTED]> <[EMAIL PROTECTED]> wrote:
On 7/20/07, Charles R Harris <[EMAIL PROTECTED]> wrote: > > I expect using sqrt(x) will be faster than x**.5. > I did test this at one point and was also surprised that sqrt(x) seemed slower than **.5. However I found out otherwise while preparing a timeit script to demonstrate this observation. Unfortunately, I didn't save the precise script I used to explore this issue the first time. On my system for arrays with more than 2 elements, sqrt is indeed faster. For smaller arrays, the different is negligible, but inches out in favor of ** 0.5.
This is just not my day. My observations above are valid for integer arrays, but not float arrays: sqrt(int array) : 6.98 usec/pass (int array)**0.5 : 22.75 usec/pass sqrt(float array) : 6.70 usec/pass (float array)**0.5: 4.66 usec/pass Generated by: import timeit n=100000 t=timeit.Timer(stmt='sqrt(arange(3))',setup='from numpy import arange,array,sqrt\nx=arange(100)') print 'sqrt(int array) : %5.2f usec/pass' % (1000000*t.timeit(number=n)/n) t=timeit.Timer(stmt='x**0.5',setup='from numpy import arange,array\nx=arange(100)') print '(int array)**0.5 : %5.2f usec/pass' % (1000000*t.timeit(number=n)/n) t=timeit.Timer(stmt='sqrt(arange(3))',setup='from numpy import arange,array,sqrt\nx=arange(100,dtype=float)') print 'sqrt(float array) : %5.2f usec/pass' % (1000000*t.timeit(number=n)/n) t=timeit.Timer(stmt='x**0.5',setup='from numpy import arange,array\nx=arange(100,dtype=float)') print '(float array)**0.5: %5.2f usec/pass' % (1000000*t.timeit(number=n)/n) -Kevin
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