[Numpy-discussion] Mean of n values within an array
Charles R Harris
charlesr.harris at gmail.com
Sat Jul 29 12:20:51 CDT 2006
Hmmm,
I rewrote the subroutine a bit.
def numpy_nmean(list,n):
a = numpy.empty(len(list),dtype=float)
b = numpy.cumsum(list)
for i in range(0,len(list)):
if i < n :
a[i] = b[i]/(i+1)
else :
a[i] = (b[i] - b[i-n])/(i+1)
return a
and got
regular python took: 0.750000 sec.
numpy took: 0.380000 sec.
The precision of the new routine is admittedly suspect. Convolve can also
be used:
def numpy_nmean_conv(list,n):
b = numpy.ones(n,dtype=float)
a = numpy.convolve(list,b,mode="full")
for i in range(0,len(list)):
if i < n :
a[i] /= i + 1
else :
a[i] /= n
return a[:len(list)]
Which gives
numpy convolve took: 0.260000 sec.
You might actually want mode="valid" which would only return averages over
runs of length n.
One oddity here is that I think convolution should be slower than cumsum and
that raises my suspicions about the latter, it seems much too slow.
Chuck
On 7/29/06, Phil Ruggera <pruggera at gmail.com> wrote:
>
> I rewrote some python code using numpy to do a performance comparison.
> The results were the opposite of what I wanted. Numpy was slower
> than Python without numpy. Is there something wrong with my approach?
>
> # mean of n values within an array
> import numpy, time
> def nmean(list,n):
> a = []
> for i in range(1,len(list)+1):
> start = i-n
> divisor = n
> if start < 0:
> start = 0
> divisor = i
> a.append(sum(list[start:i])/divisor)
> return a
>
> t = [1.0*i for i in range(1400)]
> start = time.clock()
> for x in range(100):
> nmean(t,50)
> print "regular python took: %f sec."%(time.clock() - start)
>
> def numpy_nmean(list,n):
> a = numpy.empty(len(list),dtype=float)
> for i in range(1,len(list)+1):
> start = i-n
> if start < 0:
> start = 0
> a[i-1] = list[start:i].mean(0)
> return a
>
> t = numpy.arange(0,1400,dtype=float)
> start = time.clock()
> for x in range(100):
> numpy_nmean(t,50)
> print "numpy took: %f sec."%(time.clock() - start)
>
>
> Results:
> regular python took: 1.215274 sec.
> numpy took: 2.499299 sec.
>
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