[Numpy-discussion] Using logical function on more than 2 arrays, availability of a "between" function ?
Frédéric Bastien
nouiz@nouiz....
Tue Mar 20 11:01:17 CDT 2012
I didn't try it, but I think that Theano and numexpr should be able to
make them faster.
[1] http://deeplearning.net/software/theano/
[2] https://code.google.com/p/numexpr/
Fred
On Tue, Mar 20, 2012 at 9:05 AM, Matthieu Rigal <rigal@rapideye.net> wrote:
> Auto-answer, sorry,
>
> Well, actually I made a mistake lower... that you may have noticed...
> On the faster (your) solution, even with a cleaner use of the out parameter,
> the fact that the all has then to be used with parameter axis=0 takes more
> time and makes it actually slower than the initial solution...
>
> So I may go for the "multiplier" solution.
>
> Regards,
> Matthieu
>
> On Tuesday 20 March 2012 13:13:15 you wrote:
>> Hi Richard,
>>
>> Thanks for your answer and the related help !
>>
>> In fact, I was hoping to have a less memory and more speed solution.
>> Something equivalent to a "raster calculator" for numpy. Wouldn't it make
>> sense to have some optimized function to work on more than 2 arrays for
>> numpy anyway ?
>>
>> At the end, I am rather interested by more speed.
>>
>> I tried first a code-sparing version :
>> array = numpy.asarray([(aBlueChannel < 1.0),(aNirChannel > aBlueChannel *
>> 1.0),(aNirChannel < aBlueChannel * 1.8)]).all()
>>
>> But this one is at the end more than 2 times slower than :
>> array1 = numpy.empty([3,6566,6682], dtype=numpy.bool)
>> numpy.less(aBlueChannel, 1.0, out=array1[0])
>> numpy.greater(aNirChannel, (aBlueChannel * 1.0), out=array1[1])
>> numpy.less(aNirChannel, (aBlueChannel * 1.8), out=array1[2])
>> array = array1.all()
>>
>> (and this solution is about 30% faster than the original one)
>>
>> I could find another way which was fine for me too:
>> array = (aBlueChannel < 1.0) * (aNirChannel > (aBlueChannel * 1.0)) *
>> (aNirChannel < (aBlueChannel * 1.8))
>>
>> But this one is only 5-10% faster than the original solution, even if
>> probably using less memory than the 2 previous ones. (same was possible
>> with operator +, but slower than operator *)
>>
>> Regards,
>> Matthieu Rigal
>>
>> On Monday 19 March 2012 18:00:02 numpy-discussion-request@scipy.org wrote:
>> > Message: 2
>> > Date: Mon, 19 Mar 2012 13:20:23 +0000
>> > From: Richard Hattersley <rhattersley@gmail.com>
>> > Subject: Re: [Numpy-discussion] Using logical function on more than 2
>> > arrays, availability of a "between" function ?
>> > To: Discussion of Numerical Python <numpy-discussion@scipy.org>
>> > Message-ID:
>> >
>> > <CAP=RS9=UBOc6Kmtmnne7W093t19w=T=oSrXUAW0WF8B49hqcXQ@mail.gmail.com
>> >
>> > > Content-Type: text/plain; charset=ISO-8859-1
>> >
>> > What do you mean by "efficient"? Are you trying to get it execute
>> > faster? Or using less memory? Or have more concise source code?
>> >
>> > Less memory:
>> > - numpy.vectorize would let you get to the end result without any
>> > intermediate arrays but will be slow.
>> > - Using the "out" parameter of numpy.logical_and will let you avoid
>> > one of the intermediate arrays.
>> >
>> > More speed?:
>> > Perhaps putting all three boolean temporary results into a single
>> > boolean array (using the "out" parameter of numpy.greater, etc) and
>> > using numpy.all might benefit from logical short-circuiting.
>> >
>> > And watch out for divide-by-zero from "aNirChannel/aBlueChannel".
>> >
>> > Regards,
>> > Richard Hattersley
>>
>
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