Re: [Numpy-discussion] Fixed-point arithemetic...any solution yet?

[Norbert Nemec]

Dag Sverre Seljebotn wrote:
 I haven't heard of anything, but here's what I'd do:
   - Use np.int64
   - Multiply all inputs to my code with 10^6
   - Divide all output from my code with 10^6
   - If you need to debug-print and array, simply define something like

 FIXED_POINT_FACTOR = 10**6

 def printarr(x):
  print x.astype(np.float) / FIXED_POINT_FACTOR

 Or am I missing something?
   
Indeed, you are missing that internal multiplications have to take into 
account this factor as well. To prevent loss of precision, you would 
need int128 results and shift those correctly after the multiplication.

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Re: [Numpy-discussion] min() of array containing NaN

[Norbert Nemec]

Travis E. Oliphant wrote:
 Thomas J. Duck wrote:
   
 Determining the minimum value of an array that contains NaN produces  
 a surprising result:

   x = numpy.array([0,1,2,numpy.nan,4,5,6])
   x.min()
 4.0

 I expected 0.0.  Is this the intended behaviour or a bug?  I am using  
 numpy 1.1.1.
   
 
 NAN's don't play well with comparisons because comparison with them is 
 undefined.See  numpy.nanmin
   
This is not true! Each single comparison with a NaN has a well defined 
outcome. The difficulty is only that certain logical assumptions do not 
hold any more when NaNs are involved (e.g. [AB] is not equivalent with 
[not(A=B)]). Assuming an IEEE compliant processor and C compiler, it 
should be possible to code a NaN safe min routine without additional 
overhead.

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Re: [Numpy-discussion] diff of find of diff trick

[Norbert Nemec]

Basically, what you want to do is a histogram. Numpy has that
functionality built in. However: the version built in to numpy is about
as suboptimal as yours. The problem is the unnecessary sorting of the data.

In principle, a histogram does not need any sorting, so it could be done
in strictly O(N). I don't see any simply way of doing so efficiently in
numpy without resorting to a costly loop. We had a discussion about this
a while ago but I cannot remember any resolution. The proper thing to do
would probably be a C routine.

If performance is crucial to you, I would suggest using weave. A minimal
example as starting point is given below.

Greetings,
Norbert

---
#!/usr/bin/env python

import numpy
import scipy.weave
import scipy.weave.converters

def histogram(data, nR):
res = numpy.zeros(nR,int)
dataflat = data.flatten()
Ndata = len(dataflat)
scipy.weave.inline(
for(int i=0;iNdata;i++) {
int d = dataflat(i);
if(d  nR  d = 0) {
res(d) += 1;
}
}
,
arg_names = [dataflat,res,Ndata,nR],
type_converters = scipy.weave.converters.blitz,
)
return res



a = numpy.array([0,3,1,2,3,5,2,3,2,4,5,1,3,4,2])
print a
print histogram(a,4)
---





Robin wrote:
 Hello,

 As a converting MATLAB user I am bringing over some of my code. A core
 function I need is to sample the probability distribution of a given
 set of data. Sometimes these sets are large so I would like to make it
 as efficient as possible. (the data are integers representing members
 of a discrete space)

 In MATLAB the best way I found was the diff of find of diff trick
 which resulted in the completely vectorised solution (below). Does it
 make sense to translate this into numpy? I don't have a feel yet for
 what is fast/slow - are the functions below built in and so quick (I
 thought diff might not be).

 Is there a better/more pythonic way to do it?

 
 function Pr=prob(data, nR)

 Pr= zeros(nR,1);
 % diff of find of diff trick for counting number of elements
 temp = sort(data(data0));% want vector excluding P(0)
 dtemp= diff([temp;max(temp)+1]);
 count = diff(find([1;dtemp]));
 indx = temp(dtemp0);
 Pr(indx)= count ./ numel(data);% probability
 

 Thanks

 Robin
 

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