On Mon, May 18, 2009 at 6:22 PM, Robert Kern wrote:
> On Mon, May 18, 2009 at 13:23, Pauli Virtanen wrote:
>> Mon, 18 May 2009 09:21:39 -0700, David J Strozzi wrote:
>> [clip]
>>> I also like pointing out that Yorick was a fast, free environment
>>> developed by ~1990, when matlab/IDL were probab
On Mon, May 18, 2009 at 13:23, Pauli Virtanen wrote:
> Mon, 18 May 2009 09:21:39 -0700, David J Strozzi wrote:
> [clip]
>> I also like pointing out that Yorick was a fast, free environment
>> developed by ~1990, when matlab/IDL were probably the only comparable
>> games in town, but very few peopl
Mon, 18 May 2009 09:21:39 -0700, David J Strozzi wrote:
[clip]
> I also like pointing out that Yorick was a fast, free environment
> developed by ~1990, when matlab/IDL were probably the only comparable
> games in town, but very few people ever used it. I think this is a case
> study in the triump
>
>The actual list of Yorick functions relevant here appears to be here:
>
> http:// yorick.sourceforge.net/manual/yorick_46.php#SEC46
> http:// yorick.sourceforge.net/manual/yorick_47.php#SEC47
>
>I must say that I don't see many functions missing in Numpy...
>
>David (Strozzi): are th
On Mon, May 18, 2009 at 11:38 AM, Michael S. Gilbert
wrote:
> On Mon, 18 May 2009 05:37:09 -0700 (PDT), Cristi Constantin wrote:
>> Good day.
>> I am working on this algorithm for a few weeks now, so i tried almost
>> everything...
>> I want to overlap / overwrite 2 matrices, but completely ignor
On Mon, May 18, 2009 at 9:35 AM, wrote:
> On Mon, May 18, 2009 at 10:55 AM, Charles R Harris
> wrote:
> >
> >
> > 2009/5/18 Stéfan van der Walt
> >>
> >> 2009/5/18 Sebastian Walter :
> >> > B = numpy.dot(A.T, A)
> >>
> >> This multiplication should be avoided whenever possible -- you are
> >> e
On Mon, 18 May 2009 05:37:09 -0700 (PDT), Cristi Constantin wrote:
> Good day.
> I am working on this algorithm for a few weeks now, so i tried almost
> everything...
> I want to overlap / overwrite 2 matrices, but completely ignore some values
> (in this case ignore 0)
> Let me explain:
>
> a =
On Mon, May 18, 2009 at 10:55 AM, Charles R Harris
wrote:
>
>
> 2009/5/18 Stéfan van der Walt
>>
>> 2009/5/18 Sebastian Walter :
>> > B = numpy.dot(A.T, A)
>>
>> This multiplication should be avoided whenever possible -- you are
>> effectively squaring your condition number.
>
> Although the cond
2009/5/18 Stéfan van der Walt
> 2009/5/18 Sebastian Walter :
> > B = numpy.dot(A.T, A)
>
> This multiplication should be avoided whenever possible -- you are
> effectively squaring your condition number.
>
Although the condition number doesn't mean much unless the columns are
normalized. Having
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2009/5/18 Stéfan van der Walt :
> 2009/5/18 rob steed :
>> This works fine. However, if the arrays have different lengths, we get a
>> problem.
>>
> y2=N.array([0,0,0,1])
> N.correlate(x,y2,'full')
>
> This looks like a bug to me.
>
> In [54]: N.correlate([1, 0, 0, 0], [0, 0, 0, 1],'full')
Good day.
I am working on this algorithm for a few weeks now, so i tried almost
everything...
I want to overlap / overwrite 2 matrices, but completely ignore some values (in
this case ignore 0)
Let me explain:
a = [
[1, 2, 3, 4, 5],
[9,7],
[0,0,0,0,0],
[5,5,5] ]
b = [
[0,0,9,9],
[1,1,1,1],
[2,2
2009/5/18 rob steed :
> This works fine. However, if the arrays have different lengths, we get a
> problem.
>
y2=N.array([0,0,0,1])
N.correlate(x,y2,'full')
This looks like a bug to me.
In [54]: N.correlate([1, 0, 0, 0], [0, 0, 0, 1],'full')
Out[54]: array([1, 0, 0, 0, 0, 0, 0])
In [5
Hi all,
I have been using numpy.correlate and was finding something weird. I now think
that there might be a bug.
Correlations should be order dependent eg. correlate(x,y) != correlate(y,x) in
general (whereas convolutions are symmetric)
>>> import numpy as N
>>> x = N.array([1,0,0])
>>> y =
2009/5/18 Stéfan van der Walt :
> 2009/5/18 Sebastian Walter :
>> B = numpy.dot(A.T, A)
>
> This multiplication should be avoided whenever possible -- you are
> effectively squaring your condition number.
Indeed.
>
> In the case where you have more rows than columns, use least squares.
> For squar
2009/5/18 Sebastian Walter :
> B = numpy.dot(A.T, A)
This multiplication should be avoided whenever possible -- you are
effectively squaring your condition number.
In the case where you have more rows than columns, use least squares.
For square matrices use solve. For large sparse matrices, use
Alternatively, to solve A x = b you could do
import numpy
import numpy.linalg
B = numpy.dot(A.T, A)
c = numpy.dot(A.T, b)
x = numpy.linalg(B,c)
This is not the most efficient way to do it but at least you know
exactly what's going on in your code.
On Sun, May 17, 2009 at 7:21 PM, wrote:
> O
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