If I have two recarrays with the same len and column headers, the __eq__
method returns the rich comparison, which is great. E.g.
In [20]: x =
np.rec.fromrecords([(1,2,'dd',.3),(33,2,'y',2.2),(2,3,'a',21.4),(3,4,'b',33.2)],names=['A','B','C','D'])
In [21]: y =
np.rec.fromrecords([(1,2,'dd',.3),(
I've tried the same scheme using R and it seems to give the right
answers
> quantile( rf(1000,10,10), .99)
99%
4.84548
> quantile( rf(1000,11,10), .99)
99%
4.770002
> quantile( rf(1000,11,11), .99)
99%
4.465655
> quantile( rf(1000,10,11), .99)
99%
4.539423
I either found a bug in the F distribution, or I'm really messed up.
>From a table I find
dfnum dfden F(P<.01)
10 10 4.85
11 10 4.78
11 11 4.46
10 11 4.54
So let's calculate the same quantities using numpy...
import scipy.stats as stats
import numpy as np
I
On 2009-07-03, Charles R Harris wrote:
> roots? The connection between polynomial coefficients and polynomial values
> becomes somewhat vague when the polynomial degree becomes large, it is
> numerically ill conditioned.
In addition to switching to higher precision than machine
precision, anothe
Fabrice Silva wrote:
> Le vendredi 03 juillet 2009 à 11:52 +0200, Nils Wagner a écrit :
>> You will need multiprecision arithmetic in that case.
>> It's an ill-conditioned problem.
>
> I may have said that the solution are of the same order of magnitude, so
> that the ratio between the lowest and
On Fri, Jul 3, 2009 at 3:48 AM, Fabrice Silva wrote:
> Hello
> Has anyone looked at the behaviour of the (polynomial) roots function
> for high-order polynomials ? I have an application which internally
> searches for the roots of a polynomial. It works nicely for order less
> than 20, and then h
Le vendredi 03 juillet 2009 à 14:43 +0200, Nils Wagner a écrit :
> Just curious - Can you provide us with the coefficients of
> your polynomial ?
Working case :
Polynomial.c =
[ -1.34100085e+57 +0.e+00j -2.28806781e+55 +0.e+00j
-4.34808480e+54 -3.27208577e+36j -2.44499178e+
On Fri, 03 Jul 2009 14:26:39 +0200
Fabrice Silva wrote:
> Le vendredi 03 juillet 2009 à 11:52 +0200, Nils Wagner a
>écrit :
>> You will need multiprecision arithmetic in that case.
>> It's an ill-conditioned problem.
>
> I may have said that the solution are of the same order
>of magnitude, s
Le vendredi 03 juillet 2009 à 11:52 +0200, Nils Wagner a écrit :
> You will need multiprecision arithmetic in that case.
> It's an ill-conditioned problem.
I may have said that the solution are of the same order of magnitude, so
that the ratio between the lowest and the highest absolute values of
>2009/7/3 Sebastian Haase :
> Hi,
> should this not be accepted:
N.argwhere([4,0,2,1,3])
> ?
> instead I get
>
> Traceback (most recent call last):
> File "", line 1, in
> File "./numpy/core/numeric.py", line 510, in argwhere
> AttributeError: 'list' object has no attribute 'nonzero'
N
On Fri, 03 Jul 2009 11:48:45 +0200
Fabrice Silva wrote:
> Hello
> Has anyone looked at the behaviour of the (polynomial)
>roots function
> for high-order polynomials ? I have an application which
>internally
> searches for the roots of a polynomial. It works nicely
>for order less
> than 20,
Hello
Has anyone looked at the behaviour of the (polynomial) roots function
for high-order polynomials ? I have an application which internally
searches for the roots of a polynomial. It works nicely for order less
than 20, and then has an erratic behaviour for upper values...
I looked into the so
Hi,
should this not be accepted:
>>> N.argwhere([4,0,2,1,3])
?
instead I get
Traceback (most recent call last):
File "", line 1, in
File "./numpy/core/numeric.py", line 510, in argwhere
AttributeError: 'list' object has no attribute 'nonzero'
>>> N.argwhere(N.array([4,0,2,1,3]))
[[0]
[2]
[3
Pierre GM gmail.com> writes:
> What about
> 'formats':[eval(b) for b in event_format]
>
> Should it fail, try something like:
> dtype([(x,eval(b)) for (x,b) in zip(event_fields, event_format)])
>
> At least you force dtype to have the same nb of names & formats.
>
You could use
data = np.ge
A Thursday 02 July 2009 20:15:13 Dan Yamins escrigué:
> > What's wrong with recarrays? In any case, if you need a true ndarray
> > object
> > you can always do:
> >
> > ndarr = recarr.view(np.ndarray)
> >
> > and you are done.
>
> I have a question about this though. The object "ndarr" will consi
15 matches
Mail list logo
