James Stroud j@mbia.edu wrote:
James Stroud wrote:
[pointless stuff]
OK. Nevermind. I'm rebinding encodings and so taking a sample from the
sample and thus getting the sample back. Terribly sorry.
There is truly nothing to be sorry about.
It takes guts to come right out and say
James Stroud wrote:
If I run it again on 10 (or 1000) the set is basically homogenous
but now of different values (terribly confusing):
set([12048175104.1, 12048175104.15, 12048175104.46,
12048175103.94, 12048175104.23, 12048175103.81,
12048175103.98,
Hi James
Mathematica says that the determinant of the integer version of this
matrix is 2774532096, which is another vote for the answer you have.
Mathematica says that the determinant of the 24-digit real version of
your matrix is 2.774532096*10^9, which looks very similar to me.
I'd go with
Hello All,
I'm using numpy to calculate determinants of matrices that look like
this (13x13):
[[ 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[ 1. 0. 1. 4. 1. 9. 4. 4. 1. 1. 4. 9. 4. 9.]
[ 1. 1. 0. 1. 4. 4. 9. 9. 4. 4. 1. 4. 1. 4.]
[ 1. 4. 1. 0. 9.
On 6 jun 2007, at 13.10, James Stroud wrote:
Hello All,
I'm using numpy to calculate determinants of matrices that look like
this (13x13):
[[ 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[ 1. 0. 1. 4. 1. 9. 4. 4. 1. 1. 4. 9. 4. 9.]
[ 1. 1. 0. 1. 4. 4. 9.
James Stroud je napisao/la:
Hello All,
I'm using numpy to calculate determinants of matrices that look like
this (13x13):
[[ 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[ 1. 0. 1. 4. 1. 9. 4. 4. 1. 1. 4. 9. 4. 9.]
[ 1. 1. 0. 1. 4. 4. 9. 9. 4. 4. 1.
On Jun 6, 6:47 am, Tommy Nordgren [EMAIL PROTECTED] wrote:
On 6 jun 2007, at 13.10, James Stroud wrote:
Hello All,
I'm using numpy to calculate determinants of matrices that look like
this (13x13):
[[ 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[ 1. 0. 1. 4. 1.
James Stroud wrote:
Hello All,
I'm using numpy to calculate determinants of matrices that look like
this (13x13):
[[ 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[snip]
But I have a feeling I'm exceeding the capacity of floats here. Does
anyone have an idea for how to treat
James Stroud wrote:
Hello All,
I'm using numpy to calculate determinants of matrices that look like
this (13x13):
[[ 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[ 1. 0. 1. 4. 1. 9. 4. 4. 1. 1. 4. 9. 4. 9.]
[ 1. 1. 0. 1. 4. 4. 9. 9. 4. 4. 1. 4. 1.
James Stroud wrote:
I'm using numpy to calculate determinants of matrices that look like
this (13x13):
[[ 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[ 1. 0. 1. 4. 1. 9. 4. 4. 1. 1. 4. 9. 4. 9.]
[ 1. 1. 0. 1. 4. 4. 9. 9. 4. 4. 1. 4. 1. 4.]
[ 1.
James Stroud wrote:
For this matrix, I'm getting this with numpy:
2774532095.971
But I have a feeling I'm exceeding the capacity of floats here.
Does anyone have an idea for how to treat this?
Not if you don't state your requirements more precisely. E. g. what
precision do you need?
On Wed, 06 Jun 2007 04:10:43 -0700, James Stroud wrote:
Hello All,
I'm using numpy to calculate determinants of matrices that look like
this (13x13):
[snip matrix]
For this matrix, I'm getting this with numpy:
2774532095.971
But I have a feeling I'm exceeding the capacity of
Hello,
Thank you to those who responded for your answers. They were very
helpful and I'm confident now that numpy is calculating accurate
determinants for these matrices.
But I think I need to restate my problem a little as suggested by some
becuase I'm still bewildered.
First, here is the
Steven D'Aprano wrote:
[Valuable Response]
Thank you Steven for your helpful comments. Please see my reply to
Bjoern Schliessmann where I have restated my problem.
James
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James Stroud wrote:
[pointless stuff]
OK. Nevermind. I'm rebinding encodings and so taking a sample from the
sample and thus getting the sample back. Terribly sorry.
James
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