On 5/1/07, Andrew Lazarus <[EMAIL PROTECTED]> wrote:
Let me just thank the list, especially for the references. (I found
similar papers myself with Google: and to think I have a university
library alumni card and barely need it any more!)
I'll write again on the sorts of results I get.
Looking
Let me just thank the list, especially for the references. (I found
similar papers myself with Google: and to think I have a university
library alumni card and barely need it any more!)
I'll write again on the sorts of results I get.
BEGIN:VCARD
VERSION:2.1
N:Lazarus;Andrew;;;Ph.D.
FN:Andrew Lazar
On 21-4-2007 1:42 Mark Kirkwood wrote:
I don't think that will work for the vector norm i.e:
|x - y| = sqrt(sum over j ((x[j] - y[j])^2))
I don't know if this is usefull here, but I was able to rewrite that
algorithm for a set of very sparse vectors (i.e. they had very little
overlapping fac
On Fri, 27 Apr 2007, Alexander Staubo wrote:
On 4/20/07, Andrew Lazarus <[EMAIL PROTECTED]> wrote:
I have a table with 2.5 million real[] arrays. (They are points in a
time series.) Given a new array X, I'd like to find, say, the 25
closest to X in some sense--for simplification, let's just say
On 4/20/07, Andrew Lazarus <[EMAIL PROTECTED]> wrote:
I have a table with 2.5 million real[] arrays. (They are points in a
time series.) Given a new array X, I'd like to find, say, the 25
closest to X in some sense--for simplification, let's just say in the
usual vector norm. Speed is critical he
On Apr 20, 12:07 pm, [EMAIL PROTECTED] (Andrew Lazarus) wrote:
> I have a table with 2.5 million real[] arrays. (They are points in a
> time series.) Given a new array X, I'd like to find, say, the 25
> closest to X in some sense--for simplification, let's just say in the
> usualvectornorm. Speed i
Andrew Lazarus <[EMAIL PROTECTED]> writes:
> Because I know the 25 closest are going to be fairly close in each
> coordinate, I did try a multicolumn index on the last 6 columns and
> used a +/- 0.1 or 0.2 tolerance on each. (The 25 best are very probably inside
> that hypercube on the distribution
Andrew Lazarus wrote:
Because I know the 25 closest are going to be fairly close in each
coordinate, I did try a multicolumn index on the last 6 columns and
used a +/- 0.1 or 0.2 tolerance on each. (The 25 best are very probably inside
that hypercube on the distribution of data in question.)
Thi
Because I know the 25 closest are going to be fairly close in each
coordinate, I did try a multicolumn index on the last 6 columns and
used a +/- 0.1 or 0.2 tolerance on each. (The 25 best are very probably inside
that hypercube on the distribution of data in question.)
This hypercube tended to ha
Jeff Davis wrote:
On Fri, 2007-04-20 at 12:07 -0700, Andrew Lazarus wrote:
I have a table with 2.5 million real[] arrays. (They are points in a
time series.) Given a new array X, I'd like to find, say, the 25
closest to X in some sense--for simplification, let's just say in the
usual vector norm
On Fri, 2007-04-20 at 12:07 -0700, Andrew Lazarus wrote:
> I have a table with 2.5 million real[] arrays. (They are points in a
> time series.) Given a new array X, I'd like to find, say, the 25
> closest to X in some sense--for simplification, let's just say in the
> usual vector norm. Speed is cr
I have a table with 2.5 million real[] arrays. (They are points in a
time series.) Given a new array X, I'd like to find, say, the 25
closest to X in some sense--for simplification, let's just say in the
usual vector norm. Speed is critical here, and everything I have tried
has been too slow.
I im
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