Hello,
> De : Ezequiel Soule
> Envoyé : lundi 1 septembre 2014 15:36
>
> I implemented a cell algorithm, which is used in molecular dynamics [...]
> you divide the space in "cells", with a size that has to be larger than the
> maximum
> posible distance, then you identify in which cell each point
Hi, I am working in something related, which is deteriming overlaps in a
sistem of spheres with random positions. So I have to compute the
distance between centers of spheres, and compare it with the contact
distance. I implemented a cell algorithm, which is used in molecular
dynamics (you can
Hello,
> De : Samuel Gougeon
> Envoyé : vendredi 29 août 2014 23:51
>
> Actually the algo in members() defines and runs over multiline slices,
> that shortens a lot the number of iterations in the explicit loop [...]
> but is more complex to implement.
Sounds interesting.
I'll have a look at it.
Hello,
> De : sho...@energetiq.com
> Envoyé : vendredi 29 août 2014 16:38
>
> If your goal is to find nearest the N neighbors of a given point,
> look up the K-D Tree algorithm
This is not the case, but I keep your advice in mind.
Tree algorithms are often an elegant and efficient way to solve pr
Le 29/08/2014 12:58, Dang, Christophe a écrit :
Just to close the subject:
I tried to implement the algorithm with sparse matrices, and it is less
efficient than scanning over one dimension: 7 times faster than the naive
algorithm.
Yes, it was somewhat expected. Also for memory consumption, th
Le 29/08/2014 09:29, Dang, Christophe a écrit :
Hello,
De Samuel Gougeon
Envoyé : jeudi 28 août 2014 23:59
You will need one loop over slices along one of both dimensions.
Such a slicing algorithm in an identical situation is used inside members.sci.
OK, I just overlooked the file but am lazy
distances over that much smaller set, if
necessary...
S
From:
"Dang, Christophe"
To:
"International users mailing list for Scilab." ,
Date:
08/29/2014 06:58 AM
Subject:
Re: [Scilab-users] Pairwise distance of a huge amount of points
Sent by:
"users"
Just to close the subject:
I tried to implement the algorithm with sparse matrices, and it is less
efficient than scanning over one dimension: 7 times faster than the naive
algorithm.
If I generate the sparse matrix from a n*(n-1)/2 vector,
it is even worse: less efficient than the naive algori
Hello,
> De Samuel Gougeon
> Envoyé : jeudi 28 août 2014 23:59
>
> You will need one loop over slices along one of both dimensions.
> Such a slicing algorithm in an identical situation is used inside members.sci.
OK, I just overlooked the file but am lazy to read a 400 lines script and
prefered
Hello Christophe,
Le 28/08/2014 16:22, Dang, Christophe a écrit :
Hello,
I need to compute the pairwise distance of a huge amount of points, namely n =
49545.
I re-read a previous discussion
http://mailinglists.scilab.org/Plot-overlays-on-images-tp2617675p4030599.html
So, I'm trying yo avoid
Hello,
I need to compute the pairwise distance of a huge amount of points, namely n =
49545.
I re-read a previous discussion
http://mailinglists.scilab.org/Plot-overlays-on-images-tp2617675p4030599.html
So, I'm trying yo avoid loops,
but I need then at least to have a vectors, or sparse matrice
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