Thanks Serge for your help,
Transforming a 3D array into a 2D allows to vectroize my problem in a simple
way.
I have noted the true definition of the variance... We did not talk about the
same actually..
Stéphane
________________________________
De: users-boun...@lists.scilab.org de la part de Serge Steer
Date: mar. 12/02/2013 17:50
À: International users mailing list for Scilab.
Objet : Re: [Scilab-users] Vectorization
You can try something like
// CALCULATE VARIANCE OVER LAST N POINTS
Ni = 100;
Nj = 10;
Nk = 10;
A = rand(Ni,Nj,Nk);
tic;
A=matrix(A,Ni,-1);//transform A into a 2D array
D1=zeros(Ni-N+1,Nj*Nk);
for i=N:Ni,
D1(i-(N-1),:)=variance(A(i-(N-1):i,:),1);
end
D1=matrix(D1,-1,Nj,Nk); //Transform D1 into a 3D array
toc
Are you sure of your variance definition? For me if v is a vector the variance
of v is computed by sqrt(sum( (v-mean(v))^2))
Serge Steer
INRIA
Le 12/02/2013 17:10, Stéphane Bécu a écrit :
Hello,
I have much difficulty to optimize my calculation. It consists of
calculating the variance of a vector over last N elements . See the example in
the attached file for a 3D matrix. With a PC under windows 7 and scilab 5.3, it
needs 40 sec to calculate the variance for a 10*10*1000 matrix. This is too
much long since I have much bigger files to work on.
There must be a way to vectorize the problem but I do not see how ?
Thanks in advance,
Stéphane
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