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Retrieving a random element from each row in a matrix

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Lui
Lui el 23 de Mayo de 2019
Comentada: Star Strider el 24 de Mayo de 2019
hi everyone
I have a 20 by 10 matrix. I am running the following code to retrieve a random element from each row of the matrix. In this case, I need to create a 20 by 1 matrix at the end. I have written the following code but it does give me a shuffle of the first column alone.
sz=size(R,1);
B=zeros(sz,1);
i=1:20
% for n=1:100
for j=1
for i=1:20
for row=1:20
M(i,j)=randi(length(R),20,1);
B(i,j)=R(M(i,j));
end
end
end
% end
  2 comentarios
Alex Mcaulley
Alex Mcaulley el 23 de Mayo de 2019
What is your question? Do you have any issue?
Lui
Lui el 23 de Mayo de 2019
The code does not yield the desired results and would appreciate if someone helped point out where it is wrong or an alternative methods

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Respuesta aceptada

Star Strider
Star Strider el 23 de Mayo de 2019
It is likely easier to use the sub2ind funciton to create linear indices into ‘R’ from a defined list of random column subscripts:
R=randi(1000,20,10);
idx = sub2ind(size(R), (1:size(R,1))', randi(size(R,2),size(R,1),1));
B = R(idx)
The second argument defines the row indices and the third defines the random column indices.
See if that does what you want.
  13 comentarios
Lui
Lui el 24 de Mayo de 2019
I am grateful.
Just for curiosity sake, if I were to use randi with data which is not normally distributed, the results would be normally distributed. What is the alternative to ensure that the sample data maintains follows its initial distribution?
Star Strider
Star Strider el 24 de Mayo de 2019
As always, my pleasure.
The output of the randi function is uniformly distributed, not normally distributed. If you were to use the normal distribution instead (the randn function being one such), and you did not change the mean and standard deviation to create the ‘R’ matrix, the data would remain normally distributed.
If you do not otherwise change the parameters of the distribution you are using to create the matrix, the matrix should retain the properties of that distribution, regardless of how you sample it.

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