Retrieving a random element from each row in a matrix

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 件のコメント

Alex Mcaulley
Alex Mcaulley 2019 年 5 月 23 日
What is your question? Do you have any issue?
Lui
Lui 2019 年 5 月 23 日
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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Star Strider
Star Strider 2019 年 5 月 23 日

0 投票

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 件のコメント

Adam Danz
Adam Danz 2019 年 5 月 23 日
I'd do the same (except R is a 10x10 matrix in the description but 20x10 matrix in the OP's example which is confusing).
Star Strider
Star Strider 2019 年 5 月 23 日
My code adapts automatically to the size of ‘R’.
madhan ravi
madhan ravi 2019 年 5 月 23 日
編集済み: madhan ravi 2019 年 5 月 23 日
@Adam: Whatever matrix it might be, this approach yields the desired result.
Adam Danz
Adam Danz 2019 年 5 月 23 日
編集済み: Adam Danz 2019 年 5 月 23 日
Of course.... I was about to post the exact same response (down to the character) ;)
Just pointing out that the example data in R is a [20 x 10] matrix where the OP is expecting a final vector of length 10.
"I need to create a 10 by 1 matrix at the end. "
madhan ravi
madhan ravi 2019 年 5 月 23 日
I need to create a 10 by 1 matrix at the end.
I maybe blind but I don't see that statement anywhere in the question.
Adam Danz
Adam Danz 2019 年 5 月 23 日
編集済み: Adam Danz 2019 年 5 月 23 日
Well, now it says 20 by 1 but I copy pasted that statement just seconds ago.
190523 113435-MATLAB Answers - MATLAB Central.jpg
madhan ravi
madhan ravi 2019 年 5 月 23 日
XD looks like our beloved OP played a game inbetween.
Lui
Lui 2019 年 5 月 23 日
Sorry guys. That was my mistake. I edited the question to suit what I am working with right now. It was a 10 by 10 initially. It is my mistake. Nut star riders answer worked well.
I am not sure if I wanted to do it n times if it would still work. I appreciate
Star Strider
Star Strider 2019 年 5 月 23 日
As always, my pleasure!
Lui
Lui 2019 年 5 月 24 日
編集済み: Lui 2019 年 5 月 24 日
May I add another question.
I have added a for loop to iterate the process for n times and it still works great. I can get the 20 by 1 matrix just fine. However, is it possible for me to generate 20 by20 matrix instead. This means that for every first n iterations, the loop returns the first colum then another n iterations it returns yet another column until I have a 20 by 20 matrix generated by the same process.
R=randi(1000,20,10);
for n=1:100
idx = sub2ind(size(R), (1:size(R,1))', randi(size(R,2),size(R,1),1));
B = R(idx)
end
How do I change this code to yield a 20 by 20 matrix instead of just one. And for each of the 20 columns, the above line of code should be applicable.
thanks
Star Strider
Star Strider 2019 年 5 月 24 日
To get your (20x20) matrix, you code needs to change a bit:
R=randi(1000,20,10);
B = zeros(20); % Preallocate
for n=1:20
idx = sub2ind(size(R), (1:size(R,1))', randi(size(R,2),size(R,1),1));
B(:,n) = R(idx);
end
That worked when I tried it.
Lui
Lui 2019 年 5 月 24 日
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 2019 年 5 月 24 日
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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