Using randsample on a matrix of weights

Question

Samuel Moor-Smith 2020-7-4

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/559619-using-randsample-on-a-matrix-of-weights

评论： Samuel Moor-Smith 2020-7-5

采纳的回答： Sindar

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Hi,

I have some code like this:

for k = 1:p
    new_index = randsample([1:n,nan],1,true,tot_weights(k,:));
    curr_indexes(k) = new_index;
    if ~isnan(new_index)
        currx(k) = x(i,new_index);
        curry(k) = y(i,new_index);
    else 
        currx(k) = nan;
        curry(k) = nan;  
    end
end

where tot_weights is a p x n matrix that has the weights for the sampling. I am trying to get rid of the for loop and vectorize the function. Basically I just want to get a total of p values that are between 1 to n or nan. The probability for the kth value to be a certain value is given by the weights in the kth row of tot_weights.

Any suggestions would be greatly appreciated! Right now, this for loop takes an annoying amount of time to run.

Cheers,

Sam

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Jeff Miller 2020-7-5

The weights seem to change for different values of k, so I'd guess you need to build curr_indices in a for loop. Seems like you could move everything else outside the for loop and vectorize that part of it, though.

Might be a little quicker to replace [1:n,nan] with [1:n+1] and treat n+1 as a special index pointing to nan.

Samuel Moor-Smith 2020-7-5

Yeah, you're right! I moved everything else outside and it works a little faster. Thank you!

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Answer 1

Sindar 2020-7-5

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https://ww2.mathworks.cn/matlabcentral/answers/559619-using-randsample-on-a-matrix-of-weights#answer_461180

编辑：Sindar 2020-7-5

在 MATLAB Online 中打开

it doesn't seem like the assignment statement has any reason to be in the loop. Pulling it out may save a decent amount of time (especially if nan is common):

% sample each row of tot_weights
% there may be a direct way to sample a weight matrix like this loop-free, but I don't know it
for k = 1:p
    curr_indexes(k) = randsample([1:n,nan],1,true,tot_weights(k,:));
end
% set currx and curry to all-nans of the correct size
currx = nan(1,p);
curry = nan(1,p);
% update currx where curr_indexes is not nan
% fill these with curr_indexes of x (excluding places where curr_indexes is nan)
currx(~isnan(curr_indexes)) = x(i,curr_indexes(~isnan(curr_indexes)))
% same for curry
curry(~isnan(curr_indexes)) = y(i,curr_indexes(~isnan(curr_indexes)));