# Uniform sampling with min and max

Started by July 2, 2001
 Hi Given that I have a matrix, say A = [1 2 3; 4 5 6], how do I draw new samples over the range of A columns using uniform distribution? For example, min(:,1) = 1 and max(:,1) = 4 Therefore my new sample can have data points ranging from 1 to 4 in the first column, and so on. I know I can use rand to draw random numbers that is uniformly distributed, but I do not know how to set a range for each column of the matrix. I would really appreciate any help given. Thank you very much in advance. Sincerely Mit
 First, create a matrix B having enough random numbers (distributed between 0 and 1.0). I'm doing 100 per column: M = 100; N = size(A,2); B = rand(M,N); Now multiply each column of B by the range indicated by the columns of A: B = B*diag( A(2,:) - A(1,:) ); Now the random numbers in the jth column of B are from 0 to (A(2,j) - A(1,j)). This is the correct differential range, but since the range should actually be A(1,j) to A(2,j), we must move the range up by A(1,j). B = B + ones(M,1)*A(1,:) B should now be your desired matrix. Sincerely, Glen Ragan
 hi Mit, u can use the following command: ones(size(A))*diag(min(A)) + rand(size(A))*diag((max(A) - min(A))) in the second term the "diag((max(A) - min(A)))" scales each column separately according to the required range. In the first term "diag(min(A))" adds the required bias. To use the same for rows use premultiplication instead of post-mult. -priyank --- wrote: > Hi > > Given that I have a matrix, say A = [1 2 3; 4 5 6], > how do I draw new samples over the range of A columns using uniform > distribution? > > For example, min(:,1) = 1 and max(:,1) = 4 > Therefore my new sample can have data points ranging from 1 to 4 in > the first column, and so on. I know I can use rand to draw random > numbers that is uniformly distributed, but I do not know how to set a > range for each column of the matrix. > > I would really appreciate any help given. Thank you very much in > advance. > > Sincerely > Mit > > __________________________________________________