I do not know if you will find this more elegant but with the help of a little function that uses recursion you could easily get a fixed subset of your combinations. Then if you want to get the nth vector: k=mod(n,N) One method to order them can be explained with the following example: if you have three indices i,j,k ranging from 0 to N-1 you can use a unique index n=i*N*N+j*N+k to go over all the possibilities. juliaset, a MATLAB code which computes and plots a Julia set, the set of points in the complex plane that remain bounded under a mapping of the form f(z) z2+c. The real and imaginary parts are independent normally distributed random variables with mean 0 and variance 1/2. Furthermore, the validation set is randomly chosen at every step of our. For example, let us create a row vector rv of 9 elements, then we will reference the elements 3 to 7 by writing rv (3:7) and create a new vector named subrv. By default, randn(n,'like',1i) generates random numbers from the standard complex normal distribution. for every input-target pair (pattern), where (the targets) represent our. If you want to reuse this to get other combinations you can pretty much use the same code: Nelements=100 ĮDIT: Another method for your needs would be to order the combinations to be able to get only a given subset. MATLAB allows you to select a range of elements from a vector. If you want to get exactly M combinations you can use a loop: Nelements=100 Ĭombsubset=)] You can try using randi and generate random combinations of 7 integers from 1 to Nelements and then check that you only have unique combinations: Nelements=100 To use an example from DNA sequences, if we pick a random location of DNA, the possible outcomes to observe are the letters A, C, G, T.
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