![]() The matrix analysis functions det, rcond, hess, and expm also show significant increase in speed on large double-precision arrays. The matrix multiply (X*Y) and matrix power (X^p) operators show significant increase in speed on large double-precision arrays (on order of 10,000 elements). As a general rule, complicated functions speed up more than simple functions. The operation is not memory-bound processing time is not dominated by memory access time. For example, most functions speed up only when the array contains several thousand elements or more. The returned array cR has the same underlying type, sparsity, and complexity. These Matlab functions generate m × n matrices composed of ones, zeros, uniformly distributed random numbers, normally distributed random numbers and uniformly distributed random integers, respectively. You can also specify 'noCommunication' as part of the function call. Some of the most common are ones(m,n), zeros(m,n), rand(m,n), randn(m,n), and randi(p,m,n). ![]() To increase backward compatibility i want to replace all my randi(m,n) to some function that uses. ![]() cR rand ( ,likep) uses the array p to create a codistributed array of uniformly distributed random numbers. Matlab Implement randi using rand in MATLAB. Unless you need repeatability or uniqueness, it is usually advisable to simply generate random values without reseeding the generator. The data size is large enough so that any advantages of concurrent execution outweigh the time required to partition the data and manage separate execution threads. You must specify 'noCommunication' after the size, data type and codist arguments. As with 'shuffle' there is a caveat when reseeding MATLAB's random number generator, because it affects all subsequent output from rand, randi, and randn. They should require few sequential operations. These sections must be able to execute with little communication between processes. The function performs operations that easily partition into sections that execute concurrently.
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