"state" should be used to reset the state of the rand. Rand to once again use the new generators, the keyword Rand to use the old generators, only setting the seed will. However, it should be noted that querying the seed will not cause Keyword "seed" is used to specify that the old generators should Same random sequences as produced by the old generators. However, in some circumstances it might be desirable to obtain the Old generator, and produces random numbers with a significantly longer cycle The new generator is used by default as it is significantly faster than the Older versions of Octave used a different random number generator. Returned values together, otherwise the generator state can be learned after 1,ĭo not use for cryptography without securely hashing several Mersenne Twister: A 623-dimensionally equidistributed uniformĪCM Trans. To compute the pseudo-random sequence, rand uses the Mersenne Vector in Octave’s startup files (see Startup Files). To obtainīehavior comparable to MATLAB, initialize with a deterministic state Note that this differs from MATLAB, whichĪlways initializes the state to the same state at startup. This new state will be a hash based on the value ofīy default, the generator is initialized from /dev/urandom if it isĪvailable, otherwise from CPU time, wall clock time, and the currentįraction of a second. You may also initialize the state vector from an arbitrary vector of length You can query the state of the random number generator using the form The arguments are handled the same as the arguments for eye. Return a matrix with random elements uniformly distributed on the In order to be compatible with the corresponding MATLAB function.Īlso for compatibility with MATLAB, return the right-hand side of Return a row vector with n elements logarithmically spaced from : logspace ( a, b) : logspace ( a, b, n) : logspace ( a, pi, n) Only a single value ( n = 1) is requested. Linspace transforms them to column vectors and returns a matrix whereĮach row is an independent sequence betweenįor compatibility with MATLAB, return the second argument ( end) when If one, or both, inputs are vectors, then The linspace function returns a row vector when both start andĮnd are scalars. Points is not specified, a value of 100 is used. If start is greater thanĮnd, the elements are stored in decreasing order. If the number of elements is greater than one, then the endpoints startĪnd end are always included in the range. Return a row vector with n linearly spaced elements between start : linspace ( start, end) : linspace ( start, end, n) The functions linspace and logspace make it very easy toĬreate vectors with evenly or logarithmically spaced elements. A row vector is alwaysĬonceptually the result is calculated as follows: Matrix then the columns of x are imagined to be stacked on top ofĮach other for purposes of the selection index. Second row, r(2,j), specifies the repeat count. R is a 2x N integer matrix specifying which elements to repeatĪnd how often to repeat each element. ForĬopying along more than two dimensions, specify the number of times to copyĪcross each dimension m, n, p, …, in a vector in theĬonstruct a vector of repeated elements from x. If n is not specified, form an m by m block matrix. Julia> jacobus(a,b) = reshape(reinterpret(Int, collect(Iterators.: repmat ( A, m) : repmat ( A, m, n) : repmat ( A, m, n, p …) : repmat ( A, ) : repmat ( A, )įorm a block matrix of size m by n, with a copy of matrix Julia> combination_vectors(a, b) = vcat(( for f in a for s in b).) It is about the same speed as the code using reinterpret by above, but is simpler and the results will likely be easier to use too (especially if you use StaticArrays instead of tuples). In terms of speed, if I define f(a,b) = vec(tuple.(a',b)), then compared to the combination_vectors code above it is more than 100x faster. Julia has more data structures than just matrices of numbers, and it often pays to exploit them! Tuples (and their relatives StaticArrays) are often much faster more convenient to work with than the matlab-style 2-column matrices. Or, if you want them in a vector: julia> vec(tuple.(a',b)) If you are willing to consider another data structure, you can just make a matrix of tuples with: julia> tuple.(a',b) The following code does what I want: create a 2 columns matrix containing in rows all the element-wise combination of 2 vectors.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |