butools.map.RandomMMAP¶
-
butools.map.RandomMMAP()¶ Matlab: D = RandomMMAP(order, types, mean, zeroEntries, maxTrials, prec)Mathematica: D = RandomMMAP[order, types, mean, zeroEntries, maxTrials, prec]Python/Numpy: D = RandomMMAP(order, types, mean, zeroEntries, maxTrials, prec)Returns a random Markovian arrival process with given mean value.
Parameters: order : int
The size of the MAP
types : int
The number of different arrival types
mean : double, optional
The mean inter-arrival times of the MMAP
zeroEntries : int, optional
The number of zero entries in the D0 and D1 matrices
maxTrials : int, optional
The maximum number of trials to find a proper MMAP (that has an irreducible phase process and none of its parameters is all-zero)
prec : double, optional
Numerical precision for checking the irreducibility. The default value is 1e-14.
Returns: D : list/cell of matrices of shape(M,M), length(types+1)
The D0...Dtypes matrices of the MMAP
Examples
For Matlab:
>>> Dm = RandomMMAP(4, 3, 1.62, 10); >>> disp(Dm{1}); -0.84147 0.066601 0.054766 0 0.071719 -0.67551 0 0.016834 0.016327 0.11655 -0.57318 0.043691 0.095968 0.079712 0 -0.87041 >>> disp(Dm{2}); 0.085267 0.12324 0.018852 0.08092 0.04971 0.029681 0.015256 0.10755 0.04997 0 0 0.0019056 0.098333 0.062911 0.06319 0.055274 >>> disp(Dm{3}); 0.11299 0.092558 0 0.028328 0.043418 0.10553 0.0093983 0.063497 0.10159 0.071162 0 0.0073957 0 0.053923 0.04684 0.060224 >>> disp(Dm{4}); 0.10217 0.055739 0.0096715 0.01037 0 0.017933 0.076958 0.068026 0.011933 0.0053227 0.029046 0.11829 0.0379 0.10115 0 0.11498 >>> m = MarginalMomentsFromMMAP(Dm, 1); >>> disp(m); 1.62
For Mathematica:
>>> Dm = RandomMMAP[4, 3, 1.62, 10]; >>> Print[Dm[[1]]]; {{-0.5862070911809831, 0., 0.0168137805609394, 0.08788491863058187}, {0.05283897336899656, -0.8424383930373084, 0.07960302764594157, 0.}, {0.0430532309208159, 0.09311851788523529, -0.8665132200026514, 0.09720430167174356}, {0.03143342574603682, 0.006933947818271124, 0.1026546426188773, -0.8461985523926278}} >>> Print[Dm[[2]]]; {{0.014680660704177124, 0.0645992486634905, 0.08177105711062393, 0.016642761139291848}, {0., 0.09540597852268921, 0., 0.0707635001562371}, {0.095047323249757, 0.05618623612094079, 0.07843785718105817, 0.06223373813267749}, {0.0465659166377795, 0.027686196902255576, 0.07643593698997828, 0.04091552394072304}} >>> Print[Dm[[3]]]; {{0.09941961376878797, 0., 0.08253500208040433, 0.10492138700485602}, {0.07147061429219402, 0.08132225534707489, 0., 0.01620411904838067}, {0.05528588985095624, 0.06258835562699341, 0.07840456211046513, 0.03871997575555587}, {0.09854407744754319, 0.06032740737189369, 0.06451082035845042, 0.09710176006166384}} >>> Print[Dm[[4]]]; {{0., 0.0169386615178302, 0., 0.}, {0.08694201353931666, 0.10148847810958055, 0.09477989858935744, 0.09161953441753981}, {0.004110565171334107, 0.0061642514927466065, 0.01543324456111962, 0.08052517027125213}, {0.07312825196154744, 0., 0.06649165886464029, 0.053468985672967276}} >>> m = MarginalMomentsFromMMAP[Dm, 1][[1]]; >>> Print[m]; 1.6199999999999997
For Python/Numpy:
>>> Dm = RandomMMAP(4, 3, 1.62, 10) >>> print(Dm[0]) [[-0.77004 0.07746 0.00752 0.12555] [ 0.00951 -0.96329 0.11107 0.04652] [ 0. 0.07548 -0.66821 0.03229] [ 0.12584 0. 0.03691 -0.77768]] >>> print(Dm[1]) [[ 0.05194 0.03399 0.00531 0.04182] [ 0.02984 0.07001 0.1335 0.06569] [ 0.03889 0.04339 0. 0. ] [ 0.06669 0. 0.10119 0.0133 ]] >>> print(Dm[2]) [[ 0.08729 0.03552 0.03436 0.0247 ] [ 0.11078 0.02114 0. 0.02779] [ 0.11427 0.06711 0.0964 0.12043] [ 0.05964 0.1119 0. 0.06175]] >>> print(Dm[3]) [[ 0.07868 0.04748 0.02386 0.09457] [ 0.14246 0. 0.07483 0.12014] [ 0. 0.05206 0.00753 0.02037] [ 0.0462 0.04298 0.11128 0. ]] >>> m = MarginalMomentsFromMMAP(Dm, 1)[0] >>> print(m) 1.62