butools.map.SamplesFromMMAP¶

butools.map.SamplesFromMMAP()¶

Matlab:	x = SamplesFromMMAP(D, K, prec)
Mathematica:	x = SamplesFromMMAP[D, K, prec]
Python/Numpy:	x = SamplesFromMMAP(D, K, prec)

Generates random samples from a marked Markovian arrival process.

Parameters:	D : list of matrices of shape(M,M), length(N) The D0...DN matrices of the MMAP K : integer The number of samples to generate. prec : double, optional Numerical precision to check if the input MMAP is valid. The default value is 1e-14.
Returns:	x : matrix, shape(K,2) The random samples. Each row consists of two columns: the inter-arrival time and the type of the arrival.

Examples

For Matlab:

>>> D0 = [-1.78, 0.29; 0.07, -0.92];
>>> D1 = [0.15, 0.49; 0.23, 0.36];
>>> D2 = [0.11, 0.2; 0.01, 0];
>>> D3 = [0.14, 0.4; 0.11, 0.14];
>>> Dm = {D0, D1, D2, D3};
>>> x = SamplesFromMMAP(Dm, 10000);
>>> mt = MarginalMomentsFromTrace(x(1:end, 1), 3);
>>> disp(mt);
      0.99277       2.0363       6.3483
>>> mm = MarginalMomentsFromMMAP(Dm, 3);
>>> disp(mm);
       1.0007       2.1045       6.8277

For Mathematica:

>>> D0 = {{-1.78, 0.29},{0.07, -0.92}};
>>> D1 = {{0.15, 0.49},{0.23, 0.36}};
>>> D2 = {{0.11, 0.2},{0.01, 0}};
>>> D3 = {{0.14, 0.4},{0.11, 0.14}};
>>> Dm = {D0, D1, D2, D3};
>>> x = SamplesFromMMAP[Dm, 10000];
>>> mt = MarginalMomentsFromTrace[x[[1;;-1, 1]], 3];
>>> Print[mt];
{0.9922207868979519, 2.0916288580876965, 7.085666983629913}
>>> mm = MarginalMomentsFromMMAP[Dm, 3];
>>> Print[mm];
{1.000667111407605, 2.1044966311760755, 6.827688149434602}

For Python/Numpy:

>>> D0 = ml.matrix([[-1.78, 0.29],[0.07, -0.92]])
>>> D1 = ml.matrix([[0.15, 0.49],[0.23, 0.36]])
>>> D2 = ml.matrix([[0.11, 0.2],[0.01, 0]])
>>> D3 = ml.matrix([[0.14, 0.4],[0.11, 0.14]])
>>> Dm = [D0, D1, D2, D3]
>>> x = SamplesFromMMAP(Dm, 10000)
>>> mt = MarginalMomentsFromTrace(x[:, 0], 3)
>>> print(mt)
[1.0128822115820439, 2.125093348326891, 6.8185499625793531]
>>> mm = MarginalMomentsFromMMAP(Dm, 3)
>>> print(mm)
[1.0006671114076049, 2.1044966311760755, 6.8276881494346]