butools.dmap.MarginalMomentsFromDRAP¶

butools.dmap.MarginalMomentsFromDRAP()¶

Matlab:	moms = MarginalMomentsFromDRAP(H0, H1, K, precision)
Mathematica:	moms = MarginalMomentsFromDRAP[H0, H1, K, precision]
Python/Numpy:	moms = MarginalMomentsFromDRAP(H0, H1, K, precision)

Returns the moments of the marginal distribution of a discrete rational arrival process.

Parameters:	H0 : matrix, shape (M,M) The H0 matrix of the discrete rational arrival process H1 : matrix, shape (M,M) The H1 matrix of the discrete rational arrival process K : int, optional Number of moments to compute. If K=0, 2*M-1 moments are computed. The default value is K=0. precision : double, optional Numerical precision for checking if the input is valid. The default value is 1e-14
Returns:	moms : row vector of doubles, length K The vector of moments.

Examples

For Matlab:

>>> H0 = [0, 0, 0.13; 0, 0.6, 0.18; 0.31, 0.26, 0.02];
>>> H1 = [0, 1, -0.13; 0, 0.18, 0.04; 0.03, 0.09, 0.29];
>>> moms = MarginalMomentsFromDRAP(H0, H1);
>>> disp(moms);
        3.207       16.898       130.77       1347.1        17343

For Mathematica:

>>> H0 = {{0, 0, 0.13},{0, 0.6, 0.18},{0.31, 0.26, 0.02}};
>>> H1 = {{0, 1, -0.13},{0, 0.18, 0.04},{0.03, 0.09, 0.29}};
>>> moms = MarginalMomentsFromDRAP[H0, H1];
>>> Print[moms];
{3.20702366840782, 16.897636691953394, 130.7705457435602, 1347.0743893905096, 17343.182467560622}

For Python/Numpy:

>>> H0 = ml.matrix([[0, 0, 0.13],[0, 0.6, 0.18],[0.31, 0.26, 0.02]])
>>> H1 = ml.matrix([[0, 1, -0.13],[0, 0.18, 0.04],[0.03, 0.09, 0.29]])
>>> moms = MarginalMomentsFromDRAP(H0, H1)
>>> print(moms)
[3.2070236684078202, 16.897636691953394, 130.77054574356021, 1347.0743893905096, 17343.182467560622]