butools.map.MarginalMomentsFromRAP¶

butools.map.MarginalMomentsFromRAP()¶

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

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

Parameters:	H0 : matrix, shape (M,M) The H0 matrix of the rational arrival process H1 : matrix, shape (M,M) The H1 matrix of the 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 = [-2., 0, 0; 0, -3., 1.; 0, -1., -2.];
>>> H1 = [1.8, 0.2, 0; 0.2, 1.8, 0; 0.2, 1.8, 1.];
>>> moms = MarginalMomentsFromRAP(H0, H1);
>>> disp(moms);
      0.44444      0.38095      0.48299      0.82216       1.7944

For Mathematica:

>>> H0 = {{-2., 0, 0},{0, -3., 1.},{0, -1., -2.}};
>>> H1 = {{1.8, 0.2, 0},{0.2, 1.8, 0},{0.2, 1.8, 1.}};
>>> moms = MarginalMomentsFromRAP[H0, H1];
>>> Print[moms];
{0.4444444444444444, 0.380952380952381, 0.48299319727891166, 0.8221574344023325, 1.794391225878107}

For Python/Numpy:

>>> H0 = ml.matrix([[-2., 0, 0],[0, -3., 1.],[0, -1., -2.]])
>>> H1 = ml.matrix([[1.8, 0.2, 0],[0.2, 1.8, 0],[0.2, 1.8, 1.]])
>>> moms = MarginalMomentsFromRAP(H0, H1)
>>> print(moms)
[0.44444444444444442, 0.38095238095238093, 0.48299319727891149, 0.82215743440233213, 1.7943912258781058]