butools.dmap.LagkJointMomentsFromDMMAP¶

butools.dmap.LagkJointMomentsFromDMMAP()¶

Matlab:	Nm = LagkJointMomentsFromDMMAP(D, K, L, prec)
Mathematica:	Nm = LagkJointMomentsFromDMMAP[D, K, L, prec]
Python/Numpy:	Nm = LagkJointMomentsFromDMMAP(D, K, L, prec)

Returns the lag-L joint moments of a discrete marked Markovian arrival process.

Parameters:

D : list/cell of matrices of shape(M,M), length(N) The D0...DN matrices of the DMMAP to check K : int, optional The dimension of the matrix of joint moments to compute. If K=0, the MxM joint moments will be computed. The default value is 0 L : int, optional The lag at which the joint moments are computed. The default value is 1 prec : double, optional Numerical precision to check if the input is valid. The default value is 1e-14

Returns:

Nm : list/cell of matrices of shape(K+1,K+1), length(L) The matrices containing the lag-L joint moments, starting from moment 0.

Examples

For Matlab:

>>> D0 = [0.34, 0, 0; 0.06, 0.05, 0.03; 0.11, 0.13, 0];
>>> D1 = [0.3, 0, 0; 0.16, 0.18, 0.05; 0.15, 0.04, 0.09];
>>> D2 = [0, 0.01, 0; 0.1, 0.07, 0.08; 0.13, 0.12, 0.13];
>>> D3 = [0.35, 0, 0; 0, 0.18, 0.04; 0.06, 0.03, 0.01];
>>> Nm = LagkJointMomentsFromDMMAP({D0, D1, D2, D3}, 3, 1);
>>> disp(Nm{1});
      0.45395      0.68525       1.3856       3.8671
      0.68283       1.0318       2.0887       5.8339
       1.3755       2.0807       4.2171       11.789
        3.828       5.7954       11.756       32.887
>>> disp(Nm{2});
     0.026281      0.03323     0.053055      0.11925
     0.035051     0.043866     0.068917      0.15222
     0.060653      0.07477      0.11464      0.24631
       0.1482      0.17996      0.26901      0.56074
>>> disp(Nm{3});
      0.51977      0.78522       1.5891        4.438
      0.78582       1.1881       2.4067       6.7254
       1.5917       2.4087       4.8838       13.657
       4.4481       6.7354       13.666       38.235

For Mathematica:

>>> D0 = {{0.34, 0, 0},{0.06, 0.05, 0.03},{0.11, 0.13, 0}};
>>> D1 = {{0.3, 0, 0},{0.16, 0.18, 0.05},{0.15, 0.04, 0.09}};
>>> D2 = {{0, 0.01, 0},{0.1, 0.07, 0.08},{0.13, 0.12, 0.13}};
>>> D3 = {{0.35, 0, 0},{0, 0.18, 0.04},{0.06, 0.03, 0.01}};
>>> Nm = LagkJointMomentsFromDMMAP[{D0, D1, D2, D3}, 3, 1];
>>> Print[Nm[[1]]];
{{0.4539462822484643, 0.6852510653292481, 1.3856372122857255, 3.867050666477712},
 {0.6828250179169082, 1.0317922106199087, 2.088678354109492, 5.833872336896188},
 {1.3754547360883858, 2.08069063568443, 4.2170625241275665, 11.789140254269103},
 {3.8280196252781336, 5.795448856171304, 11.75638029642303, 32.88737375149565}}
>>> Print[Nm[[2]]];
{{0.02628103195085287, 0.03323044515488185, 0.05305519981654366, 0.11925159337093362},
 {0.0350514750959702, 0.04386614712772344, 0.06891661923475792, 0.15221701375061739},
 {0.06065278939554716, 0.07477024474361887, 0.11464021882832742, 0.24630981535186194},
 {0.1481969521134317, 0.17996016640440504, 0.269014437752629, 0.5607367898805973}}
>>> Print[Nm[[3]]];
{{0.5197726858006831, 0.7852158210070554, 1.5891494452486254, 4.438003130983554},
 {0.7858208384783067, 1.1881008368374677, 2.4066502002309207, 6.725367887552774},
 {1.5917343318669612, 2.4086996142807697, 4.883779522055594, 13.657165850576511},
 {4.448088813440633, 6.7353937552523435, 13.665836661211316, 38.234808402167886}}

For Python/Numpy:

>>> D0 = ml.matrix([[0.34, 0, 0],[0.06, 0.05, 0.03],[0.11, 0.13, 0]])
>>> D1 = ml.matrix([[0.3, 0, 0],[0.16, 0.18, 0.05],[0.15, 0.04, 0.09]])
>>> D2 = ml.matrix([[0, 0.01, 0],[0.1, 0.07, 0.08],[0.13, 0.12, 0.13]])
>>> D3 = ml.matrix([[0.35, 0, 0],[0, 0.18, 0.04],[0.06, 0.03, 0.01]])
>>> Nm = LagkJointMomentsFromDMMAP([D0, D1, D2, D3], 3, 1)
>>> print(Nm[0])
[[  0.45395   0.68525   1.38564   3.86705]
 [  0.68283   1.03179   2.08868   5.83387]
 [  1.37545   2.08069   4.21706  11.78914]
 [  3.82802   5.79545  11.75638  32.88737]]
>>> print(Nm[1])
[[ 0.02628  0.03323  0.05306  0.11925]
 [ 0.03505  0.04387  0.06892  0.15222]
 [ 0.06065  0.07477  0.11464  0.24631]
 [ 0.1482   0.17996  0.26901  0.56074]]
>>> print(Nm[2])
[[  0.51977   0.78522   1.58915   4.438  ]
 [  0.78582   1.1881    2.40665   6.72537]
 [  1.59173   2.4087    4.88378  13.65717]
 [  4.44809   6.73539  13.66584  38.23481]]