butools.dmap.LagkJointMomentsFromDMRAP¶

butools.dmap.LagkJointMomentsFromDMRAP()¶

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

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

Parameters:

H : list/cell of matrices of shape(M,M), length(N) The H0...HN matrices of the DMRAP 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:

>>> H0 = [0.15, 0.2, 0.18; -0.23, 0.17, 0.22; 0.19, 0.15, 0.16];
>>> H1 = [0.01, 0.08, 0.16; 0.02, 0.2, 0.07; 0.02, 0.15, 0.17];
>>> H2 = [0.14, 0.07, 0.01; 0.19, 0.02, 0.34; 0.06, 0.1, 0];
>>> Nm = LagkJointMomentsFromDMRAP({H0, H1, H2}, 3, 2);
>>> disp(Nm{1});
      0.48798      0.78047       1.6785       4.9029
      0.77458       1.2395       2.6673       7.7945
       1.6539       2.6481       5.7016       16.669
       4.8092       7.7033       16.593       48.526
>>> disp(Nm{2});
      0.51202      0.81429       1.7401       5.0566
      0.82019       1.3036       2.7837       8.0853
       1.7647       2.8029       5.9814       17.365
       5.1503        8.177       17.442       50.619

For Mathematica:

>>> H0 = {{0.15, 0.2, 0.18},{-0.23, 0.17, 0.22},{0.19, 0.15, 0.16}};
>>> H1 = {{0.01, 0.08, 0.16},{0.02, 0.2, 0.07},{0.02, 0.15, 0.17}};
>>> H2 = {{0.14, 0.07, 0.01},{0.19, 0.02, 0.34},{0.06, 0.1, 0}};
>>> Nm = LagkJointMomentsFromDMRAP[{H0, H1, H2}, 3, 2];
>>> Print[Nm[[1]]];
{{0.4879805825563548, 0.7804739995572898, 1.6784777723131161, 4.902860723736972},
 {0.7745778986519039, 1.2395315649453407, 2.6672632669482894, 7.794493672457849},
 {1.653860986909174, 2.648092315164064, 5.701611221347518, 16.66906590441337},
 {4.80922115249553, 7.70333279203901, 16.592926073643227, 48.52561424626535}}
>>> Print[Nm[[2]]];
{{0.5120194174436454, 0.8142942617124118, 1.7400542916801724, 5.0566248687873845},
 {0.820190362617798, 1.3035610054097582, 2.7836656770888917, 8.085263953193856},
 {1.764671077084115, 2.8029046012949106, 5.98142492574121, 17.364636197177674},
 {5.1502644400288276, 8.17696708018504, 17.441960447818936, 50.61882961114629}}

For Python/Numpy:

>>> H0 = ml.matrix([[0.15, 0.2, 0.18],[-0.23, 0.17, 0.22],[0.19, 0.15, 0.16]])
>>> H1 = ml.matrix([[0.01, 0.08, 0.16],[0.02, 0.2, 0.07],[0.02, 0.15, 0.17]])
>>> H2 = ml.matrix([[0.14, 0.07, 0.01],[0.19, 0.02, 0.34],[0.06, 0.1, 0]])
>>> Nm = LagkJointMomentsFromDMRAP([H0, H1, H2], 3, 2)
>>> print(Nm[0])
[[  0.48798   0.78047   1.67848   4.90286]
 [  0.77458   1.23953   2.66726   7.79449]
 [  1.65386   2.64809   5.70161  16.66907]
 [  4.80922   7.70333  16.59293  48.52561]]
>>> print(Nm[1])
[[  0.51202   0.81429   1.74005   5.05662]
 [  0.82019   1.30356   2.78367   8.08526]
 [  1.76467   2.8029    5.98142  17.36464]
 [  5.15026   8.17697  17.44196  50.61883]]