butools.dmap.LagkJointMomentsFromDMAP¶

butools.dmap.LagkJointMomentsFromDMAP()¶

Matlab:	Nm = LagkJointMomentsFromDMAP(D0, D1, K, L, prec)
Mathematica:	Nm = LagkJointMomentsFromDMAP[D0, D1, K, L, prec]
Python/Numpy:	Nm = LagkJointMomentsFromDMAP(D0, D1, K, L, prec)

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

Parameters:

D0 : matrix, shape (M,M) The D0 matrix of the discrete Markovian arrival process D1 : matrix, shape (M,M) The D1 matrix of the discrete Markovian arrival process 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 : matrix, shape(K+1,K+1) The matrix containing the lag-L joint moments, starting from moment 0.

Examples

For Matlab:

>>> D0 = [0, 0.02, 0, 0; 0, 0.17, 0.2, 0.14; 0.16, 0.17, 0.02, 0.18; 0, 0, 0, 0.12];
>>> D1 = [0, 0.88, 0.1, 0; 0.18, 0.07, 0.14, 0.1; 0.13, 0.15, 0.15, 0.04; 0.31, 0.18, 0.12, 0.27];
>>> Nm = LagkJointMomentsFromDMAP(D0, D1, 4, 1);
>>> disp(Nm);
            1       1.4955       2.9542       7.8852       27.282
       1.4955       2.2037       4.2827       11.293       38.822
       2.9542       4.2875       8.1899       21.315       72.753
       7.8852       11.326       21.379       55.129       187.21
       27.282       38.993        73.17       187.82       636.23
>>> moms = MarginalMomentsFromDMAP(D0, D1, 4);
>>> disp(moms);
       1.4955       2.9542       7.8852       27.282
>>> cjm = zeros(1,3);
>>> for i=1:1:3
>>>     Nx = LagkJointMomentsFromDMAP(D0, D1, 1, i);
>>>     cjm(i) = (Nx(2, 2)-moms(1)^2)/(moms(2)-moms(1)^2);
>>> end
>>> disp(cjm);
    -0.045859     0.010753   -0.0047996
>>> corr = LagCorrelationsFromDMAP(D0, D1, 3);
>>> disp(corr);
    -0.045859     0.010753   -0.0047996

For Mathematica:

>>> D0 = {{0, 0.02, 0, 0},{0, 0.17, 0.2, 0.14},{0.16, 0.17, 0.02, 0.18},{0, 0, 0, 0.12}};
>>> D1 = {{0, 0.88, 0.1, 0},{0.18, 0.07, 0.14, 0.1},{0.13, 0.15, 0.15, 0.04},{0.31, 0.18, 0.12, 0.27}};
>>> Nm = LagkJointMomentsFromDMAP[D0, D1, 4, 1];
>>> Print[Nm];
{{1., 1.4955358592094412, 2.954247965436847, 7.885226907678559, 27.282328108669486},
 {1.4955358592094414, 2.2037182406034797, 4.282673397390962, 11.293317579798646, 38.82178903024472},
 {2.9542479654368474, 4.287487747878976, 8.189899409259828, 21.31527510118519, 72.75329018362508},
 {7.885226907678561, 11.326490281736413, 21.37905524531638, 55.129087442003616, 187.21290956791222},
 {27.282328108669493, 38.992776912604896, 73.17046611681856, 187.8221757842213, 636.2306227476095}}
>>> moms = MarginalMomentsFromDMAP[D0, D1, 4];
>>> Print[moms];
{1.4955358592094412, 2.9542479654368474, 7.885226907678561, 27.282328108669493}
>>> cjm = Table[0,{3}];
>>> Do[
>>>     Nx = LagkJointMomentsFromDMAP[D0, D1, 1, i];
>>>     cjm[[i]] = (Nx[[2, 2]]-moms[[1]]^2)/(moms[[2]]-moms[[1]]^2);
>>> , {i,1,3,1}];
>>> Print[cjm];
{-0.045858873104012064, 0.010753286512164551, -0.00479959597519405}
>>> corr = LagCorrelationsFromDMAP[D0, D1, 3];
>>> Print[corr];
{-0.04585887310401268, 0.010753286512163932, -0.00479959597519405}

For Python/Numpy:

>>> D0 = ml.matrix([[0, 0.02, 0, 0],[0, 0.17, 0.2, 0.14],[0.16, 0.17, 0.02, 0.18],[0, 0, 0, 0.12]])
>>> D1 = ml.matrix([[0, 0.88, 0.1, 0],[0.18, 0.07, 0.14, 0.1],[0.13, 0.15, 0.15, 0.04],[0.31, 0.18, 0.12, 0.27]])
>>> Nm = LagkJointMomentsFromDMAP(D0, D1, 4, 1)
>>> print(Nm)
[[   1.         1.49554    2.95425    7.88523   27.28233]
 [   1.49554    2.20372    4.28267   11.29332   38.82179]
 [   2.95425    4.28749    8.1899    21.31528   72.75329]
 [   7.88523   11.32649   21.37906   55.12909  187.21291]
 [  27.28233   38.99278   73.17047  187.82218  636.23062]]
>>> moms = MarginalMomentsFromDMAP(D0, D1, 4)
>>> print(moms)
[1.4955358592094412, 2.9542479654368474, 7.885226907678561, 27.282328108669493]
>>> cjm = np.zeros(3)
>>> for i in range(1,4,1):
>>>     Nx = LagkJointMomentsFromDMAP(D0, D1, 1, i)
>>>     cjm[i-1] = (Nx[1, 1]-moms[0]**2)/(moms[1]-moms[0]**2)
>>> print(cjm)
[-0.04586  0.01075 -0.0048 ]
>>> corr = LagCorrelationsFromDMAP(D0, D1, 3)
>>> print(corr)
[-0.04586  0.01075 -0.0048 ]