butools.map.LagCorrelationsFromMAP¶

butools.map.LagCorrelationsFromMAP()¶

Matlab:	acf = LagCorrelationsFromMAP(D0, D1, L, prec)
Mathematica:	acf = LagCorrelationsFromMAP[D0, D1, L, prec]
Python/Numpy:	acf = LagCorrelationsFromMAP(D0, D1, L, prec)

Returns the lag autocorrelations of a Markovian arrival process.

Parameters:	D0 : matrix, shape (M,M) The D0 matrix of the Markovian arrival process D1 : matrix, shape (M,M) The D1 matrix of the Markovian arrival process L : double, optional The number of lags to compute. The default value is 1 prec : double, optional Numerical precision to check if the input is valid. The default value is 1e-14
Returns:	acf : column vector of doubles, length (L) The lag autocorrelation function up to lag L

Examples

For Matlab:

>>> D0 = [-5., 0, 1., 1.; 1., -8., 1., 0; 1., 0, -4., 1.; 1., 2., 3., -9.];
>>> D1 = [0, 1., 0, 2.; 2., 1., 3., 0; 0, 0, 1., 1.; 1., 1., 0, 1.];
>>> corr = LagCorrelationsFromMAP(D0, D1, 3);
>>> disp(corr);
   0.00012012   0.00086176  -0.00022001

For Mathematica:

>>> D0 = {{-5., 0, 1., 1.},{1., -8., 1., 0},{1., 0, -4., 1.},{1., 2., 3., -9.}};
>>> D1 = {{0, 1., 0, 2.},{2., 1., 3., 0},{0, 0, 1., 1.},{1., 1., 0, 1.}};
>>> corr = LagCorrelationsFromMAP[D0, D1, 3];
>>> Print[corr];
{0.00012012478025411484, 0.0008617649366101062, -0.00022001393374437001}

For Python/Numpy:

>>> D0 = ml.matrix([[-5., 0, 1., 1.],[1., -8., 1., 0],[1., 0, -4., 1.],[1., 2., 3., -9.]])
>>> D1 = ml.matrix([[0, 1., 0, 2.],[2., 1., 3., 0],[0, 0, 1., 1.],[1., 1., 0, 1.]])
>>> corr = LagCorrelationsFromMAP(D0, D1, 3)
>>> print(corr)
[ 0.00012  0.00086 -0.00022]