butools.dmap.LagCorrelationsFromDMAP¶

butools.dmap.LagCorrelationsFromDMAP()¶

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

Returns the lag autocorrelations 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 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 = [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];
>>> 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}};
>>> 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]])
>>> corr = LagCorrelationsFromDMAP(D0, D1, 3)
>>> print(corr)
[-0.04586  0.01075 -0.0048 ]