butools.dmap.LagCorrelationsFromDRAP¶

butools.dmap.LagCorrelationsFromDRAP()¶

Matlab:	acf = LagCorrelationsFromDRAP(H0, H1, L, prec)
Mathematica:	acf = LagCorrelationsFromDRAP[H0, H1, L, prec]
Python/Numpy:	acf = LagCorrelationsFromDRAP(H0, H1, L, prec)

Returns the lag autocorrelations of a discrete rational arrival process.

Parameters:	H0 : matrix, shape (M,M) The H0 matrix of the discrete rational arrival process H1 : matrix, shape (M,M) The H1 matrix of the discrete rational 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:

>>> H0 = [0, 0, 0.13; 0, 0.6, 0.18; 0.31, 0.26, 0.02];
>>> H1 = [0, 1, -0.13; 0, 0.18, 0.04; 0.03, 0.09, 0.29];
>>> corr = LagCorrelationsFromDRAP(H0, H1, 3);
>>> disp(corr);
     0.014303    0.0012424   7.5868e-06

For Mathematica:

>>> H0 = {{0, 0, 0.13},{0, 0.6, 0.18},{0.31, 0.26, 0.02}};
>>> H1 = {{0, 1, -0.13},{0, 0.18, 0.04},{0.03, 0.09, 0.29}};
>>> corr = LagCorrelationsFromDRAP[H0, H1, 3];
>>> Print[corr];
{0.01430295723332723, 0.0012424024982963658, 7.586755398724169*^-6}

For Python/Numpy:

>>> H0 = ml.matrix([[0, 0, 0.13],[0, 0.6, 0.18],[0.31, 0.26, 0.02]])
>>> H1 = ml.matrix([[0, 1, -0.13],[0, 0.18, 0.04],[0.03, 0.09, 0.29]])
>>> corr = LagCorrelationsFromDRAP(H0, H1, 3)
>>> print(corr)
[  1.43030e-02   1.24240e-03   7.58676e-06]