butools.map.LagCorrelationsFromRAP¶

butools.map.LagCorrelationsFromRAP()¶

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

Returns the lag autocorrelations of a rational arrival process.

Parameters:	H0 : matrix, shape (M,M) The H0 matrix of the rational arrival process H1 : matrix, shape (M,M) The H1 matrix of the 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 = [-2., 0, 0; 0, -3., 1.; 0, -1., -2.];
>>> H1 = [1.8, 0.2, 0; 0.2, 1.8, 0; 0.2, 1.8, 1.];
>>> corr = LagCorrelationsFromRAP(H0, H1, 3);
>>> disp(corr);
   -0.0038462    0.0045604    0.0058956

For Mathematica:

>>> H0 = {{-2., 0, 0},{0, -3., 1.},{0, -1., -2.}};
>>> H1 = {{1.8, 0.2, 0},{0.2, 1.8, 0},{0.2, 1.8, 1.}};
>>> corr = LagCorrelationsFromRAP[H0, H1, 3];
>>> Print[corr];
{-0.0038461538461536634, 0.0045604395604397245, 0.005895604395604545}

For Python/Numpy:

>>> H0 = ml.matrix([[-2., 0, 0],[0, -3., 1.],[0, -1., -2.]])
>>> H1 = ml.matrix([[1.8, 0.2, 0],[0.2, 1.8, 0],[0.2, 1.8, 1.]])
>>> corr = LagCorrelationsFromRAP(H0, H1, 3)
>>> print(corr)
[-0.00385  0.00456  0.0059 ]