Package jline.lib.kpctoolbox.mmpp

Class MMPPKt

  • All Implemented Interfaces:
    
public final class MMPPKt

    • Constructor Detail

    • Method Detail

      • mmpp2_fit

         final static MatrixCell mmpp2_fit(Double E1, Double E2, Double E3, Double ACFLAG1)

        Fits a 2-state MMPP (MMPP2) to match first three moments and lag-1 autocorrelation.

        Parameters:
        E1 - First moment (mean)
        E2 - Second moment
        E3 - Third moment
        ACFLAG1 - Lag-1 autocorrelation (must be in 0, 0.
        Returns:

        Fitted MAP as {D0, D1}

      • mmpp2_fit1

         final static MatrixCell mmpp2_fit1(Double E1, Double E2, Double E3)

        Fits MMPP2 using only moments (no autocorrelation).

        Parameters:
        E1 - First moment
        E2 - Second moment
        E3 - Third moment
        Returns:

        Fitted MAP as {D0, D1}

      • mmpp2_fit2

         final static MatrixCell mmpp2_fit2(Double E1, Double E2, Double E3, Double acf1)

        Fits MMPP2 using moments and lag-1 ACF.

        Parameters:
        E1 - First moment
        E2 - Second moment
        E3 - Third moment
        acf1 - Lag-1 autocorrelation
        Returns:

        Fitted MAP as {D0, D1}

      • mmpp2_fit3

         final static MatrixCell mmpp2_fit3(Double E1, Double E2, Double E3, Double acf2)

        Fits MMPP2 using moments and lag-2 ACF (approximation).

        Parameters:
        E1 - First moment
        E2 - Second moment
        E3 - Third moment
        acf2 - Lag-2 autocorrelation
        Returns:

        Fitted MAP as {D0, D1}

      • mmpp2_fit4

         final static MatrixCell mmpp2_fit4(Double E1, Double E2, Double E3, DoubleArray acfValues)

        Fits MMPP2 using moments and multiple ACF lags.

        Parameters:
        E1 - First moment
        E2 - Second moment
        E3 - Third moment
        acfValues - Array of ACF values at lags 1, 2, ...
        Returns:

        Fitted MAP as {D0, D1}

      • mmpp2_fitc

         final static MatrixCell mmpp2_fitc(Double meanCount, Double varCount, Double scale)

        Fits MMPP2 from counting process statistics.

        Parameters:
        meanCount - Mean count
        varCount - Variance of counts
        scale - Time scale
        Returns:

        Fitted MAP as {D0, D1}

      • mmpp2_fitc_approx

         final static MatrixCell mmpp2_fitc_approx(Double meanCount, Double varCount, Double scale, Double acfCount)

        Fits MMPP2 from counting process with approximation.

        Parameters:
        meanCount - Mean count
        varCount - Variance of counts
        scale - Time scale
        acfCount - ACF of counting process
        Returns:

        Fitted MAP as {D0, D1}

      • mmpp2_fitc_theoretical

         final static MatrixCell mmpp2_fitc_theoretical(Double lambda1, Double lambda2, Double q12, Double q21)

        Theoretical MMPP2 fitting from counting process.

        Parameters:
        lambda1 - Arrival rate in state 1
        lambda2 - Arrival rate in state 2
        q12 - Transition rate from state 1 to 2
        q21 - Transition rate from state 2 to 1
        Returns:

        Fitted MAP as {D0, D1}

      • mmpp_rand

         final static DoubleArray mmpp_rand(MatrixCell MAP, Integer nSamples, Long seed)

        Generates random samples from an MMPP.

        Parameters:
        MAP - MMPP as {D0, D1}
        nSamples - Number of samples to generate
        seed - Random seed (optional)
        Returns:

        Array of inter-arrival times