Constructor Detail

Method Detail

• computeAutocorrelation

   final DoubleArray computeAutocorrelation(MatrixCell MMAP, Integer maxLag)

  Computes the autocorrelation function of an MMAP up to the specified lag. Uses the first two matrices (D0, D1) of the MMAP which form a valid MAP.

  Parameters:

  MMAP - the MMAP to analyze

  maxLag - maximum lag to compute

  Returns:

  array of autocorrelation values for lags 1 to maxLag

• computeIDC

   final Double computeIDC(MatrixCell MMAP, Double timeWindow)

  Computes the index of dispersion for counts (IDC) of an MMAP. Uses the first two matrices (D0, D1) of the MMAP which form a valid MAP.

  Parameters:

  MMAP - the MMAP to analyze

  timeWindow - the time window for IDC computation (ignored, uses asymptotic IDC)

  Returns:

  the index of dispersion for counts

• computeCoeffVar

   final Double computeCoeffVar(MatrixCell MMAP)

  Computes the coefficient of variation of an MMAP. Uses the first two matrices (D0, D1) of the MMAP which form a valid MAP.

  Parameters:

  MMAP - the MMAP to analyze

  Returns:

  the coefficient of variation

• computeMoments

   final DoubleArray computeMoments(MatrixCell MMAP, Integer n)

  Computes the first n moments of an MMAP. Uses the first two matrices (D0, D1) of the MMAP which form a valid MAP.

  Parameters:

  MMAP - the MMAP to analyze

  n - number of moments to compute

  Returns:

  array of the first n moments

• computeSpectralGap

   final Double computeSpectralGap(MatrixCell MMAP)

  Computes the spectral gap of an MMAP generator matrix.

  Parameters:

  MMAP - the MMAP to analyze

  Returns:

  the spectral gap (difference between largest and second-largest eigenvalue)

• optimizeParameters

   final DoubleArray optimizeParameters(DoubleArray initialParams, Function1<DoubleArray, Double> objectiveFunction, Array<Function1<DoubleArray, Double>> constraints, Double tolerance)

  Optimizes MMAP parameters using COBYLA optimization.

  Parameters:

  initialParams - initial parameter vector

  objectiveFunction - objective function to minimize

  constraints - constraint functions

  tolerance - optimization tolerance

  Returns:

  optimized parameter vector

• computeKLDivergence

   final Double computeKLDivergence(MatrixCell MMAP1, MatrixCell MMAP2, Integer numSamples, Integer seed)

  Computes the Kullback-Leibler divergence between two MMAPs.

  Parameters:

  MMAP1 - first MMAP

  MMAP2 - second MMAP

  numSamples - number of samples for estimation

  seed - random seed for reproducible results

  Returns:

  KL divergence estimate

• validateMMAP

   final Boolean validateMMAP(MatrixCell MMAP)

  Validates that a matrix represents a valid MMAP.

  Parameters:

  MMAP - the MMAP to validate

  Returns:

  true if the MMAP is valid, false otherwise