Package jline.api.mam

Class Mmap_count_mcovKt

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    • Method Summary

      Modifier and Type Method Description
      final static Matrix mmap_count_mcov(MatrixCell MMAP, Double t) Computes the count covariance between each pair of classes at a given time scale.
      final static Matrix mmap_count_mcov(Array<Matrix> mmap, Double t) Computes the count covariance between each pair of classes at a given time scale.

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        clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    • Constructor Detail

    • Method Detail

      • mmap_count_mcov

         final static Matrix mmap_count_mcov(MatrixCell MMAP, Double t)

        Computes the count covariance between each pair of classes at a given time scale.

        For an MMAP with m classes, this function computes the m x m covariance matrix S where S(i,j) represents the covariance between the count of class i events and class j events over time period t.

        The algorithm works as follows:

        • For diagonal elements: S(i,i) = Var(N_i(t)) (per-class variance)

        • For off-diagonal elements: S(i,j) = 1/2 * (Var(N_i(t) + N_j(t)) - Var(N_i(t)) - Var(N_j(t)))

        This uses the property that for random variables X and Y: Cov(X,Y) = 1/2 * (Var(X+Y) - Var(X) - Var(Y))

        Parameters:
        MMAP - the MMAP represented as a MatrixCell where MMAP0 = D0, MMAP1 = aggregate D1, and MMAP2+i = D_{i+1} for i = 0, 1, ...
        t - the time scale over which to compute covariances
        Returns:

        the m x m covariance matrix between class pairs

      • mmap_count_mcov

         final static Matrix mmap_count_mcov(Array<Matrix> mmap, Double t)

        Computes the count covariance between each pair of classes at a given time scale. Array-based overload for compatibility with functions expecting Array<Matrix>.

        Parameters:
        mmap - the MMAP represented as Array<Matrix> where mmap0 = D0, mmap1 = D1, mmap2 = D2, ...
        t - the time scale over which to compute covariances
        Returns:

        the m x m covariance matrix between class pairs