scalation.analytics

HiddenMarkov

class HiddenMarkov extends AnyRef

The HiddenMarkov classes provides Hidden Markov Models (HMM). An HMM model consists of a probability vector 'pi' and probability matrices 'a' and 'b'. The discrete-time system is characterized by a hidden 'state(t)' and an 'observed(t)' symbol at time 't'.

pi(j) = P(state(t) = j) a(i, j) = P(state(t+1) = j|state(t) = i) b(i, k) = P(observed(t) = k|state(t) = i)

See also

http://www.cs.sjsu.edu/faculty/stamp/RUA/HMM.pdf

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Instance Constructors

  1. new HiddenMarkov(ob: VectorI, m: Int, n: Int)

    ob

    the observation vector

    m

    the number of observation symbols

    n

    the number of states in the model

Value Members

  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. def alp_pass(): Unit

    The alpha-pass: a forward pass from time t = 0 to tt-1 that computes alpha 'alp'.

  7. final def asInstanceOf[T0]: T0

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  8. def bet_pass(): Unit

    The beta-pass: a backward pass from time t = tt-1 to 0 that computes beta 'bet'.

  9. def clone(): AnyRef

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  10. final def eq(arg0: AnyRef): Boolean

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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  13. def gam_pass(): Unit

    The gamma-pass: a forward pass from time t = 0 to tt-2 that computes gamma 'gam'.

  14. final def getClass(): Class[_]

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. def logProb(): Double

    Compute the log of the probability of the observation vector 'ob' given the model 'pi, 'a' and 'b'.

  18. final def ne(arg0: AnyRef): Boolean

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. def reestimate(): Unit

    Re-estimate the probability vector 'pi' and the probability matrices 'a' and 'b'.

  22. final def synchronized[T0](arg0: ⇒ T0): T0

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  23. def toString(): String

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  24. def train(): (VectorD, MatrixD, MatrixD)

    Train the Hidden Markov Model using the observation vector 'ob' to determine the model 'pi, 'a' and 'b'.

  25. final def wait(): Unit

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  26. final def wait(arg0: Long, arg1: Int): Unit

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  27. final def wait(arg0: Long): Unit

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