breeze.stats.distributions.MarkovChain

Combinators

object Combinators

Combinators for creating transition kernels from other kernels or things that are not quite transition kernels. A kernel is a fn of type T=<Rand[T]

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  1. class RichKernel[T] extends AnyRef

    Extension methods for kernels

  2. class RichPseudoKernel[C, T, U] extends AnyRef

    Extension methods for pseudo-kernels A pseudo-kernel is a method of type (C,T)=<Rand[U], with C being a context type

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  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. final def asInstanceOf[T0]: T0

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

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. def promoteTuple[T1, T2, T3, U1, U2, U3, T4, U4](k1: (T1, T2, T3, T4) ⇒ Rand[U1], k2: (U1, T2, T3, T4) ⇒ Rand[U2], k3: (U1, U2, T3, T4) ⇒ Rand[U3], k4: (U1, U2, U3, T4) ⇒ Rand[U4]): ((T1, T2, T3, T4)) ⇒ Rand[(U1, U2, U3, U4)]

    Tupleization of nearly-transition kernels to produce a kernel for tuples

  18. def promoteTuple[T1, T2, T3, U1, U2, U3](k1: (T1, T2, T3) ⇒ Rand[U1], k2: (U1, T2, T3) ⇒ Rand[U2], k3: (U1, U2, T3) ⇒ Rand[U3]): ((T1, T2, T3)) ⇒ Rand[(U1, U2, U3)]

    Tupleization of nearly-transition kernels to produce a kernel for tuples

  19. def promoteTuple[A, B, C, D](k1: (A, B) ⇒ Rand[C], k2: (C, B) ⇒ Rand[D]): ((A, B)) ⇒ Rand[(C, D)]

    Tupleization of nearly-transition kernels to produce a transition kernel for tuples

  20. implicit def richKernel[T](k1: (T) ⇒ Rand[T]): RichKernel[T]

  21. implicit def richPseudoKernel[C, T, U](k1: (C, T) ⇒ Rand[U]): RichPseudoKernel[C, T, U]

  22. def seqKernel[T](trans: (Seq[T], Int) ⇒ Rand[T]): (Seq[T]) ⇒ Rand[Seq[T]]

    Creates a transition kernel over a sequence, given the ability to do one index at a time.

    Creates a transition kernel over a sequence, given the ability to do one index at a time. Useful for sequence models with a markov assumption.

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

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  24. def toString(): String

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  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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