Packages

  • package root
    Definition Classes
    root
  • package provingground

    This is work towards automated theorem proving based on learning, using homotopy type theory (HoTT) as foundations and natural language processing.

    This is work towards automated theorem proving based on learning, using homotopy type theory (HoTT) as foundations and natural language processing.

    The implementation of homotopy type theory is split into:

    • the object HoTT with terms, types, functions and dependent functions, pairs etc
    • the package induction with general inductive types and recursion/induction on these.

    The learning package has the code for learning.

    Scala code, including the spire library, is integrated with homotopy type theory in the scalahott package

    We have implemented a functor based approach to translation in the translation package, used for nlp as well as serialization and parsing.

    The library package is contains basic structures implemented in HoTT.

    Definition Classes
    root
  • package andrewscurtis
    Definition Classes
    provingground
  • object AndrewsCurtis
    Definition Classes
    andrewscurtis
  • ACDeStab
  • ACFlowData
  • ACMoveType
  • ACStab
  • ACparameters
  • AtomicChain
  • Chain
  • Conj
  • DynDst
  • Inv
  • LftMult
  • Move
  • RecChain
  • RtMult

case class DynDst[V, E](vrtdst: FiniteDistribution[V], edgdst: FiniteDistribution[E], cntn: Double) extends Product with Serializable

Linear Supertypes
Serializable, Product, Equals, AnyRef, Any
Type Hierarchy
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. DynDst
  2. Serializable
  3. Product
  4. Equals
  5. AnyRef
  6. Any
Implicitly
  1. by any2stringadd
  2. by StringFormat
  3. by Ensuring
  4. by ArrowAssoc
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new DynDst(vrtdst: FiniteDistribution[V], edgdst: FiniteDistribution[E], cntn: Double)

Value Members

  1. def ++(that: DynDst[V, E]): DynDst[V, E]
  2. def addC(p: Double): DynDst[V, E]
  3. def addE(e: E, p: Double): DynDst[V, E]
  4. def addV(v: V, p: Double): DynDst[V, E]
  5. val cntn: Double
  6. val edgdst: FiniteDistribution[E]
  7. def flatten: DynDst[V, E]
  8. def normalized(c: Double): DynDst[V, E]
  9. def probE(e: E): Double
  10. def probV(v: V): Double
  11. def productElementNames: Iterator[String]
    Definition Classes
    Product
  12. def updtC(c: Double): DynDst[V, E]
  13. def updtE(ed: FiniteDistribution[E]): DynDst[V, E]
  14. def updtV(vd: FiniteDistribution[V]): DynDst[V, E]
  15. val vrtdst: FiniteDistribution[V]