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
  • object ProbabilityDistribution
    Definition Classes
    provingground
  • CondMapped
  • Conditioned
  • FiberProduct
  • FlatMapped
  • Flattened
  • Mapped
  • Mixin
  • MixinOpt
  • Mixture
  • Product
  • Scaled
  • Sum

class Mixture[A] extends ProbabilityDistribution[A]

Linear Supertypes
ProbabilityDistribution[A], AnyRef, Any
Type Hierarchy
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. Mixture
  2. ProbabilityDistribution
  3. AnyRef
  4. Any
Implicitly
  1. by any2stringadd
  2. by StringFormat
  3. by Ensuring
  4. by ArrowAssoc
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new Mixture(base: ProbabilityDistribution[A], components: => Vector[Weighted[ProbabilityDistribution[A]]])

Value Members

  1. def <++>(components: => Vector[Weighted[ProbabilityDistribution[A]]]): Mixture[A]

    mix in weighted distributions; the mixed in distributions are called by name, so can depend on this distribution.

    mix in weighted distributions; the mixed in distributions are called by name, so can depend on this distribution.

    Definition Classes
    ProbabilityDistribution
  2. def <+>(mixin: => ProbabilityDistribution[A], weight: Double): Mixin[A]

    generates from the mixed in distribution with probability _weight_, otherwise defaults to this distribution; as the mixed in distribution is called by name, it may depend on the present one.

    generates from the mixed in distribution with probability _weight_, otherwise defaults to this distribution; as the mixed in distribution is called by name, it may depend on the present one.

    Definition Classes
    ProbabilityDistribution
  3. def <+?>(mixin: => ProbabilityDistribution[Option[A]], weight: Double): MixinOpt[A]

    generates from the mixed in optional valued distribution with probability weight, otherwise, or if the optional returns None, defaults to this distribution; the mixed in distribution is call by name, so may depend on this distribution.

    generates from the mixed in optional valued distribution with probability weight, otherwise, or if the optional returns None, defaults to this distribution; the mixed in distribution is call by name, so may depend on this distribution.

    Definition Classes
    ProbabilityDistribution
  4. def condMap[B](f: (A) => Option[B]): Mixture[B]
    Definition Classes
    Mixture → ProbabilityDistribution
  5. def conditioned(p: (A) => Boolean): Mixture[A]
    Definition Classes
    Mixture → ProbabilityDistribution
  6. lazy val dists: Vector[ProbabilityDistribution[A]]
  7. def fibProduct[Q, B](quot: (A) => Q, fibers: (Q) => ProbabilityDistribution[B]): FiberProduct[A, Q, B]
    Definition Classes
    ProbabilityDistribution
  8. def findFut(p: (A) => Boolean, maxloops: Long): Future[Option[A]]

    Find element satisfying predicate, runs concurrently, returning future

    Find element satisfying predicate, runs concurrently, returning future

    Definition Classes
    ProbabilityDistribution
  9. lazy val first: ProbabilityDistribution[A]
  10. def flatMap[B](f: (A) => ProbabilityDistribution[B]): ProbabilityDistribution[B]
    Definition Classes
    ProbabilityDistribution
  11. def map[B](f: (A) => B): ProbabilityDistribution[B]
    Definition Classes
    ProbabilityDistribution
  12. def next: A

    the next random value

    the next random value

    Definition Classes
    Mixture → ProbabilityDistribution
  13. def product[B](that: ProbabilityDistribution[B]): Product[A, B]
    Definition Classes
    ProbabilityDistribution
  14. lazy val ps: Vector[Double]
  15. lazy val qs: Vector[Double]
  16. def randomVariable: Iterator[A]
    Definition Classes
    ProbabilityDistribution
  17. lazy val rest: Vector[Weighted[ProbabilityDistribution[A]]]
  18. lazy val weightedDists: Vector[(ProbabilityDistribution[A], Double)]