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object FineDeducer extends Serializable

A refined deducer, i.e., evolution of terms and derivatives of evolution. Various evolutions are defined mutually recursively - of functions, of types, of terms of a type and of all terms. Derivatives are defined mutually recursively with the evolutions.

This is refined so that, for example, arguments are chosen conditionally from the domain of a function.

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  1. type SomeFunc = FuncLike[u, v] forSome {type u <: Term with Subs[u], type v <: Term with Subs[v]}

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
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  3. final def ==(arg0: Any): Boolean
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  4. def asFuncs(pd: ProbabilityDistribution[Term]): ProbabilityDistribution[SomeFunc]
  5. final def asInstanceOf[T0]: T0
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  6. def asTyps(pd: ProbabilityDistribution[Term]): ProbabilityDistribution[Typ[Term]]
  7. def clone(): AnyRef
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  8. final def eq(arg0: AnyRef): Boolean
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  9. def equals(arg0: AnyRef): Boolean
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  10. final def getClass(): Class[_ <: AnyRef]
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  11. def hashCode(): Int
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  12. final def isInstanceOf[T0]: Boolean
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  13. def lambdaEv(varweight: Double)(typEvolve: => (FiniteDistribution[Term]) => ProbabilityDistribution[Term], valueEvolve: => (Term) => (FiniteDistribution[Term]) => ProbabilityDistribution[Term])(p: FiniteDistribution[Term]): ProbabilityDistribution[Option[Term]]

    a lambda-island for evolution:

    a lambda-island for evolution:

    typEvolve

    evolution of types, from which a variable is chosen

    valueEvolve

    evolution of terms, given initial distribution

    p

    initial distribution A type is picked at random, a variable x with this type is generated and mixed in with weight varWeight into the initial distribution p. The evolved distribution is mapped by x :-> _.

  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 piEv(varweight: Double)(typEvolve: => (FiniteDistribution[Term]) => ProbabilityDistribution[Term], valueEvolve: => (Term) => (FiniteDistribution[Term]) => ProbabilityDistribution[Term])(p: FiniteDistribution[Term]): ProbabilityDistribution[Option[Term]]

    a lambda-island for evolution:

    a lambda-island for evolution:

    typEvolve

    evolution of types, from which a variable is chosen

    valueEvolve

    evolution of terms, given initial distribution

    p

    initial distribution A type is picked at random, a variable x with this type is generated and mixed in with weight varWeight into the initial distribution p. The evolved distribution is mapped by pi(x)(_).

  18. def simpleApplnEv(funcEvolve: => (FiniteDistribution[Term]) => ProbabilityDistribution[SomeFunc], argEvolve: => (Typ[Term]) => (FiniteDistribution[Term]) => ProbabilityDistribution[Term])(p: FiniteDistribution[Term]): ProbabilityDistribution[v forSome {type v <: Term with Subs[v]}]

    evolution by function application, to be used by choosing function at random and then argument conditionally in its domain.

  19. final def synchronized[T0](arg0: => T0): T0
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  20. def termClosure(vars: Vector[Term])(fd: FiniteDistribution[Term]): FiniteDistribution[Term]
  21. def toString(): String
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  22. def typClosure(vars: Vector[Term])(fd: FiniteDistribution[Typ[Term]]): FiniteDistribution[Typ[Term]]
  23. def unif(vars: Term*)(terms: Term*)(axioms: Typ[Term]*): FiniteDistribution[Term]
  24. def unifApplnEv(funcEvolve: => (FiniteDistribution[Term]) => ProbabilityDistribution[SomeFunc], argEvolve: => (FiniteDistribution[Term]) => ProbabilityDistribution[Term])(p: FiniteDistribution[Term]): ProbabilityDistribution[Option[Term]]

    evolution by function application with unification

  25. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
  26. final def wait(arg0: Long): Unit
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  27. final def wait(): Unit
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  1. def finalize(): Unit
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    Deprecated

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