object Deducer extends Serializable
Generating terms from given ones using the main HoTT operations, and the adjoint of this generation. This is viewed as deduction. Generation is a map on probability distributions, but the adjoint regards truncated distributions as the tangent space, and restricts domain to finite distributions.
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- def appln(rec: => (ProbabilityDistribution[Term]) => ProbabilityDistribution[Term])(p: ProbabilityDistribution[Term]): ProbabilityDistribution[Option[Term]]
generating optionally using function application, with function and argument generated recursively; to be mixed in using
<+?>
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- final def eq(arg0: AnyRef): Boolean
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- def eqSubs(rec: => (ProbabilityDistribution[Term]) => ProbabilityDistribution[Term])(p: ProbabilityDistribution[Term])(save: (Term, IdentityTyp[Term], Term) => Unit): ProbabilityDistribution[Option[Term with Subs[Term]]]
- def equals(arg0: AnyRef): Boolean
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- def feedback(absTheorems: FiniteDistribution[Typ[Term]], absThmsByProofs: FiniteDistribution[Typ[Term]], proofs: Map[Typ[Term], FiniteDistribution[Term]], vars: Vector[Weighted[Term]], lambdaWeight: Double, piWeight: Double): FiniteDistribution[Term]
- def flow(fd: FiniteDistribution[Term], shifts: FiniteDistribution[Term]): FiniteDistribution[Term]
- final def getClass(): Class[_ <: AnyRef]
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- def hashCode(): Int
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- def hashedUnifInv[U <: Term with Subs[U]](term: Term, hashedInvMap: Map[ShapeTree, Vector[(Term, Set[(U, Term)])]]): Set[(U, Term)]
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- def lambda(varweight: Double)(rec: => (ProbabilityDistribution[Term]) => ProbabilityDistribution[Term])(p: ProbabilityDistribution[Term]): ProbabilityDistribution[Option[Term]]
generating optionally as lambdas, with function and argument generated recursively; to be mixed in using
<+?>
avoid having value a universe - def lambdaValue[U <: Term with Subs[U]](variable: U): (Term) => Option[Term]
given a type, returns optionally values of lambda terms with variable of the given type with variable in values from the above
variable
object - def memAppln(rec: => (ProbabilityDistribution[Term]) => ProbabilityDistribution[Term])(p: ProbabilityDistribution[Term])(save: (Term, Term, Term) => Unit): ProbabilityDistribution[Option[Term]]
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- def pi(varweight: Double)(rec: => (ProbabilityDistribution[Term]) => ProbabilityDistribution[Term])(p: ProbabilityDistribution[Term]): ProbabilityDistribution[Option[Term]]
generating optionally as pi's, with function and argument generated recursively; to be mixed in using
<+?>
- def piValue[U <: Term with Subs[U]](variable: U): (Term) => Option[Term]
- def proofEntropies(fd: FiniteDistribution[Term]): Vector[Weighted[Typ[U] forSome {type U >: x$2.type <: Term with Subs[U], val x$2: Term}]]
- final def synchronized[T0](arg0: => T0): T0
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- def toString(): String
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- def unifInv[U <: Term with Subs[U]](term: Term, invMap: Vector[(Term, Set[(U, Term)])]): Set[(U, Term)]
- def unpickle(str: String): TermPopulation
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