case class Deducer(applnWeight: Double = 0.2, lambdaWeight: Double = 0.2, piWeight: Double = 0.2, varWeight: Double = 0.3, vars: Vector[Weighted[Term]] = Vector(), propDecay: Double = 0.5, cutoff: Double = 0.01, feedbackScale: Double = 0.1, abstractionWeight: Double = 0.3, genMemory: Double = 0.8) extends Product with Serializable
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- final def !=(arg0: Any): Boolean
- Definition Classes
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- final def ##: Int
- Definition Classes
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- def +(other: String): String
- def ->[B](y: B): (Deducer, B)
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- def absFD(fd: FiniteDistribution[Term]): FiniteDistribution[Term]
- def abstractTyps(typ: Typ[Term]): Typ[Term]
- val abstractionWeight: Double
- val applnWeight: Double
- final def asInstanceOf[T0]: T0
- Definition Classes
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- def backProp(epsilon: Double, invImage: (Term) => Set[(Term, Term)])(fd: Prob): (TruncatedDistribution[Term]) => TruncatedDistribution[Term]
- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- val cutoff: Double
- def ensuring(cond: (Deducer) => Boolean, msg: => Any): Deducer
- def ensuring(cond: (Deducer) => Boolean): Deducer
- def ensuring(cond: Boolean, msg: => Any): Deducer
- def ensuring(cond: Boolean): Deducer
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- val feedbackScale: Double
- def formatted(fmtstr: String): String
- def funcUniProp(backProp: => (Prob) => (TruncatedDistribution[Term]) => TruncatedDistribution[Term])(fd: Prob, invImage: (Term) => Set[(Term, Term)])(td: TruncatedDistribution[Term]): TruncatedDistribution[Term]
- def funcUniPropTerm(backProp: => (Prob) => (TruncatedDistribution[Term]) => TruncatedDistribution[Term])(fd: Prob, invImage: (Term) => Set[(Term, Term)]): (Term) => TruncatedDistribution[Term]
- val genMemory: Double
- def getAbstractTheorems: FiniteDistribution[Typ[Term]]
- def getAbstractTheoremsByProofs: FiniteDistribution[Typ[Term]]
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
- def getElapsedTime: Long
- def getFeedback: FiniteDistribution[Term]
- def getLoops: Int
- def getProofs(absTyp: Typ[Term]): FiniteDistribution[Term]
proofs of an abstracted theorem
- def getSample(pd: ProbabilityDistribution[Term], n: Int, accum: InvMap = Vector()): TermPopulation
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- def lambdaFD(fd: Prob)(variable: Term): (Term) => Double
given a truncated distribution of terms and a type, returns the truncated distribution of
value
s of lambda terms of that type; with variable in the values from the abovevariable
object - def lambdaProb(prob: Prob)(variable: Term): Prob
- def lambdaPropValues(backProp: => (Prob) => (TruncatedDistribution[Term]) => TruncatedDistribution[Term])(fd: Prob)(td: TruncatedDistribution[Term]): TruncatedDistribution[Term]
- def lambdaPropValuesTerm(backProp: => (Prob) => (TruncatedDistribution[Term]) => TruncatedDistribution[Term])(fd: Prob): (Term) => TruncatedDistribution[Term]
- def lambdaPropVar(backProp: => (Prob) => (TruncatedDistribution[Term]) => TruncatedDistribution[Term])(fd: Prob)(td: TruncatedDistribution[Term]): TruncatedDistribution[Term]
- def lambdaPropVarTerm(backProp: => (Prob) => (TruncatedDistribution[Term]) => TruncatedDistribution[Term])(fd: Prob): (Term) => TruncatedDistribution[Term]
- val lambdaWeight: Double
- def learnerNextDistribution(fd: FiniteDistribution[Term], theorems: FiniteDistribution[Typ[Term]], n: Int, memory: Boolean = true, accum: InvMap = Vector(), smooth: (FiniteDistribution[Term]) => FiniteDistribution[Term] = identity): (FiniteDistribution[Term], InvMap)
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def nextDistribution(fd: FiniteDistribution[Term], n: Int, memory: Boolean = true, accum: InvMap = Vector(), smooth: (FiniteDistribution[Term]) => FiniteDistribution[Term] = identity): (FiniteDistribution[Term], InvMap)
- final def notify(): Unit
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- @native() @HotSpotIntrinsicCandidate()
- final def notifyAll(): Unit
- Definition Classes
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- @native() @HotSpotIntrinsicCandidate()
- def piFD(fd: Prob)(variable: Term): Prob
- def piPropValues(backProp: => (Prob) => (TruncatedDistribution[Term]) => TruncatedDistribution[Term])(fd: Prob)(td: TruncatedDistribution[Term]): TruncatedDistribution[Term]
- def piPropValuesTerm(backProp: => (Prob) => (TruncatedDistribution[Term]) => TruncatedDistribution[Term])(fd: Prob): (Term) => TruncatedDistribution[Term]
- def piPropVar(backProp: => (Prob) => (TruncatedDistribution[Term]) => TruncatedDistribution[Term])(fd: Prob)(td: TruncatedDistribution[Term]): TruncatedDistribution[Term]
- def piPropVarTerm(backProp: => (Prob) => (TruncatedDistribution[Term]) => TruncatedDistribution[Term])(fd: Prob): (Term) => TruncatedDistribution[Term]
- val piWeight: Double
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- val propDecay: Double
- def shiftFD(popln: TermPopulation, cumApplnInv: InvMap = Vector()): FiniteDistribution[Term]
- def shifted(fd: FiniteDistribution[Term], td: TruncatedDistribution[Term]): FiniteDistribution[Term]
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- val varWeight: Double
- val vars: Vector[Weighted[Term]]
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
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- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
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- @throws(classOf[java.lang.InterruptedException])
- object absBucket extends WeightedTermBucket
- object bucket extends TermBucket
Deprecated Value Members
- def finalize(): Unit
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- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
- def →[B](y: B): (Deducer, B)
- Implicit
- This member is added by an implicit conversion from Deducer toArrowAssoc[Deducer] performed by method ArrowAssoc in scala.Predef.
- Definition Classes
- ArrowAssoc
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- @deprecated
- Deprecated
(Since version 2.13.0) Use
->
instead. If you still wish to display it as one character, consider using a font with programming ligatures such as Fira Code.