object EntropyAtomWeight extends Serializable
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- def apply[A, B](genDist: FiniteDistribution[A], pDist: FiniteDistribution[B], qDist: FiniteDistribution[B], elem: B, initWeight: Double, hW: Double, klW: Double): EntropyAtomWeight
More natural (but verbose) description of spire gradients for entropy tradeoff
More natural (but verbose) description of spire gradients for entropy tradeoff
- A
the type of objects of the initial distribution, e.g. terms
- B
the type of objects of the final distribution, e.g. types
- genDist
the initial generating distribution
- pDist
the initial "theorem by proof" distribution
- qDist
the initial "theorem by statement" distribution
- elem
the lemma whose weight is learned
- initWeight
the weight of the initial distribution in the final one
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- def evolvedLemmaGens(ev: EvolvedStateLike, hW: Double, klW: Double): Vector[(Typ[Term], EntropyAtomWeight)]
- def evolvedLemmaIters(ev: EvolvedStateLike, hW: Double, klW: Double, sc: Double = 1, prune: Boolean = false): Vector[(Typ[Term], Iterator[Double])]
- def findGoal(init: TermState, base: TermState, tg: TermGenParams, steps: Int, maxDepth: Int, cutoff: Double, hW: Double, klW: Double, limit: FiniteDuration = 3.minutes, sc: Double = 1): Task[FiniteDistribution[Term]]
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- def lemmaDist(ev: EvolvedStateLike, steps: Int, hW: Double, klW: Double, sc: Double = 1): FiniteDistribution[Term]
- def lemmaWeights(ev: EvolvedStateLike, steps: Int, hW: Double, klW: Double, sc: Double = 1): Vector[(Typ[Term], Double)]
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- def proofWeightIter(ev: EvolvedStateLike, pf: Term, weight: Double, cutoff: Double, hW: Double, klW: Double, sc: Double = 1, prune: Boolean = false): Iterator[Double]
- def proofWeightTuned(ev: EvolvedStateLike, pf: Term, weight: Double, cutoff: Double, steps: Int, hW: Double, klW: Double, sc: Double = 1, prune: Boolean = false): Option[(Term, Double)]
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- def tunedProofs(ev: EvolvedStateLike, pfs: Vector[(Term, Double)], cutoff: Double, steps: Int, hW: Double, klW: Double, sc: Double = 1, prune: Boolean = false): Vector[(Term, Double)]
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