object CASCEvaluation
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- CASCEvaluation.scala
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final
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!=(arg0: Any): Boolean
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##(): Int
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def
==(arg0: Any): Boolean
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- def apply(prefix: String, print_statistics: Boolean = false): CSVFile[String]
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final
def
asInstanceOf[T0]: T0
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- protected[java.lang]
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- @native() @HotSpotIntrinsicCandidate() @throws( ... )
- def dagRatio[T <: CASCResult](pair: (TstpProofStats[T], RPProofStats[T])): BigDecimal
- def depthRatio[T <: CASCResult](pair: (TstpProofStats[T], RPProofStats[T])): BigDecimal
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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- def eval[T <: CASCResult](bundle: ResultBundle[T]): CSVFile[String]
- def eval_before_after[T <: CASCResult](before_replayed: Seq[(TstpProofStats[T], RPProofStats[T])], ratio: ((TstpProofStats[T], RPProofStats[T])) ⇒ BigDecimal, description: String = "", tex: Boolean = true): (Seq[(TstpProofStats[T], RPProofStats[T])], Seq[(TstpProofStats[T], RPProofStats[T])], Seq[(TstpProofStats[T], RPProofStats[T])])
- def eval_errors[T <: FileData](p: String, problems: Int, sstats: Set[TstpProofStats[T]], rpstats: Set[RPProofStats[T]], tstp_e_bags: ErrorBags[T], rp_e_bags: ErrorBags[T], tex: Boolean = false): Unit
- def eval_rp_stats[T <: FileData](rp_stats: Set[RPProofStats[T]]): (CSVFile[String], Statistic[Int], Statistic[BigInt], Statistic[BigDecimal], Statistic[Int], Statistic[Int])
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final
def
getClass(): Class[_]
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- @native() @HotSpotIntrinsicCandidate()
- def getProblemStats(prefix: String): List[Either[TstpError[TptpLibraryProblem] with Product, TptpInputStats[TptpLibraryProblem]]]
- def getRPstatByProperty[S, T <: FileData](rp_stats: Set[RPProofStats[T]], prop: (RPProofStats[T]) ⇒ S)(implicit num: Numeric[S], conv: (S) ⇒ BigDecimal): Statistic[S]
- def getStatisticSummary[T](s: Seq[Statistic[T]])(implicit num: Numeric[T], conv: (T) ⇒ BigDecimal): Statistic[T]
- def get_dagsize_to_minddagsize_graph[T <: CASCResult](bundle: ResultBundle[T]): (List[(BigInt, BigInt, Int, Int)], CSVFile[String])
- def get_depth_to_mindepth_graph[T <: CASCResult](bundle: ResultBundle[T]): (List[(Int, Int, Int, Int)], CSVFile[String])
- def get_prop_to_minprop_graph[T <: CASCResult, U](bundle: ResultBundle[T], prop: (RPProofStats[T]) ⇒ U)(implicit num: Numeric[U]): (List[(U, U, Int, Int)], CSVFile[String])
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hashCode(): Int
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final
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isInstanceOf[T0]: Boolean
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- def loadResult[T](filename: String): Option[T]
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final
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ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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- def processFiles[T <: FileData](data: Iterable[T], print_statistics: Boolean = false): ResultBundle[T]
- def processLeanCop[T <: FileData](data: Iterable[T], print_statistics: Boolean = false): ParIterable[Either[TstpError[T] with Product, Option[ExpansionSequent]]]
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def
roundedStatisticCSV[T](s: Statistic[T])(implicit num: Numeric[T], conv: (T) ⇒ BigDecimal): CSVRow[String]
toCSV with truncation after 2 digits
toCSV with truncation after 2 digits
- T
the type of data points in the statistic
- s
the statistic
- num
implicit numeric object for statistic data
- conv
implicit converter from statistic data to big decimals
- returns
a CSVRow with the trincated strings
- def saveResult[T](filename: String, bundle: T): Unit
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synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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final
def
wait(): Unit
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This is the API documentation for GAPT.
The main package is gapt.