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lamp.tabular

AutoLoop

object AutoLoop

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  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. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. def computeValidationErrors(pred: STen, targetType: TargetType, precision: FloatingPointPrecision, targetFullbatch: STen)(implicit scope: Scope): Double
  7. final def eq(arg0: AnyRef): Boolean
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  8. def equals(arg0: AnyRef): Boolean
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  9. implicit val ev1: Movable[ClassificationTree] { def list(t: lamp.extratrees.ClassificationTree): scala.collection.immutable.Nil.type }
  10. implicit val ev2: Movable[RegressionTree] { def list(t: lamp.extratrees.RegressionTree): scala.collection.immutable.Nil.type }
  11. def finalize(): Unit
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    @throws(classOf[java.lang.Throwable])
  12. final def getClass(): Class[_ <: AnyRef]
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    @native()
  13. def hashCode(): Int
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    @native()
  14. final def isInstanceOf[T0]: Boolean
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  15. def makeCVFolds(length: Int, k: Int, repeat: Int, rng: Generator): IndexedSeq[(Seq[Int], Seq[Int])]
  16. final def ne(arg0: AnyRef): Boolean
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  17. final def notify(): Unit
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    @native()
  18. final def notifyAll(): Unit
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    @native()
  19. final def synchronized[T0](arg0: => T0): T0
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  20. def toString(): String
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  21. def train(dataFullbatch: STen, targetFullbatch: STen, folds: Seq[(Seq[Int], Seq[Int])], targetType: TargetType, dataLayout: Seq[Metadata], epochs: Seq[Int], weighDecays: Seq[Double], dropouts: Seq[Double], hiddenSizes: Seq[Int], knnK: Seq[Int], knnMinibatchSize: Int, extratreesK: Seq[Int], extratreesM: Seq[Int], extratreesNMin: Seq[Int], extratreeParallelism: Int, learningRate: Double, device: Device, precision: FloatingPointPrecision, minibatchSize: Int, logger: Option[Logger], ensembleFolds: Seq[(Seq[Int], Seq[Int])], rng: Generator)(outerScope: Scope): IO[EnsembleModel]
  22. def trainAndAverageFolds(dataFullbatch: STen, targetFullbatch: STen, predictions: Seq[STen], folds: Seq[(Seq[Int], Seq[Int])], targetType: TargetType, dataLayout: Seq[Metadata], epochs: Seq[Int], weightDecay: Double, dropout: Double, hiddenSize: Int, learningRate: Double, device: Device, precision: FloatingPointPrecision, minibatchSize: Int, logger: Option[Logger], rng: Generator)(outerScope: Scope): IO[Seq[(Int, STen, Seq[NNBase])]]
  23. def trainAndAverageFoldsExtratrees(k: Int, m: Int, nMin: Int, parallelism: Int, dataFullbatch: STen, targetFullbatch: STen, predictions: Seq[STen], folds: Seq[(Seq[Int], Seq[Int])], targetType: TargetType, dataLayout: Seq[Metadata], logger: Option[Logger])(outerScope: Scope): IO[Seq[(Int, STen, Seq[ExtratreesBase])]]
  24. def trainAndAverageFoldsKnn(k: Int, dataFullbatch: STen, targetFullbatch: STen, predictions: Seq[STen], folds: Seq[(Seq[Int], Seq[Int])], targetType: TargetType, dataLayout: Seq[Metadata], device: Device, precision: FloatingPointPrecision, minibatchSize: Int, logger: Option[Logger])(outerScope: Scope): IO[Seq[(Int, STen, Seq[KnnBase])]]
  25. final def wait(): Unit
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    @throws(classOf[java.lang.InterruptedException])
  26. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
  27. final def wait(arg0: Long): Unit
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    @throws(classOf[java.lang.InterruptedException]) @native()

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