Datasets

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1. final def !=(arg0: AnyRef): Boolean

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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8. def crossValidate[T, R](k: Int, dataset: IndexedSeq[T])(f: (IndexedSeq[T], IndexedSeq[T]) ⇒ R): IndexedSeq[R]

   Split a training set into k-folds, with a test sets equal to 1/kth of the data and training sets the rest of it.

   Split a training set into k-folds, with a test sets equal to 1/kth of the data and training sets the rest of it. Returns a IndexedSeq of results, one for each execution.

   Syntax: crossValidate(K, myDataSet)( (trainSet,testSet) => {produce a result} )

9. final def eq(arg0: AnyRef): Boolean

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10. def equals(arg0: Any): Boolean

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11. def finalize(): Unit

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12. final def getClass(): Class[_]

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13. def hashCode(): Int

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14. final def isInstanceOf[T0]: Boolean

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15. def loocv[T](dataset: IndexedSeq[T]): AnyRef { def apply[R](f: (IndexedSeq[T], IndexedSeq[T]) => R): IndexedSeq[R] }

    Leave-one-out Cross validation Split a training set into dataset.

    Leave-one-out Cross validation Split a training set into dataset.size-folds, with a test sets equal to 1 of the data and training sets for the rest of it. Returns a IndexedSeq of results, one for each execution.

    This is probably very slow!

    Syntax: loocv(myDataSet)( (trainSet,testSet) => {produce a result} )

16. final def ne(arg0: AnyRef): Boolean

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17. final def notify(): Unit

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18. final def notifyAll(): Unit

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19. final def synchronized[T0](arg0: ⇒ T0): T0

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20. def toString(): String

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21. final def wait(): Unit

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22. final def wait(arg0: Long, arg1: Int): Unit

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23. final def wait(arg0: Long): Unit

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