Trainer

Value Members

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

   

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7. def execution: Execution[Unit]

   

8. def expand[S](path: String)(implicit splitter: Splitter[V, T], evaluator: Evaluator[V, T], stopper: Stopper[T], inj: Injection[Tree[K, V, T], String]): Trainer[K, V, T]

   

   expand each tree by one level, by attempting to split every leaf.

   expand each tree by one level, by attempting to split every leaf.

   path

   where to save the new tree

   splitter

   the splitter to use to generate candidate splits for each leaf

   evaluator

   the evaluator to use to decide which split to use for each leaf

9. def expandFrom(base: String, step: Int, to: Int)(implicit splitter: Splitter[V, T], evaluator: Evaluator[V, T], stopper: Stopper[T], inj: Injection[Tree[K, V, T], String]): Trainer[K, V, T]

   

10. def expandInMemory(path: String, times: Int)(implicit splitter: Splitter[V, T], evaluator: Evaluator[V, T], stopper: Stopper[T], inj: Injection[Tree[K, V, T], String]): Trainer[K, V, T]

    

11. def expandTimes(base: String, times: Int)(implicit splitter: Splitter[V, T], evaluator: Evaluator[V, T], stopper: Stopper[T], inj: Injection[Tree[K, V, T], String]): Trainer[K, V, T]

    

    recursively expand multiple times, writing out the new tree at each step

12. def featureImportance[P, E](error: Error[T, P, E])(fn: (TypedPipe[(K, E)]) ⇒ Execution[_])(implicit voter: Voter[T, P]): Trainer[K, V, T]

    

    featureImportance should: shuffle data randomly (group on something random then sort on something random?), then stream through and have each instance pick one feature value at random to pass on to the following instance.

    featureImportance should: shuffle data randomly (group on something random then sort on something random?), then stream through and have each instance pick one feature value at random to pass on to the following instance. then group by permuted feature and compare error.

13. def finalize(): Unit

    

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14. def flatMapSampler(fn: ((TypedPipe[Instance[K, V, T]], Sampler[K])) ⇒ Execution[Sampler[K]]): Trainer[K, V, T]

    

15. def flatMapTrees(fn: ((TypedPipe[Instance[K, V, T]], Sampler[K], Iterable[(Int, Tree[K, V, T])])) ⇒ Execution[TypedPipe[(Int, Tree[K, V, T])]]): Trainer[K, V, T]

    

16. def forceTrainingDataToDisk: Trainer[K, V, T]

    

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

    

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18. final def isInstanceOf[T0]: Boolean

    

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19. def load(path: String)(implicit inj: Injection[Tree[K, V, T], String]): Trainer[K, V, T]

    

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

    

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21. final def notify(): Unit

    

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22. final def notifyAll(): Unit

    

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23. def outOfTime(quantile: Double = 0.8): Trainer[K, V, T]

    

    add out of time validation

24. def prune[P, E](path: String, error: Error[T, P, E])(implicit voter: Voter[T, P], inj: Injection[Tree[K, V, T], String], ord: Ordering[E]): Trainer[K, V, T]

    

    prune a tree to minimize validation error

    prune a tree to minimize validation error

    Construct a Map[Int,T] from the trainingData for each tree, and then transform the trees using the prune method.

25. val reducers: Int

    

26. val samplerExecution: Execution[Sampler[K]]

    

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

    

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28. def tee[A](fn: ((TypedPipe[Instance[K, V, T]], Sampler[K], Iterable[(Int, Tree[K, V, T])])) ⇒ Execution[A]): Trainer[K, V, T]

    

29. val trainingDataExecution: Execution[TypedPipe[Instance[K, V, T]]]

    

30. val treeExecution: Execution[TypedPipe[(Int, Tree[K, V, T])]]

    

31. val unitExecution: Execution[Unit]

    

32. def updateTargets(path: String)(implicit inj: Injection[Tree[K, V, T], String]): Trainer[K, V, T]

    

    Update the leaves of the current trees from the training set.

    Update the leaves of the current trees from the training set.

    The leaves target distributions will be set to the summed distributions of the instances in the training set that would get classified to them. Often used to initialize an empty tree.

33. def validate[P, E](error: Error[T, P, E])(fn: (ValuePipe[E]) ⇒ Execution[_])(implicit voter: Voter[T, P]): Trainer[K, V, T]

    

    produce an error object from the current trees and the validation set

34. final def wait(): Unit

    

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

    

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

    

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