Utils

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 buffered_preds[IT, IO, ID, IS, I, TT, TO, TD, TS, EI, InferInput, InferOutput, ModelInferenceOutput](predictiveModel: Estimator[IT, IO, ID, IS, I, (IT, TT), (IO, TO), (ID, TD), (IS, TS), (I, EI)], workingData: InferInput, buffer: Int, dataSize: Int)(implicit getSplitByIndex: MetaPipe12[InferInput, Int, Int, InferInput], concatenateSplits: DataPipe[Iterable[InferOutput], InferOutput], evFetchableIO: Aux[IO, IT], evFetchableI: Aux[I, ModelInferenceOutput], evFetchableIIO: Aux[(IO, I), (IT, ModelInferenceOutput)], ev: SupportedInferInput[InferInput, InferOutput, IT, IO, ID, IS, ModelInferenceOutput]): InferOutput

   

6. def clone(): AnyRef

   

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7. def cross_entropy(target_prob: Tensor, prob: Tensor): Tensor

   

   Calculate the cross-entropy of two probability distributions.

8. def cross_entropy(target_prob: Output, prob: Output): Output

   

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 get_ffstack_properties(neuron_counts: Seq[Int], ff_index: Int, data_type: String): (Seq[Shape], Seq[String], Seq[String])

    

14. def hashCode(): Int

    

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15. def hellinger(target_prob: Tensor, prob: Tensor): Tensor

    

16. def hellinger(target_prob: Output, prob: Output): Output

    

    Calculate the Hellinger distance between two probability distributions.

17. final def isInstanceOf[T0]: Boolean

    

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18. def js(target_prob: Tensor, prob: Tensor): Output

    

19. def js(target_prob: Output, prob: Output): Output

    

    Calculate the Jensen Shannon divergence between a probability and a target probability.

20. def kl(prior: Tensor, p: Tensor): Output

    

21. def kl(prior: Output, p: Output): Output

    

    Calculate the Kullback Leibler divergence of a probability density from a prior density.

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

    

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23. final def notify(): Unit

    

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24. final def notifyAll(): Unit

    

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25. def predict_data[IT, IO, ID, IS, I, TT, TO, TD, TS, EI, InferOutput, ModelInferenceOutput](predictiveModel: Estimator[IT, IO, ID, IS, I, (IT, TT), (IO, TO), (ID, TD), (IS, TS), (I, EI)], data: AbstractDataSet[IT, TT], pred_flags: (Boolean, Boolean) = (false, true), buff_size: Int = 400)(implicit getSplitByIndex: MetaPipe12[IT, Int, Int, IT], concatenateSplits: DataPipe[Iterable[InferOutput], InferOutput], evFetchableIO: Aux[IO, IT], evFetchableI: Aux[I, ModelInferenceOutput], evFetchableIIO: Aux[(IO, I), (IT, ModelInferenceOutput)], ev: SupportedInferInput[IT, InferOutput, IT, IO, ID, IS, ModelInferenceOutput]): (Option[InferOutput], Option[InferOutput])

    

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

    

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27. def toDoubleSeq(t: Tensor): Iterator[Double]

    

    Convert a float tensor to a Sequence.

28. def toString(): String

    

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29. final def wait(): Unit

    

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

    

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

    

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