MathUtils

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. val LogTolerance: Double

   

   If a difference is bigger than this in log terms, then the sum or difference of them will just be the larger (to 12 or so decimal places for double, and 7 or 8 for float).

5. val LogToleranceFloat: Float

   

6. final def asInstanceOf[T0]: T0

   

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7. def clone(): AnyRef

   

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8. def denseSoftmax(vector: Array[Double], gamma: Double = 1.0): Array[Double]

   

   Puts a softmax layer over a collection of scores, so they look like probabilities

   Puts a softmax layer over a collection of scores, so they look like probabilities

   vector

   A collection of unnormalized scores

   gamma

   Indicates how spiked the probability distribution should be

9. def denseSoftmaxFloat(vector: Array[Float], gamma: Float = 1.0f): Array[Float]

   

   Puts a softmax layer over a collection of scores, so they look like probabilities

   Puts a softmax layer over a collection of scores, so they look like probabilities

   vector

   A collection of unnormalized scores

   gamma

   Indicates how spiked the probability distribution should be

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

    

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11. def equals(arg0: Any): Boolean

    

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12. def finalize(): Unit

    

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

    

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14. def hashCode(): Int

    

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15. def histogram(xs: Traversable[Double], numBuckets: Int): Map[(Double, Double), Int]

    

16. def histogram(xs: Traversable[Double], boundaries: Seq[Double]): Map[(Double, Double), Int]

    

17. final def isInstanceOf[T0]: Boolean

    

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18. def logSum(logInputs: IndexedSeq[Double], fromIndex: Int, toIndex: Int): Double

    

    Returns the log of the portion between fromIndex, inclusive, and toIndex, exclusive, of an array of numbers, which are themselves input in log form.

    Returns the log of the portion between fromIndex, inclusive, and toIndex, exclusive, of an array of numbers, which are themselves input in log form. This is all natural logarithms. Reasonable care is taken to do this as efficiently as possible (under the assumption that the numbers might differ greatly in magnitude), with high accuracy, and without numerical overflow. Throws an IllegalArgumentException if logInputs is of length zero. Otherwise, returns Double.NegativeInfinity if fromIndex >= toIndex.

    logInputs

    Numbers in log form

    fromIndex

    Start offset (inclusive)

    toIndex

    End offset (exclusive)

    returns

    log(x1 + ... + xn)

19. def logSum(logInputs: IndexedSeq[Double]): Double

    

20. def logSumFloat(logInputs: IndexedSeq[Float], fromIndex: Int, toIndex: Int): Float

    

21. def logSumFloat(logInputs: IndexedSeq[Float]): Float

    

22. def nBest[T](scoringFunction: (T) ⇒ Double)(xs: Iterable[T], howMany: Int)(implicit arg0: Manifest[T]): List[(T, Double)]

    

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

    

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24. final def notify(): Unit

    

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25. final def notifyAll(): Unit

    

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26. def randomize[T](l: Array[T], rand: Random): Array[T]

    

27. def round(d: Double, decimals: Int): Double

    

    Rounds a number between 0 and 1 to a percentage value, with up to the indicated number of decimals This is useful to pretty print P/R/F1 scores

28. def sampleStream[T](xs: Iterable[T], howMany: Int)(implicit arg0: Manifest[T]): List[T]

    

    sample howMany elements uniformly from xs.

    sample howMany elements uniformly from xs. Doesn't retain order of xs

29. def softmax(scores: Iterable[Double], gamma: Double = 1.0): IndexedSeq[Double]

    

    Puts a softmax layer over a collection of scores, so they look like probabilities

    Puts a softmax layer over a collection of scores, so they look like probabilities

    scores

    A collection of unnormalized scores

    gamma

    Indicates how spiked the probability distribution should be

30. def softmaxFloat(scores: Iterable[Float], gamma: Float = 1.0f): IndexedSeq[Float]

    

    Puts a softmax layer over a collection of scores, so they look like probabilities

    Puts a softmax layer over a collection of scores, so they look like probabilities

    scores

    A collection of unnormalized scores

    gamma

    Indicates how spiked the probability distribution should be

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

    

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32. def toString(): String

    

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33. final def wait(): Unit

    

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

    

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

    

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