LogUtils

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 equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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

   

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10. def hashCode(): Int

    

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11. final def isInstanceOf[T0]: Boolean

    

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12. def logAdditiveInverse(logP: Double): Double

    

    Computes the log of the additive inverse of a log value.

    Computes the log of the additive inverse of a log value. E.g., given log p, we compute log(1 - p).

    logP

    The log of the value.

    returns

    The log of 1 minus the value.

    Note

    From the perspective of numerical precision, we expect logP to be less than 0. This implies that P is expected to be between 0 and 1, non-inclusive.

13. def logNormalize(pArray: Array[Double]): Array[Double]

    

    Normalizes an array of log likelihoods.

    Normalizes an array of log likelihoods.

    pArray

    The array to normalize.

    returns

    Returns a normalized array.

14. def logSum(logP: Double, logQ: Double): Double

    

    Given two log scaled values, compute the log of the sum of the values.

    Given two log scaled values, compute the log of the sum of the values. E.g., log(p + q) = logSum(log(p), log(q))

    logP

    The log of the first value.

    logQ

    The log of the second value.

    returns

    The log of the sums of the first value and the second value.

    Note

    This function is not sensitive to the relative value of P and Q. We check inside of the function to determine whether P or Q is larger.

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

    

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16. final def notify(): Unit

    

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17. final def notifyAll(): Unit

    

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18. def sumLogProbabilities(pArray: Array[Double]): Double

    

    Given an array of log probabilities, computes the log of the sum of the probabilities.

    Given an array of log probabilities, computes the log of the sum of the probabilities. E.g., computes:

    log(p0 + ... + pn) = sumLogProbabilities(Array(log(p0), ..., log(pn))) ~ log(Array.map(exp(_)).sum)

    pArray

    Array of log probabilities to sum.

    returns

    The log of the sum of the probabilities.

    Note

    This function is not numerically sensitive to the order of the values in the array. The array is copied and sorted before the sum is applied.

    See also

    logSum

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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