object Merit

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  8. def estimateMerits[T](pva: Iterator[(PredictionResult[T], Seq[T])], merits: Map[String, Merit[T]], rng: Random = Random): Map[String, (Double, Double)]

    Estimate a set of named merits by applying them to multiple sets of predictions and actual values

    Estimate a set of named merits by applying them to multiple sets of predictions and actual values

    The uncertainty in the estimate of each merit is calculated by looking at the variance across the batches

    pva

    predicted-vs-actual data in a series of batches

    merits

    to apply to the predicted-vs-actual data

    returns

    map from the merit name to its (value, uncertainty)

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  15. final def notifyAll(): Unit
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  16. def plotMeritScan[T](parameterName: String, parameterValues: Seq[Double], merits: Map[String, Merit[T]], logScale: Boolean = false, yMin: Option[Double] = None, yMax: Option[Double] = None, rng: Random = Random)(pvaBuilder: (Double) => Iterator[(PredictionResult[T], Seq[T])]): XYChart

    Compute merits as a function of a parameter, given a builder that takes the parameter to predicted-vs-actual data

    Compute merits as a function of a parameter, given a builder that takes the parameter to predicted-vs-actual data

    parameterName

    name of the parameter that's being scanned over

    parameterValues

    values of the parameter to try

    merits

    to apply at each parameter value

    logScale

    whether the parameters should be plotted on a log scale

    rng

    random number generator to use

    pvaBuilder

    function that takes the parameter to predicted-vs-actual data

    returns

    an XYChart that plots the merits vs the parameter value

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