object Merit
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- 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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- 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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