kumulant/com.eignex.kumulant.schema/Accuracy

Accuracy

@Serializable

@SerialName(value = "Accuracy")

data object Accuracy : PairedStatSpec<WeightedMeanResult> (source)

Spec for AccuracyStat: weighted classification accuracy over (predictedClass, trueClass).

Functions

atIndices

fun <R : Result> PairedStatSpec<R>.atIndices(indexX: Int, indexY: Int): VectorStatSpec<R>

Adapt a paired spec into a vector spec by consuming the indexX / indexY coordinates.

filter

fun <R : Result> PairedStatSpec<R>.filter(pred: BoolExpr): PairedStatSpec<R>

Wrap this paired spec so updates are forwarded only when pred evaluates true on (x, y).

foldVector

fun <R : Result> PairedStatSpec<R>.foldVector(xExpr: ScalarExpr, yExpr: ScalarExpr): VectorStatSpec<R>

Lift this paired spec to a vector spec, reducing every vector to a pair (xExpr, yExpr) of scalars via xExpr and yExpr.

materialize

fun <R : Result> PairedStatSpec<R>.materialize(concurrency: Concurrency = Concurrency.None): PairedStat<R>

Construct a live PairedStat from a PairedStatSpec. See SeriesStatSpec.materialize.

fun StatSpec.materialize(concurrency: Concurrency = Concurrency.None): Stat<*>

Construct a live stat from any StatSpec, dispatching on its modality. Useful for code paths (like StatSchemaDef.materialize) that iterate over an erased Map<String, StatSpec> and don't statically know the modality.

minMaxScaler

fun <R : Result> PairedStatSpec<R>.minMaxScaler(targetLow: Double = 0.0, targetHigh: Double = 1.0): PairedStatSpec<R>

Min-max scale both axes of a paired spec against per-axis Range primaries.

sample

fun <R : Result> PairedStatSpec<R>.sample(rate: Double, seed: Long): PairedStatSpec<R>

Wrap this paired spec to keep each update with probability rate; seed feeds the PRNG.

standardScaler

fun <R : Result> PairedStatSpec<R>.standardScaler(): PairedStatSpec<R>

Z-score both axes of a paired spec against per-axis Variance primaries.

throttle

fun <R : Result> PairedStatSpec<R>.throttle(every: Int): PairedStatSpec<R>

Wrap this paired spec so it only sees one in every every updates.

transformPair

fun <R : Result> PairedStatSpec<R>.transformPair(xExpr: ScalarExpr, yExpr: ScalarExpr): PairedStatSpec<R>

Wrap this paired spec so each (x, y) is remapped via xExpr/yExpr before the inner stat sees it.

transformX

fun <R : Result> PairedStatSpec<R>.transformX(expr: ScalarExpr): PairedStatSpec<R>

Map only the x coordinate; y stays as-is.

transformY

fun <R : Result> PairedStatSpec<R>.transformY(expr: ScalarExpr): PairedStatSpec<R>

Map only the y coordinate; x stays as-is.

weightBy

fun <R : Result> PairedStatSpec<R>.weightBy(expr: ScalarExpr): PairedStatSpec<R>

Wrap this paired spec so every update's weight is multiplied by expr.eval(x, y).

windowed

fun <R : Result> PairedStatSpec<R>.windowed(durationMillis: Long, slices: Int = 10): PairedStatSpec<R>

Wrap this paired spec in a sliding time window of durationMillis split into slices buckets.

withFixedX

fun <R : Result> PairedStatSpec<R>.withFixedX(fixedX: Double): SeriesStatSpec<R>

Adapt a paired spec into a series spec by pinning x to fixedX.

withFixedY

fun <R : Result> PairedStatSpec<R>.withFixedY(fixedY: Double): SeriesStatSpec<R>

Adapt a paired spec into a series spec by pinning y to fixedY.

withSelfLag

fun <R : Result> PairedStatSpec<R>.withSelfLag(k: Int): SeriesStatSpec<R>

Lift a paired spec into a series spec by self-pairing each input with the value seen k updates ago.

withTimeAsX

fun <R : Result> PairedStatSpec<R>.withTimeAsX(): SeriesStatSpec<R>

Adapt a paired spec into a series spec by using the update timestamp as x.

withTimeAsY

fun <R : Result> PairedStatSpec<R>.withTimeAsY(): SeriesStatSpec<R>

Adapt a paired spec into a series spec by using the update timestamp as y.

withWeight

fun <R : Result> PairedStatSpec<R>.withWeight(weight: Double): PairedStatSpec<R>

Wrap this paired spec so every update applies the per-observation weight multiplier.