kumulant/com.eignex.kumulant.schema/LinearCounting

LinearCounting

@Serializable

@SerialName(value = "LinearCounting")

data class LinearCounting(val bits: Int = 4096, val hasher: HasherRef = HasherRef.SplitMix64) : DiscreteStatSpec<LinearCountingResult> (source)

Spec for LinearCountingStat: cardinality estimator backed by a bitset of bits cells.

Constructors

LinearCounting

constructor(bits: Int = 4096, hasher: HasherRef = HasherRef.SplitMix64)

Properties

bits

val bits: Int

Bitset size; trade-off between memory and accuracy near the saturation cap.

hasher

val hasher: HasherRef

HasherRef for the mixer applied before indexing; resolved via the Hashers registry.

Functions

asSeries

fun <R : Result> DiscreteStatSpec<R>.asSeries(): SeriesStatSpec<R>

Adapt a discrete spec into a series spec - the series sees value.toDouble() per update.

filter

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

Wrap this discrete spec so updates are forwarded only when pred evaluates true.

materialize

fun <R : Result> DiscreteStatSpec<R>.materialize(concurrency: Concurrency = Concurrency.None): DiscreteStat<R>

Construct a live DiscreteStat from a DiscreteStatSpec. 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.

sample

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

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

throttle

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

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

transform

fun <R : Result> DiscreteStatSpec<R>.transform(expr: ScalarExpr): DiscreteStatSpec<R>

Wrap this discrete spec to apply expr to every update before the inner stat sees it.

weightBy

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

Wrap this discrete spec so every update's weight is multiplied by expr.eval(value.toDouble()).

windowed

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

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

withValue

fun <R : Result> DiscreteStatSpec<R>.withValue(value: Long): DiscreteStatSpec<R>

Wrap this discrete spec so every update pushes the constant value regardless of input.

withWeight

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

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

LinearCounting

constructor(bits: Int = 4096, hasher: HasherRef = HasherRef.SplitMix64)(source)

bits

val bits: Int(source)

Bitset size; trade-off between memory and accuracy near the saturation cap.

hasher

val hasher: HasherRef(source)

HasherRef for the mixer applied before indexing; resolved via the Hashers registry.