kumulant/com.eignex.kumulant.schema/DiagonalRegression

DiagonalRegression

@Serializable

@SerialName(value = "DiagonalRegression")

data class DiagonalRegression(val featureSize: Int, val priorPrecision: Double = 1.0, val learningRate: ScalarExpr = ConstantRate(1.0), val penalty: Penalty = Penalty.None, val link: Link = Link.Identity) : RegressionStatSpec<DiagonalRegressionResult> (source)

Spec for DiagonalRegressionStat: factorised-Gaussian posterior with per-coordinate precision.

Constructors

DiagonalRegression

constructor(featureSize: Int, priorPrecision: Double = 1.0, learningRate: ScalarExpr = ConstantRate(1.0), penalty: Penalty = Penalty.None, link: Link = Link.Identity)

Properties

featureSize

val featureSize: Int

Number of input features.

learningRate

val learningRate: ScalarExpr

Per-step learning rate.

link

val link: Link

GLM link function.

penalty

val penalty: Penalty

Gradient-step regulariser.

priorPrecision

val priorPrecision: Double

Initial per-coordinate precision (inverse variance) seeded into every weight.

Functions

filter

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

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

materialize

fun <R : Result> RegressionStatSpec<R>.materialize(concurrency: Concurrency = Concurrency.None): RegressionStat<R>

Construct a live RegressionStat from a RegressionStatSpec. 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.

minMaxScaleFeatures

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

Element-wise min-max scale a regression spec's feature vector.

sample

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

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

standardScaleFeatures

fun <R : Result> RegressionStatSpec<R>.standardScaleFeatures(): RegressionStatSpec<R>

Element-wise standardise a regression spec's feature vector.

throttle

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

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

transformX

fun <R : Result> RegressionStatSpec<R>.transformX(expr: VectorExpr): RegressionStatSpec<R>

Wrap this regression spec so x is remapped by expr before the inner stat sees it.

transformY

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

Wrap this regression spec so y is remapped by expr before the inner stat sees it.

weightBy

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

Wrap this regression spec so every update's weight is multiplied by expr.eval(0, y, v).

withWeight

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

Wrap this regression spec so every update uses weight regardless of caller input.