kumulant/com.eignex.kumulant.schema/StochasticRegression

StochasticRegression

@Serializable

@SerialName(value = "StochasticRegression")

data class StochasticRegression(val featureSize: Int, val optimizer: OptimizerSpec = Sgd(), val biasOptimizer: OptimizerSpec = optimizer, val penalty: Penalty = Penalty.None, val link: Link = Link.Identity) : RegressionStatSpec<StochasticRegressionResult> (source)

Spec for StochasticRegressionStat: online GLM with a configurable optimizer.

Constructors

StochasticRegression

constructor(featureSize: Int, optimizer: OptimizerSpec = Sgd(), biasOptimizer: OptimizerSpec = optimizer, penalty: Penalty = Penalty.None, link: Link = Link.Identity)

Properties

biasOptimizer

val biasOptimizer: OptimizerSpec

Update rule for the bias scalar; defaults to optimizer.

featureSize

val featureSize: Int

Number of input features.

link

val link: Link

GLM link function; Link.Identity gives plain OLS.

optimizer

val optimizer: OptimizerSpec

Per-coordinate update rule for the weight vector.

penalty

val penalty: Penalty

Gradient-step regulariser. Requires Sgd optimizers.

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.