LinearRegressor

abstract def copy(model: A): A

A function that creates an identical regressor.

abstract def copy(model: A, w: RealVector): A

A function that creates a new linear model with parameters w.

abstract def lossGradStateless(model: A, w: RealVector, x: Features, y: Target): RealVector

A stateless function that calculates the gradient of the loss function wrt.

abstract def lossStateless(model: A, w: RealVector, x: Features, y: Target): Double

A stateless function that calculates the value of the loss function.

abstract def predictStateless(model: A, w: RealVector, x: Features): Target

A stateless function that predicts a target variable.

abstract def targetVariableAppropriate(y: Target): Boolean

A function that checks whether the target variable contains valid data.

abstract def w(model: A): Option[RealVector]

Parameters (weights) of a linear model, i.e.

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def fit(model: A, x: Features, y: Target): A

def fitSafe(model: A, x: Features, y: Target): A

A function that is guaranteed to receive an appropriate target variable when called.

final def getClass(): Class[_]

def hashCode(): Int

def isFitted(model: A): Boolean

final def isInstanceOf[T0]: Boolean

def maximumLikelihood(model: A, x: Features, y: Target, init: RealVector): RealVector

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def predict(model: A, x: Features): Target

def predictSafe(model: A, x: Features): Target

def save(model: A, filePath: String): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

def xWithBiasTerm(x: Features): Features