OptimizingBaseLinearTrainer

abstract def maxIterations: Int

abstract def miniBatch: Int

abstract def nThreads: Int

abstract def newModel(featureSize: Int, labelSize: Int): C

abstract def objective: OptimizableObjective[Prediction, Output]

abstract def optimizer: GradientOptimizer

implicit abstract def random: Random

abstract def useOnlineTrainer: Boolean

abstract def useParallelTrainer: Boolean

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final def baseTrain(classifier: C, labels: Seq[Output], features: Seq[Input], weights: Seq[Double], evaluate: (C) ⇒ Unit): Unit

Estimate the parameters of a classifier that has already been created.

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def simpleTrain(labelSize: Int, featureSize: Int, labels: Seq[Output], features: Seq[Input], weights: Seq[Double], evaluate: (C) ⇒ Unit): C

Create a Classifier and estimate its parameters.

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