GPCommitteeRegression

abstract val optimizer: RegularizedOptimizer[DenseVector[Double], I, Double, D]

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val baseNetworks: List[AbstractGPRegressionModel[Seq[(I, Double)], I]]

def clearParameters(): Unit

def clone(): AnyRef

def data: D

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

var featureMap: (I) ⇒ I

The non linear feature mapping implicitly defined by the kernel applied, this is initialized to an identity map.

def finalize(): Unit

val g: D

The training data

final def getClass(): Class[_]

def hashCode(): Int

def initParams(): DenseVector[Double]

final def isInstanceOf[T0]: Boolean

def learn(): Unit

Learn the parameters of the model which are in a node of the graph.

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val num_points: Int

def parameters(): DenseVector[Double]

Get the value of the parameters of the model.

var params: DenseVector[Double]

val phi: (I) ⇒ DenseVector[Double]

def predict(point: I): Double

Predict the value of the target variable given a point.

def setBatchFraction(f: Double): GPCommitteeRegression.this.type

def setLearningRate(alpha: Double): GPCommitteeRegression.this.type

def setMaxIterations(i: Int): GPCommitteeRegression.this.type

def setRegParam(r: Double): GPCommitteeRegression.this.type

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

def toString(): String

def updateParameters(param: DenseVector[Double]): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit