AbstractGPRegressionModel

abstract def dataAsSeq(data: T): Seq[(I, Double)]

Convert from the underlying data structure to Seq[(I, Y)] where I is the index set of the GP and Y is the value/label type.

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def _blockSize: Int

def _current_state: Map[String, Double]

def _errorSigma: Int

def _hyper_parameters: List[String]

final def asInstanceOf[T0]: T0

var blockSize: Int

def blockSize_(b: Int): Unit

var caching: Boolean

def calculateEnergyPipe(h: Map[String, Double], options: Map[String, String]): DataPipe2[Seq[I], PartitionedVector, Double]

Returns a DataPipe2 which calculates the energy of data: T.

def calculateGradEnergyPipe(h: Map[String, Double]): DataPipe2[Seq[I], PartitionedVector, Map[String, Double]]

Returns a DataPipe which calculates the gradient of the energy, E(.) of data: T with respect to the model hyper-parameters.

def clone(): AnyRef

val covariance: LocalScalarKernel[I]

Underlying covariance function of the Gaussian Processes.

var current_state: Map[String, Double]

A Map which stores the current state of the system.

def data: T

def dataAsIndexSeq(data: T): Seq[I]

Convert from the underlying data structure to Seq[I] where I is the index set of the GP

def energy(h: Map[String, Double], options: Map[String, String]): Double

Calculates the energy of the configuration, in most global optimization algorithms we aim to find an approximate value of the hyper-parameters such that this function is minimized.

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def errorSigma_(s: Int): Unit

def finalize(): Unit

val g: T

The training data

final def getClass(): Class[_]

def getCrossKernelMatrix[U <: Seq[I]](test: U): PartitionedMatrix

def getTestKernelMatrix[U <: Seq[I]](test: U): PartitionedPSDMatrix

def getTrainKernelMatrix[U <: Seq[I]]: PartitionedPSDMatrix

def gradEnergy(h: Map[String, Double]): Map[String, Double]

Calculates the gradient energy of the configuration and subtracts this from the current value of h to yield a new hyper-parameter configuration.

def hashCode(): Int

var hyper_parameters: List[String]

Stores the names of the hyper-parameters

final def isInstanceOf[T0]: Boolean

var kernelMatrixCache: DenseMatrix[Double]

val mean: DataPipe[I, Double]

The GP is taken to be zero mean, or centered.

final def ne(arg0: AnyRef): Boolean

val noiseModel: LocalScalarKernel[I]

final def notify(): Unit

final def notifyAll(): Unit

val npoints: Int

var partitionedKernelMatrixCache: PartitionedPSDMatrix

def persist(state: Map[String, Double]): Unit

Cache the training kernel and noise matrices for fast access in future predictions.

def predict(point: I): Double

Predict the value of the target variable given a point.

def predictionWithErrorBars[U <: Seq[I]](testData: U, sigma: Int): Seq[(I, Double, Double, Double)]

Draw three predictions from the posterior predictive distribution

def predictiveDistribution[U <: Seq[I]](test: U): MultGaussianPRV

Calculates posterior predictive distribution for a particular set of test data points.

def setState(s: Map[String, Double]): AbstractGPRegressionModel.this.type

Set the model "state" which contains values of its hyper-parameters with respect to the covariance and noise kernels.

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

def test(testData: T): Seq[(I, Double, Double, Double, Double)]

Returns a prediction with error bars for a test set of indexes and labels.

def toString(): String

lazy val trainingData: Seq[I]

lazy val trainingDataLabels: PartitionedVector

def unpersist(): Unit

Forget the cached kernel & noise matrices.

final def wait(): Unit

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

final def wait(arg0: Long): Unit