StochasticProcessMixtureModel

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

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.

abstract val g: Seq[(I, Y)]

The training data

abstract def predict(point: I): Y

Predict the value of the target variable given a point.

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

Draw three predictions from the posterior predictive distribution 1) Mean or MAP estimate Y 2) Y- : The lower error bar estimate (mean - sigma*stdDeviation) 3) Y+ : The upper error bar.

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

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

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def _errorSigma: Int

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def data: Seq[(I, Y)]

def dataAsIndexSeq(data: Seq[(I, Y)]): Seq[I]

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

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def errorSigma_(s: Int): Unit

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 synchronized[T0](arg0: ⇒ T0): T0

def test(testData: Seq[(I, Y)]): Seq[(I, Y, Y, Y, Y)]

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit