GenContinuousMixtureModel

A multinomial mixture of component processes, each of which can output predictive distributions which have error bars around the mean/mode.

T: The training data type of each component
I: The input feature type accepted by each component
Y: The type of the output label
YDomain: The type of a collection of outputs, e.g. vector
YDomainVar: The type of the second moment (variance) returned by the predictive distribution of each component process
BaseDistr: The type of the predictive distribution of each process.
W1: The random variable type returned by the predictiveDistribution() method of each component.
BaseProcesses: The type of the stochastic process components

Linear Supertypes

StochasticProcessMixtureModel[I, Y, ContMixtureRVBars[YDomain, YDomainVar, BaseDistr]], ContinuousProcessModel[Seq[(I, Y)], I, Y, ContMixtureRVBars[YDomain, YDomainVar, BaseDistr]], StochasticProcessModel[Seq[(I, Y)], I, Y, ContMixtureRVBars[YDomain, YDomainVar, BaseDistr]], Model[Seq[(I, Y)], I, Y], AnyRef, Any

Known Subclasses

GaussianProcessMixture, MVTMixture, StudentTProcessMixture

Instance Constructors

new GenContinuousMixtureModel(component_processes: Seq[BaseProcesses], weights: DenseVector[Double])(implicit arg0: ClassTag[I])

component_processes
The stochastic processes which form the components of the mixture
weights
The probability weights assigned to each component.

Abstract Value Members

abstract def getVectorSpace(num_dim: Int): VectorSpace[YDomain, Double]

Attributes
protected
abstract def toStream(y: YDomain): Stream[Y]

Attributes
protected

Concrete Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def _errorSigma: Int

Definition Classes
ContinuousProcessModel
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val component_processes: Seq[BaseProcesses]

The stochastic processes which form the components of the mixture
def data: Seq[(I, Y)]

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

Definition Classes
StochasticProcessModel
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.
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.

Definition Classes
GenContinuousMixtureModel → StochasticProcessModel
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def errorSigma_(s: Int): Unit

Definition Classes
ContinuousProcessModel
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
val g: Seq[(I, Y)]

The training data
The training data

Attributes
protected
Definition Classes
GenContinuousMixtureModel → Model
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def predict(point: I): Y

Predict the value of the target variable given a point.
Predict the value of the target variable given a point.

Definition Classes
GenContinuousMixtureModel → Model
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.
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. (mean + sigma*stdDeviation)

Definition Classes
GenContinuousMixtureModel → ContinuousProcessModel
def predictiveDistribution[U <: Seq[I]](test: U): ContMixtureRVBars[YDomain, YDomainVar, BaseDistr]

Calculates posterior predictive distribution for a particular set of test data points.
Calculates posterior predictive distribution for a particular set of test data points.
test
A Sequence or Sequence like data structure storing the values of the input patters.

Definition Classes
GenContinuousMixtureModel → StochasticProcessModel
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
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.
Returns a prediction with error bars for a test set of indexes and labels. (Index, Actual Value, Prediction, Lower Bar, Higher Bar)

Definition Classes
ContinuousProcessModel
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
val weights: DenseVector[Double]

The probability weights assigned to each component.

Related Doc: package models

Instance Constructors

new GenContinuousMixtureModel(component_processes: Seq[BaseProcesses], weights: DenseVector[Double])(implicit arg0: ClassTag[I])

Abstract Value Members

abstract def getVectorSpace(num_dim: Int): VectorSpace[YDomain, Double]

abstract def toStream(y: YDomain): Stream[Y]

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def _errorSigma: Int

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val component_processes: Seq[BaseProcesses]

def data: Seq[(I, Y)]

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

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

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def errorSigma_(s: Int): Unit

def finalize(): Unit

val g: Seq[(I, Y)]

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

def predict(point: I): Y

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

def predictiveDistribution[U <: Seq[I]](test: U): ContMixtureRVBars[YDomain, YDomainVar, BaseDistr]

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

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

val weights: DenseVector[Double]

Inherited from StochasticProcessMixtureModel[I, Y, ContMixtureRVBars[YDomain, YDomainVar, BaseDistr]]

Inherited from ContinuousProcessModel[Seq[(I, Y)], I, Y, ContMixtureRVBars[YDomain, YDomainVar, BaseDistr]]

Inherited from StochasticProcessModel[Seq[(I, Y)], I, Y, ContMixtureRVBars[YDomain, YDomainVar, BaseDistr]]

Inherited from Model[Seq[(I, Y)], I, Y]

Inherited from AnyRef

Inherited from Any

Ungrouped