MetaModel

Defines the basic skeleton of a "meta-model" or a model of models.

A set of base models are trained on sub-sampled versions of the training data set and finally a predictor of the form.

y(x) = f(y1(x), y2(x), ..., yb(x))

Where f is some combination function and b is the number of base models used.

D: The type of the data structure containing the training data set.
D1: The type of data structure containing the data of the base models.
BaseModel: The type of model used as base model for the meta model. example: FeedForwardNetwork, GPRegression, etc
Pipe: A sub-type of ModelPipe which yields a BaseModel with D1 as the base data structure given a data structure of type D

Linear Supertypes

Model[D, DenseVector[Double], Double], AnyRef, Any

Known Subclasses

CommitteeModel, LSSVMCommittee

Instance Constructors

new MetaModel(num: Long, data: D, networks: Pipe*)

num
The number of training data points.
data
The actual training data
networks
A sequence of Pipe objects yielding BaseModel

Abstract Value Members

abstract def predict(point: DenseVector[Double]): Double

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

Definition Classes
Model

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
final def asInstanceOf[T0]: T0

Definition Classes
Any
val baseNetworks: List[BaseModel]
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def data: D

Definition Classes
Model
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def finalize(): Unit

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

The training data
The training data

Attributes
protected
Definition Classes
MetaModel → 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
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
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( ... )

Related Doc: package ensemble

abstract class MetaModel[D, D1, BaseModel <: Model[D1, DenseVector[Double], Double], Pipe <: ModelPipe[D, D1, DenseVector[Double], Double, BaseModel]] extends Model[D, DenseVector[Double], Double]

Instance Constructors

new MetaModel(num: Long, data: D, networks: Pipe*)

Abstract Value Members

abstract def predict(point: DenseVector[Double]): Double

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val baseNetworks: List[BaseModel]

def clone(): AnyRef

def data: D

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

val g: D

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 toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Model[D, DenseVector[Double], Double]

Inherited from AnyRef

Inherited from Any

Ungrouped