ParameterizedLearner

Skeleton of Parameterized Model

G: The type of the underlying data.
T: The type of the parameters
Q: A Vector/Matrix representing the features of a point
R: The type of the output of the predictive model i.e. A Real Number or a Vector of outputs.
S: The type of the edge containing the features and label.

Linear Supertypes

Model[G, Q, R], AnyRef, Any

Abstract Value Members

abstract val g: G

The training data
The training data

Attributes
protected
Definition Classes
Model
abstract def initParams(): T
abstract def learn(): Unit

Learn the parameters of the model.
abstract val optimizer: RegularizedOptimizer[T, Q, R, S]

Attributes
protected
abstract val params: T

Attributes
protected
abstract def predict(point: Q): R

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
def clone(): AnyRef

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

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] )
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 parameters(): T

Get the value of the parameters of the model.
def setBatchFraction(f: Double): ParameterizedLearner.this.type
def setLearningRate(alpha: Double): ParameterizedLearner.this.type
def setMaxIterations(i: Int): ParameterizedLearner.this.type
def setRegParam(r: Double): ParameterizedLearner.this.type
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def updateParameters(param: T): Unit
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 models

trait ParameterizedLearner[G, T, Q, R, S] extends Model[G, Q, R]

Abstract Value Members

abstract val g: G

abstract def initParams(): T

abstract def learn(): Unit

abstract val optimizer: RegularizedOptimizer[T, Q, R, S]

abstract val params: T

abstract def predict(point: Q): R

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def data: G

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

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

def parameters(): T

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

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

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

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

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

def toString(): String

def updateParameters(param: T): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Model[G, Q, R]

Inherited from AnyRef

Inherited from Any

Ungrouped