LinearModel

Represents skeleton of a Linear Model.

T: The underlying type of the data structure ex. Gremlin, Neo4j, Spark RDD etc
P: A Vector/Matrix of Doubles
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 data containing the features and label.

Linear Supertypes

ParameterizedLearner[T, P, Q, R, S], Model[T, Q, R], AnyRef, Any

Known Subclasses

AbstractDualLSSVM, CommitteeModel, CommitteeNetwork, DLSSVM, GPCommitteeRegression, GeneralizedLeastSquaresModel, GeneralizedLinearModel, GenericGLM, KernelSparkModel, KernelizedModel, LSSVMCommittee, LSSVMSparkModel, LogisticGLM, ProbitGLM, RegularizedGLM, SparkGLM, SparkLogisticGLM, SparkProbitGLM, SubsampledDualLSSVM

Abstract Value Members

abstract def clearParameters(): Unit
abstract val g: T

The training data
The training data

Attributes
protected
Definition Classes
Model
abstract def initParams(): P

Definition Classes
ParameterizedLearner
abstract def learn(): Unit

Learn the parameters of the model.
Learn the parameters of the model.

Definition Classes
ParameterizedLearner
abstract val optimizer: RegularizedOptimizer[P, Q, R, S]

Attributes
protected
Definition Classes
ParameterizedLearner
abstract val params: P

Attributes
protected
Definition Classes
ParameterizedLearner
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: T

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

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

Definition Classes
AnyRef → Any
var featureMap: (Q) ⇒ Q

The non linear feature mapping implicitly defined by the kernel applied, this is initialized to an identity map.
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(): P

Get the value of the parameters of the model.
Get the value of the parameters of the model.

Definition Classes
ParameterizedLearner
def setBatchFraction(f: Double): LinearModel.this.type

Definition Classes
ParameterizedLearner
def setLearningRate(alpha: Double): LinearModel.this.type

Definition Classes
ParameterizedLearner
def setMaxIterations(i: Int): LinearModel.this.type

Definition Classes
ParameterizedLearner
def setRegParam(r: Double): LinearModel.this.type

Definition Classes
ParameterizedLearner
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

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

Definition Classes
ParameterizedLearner
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 LinearModel[T, P, Q, R, S] extends ParameterizedLearner[T, P, Q, R, S]

Abstract Value Members

abstract def clearParameters(): Unit

abstract val g: T

abstract def initParams(): P

abstract def learn(): Unit

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

abstract val params: P

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: T

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

var featureMap: (Q) ⇒ Q

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(): P

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

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

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

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

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

def toString(): String

def updateParameters(param: P): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from ParameterizedLearner[T, P, Q, R, S]

Inherited from Model[T, Q, R]

Inherited from AnyRef

Inherited from Any

Ungrouped