GeneralizedLinearAlgorithm

Instance Constructors

new GeneralizedLinearAlgorithm()

Abstract Value Members

abstract def createModel(weights: Vector, intercept: Double): M

Create a model given the weights and intercept
Create a model given the weights and intercept

Attributes
protected
abstract def optimizer: Optimizer

The optimizer to solve the problem.
The optimizer to solve the problem.

Annotations
@Since( "0.8.0" )

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
var addIntercept: Boolean

Whether to add intercept (default: false).
Whether to add intercept (default: false).

Attributes
protected
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
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] )
def generateInitialWeights(input: RDD[LabeledPoint]): Vector

Generate the initial weights when the user does not supply them
Generate the initial weights when the user does not supply them

Attributes
protected
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getNumFeatures: Int

The dimension of training features.
The dimension of training features.

Annotations
@Since( "1.4.0" )
def hashCode(): Int

Definition Classes
AnyRef → Any
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
def isAddIntercept: Boolean

Get if the algorithm uses addIntercept
Get if the algorithm uses addIntercept

Annotations
@Since( "1.4.0" )
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
var numFeatures: Int

The dimension of training features.
The dimension of training features.

Attributes
protected
var numOfLinearPredictor: Int

In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept.
In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept. However, for multinomial logistic regression, with K possible outcomes, we are training K-1 independent binary logistic regression models which requires K-1 sets of linear predictor.
As a result, the workaround here is if more than two sets of linear predictors are needed, we construct bigger weights vector which can hold both weights and intercepts. If the intercepts are added, the dimension of weights will be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension of weights will be (numOfLinearPredictor) * numFeatures.
Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero.

Attributes
protected
def run(input: RDD[LabeledPoint], initialWeights: Vector): M

Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.

Annotations
@Since( "1.0.0" )
def run(input: RDD[LabeledPoint]): M

Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.

Annotations
@Since( "0.8.0" )
def setIntercept(addIntercept: Boolean): GeneralizedLinearAlgorithm.this.type

Set if the algorithm should add an intercept.
Set if the algorithm should add an intercept. Default false. We set the default to false because adding the intercept will cause memory allocation.

Annotations
@Since( "0.8.0" )
def setValidateData(validateData: Boolean): GeneralizedLinearAlgorithm.this.type

Set if the algorithm should validate data before training.
Set if the algorithm should validate data before training. Default true.

Annotations
@Since( "0.8.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
var validateData: Boolean

Attributes
protected
val validators: Seq[(RDD[LabeledPoint]) ⇒ Boolean]

Attributes
protected
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 regression

abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] extends Logging with Serializable

Instance Constructors

new GeneralizedLinearAlgorithm()

Abstract Value Members

abstract def createModel(weights: Vector, intercept: Double): M

abstract def optimizer: Optimizer

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

var addIntercept: Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def generateInitialWeights(input: RDD[LabeledPoint]): Vector

final def getClass(): Class[_]

def getNumFeatures: Int

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

def isAddIntercept: Boolean

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

var numFeatures: Int

var numOfLinearPredictor: Int

def run(input: RDD[LabeledPoint], initialWeights: Vector): M

def run(input: RDD[LabeledPoint]): M

def setIntercept(addIntercept: Boolean): GeneralizedLinearAlgorithm.this.type

def setValidateData(validateData: Boolean): GeneralizedLinearAlgorithm.this.type

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

def toString(): String

var validateData: Boolean

val validators: Seq[(RDD[LabeledPoint]) ⇒ Boolean]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped