LogisticRegressionWithSGD

Instance Constructors

new LogisticRegressionWithSGD()

Construct a LogisticRegression object with default parameters

Value Members

final def !=(arg0: AnyRef): Boolean

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

Definition Classes
Any
final def ##(): Int

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

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

Definition Classes
Any
var addIntercept: Boolean

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

Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def createModel(weights: Vector, intercept: Double): LogisticRegressionModel

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

Attributes
protected
Definition Classes
LogisticRegressionWithSGD → GeneralizedLinearAlgorithm
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
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 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
val optimizer: GradientDescent

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

Definition Classes
LogisticRegressionWithSGD → GeneralizedLinearAlgorithm
def run(input: RDD[LabeledPoint], initialWeights: Vector): LogisticRegressionModel

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.

Definition Classes
GeneralizedLinearAlgorithm
def run(input: RDD[LabeledPoint]): LogisticRegressionModel

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.

Definition Classes
GeneralizedLinearAlgorithm
def setIntercept(addIntercept: Boolean): LogisticRegressionWithSGD.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.

Definition Classes
GeneralizedLinearAlgorithm
def setValidateData(validateData: Boolean): LogisticRegressionWithSGD.this.type

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

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
var validateData: Boolean

Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
val validators: List[(RDD[LabeledPoint]) ⇒ Boolean]

Attributes
protected
Definition Classes
LogisticRegressionWithSGD → GeneralizedLinearAlgorithm
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( ... )

class LogisticRegressionWithSGD extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable

Instance Constructors

new LogisticRegressionWithSGD()

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

var addIntercept: Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def createModel(weights: Vector, intercept: Double): LogisticRegressionModel

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

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 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

val optimizer: GradientDescent

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

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

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

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

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

def toString(): String

var validateData: Boolean

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

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from GeneralizedLinearAlgorithm[LogisticRegressionModel]

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped