DenseLBFGSwithL2

Instance Constructors

new DenseLBFGSwithL2(gradient: DenseGradient, fitIntercept: Boolean = true, numCorrections: Int = 10, convergenceTol: Double = 1.0E-4, numIterations: Int = 100, regParam: Double = 0.0)

gradient
Gradient function to be used.
fitIntercept
Whether to fit the intercepts or not.
numCorrections
3 < numCorrections < 10 is recommended.
convergenceTol
convergence tolerance for L-BFGS
numIterations
max number of iterations to run
regParam
L2 regularization

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
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val convergenceTol: Double

convergence tolerance for L-BFGS
def cost(n: Long, d: Int, k: Int, sparsity: Double, numMachines: Int, cpuWeight: Double, memWeight: Double, networkWeight: Double): Double

Definition Classes
DenseLBFGSwithL2 → CostModel
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def execute(deps: Seq[Expression]): TransformerExpression

Definition Classes
EstimatorOperator → Operator
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fit(data: RDD[T], labels: RDD[DenseVector[Double]]): LinearMapper[T]

The type-safe method that ML developers need to implement when writing new Estimators.
The type-safe method that ML developers need to implement when writing new Estimators.
data
The estimator's training data.
labels
The estimator's training labels
returns
A new transformer

Definition Classes
DenseLBFGSwithL2 → LabelEstimator
val fitIntercept: Boolean

Whether to fit the intercepts or not.
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
val gradient: DenseGradient

Gradient function to be used.
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def label: String

Definition Classes
Operator
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numCorrections: Int

3 < numCorrections < 10 is recommended.
val numIterations: Int

max number of iterations to run
val regParam: Double

L2 regularization
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( ... )
val weight: Int

Definition Classes
DenseLBFGSwithL2 → WeightedNode
final def withData(data: PipelineDataset[T], labels: PipelineDataset[DenseVector[Double]]): Pipeline[T, DenseVector[Double]]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
data
The training data
labels
The training labels
returns
A pipeline that fits this label estimator and applies the result to inputs.

Definition Classes
LabelEstimator
final def withData(data: RDD[T], labels: RDD[DenseVector[Double]]): Pipeline[T, DenseVector[Double]]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
data
The training data
labels
The training labels
returns
A pipeline that fits this label estimator and applies the result to inputs.

Definition Classes
LabelEstimator
final def withData(data: PipelineDataset[T], labels: RDD[DenseVector[Double]]): Pipeline[T, DenseVector[Double]]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
data
The training data
labels
The training labels
returns
A pipeline that fits this label estimator and applies the result to inputs.

Definition Classes
LabelEstimator
final def withData(data: RDD[T], labels: PipelineDataset[DenseVector[Double]]): Pipeline[T, DenseVector[Double]]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.
data
The training data
labels
The training labels
returns
A pipeline that fits this label estimator and applies the result to inputs.

Definition Classes
LabelEstimator

class DenseLBFGSwithL2[T <: Vector[Double]] extends LabelEstimator[T, DenseVector[Double], DenseVector[Double]] with WeightedNode with CostModel

Instance Constructors

new DenseLBFGSwithL2(gradient: DenseGradient, fitIntercept: Boolean = true, numCorrections: Int = 10, convergenceTol: Double = 1.0E-4, numIterations: Int = 100, regParam: Double = 0.0)

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

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val convergenceTol: Double

def cost(n: Long, d: Int, k: Int, sparsity: Double, numMachines: Int, cpuWeight: Double, memWeight: Double, networkWeight: Double): Double

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def execute(deps: Seq[Expression]): TransformerExpression

def finalize(): Unit

def fit(data: RDD[T], labels: RDD[DenseVector[Double]]): LinearMapper[T]

val fitIntercept: Boolean

final def getClass(): Class[_]

val gradient: DenseGradient

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def label: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numCorrections: Int

val numIterations: Int

val regParam: Double

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

val weight: Int

final def withData(data: PipelineDataset[T], labels: PipelineDataset[DenseVector[Double]]): Pipeline[T, DenseVector[Double]]

final def withData(data: RDD[T], labels: RDD[DenseVector[Double]]): Pipeline[T, DenseVector[Double]]

final def withData(data: PipelineDataset[T], labels: RDD[DenseVector[Double]]): Pipeline[T, DenseVector[Double]]

final def withData(data: RDD[T], labels: PipelineDataset[DenseVector[Double]]): Pipeline[T, DenseVector[Double]]

Inherited from CostModel

Inherited from WeightedNode

Inherited from LabelEstimator[T, DenseVector[Double], DenseVector[Double]]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

Ungrouped