LeastSquaresEstimator

Instance Constructors

new LeastSquaresEstimator(lambda: Double = 0, numMachines: Option[Int] = scala.None, cpuWeight: Double = 3.8E-4, memWeight: Double = 0.29, networkWeight: Double = 1.32)(implicit arg0: ClassTag[T])

lambda
The L2 regularization parameter to use, defaults to 0
numMachines
cpuWeight
memWeight
networkWeight

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 default: LabelEstimator[T, DenseVector[Double], DenseVector[Double]] with WeightedNode

Definition Classes
LeastSquaresEstimator → OptimizableLabelEstimator
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]]): Transformer[T, DenseVector[Double]]

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
OptimizableLabelEstimator → LabelEstimator
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

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

Definition Classes
Any
def label: String

Definition Classes
Operator
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
def optimize(sample: RDD[T], sampleLabels: RDD[DenseVector[Double]], numPerPartition: Map[Int, Int]): LabelEstimator[T, DenseVector[Double], DenseVector[Double]]

Definition Classes
LeastSquaresEstimator → OptimizableLabelEstimator
val options: Seq[(CostModel, LabelEstimator[T, DenseVector[Double], DenseVector[Double]])]
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
LeastSquaresEstimator → 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 LeastSquaresEstimator[T <: Vector[Double]] extends OptimizableLabelEstimator[T, DenseVector[Double], DenseVector[Double]] with WeightedNode with Logging

Instance Constructors

new LeastSquaresEstimator(lambda: Double = 0, numMachines: Option[Int] = scala.None, cpuWeight: Double = 3.8E-4, memWeight: Double = 0.29, networkWeight: Double = 1.32)(implicit arg0: ClassTag[T])

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 default: LabelEstimator[T, DenseVector[Double], DenseVector[Double]] with WeightedNode

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]]): Transformer[T, DenseVector[Double]]

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def label: String

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

def optimize(sample: RDD[T], sampleLabels: RDD[DenseVector[Double]], numPerPartition: Map[Int, Int]): LabelEstimator[T, DenseVector[Double], DenseVector[Double]]

val options: Seq[(CostModel, LabelEstimator[T, DenseVector[Double], DenseVector[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 Logging

Inherited from WeightedNode

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

Inherited from Optimizable

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