Estimator

Base trait for Flink's pipeline operators.

An estimator can be fitted to input data. In order to do that the implementing class has to provide an implementation of a FitOperation with the correct input type. In order to make the FitOperation retrievable by the Scala compiler, the implementation should be placed in the companion object of the implementing class.

The pipeline mechanism has been inspired by scikit-learn

Self Type: Estimator[Self] with Self

Linear Supertypes

WithParameters, AnyRef, Any

Known Subclasses

ALS, ChainedPredictor, ChainedTransformer, KNN, MinMaxScaler, MultipleLinearRegression, PolynomialFeatures, Predictor, SVM, StandardScaler, StochasticOutlierSelection, Transformer

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( ... )
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 fit[Training](training: DataSet[Training], fitParameters: ParameterMap = ParameterMap.Empty)(implicit fitOperation: FitOperation[Self, Training]): Unit

Fits the estimator to the given input data.
Fits the estimator to the given input data. The fitting logic is contained in the FitOperation. The computed state will be stored in the implementing class.
Training
Type of the training data
training
Training data
fitParameters
Additional parameters for the FitOperation
fitOperation
FitOperation which encapsulates the algorithm logic
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
val parameters: ParameterMap

Definition Classes
WithParameters
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( ... )

Related Docs: object Estimator | package pipeline

trait Estimator[Self] extends WithParameters

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def fit[Training](training: DataSet[Training], fitParameters: ParameterMap = ParameterMap.Empty)(implicit fitOperation: FitOperation[Self, Training]): 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

val parameters: ParameterMap

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

Inherited from WithParameters

Inherited from AnyRef

Inherited from Any

Ungrouped