NaiveBayesEstimator

Instance Constructors

new NaiveBayesEstimator(numClasses: Int, lambda: Double = 1.0)(implicit arg0: ClassTag[T])

lambda
The lambda parameter to use for the naive bayes model

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( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
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(in: RDD[T], labels: RDD[Int]): NaiveBayesModel[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.
labels
The estimator's training labels
returns
A new transformer

Definition Classes
NaiveBayesEstimator → LabelEstimator
final def getClass(): Class[_]

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

Definition Classes
Any
def label: String

Definition Classes
Operator
val lambda: Double

The lambda parameter to use for the naive bayes model
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numClasses: Int
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
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( ... )
final def withData(data: PipelineDataset[T], labels: PipelineDataset[Int]): 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[Int]): 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[Int]): 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[Int]): 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

case class NaiveBayesEstimator[T <: Vector[Double]](numClasses: Int, lambda: Double = 1.0)(implicit evidence$1: ClassTag[T]) extends LabelEstimator[T, DenseVector[Double], Int] with Product with Serializable

Instance Constructors

new NaiveBayesEstimator(numClasses: Int, lambda: Double = 1.0)(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

final def eq(arg0: AnyRef): Boolean

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

def finalize(): Unit

def fit(in: RDD[T], labels: RDD[Int]): NaiveBayesModel[T]

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

def label: String

val lambda: Double

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numClasses: Int

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

final def wait(): Unit

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

final def wait(arg0: Long): Unit

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

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

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

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

Inherited from Product

Inherited from Equals

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

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

Ungrouped