NaiveBayesModel

Instance Constructors

new NaiveBayesModel(labels: Array[Int], pi: Array[Double], theta: Array[Array[Double]])

labels
list of class labels, ranging from 0 to (C - 1) inclusive
pi
log of class priors, whose dimension is C, number of labels
theta
log of class conditional probabilities, whose dimension is C-by-D, where D is number of features

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 andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: RDD[T], labels: RDD[L]): PipelineWithFittedTransformer[T, DenseVector[Double], C]

Definition Classes
Pipeline
final def andThen[C](est: Estimator[DenseVector[Double], C], data: RDD[T]): PipelineWithFittedTransformer[T, DenseVector[Double], C]

Definition Classes
Pipeline
final def andThen[C](next: Pipeline[DenseVector[Double], C]): Pipeline[T, C]

Chains a pipeline onto the end of this one, producing a new pipeline.
Chains a pipeline onto the end of this one, producing a new pipeline.
next
the pipeline to chain

Definition Classes
Pipeline
def apply(in: T): DenseVector[Double]

Transforms a feature vector to a vector containing the log(posterior probabilities) of the different classes according to this naive bayes model.
Transforms a feature vector to a vector containing the log(posterior probabilities) of the different classes according to this naive bayes model.
in
The input feature vector
returns
Log-posterior probabilites of the classes for the input features

Definition Classes
NaiveBayesModel → Transformer → Pipeline
def apply(in: RDD[T]): RDD[DenseVector[Double]]

Apply this Transformer to an RDD of input items
Apply this Transformer to an RDD of input items
in
The bulk RDD input to pass into this transformer
returns
The bulk RDD output for the given input

Definition Classes
Transformer → Pipeline
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] )
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
Node
val labels: Array[Int]

list of class labels, ranging from 0 to (C - 1) inclusive
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val pi: Array[Double]

log of class priors, whose dimension is C, number of labels
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val theta: Array[Array[Double]]

log of class conditional probabilities, whose dimension is C-by-D, where D is number of features
final def toDOTString: String

returns
A graphviz dot representation of this pipeline

Definition Classes
Pipeline
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( ... )

class NaiveBayesModel[T <: Vector[Double]] extends Transformer[T, DenseVector[Double]]

Instance Constructors

new NaiveBayesModel(labels: Array[Int], pi: Array[Double], theta: Array[Array[Double]])

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 andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: RDD[T], labels: RDD[L]): PipelineWithFittedTransformer[T, DenseVector[Double], C]

final def andThen[C](est: Estimator[DenseVector[Double], C], data: RDD[T]): PipelineWithFittedTransformer[T, DenseVector[Double], C]

final def andThen[C](next: Pipeline[DenseVector[Double], C]): Pipeline[T, C]

def apply(in: T): DenseVector[Double]

def apply(in: RDD[T]): RDD[DenseVector[Double]]

final def asInstanceOf[T0]: T0

def clone(): AnyRef

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 label: String

val labels: Array[Int]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val pi: Array[Double]

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

val theta: Array[Array[Double]]

final def toDOTString: String

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Transformer[T, DenseVector[Double]]

Inherited from Pipeline[T, DenseVector[Double]]

Inherited from TransformerNode[DenseVector[Double]]

Inherited from Serializable

Inherited from Serializable

Inherited from Node

Inherited from AnyRef

Inherited from Any

Ungrouped