KMeansPlusPlusEstimator

Instance Constructors

new KMeansPlusPlusEstimator(numMeans: Int, maxIterations: Int, stopTolerance: Double = 0.001, seed: Int = 0)

numMeans
maxIterations
stopTolerance
Tolerance used to decide when to terminate Lloyd's algorithm

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(X: DenseMatrix[Double]): KMeansModel
def fit(data: RDD[DenseVector[Double]]): KMeansModel

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.
returns
A new transformer

Definition Classes
KMeansPlusPlusEstimator → Estimator
final def getClass(): Class[_]

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
val maxIterations: Int
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numMeans: Int
val seed: Int
val stopTolerance: Double

Tolerance used to decide when to terminate Lloyd's algorithm
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[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

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

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

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

Definition Classes
Estimator

case class KMeansPlusPlusEstimator(numMeans: Int, maxIterations: Int, stopTolerance: Double = 0.001, seed: Int = 0) extends Estimator[DenseVector[Double], DenseVector[Double]] with Logging with Product with Serializable

Instance Constructors

new KMeansPlusPlusEstimator(numMeans: Int, maxIterations: Int, stopTolerance: Double = 0.001, seed: Int = 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

final def eq(arg0: AnyRef): Boolean

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

def finalize(): Unit

def fit(X: DenseMatrix[Double]): KMeansModel

def fit(data: RDD[DenseVector[Double]]): KMeansModel

final def getClass(): Class[_]

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

val maxIterations: Int

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numMeans: Int

val seed: Int

val stopTolerance: Double

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[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

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

Inherited from Product

Inherited from Equals

Inherited from Logging

Inherited from Estimator[DenseVector[Double], DenseVector[Double]]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

Ungrouped