RaRParamsAdaptive

Instance Constructors

new RaRParamsAdaptive(k: Int, m: Int, beta: Double, min: Int, parallelismFactor: Int = 1)

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
val beta: Double
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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

Definition Classes
Any
val k: Int

Maximum size of the subset to select in each random trial.
Maximum size of the subset to select in each random trial. This inherently defines the maximal size of the cluster that can be detected. Because the algorithm always only evaluates a subset of the features, it will never find correlated clusters of a size bigger than k. Nevertheless, setting k to high leads to a low specificity of the contrast measure.

Definition Classes
RaRParamsAdaptive → RaRParams
lazy val logger: Logger

Attributes
protected
Definition Classes
LazyLogging
val m: Int
val min: Int
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def numberOfMonteCarlos(numFeatures: Int): Int

Defines how many random trials are used to fill the tables.
Defines how many random trials are used to fill the tables. The more tries we allow the more accurate the results will be and the smaller correlations we will find. Can be static or dependent on the number of features.
numFeatures
number of features in the dataset

Definition Classes
RaRParamsAdaptive → RaRParams
val parallelismFactor: Int

Defines how many operations are allowed to be performed in parallel
Defines how many operations are allowed to be performed in parallel

Definition Classes
RaRParamsAdaptive → RaRParams
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( ... )

Related Doc: package RaRSearch

case class RaRParamsAdaptive(k: Int, m: Int, beta: Double, min: Int, parallelismFactor: Int = 1) extends RaRParams with LazyLogging with Product with Serializable

Instance Constructors

new RaRParamsAdaptive(k: Int, m: Int, beta: Double, min: Int, parallelismFactor: Int = 1)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val beta: Double

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

val k: Int

lazy val logger: Logger

val m: Int

val min: Int

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def numberOfMonteCarlos(numFeatures: Int): Int

val parallelismFactor: 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

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from LazyLogging

Inherited from RaRParams

Inherited from AnyRef

Inherited from Any

Ungrouped