Packages

object BoltzmannSplitter extends Serializable

Linear Supertypes
Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. BoltzmannSplitter
  2. Serializable
  3. Serializable
  4. AnyRef
  5. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Type Members

  1. case class SplitterResult(split: Split, variance: Double, rebasedScore: Double, base: Double) extends Product with Serializable

    Container for function returns, like a decorated tuple

    Container for function returns, like a decorated tuple

    The true score (proportional to draw probability) is rebasedScore * Math.exp(base). This decomposition is such that rebasedScore should always be >= 1.0.

    Attributes
    protected

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. def getBestCategoricalSplit(data: Seq[(Vector[AnyVal], Double, Double)], calculator: VarianceCalculator, index: Int, minCount: Int, beta: Double): Option[SplitterResult]

    Find the best categorical splitter.

    Find the best categorical splitter.

    data

    to split

    calculator

    that will efficiently compute the impurity (variance in this case)

    index

    of the feature to split on

    minCount

    minimum number of training instances to leave in each of the children nodes

    beta

    the inverse temperature (1.0 / (temperature * initial variance)) to scale the variances by

    returns

    the best split of this feature, along with its score, base, and result variance

  10. def getBestRealSplit(data: Seq[(Vector[AnyVal], Double, Double)], calculator: VarianceCalculator, index: Int, minCount: Int, beta: Double): Option[SplitterResult]

    Find the best split on a continuous variable

    Find the best split on a continuous variable

    data

    to split

    calculator

    that will efficiently compute the impurity (variance in this case)

    index

    of the feature to split on

    minCount

    minimum number of training instances to leave in each of the children nodes

    beta

    the inverse temperature (1.0 / (temperature * initial variance)) to scale the variances by

    returns

    the best split of this feature, along with its score, base, and result variance

  11. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  12. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  13. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  14. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  16. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  17. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  18. def toString(): String
    Definition Classes
    AnyRef → Any
  19. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  20. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  21. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped