org.apache.spark.streamdm.classifiers.model

LinearModel

class LinearModel extends ClassificationModel with Serializable

A Model trait defines the needed operations on any learning Model. It provides methods for updating the model and for predicting the label of a given Instance

Linear Supertypes
ClassificationModel, Model, Serializable, Serializable, AnyRef, Any
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Inherited
  1. LinearModel
  2. ClassificationModel
  3. Model
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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Instance Constructors

  1. new LinearModel(lossFunction: Loss, initialModel: Instance, numberFeatures: Int)

Type Members

  1. type T = LinearModel

    Definition Classes
    LinearModel → Model

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  11. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  12. def gradient(instance: Example): Instance

  13. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  15. val loss: Loss

  16. val modelInstance: Instance

  17. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  18. final def notify(): Unit

    Definition Classes
    AnyRef

  19. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  20. val numFeatures: Int

  21. def predict(instance: Example): Double

    Definition Classes
    LinearModel → ClassificationModel

  22. def prob(instance: Example): Double

    Computes the probability for a given label class, given the current Model

    Computes the probability for a given label class, given the current Model

    instance

    the Instance which needs a class predicted

    returns

    the predicted probability

    Definition Classes
    LinearModel → ClassificationModel

  23. def regularize(regularizer: Regularizer): Instance

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

    Definition Classes
    AnyRef

  25. def toString(): String

    Definition Classes
    LinearModel → AnyRef → Any

  26. def update(change: Example): LinearModel

    Update the model, depending on the Instance given for training.

    Update the model, depending on the Instance given for training.

    change

    the example based on which the Model is updated

    returns

    the updated Model

    Definition Classes
    LinearModel → Model

  27. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  29. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from ClassificationModel

Inherited from Model

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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