org.apache.spark.streamdm.classifiers.model

PerceptronLoss

class PerceptronLoss extends SquaredLoss with Serializable

Implementation of the perceptron loss function. Essentially, the perceptron is using the squared loss function except for the "gradient".

Linear Supertypes
SquaredLoss, Loss, Serializable, Serializable, AnyRef, Any
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  1. PerceptronLoss
  2. SquaredLoss
  3. Loss
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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Instance Constructors

  1. new PerceptronLoss()

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(label: Double, dot: Double): Double

    Computes the value of the perceptron update function

    Computes the value of the perceptron update function

    dot

    the dot product of the linear model and the instance

    returns

    the update value

    Definition Classes
    PerceptronLoss → SquaredLoss → Loss

  13. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  15. def loss(label: Double, dot: Double): Double

    Computes the value of the loss function

    Computes the value of the loss function

    dot

    the dot product of the linear model and the instance

    returns

    the loss value

    Definition Classes
    SquaredLoss → Loss

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

    Definition Classes
    AnyRef

  17. final def notify(): Unit

    Definition Classes
    AnyRef

  18. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  19. def predict(dot: Double): Double

    Computes the binary prediction based on a dot prodcut

    Computes the binary prediction based on a dot prodcut

    dot

    the dot product of the linear model and the instance

    returns

    the predicted binary class

    Definition Classes
    SquaredLoss → Loss

  20. def prob(dot: Double): Double

    Computes the probability of a binary prediction based on a dot product

    Computes the probability of a binary prediction based on a dot product

    dot

    the dot product of the linear model and the instance

    returns

    the predicted probability

    Definition Classes
    Loss

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

    Definition Classes
    AnyRef

  22. def toString(): String

    Definition Classes
    AnyRef → Any

  23. final def wait(): Unit

    Definition Classes
    AnyRef
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    @throws( ... )

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

    Definition Classes
    AnyRef
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    @throws( ... )

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

    Definition Classes
    AnyRef
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    @throws( ... )

Inherited from SquaredLoss

Inherited from Loss

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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