Object

org.emmalanguage.lib.ml.optimization.loss

squared

Related Doc: package loss

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object squared extends Loss

Represents a loss function of the form

E(w) = (wx - y)**2

where w: Weights x: Instance features y: Instance label

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

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  2. final def ##(): Int

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

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  4. def apply[ID](w: DVector, x: LDPoint[ID, Double]): Double

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    Compute the squared loss Loss function.

    Compute the squared loss Loss function.

    w

    The weights that are used to evaluate the loss.

    x

    The instance that is evaluated.

    returns

    The loss as measured by the least squares solution.

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    squared → Loss

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )

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

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  11. def gradient[ID](w: DVector, x: LDPoint[ID, Double]): DVector

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    Compute the gradient of the squared loss Loss function.

    Compute the gradient of the squared loss Loss function.

    dE(w) = (wx - y)x
    w

    The weights for which the gradient is computed.

    x

    The instance for which the gradient is computed.

    returns

    The computed gradient.

    Definition Classes
    squared → Loss

  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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

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  18. def toString(): String

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  19. final def wait(): Unit

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

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

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Inherited from Loss

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