Class

org.apache.spark.mllib.optimization

LeastSquaresGradient

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class LeastSquaresGradient extends Gradient

:: DeveloperApi :: Compute gradient and loss for a Least-squared loss function, as used in linear regression. This is correct for the averaged least squares loss function (mean squared error) L = 1/2n ||A weights-y||^2 See also the documentation for the precise formulation.

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@DeveloperApi()
Source
Gradient.scala
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Gradient, Serializable, Serializable, AnyRef, Any
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  1. LeastSquaresGradient
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Instance Constructors

  1. new LeastSquaresGradient()

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Value Members

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

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  2. final def ##(): Int

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. def compute(data: Vector, label: Double, weights: Vector, cumGradient: Vector): Double

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    Compute the gradient and loss given the features of a single data point, add the gradient to a provided vector to avoid creating new objects, and return loss.

    Compute the gradient and loss given the features of a single data point, add the gradient to a provided vector to avoid creating new objects, and return loss.

    data

    features for one data point

    label

    label for this data point

    weights

    weights/coefficients corresponding to features

    cumGradient

    the computed gradient will be added to this vector

    returns

    loss

    Definition Classes
    LeastSquaresGradientGradient
  7. def compute(data: Vector, label: Double, weights: Vector): (Vector, Double)

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    Compute the gradient and loss given the features of a single data point.

    Compute the gradient and loss given the features of a single data point.

    data

    features for one data point

    label

    label for this data point

    weights

    weights/coefficients corresponding to features

    returns

    (gradient: Vector, loss: Double)

    Definition Classes
    LeastSquaresGradientGradient
  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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

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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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Inherited from Gradient

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