org.apache.spark.mllib.optimization

HingeGradient

class HingeGradient extends Gradient

Compute gradient and loss for a Hinge loss function. NOTE: This assumes that the labels are {0,1}

Linear Supertypes
Gradient, Serializable, Serializable, AnyRef, Any
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  1. HingeGradient
  2. Gradient
  3. Serializable
  4. Serializable
  5. AnyRef
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Instance Constructors

  1. new HingeGradient()

Value Members

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

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

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    @throws()

  8. def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): (DoubleMatrix, Double)

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

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

    data

    - Feature values for one data point. Column matrix of size nx1 where n is the number of features.

    label

    - Label for this data item.

    weights

    - Column matrix containing weights for every feature.

    returns

    A tuple of 2 elements. The first element is a column matrix containing the computed gradient and the second element is the loss computed at this data point.

    Definition Classes
    HingeGradient → Gradient

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

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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  17. final def notifyAll(): Unit

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

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  19. def toString(): String

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Inherited from Gradient

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