org.apache.spark.mllib.optimization

Gradient

abstract class Gradient extends Serializable

:: DeveloperApi :: Class used to compute the gradient for a loss function, given a single data point.

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@DeveloperApi()
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HingeGradient, LeastSquaresGradient, LogisticGradient
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  1. new Gradient()

Abstract Value Members

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

    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

  2. abstract def compute(data: Vector, label: Double, weights: Vector): (Vector, Double)

    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)

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