org.apache.spark.mllib.optimization
Class HingeGradient

Object
  org.apache.spark.mllib.optimization.Gradient
      org.apache.spark.mllib.optimization.HingeGradient

All Implemented Interfaces:

java.io.Serializable

public class HingeGradient
extends Gradient

:: DeveloperApi :: Compute gradient and loss for a Hinge loss function, as used in SVM binary classification. See also the documentation for the precise formulation. NOTE: This assumes that the labels are {0,1}

See Also:

Serialized Form

Constructor Summary

HingeGradient()

Method Summary

scala.Tuple2<Vector,Object>	compute(Vector data, double label, Vector weights) Compute the gradient and loss given the features of a single data point.
double	compute(Vector data, double label, Vector weights, Vector cumGradient) 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.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

HingeGradient

public HingeGradient()

Method Detail

compute

public scala.Tuple2<Vector,Object> compute(Vector data,
                                           double label,
                                           Vector weights)

Description copied from class: Gradient

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

Overrides:

compute in class Gradient

Parameters:

data - features for one data point

label - label for this data point

weights - weights/coefficients corresponding to features

Returns:

(gradient: Vector, loss: Double)

compute

public double compute(Vector data,
                      double label,
                      Vector weights,
                      Vector cumGradient)

Description copied from class: Gradient

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.

Specified by:

compute in class Gradient

Parameters:

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