Package org.apache.spark.mllib.optimization

Class LeastSquaresGradient

Object

org.apache.spark.mllib.optimization.Gradient

org.apache.spark.mllib.optimization.LeastSquaresGradient

All Implemented Interfaces:

Serializable, scala.Serializable

public class LeastSquaresGradient
extends Gradient

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.

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
LeastSquaresGradient()

Method Summary

Modifier and Type	Method	Description
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 java.lang.Object

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

Constructor Details

LeastSquaresGradient

public LeastSquaresGradient()

Method Details

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