Package org.tribuo.regression.sgd.objectives

Class AbsoluteLoss

java.lang.Object

org.tribuo.regression.sgd.objectives.AbsoluteLoss

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>, SGDObjective<DenseVector>, RegressionObjective

public class AbsoluteLoss
extends Object
implements RegressionObjective

Absolute loss (i.e., l1).

Constructor Summary

Constructors

Constructor	Description
AbsoluteLoss()	Constructs an absolute loss.

Method Summary

Modifier and Type	Method	Description
com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance	getProvenance()
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	loss(DenseVector truth, SGDVector prediction)	Deprecated.
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	lossAndGradient(DenseVector truth, SGDVector prediction)	Scores a prediction, returning the loss and a vector of per output dimension gradients.
String	toString()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Configurable

postConfig

Constructor Details

AbsoluteLoss

public AbsoluteLoss()

Constructs an absolute loss.

Method Details

loss

@Deprecated
public com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector> loss(DenseVector truth,
 SGDVector prediction)

Deprecated.

Description copied from interface: RegressionObjective

Scores a prediction, returning the loss.

Specified by:

loss in interface RegressionObjective

Parameters:

truth - The true regression value.

prediction - The predicted regression value.

Returns:

A pair with the loss and gradient.

lossAndGradient

public com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector> lossAndGradient(DenseVector truth,
 SGDVector prediction)

Description copied from interface: SGDObjective

Scores a prediction, returning the loss and a vector of per output dimension gradients.

Specified by:

lossAndGradient in interface RegressionObjective

Specified by:

lossAndGradient in interface SGDObjective<DenseVector>

Parameters:

truth - The true output.

prediction - The prediction for each dimension.

Returns:

The score and per dimension gradient.

toString

public String toString()

Overrides:

toString in class Object

getProvenance

public com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance getProvenance()

Specified by:

getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>