Package de.jungblut.math.loss

Class SquaredLoss

java.lang.Object

de.jungblut.math.loss.SquaredLoss

All Implemented Interfaces:

LossFunction

public final class SquaredLoss
extends java.lang.Object
implements LossFunction

Squared mean error function for regression problems and LinearActivationFunction.

Author:

thomas.jungblut

Constructor Summary

Constructors

Constructor	Description
SquaredLoss()

Method Summary

Modifier and Type	Method	Description
de.jungblut.math.DoubleVector	calculateGradient(de.jungblut.math.DoubleVector feature, de.jungblut.math.DoubleVector y, de.jungblut.math.DoubleVector hypothesis)	Calculate the gradient with the given parameters.
double	calculateLoss(de.jungblut.math.DoubleMatrix y, de.jungblut.math.DoubleMatrix hypothesis)	Calculate the error with the given parameters.
double	calculateLoss(de.jungblut.math.DoubleVector y, de.jungblut.math.DoubleVector hypothesis)	Calculate the error with the given parameters.

Methods inherited from class java.lang.Object

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

Constructor Detail

SquaredLoss

public SquaredLoss()

Method Detail

calculateLoss

public double calculateLoss(de.jungblut.math.DoubleMatrix y,
                            de.jungblut.math.DoubleMatrix hypothesis)

Description copied from interface: LossFunction

Calculate the error with the given parameters.

Specified by:

calculateLoss in interface LossFunction

Parameters:

y - the real outcome as a matrix- rows contain the examples, columns the examples' output.

hypothesis - the hypothesis as a matrix- rows contain the examples, columns the predicted output.

Returns:

a positive value that denotes the error between the two matrices.

calculateLoss

public double calculateLoss(de.jungblut.math.DoubleVector y,
                            de.jungblut.math.DoubleVector hypothesis)

Description copied from interface: LossFunction

Calculate the error with the given parameters.

Specified by:

calculateLoss in interface LossFunction

Parameters:

y - the real outcome as a vector single example.

hypothesis - the hypothesis as a vector single example.

Returns:

a positive value that denotes the error between the two vectors.

calculateGradient

public de.jungblut.math.DoubleVector calculateGradient(de.jungblut.math.DoubleVector feature,
                                                       de.jungblut.math.DoubleVector y,
                                                       de.jungblut.math.DoubleVector hypothesis)

Description copied from interface: LossFunction

Calculate the gradient with the given parameters.

Specified by:

calculateGradient in interface LossFunction

y - the real outcome as a vector single example.

hypothesis - the hypothesis as a vector single example.

Returns:

a vector that denotes the gradient given the hypothesis and real outcome.