public interface LossFunction
| Modifier and Type | Method and 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.
|
double calculateLoss(de.jungblut.math.DoubleMatrix y,
de.jungblut.math.DoubleMatrix hypothesis)
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.double calculateLoss(de.jungblut.math.DoubleVector y,
de.jungblut.math.DoubleVector hypothesis)
y - the real outcome as a vector single example.hypothesis - the hypothesis as a vector single example.de.jungblut.math.DoubleVector calculateGradient(de.jungblut.math.DoubleVector feature,
de.jungblut.math.DoubleVector y,
de.jungblut.math.DoubleVector hypothesis)
y - the real outcome as a vector single example.hypothesis - the hypothesis as a vector single example.Copyright © 2016. All rights reserved.