Module io.jenetics.prog
Package io.jenetics.prog.regression

Interface LossFunction<T>

Functional Interface:
This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
@FunctionalInterface public interface LossFunction<T>
This function evaluates how well an evolved program tree fits the given sample data set. If the predictions are totally off, the loss function will output a higher value. If they're pretty good, it’ll output a lower number. It is the essential part of the overall Error function.
final Error<Double> error = Error.of(LossFunction::mse);
Since:
5.0
Version:
5.0
See Also:

  • Method Summary

    Modifier and Type
    Method
    Description
    double
    apply(T[] calculated, T[] expected)
    Calculates the error between the expected function values and the values calculated by the actual ProgramGene.
    static double
    mae(Double[] calculated, Double[] expected)
    Mean absolute error is measured as the average of sum of absolute differences between predictions and actual observations.
    static double
    mse(Double[] calculated, Double[] expected)
    Mean square error is measured as the average of squared difference between predictions and actual observations.
    static double
    rmse(Double[] calculated, Double[] expected)
    Root-mean-square error is measured as the average of squared difference between predictions and actual observations.

  • Method Details

    • apply

      double apply(T[] calculated, T[] expected)
      Calculates the error between the expected function values and the values calculated by the actual ProgramGene.
      Parameters:
      calculated - the currently calculated function value
      expected - the expected function values
      Returns:
      the error value
      Throws:
      IllegalArgumentException - if the length of the two arrays is not equal
      NullPointerException - if one of the double[] arrays is null

    • mse

      static double mse(Double[] calculated, Double[] expected)
      Mean square error is measured as the average of squared difference between predictions and actual observations.
      Parameters:
      calculated - the function values calculated with the current program tree
      expected - the expected function value as given by the sample points
      Returns:
      the mean square error
      Throws:
      IllegalArgumentException - if the length of the two arrays is not equal
      NullPointerException - if one of the double[] arrays is null
      See Also:

    • rmse

      static double rmse(Double[] calculated, Double[] expected)
      Root-mean-square error is measured as the average of squared difference between predictions and actual observations.
      Parameters:
      calculated - the function values calculated with the current program tree
      expected - the expected function value as given by the sample points
      Returns:
      the mean square error
      Throws:
      IllegalArgumentException - if the length of the two arrays is not equal
      NullPointerException - if one of the double[] arrays is null
      See Also:

    • mae

      static double mae(Double[] calculated, Double[] expected)
      Mean absolute error is measured as the average of sum of absolute differences between predictions and actual observations.
      Parameters:
      calculated - the function values calculated with the current program tree
      expected - the expected function value as given by the sample points
      Returns:
      the mean absolute error
      Throws:
      IllegalArgumentException - if the length of the two arrays is not equal
      NullPointerException - if one of the double[] arrays is null