Class AbstractEvaluation

java.lang.Object
org.apache.commons.math3.fitting.leastsquares.AbstractEvaluation
All Implemented Interfaces:
LeastSquaresProblem.Evaluation
Direct Known Subclasses:
DenseWeightedEvaluation, LeastSquaresFactory.LocalLeastSquaresProblem.LazyUnweightedEvaluation, LeastSquaresFactory.LocalLeastSquaresProblem.UnweightedEvaluation
public abstract class AbstractEvaluation extends Object implements LeastSquaresProblem.Evaluation
An implementation of LeastSquaresProblem.Evaluation that is designed for extension. All of the methods implemented here use the methods that are left unimplemented.

TODO cache results?

Since:
3.3

  • Field Details

    • observationSize

      private final int observationSize
      number of observations

  • Constructor Details

    • AbstractEvaluation

      AbstractEvaluation(int observationSize)
      Constructor.
      Parameters:
      observationSize - the number of observation. Needed for getRMS().

  • Method Details

    • getCovariances

      public RealMatrix getCovariances(double threshold)
      Get the covariance matrix of the optimized parameters.
      Note that this operation involves the inversion of the JTJ matrix, where J is the Jacobian matrix. The threshold parameter is a way for the caller to specify that the result of this computation should be considered meaningless, and thus trigger an exception.
      Specified by:
      getCovariances in interface LeastSquaresProblem.Evaluation
      Parameters:
      threshold - Singularity threshold.
      Returns:
      the covariance matrix.

    • getSigma

      public RealVector getSigma(double covarianceSingularityThreshold)
      Get an estimate of the standard deviation of the parameters. The returned values are the square root of the diagonal coefficients of the covariance matrix, sd(a[i]) ~= sqrt(C[i][i]), where a[i] is the optimized value of the i-th parameter, and C is the covariance matrix.
      Specified by:
      getSigma in interface LeastSquaresProblem.Evaluation
      Parameters:
      covarianceSingularityThreshold - Singularity threshold (see computeCovariances).
      Returns:
      an estimate of the standard deviation of the optimized parameters

    • getRMS

      public double getRMS()
      Get the normalized cost. It is the square-root of the sum of squared of the residuals, divided by the number of measurements.
      Specified by:
      getRMS in interface LeastSquaresProblem.Evaluation
      Returns:
      the cost.

    • getCost

      public double getCost()
      Get the cost.
      Specified by:
      getCost in interface LeastSquaresProblem.Evaluation
      Returns:
      the cost.
      See Also: