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See:
Description
Interface Summary | |
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EstimationProblem | This interface represents an estimation problem. |
Estimator | This interface represents solvers for estimation problems. |
Class Summary | |
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AbstractEstimator | Base class for implementing estimators. |
EstimatedParameter | This class represents the estimated parameters of an estimation problem. |
GaussNewtonEstimator | This class implements a solver for estimation problems. |
LevenbergMarquardtEstimator | This class solves a least squares problem. |
SimpleEstimationProblem | Simple implementation of the EstimationProblem interface for boilerplate data handling. |
WeightedMeasurement | This class represents measurements in estimation problems. |
Exception Summary | |
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EstimationException | This class represents exceptions thrown by the estimation solvers. |
This package provides classes to solve estimation problems.
The estimation problems considered here are parametric problems where a user model depends on initially unknown scalar parameters and several measurements made on values that depend on the model are available. As an example, one can consider the flow rate of a river given rain data on its vicinity, or the center and radius of a circle given points on a ring.
One important class of estimation problems is weighted least squares problems. They basically consist in finding the values for some parameters pk such that a cost function J = sum(wi ri2) is minimized. The various ri terms represent the deviation ri = mesi - modi between the measurements and the parameterized models. The wi factors are the measurements weights, they are often chosen either all equal to 1.0 or proportional to the inverse of the variance of the measurement type. The solver adjusts the values of the estimated parameters pk which are not bound. It does not touch the parameters which have been put in a bound state by the user.
This package provides the EstimatedParameter
class to
represent each estimated parameter, and the WeightedMeasurement
abstract
class the user can extend to define its measurements. All parameters and measurements
are then provided to some Estimator
packed together in an EstimationProblem
instance
which acts only as a container. The package provides two common estimators for
weighted least squares problems, one based on the Gauss-Newton
method and the
other one based on the Levenberg-Marquardt
method.
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