AbstractCurveFitter (Hipparchus 3.0 API)

java.lang.Object
- org.hipparchus.fitting.AbstractCurveFitter

Direct Known Subclasses:

GaussianCurveFitter, HarmonicCurveFitter, PolynomialCurveFitter, SimpleCurveFitter
```
public abstract class AbstractCurveFitter
extends Object
```
Base class that contains common code for fitting parametric univariate real functions y = f(p_i;x), where x is the independent variable and the p_i are the parameters.
A fitter will find the optimal values of the parameters by fitting the curve so it remains very close to a set of N observed points (x_k, y_k), 0 <= k < N.
An algorithm usually performs the fit by finding the parameter values that minimizes the objective function
```
  ∑y_k - f(x_k)²,
 
```
which is actually a least-squares problem. This class contains boilerplate code for calling the fit(Collection) method for obtaining the parameters. The problem setup, such as the choice of optimization algorithm for fitting a specific function is delegated to subclasses.

Nested Class Summary

Nested Classes
Modifier and Type Class Description

protected static class AbstractCurveFitter.TheoreticalValuesFunction
Vector function for computing function theoretical values.

Constructor Summary

Constructors
Constructor Description

AbstractCurveFitter()
Empty constructor.

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method	Description
`double[]`	`fit(Collection<WeightedObservedPoint> points)`	Fits a curve.
`protected LeastSquaresOptimizer`	`getOptimizer()`	Creates an optimizer set up to fit the appropriate curve.
`protected abstract LeastSquaresProblem`	`getProblem(Collection<WeightedObservedPoint> points)`	Creates a least squares problem corresponding to the appropriate curve.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - AbstractCurveFitter
```
public AbstractCurveFitter()
```
    Empty constructor.
    This constructor is not strictly necessary, but it prevents spurious javadoc warnings with JDK 18 and later.
    
    Since:
    
    3.0
- Method Detail
  - fit
```
public double[] fit(Collection<WeightedObservedPoint> points)
```
    Fits a curve. This method computes the coefficients of the curve that best fit the sample of observed points.
    
    Parameters:
    
    points - Observations.
    
    Returns:
    
    the fitted parameters.
  - getOptimizer
```
protected LeastSquaresOptimizer getOptimizer()
```
    Creates an optimizer set up to fit the appropriate curve.
    The default implementation uses a Levenberg-Marquardt optimizer.
    
    Returns:
    
    the optimizer to use for fitting the curve to the given points.
  - getProblem
```
protected abstract LeastSquaresProblem getProblem(Collection<WeightedObservedPoint> points)
```
    Creates a least squares problem corresponding to the appropriate curve.
    
    Parameters:
    
    points - Sample points.
    
    Returns:
    
    the least squares problem to use for fitting the curve to the given points.