## org.apache.commons.math.optimization.fitting
Class CurveFitter

    java.lang.Object
      org.apache.commons.math.optimization.fitting.CurveFitter

    public class CurveFitter

extends Object

Fitter for parametric univariate real functions y = f(x).

When a univariate real function y = f(x) does depend on some unknown parameters p0, p1 ... pn-1, this class can be used to find these parameters. It does this by fitting the curve so it remains very close to a set of observed points (x0, y0), (x1, y1) ... (xk-1, yk-1). This fitting is done by finding the parameters values that minimizes the objective function ∑(yi-f(xi))2. This is really a least squares problem.

Since:
  2.0
Version:
  $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $

Nested Class Summary
`private class` `CurveFitter.TheoreticalValuesFunction` Vectorial function computing function theoretical values.

Field Summary
`private List<WeightedObservedPoint>` `observations` Observed points.
`private DifferentiableMultivariateVectorialOptimizer` `optimizer` Optimizer to use for the fitting.

Constructor Summary
`CurveFitter(DifferentiableMultivariateVectorialOptimizer optimizer)` Simple constructor.

Method Summary
`void` `addObservedPoint(double x, double y)` Add an observed (x,y) point to the sample with unit weight.
`void` `addObservedPoint(double weight, double x, double y)` Add an observed weighted (x,y) point to the sample.
`void` `addObservedPoint(WeightedObservedPoint observed)` Add an observed weighted (x,y) point to the sample.
`void` `clearObservations()` Remove all observations.
`double[]` `fit(ParametricRealFunction f, double[] initialGuess)` Fit a curve.
`WeightedObservedPoint[]` `getObservations()` Get the observed points.

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

Field Detail

### optimizer

    private final DifferentiableMultivariateVectorialOptimizer optimizer

  Optimizer to use for the fitting.

### observations

    private final List<WeightedObservedPoint> observations

  Observed points.

Constructor Detail

### CurveFitter

    public CurveFitter(DifferentiableMultivariateVectorialOptimizer optimizer)

  Simple constructor.

  Parameters:
    `optimizer` - optimizer to use for the fitting

Method Detail

### addObservedPoint

    public void addObservedPoint(double x,
                                 double y)

  Add an observed (x,y) point to the sample with unit weight.

  Calling this method is equivalent to call `addObservedPoint(1.0, x, y)`.

  Parameters:
    `x` - abscissa of the point
    `y` - observed value of the point at x, after fitting we should have f(x) as close as possible to this value
  See Also:
    `addObservedPoint(double, double, double)`, `addObservedPoint(WeightedObservedPoint)`, `getObservations()`

### addObservedPoint

    public void addObservedPoint(double weight,
                                 double x,
                                 double y)

  Add an observed weighted (x,y) point to the sample.

  Parameters:
    `weight` - weight of the observed point in the fit
    `x` - abscissa of the point
    `y` - observed value of the point at x, after fitting we should have f(x) as close as possible to this value
  See Also:
    `addObservedPoint(double, double)`, `addObservedPoint(WeightedObservedPoint)`, `getObservations()`

### addObservedPoint

    public void addObservedPoint(WeightedObservedPoint observed)

  Add an observed weighted (x,y) point to the sample.

  Parameters:
    `observed` - observed point to add
  See Also:
    `addObservedPoint(double, double)`, `addObservedPoint(double, double, double)`, `getObservations()`

### getObservations

    public WeightedObservedPoint[] getObservations()

  Get the observed points.

  Returns:
    observed points
  See Also:
    `addObservedPoint(double, double)`, `addObservedPoint(double, double, double)`, `addObservedPoint(WeightedObservedPoint)`

### clearObservations

    public void clearObservations()

  Remove all observations.

### fit

    public double[] fit(ParametricRealFunction f,
                        double[] initialGuess)
                 throws FunctionEvaluationException,
                        OptimizationException,
                        IllegalArgumentException

  Fit a curve.

  This method compute the coefficients of the curve that best fit the sample of observed points previously given through calls to the `addObservedPoint` method.

  Parameters:
    `f` - parametric function to fit
    `initialGuess` - first guess of the function parameters
  Returns:
    fitted parameters
  Throws:
    `FunctionEvaluationException` - if the objective function throws one during the search
    `OptimizationException` - if the algorithm failed to converge
    `IllegalArgumentException` - if the start point dimension is wrong

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