HarmonicFitter (The Adobe Experience Manager SDK 2022.11.9850.20221116T162329Z-220900)

java.lang.Object
- org.apache.commons.math.optimization.fitting.HarmonicFitter

```
public class HarmonicFitter
extends java.lang.Object
```
This class implements a curve fitting specialized for sinusoids.
Harmonic fitting is a very simple case of curve fitting. The estimated coefficients are the amplitude a, the pulsation ω and the phase φ: f (t) = a cos (ω t + φ). They are searched by a least square estimator initialized with a rough guess based on integrals.

Since:

2.0

Constructor Summary

Constructors
Constructor	Description
`HarmonicFitter(DifferentiableMultivariateVectorialOptimizer optimizer)`	Simple constructor.
`HarmonicFitter(DifferentiableMultivariateVectorialOptimizer optimizer, double[] initialGuess)`	Simple constructor.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`addObservedPoint(double weight, double x, double y)`	Add an observed weighted (x,y) point to the sample.
`HarmonicFunction`	`fit()`	Fit an harmonic function to the observed points.

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

- Constructor Detail
  - HarmonicFitter
```
public HarmonicFitter(DifferentiableMultivariateVectorialOptimizer optimizer)
```
    Simple constructor.
    
    Parameters:
    
    optimizer - optimizer to use for the fitting
  - HarmonicFitter
```
public HarmonicFitter(DifferentiableMultivariateVectorialOptimizer optimizer,
                      double[] initialGuess)
```
    Simple constructor.
    This constructor can be used when a first guess of the coefficients is already known.
    
    Parameters:
    
    optimizer - optimizer to use for the fitting
    
    initialGuess - guessed values for amplitude (index 0), pulsation ω (index 1) and phase φ (index 2)
- Method Detail
  - 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 P(x) as close as possible to this value
  - fit
```
public HarmonicFunction fit()
                     throws OptimizationException
```
    Fit an harmonic function to the observed points.
    
    Returns:
    
    harmonic function best fitting the observed points
    
    Throws:
    
    OptimizationException - if the sample is too short or if the first guess cannot be computed