org.apache.commons.math.optimization.fitting.CurveFitter Maven / Gradle / Ivy
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* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
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* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
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package org.apache.commons.math.optimization.fitting;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math.analysis.DifferentiableMultivariateVectorialFunction;
import org.apache.commons.math.analysis.MultivariateMatrixFunction;
import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.optimization.DifferentiableMultivariateVectorialOptimizer;
import org.apache.commons.math.optimization.OptimizationException;
import org.apache.commons.math.optimization.VectorialPointValuePair;
/** 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.
* @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $
* @since 2.0
*/
public class CurveFitter {
/** Optimizer to use for the fitting. */
private final DifferentiableMultivariateVectorialOptimizer optimizer;
/** Observed points. */
private final List observations;
/** Simple constructor.
* @param optimizer optimizer to use for the fitting
*/
public CurveFitter(final DifferentiableMultivariateVectorialOptimizer optimizer) {
this.optimizer = optimizer;
observations = new ArrayList();
}
/** Add an observed (x,y) point to the sample with unit weight.
* Calling this method is equivalent to call
* addObservedPoint(1.0, x, y).
* @param x abscissa of the point
* @param y observed value of the point at x, after fitting we should
* have f(x) as close as possible to this value
* @see #addObservedPoint(double, double, double)
* @see #addObservedPoint(WeightedObservedPoint)
* @see #getObservations()
*/
public void addObservedPoint(double x, double y) {
addObservedPoint(1.0, x, y);
}
/** Add an observed weighted (x,y) point to the sample.
* @param weight weight of the observed point in the fit
* @param x abscissa of the point
* @param y observed value of the point at x, after fitting we should
* have f(x) as close as possible to this value
* @see #addObservedPoint(double, double)
* @see #addObservedPoint(WeightedObservedPoint)
* @see #getObservations()
*/
public void addObservedPoint(double weight, double x, double y) {
observations.add(new WeightedObservedPoint(weight, x, y));
}
/** Add an observed weighted (x,y) point to the sample.
* @param observed observed point to add
* @see #addObservedPoint(double, double)
* @see #addObservedPoint(double, double, double)
* @see #getObservations()
*/
public void addObservedPoint(WeightedObservedPoint observed) {
observations.add(observed);
}
/** Get the observed points.
* @return observed points
* @see #addObservedPoint(double, double)
* @see #addObservedPoint(double, double, double)
* @see #addObservedPoint(WeightedObservedPoint)
*/
public WeightedObservedPoint[] getObservations() {
return observations.toArray(new WeightedObservedPoint[observations.size()]);
}
/**
* Remove all observations.
*/
public void clearObservations() {
observations.clear();
}
/** 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 {@link #addObservedPoint(WeightedObservedPoint)
* addObservedPoint} method.
* @param f parametric function to fit
* @param initialGuess first guess of the function parameters
* @return fitted parameters
* @exception FunctionEvaluationException if the objective function throws one during the search
* @exception OptimizationException if the algorithm failed to converge
* @exception IllegalArgumentException if the start point dimension is wrong
*/
public double[] fit(final ParametricRealFunction f,
final double[] initialGuess)
throws FunctionEvaluationException, OptimizationException, IllegalArgumentException {
// prepare least squares problem
double[] target = new double[observations.size()];
double[] weights = new double[observations.size()];
int i = 0;
for (WeightedObservedPoint point : observations) {
target[i] = point.getY();
weights[i] = point.getWeight();
++i;
}
// perform the fit
VectorialPointValuePair optimum =
optimizer.optimize(new TheoreticalValuesFunction(f), target, weights, initialGuess);
// extract the coefficients
return optimum.getPointRef();
}
/** Vectorial function computing function theoretical values. */
private class TheoreticalValuesFunction
implements DifferentiableMultivariateVectorialFunction {
/** Function to fit. */
private final ParametricRealFunction f;
/** Simple constructor.
* @param f function to fit.
*/
public TheoreticalValuesFunction(final ParametricRealFunction f) {
this.f = f;
}
/** {@inheritDoc} */
public MultivariateMatrixFunction jacobian() {
return new MultivariateMatrixFunction() {
public double[][] value(double[] point)
throws FunctionEvaluationException, IllegalArgumentException {
final double[][] jacobian = new double[observations.size()][];
int i = 0;
for (WeightedObservedPoint observed : observations) {
jacobian[i++] = f.gradient(observed.getX(), point);
}
return jacobian;
}
};
}
/** {@inheritDoc} */
public double[] value(double[] point) throws FunctionEvaluationException, IllegalArgumentException {
// compute the residuals
final double[] values = new double[observations.size()];
int i = 0;
for (WeightedObservedPoint observed : observations) {
values[i++] = f.value(observed.getX(), point);
}
return values;
}
}
}