/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* 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
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math.optimization.fitting;
import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.analysis.polynomials.PolynomialFunction;
import org.apache.commons.math.optimization.DifferentiableMultivariateVectorialOptimizer;
import org.apache.commons.math.optimization.OptimizationException;
/** This class implements a curve fitting specialized for polynomials.
* <p>Polynomial fitting is a very simple case of curve fitting. The
* estimated coefficients are the polynomial coefficients. They are
* searched by a least square estimator.</p>
* @version $Revision: 1073270 $ $Date: 2011-02-22 10:19:27 +0100 (mar. 22 févr. 2011) $
* @since 2.0
*/
public class PolynomialFitter {
/** Fitter for the coefficients. */
private final CurveFitter fitter;
/** Polynomial degree. */
private final int degree;
/** Simple constructor.
* <p>The polynomial fitter built this way are complete polynomials,
* ie. a n-degree polynomial has n+1 coefficients.</p>
* @param degree maximal degree of the polynomial
* @param optimizer optimizer to use for the fitting
*/
public PolynomialFitter(int degree, final DifferentiableMultivariateVectorialOptimizer optimizer) {
this.fitter = new CurveFitter(optimizer);
this.degree = degree;
}
/** 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 P(x) as close as possible to this value
*/
public void addObservedPoint(double weight, double x, double y) {
fitter.addObservedPoint(weight, x, y);
}
/**
* Remove all observations.
* @since 2.2
*/
public void clearObservations() {
fitter.clearObservations();
}
/** Get the polynomial fitting the weighted (x, y) points.
* @return polynomial function best fitting the observed points
* @exception OptimizationException if the algorithm failed to converge
*/
public PolynomialFunction fit() throws OptimizationException {
try {
return new PolynomialFunction(fitter.fit(new ParametricPolynomial(), new double[degree + 1]));
} catch (FunctionEvaluationException fee) {
// should never happen
throw new RuntimeException(fee);
}
}
/** Dedicated parametric polynomial class. */
private static class ParametricPolynomial implements ParametricRealFunction {
/** {@inheritDoc} */
public double[] gradient(double x, double[] parameters) {
final double[] gradient = new double[parameters.length];
double xn = 1.0;
for (int i = 0; i < parameters.length; ++i) {
gradient[i] = xn;
xn *= x;
}
return gradient;
}
/** {@inheritDoc} */
public double value(final double x, final double[] parameters) {
double y = 0;
for (int i = parameters.length - 1; i >= 0; --i) {
y = y * x + parameters[i];
}
return y;
}
}
}