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/*
* 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.math3.fitting;
import org.apache.commons.math3.analysis.polynomials.PolynomialFunction;
import org.apache.commons.math3.exception.MathInternalError;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresBuilder;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem;
import org.apache.commons.math3.linear.DiagonalMatrix;
import java.util.Collection;
/**
* Fits points to a {@link
* org.apache.commons.math3.analysis.polynomials.PolynomialFunction.Parametric polynomial} function.
* <br>
* The size of the {@link #withStartPoint(double[]) initial guess} array defines the degree of the
* polynomial to be fitted. They must be sorted in increasing order of the polynomial's degree. The
* optimal values of the coefficients will be returned in the same order.
*
* @since 3.3
*/
public class PolynomialCurveFitter extends AbstractCurveFitter {
/** Parametric function to be fitted. */
private static final PolynomialFunction.Parametric FUNCTION =
new PolynomialFunction.Parametric();
/** Initial guess. */
private final double[] initialGuess;
/** Maximum number of iterations of the optimization algorithm. */
private final int maxIter;
/**
* Contructor used by the factory methods.
*
* @param initialGuess Initial guess.
* @param maxIter Maximum number of iterations of the optimization algorithm.
* @throws MathInternalError if {@code initialGuess} is {@code null}.
*/
private PolynomialCurveFitter(double[] initialGuess, int maxIter) {
this.initialGuess = initialGuess;
this.maxIter = maxIter;
}
/**
* Creates a default curve fitter. Zero will be used as initial guess for the coefficients, and
* the maximum number of iterations of the optimization algorithm is set to {@link
* Integer#MAX_VALUE}.
*
* @param degree Degree of the polynomial to be fitted.
* @return a curve fitter.
* @see #withStartPoint(double[])
* @see #withMaxIterations(int)
*/
public static PolynomialCurveFitter create(int degree) {
return new PolynomialCurveFitter(new double[degree + 1], Integer.MAX_VALUE);
}
/**
* Configure the start point (initial guess).
*
* @param newStart new start point (initial guess)
* @return a new instance.
*/
public PolynomialCurveFitter withStartPoint(double[] newStart) {
return new PolynomialCurveFitter(newStart.clone(), maxIter);
}
/**
* Configure the maximum number of iterations.
*
* @param newMaxIter maximum number of iterations
* @return a new instance.
*/
public PolynomialCurveFitter withMaxIterations(int newMaxIter) {
return new PolynomialCurveFitter(initialGuess, newMaxIter);
}
/** {@inheritDoc} */
@Override
protected LeastSquaresProblem getProblem(Collection<WeightedObservedPoint> observations) {
// Prepare least-squares problem.
final int len = observations.size();
final double[] target = new double[len];
final double[] weights = new double[len];
int i = 0;
for (WeightedObservedPoint obs : observations) {
target[i] = obs.getY();
weights[i] = obs.getWeight();
++i;
}
final AbstractCurveFitter.TheoreticalValuesFunction model =
new AbstractCurveFitter.TheoreticalValuesFunction(FUNCTION, observations);
if (initialGuess == null) {
throw new MathInternalError();
}
// Return a new least squares problem set up to fit a polynomial curve to the
// observed points.
return new LeastSquaresBuilder()
.maxEvaluations(Integer.MAX_VALUE)
.maxIterations(maxIter)
.start(initialGuess)
.target(target)
.weight(new DiagonalMatrix(weights))
.model(model.getModelFunction(), model.getModelFunctionJacobian())
.build();
}
}
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