AbstractCurveFitter.java
- /*
- * 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.math4.legacy.fitting;
- import java.util.Arrays;
- import java.util.Collection;
- import org.apache.commons.math4.legacy.analysis.MultivariateMatrixFunction;
- import org.apache.commons.math4.legacy.analysis.MultivariateVectorFunction;
- import org.apache.commons.math4.legacy.analysis.ParametricUnivariateFunction;
- import org.apache.commons.math4.legacy.fitting.leastsquares.LeastSquaresOptimizer;
- import org.apache.commons.math4.legacy.fitting.leastsquares.LeastSquaresProblem;
- import org.apache.commons.math4.legacy.fitting.leastsquares.LevenbergMarquardtOptimizer;
- /**
- * Base class that contains common code for fitting parametric univariate
- * real functions <code>y = f(p<sub>i</sub>;x)</code>, where {@code x} is
- * the independent variable and the <code>p<sub>i</sub></code> are the
- * <em>parameters</em>.
- * <br>
- * A fitter will find the optimal values of the parameters by
- * <em>fitting</em> the curve so it remains very close to a set of
- * {@code N} observed points <code>(x<sub>k</sub>, y<sub>k</sub>)</code>,
- * {@code 0 <= k < N}.
- * <br>
- * An algorithm usually performs the fit by finding the parameter
- * values that minimizes the objective function
- * <pre><code>
- * ∑y<sub>k</sub> - f(x<sub>k</sub>)<sup>2</sup>,
- * </code></pre>
- * which is actually a least-squares problem.
- * This class contains boilerplate code for calling the
- * {@link #fit(Collection)} method for obtaining the parameters.
- * The problem setup, such as the choice of optimization algorithm
- * for fitting a specific function is delegated to subclasses.
- *
- * @since 3.3
- */
- public abstract class AbstractCurveFitter {
- /**
- * Fits a curve.
- * This method computes the coefficients of the curve that best
- * fit the sample of observed points.
- *
- * @param points Observations.
- * @return the fitted parameters.
- */
- public double[] fit(Collection<WeightedObservedPoint> points) {
- // Perform the fit.
- return getOptimizer().optimize(getProblem(points)).getPoint().toArray();
- }
- /**
- * Creates an optimizer set up to fit the appropriate curve.
- * <p>
- * The default implementation uses a {@link LevenbergMarquardtOptimizer
- * Levenberg-Marquardt} optimizer.
- * </p>
- * @return the optimizer to use for fitting the curve to the
- * given {@code points}.
- */
- protected LeastSquaresOptimizer getOptimizer() {
- return new LevenbergMarquardtOptimizer();
- }
- /**
- * Creates a least squares problem corresponding to the appropriate curve.
- *
- * @param points Sample points.
- * @return the least squares problem to use for fitting the curve to the
- * given {@code points}.
- */
- protected abstract LeastSquaresProblem getProblem(Collection<WeightedObservedPoint> points);
- /**
- * Vector function for computing function theoretical values.
- */
- protected static class TheoreticalValuesFunction {
- /** Function to fit. */
- private final ParametricUnivariateFunction f;
- /** Observations. */
- private final double[] points;
- /**
- * @param f function to fit.
- * @param observations Observations.
- */
- public TheoreticalValuesFunction(final ParametricUnivariateFunction f,
- final Collection<WeightedObservedPoint> observations) {
- this.f = f;
- this.points = observations.stream().mapToDouble(WeightedObservedPoint::getX).toArray();
- }
- /**
- * @return the model function values.
- */
- public MultivariateVectorFunction getModelFunction() {
- return new MultivariateVectorFunction() {
- /** {@inheritDoc} */
- @Override
- public double[] value(double[] p) {
- return Arrays.stream(points).map(point -> f.value(point, p)).toArray();
- }
- };
- }
- /**
- * @return the model function Jacobian.
- */
- public MultivariateMatrixFunction getModelFunctionJacobian() {
- return new MultivariateMatrixFunction() {
- /** {@inheritDoc} */
- @Override
- public double[][] value(double[] p) {
- final int len = points.length;
- final double[][] jacobian = new double[len][];
- for (int i = 0; i < len; i++) {
- jacobian[i] = f.gradient(points[i], p);
- }
- return jacobian;
- }
- };
- }
- }
- }