CurveFitter.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.math3.optimization.fitting;
- import java.util.ArrayList;
- import java.util.List;
- import org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction;
- import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
- import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
- import org.apache.commons.math3.analysis.differentiation.DerivativeStructure;
- import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction;
- import org.apache.commons.math3.optimization.DifferentiableMultivariateVectorOptimizer;
- import org.apache.commons.math3.optimization.MultivariateDifferentiableVectorOptimizer;
- import org.apache.commons.math3.optimization.PointVectorValuePair;
- /** Fitter for parametric univariate real functions y = f(x).
- * <br/>
- * When a univariate real function y = f(x) does depend on some
- * unknown parameters p<sub>0</sub>, p<sub>1</sub> ... p<sub>n-1</sub>,
- * this class can be used to find these parameters. It does this
- * by <em>fitting</em> the curve so it remains very close to a set of
- * observed points (x<sub>0</sub>, y<sub>0</sub>), (x<sub>1</sub>,
- * y<sub>1</sub>) ... (x<sub>k-1</sub>, y<sub>k-1</sub>). This fitting
- * is done by finding the parameters values that minimizes the objective
- * function ∑(y<sub>i</sub>-f(x<sub>i</sub>))<sup>2</sup>. This is
- * really a least squares problem.
- *
- * @param <T> Function to use for the fit.
- *
- * @deprecated As of 3.1 (to be removed in 4.0).
- * @since 2.0
- */
- @Deprecated
- public class CurveFitter<T extends ParametricUnivariateFunction> {
- /** Optimizer to use for the fitting.
- * @deprecated as of 3.1 replaced by {@link #optimizer}
- */
- @Deprecated
- private final DifferentiableMultivariateVectorOptimizer oldOptimizer;
- /** Optimizer to use for the fitting. */
- private final MultivariateDifferentiableVectorOptimizer optimizer;
- /** Observed points. */
- private final List<WeightedObservedPoint> observations;
- /** Simple constructor.
- * @param optimizer optimizer to use for the fitting
- * @deprecated as of 3.1 replaced by {@link #CurveFitter(MultivariateDifferentiableVectorOptimizer)}
- */
- @Deprecated
- public CurveFitter(final DifferentiableMultivariateVectorOptimizer optimizer) {
- this.oldOptimizer = optimizer;
- this.optimizer = null;
- observations = new ArrayList<WeightedObservedPoint>();
- }
- /** Simple constructor.
- * @param optimizer optimizer to use for the fitting
- * @since 3.1
- */
- public CurveFitter(final MultivariateDifferentiableVectorOptimizer optimizer) {
- this.oldOptimizer = null;
- this.optimizer = optimizer;
- observations = new ArrayList<WeightedObservedPoint>();
- }
- /** Add an observed (x,y) point to the sample with unit weight.
- * <p>Calling this method is equivalent to call
- * {@code addObservedPoint(1.0, x, y)}.</p>
- * @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 the fitted parameters.
- * @throws org.apache.commons.math3.exception.DimensionMismatchException
- * if the start point dimension is wrong.
- */
- public double[] fit(T f, final double[] initialGuess) {
- return fit(Integer.MAX_VALUE, f, initialGuess);
- }
- /**
- * 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.
- * @param maxEval Maximum number of function evaluations.
- * @return the fitted parameters.
- * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
- * if the number of allowed evaluations is exceeded.
- * @throws org.apache.commons.math3.exception.DimensionMismatchException
- * if the start point dimension is wrong.
- * @since 3.0
- */
- public double[] fit(int maxEval, T f,
- final double[] initialGuess) {
- // 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
- final PointVectorValuePair optimum;
- if (optimizer == null) {
- // to be removed in 4.0
- optimum = oldOptimizer.optimize(maxEval, new OldTheoreticalValuesFunction(f),
- target, weights, initialGuess);
- } else {
- optimum = optimizer.optimize(maxEval, new TheoreticalValuesFunction(f),
- target, weights, initialGuess);
- }
- // extract the coefficients
- return optimum.getPointRef();
- }
- /** Vectorial function computing function theoretical values. */
- @Deprecated
- private class OldTheoreticalValuesFunction
- implements DifferentiableMultivariateVectorFunction {
- /** Function to fit. */
- private final ParametricUnivariateFunction f;
- /** Simple constructor.
- * @param f function to fit.
- */
- public OldTheoreticalValuesFunction(final ParametricUnivariateFunction f) {
- this.f = f;
- }
- /** {@inheritDoc} */
- public MultivariateMatrixFunction jacobian() {
- return new MultivariateMatrixFunction() {
- public double[][] value(double[] point) {
- 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) {
- // 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;
- }
- }
- /** Vectorial function computing function theoretical values. */
- private class TheoreticalValuesFunction implements MultivariateDifferentiableVectorFunction {
- /** Function to fit. */
- private final ParametricUnivariateFunction f;
- /** Simple constructor.
- * @param f function to fit.
- */
- public TheoreticalValuesFunction(final ParametricUnivariateFunction f) {
- this.f = f;
- }
- /** {@inheritDoc} */
- public double[] value(double[] point) {
- // 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;
- }
- /** {@inheritDoc} */
- public DerivativeStructure[] value(DerivativeStructure[] point) {
- // extract parameters
- final double[] parameters = new double[point.length];
- for (int k = 0; k < point.length; ++k) {
- parameters[k] = point[k].getValue();
- }
- // compute the residuals
- final DerivativeStructure[] values = new DerivativeStructure[observations.size()];
- int i = 0;
- for (WeightedObservedPoint observed : observations) {
- // build the DerivativeStructure by adding first the value as a constant
- // and then adding derivatives
- DerivativeStructure vi = new DerivativeStructure(point.length, 1, f.value(observed.getX(), parameters));
- for (int k = 0; k < point.length; ++k) {
- vi = vi.add(new DerivativeStructure(point.length, 1, k, 0.0));
- }
- values[i++] = vi;
- }
- return values;
- }
- }
- }