| /* |
| * 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 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). |
| * <p>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.</p> |
| * @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<WeightedObservedPoint> observations; |
| |
| /** Simple constructor. |
| * @param optimizer optimizer to use for the fitting |
| */ |
| public CurveFitter(final DifferentiableMultivariateVectorialOptimizer optimizer) { |
| 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)</code>.</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. |
| * <p>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.</p> |
| * @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; |
| |
| } |
| |
| } |
| |
| } |
