001    /*
002     * Licensed to the Apache Software Foundation (ASF) under one or more
003     * contributor license agreements.  See the NOTICE file distributed with
004     * this work for additional information regarding copyright ownership.
005     * The ASF licenses this file to You under the Apache License, Version 2.0
006     * (the "License"); you may not use this file except in compliance with
007     * the License.  You may obtain a copy of the License at
008     *
009     *      http://www.apache.org/licenses/LICENSE-2.0
010     *
011     * Unless required by applicable law or agreed to in writing, software
012     * distributed under the License is distributed on an "AS IS" BASIS,
013     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014     * See the License for the specific language governing permissions and
015     * limitations under the License.
016     */
017    
018    package org.apache.commons.math.optimization.fitting;
019    
020    import org.apache.commons.math.FunctionEvaluationException;
021    import org.apache.commons.math.analysis.polynomials.PolynomialFunction;
022    import org.apache.commons.math.optimization.DifferentiableMultivariateVectorialOptimizer;
023    import org.apache.commons.math.optimization.OptimizationException;
024    
025    /** This class implements a curve fitting specialized for polynomials.
026     * <p>Polynomial fitting is a very simple case of curve fitting. The
027     * estimated coefficients are the polynomial coefficients. They are
028     * searched by a least square estimator.</p>
029     * @version $Revision: 1073270 $ $Date: 2011-02-22 10:19:27 +0100 (mar. 22 f??vr. 2011) $
030     * @since 2.0
031     */
032    
033    public class PolynomialFitter {
034    
035        /** Fitter for the coefficients. */
036        private final CurveFitter fitter;
037    
038        /** Polynomial degree. */
039        private final int degree;
040    
041        /** Simple constructor.
042         * <p>The polynomial fitter built this way are complete polynomials,
043         * ie. a n-degree polynomial has n+1 coefficients.</p>
044         * @param degree maximal degree of the polynomial
045         * @param optimizer optimizer to use for the fitting
046         */
047        public PolynomialFitter(int degree, final DifferentiableMultivariateVectorialOptimizer optimizer) {
048            this.fitter = new CurveFitter(optimizer);
049            this.degree = degree;
050        }
051    
052        /** Add an observed weighted (x,y) point to the sample.
053         * @param weight weight of the observed point in the fit
054         * @param x abscissa of the point
055         * @param y observed value of the point at x, after fitting we should
056         * have P(x) as close as possible to this value
057         */
058        public void addObservedPoint(double weight, double x, double y) {
059            fitter.addObservedPoint(weight, x, y);
060        }
061    
062        /**
063         * Remove all observations.
064         * @since 2.2
065         */
066        public void clearObservations() {
067            fitter.clearObservations();
068        }
069    
070        /** Get the polynomial fitting the weighted (x, y) points.
071         * @return polynomial function best fitting the observed points
072         * @exception OptimizationException if the algorithm failed to converge
073         */
074        public PolynomialFunction fit() throws OptimizationException {
075            try {
076                return new PolynomialFunction(fitter.fit(new ParametricPolynomial(), new double[degree + 1]));
077            } catch (FunctionEvaluationException fee) {
078                // should never happen
079                throw new RuntimeException(fee);
080            }
081        }
082    
083        /** Dedicated parametric polynomial class. */
084        private static class ParametricPolynomial implements ParametricRealFunction {
085    
086            /** {@inheritDoc} */
087            public double[] gradient(double x, double[] parameters) {
088                final double[] gradient = new double[parameters.length];
089                double xn = 1.0;
090                for (int i = 0; i < parameters.length; ++i) {
091                    gradient[i] = xn;
092                    xn *= x;
093                }
094                return gradient;
095            }
096    
097            /** {@inheritDoc} */
098            public double value(final double x, final double[] parameters) {
099                double y = 0;
100                for (int i = parameters.length - 1; i >= 0; --i) {
101                    y = y * x + parameters[i];
102                }
103                return y;
104            }
105    
106        }
107    
108    }