    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    }