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.MathRuntimeException;
022    import org.apache.commons.math.analysis.polynomials.PolynomialFunction;
023    import org.apache.commons.math.optimization.DifferentiableMultivariateVectorialOptimizer;
024    import org.apache.commons.math.optimization.OptimizationException;
025    
026    /** This class implements a curve fitting specialized for polynomials.
027     * <p>Polynomial fitting is a very simple case of curve fitting. The
028     * estimated coefficients are the polynomial coefficients. They are
029     * searched by a least square estimator.</p>
030     * @version $Revision: 786479 $ $Date: 2009-06-19 08:36:16 -0400 (Fri, 19 Jun 2009) $
031     * @since 2.0
032     */
033    
034    public class PolynomialFitter {
035    
036        /** Fitter for the coefficients. */
037        private final CurveFitter fitter;
038    
039        /** Polynomial degree. */
040        private final int degree;
041    
042        /** Simple constructor.
043         * <p>The polynomial fitter built this way are complete polynomials,
044         * ie. a n-degree polynomial has n+1 coefficients.</p>
045         * @param degree maximal degree of the polynomial
046         * @param optimizer optimizer to use for the fitting
047         */
048        public PolynomialFitter(int degree, final DifferentiableMultivariateVectorialOptimizer optimizer) {
049            this.fitter = new CurveFitter(optimizer);
050            this.degree = degree;
051        }
052    
053        /** Add an observed weighted (x,y) point to the sample.
054         * @param weight weight of the observed point in the fit
055         * @param x abscissa of the point
056         * @param y observed value of the point at x, after fitting we should
057         * have P(x) as close as possible to this value
058         */
059        public void addObservedPoint(double weight, double x, double y) {
060            fitter.addObservedPoint(weight, x, y);
061        }
062    
063        /** Get the polynomial fitting the weighted (x, y) points.
064         * @return polynomial function best fitting the observed points
065         * @exception OptimizationException if the algorithm failed to converge
066         */
067        public PolynomialFunction fit()
068            throws OptimizationException {
069            try {
070                return new PolynomialFunction(fitter.fit(new ParametricPolynomial(), new double[degree + 1]));
071            } catch (FunctionEvaluationException fee) {
072                // this should never happen
073                throw MathRuntimeException.createInternalError(fee);
074            }
075        }
076    
077        /** Dedicated parametric polynomial class. */
078        private static class ParametricPolynomial implements ParametricRealFunction {
079    
080            /** {@inheritDoc} */
081            public double[] gradient(double x, double[] parameters)
082                    throws FunctionEvaluationException {
083                final double[] gradient = new double[parameters.length];
084                double xn = 1.0;
085                for (int i = 0; i < parameters.length; ++i) {
086                    gradient[i] = xn;
087                    xn *= x;
088                }
089                return gradient;
090            }
091    
092            /** {@inheritDoc} */
093            public double value(final double x, final double[] parameters) {
094                double y = 0;
095                for (int i = parameters.length - 1; i >= 0; --i) {
096                    y = y * x + parameters[i];
097                }
098                return y;
099            }
100            
101        }
102    
103    }