1 /* 2 * Licensed to the Apache Software Foundation (ASF) under one or more 3 * contributor license agreements. See the NOTICE file distributed with 4 * this work for additional information regarding copyright ownership. 5 * The ASF licenses this file to You under the Apache License, Version 2.0 6 * (the "License"); you may not use this file except in compliance with 7 * the License. You may obtain a copy of the License at 8 * 9 * http://www.apache.org/licenses/LICENSE-2.0 10 * 11 * Unless required by applicable law or agreed to in writing, software 12 * distributed under the License is distributed on an "AS IS" BASIS, 13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 * See the License for the specific language governing permissions and 15 * limitations under the License. 16 */ 17 18 package org.apache.commons.math.optimization.fitting; 19 20 import org.apache.commons.math.FunctionEvaluationException; 21 import org.apache.commons.math.analysis.polynomials.PolynomialFunction; 22 import org.apache.commons.math.optimization.DifferentiableMultivariateVectorialOptimizer; 23 import org.apache.commons.math.optimization.OptimizationException; 24 25 /** This class implements a curve fitting specialized for polynomials. 26 * <p>Polynomial fitting is a very simple case of curve fitting. The 27 * estimated coefficients are the polynomial coefficients. They are 28 * searched by a least square estimator.</p> 29 * @version $Revision: 1073270 $ $Date: 2011-02-22 10:19:27 +0100 (mar. 22 fvr. 2011) $ 30 * @since 2.0 31 */ 32 33 public class PolynomialFitter { 34 35 /** Fitter for the coefficients. */ 36 private final CurveFitter fitter; 37 38 /** Polynomial degree. */ 39 private final int degree; 40 41 /** Simple constructor. 42 * <p>The polynomial fitter built this way are complete polynomials, 43 * ie. a n-degree polynomial has n+1 coefficients.</p> 44 * @param degree maximal degree of the polynomial 45 * @param optimizer optimizer to use for the fitting 46 */ 47 public PolynomialFitter(int degree, final DifferentiableMultivariateVectorialOptimizer optimizer) { 48 this.fitter = new CurveFitter(optimizer); 49 this.degree = degree; 50 } 51 52 /** Add an observed weighted (x,y) point to the sample. 53 * @param weight weight of the observed point in the fit 54 * @param x abscissa of the point 55 * @param y observed value of the point at x, after fitting we should 56 * have P(x) as close as possible to this value 57 */ 58 public void addObservedPoint(double weight, double x, double y) { 59 fitter.addObservedPoint(weight, x, y); 60 } 61 62 /** 63 * Remove all observations. 64 * @since 2.2 65 */ 66 public void clearObservations() { 67 fitter.clearObservations(); 68 } 69 70 /** Get the polynomial fitting the weighted (x, y) points. 71 * @return polynomial function best fitting the observed points 72 * @exception OptimizationException if the algorithm failed to converge 73 */ 74 public PolynomialFunction fit() throws OptimizationException { 75 try { 76 return new PolynomialFunction(fitter.fit(new ParametricPolynomial(), new double[degree + 1])); 77 } catch (FunctionEvaluationException fee) { 78 // should never happen 79 throw new RuntimeException(fee); 80 } 81 } 82 83 /** Dedicated parametric polynomial class. */ 84 private static class ParametricPolynomial implements ParametricRealFunction { 85 86 /** {@inheritDoc} */ 87 public double[] gradient(double x, double[] parameters) { 88 final double[] gradient = new double[parameters.length]; 89 double xn = 1.0; 90 for (int i = 0; i < parameters.length; ++i) { 91 gradient[i] = xn; 92 xn *= x; 93 } 94 return gradient; 95 } 96 97 /** {@inheritDoc} */ 98 public double value(final double x, final double[] parameters) { 99 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 } 109
