1dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond/* 2dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * Licensed to the Apache Software Foundation (ASF) under one or more 3dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * contributor license agreements. See the NOTICE file distributed with 4dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * this work for additional information regarding copyright ownership. 5dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * The ASF licenses this file to You under the Apache License, Version 2.0 6dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * (the "License"); you may not use this file except in compliance with 7dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * the License. You may obtain a copy of the License at 8dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * 9dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * http://www.apache.org/licenses/LICENSE-2.0 10dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * 11dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * Unless required by applicable law or agreed to in writing, software 12dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * distributed under the License is distributed on an "AS IS" BASIS, 13dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * See the License for the specific language governing permissions and 15dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * limitations under the License. 16dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond */ 17dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond 18dee0849a9704d532af0b550146cbafbaa6ee1d19Raymondpackage org.apache.commons.math.optimization.fitting; 19dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond 20dee0849a9704d532af0b550146cbafbaa6ee1d19Raymondimport org.apache.commons.math.FunctionEvaluationException; 21dee0849a9704d532af0b550146cbafbaa6ee1d19Raymondimport org.apache.commons.math.optimization.DifferentiableMultivariateVectorialOptimizer; 22dee0849a9704d532af0b550146cbafbaa6ee1d19Raymondimport org.apache.commons.math.optimization.OptimizationException; 23dee0849a9704d532af0b550146cbafbaa6ee1d19Raymondimport org.apache.commons.math.optimization.fitting.CurveFitter; 24dee0849a9704d532af0b550146cbafbaa6ee1d19Raymondimport org.apache.commons.math.optimization.fitting.WeightedObservedPoint; 25dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond 26dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond/** 27dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * Fits points to a Gaussian function (that is, a {@link GaussianFunction}). 28dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * <p> 29dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * Usage example: 30dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * <pre> 31dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * GaussianFitter fitter = new GaussianFitter( 32dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * new LevenbergMarquardtOptimizer()); 33dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.0254623, 531026.0); 34dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.03128248, 984167.0); 35dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.03839603, 1887233.0); 36dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.04421621, 2687152.0); 37dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.05132976, 3461228.0); 38dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.05326982, 3580526.0); 39dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.05779662, 3439750.0); 40dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.0636168, 2877648.0); 41dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.06943698, 2175960.0); 42dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.07525716, 1447024.0); 43dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.08237071, 717104.0); 44dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * fitter.addObservedPoint(4.08366408, 620014.0); 45dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * GaussianFunction fitFunction = fitter.fit(); 46dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * </pre> 47dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * 48dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @see ParametricGaussianFunction 49dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @since 2.2 50dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $ 51dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond */ 52dee0849a9704d532af0b550146cbafbaa6ee1d19Raymondpublic class GaussianFitter { 53dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond 54dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond /** Fitter used for fitting. */ 55dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond private final CurveFitter fitter; 56dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond 57dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond /** 58dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * Constructs an instance using the specified optimizer. 59dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * 60dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @param optimizer optimizer to use for the fitting 61dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond */ 62dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond public GaussianFitter(DifferentiableMultivariateVectorialOptimizer optimizer) { 63dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond fitter = new CurveFitter(optimizer); 64dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond } 65dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond 66dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond /** 67dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * Adds point (<code>x</code>, <code>y</code>) to list of observed points 68dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * with a weight of 1.0. 69dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * 70dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @param x <tt>x</tt> point value 71dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @param y <tt>y</tt> point value 72dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond */ 73dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond public void addObservedPoint(double x, double y) { 74dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond addObservedPoint(1.0, x, y); 75dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond } 76dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond 77dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond /** 78dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * Adds point (<code>x</code>, <code>y</code>) to list of observed points 79dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * with a weight of <code>weight</code>. 80dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * 81dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @param weight weight assigned to point 82dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @param x <tt>x</tt> point value 83dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @param y <tt>y</tt> point value 84dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond */ 85dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond public void addObservedPoint(double weight, double x, double y) { 86dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond fitter.addObservedPoint(weight, x, y); 87dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond } 88dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond 89dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond /** 90dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * Fits Gaussian function to the observed points. 91dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * 92dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @return Gaussian function best fitting the observed points 93dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * 94dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @throws FunctionEvaluationException if <code>CurveFitter.fit</code> throws it 95dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @throws OptimizationException if <code>CurveFitter.fit</code> throws it 96dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @throws IllegalArgumentException if <code>CurveFitter.fit</code> throws it 97dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * 98dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @see CurveFitter 99dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond */ 100dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond public GaussianFunction fit() throws FunctionEvaluationException, OptimizationException { 101dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond return new GaussianFunction(fitter.fit(new ParametricGaussianFunction(), 102dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond createParametersGuesser(fitter.getObservations()).guess())); 103dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond } 104dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond 105dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond /** 106dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * Factory method to create a <code>GaussianParametersGuesser</code> 107dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * instance initialized with the specified observations. 108dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * 109dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @param observations points used to initialize the created 110dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * <code>GaussianParametersGuesser</code> instance 111dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * 112dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond * @return new <code>GaussianParametersGuesser</code> instance 113dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond */ 114dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond protected GaussianParametersGuesser createParametersGuesser(WeightedObservedPoint[] observations) { 115dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond return new GaussianParametersGuesser(observations); 116dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond } 117dee0849a9704d532af0b550146cbafbaa6ee1d19Raymond} 118