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.optimization.DifferentiableMultivariateVectorialOptimizer;
022 import org.apache.commons.math.optimization.OptimizationException;
023 import org.apache.commons.math.optimization.fitting.CurveFitter;
024 import org.apache.commons.math.optimization.fitting.WeightedObservedPoint;
025
026 /**
027 * Fits points to a Gaussian function (that is, a {@link GaussianFunction}).
028 * <p>
029 * Usage example:
030 * <pre>
031 * GaussianFitter fitter = new GaussianFitter(
032 * new LevenbergMarquardtOptimizer());
033 * fitter.addObservedPoint(4.0254623, 531026.0);
034 * fitter.addObservedPoint(4.03128248, 984167.0);
035 * fitter.addObservedPoint(4.03839603, 1887233.0);
036 * fitter.addObservedPoint(4.04421621, 2687152.0);
037 * fitter.addObservedPoint(4.05132976, 3461228.0);
038 * fitter.addObservedPoint(4.05326982, 3580526.0);
039 * fitter.addObservedPoint(4.05779662, 3439750.0);
040 * fitter.addObservedPoint(4.0636168, 2877648.0);
041 * fitter.addObservedPoint(4.06943698, 2175960.0);
042 * fitter.addObservedPoint(4.07525716, 1447024.0);
043 * fitter.addObservedPoint(4.08237071, 717104.0);
044 * fitter.addObservedPoint(4.08366408, 620014.0);
045 * GaussianFunction fitFunction = fitter.fit();
046 * </pre>
047 *
048 * @see ParametricGaussianFunction
049 * @since 2.2
050 * @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 f??vr. 2011) $
051 */
052 public class GaussianFitter {
053
054 /** Fitter used for fitting. */
055 private final CurveFitter fitter;
056
057 /**
058 * Constructs an instance using the specified optimizer.
059 *
060 * @param optimizer optimizer to use for the fitting
061 */
062 public GaussianFitter(DifferentiableMultivariateVectorialOptimizer optimizer) {
063 fitter = new CurveFitter(optimizer);
064 }
065
066 /**
067 * Adds point (<code>x</code>, <code>y</code>) to list of observed points
068 * with a weight of 1.0.
069 *
070 * @param x <tt>x</tt> point value
071 * @param y <tt>y</tt> point value
072 */
073 public void addObservedPoint(double x, double y) {
074 addObservedPoint(1.0, x, y);
075 }
076
077 /**
078 * Adds point (<code>x</code>, <code>y</code>) to list of observed points
079 * with a weight of <code>weight</code>.
080 *
081 * @param weight weight assigned to point
082 * @param x <tt>x</tt> point value
083 * @param y <tt>y</tt> point value
084 */
085 public void addObservedPoint(double weight, double x, double y) {
086 fitter.addObservedPoint(weight, x, y);
087 }
088
089 /**
090 * Fits Gaussian function to the observed points.
091 *
092 * @return Gaussian function best fitting the observed points
093 *
094 * @throws FunctionEvaluationException if <code>CurveFitter.fit</code> throws it
095 * @throws OptimizationException if <code>CurveFitter.fit</code> throws it
096 * @throws IllegalArgumentException if <code>CurveFitter.fit</code> throws it
097 *
098 * @see CurveFitter
099 */
100 public GaussianFunction fit() throws FunctionEvaluationException, OptimizationException {
101 return new GaussianFunction(fitter.fit(new ParametricGaussianFunction(),
102 createParametersGuesser(fitter.getObservations()).guess()));
103 }
104
105 /**
106 * Factory method to create a <code>GaussianParametersGuesser</code>
107 * instance initialized with the specified observations.
108 *
109 * @param observations points used to initialize the created
110 * <code>GaussianParametersGuesser</code> instance
111 *
112 * @return new <code>GaussianParametersGuesser</code> instance
113 */
114 protected GaussianParametersGuesser createParametersGuesser(WeightedObservedPoint[] observations) {
115 return new GaussianParametersGuesser(observations);
116 }
117 }