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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math.optimization.fitting;
import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.optimization.DifferentiableMultivariateVectorialOptimizer;
import org.apache.commons.math.optimization.OptimizationException;
import org.apache.commons.math.optimization.fitting.CurveFitter;
import org.apache.commons.math.optimization.fitting.WeightedObservedPoint;
/**
* Fits points to a Gaussian function (that is, a {@link GaussianFunction}).
* <p>
* Usage example:
* <pre>
* GaussianFitter fitter = new GaussianFitter(
* new LevenbergMarquardtOptimizer());
* fitter.addObservedPoint(4.0254623, 531026.0);
* fitter.addObservedPoint(4.03128248, 984167.0);
* fitter.addObservedPoint(4.03839603, 1887233.0);
* fitter.addObservedPoint(4.04421621, 2687152.0);
* fitter.addObservedPoint(4.05132976, 3461228.0);
* fitter.addObservedPoint(4.05326982, 3580526.0);
* fitter.addObservedPoint(4.05779662, 3439750.0);
* fitter.addObservedPoint(4.0636168, 2877648.0);
* fitter.addObservedPoint(4.06943698, 2175960.0);
* fitter.addObservedPoint(4.07525716, 1447024.0);
* fitter.addObservedPoint(4.08237071, 717104.0);
* fitter.addObservedPoint(4.08366408, 620014.0);
* GaussianFunction fitFunction = fitter.fit();
* </pre>
*
* @see ParametricGaussianFunction
* @since 2.2
* @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $
*/
public class GaussianFitter {
/** Fitter used for fitting. */
private final CurveFitter fitter;
/**
* Constructs an instance using the specified optimizer.
*
* @param optimizer optimizer to use for the fitting
*/
public GaussianFitter(DifferentiableMultivariateVectorialOptimizer optimizer) {
fitter = new CurveFitter(optimizer);
}
/**
* Adds point (<code>x</code>, <code>y</code>) to list of observed points
* with a weight of 1.0.
*
* @param x <tt>x</tt> point value
* @param y <tt>y</tt> point value
*/
public void addObservedPoint(double x, double y) {
addObservedPoint(1.0, x, y);
}
/**
* Adds point (<code>x</code>, <code>y</code>) to list of observed points
* with a weight of <code>weight</code>.
*
* @param weight weight assigned to point
* @param x <tt>x</tt> point value
* @param y <tt>y</tt> point value
*/
public void addObservedPoint(double weight, double x, double y) {
fitter.addObservedPoint(weight, x, y);
}
/**
* Fits Gaussian function to the observed points.
*
* @return Gaussian function best fitting the observed points
*
* @throws FunctionEvaluationException if <code>CurveFitter.fit</code> throws it
* @throws OptimizationException if <code>CurveFitter.fit</code> throws it
* @throws IllegalArgumentException if <code>CurveFitter.fit</code> throws it
*
* @see CurveFitter
*/
public GaussianFunction fit() throws FunctionEvaluationException, OptimizationException {
return new GaussianFunction(fitter.fit(new ParametricGaussianFunction(),
createParametersGuesser(fitter.getObservations()).guess()));
}
/**
* Factory method to create a <code>GaussianParametersGuesser</code>
* instance initialized with the specified observations.
*
* @param observations points used to initialize the created
* <code>GaussianParametersGuesser</code> instance
*
* @return new <code>GaussianParametersGuesser</code> instance
*/
protected GaussianParametersGuesser createParametersGuesser(WeightedObservedPoint[] observations) {
return new GaussianParametersGuesser(observations);
}
}
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