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Java example source code file (GaussianCurveFitter.java)
The GaussianCurveFitter.java Java example source code/* * 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.math3.fitting; import java.util.ArrayList; import java.util.Collection; import java.util.Collections; import java.util.Comparator; import java.util.List; import org.apache.commons.math3.analysis.function.Gaussian; import org.apache.commons.math3.exception.NotStrictlyPositiveException; import org.apache.commons.math3.exception.NullArgumentException; import org.apache.commons.math3.exception.NumberIsTooSmallException; import org.apache.commons.math3.exception.OutOfRangeException; import org.apache.commons.math3.exception.ZeroException; import org.apache.commons.math3.exception.util.LocalizedFormats; import org.apache.commons.math3.fitting.leastsquares.LeastSquaresBuilder; import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem; import org.apache.commons.math3.linear.DiagonalMatrix; import org.apache.commons.math3.util.FastMath; /** * Fits points to a {@link * org.apache.commons.math3.analysis.function.Gaussian.Parametric Gaussian} * function. * <br/> * The {@link #withStartPoint(double[]) initial guess values} must be passed * in the following order: * <ul> * <li>Normalization * <li>Mean * <li>Sigma * </ul> * The optimal values will be returned in the same order. * * <p> * Usage example: * <pre> * WeightedObservedPoints obs = new WeightedObservedPoints(); * obs.add(4.0254623, 531026.0); * obs.add(4.03128248, 984167.0); * obs.add(4.03839603, 1887233.0); * obs.add(4.04421621, 2687152.0); * obs.add(4.05132976, 3461228.0); * obs.add(4.05326982, 3580526.0); * obs.add(4.05779662, 3439750.0); * obs.add(4.0636168, 2877648.0); * obs.add(4.06943698, 2175960.0); * obs.add(4.07525716, 1447024.0); * obs.add(4.08237071, 717104.0); * obs.add(4.08366408, 620014.0); * double[] parameters = GaussianCurveFitter.create().fit(obs.toList()); * </pre> * * @since 3.3 */ public class GaussianCurveFitter extends AbstractCurveFitter { /** Parametric function to be fitted. */ private static final Gaussian.Parametric FUNCTION = new Gaussian.Parametric() { /** {@inheritDoc} */ @Override public double value(double x, double ... p) { double v = Double.POSITIVE_INFINITY; try { v = super.value(x, p); } catch (NotStrictlyPositiveException e) { // NOPMD // Do nothing. } return v; } /** {@inheritDoc} */ @Override public double[] gradient(double x, double ... p) { double[] v = { Double.POSITIVE_INFINITY, Double.POSITIVE_INFINITY, Double.POSITIVE_INFINITY }; try { v = super.gradient(x, p); } catch (NotStrictlyPositiveException e) { // NOPMD // Do nothing. } return v; } }; /** Initial guess. */ private final double[] initialGuess; /** Maximum number of iterations of the optimization algorithm. */ private final int maxIter; /** * Contructor used by the factory methods. * * @param initialGuess Initial guess. If set to {@code null}, the initial guess * will be estimated using the {@link ParameterGuesser}. * @param maxIter Maximum number of iterations of the optimization algorithm. */ private GaussianCurveFitter(double[] initialGuess, int maxIter) { this.initialGuess = initialGuess; this.maxIter = maxIter; } /** * Creates a default curve fitter. * The initial guess for the parameters will be {@link ParameterGuesser} * computed automatically, and the maximum number of iterations of the * optimization algorithm is set to {@link Integer#MAX_VALUE}. * * @return a curve fitter. * * @see #withStartPoint(double[]) * @see #withMaxIterations(int) */ public static GaussianCurveFitter create() { return new GaussianCurveFitter(null, Integer.MAX_VALUE); } /** * Configure the start point (initial guess). * @param newStart new start point (initial guess) * @return a new instance. */ public GaussianCurveFitter withStartPoint(double[] newStart) { return new GaussianCurveFitter(newStart.clone(), maxIter); } /** * Configure the maximum number of iterations. * @param newMaxIter maximum number of iterations * @return a new instance. */ public GaussianCurveFitter withMaxIterations(int newMaxIter) { return new GaussianCurveFitter(initialGuess, newMaxIter); } /** {@inheritDoc} */ @Override protected LeastSquaresProblem getProblem(Collection<WeightedObservedPoint> observations) { // Prepare least-squares problem. final int len = observations.size(); final double[] target = new double[len]; final double[] weights = new double[len]; int i = 0; for (WeightedObservedPoint obs : observations) { target[i] = obs.getY(); weights[i] = obs.getWeight(); ++i; } final AbstractCurveFitter.TheoreticalValuesFunction model = new AbstractCurveFitter.TheoreticalValuesFunction(FUNCTION, observations); final double[] startPoint = initialGuess != null ? initialGuess : // Compute estimation. new ParameterGuesser(observations).guess(); // Return a new least squares problem set up to fit a Gaussian curve to the // observed points. return new LeastSquaresBuilder(). maxEvaluations(Integer.MAX_VALUE). maxIterations(maxIter). start(startPoint). target(target). weight(new DiagonalMatrix(weights)). model(model.getModelFunction(), model.getModelFunctionJacobian()). build(); } /** * Guesses the parameters {@code norm}, {@code mean}, and {@code sigma} * of a {@link org.apache.commons.math3.analysis.function.Gaussian.Parametric} * based on the specified observed points. */ public static class ParameterGuesser { /** Normalization factor. */ private final double norm; /** Mean. */ private final double mean; /** Standard deviation. */ private final double sigma; /** * Constructs instance with the specified observed points. * * @param observations Observed points from which to guess the * parameters of the Gaussian. * @throws NullArgumentException if {@code observations} is * {@code null}. * @throws NumberIsTooSmallException if there are less than 3 * observations. */ public ParameterGuesser(Collection<WeightedObservedPoint> observations) { if (observations == null) { throw new NullArgumentException(LocalizedFormats.INPUT_ARRAY); } if (observations.size() < 3) { throw new NumberIsTooSmallException(observations.size(), 3, true); } final List<WeightedObservedPoint> sorted = sortObservations(observations); final double[] params = basicGuess(sorted.toArray(new WeightedObservedPoint[0])); norm = params[0]; mean = params[1]; sigma = params[2]; } /** * Gets an estimation of the parameters. * * @return the guessed parameters, in the following order: * <ul> * <li>Normalization factor * <li>Mean * <li>Standard deviation * </ul> */ public double[] guess() { return new double[] { norm, mean, sigma }; } /** * Sort the observations. * * @param unsorted Input observations. * @return the input observations, sorted. */ private List<WeightedObservedPoint> sortObservations(Collection Other Java examples (source code examples)Here is a short list of links related to this Java GaussianCurveFitter.java source code file: |
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