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The Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.

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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
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 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
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 * See the License for the specific language governing permissions and
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package org.apache.commons.math.optimization.fitting;

import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.exception.util.LocalizedFormats;
import org.apache.commons.math.optimization.DifferentiableMultivariateVectorialOptimizer;
import org.apache.commons.math.optimization.OptimizationException;
import org.apache.commons.math.util.FastMath;

/** This class implements a curve fitting specialized for sinusoids.
 * 

Harmonic fitting is a very simple case of curve fitting. The * estimated coefficients are the amplitude a, the pulsation ω and * the phase φ: f (t) = a cos (ω t + φ). They are * searched by a least square estimator initialized with a rough guess * based on integrals.

* @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $ * @since 2.0 */ public class HarmonicFitter { /** Fitter for the coefficients. */ private final CurveFitter fitter; /** Values for amplitude, pulsation ω and phase φ. */ private double[] parameters; /** Simple constructor. * @param optimizer optimizer to use for the fitting */ public HarmonicFitter(final DifferentiableMultivariateVectorialOptimizer optimizer) { this.fitter = new CurveFitter(optimizer); parameters = null; } /** Simple constructor. *

This constructor can be used when a first guess of the * coefficients is already known.

* @param optimizer optimizer to use for the fitting * @param initialGuess guessed values for amplitude (index 0), * pulsation ω (index 1) and phase φ (index 2) */ public HarmonicFitter(final DifferentiableMultivariateVectorialOptimizer optimizer, final double[] initialGuess) { this.fitter = new CurveFitter(optimizer); this.parameters = initialGuess.clone(); } /** Add an observed weighted (x,y) point to the sample. * @param weight weight of the observed point in the fit * @param x abscissa of the point * @param y observed value of the point at x, after fitting we should * have P(x) as close as possible to this value */ public void addObservedPoint(double weight, double x, double y) { fitter.addObservedPoint(weight, x, y); } /** Fit an harmonic function to the observed points. * @return harmonic function best fitting the observed points * @throws OptimizationException if the sample is too short or if * the first guess cannot be computed */ public HarmonicFunction fit() throws OptimizationException { // shall we compute the first guess of the parameters ourselves ? if (parameters == null) { final WeightedObservedPoint[] observations = fitter.getObservations(); if (observations.length < 4) { throw new OptimizationException(LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, observations.length, 4); } HarmonicCoefficientsGuesser guesser = new HarmonicCoefficientsGuesser(observations); guesser.guess(); parameters = new double[] { guesser.getGuessedAmplitude(), guesser.getGuessedPulsation(), guesser.getGuessedPhase() }; } try { double[] fitted = fitter.fit(new ParametricHarmonicFunction(), parameters); return new HarmonicFunction(fitted[0], fitted[1], fitted[2]); } catch (FunctionEvaluationException fee) { // should never happen throw new RuntimeException(fee); } } /** Parametric harmonic function. */ private static class ParametricHarmonicFunction implements ParametricRealFunction { /** {@inheritDoc} */ public double value(double x, double[] parameters) { final double a = parameters[0]; final double omega = parameters[1]; final double phi = parameters[2]; return a * FastMath.cos(omega * x + phi); } /** {@inheritDoc} */ public double[] gradient(double x, double[] parameters) { final double a = parameters[0]; final double omega = parameters[1]; final double phi = parameters[2]; final double alpha = omega * x + phi; final double cosAlpha = FastMath.cos(alpha); final double sinAlpha = FastMath.sin(alpha); return new double[] { cosAlpha, -a * x * sinAlpha, -a * sinAlpha }; } } }




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