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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.exception.util.LocalizedFormats;
import org.apache.commons.math.optimization.OptimizationException;
import org.apache.commons.math.util.FastMath;
/** This class guesses harmonic coefficients from a sample.
* <p>The algorithm used to guess the coefficients is as follows:</p>
* <p>We know f (t) at some sampling points t<sub>i</sub> and want to find a,
* ω and φ such that f (t) = a cos (ω t + φ).
* </p>
*
* <p>From the analytical expression, we can compute two primitives :
* <pre>
* If2 (t) = ∫ f<sup>2</sup> = a<sup>2</sup> × [t + S (t)] / 2
* If'2 (t) = ∫ f'<sup>2</sup> = a<sup>2</sup> ω<sup>2</sup> × [t - S (t)] / 2
* where S (t) = sin (2 (ω t + φ)) / (2 ω)
* </pre>
* </p>
*
* <p>We can remove S between these expressions :
* <pre>
* If'2 (t) = a<sup>2</sup> ω<sup>2</sup> t - ω<sup>2</sup> If2 (t)
* </pre>
* </p>
*
* <p>The preceding expression shows that If'2 (t) is a linear
* combination of both t and If2 (t): If'2 (t) = A × t + B × If2 (t)
* </p>
*
* <p>From the primitive, we can deduce the same form for definite
* integrals between t<sub>1</sub> and t<sub>i</sub> for each t<sub>i</sub> :
* <pre>
* If2 (t<sub>i</sub>) - If2 (t<sub>1</sub>) = A × (t<sub>i</sub> - t<sub>1</sub>) + B × (If2 (t<sub>i</sub>) - If2 (t<sub>1</sub>))
* </pre>
* </p>
*
* <p>We can find the coefficients A and B that best fit the sample
* to this linear expression by computing the definite integrals for
* each sample points.
* </p>
*
* <p>For a bilinear expression z (x<sub>i</sub>, y<sub>i</sub>) = A × x<sub>i</sub> + B × y<sub>i</sub>, the
* coefficients A and B that minimize a least square criterion
* ∑ (z<sub>i</sub> - z (x<sub>i</sub>, y<sub>i</sub>))<sup>2</sup> are given by these expressions:</p>
* <pre>
*
* ∑y<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>z<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑y<sub>i</sub>z<sub>i</sub>
* A = ------------------------
* ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>y<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>y<sub>i</sub>
*
* ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>z<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>z<sub>i</sub>
* B = ------------------------
* ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>y<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>y<sub>i</sub>
* </pre>
* </p>
*
*
* <p>In fact, we can assume both a and ω are positive and
* compute them directly, knowing that A = a<sup>2</sup> ω<sup>2</sup> and that
* B = - ω<sup>2</sup>. The complete algorithm is therefore:</p>
* <pre>
*
* for each t<sub>i</sub> from t<sub>1</sub> to t<sub>n-1</sub>, compute:
* f (t<sub>i</sub>)
* f' (t<sub>i</sub>) = (f (t<sub>i+1</sub>) - f(t<sub>i-1</sub>)) / (t<sub>i+1</sub> - t<sub>i-1</sub>)
* x<sub>i</sub> = t<sub>i</sub> - t<sub>1</sub>
* y<sub>i</sub> = ∫ f<sup>2</sup> from t<sub>1</sub> to t<sub>i</sub>
* z<sub>i</sub> = ∫ f'<sup>2</sup> from t<sub>1</sub> to t<sub>i</sub>
* update the sums ∑x<sub>i</sub>x<sub>i</sub>, ∑y<sub>i</sub>y<sub>i</sub>, ∑x<sub>i</sub>y<sub>i</sub>, ∑x<sub>i</sub>z<sub>i</sub> and ∑y<sub>i</sub>z<sub>i</sub>
* end for
*
* |--------------------------
* \ | ∑y<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>z<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑y<sub>i</sub>z<sub>i</sub>
* a = \ | ------------------------
* \| ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>z<sub>i</sub> - ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>z<sub>i</sub>
*
*
* |--------------------------
* \ | ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>z<sub>i</sub> - ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>z<sub>i</sub>
* ω = \ | ------------------------
* \| ∑x<sub>i</sub>x<sub>i</sub> ∑y<sub>i</sub>y<sub>i</sub> - ∑x<sub>i</sub>y<sub>i</sub> ∑x<sub>i</sub>y<sub>i</sub>
*
* </pre>
* </p>
* <p>Once we know ω, we can compute:
* <pre>
* fc = ω f (t) cos (ω t) - f' (t) sin (ω t)
* fs = ω f (t) sin (ω t) + f' (t) cos (ω t)
* </pre>
* </p>
* <p>It appears that <code>fc = a ω cos (φ)</code> and
* <code>fs = -a ω sin (φ)</code>, so we can use these
* expressions to compute φ. The best estimate over the sample is
* given by averaging these expressions.
* </p>
* <p>Since integrals and means are involved in the preceding
* estimations, these operations run in O(n) time, where n is the
* number of measurements.</p>
* @version $Revision: 1056034 $ $Date: 2011-01-06 20:41:43 +0100 (jeu. 06 janv. 2011) $
* @since 2.0
*/
public class HarmonicCoefficientsGuesser {
/** Sampled observations. */
private final WeightedObservedPoint[] observations;
/** Guessed amplitude. */
private double a;
/** Guessed pulsation ω. */
private double omega;
/** Guessed phase φ. */
private double phi;
/** Simple constructor.
* @param observations sampled observations
*/
public HarmonicCoefficientsGuesser(WeightedObservedPoint[] observations) {
this.observations = observations.clone();
a = Double.NaN;
omega = Double.NaN;
}
/** Estimate a first guess of the coefficients.
* @exception OptimizationException if the sample is too short or if
* the first guess cannot be computed (when the elements under the
* square roots are negative).
* */
public void guess() throws OptimizationException {
sortObservations();
guessAOmega();
guessPhi();
}
/** Sort the observations with respect to the abscissa.
*/
private void sortObservations() {
// Since the samples are almost always already sorted, this
// method is implemented as an insertion sort that reorders the
// elements in place. Insertion sort is very efficient in this case.
WeightedObservedPoint curr = observations[0];
for (int j = 1; j < observations.length; ++j) {
WeightedObservedPoint prec = curr;
curr = observations[j];
if (curr.getX() < prec.getX()) {
// the current element should be inserted closer to the beginning
int i = j - 1;
WeightedObservedPoint mI = observations[i];
while ((i >= 0) && (curr.getX() < mI.getX())) {
observations[i + 1] = mI;
if (i-- != 0) {
mI = observations[i];
}
}
observations[i + 1] = curr;
curr = observations[j];
}
}
}
/** Estimate a first guess of the a and ω coefficients.
* @exception OptimizationException if the sample is too short or if
* the first guess cannot be computed (when the elements under the
* square roots are negative).
*/
private void guessAOmega() throws OptimizationException {
// initialize the sums for the linear model between the two integrals
double sx2 = 0.0;
double sy2 = 0.0;
double sxy = 0.0;
double sxz = 0.0;
double syz = 0.0;
double currentX = observations[0].getX();
double currentY = observations[0].getY();
double f2Integral = 0;
double fPrime2Integral = 0;
final double startX = currentX;
for (int i = 1; i < observations.length; ++i) {
// one step forward
final double previousX = currentX;
final double previousY = currentY;
currentX = observations[i].getX();
currentY = observations[i].getY();
// update the integrals of f<sup>2</sup> and f'<sup>2</sup>
// considering a linear model for f (and therefore constant f')
final double dx = currentX - previousX;
final double dy = currentY - previousY;
final double f2StepIntegral =
dx * (previousY * previousY + previousY * currentY + currentY * currentY) / 3;
final double fPrime2StepIntegral = dy * dy / dx;
final double x = currentX - startX;
f2Integral += f2StepIntegral;
fPrime2Integral += fPrime2StepIntegral;
sx2 += x * x;
sy2 += f2Integral * f2Integral;
sxy += x * f2Integral;
sxz += x * fPrime2Integral;
syz += f2Integral * fPrime2Integral;
}
// compute the amplitude and pulsation coefficients
double c1 = sy2 * sxz - sxy * syz;
double c2 = sxy * sxz - sx2 * syz;
double c3 = sx2 * sy2 - sxy * sxy;
if ((c1 / c2 < 0.0) || (c2 / c3 < 0.0)) {
throw new OptimizationException(LocalizedFormats.UNABLE_TO_FIRST_GUESS_HARMONIC_COEFFICIENTS);
}
a = FastMath.sqrt(c1 / c2);
omega = FastMath.sqrt(c2 / c3);
}
/** Estimate a first guess of the φ coefficient.
*/
private void guessPhi() {
// initialize the means
double fcMean = 0.0;
double fsMean = 0.0;
double currentX = observations[0].getX();
double currentY = observations[0].getY();
for (int i = 1; i < observations.length; ++i) {
// one step forward
final double previousX = currentX;
final double previousY = currentY;
currentX = observations[i].getX();
currentY = observations[i].getY();
final double currentYPrime = (currentY - previousY) / (currentX - previousX);
double omegaX = omega * currentX;
double cosine = FastMath.cos(omegaX);
double sine = FastMath.sin(omegaX);
fcMean += omega * currentY * cosine - currentYPrime * sine;
fsMean += omega * currentY * sine + currentYPrime * cosine;
}
phi = FastMath.atan2(-fsMean, fcMean);
}
/** Get the guessed amplitude a.
* @return guessed amplitude a;
*/
public double getGuessedAmplitude() {
return a;
}
/** Get the guessed pulsation ω.
* @return guessed pulsation ω
*/
public double getGuessedPulsation() {
return omega;
}
/** Get the guessed phase φ.
* @return guessed phase φ
*/
public double getGuessedPhase() {
return phi;
}
}
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