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MATH-1172: Simple curve fitter 

Provides boiler-plate code so that users can readily fit any parametric function.
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Gilles 2014-12-14 18:48:01 +01:00
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commit 491786ce41
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src/changes/changes.xml
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@ -79,6 +79,10 @@ Users are encouraged to upgrade to this version as this release not
2. A few methods in the FastMath class are in fact slower that their
counterpart in either Math or StrictMath (cf. MATH-740 and MATH-901).
">
<action dev="erans" type="add" issue="MATH-1172">
New class "SimpleCurveFitter": Boiler-plate code to allow fitting of
a user-defined parametric function.
</action>
<action dev="erans" type="add" issue="MATH-1173">
New classes "TricubicInterpolatingFunction" and "TricubicInterpolator" to
replace "TricubicSplineInterpolatingFunction" and "TricubicSplineInterpolator".

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src/main/java/org/apache/commons/math3/fitting/SimpleCurveFitter.java Normal file
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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.math3.fitting;
import java.util.Collection;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresBuilder;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem;
import org.apache.commons.math3.linear.DiagonalMatrix;
/**
* Fits points to a user-defined {@link ParametricUnivariateFunction function}.
*
* @since 3.4
*/
public class SimpleCurveFitter extends AbstractCurveFitter {
/** Function to fit. */
private final ParametricUnivariateFunction function;
/** Initial guess for the parameters. */
private final double[] initialGuess;
/** Maximum number of iterations of the optimization algorithm. */
private final int maxIter;
/**
* Contructor used by the factory methods.
*
* @param function Function to fit.
* @param initialGuess Initial guess. Cannot be {@code null}. Its length must
* be consistent with the number of parameters of the {@code function} to fit.
* @param maxIter Maximum number of iterations of the optimization algorithm.
*/
private SimpleCurveFitter(ParametricUnivariateFunction function,
double[] initialGuess,
int maxIter) {
this.function = function;
this.initialGuess = initialGuess;
this.maxIter = maxIter;
}
/**
* Creates a 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}.
*
* @param f Function to fit.
* @param start Initial guess for the parameters. Cannot be {@code null}.
* Its length must be consistent with the number of parameters of the
* function to fit.
* @return a curve fitter.
*
* @see #withStartPoint(double[])
* @see #withMaxIterations(int)
*/
public static SimpleCurveFitter create(ParametricUnivariateFunction f,
double[] start) {
return new SimpleCurveFitter(f, start, Integer.MAX_VALUE);
}
/**
* Configure the start point (initial guess).
* @param newStart new start point (initial guess)
* @return a new instance.
*/
public SimpleCurveFitter withStartPoint(double[] newStart) {
return new SimpleCurveFitter(function,
newStart.clone(),
maxIter);
}
/**
* Configure the maximum number of iterations.
* @param newMaxIter maximum number of iterations
* @return a new instance.
*/
public SimpleCurveFitter withMaxIterations(int newMaxIter) {
return new SimpleCurveFitter(function,
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 count = 0;
for (WeightedObservedPoint obs : observations) {
target[count] = obs.getY();
weights[count] = obs.getWeight();
++count;
}
final AbstractCurveFitter.TheoreticalValuesFunction model
= new AbstractCurveFitter.TheoreticalValuesFunction(function,
observations);
// Create an optimizer for fitting the curve to the observed points.
return new LeastSquaresBuilder().
maxEvaluations(Integer.MAX_VALUE).
maxIterations(maxIter).
start(initialGuess).
target(target).
weight(new DiagonalMatrix(weights)).
model(model.getModelFunction(), model.getModelFunctionJacobian()).
build();
}
}

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src/test/java/org/apache/commons/math3/fitting/SimpleCurveFitterTest.java Normal file
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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.math3.fitting;
import java.util.Random;
import org.apache.commons.math3.TestUtils;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.analysis.polynomials.PolynomialFunction;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.distribution.UniformRealDistribution;
import org.apache.commons.math3.exception.ConvergenceException;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Test;
/**
* Test for class {@link SimpleCurveFitter}.
*/
public class SimpleCurveFitterTest {
@Test
public void testPolynomialFit() {
final Random randomizer = new Random(53882150042L);
final RealDistribution rng = new UniformRealDistribution(-100, 100);
rng.reseedRandomGenerator(64925784252L);
final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
final PolynomialFunction f = new PolynomialFunction(coeff);
// Collect data from a known polynomial.
final WeightedObservedPoints obs = new WeightedObservedPoints();
for (int i = 0; i < 100; i++) {
final double x = rng.sample();
obs.add(x, f.value(x) + 0.1 * randomizer.nextGaussian());
}
final ParametricUnivariateFunction function = new PolynomialFunction.Parametric();
// Start fit from initial guesses that are far from the optimal values.
final SimpleCurveFitter fitter
= SimpleCurveFitter.create(function,
new double[] { -1e20, 3e15, -5e25 });
final double[] best = fitter.fit(obs.toList());
TestUtils.assertEquals("best != coeff", coeff, best, 2e-2);
}
}