org.apache.commons.math3.fitting.AbstractCurveFitter Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of commons-math3 Show documentation
Show all versions of commons-math3 Show documentation
The Apache Commons 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.
/*
* 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.MultivariateVectorFunction;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem;
import org.apache.commons.math3.fitting.leastsquares.LevenbergMarquardtOptimizer;
/**
* Base class that contains common code for fitting parametric univariate
* real functions y = f(pi;x), where {@code x} is
* the independent variable and the pi are the
* parameters.
*
* A fitter will find the optimal values of the parameters by
* fitting the curve so it remains very close to a set of
* {@code N} observed points (xk, yk),
* {@code 0 <= k < N}.
*
* An algorithm usually performs the fit by finding the parameter
* values that minimizes the objective function
*
* ∑yk - f(xk)2,
*
* which is actually a least-squares problem.
* This class contains boilerplate code for calling the
* {@link #fit(Collection)} method for obtaining the parameters.
* The problem setup, such as the choice of optimization algorithm
* for fitting a specific function is delegated to subclasses.
*
* @since 3.3
*/
public abstract class AbstractCurveFitter {
/**
* Fits a curve.
* This method computes the coefficients of the curve that best
* fit the sample of observed points.
*
* @param points Observations.
* @return the fitted parameters.
*/
public double[] fit(Collection points) {
// Perform the fit.
return getOptimizer().optimize(getProblem(points)).getPoint().toArray();
}
/**
* Creates an optimizer set up to fit the appropriate curve.
*
* The default implementation uses a {@link LevenbergMarquardtOptimizer
* Levenberg-Marquardt} optimizer.
*
* @return the optimizer to use for fitting the curve to the
* given {@code points}.
*/
protected LeastSquaresOptimizer getOptimizer() {
return new LevenbergMarquardtOptimizer();
}
/**
* Creates a least squares problem corresponding to the appropriate curve.
*
* @param points Sample points.
* @return the least squares problem to use for fitting the curve to the
* given {@code points}.
*/
protected abstract LeastSquaresProblem getProblem(Collection points);
/**
* Vector function for computing function theoretical values.
*/
protected static class TheoreticalValuesFunction {
/** Function to fit. */
private final ParametricUnivariateFunction f;
/** Observations. */
private final double[] points;
/**
* @param f function to fit.
* @param observations Observations.
*/
public TheoreticalValuesFunction(final ParametricUnivariateFunction f,
final Collection observations) {
this.f = f;
final int len = observations.size();
this.points = new double[len];
int i = 0;
for (WeightedObservedPoint obs : observations) {
this.points[i++] = obs.getX();
}
}
/**
* @return the model function values.
*/
public MultivariateVectorFunction getModelFunction() {
return new MultivariateVectorFunction() {
/** {@inheritDoc} */
public double[] value(double[] p) {
final int len = points.length;
final double[] values = new double[len];
for (int i = 0; i < len; i++) {
values[i] = f.value(points[i], p);
}
return values;
}
};
}
/**
* @return the model function Jacobian.
*/
public MultivariateMatrixFunction getModelFunctionJacobian() {
return new MultivariateMatrixFunction() {
/** {@inheritDoc} */
public double[][] value(double[] p) {
final int len = points.length;
final double[][] jacobian = new double[len][];
for (int i = 0; i < len; i++) {
jacobian[i] = f.gradient(points[i], p);
}
return jacobian;
}
};
}
}
}