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 * 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
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 *      http://www.apache.org/licenses/LICENSE-2.0
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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 function.
Since:3.4
/** * Fits points to a user-defined {@link ParametricUnivariateFunction function}. * * @since 3.4 */
public class SimpleCurveFitter extends AbstractCurveFitter {
Function to fit.
/** Function to fit. */
private final ParametricUnivariateFunction function;
Initial guess for the parameters.
/** Initial guess for the parameters. */
private final double[] initialGuess;
Maximum number of iterations of the optimization algorithm.
/** Maximum number of iterations of the optimization algorithm. */
private final int maxIter;
Contructor used by the factory methods.
Params:
  • function – Function to fit.
  • initialGuess – Initial guess. Cannot be null. Its length must be consistent with the number of parameters of the function to fit.
  • maxIter – Maximum number of iterations of the optimization algorithm.
/** * 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 maximum number of iterations of the optimization algorithm is set to Integer.MAX_VALUE.
Params:
  • f – Function to fit.
  • start – Initial guess for the parameters. Cannot be null. Its length must be consistent with the number of parameters of the function to fit.
See Also:
Returns:a curve fitter.
/** * Creates a curve fitter. * 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).
Params:
  • newStart – new start point (initial guess)
Returns:a new instance.
/** * 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.
Params:
  • newMaxIter – maximum number of iterations
Returns:a new instance.
/** * 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}
/** {@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(); } }