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Java example source code file (SimpleCurveFitter.java)

This example Java source code file (SimpleCurveFitter.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

abstractcurvefitter, diagonalmatrix, leastsquaresbuilder, leastsquaresproblem, override, parametricunivariatefunction, simplecurvefitter, util, weightedobservedpoint

The SimpleCurveFitter.java Java example source code

 * 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,
 * 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 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,

     * 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,

    /** {@inheritDoc} */
    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();

        final AbstractCurveFitter.TheoreticalValuesFunction model
            = new AbstractCurveFitter.TheoreticalValuesFunction(function,

        // Create an optimizer for fitting the curve to the observed points.
        return new LeastSquaresBuilder().
                weight(new DiagonalMatrix(weights)).
                model(model.getModelFunction(), model.getModelFunctionJacobian()).

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