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Java example source code file (CurveFitterTest.java)

This example Java source code file (CurveFitterTest.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

curvefitter, curvefittertest, deprecated, levenbergmarquardtoptimizer, parametricunivariatefunction, simpleinversefunction, test

The CurveFitterTest.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, 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.optimization.fitting;

import org.apache.commons.math3.optimization.general.LevenbergMarquardtOptimizer;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Test;

@Deprecated
public class CurveFitterTest {

    @Test
    public void testMath303() {

        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter(optimizer);
        fitter.addObservedPoint(2.805d, 0.6934785852953367d);
        fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
        fitter.addObservedPoint(1.655d, 0.9474675497289684);
        fitter.addObservedPoint(1.725d, 0.9013594835804194d);

        ParametricUnivariateFunction sif = new SimpleInverseFunction();

        double[] initialguess1 = new double[1];
        initialguess1[0] = 1.0d;
        Assert.assertEquals(1, fitter.fit(sif, initialguess1).length);

        double[] initialguess2 = new double[2];
        initialguess2[0] = 1.0d;
        initialguess2[1] = .5d;
        Assert.assertEquals(2, fitter.fit(sif, initialguess2).length);

    }

    @Test
    public void testMath304() {

        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter(optimizer);
        fitter.addObservedPoint(2.805d, 0.6934785852953367d);
        fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
        fitter.addObservedPoint(1.655d, 0.9474675497289684);
        fitter.addObservedPoint(1.725d, 0.9013594835804194d);

        ParametricUnivariateFunction sif = new SimpleInverseFunction();

        double[] initialguess1 = new double[1];
        initialguess1[0] = 1.0d;
        Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14);

        double[] initialguess2 = new double[1];
        initialguess2[0] = 10.0d;
        Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14);

    }

    @Test
    public void testMath372() {
        LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
        CurveFitter<ParametricUnivariateFunction> curveFitter = new CurveFitter(optimizer);

        curveFitter.addObservedPoint( 15,  4443);
        curveFitter.addObservedPoint( 31,  8493);
        curveFitter.addObservedPoint( 62, 17586);
        curveFitter.addObservedPoint(125, 30582);
        curveFitter.addObservedPoint(250, 45087);
        curveFitter.addObservedPoint(500, 50683);

        ParametricUnivariateFunction f = new ParametricUnivariateFunction() {

            public double value(double x, double ... parameters) {

                double a = parameters[0];
                double b = parameters[1];
                double c = parameters[2];
                double d = parameters[3];

                return d + ((a - d) / (1 + FastMath.pow(x / c, b)));
            }

            public double[] gradient(double x, double ... parameters) {

                double a = parameters[0];
                double b = parameters[1];
                double c = parameters[2];
                double d = parameters[3];

                double[] gradients = new double[4];
                double den = 1 + FastMath.pow(x / c, b);

                // derivative with respect to a
                gradients[0] = 1 / den;

                // derivative with respect to b
                // in the reported (invalid) issue, there was a sign error here
                gradients[1] = -((a - d) * FastMath.pow(x / c, b) * FastMath.log(x / c)) / (den * den);

                // derivative with respect to c
                gradients[2] = (b * FastMath.pow(x / c, b - 1) * (x / (c * c)) * (a - d)) / (den * den);

                // derivative with respect to d
                gradients[3] = 1 - (1 / den);

                return gradients;

            }
        };

        double[] initialGuess = new double[] { 1500, 0.95, 65, 35000 };
        double[] estimatedParameters = curveFitter.fit(f, initialGuess);

        Assert.assertEquals( 2411.00, estimatedParameters[0], 500.00);
        Assert.assertEquals(    1.62, estimatedParameters[1],   0.04);
        Assert.assertEquals(  111.22, estimatedParameters[2],   0.30);
        Assert.assertEquals(55347.47, estimatedParameters[3], 300.00);
        Assert.assertTrue(optimizer.getRMS() < 600.0);

    }

    private static class SimpleInverseFunction implements ParametricUnivariateFunction {

        public double value(double x, double ... parameters) {
            return parameters[0] / x + (parameters.length < 2 ? 0 : parameters[1]);
        }

        public double[] gradient(double x, double ... doubles) {
            double[] gradientVector = new double[doubles.length];
            gradientVector[0] = 1 / x;
            if (doubles.length >= 2) {
                gradientVector[1] = 1;
            }
            return gradientVector;
        }
    }

}

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