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Java example source code file (CurveFitter.java)

This example Java source code file (CurveFitter.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

arraylist, curvefitter, deprecated, derivativestructure, differentiablemultivariatevectorfunction, differentiablemultivariatevectoroptimizer, multivariatedifferentiablevectorfunction, multivariatedifferentiablevectoroptimizer, multivariatematrixfunction, oldtheoreticalvaluesfunction, parametricunivariatefunction, pointvectorvaluepair, theoreticalvaluesfunction, util, weightedobservedpoint

The CurveFitter.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 java.util.ArrayList;
import java.util.List;

import org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.analysis.differentiation.DerivativeStructure;
import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction;
import org.apache.commons.math3.optimization.DifferentiableMultivariateVectorOptimizer;
import org.apache.commons.math3.optimization.MultivariateDifferentiableVectorOptimizer;
import org.apache.commons.math3.optimization.PointVectorValuePair;

/** Fitter for parametric univariate real functions y = f(x).
 * <br/>
 * When a univariate real function y = f(x) does depend on some
 * unknown parameters p<sub>0, p1 ... pn-1,
 * this class can be used to find these parameters. It does this
 * by <em>fitting the curve so it remains very close to a set of
 * observed points (x<sub>0, y0), (x1,
 * y<sub>1) ... (xk-1, yk-1). This fitting
 * is done by finding the parameters values that minimizes the objective
 * function ∑(y<sub>i-f(xi))2. This is
 * really a least squares problem.
 *
 * @param <T> Function to use for the fit.
 *
 * @deprecated As of 3.1 (to be removed in 4.0).
 * @since 2.0
 */
@Deprecated
public class CurveFitter<T extends ParametricUnivariateFunction> {

    /** Optimizer to use for the fitting.
     * @deprecated as of 3.1 replaced by {@link #optimizer}
     */
    @Deprecated
    private final DifferentiableMultivariateVectorOptimizer oldOptimizer;

    /** Optimizer to use for the fitting. */
    private final MultivariateDifferentiableVectorOptimizer optimizer;

    /** Observed points. */
    private final List<WeightedObservedPoint> observations;

    /** Simple constructor.
     * @param optimizer optimizer to use for the fitting
     * @deprecated as of 3.1 replaced by {@link #CurveFitter(MultivariateDifferentiableVectorOptimizer)}
     */
    @Deprecated
    public CurveFitter(final DifferentiableMultivariateVectorOptimizer optimizer) {
        this.oldOptimizer = optimizer;
        this.optimizer    = null;
        observations      = new ArrayList<WeightedObservedPoint>();
    }

    /** Simple constructor.
     * @param optimizer optimizer to use for the fitting
     * @since 3.1
     */
    public CurveFitter(final MultivariateDifferentiableVectorOptimizer optimizer) {
        this.oldOptimizer = null;
        this.optimizer    = optimizer;
        observations      = new ArrayList<WeightedObservedPoint>();
    }

    /** Add an observed (x,y) point to the sample with unit weight.
     * <p>Calling this method is equivalent to call
     * {@code addObservedPoint(1.0, x, y)}.</p>
     * @param x abscissa of the point
     * @param y observed value of the point at x, after fitting we should
     * have f(x) as close as possible to this value
     * @see #addObservedPoint(double, double, double)
     * @see #addObservedPoint(WeightedObservedPoint)
     * @see #getObservations()
     */
    public void addObservedPoint(double x, double y) {
        addObservedPoint(1.0, x, y);
    }

    /** Add an observed weighted (x,y) point to the sample.
     * @param weight weight of the observed point in the fit
     * @param x abscissa of the point
     * @param y observed value of the point at x, after fitting we should
     * have f(x) as close as possible to this value
     * @see #addObservedPoint(double, double)
     * @see #addObservedPoint(WeightedObservedPoint)
     * @see #getObservations()
     */
    public void addObservedPoint(double weight, double x, double y) {
        observations.add(new WeightedObservedPoint(weight, x, y));
    }

    /** Add an observed weighted (x,y) point to the sample.
     * @param observed observed point to add
     * @see #addObservedPoint(double, double)
     * @see #addObservedPoint(double, double, double)
     * @see #getObservations()
     */
    public void addObservedPoint(WeightedObservedPoint observed) {
        observations.add(observed);
    }

    /** Get the observed points.
     * @return observed points
     * @see #addObservedPoint(double, double)
     * @see #addObservedPoint(double, double, double)
     * @see #addObservedPoint(WeightedObservedPoint)
     */
    public WeightedObservedPoint[] getObservations() {
        return observations.toArray(new WeightedObservedPoint[observations.size()]);
    }

    /**
     * Remove all observations.
     */
    public void clearObservations() {
        observations.clear();
    }

    /**
     * Fit a curve.
     * This method compute the coefficients of the curve that best
     * fit the sample of observed points previously given through calls
     * to the {@link #addObservedPoint(WeightedObservedPoint)
     * addObservedPoint} method.
     *
     * @param f parametric function to fit.
     * @param initialGuess first guess of the function parameters.
     * @return the fitted parameters.
     * @throws org.apache.commons.math3.exception.DimensionMismatchException
     * if the start point dimension is wrong.
     */
    public double[] fit(T f, final double[] initialGuess) {
        return fit(Integer.MAX_VALUE, f, initialGuess);
    }

    /**
     * Fit a curve.
     * This method compute the coefficients of the curve that best
     * fit the sample of observed points previously given through calls
     * to the {@link #addObservedPoint(WeightedObservedPoint)
     * addObservedPoint} method.
     *
     * @param f parametric function to fit.
     * @param initialGuess first guess of the function parameters.
     * @param maxEval Maximum number of function evaluations.
     * @return the fitted parameters.
     * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
     * if the number of allowed evaluations is exceeded.
     * @throws org.apache.commons.math3.exception.DimensionMismatchException
     * if the start point dimension is wrong.
     * @since 3.0
     */
    public double[] fit(int maxEval, T f,
                        final double[] initialGuess) {
        // prepare least squares problem
        double[] target  = new double[observations.size()];
        double[] weights = new double[observations.size()];
        int i = 0;
        for (WeightedObservedPoint point : observations) {
            target[i]  = point.getY();
            weights[i] = point.getWeight();
            ++i;
        }

        // perform the fit
        final PointVectorValuePair optimum;
        if (optimizer == null) {
            // to be removed in 4.0
            optimum = oldOptimizer.optimize(maxEval, new OldTheoreticalValuesFunction(f),
                                            target, weights, initialGuess);
        } else {
            optimum = optimizer.optimize(maxEval, new TheoreticalValuesFunction(f),
                                         target, weights, initialGuess);
        }

        // extract the coefficients
        return optimum.getPointRef();
    }

    /** Vectorial function computing function theoretical values. */
    @Deprecated
    private class OldTheoreticalValuesFunction
        implements DifferentiableMultivariateVectorFunction {
        /** Function to fit. */
        private final ParametricUnivariateFunction f;

        /** Simple constructor.
         * @param f function to fit.
         */
        OldTheoreticalValuesFunction(final ParametricUnivariateFunction f) {
            this.f = f;
        }

        /** {@inheritDoc} */
        public MultivariateMatrixFunction jacobian() {
            return new MultivariateMatrixFunction() {
                /** {@inheritDoc} */
                public double[][] value(double[] point) {
                    final double[][] jacobian = new double[observations.size()][];

                    int i = 0;
                    for (WeightedObservedPoint observed : observations) {
                        jacobian[i++] = f.gradient(observed.getX(), point);
                    }

                    return jacobian;
                }
            };
        }

        /** {@inheritDoc} */
        public double[] value(double[] point) {
            // compute the residuals
            final double[] values = new double[observations.size()];
            int i = 0;
            for (WeightedObservedPoint observed : observations) {
                values[i++] = f.value(observed.getX(), point);
            }

            return values;
        }
    }

    /** Vectorial function computing function theoretical values. */
    private class TheoreticalValuesFunction implements MultivariateDifferentiableVectorFunction {

        /** Function to fit. */
        private final ParametricUnivariateFunction f;

        /** Simple constructor.
         * @param f function to fit.
         */
        TheoreticalValuesFunction(final ParametricUnivariateFunction f) {
            this.f = f;
        }

        /** {@inheritDoc} */
        public double[] value(double[] point) {
            // compute the residuals
            final double[] values = new double[observations.size()];
            int i = 0;
            for (WeightedObservedPoint observed : observations) {
                values[i++] = f.value(observed.getX(), point);
            }

            return values;
        }

        /** {@inheritDoc} */
        public DerivativeStructure[] value(DerivativeStructure[] point) {

            // extract parameters
            final double[] parameters = new double[point.length];
            for (int k = 0; k < point.length; ++k) {
                parameters[k] = point[k].getValue();
            }

            // compute the residuals
            final DerivativeStructure[] values = new DerivativeStructure[observations.size()];
            int i = 0;
            for (WeightedObservedPoint observed : observations) {

                // build the DerivativeStructure by adding first the value as a constant
                // and then adding derivatives
                DerivativeStructure vi = new DerivativeStructure(point.length, 1, f.value(observed.getX(), parameters));
                for (int k = 0; k < point.length; ++k) {
                    vi = vi.add(new DerivativeStructure(point.length, 1, k, 0.0));
                }

                values[i++] = vi;

            }

            return values;
        }

    }

}

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