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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, initialguess, maxeval, modelfunction, modelfunctionjacobian, multivariatematrixfunction, multivariatevectoroptimizer, parametricunivariatefunction, pointvectorvaluepair, target, 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.fitting;

import java.util.ArrayList;
import java.util.List;
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.optim.MaxEval;
import org.apache.commons.math3.optim.InitialGuess;
import org.apache.commons.math3.optim.PointVectorValuePair;
import org.apache.commons.math3.optim.nonlinear.vector.MultivariateVectorOptimizer;
import org.apache.commons.math3.optim.nonlinear.vector.ModelFunction;
import org.apache.commons.math3.optim.nonlinear.vector.ModelFunctionJacobian;
import org.apache.commons.math3.optim.nonlinear.vector.Target;
import org.apache.commons.math3.optim.nonlinear.vector.Weight;

/**
 * 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.
 *
 * @since 2.0
 * @deprecated As of 3.3. Please use {@link AbstractCurveFitter} and
 * {@link WeightedObservedPoints} instead.
 */
@Deprecated
public class CurveFitter<T extends ParametricUnivariateFunction> {
    /** Optimizer to use for the fitting. */
    private final MultivariateVectorOptimizer optimizer;
    /** Observed points. */
    private final List<WeightedObservedPoint> observations;

    /**
     * Simple constructor.
     *
     * @param optimizer Optimizer to use for the fitting.
     * @since 3.1
     */
    public CurveFitter(final MultivariateVectorOptimizer optimizer) {
        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;
        }

        // Input to the optimizer: the model and its Jacobian.
        final TheoreticalValuesFunction model = new TheoreticalValuesFunction(f);

        // Perform the fit.
        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(maxEval),
                                 model.getModelFunction(),
                                 model.getModelFunctionJacobian(),
                                 new Target(target),
                                 new Weight(weights),
                                 new InitialGuess(initialGuess));
        // Extract the coefficients.
        return optimum.getPointRef();
    }

    /** Vectorial function computing function theoretical values. */
    private class TheoreticalValuesFunction {
        /** Function to fit. */
        private final ParametricUnivariateFunction f;

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

        /**
         * @return the model function values.
         */
        public ModelFunction getModelFunction() {
            return new ModelFunction(new MultivariateVectorFunction() {
                    /** {@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;
                    }
                });
        }

        /**
         * @return the model function Jacobian.
         */
        public ModelFunctionJacobian getModelFunctionJacobian() {
            return new ModelFunctionJacobian(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;
                    }
                });
        }
    }
}

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