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Commons Math example source code file (CurveFitter.java)

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

Java - Commons Math tags/keywords

arraylist, curvefitter, differentiablemultivariatevectorialfunction, differentiablemultivariatevectorialoptimizer, functionevaluationexception, illegalargumentexception, illegalargumentexception, multivariatematrixfunction, parametricrealfunction, parametricrealfunction, theoreticalvaluesfunction, theoreticalvaluesfunction, util, weightedobservedpoint, weightedobservedpoint

The Commons Math CurveFitter.java 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.math.optimization.fitting;

import java.util.ArrayList;
import java.util.List;

import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.analysis.DifferentiableMultivariateVectorialFunction;
import org.apache.commons.math.analysis.MultivariateMatrixFunction;
import org.apache.commons.math.optimization.DifferentiableMultivariateVectorialOptimizer;
import org.apache.commons.math.optimization.OptimizationException;
import org.apache.commons.math.optimization.VectorialPointValuePair;

/** Fitter for parametric univariate real functions y = f(x).
 * <p>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.</p>
 * @version $Revision: 927009 $ $Date: 2010-03-24 07:14:07 -0400 (Wed, 24 Mar 2010) $
 * @since 2.0
 */
public class CurveFitter {

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

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

    /** Simple constructor.
     * @param optimizer optimizer to use for the fitting
     */
    public CurveFitter(final DifferentiableMultivariateVectorialOptimizer 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).

* @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. * <p>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.</p> * @param f parametric function to fit * @param initialGuess first guess of the function parameters * @return fitted parameters * @exception FunctionEvaluationException if the objective function throws one during * the search * @exception OptimizationException if the algorithm failed to converge * @exception IllegalArgumentException if the start point dimension is wrong */ public double[] fit(final ParametricRealFunction f, final double[] initialGuess) throws FunctionEvaluationException, OptimizationException, IllegalArgumentException { // 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 VectorialPointValuePair optimum = optimizer.optimize(new TheoreticalValuesFunction(f), target, weights, initialGuess); // extract the coefficients return optimum.getPointRef(); } /** Vectorial function computing function theoretical values. */ private class TheoreticalValuesFunction implements DifferentiableMultivariateVectorialFunction { /** Function to fit. */ private final ParametricRealFunction f; /** Simple constructor. * @param f function to fit. */ public TheoreticalValuesFunction(final ParametricRealFunction f) { this.f = f; } /** {@inheritDoc} */ public MultivariateMatrixFunction jacobian() { return new MultivariateMatrixFunction() { public double[][] value(double[] point) throws FunctionEvaluationException, IllegalArgumentException { 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) throws FunctionEvaluationException, IllegalArgumentException { // 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; } } }

Other Commons Math examples (source code examples)

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