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Commons Math example source code file (

This example Commons Math source code file ( is included in the "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 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
 * 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.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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