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Diffstat (limited to 'src/main/java/org/apache/commons/math/optimization/fitting/CurveFitter.java')
| -rw-r--r-- | src/main/java/org/apache/commons/math/optimization/fitting/CurveFitter.java | 197 |
1 files changed, 197 insertions, 0 deletions
diff --git a/src/main/java/org/apache/commons/math/optimization/fitting/CurveFitter.java b/src/main/java/org/apache/commons/math/optimization/fitting/CurveFitter.java new file mode 100644 index 0000000..9bb70d1 --- /dev/null +++ b/src/main/java/org/apache/commons/math/optimization/fitting/CurveFitter.java @@ -0,0 +1,197 @@ +/* + * 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.analysis.DifferentiableMultivariateVectorialFunction; +import org.apache.commons.math.analysis.MultivariateMatrixFunction; +import org.apache.commons.math.FunctionEvaluationException; +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</sub>, p<sub>1</sub> ... p<sub>n-1</sub>, + * this class can be used to find these parameters. It does this + * by <em>fitting</em> the curve so it remains very close to a set of + * observed points (x<sub>0</sub>, y<sub>0</sub>), (x<sub>1</sub>, + * y<sub>1</sub>) ... (x<sub>k-1</sub>, y<sub>k-1</sub>). This fitting + * is done by finding the parameters values that minimizes the objective + * function ∑(y<sub>i</sub>-f(x<sub>i</sub>))<sup>2</sup>. This is + * really a least squares problem.</p> + * @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $ + * @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)</code>.</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. + * <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; + + } + + } + +} |