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Diffstat (limited to 'src/main/java/org/apache/commons/math3/optimization/fitting/CurveFitter.java')
| -rw-r--r-- | src/main/java/org/apache/commons/math3/optimization/fitting/CurveFitter.java | 299 |
1 files changed, 299 insertions, 0 deletions
diff --git a/src/main/java/org/apache/commons/math3/optimization/fitting/CurveFitter.java b/src/main/java/org/apache/commons/math3/optimization/fitting/CurveFitter.java new file mode 100644 index 0000000..26e39f5 --- /dev/null +++ b/src/main/java/org/apache/commons/math3/optimization/fitting/CurveFitter.java @@ -0,0 +1,299 @@ +/* + * 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</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. + * + * @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; + } + + } + +} |