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Diffstat (limited to 'src/main/java/org/apache/commons/math3/fitting/CurveFitter.java')
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diff --git a/src/main/java/org/apache/commons/math3/fitting/CurveFitter.java b/src/main/java/org/apache/commons/math3/fitting/CurveFitter.java new file mode 100644 index 0000000..09dd7f2 --- /dev/null +++ b/src/main/java/org/apache/commons/math3/fitting/CurveFitter.java @@ -0,0 +1,235 @@ +/* + * 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 org.apache.commons.math3.analysis.MultivariateMatrixFunction; +import org.apache.commons.math3.analysis.MultivariateVectorFunction; +import org.apache.commons.math3.analysis.ParametricUnivariateFunction; +import org.apache.commons.math3.optim.InitialGuess; +import org.apache.commons.math3.optim.MaxEval; +import org.apache.commons.math3.optim.PointVectorValuePair; +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.MultivariateVectorOptimizer; +import org.apache.commons.math3.optim.nonlinear.vector.Target; +import org.apache.commons.math3.optim.nonlinear.vector.Weight; + +import java.util.ArrayList; +import java.util.List; + +/** + * 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. + * @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)}. + * + * @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; + } + }); + } + } +} |