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Liferay Dynamic Data Lists Service
/*
* 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 java.util.ArrayList;
import java.util.List;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.optim.MaxEval;
import org.apache.commons.math3.optim.InitialGuess;
import org.apache.commons.math3.optim.PointVectorValuePair;
import org.apache.commons.math3.optim.nonlinear.vector.MultivariateVectorOptimizer;
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.Target;
import org.apache.commons.math3.optim.nonlinear.vector.Weight;
/**
* Fitter for parametric univariate real functions y = f(x).
*
* When a univariate real function y = f(x) does depend on some
* unknown parameters p0, p1 ... pn-1,
* this class can be used to find these parameters. It does this
* by fitting the curve so it remains very close to a set of
* observed points (x0, y0), (x1,
* y1) ... (xk-1, yk-1). This fitting
* is done by finding the parameters values that minimizes the objective
* function ∑(yi-f(xi))2. This is
* really a least squares problem.
*
* @param 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 {
/** Optimizer to use for the fitting. */
private final MultivariateVectorOptimizer optimizer;
/** Observed points. */
private final List 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();
}
/** Add an observed (x,y) point to the sample with unit weight.
* 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;
}
});
}
}
}