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* 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.
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package org.apache.commons.math3.fitting;
import java.util.Collection;
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresBuilder;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem;
import org.apache.commons.math3.linear.DiagonalMatrix;
Fits points to a user-defined function
. Since: 3.4
/**
* Fits points to a user-defined {@link ParametricUnivariateFunction function}.
*
* @since 3.4
*/
public class SimpleCurveFitter extends AbstractCurveFitter {
Function to fit. /** Function to fit. */
private final ParametricUnivariateFunction function;
Initial guess for the parameters. /** Initial guess for the parameters. */
private final double[] initialGuess;
Maximum number of iterations of the optimization algorithm. /** Maximum number of iterations of the optimization algorithm. */
private final int maxIter;
Contructor used by the factory methods.
Params: - function – Function to fit.
- initialGuess – Initial guess. Cannot be
null
. Its length must be consistent with the number of parameters of the function
to fit. - maxIter – Maximum number of iterations of the optimization algorithm.
/**
* Contructor used by the factory methods.
*
* @param function Function to fit.
* @param initialGuess Initial guess. Cannot be {@code null}. Its length must
* be consistent with the number of parameters of the {@code function} to fit.
* @param maxIter Maximum number of iterations of the optimization algorithm.
*/
private SimpleCurveFitter(ParametricUnivariateFunction function,
double[] initialGuess,
int maxIter) {
this.function = function;
this.initialGuess = initialGuess;
this.maxIter = maxIter;
}
Creates a curve fitter. The maximum number of iterations of the optimization algorithm is set to Integer.MAX_VALUE
. Params: - f – Function to fit.
- start – Initial guess for the parameters. Cannot be
null
. Its length must be consistent with the number of parameters of the function to fit.
See Also: Returns: a curve fitter.
/**
* Creates a curve fitter.
* The maximum number of iterations of the optimization algorithm is set
* to {@link Integer#MAX_VALUE}.
*
* @param f Function to fit.
* @param start Initial guess for the parameters. Cannot be {@code null}.
* Its length must be consistent with the number of parameters of the
* function to fit.
* @return a curve fitter.
*
* @see #withStartPoint(double[])
* @see #withMaxIterations(int)
*/
public static SimpleCurveFitter create(ParametricUnivariateFunction f,
double[] start) {
return new SimpleCurveFitter(f, start, Integer.MAX_VALUE);
}
Configure the start point (initial guess).
Params: - newStart – new start point (initial guess)
Returns: a new instance.
/**
* Configure the start point (initial guess).
* @param newStart new start point (initial guess)
* @return a new instance.
*/
public SimpleCurveFitter withStartPoint(double[] newStart) {
return new SimpleCurveFitter(function,
newStart.clone(),
maxIter);
}
Configure the maximum number of iterations.
Params: - newMaxIter – maximum number of iterations
Returns: a new instance.
/**
* Configure the maximum number of iterations.
* @param newMaxIter maximum number of iterations
* @return a new instance.
*/
public SimpleCurveFitter withMaxIterations(int newMaxIter) {
return new SimpleCurveFitter(function,
initialGuess,
newMaxIter);
}
{@inheritDoc} /** {@inheritDoc} */
@Override
protected LeastSquaresProblem getProblem(Collection<WeightedObservedPoint> observations) {
// Prepare least-squares problem.
final int len = observations.size();
final double[] target = new double[len];
final double[] weights = new double[len];
int count = 0;
for (WeightedObservedPoint obs : observations) {
target[count] = obs.getY();
weights[count] = obs.getWeight();
++count;
}
final AbstractCurveFitter.TheoreticalValuesFunction model
= new AbstractCurveFitter.TheoreticalValuesFunction(function,
observations);
// Create an optimizer for fitting the curve to the observed points.
return new LeastSquaresBuilder().
maxEvaluations(Integer.MAX_VALUE).
maxIterations(maxIter).
start(initialGuess).
target(target).
weight(new DiagonalMatrix(weights)).
model(model.getModelFunction(), model.getModelFunctionJacobian()).
build();
}
}