/*
* 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.optim.nonlinear.vector;
import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.optim.OptimizationData;
import org.apache.commons.math3.optim.BaseMultivariateOptimizer;
import org.apache.commons.math3.optim.ConvergenceChecker;
import org.apache.commons.math3.optim.PointVectorValuePair;
import org.apache.commons.math3.linear.RealMatrix;
/**
* Base class for a multivariate vector function optimizer.
*
* @since 3.1
*/
@Deprecated
public abstract class MultivariateVectorOptimizer
extends BaseMultivariateOptimizer<PointVectorValuePair> {
/** Target values for the model function at optimum. */
private double[] target;
/** Weight matrix. */
private RealMatrix weightMatrix;
/** Model function. */
private MultivariateVectorFunction model;
/**
* @param checker Convergence checker.
*/
protected MultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
super(checker);
}
/**
* Computes the objective function value.
* This method <em>must</em> be called by subclasses to enforce the
* evaluation counter limit.
*
* @param params Point at which the objective function must be evaluated.
* @return the objective function value at the specified point.
* @throws TooManyEvaluationsException if the maximal number of evaluations
* (of the model vector function) is exceeded.
*/
protected double[] computeObjectiveValue(double[] params) {
super.incrementEvaluationCount();
return model.value(params);
}
/**
* {@inheritDoc}
*
* @param optData Optimization data. In addition to those documented in
* {@link BaseMultivariateOptimizer#parseOptimizationData(OptimizationData[])
* BaseMultivariateOptimizer}, this method will register the following data:
* <ul>
* <li>{@link Target}</li>
* <li>{@link Weight}</li>
* <li>{@link ModelFunction}</li>
* </ul>
* @return {@inheritDoc}
* @throws TooManyEvaluationsException if the maximal number of
* evaluations is exceeded.
* @throws DimensionMismatchException if the initial guess, target, and weight
* arguments have inconsistent dimensions.
*/
@Override
public PointVectorValuePair optimize(OptimizationData... optData)
throws TooManyEvaluationsException,
DimensionMismatchException {
// Set up base class and perform computation.
return super.optimize(optData);
}
/**
* Gets the weight matrix of the observations.
*
* @return the weight matrix.
*/
public RealMatrix getWeight() {
return weightMatrix.copy();
}
/**
* Gets the observed values to be matched by the objective vector
* function.
*
* @return the target values.
*/
public double[] getTarget() {
return target.clone();
}
/**
* Gets the number of observed values.
*
* @return the length of the target vector.
*/
public int getTargetSize() {
return target.length;
}
/**
* Scans the list of (required and optional) optimization data that
* characterize the problem.
*
* @param optData Optimization data. The following data will be looked for:
* <ul>
* <li>{@link Target}</li>
* <li>{@link Weight}</li>
* <li>{@link ModelFunction}</li>
* </ul>
*/
@Override
protected void parseOptimizationData(OptimizationData... optData) {
// Allow base class to register its own data.
super.parseOptimizationData(optData);
// The existing values (as set by the previous call) are reused if
// not provided in the argument list.
for (OptimizationData data : optData) {
if (data instanceof ModelFunction) {
model = ((ModelFunction) data).getModelFunction();
continue;
}
if (data instanceof Target) {
target = ((Target) data).getTarget();
continue;
}
if (data instanceof Weight) {
weightMatrix = ((Weight) data).getWeight();
continue;
}
}
// Check input consistency.
checkParameters();
}
/**
* Check parameters consistency.
*
* @throws DimensionMismatchException if {@link #target} and
* {@link #weightMatrix} have inconsistent dimensions.
*/
private void checkParameters() {
if (target.length != weightMatrix.getColumnDimension()) {
throw new DimensionMismatchException(target.length,
weightMatrix.getColumnDimension());
}
}
}