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* 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.jacobian;
import java.io.IOException;
import java.util.Arrays;
import org.apache.commons.math3.optim.PointVectorValuePair;
import org.apache.commons.math3.optim.InitialGuess;
import org.apache.commons.math3.optim.MaxEval;
import org.apache.commons.math3.optim.nonlinear.vector.Target;
import org.apache.commons.math3.optim.nonlinear.vector.Weight;
import org.apache.commons.math3.util.FastMath;
import org.junit.Test;
import org.junit.Assert;
@Deprecated
public class AbstractLeastSquaresOptimizerTest {
public static AbstractLeastSquaresOptimizer createOptimizer() {
return new AbstractLeastSquaresOptimizer(null) {
@Override
protected PointVectorValuePair doOptimize() {
final double[] params = getStartPoint();
final double[] res = computeResiduals(computeObjectiveValue(params));
setCost(computeCost(res));
return new PointVectorValuePair(params, null);
}
};
}
@Test
public void testGetChiSquare() throws IOException {
final StatisticalReferenceDataset dataset
= StatisticalReferenceDatasetFactory.createKirby2();
final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
final double[] a = dataset.getParameters();
final double[] y = dataset.getData()[1];
final double[] w = new double[y.length];
Arrays.fill(w, 1.0);
StatisticalReferenceDataset.LeastSquaresProblem problem
= dataset.getLeastSquaresProblem();
optimizer.optimize(new MaxEval(1),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(y),
new Weight(w),
new InitialGuess(a));
final double expected = dataset.getResidualSumOfSquares();
final double actual = optimizer.getChiSquare();
Assert.assertEquals(dataset.getName(), expected, actual,
1E-11 * expected);
}
@Test
public void testGetRMS() throws IOException {
final StatisticalReferenceDataset dataset
= StatisticalReferenceDatasetFactory.createKirby2();
final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
final double[] a = dataset.getParameters();
final double[] y = dataset.getData()[1];
final double[] w = new double[y.length];
Arrays.fill(w, 1);
StatisticalReferenceDataset.LeastSquaresProblem problem
= dataset.getLeastSquaresProblem();
optimizer.optimize(new MaxEval(1),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(y),
new Weight(w),
new InitialGuess(a));
final double expected = FastMath
.sqrt(dataset.getResidualSumOfSquares() /
dataset.getNumObservations());
final double actual = optimizer.getRMS();
Assert.assertEquals(dataset.getName(), expected, actual,
1E-11 * expected);
}
@Test
public void testComputeSigma() throws IOException {
final StatisticalReferenceDataset dataset
= StatisticalReferenceDatasetFactory.createKirby2();
final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
final double[] a = dataset.getParameters();
final double[] y = dataset.getData()[1];
final double[] w = new double[y.length];
Arrays.fill(w, 1);
StatisticalReferenceDataset.LeastSquaresProblem problem
= dataset.getLeastSquaresProblem();
final PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(1),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
new Target(y),
new Weight(w),
new InitialGuess(a));
final double[] sig = optimizer.computeSigma(optimum.getPoint(), 1e-14);
final int dof = y.length - a.length;
final double[] expected = dataset.getParametersStandardDeviations();
for (int i = 0; i < sig.length; i++) {
final double actual = FastMath.sqrt(optimizer.getChiSquare() / dof) * sig[i];
Assert.assertEquals(dataset.getName() + ", parameter #" + i,
expected[i], actual, 1e-6 * expected[i]);
}
}
}