| /* |
| * 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.optimization.general; |
| |
| import java.io.IOException; |
| import java.util.Arrays; |
| |
| import org.junit.Assert; |
| import org.apache.commons.math3.optimization.PointVectorValuePair; |
| import org.apache.commons.math3.util.FastMath; |
| import org.junit.Test; |
| |
| @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; |
| dataset = StatisticalReferenceDatasetFactory.createKirby2(); |
| final AbstractLeastSquaresOptimizer optimizer; |
| 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); |
| |
| optimizer.optimize(1, dataset.getLeastSquaresProblem(), y, w, 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; |
| dataset = StatisticalReferenceDatasetFactory.createKirby2(); |
| final AbstractLeastSquaresOptimizer optimizer; |
| 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); |
| |
| optimizer.optimize(1, dataset.getLeastSquaresProblem(), y, w, 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; |
| dataset = StatisticalReferenceDatasetFactory.createKirby2(); |
| final AbstractLeastSquaresOptimizer optimizer; |
| 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); |
| |
| final int dof = y.length - a.length; |
| final PointVectorValuePair optimum = optimizer.optimize(1, dataset.getLeastSquaresProblem(), y, w, a); |
| final double[] sig = optimizer.computeSigma(optimum.getPoint(), 1e-14); |
| 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-7 * expected[i]); |
| } |
| } |
| } |