| /* |
| * 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.math4.fitting.leastsquares; |
| |
| import org.apache.commons.geometry.euclidean.twod.Vector2D; |
| import org.apache.commons.math4.analysis.MultivariateMatrixFunction; |
| import org.apache.commons.math4.analysis.MultivariateVectorFunction; |
| import org.apache.commons.math4.exception.ConvergenceException; |
| import org.apache.commons.math4.exception.DimensionMismatchException; |
| import org.apache.commons.math4.fitting.leastsquares.LeastSquaresBuilder; |
| import org.apache.commons.math4.fitting.leastsquares.LeastSquaresOptimizer; |
| import org.apache.commons.math4.fitting.leastsquares.LeastSquaresProblem; |
| import org.apache.commons.math4.fitting.leastsquares.MultivariateJacobianFunction; |
| import org.apache.commons.math4.fitting.leastsquares.LeastSquaresOptimizer.Optimum; |
| import org.apache.commons.math4.fitting.leastsquares.LeastSquaresProblem.Evaluation; |
| import org.apache.commons.math4.linear.Array2DRowRealMatrix; |
| import org.apache.commons.math4.linear.ArrayRealVector; |
| import org.apache.commons.math4.linear.BlockRealMatrix; |
| import org.apache.commons.math4.linear.DiagonalMatrix; |
| import org.apache.commons.math4.linear.RealMatrix; |
| import org.apache.commons.math4.linear.RealVector; |
| import org.apache.commons.math4.optim.ConvergenceChecker; |
| import org.apache.commons.math4.optim.SimpleVectorValueChecker; |
| import org.apache.commons.math4.util.FastMath; |
| import org.apache.commons.math4.util.Pair; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| import java.io.IOException; |
| import java.util.Arrays; |
| |
| import static org.hamcrest.CoreMatchers.is; |
| import static org.hamcrest.CoreMatchers.not; |
| import static org.hamcrest.CoreMatchers.sameInstance; |
| |
| /** |
| * Some of the unit tests are re-implementations of the MINPACK <a |
| * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a |
| * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files. The |
| * redistribution policy for MINPACK is available <a href="http://www.netlib.org/minpack/disclaimer">here</a>. |
| * <p> |
| * <T> Concrete implementation of an optimizer. |
| * |
| */ |
| public abstract class AbstractLeastSquaresOptimizerAbstractTest { |
| |
| /** default absolute tolerance of comparisons */ |
| public static final double TOl = 1e-10; |
| |
| public LeastSquaresBuilder base() { |
| return new LeastSquaresBuilder() |
| .checkerPair(new SimpleVectorValueChecker(1e-6, 1e-6)) |
| .maxEvaluations(100) |
| .maxIterations(getMaxIterations()); |
| } |
| |
| public LeastSquaresBuilder builder(CircleVectorial c) { |
| final double[] weights = new double[c.getN()]; |
| Arrays.fill(weights, 1.0); |
| return base() |
| .model(c.getModelFunction(), c.getModelFunctionJacobian()) |
| .target(new double[c.getN()]) |
| .weight(new DiagonalMatrix(weights)); |
| } |
| |
| public LeastSquaresBuilder builder(StatisticalReferenceDataset dataset) { |
| StatisticalReferenceDataset.LeastSquaresProblem problem |
| = dataset.getLeastSquaresProblem(); |
| final double[] weights = new double[dataset.getNumObservations()]; |
| Arrays.fill(weights, 1.0); |
| return base() |
| .model(problem.getModelFunction(), problem.getModelFunctionJacobian()) |
| .target(dataset.getData()[1]) |
| .weight(new DiagonalMatrix(weights)) |
| .start(dataset.getStartingPoint(0)); |
| } |
| |
| public void fail(LeastSquaresOptimizer optimizer) { |
| Assert.fail("Expected Exception from: " + optimizer.toString()); |
| } |
| |
| /** |
| * Check the value of a vector. |
| * @param tolerance the absolute tolerance of comparisons |
| * @param actual the vector to test |
| * @param expected the expected values |
| */ |
| public void assertEquals(double tolerance, RealVector actual, double... expected){ |
| for (int i = 0; i < expected.length; i++) { |
| Assert.assertEquals(expected[i], actual.getEntry(i), tolerance); |
| } |
| Assert.assertEquals(expected.length, actual.getDimension()); |
| } |
| |
| /** |
| * @return the default number of allowed iterations (which will be used when not |
| * specified otherwise). |
| */ |
| public abstract int getMaxIterations(); |
| |
| /** |
| * Get an instance of the optimizer under test. |
| * |
| * @return the subject under test. |
| */ |
| public abstract LeastSquaresOptimizer getOptimizer(); |
| |
| /** |
| * The subject under test. |
| */ |
| public final LeastSquaresOptimizer optimizer = this.getOptimizer(); |
| |
| @Test |
| public void testGetIterations() { |
| LeastSquaresProblem lsp = base() |
| .target(new double[]{1}) |
| .weight(new DiagonalMatrix(new double[]{1})) |
| .start(new double[]{3}) |
| .model(new MultivariateJacobianFunction() { |
| @Override |
| public Pair<RealVector, RealMatrix> value(final RealVector point) { |
| return new Pair<RealVector, RealMatrix>( |
| new ArrayRealVector( |
| new double[]{ |
| FastMath.pow(point.getEntry(0), 4) |
| }, |
| false), |
| new Array2DRowRealMatrix( |
| new double[][]{ |
| {0.25 * FastMath.pow(point.getEntry(0), 3)} |
| }, |
| false) |
| ); |
| } |
| }) |
| .build(); |
| |
| Optimum optimum = optimizer.optimize(lsp); |
| |
| //TODO more specific test? could pass with 'return 1;' |
| Assert.assertTrue(optimum.getIterations() > 0); |
| } |
| |
| @Test |
| public void testTrivial() { |
| LinearProblem problem |
| = new LinearProblem(new double[][]{{2}}, |
| new double[]{3}); |
| LeastSquaresProblem ls = problem.getBuilder().build(); |
| |
| Optimum optimum = optimizer.optimize(ls); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| assertEquals(TOl, optimum.getPoint(), 1.5); |
| Assert.assertEquals(0.0, optimum.getResiduals().getEntry(0), TOl); |
| } |
| |
| @Test |
| public void testQRColumnsPermutation() { |
| LinearProblem problem |
| = new LinearProblem(new double[][]{{1, -1}, {0, 2}, {1, -2}}, |
| new double[]{4, 6, 1}); |
| |
| Optimum optimum = optimizer.optimize(problem.getBuilder().build()); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| assertEquals(TOl, optimum.getPoint(), 7, 3); |
| assertEquals(TOl, optimum.getResiduals(), 0, 0, 0); |
| } |
| |
| @Test |
| public void testNoDependency() { |
| LinearProblem problem = new LinearProblem(new double[][]{ |
| {2, 0, 0, 0, 0, 0}, |
| {0, 2, 0, 0, 0, 0}, |
| {0, 0, 2, 0, 0, 0}, |
| {0, 0, 0, 2, 0, 0}, |
| {0, 0, 0, 0, 2, 0}, |
| {0, 0, 0, 0, 0, 2} |
| }, new double[]{0, 1.1, 2.2, 3.3, 4.4, 5.5}); |
| |
| Optimum optimum = optimizer.optimize(problem.getBuilder().build()); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| for (int i = 0; i < problem.target.length; ++i) { |
| Assert.assertEquals(0.55 * i, optimum.getPoint().getEntry(i), TOl); |
| } |
| } |
| |
| @Test |
| public void testOneSet() { |
| LinearProblem problem = new LinearProblem(new double[][]{ |
| {1, 0, 0}, |
| {-1, 1, 0}, |
| {0, -1, 1} |
| }, new double[]{1, 1, 1}); |
| |
| Optimum optimum = optimizer.optimize(problem.getBuilder().build()); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| assertEquals(TOl, optimum.getPoint(), 1, 2, 3); |
| } |
| |
| @Test |
| public void testTwoSets() { |
| double epsilon = 1e-7; |
| LinearProblem problem = new LinearProblem(new double[][]{ |
| {2, 1, 0, 4, 0, 0}, |
| {-4, -2, 3, -7, 0, 0}, |
| {4, 1, -2, 8, 0, 0}, |
| {0, -3, -12, -1, 0, 0}, |
| {0, 0, 0, 0, epsilon, 1}, |
| {0, 0, 0, 0, 1, 1} |
| }, new double[]{2, -9, 2, 2, 1 + epsilon * epsilon, 2}); |
| |
| Optimum optimum = optimizer.optimize(problem.getBuilder().build()); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| assertEquals(TOl, optimum.getPoint(), 3, 4, -1, -2, 1 + epsilon, 1 - epsilon); |
| } |
| |
| @Test |
| public void testNonInvertible() throws Exception { |
| try { |
| LinearProblem problem = new LinearProblem(new double[][]{ |
| {1, 2, -3}, |
| {2, 1, 3}, |
| {-3, 0, -9} |
| }, new double[]{1, 1, 1}); |
| |
| optimizer.optimize(problem.getBuilder().build()); |
| |
| fail(optimizer); |
| } catch (ConvergenceException e) { |
| //expected |
| } |
| } |
| |
| @Test |
| public void testIllConditioned() { |
| LinearProblem problem1 = new LinearProblem(new double[][]{ |
| {10, 7, 8, 7}, |
| {7, 5, 6, 5}, |
| {8, 6, 10, 9}, |
| {7, 5, 9, 10} |
| }, new double[]{32, 23, 33, 31}); |
| final double[] start = {0, 1, 2, 3}; |
| |
| Optimum optimum = optimizer |
| .optimize(problem1.getBuilder().start(start).build()); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| assertEquals(TOl, optimum.getPoint(), 1, 1, 1, 1); |
| |
| LinearProblem problem2 = new LinearProblem(new double[][]{ |
| {10.00, 7.00, 8.10, 7.20}, |
| {7.08, 5.04, 6.00, 5.00}, |
| {8.00, 5.98, 9.89, 9.00}, |
| {6.99, 4.99, 9.00, 9.98} |
| }, new double[]{32, 23, 33, 31}); |
| |
| optimum = optimizer.optimize(problem2.getBuilder().start(start).build()); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| assertEquals(1e-8, optimum.getPoint(), -81, 137, -34, 22); |
| } |
| |
| @Test |
| public void testMoreEstimatedParametersSimple() { |
| LinearProblem problem = new LinearProblem(new double[][]{ |
| {3, 2, 0, 0}, |
| {0, 1, -1, 1}, |
| {2, 0, 1, 0} |
| }, new double[]{7, 3, 5}); |
| |
| Optimum optimum = optimizer |
| .optimize(problem.getBuilder().start(new double[]{7, 6, 5, 4}).build()); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| } |
| |
| @Test |
| public void testMoreEstimatedParametersUnsorted() { |
| LinearProblem problem = new LinearProblem(new double[][]{ |
| {1, 1, 0, 0, 0, 0}, |
| {0, 0, 1, 1, 1, 0}, |
| {0, 0, 0, 0, 1, -1}, |
| {0, 0, -1, 1, 0, 1}, |
| {0, 0, 0, -1, 1, 0} |
| }, new double[]{3, 12, -1, 7, 1}); |
| |
| Optimum optimum = optimizer.optimize( |
| problem.getBuilder().start(new double[]{2, 2, 2, 2, 2, 2}).build()); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| RealVector point = optimum.getPoint(); |
| //the first two elements are under constrained |
| //check first two elements obey the constraint: sum to 3 |
| Assert.assertEquals(3, point.getEntry(0) + point.getEntry(1), TOl); |
| //#constrains = #states fro the last 4 elements |
| assertEquals(TOl, point.getSubVector(2, 4), 3, 4, 5, 6); |
| } |
| |
| @Test |
| public void testRedundantEquations() { |
| LinearProblem problem = new LinearProblem(new double[][]{ |
| {1, 1}, |
| {1, -1}, |
| {1, 3} |
| }, new double[]{3, 1, 5}); |
| |
| Optimum optimum = optimizer |
| .optimize(problem.getBuilder().start(new double[]{1, 1}).build()); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| assertEquals(TOl, optimum.getPoint(), 2, 1); |
| } |
| |
| @Test |
| public void testInconsistentEquations() { |
| LinearProblem problem = new LinearProblem(new double[][]{ |
| {1, 1}, |
| {1, -1}, |
| {1, 3} |
| }, new double[]{3, 1, 4}); |
| |
| Optimum optimum = optimizer |
| .optimize(problem.getBuilder().start(new double[]{1, 1}).build()); |
| |
| //TODO what is this actually testing? |
| Assert.assertTrue(optimum.getRMS() > 0.1); |
| } |
| |
| @Test |
| public void testInconsistentSizes1() { |
| try { |
| LinearProblem problem |
| = new LinearProblem(new double[][]{{1, 0}, |
| {0, 1}}, |
| new double[]{-1, 1}); |
| |
| //TODO why is this part here? hasn't it been tested already? |
| Optimum optimum = optimizer.optimize(problem.getBuilder().build()); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| assertEquals(TOl, optimum.getPoint(), -1, 1); |
| |
| //TODO move to builder test |
| optimizer.optimize( |
| problem.getBuilder().weight(new DiagonalMatrix(new double[]{1})).build()); |
| |
| fail(optimizer); |
| } catch (DimensionMismatchException e) { |
| //expected |
| } |
| } |
| |
| @Test |
| public void testInconsistentSizes2() { |
| try { |
| LinearProblem problem |
| = new LinearProblem(new double[][]{{1, 0}, {0, 1}}, |
| new double[]{-1, 1}); |
| |
| Optimum optimum = optimizer.optimize(problem.getBuilder().build()); |
| |
| Assert.assertEquals(0, optimum.getRMS(), TOl); |
| assertEquals(TOl, optimum.getPoint(), -1, 1); |
| |
| //TODO move to builder test |
| optimizer.optimize( |
| problem.getBuilder() |
| .target(new double[]{1}) |
| .weight(new DiagonalMatrix(new double[]{1})) |
| .build() |
| ); |
| |
| fail(optimizer); |
| } catch (DimensionMismatchException e) { |
| //expected |
| } |
| } |
| |
| @Test |
| public void testCircleFitting() { |
| CircleVectorial circle = new CircleVectorial(); |
| circle.addPoint(30, 68); |
| circle.addPoint(50, -6); |
| circle.addPoint(110, -20); |
| circle.addPoint(35, 15); |
| circle.addPoint(45, 97); |
| final double[] start = {98.680, 47.345}; |
| |
| Optimum optimum = optimizer.optimize(builder(circle).start(start).build()); |
| |
| Assert.assertTrue(optimum.getEvaluations() < 10); |
| |
| double rms = optimum.getRMS(); |
| Assert.assertEquals(1.768262623567235, FastMath.sqrt(circle.getN()) * rms, TOl); |
| |
| Vector2D center = Vector2D.of(optimum.getPoint().getEntry(0), optimum.getPoint().getEntry(1)); |
| Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1e-6); |
| Assert.assertEquals(96.07590211815305, center.getX(), 1e-6); |
| Assert.assertEquals(48.13516790438953, center.getY(), 1e-6); |
| |
| double[][] cov = optimum.getCovariances(1e-14).getData(); |
| Assert.assertEquals(1.839, cov[0][0], 0.001); |
| Assert.assertEquals(0.731, cov[0][1], 0.001); |
| Assert.assertEquals(cov[0][1], cov[1][0], 1e-14); |
| Assert.assertEquals(0.786, cov[1][1], 0.001); |
| |
| // add perfect measurements and check formal errors are reduced |
| double r = circle.getRadius(center); |
| for (double d = 0; d < 2 * FastMath.PI; d += 0.01) { |
| circle.addPoint(center.getX() + r * FastMath.cos(d), center.getY() + r * FastMath.sin(d)); |
| } |
| |
| double[] weights = new double[circle.getN()]; |
| Arrays.fill(weights, 2); |
| |
| optimum = optimizer.optimize( |
| builder(circle).weight(new DiagonalMatrix(weights)).start(start).build()); |
| |
| cov = optimum.getCovariances(1e-14).getData(); |
| Assert.assertEquals(0.0016, cov[0][0], 0.001); |
| Assert.assertEquals(3.2e-7, cov[0][1], 1e-9); |
| Assert.assertEquals(cov[0][1], cov[1][0], 1e-14); |
| Assert.assertEquals(0.0016, cov[1][1], 0.001); |
| } |
| |
| @Test |
| public void testCircleFittingBadInit() { |
| CircleVectorial circle = new CircleVectorial(); |
| double[][] points = circlePoints; |
| double[] weights = new double[points.length]; |
| final double[] start = {-12, -12}; |
| Arrays.fill(weights, 2); |
| for (int i = 0; i < points.length; ++i) { |
| circle.addPoint(points[i][0], points[i][1]); |
| } |
| |
| Optimum optimum = optimizer.optimize(builder(circle).weight(new DiagonalMatrix(weights)).start(start).build()); |
| |
| Vector2D center = Vector2D.of(optimum.getPoint().getEntry(0), optimum.getPoint().getEntry(1)); |
| Assert.assertTrue(optimum.getEvaluations() < 25); |
| Assert.assertEquals(0.043, optimum.getRMS(), 1e-3); |
| Assert.assertEquals(0.292235, circle.getRadius(center), 1e-6); |
| Assert.assertEquals(-0.151738, center.getX(), 1e-6); |
| Assert.assertEquals(0.2075001, center.getY(), 1e-6); |
| } |
| |
| @Test |
| public void testCircleFittingGoodInit() { |
| CircleVectorial circle = new CircleVectorial(); |
| double[][] points = circlePoints; |
| double[] weights = new double[points.length]; |
| Arrays.fill(weights, 2); |
| for (int i = 0; i < points.length; ++i) { |
| circle.addPoint(points[i][0], points[i][1]); |
| } |
| final double[] start = {0, 0}; |
| |
| Optimum optimum = optimizer.optimize( |
| builder(circle).weight(new DiagonalMatrix(weights)).start(start).build()); |
| |
| assertEquals(1e-6, optimum.getPoint(), -0.1517383071957963, 0.2074999736353867); |
| Assert.assertEquals(0.04268731682389561, optimum.getRMS(), 1e-8); |
| } |
| |
| private final double[][] circlePoints = new double[][]{ |
| {-0.312967, 0.072366}, {-0.339248, 0.132965}, {-0.379780, 0.202724}, |
| {-0.390426, 0.260487}, {-0.361212, 0.328325}, {-0.346039, 0.392619}, |
| {-0.280579, 0.444306}, {-0.216035, 0.470009}, {-0.149127, 0.493832}, |
| {-0.075133, 0.483271}, {-0.007759, 0.452680}, {0.060071, 0.410235}, |
| {0.103037, 0.341076}, {0.118438, 0.273884}, {0.131293, 0.192201}, |
| {0.115869, 0.129797}, {0.072223, 0.058396}, {0.022884, 0.000718}, |
| {-0.053355, -0.020405}, {-0.123584, -0.032451}, {-0.216248, -0.032862}, |
| {-0.278592, -0.005008}, {-0.337655, 0.056658}, {-0.385899, 0.112526}, |
| {-0.405517, 0.186957}, {-0.415374, 0.262071}, {-0.387482, 0.343398}, |
| {-0.347322, 0.397943}, {-0.287623, 0.458425}, {-0.223502, 0.475513}, |
| {-0.135352, 0.478186}, {-0.061221, 0.483371}, {0.003711, 0.422737}, |
| {0.065054, 0.375830}, {0.108108, 0.297099}, {0.123882, 0.222850}, |
| {0.117729, 0.134382}, {0.085195, 0.056820}, {0.029800, -0.019138}, |
| {-0.027520, -0.072374}, {-0.102268, -0.091555}, {-0.200299, -0.106578}, |
| {-0.292731, -0.091473}, {-0.356288, -0.051108}, {-0.420561, 0.014926}, |
| {-0.471036, 0.074716}, {-0.488638, 0.182508}, {-0.485990, 0.254068}, |
| {-0.463943, 0.338438}, {-0.406453, 0.404704}, {-0.334287, 0.466119}, |
| {-0.254244, 0.503188}, {-0.161548, 0.495769}, {-0.075733, 0.495560}, |
| {0.001375, 0.434937}, {0.082787, 0.385806}, {0.115490, 0.323807}, |
| {0.141089, 0.223450}, {0.138693, 0.131703}, {0.126415, 0.049174}, |
| {0.066518, -0.010217}, {-0.005184, -0.070647}, {-0.080985, -0.103635}, |
| {-0.177377, -0.116887}, {-0.260628, -0.100258}, {-0.335756, -0.056251}, |
| {-0.405195, -0.000895}, {-0.444937, 0.085456}, {-0.484357, 0.175597}, |
| {-0.472453, 0.248681}, {-0.438580, 0.347463}, {-0.402304, 0.422428}, |
| {-0.326777, 0.479438}, {-0.247797, 0.505581}, {-0.152676, 0.519380}, |
| {-0.071754, 0.516264}, {0.015942, 0.472802}, {0.076608, 0.419077}, |
| {0.127673, 0.330264}, {0.159951, 0.262150}, {0.153530, 0.172681}, |
| {0.140653, 0.089229}, {0.078666, 0.024981}, {0.023807, -0.037022}, |
| {-0.048837, -0.077056}, {-0.127729, -0.075338}, {-0.221271, -0.067526} |
| }; |
| |
| public void doTestStRD(final StatisticalReferenceDataset dataset, |
| final LeastSquaresOptimizer optimizer, |
| final double errParams, |
| final double errParamsSd) { |
| |
| final Optimum optimum = optimizer.optimize(builder(dataset).build()); |
| |
| final RealVector actual = optimum.getPoint(); |
| for (int i = 0; i < actual.getDimension(); i++) { |
| double expected = dataset.getParameter(i); |
| double delta = FastMath.abs(errParams * expected); |
| Assert.assertEquals(dataset.getName() + ", param #" + i, |
| expected, actual.getEntry(i), delta); |
| } |
| } |
| |
| @Test |
| public void testKirby2() throws IOException { |
| doTestStRD(StatisticalReferenceDatasetFactory.createKirby2(), optimizer, 1E-7, 1E-7); |
| } |
| |
| @Test |
| public void testHahn1() throws IOException { |
| doTestStRD(StatisticalReferenceDatasetFactory.createHahn1(), optimizer, 1E-7, 1E-4); |
| } |
| |
| @Test |
| public void testPointCopy() { |
| LinearProblem problem = new LinearProblem(new double[][]{ |
| {1, 0, 0}, |
| {-1, 1, 0}, |
| {0, -1, 1} |
| }, new double[]{1, 1, 1}); |
| //mutable boolean |
| final boolean[] checked = {false}; |
| |
| final LeastSquaresBuilder builder = problem.getBuilder() |
| .checker(new ConvergenceChecker<Evaluation>() { |
| @Override |
| public boolean converged(int iteration, Evaluation previous, Evaluation current) { |
| Assert.assertThat( |
| previous.getPoint(), |
| not(sameInstance(current.getPoint()))); |
| Assert.assertArrayEquals(new double[3], previous.getPoint().toArray(), 0); |
| Assert.assertArrayEquals(new double[] {1, 2, 3}, current.getPoint().toArray(), TOl); |
| checked[0] = true; |
| return true; |
| } |
| }); |
| optimizer.optimize(builder.build()); |
| |
| Assert.assertThat(checked[0], is(true)); |
| } |
| |
| class LinearProblem { |
| private final RealMatrix factors; |
| private final double[] target; |
| |
| public LinearProblem(double[][] factors, double[] target) { |
| this.factors = new BlockRealMatrix(factors); |
| this.target = target; |
| } |
| |
| public double[] getTarget() { |
| return target; |
| } |
| |
| public MultivariateVectorFunction getModelFunction() { |
| return new MultivariateVectorFunction() { |
| @Override |
| public double[] value(double[] params) { |
| return factors.operate(params); |
| } |
| }; |
| } |
| |
| public MultivariateMatrixFunction getModelFunctionJacobian() { |
| return new MultivariateMatrixFunction() { |
| @Override |
| public double[][] value(double[] params) { |
| return factors.getData(); |
| } |
| }; |
| } |
| |
| public LeastSquaresBuilder getBuilder() { |
| final double[] weights = new double[target.length]; |
| Arrays.fill(weights, 1.0); |
| return base() |
| .model(getModelFunction(), getModelFunctionJacobian()) |
| .target(target) |
| .weight(new DiagonalMatrix(weights)) |
| .start(new double[factors.getColumnDimension()]); |
| } |
| } |
| } |