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Posted to commits@commons.apache.org by er...@apache.org on 2012/12/12 15:11:04 UTC
svn commit: r1420684 [12/15] - in /commons/proper/math/trunk/src:
main/java/org/apache/commons/math3/exception/
main/java/org/apache/commons/math3/exception/util/
main/java/org/apache/commons/math3/fitting/
main/java/org/apache/commons/math3/optim/ mai...
Added: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/scalar/noderiv/SimplexOptimizerMultiDirectionalTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/scalar/noderiv/SimplexOptimizerMultiDirectionalTest.java?rev=1420684&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/scalar/noderiv/SimplexOptimizerMultiDirectionalTest.java (added)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/scalar/noderiv/SimplexOptimizerMultiDirectionalTest.java Wed Dec 12 14:10:38 2012
@@ -0,0 +1,228 @@
+/*
+ * 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.scalar.noderiv;
+
+import org.apache.commons.math3.analysis.MultivariateFunction;
+import org.apache.commons.math3.optim.MaxEval;
+import org.apache.commons.math3.optim.GoalType;
+import org.apache.commons.math3.optim.InitialGuess;
+import org.apache.commons.math3.optim.PointValuePair;
+import org.apache.commons.math3.optim.SimpleValueChecker;
+import org.apache.commons.math3.optim.ObjectiveFunction;
+import org.apache.commons.math3.util.FastMath;
+import org.junit.Assert;
+import org.junit.Test;
+
+public class SimplexOptimizerMultiDirectionalTest {
+ @Test
+ public void testMinimize1() {
+ SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
+ final FourExtrema fourExtrema = new FourExtrema();
+
+ final PointValuePair optimum
+ = optimizer.optimize(new MaxEval(200),
+ new ObjectiveFunction(fourExtrema),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { -3, 0 }),
+ new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
+ Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 4e-6);
+ Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
+ Assert.assertEquals(fourExtrema.valueXmYp, optimum.getValue(), 8e-13);
+ Assert.assertTrue(optimizer.getEvaluations() > 120);
+ Assert.assertTrue(optimizer.getEvaluations() < 150);
+ }
+
+ @Test
+ public void testMinimize2() {
+ SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
+ final FourExtrema fourExtrema = new FourExtrema();
+
+ final PointValuePair optimum
+ = optimizer.optimize(new MaxEval(200),
+ new ObjectiveFunction(fourExtrema),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { 1, 0 }),
+ new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
+ Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
+ Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
+ Assert.assertEquals(fourExtrema.valueXpYm, optimum.getValue(), 2e-12);
+ Assert.assertTrue(optimizer.getEvaluations() > 120);
+ Assert.assertTrue(optimizer.getEvaluations() < 150);
+ }
+
+ @Test
+ public void testMaximize1() {
+ SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
+ final FourExtrema fourExtrema = new FourExtrema();
+
+ final PointValuePair optimum
+ = optimizer.optimize(new MaxEval(200),
+ new ObjectiveFunction(fourExtrema),
+ GoalType.MAXIMIZE,
+ new InitialGuess(new double[] { -3.0, 0.0 }),
+ new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
+ Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 7e-7);
+ Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-7);
+ Assert.assertEquals(fourExtrema.valueXmYm, optimum.getValue(), 2e-14);
+ Assert.assertTrue(optimizer.getEvaluations() > 120);
+ Assert.assertTrue(optimizer.getEvaluations() < 150);
+ }
+
+ @Test
+ public void testMaximize2() {
+ SimplexOptimizer optimizer = new SimplexOptimizer(new SimpleValueChecker(1e-15, 1e-30));
+ final FourExtrema fourExtrema = new FourExtrema();
+
+ final PointValuePair optimum
+ = optimizer.optimize(new MaxEval(200),
+ new ObjectiveFunction(fourExtrema),
+ GoalType.MAXIMIZE,
+ new InitialGuess(new double[] { 1, 0 }),
+ new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
+ Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
+ Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
+ Assert.assertEquals(fourExtrema.valueXpYp, optimum.getValue(), 2e-12);
+ Assert.assertTrue(optimizer.getEvaluations() > 180);
+ Assert.assertTrue(optimizer.getEvaluations() < 220);
+ }
+
+ @Test
+ public void testRosenbrock() {
+ MultivariateFunction rosenbrock
+ = new MultivariateFunction() {
+ public double value(double[] x) {
+ ++count;
+ double a = x[1] - x[0] * x[0];
+ double b = 1.0 - x[0];
+ return 100 * a * a + b * b;
+ }
+ };
+
+ count = 0;
+ SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
+ PointValuePair optimum
+ = optimizer.optimize(new MaxEval(100),
+ new ObjectiveFunction(rosenbrock),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { -1.2, 1 }),
+ new MultiDirectionalSimplex(new double[][] {
+ { -1.2, 1.0 },
+ { 0.9, 1.2 },
+ { 3.5, -2.3 } }));
+
+ Assert.assertEquals(count, optimizer.getEvaluations());
+ Assert.assertTrue(optimizer.getEvaluations() > 50);
+ Assert.assertTrue(optimizer.getEvaluations() < 100);
+ Assert.assertTrue(optimum.getValue() > 1e-2);
+ }
+
+ @Test
+ public void testPowell() {
+ MultivariateFunction powell
+ = new MultivariateFunction() {
+ public double value(double[] x) {
+ ++count;
+ double a = x[0] + 10 * x[1];
+ double b = x[2] - x[3];
+ double c = x[1] - 2 * x[2];
+ double d = x[0] - x[3];
+ return a * a + 5 * b * b + c * c * c * c + 10 * d * d * d * d;
+ }
+ };
+
+ count = 0;
+ SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
+ PointValuePair optimum
+ = optimizer.optimize(new MaxEval(1000),
+ new ObjectiveFunction(powell),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { 3, -1, 0, 1 }),
+ new MultiDirectionalSimplex(4));
+ Assert.assertEquals(count, optimizer.getEvaluations());
+ Assert.assertTrue(optimizer.getEvaluations() > 800);
+ Assert.assertTrue(optimizer.getEvaluations() < 900);
+ Assert.assertTrue(optimum.getValue() > 1e-2);
+ }
+
+ @Test
+ public void testMath283() {
+ // fails because MultiDirectional.iterateSimplex is looping forever
+ // the while(true) should be replaced with a convergence check
+ SimplexOptimizer optimizer = new SimplexOptimizer(1e-14, 1e-14);
+ final Gaussian2D function = new Gaussian2D(0, 0, 1);
+ PointValuePair estimate = optimizer.optimize(new MaxEval(1000),
+ new ObjectiveFunction(function),
+ GoalType.MAXIMIZE,
+ new InitialGuess(function.getMaximumPosition()),
+ new MultiDirectionalSimplex(2));
+ final double EPSILON = 1e-5;
+ final double expectedMaximum = function.getMaximum();
+ final double actualMaximum = estimate.getValue();
+ Assert.assertEquals(expectedMaximum, actualMaximum, EPSILON);
+
+ final double[] expectedPosition = function.getMaximumPosition();
+ final double[] actualPosition = estimate.getPoint();
+ Assert.assertEquals(expectedPosition[0], actualPosition[0], EPSILON );
+ Assert.assertEquals(expectedPosition[1], actualPosition[1], EPSILON );
+ }
+
+ private static class FourExtrema implements MultivariateFunction {
+ // The following function has 4 local extrema.
+ final double xM = -3.841947088256863675365;
+ final double yM = -1.391745200270734924416;
+ final double xP = 0.2286682237349059125691;
+ final double yP = -yM;
+ final double valueXmYm = 0.2373295333134216789769; // Local maximum.
+ final double valueXmYp = -valueXmYm; // Local minimum.
+ final double valueXpYm = -0.7290400707055187115322; // Global minimum.
+ final double valueXpYp = -valueXpYm; // Global maximum.
+
+ public double value(double[] variables) {
+ final double x = variables[0];
+ final double y = variables[1];
+ return (x == 0 || y == 0) ? 0 :
+ FastMath.atan(x) * FastMath.atan(x + 2) * FastMath.atan(y) * FastMath.atan(y) / (x * y);
+ }
+ }
+
+ private static class Gaussian2D implements MultivariateFunction {
+ private final double[] maximumPosition;
+ private final double std;
+
+ public Gaussian2D(double xOpt, double yOpt, double std) {
+ maximumPosition = new double[] { xOpt, yOpt };
+ this.std = std;
+ }
+
+ public double getMaximum() {
+ return value(maximumPosition);
+ }
+
+ public double[] getMaximumPosition() {
+ return maximumPosition.clone();
+ }
+
+ public double value(double[] point) {
+ final double x = point[0], y = point[1];
+ final double twoS2 = 2.0 * std * std;
+ return 1.0 / (twoS2 * FastMath.PI) * FastMath.exp(-(x * x + y * y) / twoS2);
+ }
+ }
+
+ private int count;
+}
Propchange: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/scalar/noderiv/SimplexOptimizerMultiDirectionalTest.java
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Added: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/scalar/noderiv/SimplexOptimizerNelderMeadTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/scalar/noderiv/SimplexOptimizerNelderMeadTest.java?rev=1420684&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/scalar/noderiv/SimplexOptimizerNelderMeadTest.java (added)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/scalar/noderiv/SimplexOptimizerNelderMeadTest.java Wed Dec 12 14:10:38 2012
@@ -0,0 +1,295 @@
+/*
+ * 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.scalar.noderiv;
+
+
+import org.apache.commons.math3.exception.TooManyEvaluationsException;
+import org.apache.commons.math3.analysis.MultivariateFunction;
+import org.apache.commons.math3.analysis.MultivariateVectorFunction;
+import org.apache.commons.math3.linear.Array2DRowRealMatrix;
+import org.apache.commons.math3.linear.RealMatrix;
+import org.apache.commons.math3.optim.GoalType;
+import org.apache.commons.math3.optim.InitialGuess;
+import org.apache.commons.math3.optim.MaxEval;
+import org.apache.commons.math3.optim.ObjectiveFunction;
+import org.apache.commons.math3.optim.PointValuePair;
+import org.apache.commons.math3.optim.nonlinear.scalar.LeastSquaresConverter;
+import org.apache.commons.math3.util.FastMath;
+import org.junit.Assert;
+import org.junit.Test;
+
+public class SimplexOptimizerNelderMeadTest {
+ @Test
+ public void testMinimize1() {
+ SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
+ final FourExtrema fourExtrema = new FourExtrema();
+
+ final PointValuePair optimum
+ = optimizer.optimize(new MaxEval(100),
+ new ObjectiveFunction(fourExtrema),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { -3, 0 }),
+ new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
+ Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 2e-7);
+ Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 2e-5);
+ Assert.assertEquals(fourExtrema.valueXmYp, optimum.getValue(), 6e-12);
+ Assert.assertTrue(optimizer.getEvaluations() > 60);
+ Assert.assertTrue(optimizer.getEvaluations() < 90);
+ }
+
+ @Test
+ public void testMinimize2() {
+ SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
+ final FourExtrema fourExtrema = new FourExtrema();
+
+ final PointValuePair optimum
+ = optimizer.optimize(new MaxEval(100),
+ new ObjectiveFunction(fourExtrema),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { 1, 0 }),
+ new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
+ Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 5e-6);
+ Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 6e-6);
+ Assert.assertEquals(fourExtrema.valueXpYm, optimum.getValue(), 1e-11);
+ Assert.assertTrue(optimizer.getEvaluations() > 60);
+ Assert.assertTrue(optimizer.getEvaluations() < 90);
+ }
+
+ @Test
+ public void testMaximize1() {
+ SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
+ final FourExtrema fourExtrema = new FourExtrema();
+
+ final PointValuePair optimum
+ = optimizer.optimize(new MaxEval(100),
+ new ObjectiveFunction(fourExtrema),
+ GoalType.MAXIMIZE,
+ new InitialGuess(new double[] { -3, 0 }),
+ new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
+ Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 1e-5);
+ Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
+ Assert.assertEquals(fourExtrema.valueXmYm, optimum.getValue(), 3e-12);
+ Assert.assertTrue(optimizer.getEvaluations() > 60);
+ Assert.assertTrue(optimizer.getEvaluations() < 90);
+ }
+
+ @Test
+ public void testMaximize2() {
+ SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
+ final FourExtrema fourExtrema = new FourExtrema();
+
+ final PointValuePair optimum
+ = optimizer.optimize(new MaxEval(100),
+ new ObjectiveFunction(fourExtrema),
+ GoalType.MAXIMIZE,
+ new InitialGuess(new double[] { 1, 0 }),
+ new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
+ Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 4e-6);
+ Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 5e-6);
+ Assert.assertEquals(fourExtrema.valueXpYp, optimum.getValue(), 7e-12);
+ Assert.assertTrue(optimizer.getEvaluations() > 60);
+ Assert.assertTrue(optimizer.getEvaluations() < 90);
+ }
+
+ @Test
+ public void testRosenbrock() {
+
+ Rosenbrock rosenbrock = new Rosenbrock();
+ SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
+ PointValuePair optimum
+ = optimizer.optimize(new MaxEval(100),
+ new ObjectiveFunction(rosenbrock),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { -1.2, 1 }),
+ new NelderMeadSimplex(new double[][] {
+ { -1.2, 1 },
+ { 0.9, 1.2 },
+ { 3.5, -2.3 } }));
+
+ Assert.assertEquals(rosenbrock.getCount(), optimizer.getEvaluations());
+ Assert.assertTrue(optimizer.getEvaluations() > 40);
+ Assert.assertTrue(optimizer.getEvaluations() < 50);
+ Assert.assertTrue(optimum.getValue() < 8e-4);
+ }
+
+ @Test
+ public void testPowell() {
+ Powell powell = new Powell();
+ SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
+ PointValuePair optimum =
+ optimizer.optimize(new MaxEval(200),
+ new ObjectiveFunction(powell),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { 3, -1, 0, 1 }),
+ new NelderMeadSimplex(4));
+ Assert.assertEquals(powell.getCount(), optimizer.getEvaluations());
+ Assert.assertTrue(optimizer.getEvaluations() > 110);
+ Assert.assertTrue(optimizer.getEvaluations() < 130);
+ Assert.assertTrue(optimum.getValue() < 2e-3);
+ }
+
+ @Test
+ public void testLeastSquares1() {
+ final RealMatrix factors
+ = new Array2DRowRealMatrix(new double[][] {
+ { 1, 0 },
+ { 0, 1 }
+ }, false);
+ LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
+ public double[] value(double[] variables) {
+ return factors.operate(variables);
+ }
+ }, new double[] { 2.0, -3.0 });
+ SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
+ PointValuePair optimum =
+ optimizer.optimize(new MaxEval(200),
+ new ObjectiveFunction(ls),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { 10, 10 }),
+ new NelderMeadSimplex(2));
+ Assert.assertEquals( 2, optimum.getPointRef()[0], 3e-5);
+ Assert.assertEquals(-3, optimum.getPointRef()[1], 4e-4);
+ Assert.assertTrue(optimizer.getEvaluations() > 60);
+ Assert.assertTrue(optimizer.getEvaluations() < 80);
+ Assert.assertTrue(optimum.getValue() < 1.0e-6);
+ }
+
+ @Test
+ public void testLeastSquares2() {
+ final RealMatrix factors
+ = new Array2DRowRealMatrix(new double[][] {
+ { 1, 0 },
+ { 0, 1 }
+ }, false);
+ LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
+ public double[] value(double[] variables) {
+ return factors.operate(variables);
+ }
+ }, new double[] { 2, -3 }, new double[] { 10, 0.1 });
+ SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
+ PointValuePair optimum =
+ optimizer.optimize(new MaxEval(200),
+ new ObjectiveFunction(ls),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { 10, 10 }),
+ new NelderMeadSimplex(2));
+ Assert.assertEquals( 2, optimum.getPointRef()[0], 5e-5);
+ Assert.assertEquals(-3, optimum.getPointRef()[1], 8e-4);
+ Assert.assertTrue(optimizer.getEvaluations() > 60);
+ Assert.assertTrue(optimizer.getEvaluations() < 80);
+ Assert.assertTrue(optimum.getValue() < 1e-6);
+ }
+
+ @Test
+ public void testLeastSquares3() {
+ final RealMatrix factors =
+ new Array2DRowRealMatrix(new double[][] {
+ { 1, 0 },
+ { 0, 1 }
+ }, false);
+ LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
+ public double[] value(double[] variables) {
+ return factors.operate(variables);
+ }
+ }, new double[] { 2, -3 }, new Array2DRowRealMatrix(new double [][] {
+ { 1, 1.2 }, { 1.2, 2 }
+ }));
+ SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
+ PointValuePair optimum
+ = optimizer.optimize(new MaxEval(200),
+ new ObjectiveFunction(ls),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { 10, 10 }),
+ new NelderMeadSimplex(2));
+ Assert.assertEquals( 2, optimum.getPointRef()[0], 2e-3);
+ Assert.assertEquals(-3, optimum.getPointRef()[1], 8e-4);
+ Assert.assertTrue(optimizer.getEvaluations() > 60);
+ Assert.assertTrue(optimizer.getEvaluations() < 80);
+ Assert.assertTrue(optimum.getValue() < 1e-6);
+ }
+
+ @Test(expected=TooManyEvaluationsException.class)
+ public void testMaxIterations() {
+ Powell powell = new Powell();
+ SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
+ optimizer.optimize(new MaxEval(20),
+ new ObjectiveFunction(powell),
+ GoalType.MINIMIZE,
+ new InitialGuess(new double[] { 3, -1, 0, 1 }),
+ new NelderMeadSimplex(4));
+ }
+
+ private static class FourExtrema implements MultivariateFunction {
+ // The following function has 4 local extrema.
+ final double xM = -3.841947088256863675365;
+ final double yM = -1.391745200270734924416;
+ final double xP = 0.2286682237349059125691;
+ final double yP = -yM;
+ final double valueXmYm = 0.2373295333134216789769; // Local maximum.
+ final double valueXmYp = -valueXmYm; // Local minimum.
+ final double valueXpYm = -0.7290400707055187115322; // Global minimum.
+ final double valueXpYp = -valueXpYm; // Global maximum.
+
+ public double value(double[] variables) {
+ final double x = variables[0];
+ final double y = variables[1];
+ return (x == 0 || y == 0) ? 0 :
+ FastMath.atan(x) * FastMath.atan(x + 2) * FastMath.atan(y) * FastMath.atan(y) / (x * y);
+ }
+ }
+
+ private static class Rosenbrock implements MultivariateFunction {
+ private int count;
+
+ public Rosenbrock() {
+ count = 0;
+ }
+
+ public double value(double[] x) {
+ ++count;
+ double a = x[1] - x[0] * x[0];
+ double b = 1.0 - x[0];
+ return 100 * a * a + b * b;
+ }
+
+ public int getCount() {
+ return count;
+ }
+ }
+
+ private static class Powell implements MultivariateFunction {
+ private int count;
+
+ public Powell() {
+ count = 0;
+ }
+
+ public double value(double[] x) {
+ ++count;
+ double a = x[0] + 10 * x[1];
+ double b = x[2] - x[3];
+ double c = x[1] - 2 * x[2];
+ double d = x[0] - x[3];
+ return a * a + 5 * b * b + c * c * c * c + 10 * d * d * d * d;
+ }
+
+ public int getCount() {
+ return count;
+ }
+ }
+}
Propchange: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/scalar/noderiv/SimplexOptimizerNelderMeadTest.java
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Added: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java?rev=1420684&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java (added)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java Wed Dec 12 14:10:38 2012
@@ -0,0 +1,205 @@
+/*
+ * 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.analysis.MultivariateVectorFunction;
+import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
+import org.apache.commons.math3.exception.MathIllegalStateException;
+import org.apache.commons.math3.linear.BlockRealMatrix;
+import org.apache.commons.math3.linear.RealMatrix;
+import org.apache.commons.math3.optim.MaxEval;
+import org.apache.commons.math3.optim.InitialGuess;
+import org.apache.commons.math3.optim.PointVectorValuePair;
+import org.apache.commons.math3.optim.SimpleVectorValueChecker;
+import org.apache.commons.math3.optim.nonlinear.vector.jacobian.GaussNewtonOptimizer;
+import org.apache.commons.math3.random.GaussianRandomGenerator;
+import org.apache.commons.math3.random.JDKRandomGenerator;
+import org.apache.commons.math3.random.RandomVectorGenerator;
+import org.apache.commons.math3.random.UncorrelatedRandomVectorGenerator;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * <p>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>, for
+ * convenience, it is reproduced below.</p>
+ *
+ * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
+ * <tr><td>
+ * Minpack Copyright Notice (1999) University of Chicago.
+ * All rights reserved
+ * </td></tr>
+ * <tr><td>
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ * <ol>
+ * <li>Redistributions of source code must retain the above copyright
+ * notice, this list of conditions and the following disclaimer.</li>
+ * <li>Redistributions in binary form must reproduce the above
+ * copyright notice, this list of conditions and the following
+ * disclaimer in the documentation and/or other materials provided
+ * with the distribution.</li>
+ * <li>The end-user documentation included with the redistribution, if any,
+ * must include the following acknowledgment:
+ * <code>This product includes software developed by the University of
+ * Chicago, as Operator of Argonne National Laboratory.</code>
+ * Alternately, this acknowledgment may appear in the software itself,
+ * if and wherever such third-party acknowledgments normally appear.</li>
+ * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
+ * WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
+ * UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
+ * THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
+ * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
+ * OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
+ * OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
+ * USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
+ * THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
+ * DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
+ * UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
+ * BE CORRECTED.</strong></li>
+ * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
+ * HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
+ * ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
+ * INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
+ * ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
+ * PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
+ * SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
+ * (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
+ * EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
+ * POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
+ * <ol></td></tr>
+ * </table>
+ *
+ * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
+ * @author Burton S. Garbow (original fortran minpack tests)
+ * @author Kenneth E. Hillstrom (original fortran minpack tests)
+ * @author Jorge J. More (original fortran minpack tests)
+ * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
+ */
+public class MultiStartMultivariateVectorOptimizerTest {
+ @Test(expected=NullPointerException.class)
+ public void testGetOptimaBeforeOptimize() {
+ LinearProblem problem
+ = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
+ JacobianMultivariateVectorOptimizer underlyingOptimizer
+ = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
+ JDKRandomGenerator g = new JDKRandomGenerator();
+ g.setSeed(16069223052l);
+ RandomVectorGenerator generator
+ = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
+ MultiStartMultivariateVectorOptimizer optimizer
+ = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
+
+ optimizer.getOptima();
+ }
+
+ @Test
+ public void testTrivial() {
+ LinearProblem problem
+ = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
+ JacobianMultivariateVectorOptimizer underlyingOptimizer
+ = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
+ JDKRandomGenerator g = new JDKRandomGenerator();
+ g.setSeed(16069223052l);
+ RandomVectorGenerator generator
+ = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
+ MultiStartMultivariateVectorOptimizer optimizer
+ = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
+
+ PointVectorValuePair optimum
+ = optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1 }),
+ new InitialGuess(new double[] { 0 }));
+ Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
+ Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
+ PointVectorValuePair[] optima = optimizer.getOptima();
+ Assert.assertEquals(10, optima.length);
+ for (int i = 0; i < optima.length; i++) {
+ Assert.assertEquals(1.5, optima[i].getPoint()[0], 1e-10);
+ Assert.assertEquals(3.0, optima[i].getValue()[0], 1e-10);
+ }
+ Assert.assertTrue(optimizer.getEvaluations() > 20);
+ Assert.assertTrue(optimizer.getEvaluations() < 50);
+ Assert.assertEquals(100, optimizer.getMaxEvaluations());
+ }
+
+ /**
+ * Test demonstrating that the user exception is fnally thrown if none
+ * of the runs succeed.
+ */
+ @Test(expected=TestException.class)
+ public void testNoOptimum() {
+ JacobianMultivariateVectorOptimizer underlyingOptimizer
+ = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
+ JDKRandomGenerator g = new JDKRandomGenerator();
+ g.setSeed(12373523445l);
+ RandomVectorGenerator generator
+ = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
+ MultiStartMultivariateVectorOptimizer optimizer
+ = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
+ optimizer.optimize(new MaxEval(100),
+ new Target(new double[] { 0 }),
+ new Weight(new double[] { 1 }),
+ new InitialGuess(new double[] { 0 }),
+ new ModelFunction(new MultivariateVectorFunction() {
+ public double[] value(double[] point) {
+ throw new TestException();
+ }
+ }));
+ }
+
+ private static class TestException extends RuntimeException {}
+
+ private static 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 Target getTarget() {
+ return new Target(target);
+ }
+
+ public ModelFunction getModelFunction() {
+ return new ModelFunction(new MultivariateVectorFunction() {
+ public double[] value(double[] variables) {
+ return factors.operate(variables);
+ }
+ });
+ }
+
+ public ModelFunctionJacobian getModelFunctionJacobian() {
+ return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
+ public double[][] value(double[] point) {
+ return factors.getData();
+ }
+ });
+ }
+ }
+}
Propchange: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java
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Added: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java?rev=1420684&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java (added)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java Wed Dec 12 14:10:38 2012
@@ -0,0 +1,617 @@
+/*
+ * 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.io.Serializable;
+import java.util.Arrays;
+import org.apache.commons.math3.analysis.MultivariateVectorFunction;
+import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
+import org.apache.commons.math3.exception.ConvergenceException;
+import org.apache.commons.math3.exception.DimensionMismatchException;
+import org.apache.commons.math3.exception.NumberIsTooSmallException;
+import org.apache.commons.math3.geometry.euclidean.twod.Vector2D;
+import org.apache.commons.math3.linear.BlockRealMatrix;
+import org.apache.commons.math3.linear.RealMatrix;
+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.optim.nonlinear.vector.ModelFunction;
+import org.apache.commons.math3.optim.nonlinear.vector.ModelFunctionJacobian;
+import org.apache.commons.math3.util.FastMath;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * <p>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>, for
+ * convenience, it is reproduced below.</p>
+
+ * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
+ * <tr><td>
+ * Minpack Copyright Notice (1999) University of Chicago.
+ * All rights reserved
+ * </td></tr>
+ * <tr><td>
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ * <ol>
+ * <li>Redistributions of source code must retain the above copyright
+ * notice, this list of conditions and the following disclaimer.</li>
+ * <li>Redistributions in binary form must reproduce the above
+ * copyright notice, this list of conditions and the following
+ * disclaimer in the documentation and/or other materials provided
+ * with the distribution.</li>
+ * <li>The end-user documentation included with the redistribution, if any,
+ * must include the following acknowledgment:
+ * <code>This product includes software developed by the University of
+ * Chicago, as Operator of Argonne National Laboratory.</code>
+ * Alternately, this acknowledgment may appear in the software itself,
+ * if and wherever such third-party acknowledgments normally appear.</li>
+ * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
+ * WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
+ * UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
+ * THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
+ * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
+ * OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
+ * OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
+ * USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
+ * THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
+ * DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
+ * UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
+ * BE CORRECTED.</strong></li>
+ * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
+ * HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
+ * ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
+ * INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
+ * ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
+ * PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
+ * SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
+ * (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
+ * EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
+ * POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
+ * <ol></td></tr>
+ * </table>
+
+ * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
+ * @author Burton S. Garbow (original fortran minpack tests)
+ * @author Kenneth E. Hillstrom (original fortran minpack tests)
+ * @author Jorge J. More (original fortran minpack tests)
+ * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
+ * @version $Id: AbstractLeastSquaresOptimizerAbstractTest.java 1407467 2012-11-09 14:30:49Z erans $
+ */
+public abstract class AbstractLeastSquaresOptimizerAbstractTest {
+
+ public abstract AbstractLeastSquaresOptimizer createOptimizer();
+
+ @Test
+ public void testTrivial() {
+ LinearProblem problem
+ = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum =
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1 }),
+ new InitialGuess(new double[] { 0 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
+ Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
+ }
+
+ @Test
+ public void testQRColumnsPermutation() {
+
+ LinearProblem problem
+ = new LinearProblem(new double[][] { { 1, -1 }, { 0, 2 }, { 1, -2 } },
+ new double[] { 4, 6, 1 });
+
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum =
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1, 1, 1 }),
+ new InitialGuess(new double[] { 0, 0 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ Assert.assertEquals(7, optimum.getPoint()[0], 1e-10);
+ Assert.assertEquals(3, optimum.getPoint()[1], 1e-10);
+ Assert.assertEquals(4, optimum.getValue()[0], 1e-10);
+ Assert.assertEquals(6, optimum.getValue()[1], 1e-10);
+ Assert.assertEquals(1, optimum.getValue()[2], 1e-10);
+ }
+
+ @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 });
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum =
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
+ new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ for (int i = 0; i < problem.target.length; ++i) {
+ Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1e-10);
+ }
+ }
+
+ @Test
+ public void testOneSet() {
+
+ LinearProblem problem = new LinearProblem(new double[][] {
+ { 1, 0, 0 },
+ { -1, 1, 0 },
+ { 0, -1, 1 }
+ }, new double[] { 1, 1, 1});
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum =
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1, 1, 1 }),
+ new InitialGuess(new double[] { 0, 0, 0 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ Assert.assertEquals(1, optimum.getPoint()[0], 1e-10);
+ Assert.assertEquals(2, optimum.getPoint()[1], 1e-10);
+ Assert.assertEquals(3, optimum.getPoint()[2], 1e-10);
+ }
+
+ @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});
+
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum =
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
+ new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ Assert.assertEquals(3, optimum.getPoint()[0], 1e-10);
+ Assert.assertEquals(4, optimum.getPoint()[1], 1e-10);
+ Assert.assertEquals(-1, optimum.getPoint()[2], 1e-10);
+ Assert.assertEquals(-2, optimum.getPoint()[3], 1e-10);
+ Assert.assertEquals(1 + epsilon, optimum.getPoint()[4], 1e-10);
+ Assert.assertEquals(1 - epsilon, optimum.getPoint()[5], 1e-10);
+ }
+
+ @Test(expected=ConvergenceException.class)
+ public void testNonInvertible() throws Exception {
+
+ LinearProblem problem = new LinearProblem(new double[][] {
+ { 1, 2, -3 },
+ { 2, 1, 3 },
+ { -3, 0, -9 }
+ }, new double[] { 1, 1, 1 });
+
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1, 1, 1 }),
+ new InitialGuess(new double[] { 0, 0, 0 }));
+ }
+
+ @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 });
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum1 =
+ optimizer.optimize(new MaxEval(100),
+ problem1.getModelFunction(),
+ problem1.getModelFunctionJacobian(),
+ problem1.getTarget(),
+ new Weight(new double[] { 1, 1, 1, 1 }),
+ new InitialGuess(new double[] { 0, 1, 2, 3 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ Assert.assertEquals(1, optimum1.getPoint()[0], 1e-10);
+ Assert.assertEquals(1, optimum1.getPoint()[1], 1e-10);
+ Assert.assertEquals(1, optimum1.getPoint()[2], 1e-10);
+ Assert.assertEquals(1, optimum1.getPoint()[3], 1e-10);
+
+ 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 });
+ PointVectorValuePair optimum2 =
+ optimizer.optimize(new MaxEval(100),
+ problem2.getModelFunction(),
+ problem2.getModelFunctionJacobian(),
+ problem2.getTarget(),
+ new Weight(new double[] { 1, 1, 1, 1 }),
+ new InitialGuess(new double[] { 0, 1, 2, 3 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ Assert.assertEquals(-81, optimum2.getPoint()[0], 1e-8);
+ Assert.assertEquals(137, optimum2.getPoint()[1], 1e-8);
+ Assert.assertEquals(-34, optimum2.getPoint()[2], 1e-8);
+ Assert.assertEquals( 22, optimum2.getPoint()[3], 1e-8);
+ }
+
+ @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 });
+
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1, 1, 1 }),
+ new InitialGuess(new double[] { 7, 6, 5, 4 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ }
+
+ @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 });
+
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum =
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1, 1, 1, 1, 1 }),
+ new InitialGuess(new double[] { 2, 2, 2, 2, 2, 2 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ Assert.assertEquals(3, optimum.getPointRef()[2], 1e-10);
+ Assert.assertEquals(4, optimum.getPointRef()[3], 1e-10);
+ Assert.assertEquals(5, optimum.getPointRef()[4], 1e-10);
+ Assert.assertEquals(6, optimum.getPointRef()[5], 1e-10);
+ }
+
+ @Test
+ public void testRedundantEquations() {
+ LinearProblem problem = new LinearProblem(new double[][] {
+ { 1, 1 },
+ { 1, -1 },
+ { 1, 3 }
+ }, new double[] { 3, 1, 5 });
+
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum =
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1, 1, 1 }),
+ new InitialGuess(new double[] { 1, 1 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ Assert.assertEquals(2, optimum.getPointRef()[0], 1e-10);
+ Assert.assertEquals(1, optimum.getPointRef()[1], 1e-10);
+ }
+
+ @Test
+ public void testInconsistentEquations() {
+ LinearProblem problem = new LinearProblem(new double[][] {
+ { 1, 1 },
+ { 1, -1 },
+ { 1, 3 }
+ }, new double[] { 3, 1, 4 });
+
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1, 1, 1 }),
+ new InitialGuess(new double[] { 1, 1 }));
+ Assert.assertTrue(optimizer.getRMS() > 0.1);
+ }
+
+ @Test(expected=DimensionMismatchException.class)
+ public void testInconsistentSizes1() {
+ LinearProblem problem
+ = new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
+ new double[] { -1, 1 });
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum =
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1, 1 }),
+ new InitialGuess(new double[] { 0, 0 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
+ Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
+
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1 }),
+ new InitialGuess(new double[] { 0, 0 }));
+ }
+
+ @Test(expected=DimensionMismatchException.class)
+ public void testInconsistentSizes2() {
+ LinearProblem problem
+ = new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
+ new double[] { -1, 1 });
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum
+ = optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ problem.getTarget(),
+ new Weight(new double[] { 1, 1 }),
+ new InitialGuess(new double[] { 0, 0 }));
+ Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
+ Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
+ Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
+
+ optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ new Target(new double[] { 1 }),
+ new Weight(new double[] { 1 }),
+ new InitialGuess(new double[] { 0, 0 }));
+ }
+
+ @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);
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum
+ = optimizer.optimize(new MaxEval(100),
+ circle.getModelFunction(),
+ circle.getModelFunctionJacobian(),
+ new Target(new double[] { 0, 0, 0, 0, 0 }),
+ new Weight(new double[] { 1, 1, 1, 1, 1 }),
+ new InitialGuess(new double[] { 98.680, 47.345 }));
+ Assert.assertTrue(optimizer.getEvaluations() < 10);
+ double rms = optimizer.getRMS();
+ Assert.assertEquals(1.768262623567235, FastMath.sqrt(circle.getN()) * rms, 1e-10);
+ Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[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 = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
+ 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 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[] target = new double[circle.getN()];
+ Arrays.fill(target, 0);
+ double[] weights = new double[circle.getN()];
+ Arrays.fill(weights, 2);
+ optimum = optimizer.optimize(new MaxEval(100),
+ circle.getModelFunction(),
+ circle.getModelFunctionJacobian(),
+ new Target(target),
+ new Weight(weights),
+ new InitialGuess(new double[] { 98.680, 47.345 }));
+ cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
+ 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[] target = new double[points.length];
+ Arrays.fill(target, 0);
+ 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]);
+ }
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum
+ = optimizer.optimize(new MaxEval(100),
+ circle.getModelFunction(),
+ circle.getModelFunctionJacobian(),
+ new Target(target),
+ new Weight(weights),
+ new InitialGuess(new double[] { -12, -12 }));
+ Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
+ Assert.assertTrue(optimizer.getEvaluations() < 25);
+ Assert.assertEquals( 0.043, optimizer.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[] target = new double[points.length];
+ Arrays.fill(target, 0);
+ 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]);
+ }
+ AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ PointVectorValuePair optimum =
+ optimizer.optimize(new MaxEval(100),
+ circle.getModelFunction(),
+ circle.getModelFunctionJacobian(),
+ new Target(target),
+ new Weight(weights),
+ new InitialGuess(new double[] { 0, 0 }));
+ Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1e-6);
+ Assert.assertEquals(0.2074999736353867, optimum.getPointRef()[1], 1e-6);
+ Assert.assertEquals(0.04268731682389561, optimizer.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 double errParams,
+ final double errParamsSd) {
+ final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
+ final double[] w = new double[dataset.getNumObservations()];
+ Arrays.fill(w, 1);
+
+ final double[][] data = dataset.getData();
+ final double[] initial = dataset.getStartingPoint(0);
+ final StatisticalReferenceDataset.LeastSquaresProblem problem = dataset.getLeastSquaresProblem();
+ final PointVectorValuePair optimum
+ = optimizer.optimize(new MaxEval(100),
+ problem.getModelFunction(),
+ problem.getModelFunctionJacobian(),
+ new Target(data[1]),
+ new Weight(w),
+ new InitialGuess(initial));
+
+ final double[] actual = optimum.getPoint();
+ for (int i = 0; i < actual.length; i++) {
+ double expected = dataset.getParameter(i);
+ double delta = FastMath.abs(errParams * expected);
+ Assert.assertEquals(dataset.getName() + ", param #" + i,
+ expected, actual[i], delta);
+ }
+ }
+
+ @Test
+ public void testKirby2() throws IOException {
+ doTestStRD(StatisticalReferenceDatasetFactory.createKirby2(), 1E-7, 1E-7);
+ }
+
+ @Test
+ public void testHahn1() throws IOException {
+ doTestStRD(StatisticalReferenceDatasetFactory.createHahn1(), 1E-7, 1E-4);
+ }
+
+ static 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 Target getTarget() {
+ return new Target(target);
+ }
+
+ public ModelFunction getModelFunction() {
+ return new ModelFunction(new MultivariateVectorFunction() {
+ public double[] value(double[] params) {
+ return factors.operate(params);
+ }
+ });
+ }
+
+ public ModelFunctionJacobian getModelFunctionJacobian() {
+ return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
+ public double[][] value(double[] params) {
+ return factors.getData();
+ }
+ });
+ }
+ }
+}
Propchange: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java
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Added: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java?rev=1420684&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java (added)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java Wed Dec 12 14:10:38 2012
@@ -0,0 +1,126 @@
+/*
+ * 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;
+
+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]);
+ }
+ }
+}
Propchange: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java
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