Unthrown Exceptions
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1003355 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Sebastian Bazley
parent
2d1d3187ec
commit
5321415bc5
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@ -33,7 +33,7 @@ public final class TricubicSplineInterpolatingFunctionTest {
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* Test preconditions.
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*/
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@Test
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public void testPreconditions() throws MathException {
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public void testPreconditions() throws Exception {
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double[] xval = new double[] {3, 4, 5, 6.5};
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double[] yval = new double[] {-4, -3, -1, 2.5};
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double[] zval = new double[] {-12, -8, -5.5, -3, 0, 2.5};
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@ -150,7 +150,7 @@ public class BetaDistributionTest extends TestCase {
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}
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}
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public void testDensity() throws MathException {
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public void testDensity() {
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double[] x = new double[]{1e-6, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9};
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checkDensity(0.1, 0.1,
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x, new double[]{
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@ -280,7 +280,7 @@ public class BetaDistributionTest extends TestCase {
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}
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private void checkDensity(double alpha, double beta, double[] x, double[] expected) throws MathException {
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private void checkDensity(double alpha, double beta, double[] x, double[] expected) {
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BetaDistribution d = new BetaDistributionImpl(alpha, beta);
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for (int i = 0; i < x.length; i++) {
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assertEquals(String.format("density at x=%.1f for alpha=%.1f, beta=%.1f", x[i], alpha, beta), expected[i], d.density(x[i]), 1e-5);
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@ -101,7 +101,7 @@ import org.junit.Test;
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public class MultiStartDifferentiableMultivariateVectorialOptimizerTest {
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@Test
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public void testTrivial() throws FunctionEvaluationException, OptimizationException {
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public void testTrivial() throws FunctionEvaluationException {
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LinearProblem problem =
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new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
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DifferentiableMultivariateVectorialOptimizer underlyingOptimizer =
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@ -139,7 +139,7 @@ public class MultiStartDifferentiableMultivariateVectorialOptimizerTest {
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}
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@Test(expected = ConvergenceException.class)
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public void testNoOptimum() throws FunctionEvaluationException, OptimizationException {
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public void testNoOptimum() throws FunctionEvaluationException {
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DifferentiableMultivariateVectorialOptimizer underlyingOptimizer =
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new GaussNewtonOptimizer(true);
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JDKRandomGenerator g = new JDKRandomGenerator();
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@ -20,7 +20,6 @@ package org.apache.commons.math.optimization;
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import static org.junit.Assert.assertEquals;
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import static org.junit.Assert.assertTrue;
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import org.apache.commons.math.ConvergenceException;
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import org.apache.commons.math.FunctionEvaluationException;
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import org.apache.commons.math.analysis.MultivariateRealFunction;
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import org.apache.commons.math.optimization.direct.NelderMead;
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@ -34,7 +33,7 @@ public class MultiStartMultivariateRealOptimizerTest {
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@Test
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public void testRosenbrock()
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throws FunctionEvaluationException, ConvergenceException {
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throws FunctionEvaluationException {
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Rosenbrock rosenbrock = new Rosenbrock();
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NelderMead underlying = new NelderMead();
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@ -17,11 +17,9 @@
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package org.apache.commons.math.optimization.direct;
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import org.apache.commons.math.ConvergenceException;
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import org.apache.commons.math.FunctionEvaluationException;
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import org.apache.commons.math.analysis.MultivariateRealFunction;
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import org.apache.commons.math.optimization.GoalType;
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import org.apache.commons.math.optimization.OptimizationException;
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import org.apache.commons.math.optimization.RealPointValuePair;
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import org.apache.commons.math.optimization.SimpleScalarValueChecker;
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import org.apache.commons.math.util.FastMath;
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@ -69,7 +67,7 @@ public class MultiDirectionalTest {
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@Test
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public void testMinimizeMaximize()
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throws FunctionEvaluationException, ConvergenceException {
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throws FunctionEvaluationException {
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// the following function has 4 local extrema:
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final double xM = -3.841947088256863675365;
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@ -130,7 +128,7 @@ public class MultiDirectionalTest {
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@Test
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public void testRosenbrock()
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throws FunctionEvaluationException, ConvergenceException {
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throws FunctionEvaluationException {
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MultivariateRealFunction rosenbrock =
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new MultivariateRealFunction() {
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@ -162,7 +160,7 @@ public class MultiDirectionalTest {
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@Test
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public void testPowell()
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throws FunctionEvaluationException, ConvergenceException {
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throws FunctionEvaluationException {
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MultivariateRealFunction powell =
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new MultivariateRealFunction() {
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@ -192,7 +190,7 @@ public class MultiDirectionalTest {
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@Test
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public void testMath283()
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throws FunctionEvaluationException, OptimizationException {
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throws FunctionEvaluationException {
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// fails because MultiDirectional.iterateSimplex is looping forever
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// the while(true) should be replaced with a convergence check
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MultiDirectional multiDirectional = new MultiDirectional();
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@ -18,7 +18,6 @@
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package org.apache.commons.math.optimization.fitting;
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import org.apache.commons.math.FunctionEvaluationException;
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import org.apache.commons.math.optimization.OptimizationException;
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import org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer;
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import org.apache.commons.math.util.FastMath;
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import org.junit.Assert;
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@ -28,7 +27,7 @@ public class CurveFitterTest {
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@Test
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public void testMath303()
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throws OptimizationException, FunctionEvaluationException {
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throws FunctionEvaluationException {
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LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
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CurveFitter fitter = new CurveFitter(optimizer);
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@ -52,7 +51,7 @@ public class CurveFitterTest {
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@Test
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public void testMath304()
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throws OptimizationException, FunctionEvaluationException {
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throws FunctionEvaluationException {
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LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
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CurveFitter fitter = new CurveFitter(optimizer);
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@ -75,7 +74,7 @@ public class CurveFitterTest {
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@Test
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public void testMath372()
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throws OptimizationException, FunctionEvaluationException {
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throws FunctionEvaluationException {
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LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
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CurveFitter curveFitter = new CurveFitter(optimizer);
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@ -31,7 +31,6 @@ import org.apache.commons.math.analysis.solvers.BrentSolver;
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import org.apache.commons.math.linear.BlockRealMatrix;
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import org.apache.commons.math.linear.RealMatrix;
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import org.apache.commons.math.optimization.GoalType;
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import org.apache.commons.math.optimization.OptimizationException;
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import org.apache.commons.math.optimization.RealPointValuePair;
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import org.apache.commons.math.optimization.SimpleScalarValueChecker;
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@ -104,7 +103,7 @@ extends TestCase {
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super(name);
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}
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public void testTrivial() throws FunctionEvaluationException, OptimizationException {
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public void testTrivial() throws FunctionEvaluationException {
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LinearProblem problem =
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new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
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NonLinearConjugateGradientOptimizer optimizer =
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@ -117,7 +116,7 @@ extends TestCase {
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assertEquals(0.0, optimum.getValue(), 1.0e-10);
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}
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public void testColumnsPermutation() throws FunctionEvaluationException, OptimizationException {
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public void testColumnsPermutation() throws FunctionEvaluationException {
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LinearProblem problem =
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new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
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@ -135,7 +134,7 @@ extends TestCase {
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}
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public void testNoDependency() throws FunctionEvaluationException, OptimizationException {
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public void testNoDependency() throws FunctionEvaluationException {
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LinearProblem problem = new LinearProblem(new double[][] {
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{ 2, 0, 0, 0, 0, 0 },
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{ 0, 2, 0, 0, 0, 0 },
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@ -155,7 +154,7 @@ extends TestCase {
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}
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}
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public void testOneSet() throws FunctionEvaluationException, OptimizationException {
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public void testOneSet() throws FunctionEvaluationException {
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LinearProblem problem = new LinearProblem(new double[][] {
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{ 1, 0, 0 },
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@ -174,7 +173,7 @@ extends TestCase {
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}
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public void testTwoSets() throws FunctionEvaluationException, OptimizationException {
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public void testTwoSets() throws FunctionEvaluationException {
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final double epsilon = 1.0e-7;
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LinearProblem problem = new LinearProblem(new double[][] {
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{ 2, 1, 0, 4, 0, 0 },
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@ -213,7 +212,7 @@ extends TestCase {
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}
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public void testNonInversible() throws FunctionEvaluationException, OptimizationException {
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public void testNonInversible() throws FunctionEvaluationException {
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LinearProblem problem = new LinearProblem(new double[][] {
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{ 1, 2, -3 },
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@ -229,7 +228,7 @@ extends TestCase {
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assertTrue(optimum.getValue() > 0.5);
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}
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public void testIllConditioned() throws FunctionEvaluationException, OptimizationException {
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public void testIllConditioned() throws FunctionEvaluationException {
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LinearProblem problem1 = new LinearProblem(new double[][] {
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{ 10.0, 7.0, 8.0, 7.0 },
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{ 7.0, 5.0, 6.0, 5.0 },
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@ -267,7 +266,7 @@ extends TestCase {
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}
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public void testMoreEstimatedParametersSimple()
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throws FunctionEvaluationException, OptimizationException {
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throws FunctionEvaluationException {
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LinearProblem problem = new LinearProblem(new double[][] {
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{ 3.0, 2.0, 0.0, 0.0 },
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@ -286,7 +285,7 @@ extends TestCase {
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}
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public void testMoreEstimatedParametersUnsorted()
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throws FunctionEvaluationException, OptimizationException {
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throws FunctionEvaluationException {
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LinearProblem problem = new LinearProblem(new double[][] {
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{ 1.0, 1.0, 0.0, 0.0, 0.0, 0.0 },
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{ 0.0, 0.0, 1.0, 1.0, 1.0, 0.0 },
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@ -303,7 +302,7 @@ extends TestCase {
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assertEquals(0, optimum.getValue(), 1.0e-10);
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}
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public void testRedundantEquations() throws FunctionEvaluationException, OptimizationException {
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public void testRedundantEquations() throws FunctionEvaluationException {
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LinearProblem problem = new LinearProblem(new double[][] {
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{ 1.0, 1.0 },
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{ 1.0, -1.0 },
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@ -321,7 +320,7 @@ extends TestCase {
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}
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public void testInconsistentEquations() throws FunctionEvaluationException, OptimizationException {
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public void testInconsistentEquations() throws FunctionEvaluationException {
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LinearProblem problem = new LinearProblem(new double[][] {
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{ 1.0, 1.0 },
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{ 1.0, -1.0 },
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@ -338,7 +337,7 @@ extends TestCase {
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}
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public void testCircleFitting() throws FunctionEvaluationException, OptimizationException {
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public void testCircleFitting() throws FunctionEvaluationException {
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Circle circle = new Circle();
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circle.addPoint( 30.0, 68.0);
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circle.addPoint( 50.0, -6.0);
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