Modified: commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/direct/MultiDirectionalTest.java URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/direct/MultiDirectionalTest.java?rev=754499&r1=754498&r2=754499&view=diff ============================================================================== --- commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/direct/MultiDirectionalTest.java (original) +++ commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/direct/MultiDirectionalTest.java Sat Mar 14 17:35:49 2009 @@ -25,9 +25,9 @@ import org.apache.commons.math.linear.decomposition.NotPositiveDefiniteMatrixException; import org.apache.commons.math.optimization.GoalType; import org.apache.commons.math.optimization.ObjectiveException; -import org.apache.commons.math.optimization.ObjectiveFunction; -import org.apache.commons.math.optimization.PointValuePair; -import org.apache.commons.math.optimization.ObjectiveValueChecker; +import org.apache.commons.math.optimization.ScalarObjectiveFunction; +import org.apache.commons.math.optimization.ScalarPointValuePair; +import org.apache.commons.math.optimization.SimpleValueChecker; public class MultiDirectionalTest extends TestCase { @@ -37,8 +37,8 @@ } public void testObjectiveExceptions() throws ConvergenceException { - ObjectiveFunction wrong = - new ObjectiveFunction() { + ScalarObjectiveFunction wrong = + new ScalarObjectiveFunction() { private static final long serialVersionUID = 4751314470965489371L; public double objective(double[] x) throws ObjectiveException { if (x[0] < 0) { @@ -84,7 +84,7 @@ final double valueXmYp = -valueXmYm; // local minimum final double valueXpYm = -0.7290400707055187115322; // global minimum final double valueXpYp = -valueXpYm; // global maximum - ObjectiveFunction fourExtrema = new ObjectiveFunction() { + ScalarObjectiveFunction fourExtrema = new ScalarObjectiveFunction() { private static final long serialVersionUID = -7039124064449091152L; public double objective(double[] variables) { final double x = variables[0]; @@ -94,10 +94,10 @@ }; MultiDirectional optimizer = new MultiDirectional(); - optimizer.setConvergenceChecker(new ObjectiveValueChecker(1.0e-10, 1.0e-30)); + optimizer.setConvergenceChecker(new SimpleValueChecker(1.0e-10, 1.0e-30)); optimizer.setMaxEvaluations(200); optimizer.setStartConfiguration(new double[] { 0.2, 0.2 }); - PointValuePair optimum; + ScalarPointValuePair optimum; // minimization optimum = optimizer.optimize(fourExtrema, GoalType.MINIMIZE, new double[] { -3.0, 0 }); @@ -134,8 +134,8 @@ public void testRosenbrock() throws ObjectiveException, ConvergenceException { - ObjectiveFunction rosenbrock = - new ObjectiveFunction() { + ScalarObjectiveFunction rosenbrock = + new ScalarObjectiveFunction() { private static final long serialVersionUID = -9044950469615237490L; public double objective(double[] x) { ++count; @@ -147,12 +147,12 @@ count = 0; MultiDirectional optimizer = new MultiDirectional(); - optimizer.setConvergenceChecker(new ObjectiveValueChecker(-1, 1.0e-3)); + optimizer.setConvergenceChecker(new SimpleValueChecker(-1, 1.0e-3)); optimizer.setMaxEvaluations(100); optimizer.setStartConfiguration(new double[][] { { -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 } }); - PointValuePair optimum = + ScalarPointValuePair optimum = optimizer.optimize(rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1.0 }); assertEquals(count, optimizer.getEvaluations()); @@ -165,8 +165,8 @@ public void testPowell() throws ObjectiveException, ConvergenceException { - ObjectiveFunction powell = - new ObjectiveFunction() { + ScalarObjectiveFunction powell = + new ScalarObjectiveFunction() { private static final long serialVersionUID = -832162886102041840L; public double objective(double[] x) { ++count; @@ -180,9 +180,9 @@ count = 0; MultiDirectional optimizer = new MultiDirectional(); - optimizer.setConvergenceChecker(new ObjectiveValueChecker(-1.0, 1.0e-3)); + optimizer.setConvergenceChecker(new SimpleValueChecker(-1.0, 1.0e-3)); optimizer.setMaxEvaluations(1000); - PointValuePair optimum = + ScalarPointValuePair optimum = optimizer.optimize(powell, GoalType.MINIMIZE, new double[] { 3.0, -1.0, 0.0, 1.0 }); assertEquals(count, optimizer.getEvaluations()); assertTrue(optimizer.getEvaluations() > 800);
Modified: commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/direct/NelderMeadTest.java URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/direct/NelderMeadTest.java?rev=754499&r1=754498&r2=754499&view=diff ============================================================================== --- commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/direct/NelderMeadTest.java (original) +++ commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/direct/NelderMeadTest.java Sat Mar 14 17:35:49 2009 @@ -25,9 +25,9 @@ import org.apache.commons.math.linear.decomposition.NotPositiveDefiniteMatrixException; import org.apache.commons.math.optimization.GoalType; import org.apache.commons.math.optimization.ObjectiveException; -import org.apache.commons.math.optimization.ObjectiveFunction; -import org.apache.commons.math.optimization.PointValuePair; -import org.apache.commons.math.optimization.ObjectiveValueChecker; +import org.apache.commons.math.optimization.ScalarObjectiveFunction; +import org.apache.commons.math.optimization.ScalarPointValuePair; +import org.apache.commons.math.optimization.SimpleValueChecker; public class NelderMeadTest extends TestCase { @@ -37,8 +37,8 @@ } public void testObjectiveExceptions() throws ConvergenceException { - ObjectiveFunction wrong = - new ObjectiveFunction() { + ScalarObjectiveFunction wrong = + new ScalarObjectiveFunction() { private static final long serialVersionUID = 4751314470965489371L; public double objective(double[] x) throws ObjectiveException { if (x[0] < 0) { @@ -84,7 +84,7 @@ final double valueXmYp = -valueXmYm; // local minimum final double valueXpYm = -0.7290400707055187115322; // global minimum final double valueXpYp = -valueXpYm; // global maximum - ObjectiveFunction fourExtrema = new ObjectiveFunction() { + ScalarObjectiveFunction fourExtrema = new ScalarObjectiveFunction() { private static final long serialVersionUID = -7039124064449091152L; public double objective(double[] variables) { final double x = variables[0]; @@ -94,10 +94,10 @@ }; NelderMead optimizer = new NelderMead(); - optimizer.setConvergenceChecker(new ObjectiveValueChecker(1.0e-10, 1.0e-30)); + optimizer.setConvergenceChecker(new SimpleValueChecker(1.0e-10, 1.0e-30)); optimizer.setMaxEvaluations(100); optimizer.setStartConfiguration(new double[] { 0.2, 0.2 }); - PointValuePair optimum; + ScalarPointValuePair optimum; // minimization optimum = optimizer.optimize(fourExtrema, GoalType.MINIMIZE, new double[] { -3.0, 0 }); @@ -134,8 +134,8 @@ public void testRosenbrock() throws ObjectiveException, ConvergenceException { - ObjectiveFunction rosenbrock = - new ObjectiveFunction() { + ScalarObjectiveFunction rosenbrock = + new ScalarObjectiveFunction() { private static final long serialVersionUID = -9044950469615237490L; public double objective(double[] x) { ++count; @@ -147,12 +147,12 @@ count = 0; NelderMead optimizer = new NelderMead(); - optimizer.setConvergenceChecker(new ObjectiveValueChecker(-1, 1.0e-3)); + optimizer.setConvergenceChecker(new SimpleValueChecker(-1, 1.0e-3)); optimizer.setMaxEvaluations(100); optimizer.setStartConfiguration(new double[][] { { -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 } }); - PointValuePair optimum = + ScalarPointValuePair optimum = optimizer.optimize(rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1.0 }); assertEquals(count, optimizer.getEvaluations()); @@ -165,8 +165,8 @@ public void testPowell() throws ObjectiveException, ConvergenceException { - ObjectiveFunction powell = - new ObjectiveFunction() { + ScalarObjectiveFunction powell = + new ScalarObjectiveFunction() { private static final long serialVersionUID = -832162886102041840L; public double objective(double[] x) { ++count; @@ -180,9 +180,9 @@ count = 0; NelderMead optimizer = new NelderMead(); - optimizer.setConvergenceChecker(new ObjectiveValueChecker(-1.0, 1.0e-3)); + optimizer.setConvergenceChecker(new SimpleValueChecker(-1.0, 1.0e-3)); optimizer.setMaxEvaluations(200); - PointValuePair optimum = + ScalarPointValuePair optimum = optimizer.optimize(powell, GoalType.MINIMIZE, new double[] { 3.0, -1.0, 0.0, 1.0 }); assertEquals(count, optimizer.getEvaluations()); assertTrue(optimizer.getEvaluations() > 110);
