Apache
/
commons-math
mirror of https://github.com/apache/commons-math.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

improved test coverage 

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@796040 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe 2009-07-20 22:01:30 +00:00
parent b6d4e8649a
commit 5fd4a00932
1 changed files with 84 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

84
src/test/org/apache/commons/math/optimization/direct/NelderMeadTest.java
View File

@ -29,7 +29,11 @@ import org.apache.commons.math.MathException;
import org.apache.commons.math.MaxEvaluationsExceededException;
import org.apache.commons.math.MaxIterationsExceededException;
import org.apache.commons.math.analysis.MultivariateRealFunction;
import org.apache.commons.math.analysis.MultivariateVectorialFunction;
import org.apache.commons.math.linear.Array2DRowRealMatrix;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.optimization.GoalType;
import org.apache.commons.math.optimization.LeastSquaresConverter;
import org.apache.commons.math.optimization.OptimizationException;
import org.apache.commons.math.optimization.RealPointValuePair;
import org.apache.commons.math.optimization.SimpleRealPointChecker;
@ -173,6 +177,86 @@ public class NelderMeadTest {
}
@Test
public void testLeastSquares1()
throws FunctionEvaluationException, ConvergenceException {
final RealMatrix factors =
new Array2DRowRealMatrix(new double[][] {
{ 1.0, 0.0 },
{ 0.0, 1.0 }
}, false);
LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorialFunction() {
public double[] value(double[] variables) {
return factors.operate(variables);
}
}, new double[] { 2.0, -3.0 });
NelderMead optimizer = new NelderMead();
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
optimizer.setMaxIterations(200);
RealPointValuePair optimum =
optimizer.optimize(ls, GoalType.MINIMIZE, new double[] { 10.0, 10.0 });
assertEquals( 2.0, optimum.getPointRef()[0], 3.0e-5);
assertEquals(-3.0, optimum.getPointRef()[1], 4.0e-4);
assertTrue(optimizer.getEvaluations() > 60);
assertTrue(optimizer.getEvaluations() < 80);
assertTrue(optimum.getValue() < 1.0e-6);
}
@Test
public void testLeastSquares2()
throws FunctionEvaluationException, ConvergenceException {
final RealMatrix factors =
new Array2DRowRealMatrix(new double[][] {
{ 1.0, 0.0 },
{ 0.0, 1.0 }
}, false);
LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorialFunction() {
public double[] value(double[] variables) {
return factors.operate(variables);
}
}, new double[] { 2.0, -3.0 }, new double[] { 10.0, 0.1 });
NelderMead optimizer = new NelderMead();
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
optimizer.setMaxIterations(200);
RealPointValuePair optimum =
optimizer.optimize(ls, GoalType.MINIMIZE, new double[] { 10.0, 10.0 });
assertEquals( 2.0, optimum.getPointRef()[0], 5.0e-5);
assertEquals(-3.0, optimum.getPointRef()[1], 8.0e-4);
assertTrue(optimizer.getEvaluations() > 60);
assertTrue(optimizer.getEvaluations() < 80);
assertTrue(optimum.getValue() < 1.0e-6);
}
@Test
public void testLeastSquares3()
throws FunctionEvaluationException, ConvergenceException {
final RealMatrix factors =
new Array2DRowRealMatrix(new double[][] {
{ 1.0, 0.0 },
{ 0.0, 1.0 }
}, false);
LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorialFunction() {
public double[] value(double[] variables) {
return factors.operate(variables);
}
}, new double[] { 2.0, -3.0 }, new Array2DRowRealMatrix(new double [][] {
{ 1.0, 1.2 }, { 1.2, 2.0 }
}));
NelderMead optimizer = new NelderMead();
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
optimizer.setMaxIterations(200);
RealPointValuePair optimum =
optimizer.optimize(ls, GoalType.MINIMIZE, new double[] { 10.0, 10.0 });
assertEquals( 2.0, optimum.getPointRef()[0], 2.0e-3);
assertEquals(-3.0, optimum.getPointRef()[1], 8.0e-4);
assertTrue(optimizer.getEvaluations() > 60);
assertTrue(optimizer.getEvaluations() < 80);
assertTrue(optimum.getValue() < 1.0e-6);
}
@Test(expected = MaxIterationsExceededException.class)
public void testMaxIterations() throws MathException {
try {