Author: erans
Date: Thu Aug 18 13:38:29 2011
New Revision: 1159214
URL: http://svn.apache.org/viewvc?rev=1159214&view=rev
Log:
Removed "try" blocks.
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math/optimization/general/GaussNewtonOptimizerTest.java
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math/optimization/general/GaussNewtonOptimizerTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math/optimization/general/GaussNewtonOptimizerTest.java?rev=1159214&r1=1159213&r2=1159214&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math/optimization/general/GaussNewtonOptimizerTest.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math/optimization/general/GaussNewtonOptimizerTest.java
Thu Aug 18 13:38:29 2011
@@ -201,7 +201,7 @@ public class GaussNewtonOptimizerTest {
}
- @Test
+ @Test(expected=ConvergenceException.class)
public void testNonInversible() throws Exception {
LinearProblem problem = new LinearProblem(new double[][] {
@@ -211,12 +211,7 @@ public class GaussNewtonOptimizerTest {
}, new double[] { 1, 1, 1 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new
SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
- try {
- optimizer.optimize(100, problem, problem.target, new double[] { 1,
1, 1 }, new double[] { 0, 0, 0 });
- Assert.fail("an exception should have been caught");
- } catch (ConvergenceException ee) {
- // expected behavior
- }
+ optimizer.optimize(100, problem, problem.target, new double[] { 1, 1,
1 }, new double[] { 0, 0, 0 });
}
@Test
@@ -255,7 +250,7 @@ public class GaussNewtonOptimizerTest {
}
- @Test
+ @Test(expected=ConvergenceException.class)
public void testMoreEstimatedParametersSimple() throws Exception {
LinearProblem problem = new LinearProblem(new double[][] {
@@ -266,16 +261,11 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new
SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
- try {
- optimizer.optimize(100, problem, problem.target, new double[] { 1,
1, 1 },
- new double[] { 7, 6, 5, 4 });
- Assert.fail("an exception should have been caught");
- } catch (ConvergenceException ee) {
- // expected behavior
- }
+ optimizer.optimize(100, problem, problem.target, new double[] { 1, 1,
1 },
+ new double[] { 7, 6, 5, 4 });
}
- @Test
+ @Test(expected=ConvergenceException.class)
public void testMoreEstimatedParametersUnsorted() throws Exception {
LinearProblem problem = new LinearProblem(new double[][] {
{ 1.0, 1.0, 0.0, 0.0, 0.0, 0.0 },
@@ -286,13 +276,8 @@ public class GaussNewtonOptimizerTest {
}, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new
SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
- try {
- optimizer.optimize(100, problem, problem.target, new double[] { 1,
1, 1, 1, 1 },
- new double[] { 2, 2, 2, 2, 2, 2 });
- Assert.fail("an exception should have been caught");
- } catch (ConvergenceException ee) {
- // expected behavior
- }
+ optimizer.optimize(100, problem, problem.target, new double[] { 1, 1,
1, 1, 1 },
+ new double[] { 2, 2, 2, 2, 2, 2 });
}
@Test
@@ -311,7 +296,6 @@ public class GaussNewtonOptimizerTest {
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
Assert.assertEquals(2.0, optimum.getPoint()[0], 1.0e-8);
Assert.assertEquals(1.0, optimum.getPoint()[1], 1.0e-8);
-
}
@Test
@@ -329,8 +313,8 @@ public class GaussNewtonOptimizerTest {
}
- @Test
- public void testInconsistentSizes() throws MathUserException {
+ @Test(expected=DimensionMismatchException.class)
+ public void testInconsistentSizes1() throws MathUserException {
LinearProblem problem =
new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new
double[] { -1, 1 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
@@ -342,27 +326,30 @@ public class GaussNewtonOptimizerTest {
Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
Assert.assertEquals(+1, optimum.getPoint()[1], 1.0e-10);
- try {
- optimizer.optimize(100, problem, problem.target,
- new double[] { 1 },
- new double[] { 0, 0 });
- Assert.fail("an exception should have been thrown");
- } catch (DimensionMismatchException oe) {
- // expected behavior
- }
+ optimizer.optimize(100, problem, problem.target,
+ new double[] { 1 },
+ new double[] { 0, 0 });
+ }
- try {
- optimizer.optimize(100, problem, new double[] { 1 },
- new double[] { 1 },
- new double[] { 0, 0 });
- Assert.fail("an exception should have been thrown");
- } catch (DimensionMismatchException oe) {
- // expected behavior
- }
+ @Test(expected=DimensionMismatchException.class)
+ public void testInconsistentSizes2() throws MathUserException {
+ LinearProblem problem =
+ new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new
double[] { -1, 1 });
+ GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
+ optimizer.setConvergenceChecker(new
SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
+
+ VectorialPointValuePair optimum =
+ optimizer.optimize(100, problem, problem.target, new double[] { 1,
1 }, new double[] { 0, 0 });
+ Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
+ Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
+ Assert.assertEquals(+1, optimum.getPoint()[1], 1.0e-10);
+ optimizer.optimize(100, problem, new double[] { 1 },
+ new double[] { 1 },
+ new double[] { 0, 0 });
}
- @Test
+ @Test(expected=TooManyEvaluationsException.class)
public void testMaxEvaluations() throws Exception {
CircleVectorial circle = new CircleVectorial();
circle.addPoint( 30.0, 68.0);
@@ -372,14 +359,10 @@ public class GaussNewtonOptimizerTest {
circle.addPoint( 45.0, 97.0);
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new
SimpleVectorialPointChecker(1.0e-30, 1.0e-30));
- try {
- optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 },
- new double[] { 1, 1, 1, 1, 1 },
- new double[] { 98.680, 47.345 });
- Assert.fail("an exception should have been caught");
- } catch (TooManyEvaluationsException ee) {
- // expected behavior
- }
+
+ optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 },
+ new double[] { 1, 1, 1, 1, 1 },
+ new double[] { 98.680, 47.345 });
}
@Test
@@ -403,40 +386,10 @@ public class GaussNewtonOptimizerTest {
Assert.assertEquals(48.135167894714, center.y, 1.0e-10);
}
- @Test
+ @Test(expected=ConvergenceException.class)
public void testCircleFittingBadInit() throws MathUserException {
CircleVectorial circle = new CircleVectorial();
- double[][] points = 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}
- };
+ double[][] points = circlePoints;
double[] target = new double[points.length];
Arrays.fill(target, 0.0);
double[] weights = new double[points.length];
@@ -446,19 +399,29 @@ public class GaussNewtonOptimizerTest {
}
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new
SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
- try {
- optimizer.optimize(100, circle, target, weights, new double[] {
-12, -12 });
- Assert.fail("an exception should have been caught");
- } catch (ConvergenceException ee) {
- // expected behavior
+
+ optimizer.optimize(100, circle, target, weights, new double[] { -12,
-12 });
+ }
+
+ @Test
+ public void testCircleFittingGoodInit() throws MathUserException {
+ CircleVectorial circle = new CircleVectorial();
+ double[][] points = circlePoints;
+ double[] target = new double[points.length];
+ Arrays.fill(target, 0.0);
+ double[] weights = new double[points.length];
+ Arrays.fill(weights, 2.0);
+ for (int i = 0; i < points.length; ++i) {
+ circle.addPoint(points[i][0], points[i][1]);
}
+ GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
+ optimizer.setConvergenceChecker(new
SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
optimizer.optimize(100, circle, target, weights, new double[] { 0,
0 });
Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0],
1.0e-6);
Assert.assertEquals(0.2074999736353867, optimum.getPointRef()[1],
1.0e-6);
Assert.assertEquals(0.04268731682389561, optimizer.getRMS(),
1.0e-8);
-
}
private static class LinearProblem implements
DifferentiableMultivariateVectorialFunction, Serializable {
@@ -483,4 +446,36 @@ public class GaussNewtonOptimizerTest {
};
}
}
+
+ 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}
+ };
}
