Author: celestin
Date: Wed May 2 05:50:59 2012
New Revision: 1332924
URL: http://svn.apache.org/viewvc?rev=1332924&view=rev
Log:
Standard unit test of least-squares optimizers based on NIST's Statistical
Reference Datasets (StRD).
Added:
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Lanczos1.dat
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/GaussNewtonOptimizerTest.java
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDatasetFactory.java
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java?rev=1332924&r1=1332923&r2=1332924&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java
Wed May 2 05:50:59 2012
@@ -17,6 +17,7 @@
package org.apache.commons.math3.optimization.general;
import java.awt.geom.Point2D;
+import java.io.IOException;
import java.io.Serializable;
import java.util.Arrays;
@@ -466,6 +467,43 @@ public abstract class AbstractLeastSquar
{-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, 1.0);
+
+ final double[][] data = dataset.getData();
+ final double[] initial = dataset.getStartingPoint(0);
+ final DifferentiableMultivariateVectorFunction problem;
+ problem = dataset.getLeastSquaresProblem();
+ final PointVectorValuePair optimum;
+ optimum = optimizer.optimize(100, problem, data[1], w, initial);
+
+ final double[] actual = optimum.getPoint();
+ final double[] actualSig = optimizer.guessParametersErrors();
+ 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);
+ expected = dataset.getParameterStandardDeviation(i);
+ delta = FastMath.abs(errParamsSd * expected);
+ Assert.assertEquals(dataset.getName() + ", sd of param #" + i,
+ expected, actualSig[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 implements
DifferentiableMultivariateVectorFunction, Serializable {
private static final long serialVersionUID = 703247177355019415L;
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/GaussNewtonOptimizerTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/GaussNewtonOptimizerTest.java?rev=1332924&r1=1332923&r2=1332924&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/GaussNewtonOptimizerTest.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/GaussNewtonOptimizerTest.java
Wed May 2 05:50:59 2012
@@ -17,6 +17,8 @@
package org.apache.commons.math3.optimization.general;
+import java.io.IOException;
+
import org.apache.commons.math3.exception.ConvergenceException;
import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.optimization.SimpleVectorValueChecker;
@@ -131,8 +133,19 @@ public class GaussNewtonOptimizerTest
@Test(expected=ConvergenceException.class)
public void testCircleFittingBadInit() {
/*
- * This test does not converge with this optimizer
+ * This test does not converge with this optimizer.
*/
super.testCircleFittingBadInit();
}
+
+ @Override
+ @Test(expected = ConvergenceException.class)
+ public void testHahn1()
+ throws IOException {
+ /*
+ * TODO This test leads to a singular problem with the Gauss-Newton
+ * optimizer. This should be inquired.
+ */
+ super.testHahn1();
+ }
}
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDatasetFactory.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDatasetFactory.java?rev=1332924&r1=1332923&r2=1332924&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDatasetFactory.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/StatisticalReferenceDatasetFactory.java
Wed May 2 05:50:59 2012
@@ -152,6 +152,41 @@ public class StatisticalReferenceDataset
return dataset;
}
+ public static StatisticalReferenceDataset createLanczos1()
+ throws IOException {
+ final BufferedReader in =
+ createBufferedReaderFromResource("Lanczos1.dat");
+ StatisticalReferenceDataset dataset = null;
+ try {
+ dataset = new StatisticalReferenceDataset(in) {
+
+ @Override
+ public double getModelValue(final double x, final double[] a) {
+ System.out.println(a[0]+", "+a[1]+", "+a[2]+", "+a[3]+",
"+a[4]+", "+a[5]);
+ return a[0] * FastMath.exp(-a[3] * x) +
+ a[1] * FastMath.exp(-a[4] * x) +
+ a[2] * FastMath.exp(-a[5] * x);
+ }
+
+ @Override
+ public double[] getModelDerivatives(final double x,
+ final double[] a) {
+ final double[] dy = new double[6];
+ dy[0] = FastMath.exp(-x * a[3]);
+ dy[1] = FastMath.exp(-x * a[4]);
+ dy[2] = FastMath.exp(-x * a[5]);
+ dy[3] = -x * a[0] * dy[0];
+ dy[4] = -x * a[1] * dy[1];
+ dy[5] = -x * a[2] * dy[2];
+ return dy;
+ }
+ };
+ } finally {
+ in.close();
+ }
+ return dataset;
+ }
+
/**
* Returns an array with all available reference datasets.
*
Added:
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Lanczos1.dat
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Lanczos1.dat?rev=1332924&view=auto
==============================================================================
---
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Lanczos1.dat
(added)
+++
commons/proper/math/trunk/src/test/resources/org/apache/commons/math3/optimization/general/Lanczos1.dat
Wed May 2 05:50:59 2012
@@ -0,0 +1,84 @@
+NIST/ITL StRD
+Dataset Name: Lanczos1 (Lanczos1.dat)
+
+File Format: ASCII
+ Starting Values (lines 41 to 46)
+ Certified Values (lines 41 to 51)
+ Data (lines 61 to 84)
+
+Procedure: Nonlinear Least Squares Regression
+
+Description: These data are taken from an example discussed in
+ Lanczos (1956). The data were generated to 14-digits
+ of accuracy using
+ f(x) = 0.0951*exp(-x) + 0.8607*exp(-3*x)
+ + 1.5576*exp(-5*x).
+
+
+Reference: Lanczos, C. (1956).
+ Applied Analysis.
+ Englewood Cliffs, NJ: Prentice Hall, pp. 272-280.
+
+
+
+
+Data: 1 Response (y)
+ 1 Predictor (x)
+ 24 Observations
+ Average Level of Difficulty
+ Generated Data
+
+Model: Exponential Class
+ 6 Parameters (b1 to b6)
+
+ y = b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x) + e
+
+
+
+ Starting values Certified Values
+
+ Start 1 Start 2 Parameter Standard Deviation
+ b1 = 1.2 0.5 9.5100000027E-02 5.3347304234E-11
+ b2 = 0.3 0.7 1.0000000001E+00 2.7473038179E-10
+ b3 = 5.6 3.6 8.6070000013E-01 1.3576062225E-10
+ b4 = 5.5 4.2 3.0000000002E+00 3.3308253069E-10
+ b5 = 6.5 4 1.5575999998E+00 1.8815731448E-10
+ b6 = 7.6 6.3 5.0000000001E+00 1.1057500538E-10
+
+Residual Sum of Squares: 1.4307867721E-25
+Residual Standard Deviation: 8.9156129349E-14
+Degrees of Freedom: 18
+Number of Observations: 24
+
+
+
+
+
+
+
+
+Data: y x
+ 2.513400000000E+00 0.000000000000E+00
+ 2.044333373291E+00 5.000000000000E-02
+ 1.668404436564E+00 1.000000000000E-01
+ 1.366418021208E+00 1.500000000000E-01
+ 1.123232487372E+00 2.000000000000E-01
+ 9.268897180037E-01 2.500000000000E-01
+ 7.679338563728E-01 3.000000000000E-01
+ 6.388775523106E-01 3.500000000000E-01
+ 5.337835317402E-01 4.000000000000E-01
+ 4.479363617347E-01 4.500000000000E-01
+ 3.775847884350E-01 5.000000000000E-01
+ 3.197393199326E-01 5.500000000000E-01
+ 2.720130773746E-01 6.000000000000E-01
+ 2.324965529032E-01 6.500000000000E-01
+ 1.996589546065E-01 7.000000000000E-01
+ 1.722704126914E-01 7.500000000000E-01
+ 1.493405660168E-01 8.000000000000E-01
+ 1.300700206922E-01 8.500000000000E-01
+ 1.138119324644E-01 9.000000000000E-01
+ 1.000415587559E-01 9.500000000000E-01
+ 8.833209084540E-02 1.000000000000E+00
+ 7.833544019350E-02 1.050000000000E+00
+ 6.976693743449E-02 1.100000000000E+00
+ 6.239312536719E-02 1.150000000000E+00
