Repository: commons-math Updated Branches: refs/heads/master 301ad5921 -> 8aec465b4
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Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/f8a8ea74 Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/f8a8ea74 Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/f8a8ea74 Branch: refs/heads/master Commit: f8a8ea748a0b3015490ccecec7b97f0ab2daf9c4 Parents: 301ad59 Author: Gilles <er...@apache.org> Authored: Thu Nov 20 12:50:41 2014 +0100 Committer: Gilles <er...@apache.org> Committed: Thu Nov 20 12:50:41 2014 +0100 ---------------------------------------------------------------------- ...eBicubicSplineInterpolatingFunctionTest.java | 170 +++++++++++-------- 1 file changed, 102 insertions(+), 68 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/commons-math/blob/f8a8ea74/src/test/java/org/apache/commons/math3/analysis/interpolation/PiecewiseBicubicSplineInterpolatingFunctionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math3/analysis/interpolation/PiecewiseBicubicSplineInterpolatingFunctionTest.java b/src/test/java/org/apache/commons/math3/analysis/interpolation/PiecewiseBicubicSplineInterpolatingFunctionTest.java index 19a060c..35cb18e 100644 --- a/src/test/java/org/apache/commons/math3/analysis/interpolation/PiecewiseBicubicSplineInterpolatingFunctionTest.java +++ b/src/test/java/org/apache/commons/math3/analysis/interpolation/PiecewiseBicubicSplineInterpolatingFunctionTest.java @@ -16,9 +16,6 @@ */ package org.apache.commons.math3.analysis.interpolation; -import static org.junit.Assert.assertEquals; -import static org.junit.Assert.assertTrue; - import org.apache.commons.math3.exception.DimensionMismatchException; import org.apache.commons.math3.exception.InsufficientDataException; import org.apache.commons.math3.exception.NonMonotonicSequenceException; @@ -46,84 +43,84 @@ public final class PiecewiseBicubicSplineInterpolatingFunctionTest { double[][] zval = new double[xval.length][yval.length]; @SuppressWarnings("unused") - PiecewiseBicubicSplineInterpolatingFunction bcf = new PiecewiseBicubicSplineInterpolatingFunction( xval, yval, zval ); + PiecewiseBicubicSplineInterpolatingFunction bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval, yval, zval); try { - bcf = new PiecewiseBicubicSplineInterpolatingFunction( null, yval, zval ); - Assert.fail( "Failed to detect x null pointer" ); - } catch ( NullArgumentException iae ) { + bcf = new PiecewiseBicubicSplineInterpolatingFunction(null, yval, zval); + Assert.fail("Failed to detect x null pointer"); + } catch (NullArgumentException iae) { // Expected. } try { - bcf = new PiecewiseBicubicSplineInterpolatingFunction( xval, null, zval ); - Assert.fail( "Failed to detect y null pointer" ); - } catch ( NullArgumentException iae ) { + bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval, null, zval); + Assert.fail("Failed to detect y null pointer"); + } catch (NullArgumentException iae) { // Expected. } try { - bcf = new PiecewiseBicubicSplineInterpolatingFunction( xval, yval, null ); - Assert.fail( "Failed to detect z null pointer" ); - } catch ( NullArgumentException iae ) { + bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval, yval, null); + Assert.fail("Failed to detect z null pointer"); + } catch (NullArgumentException iae) { // Expected. } try { double xval1[] = { 0.0, 1.0, 2.0, 3.0 }; - bcf = new PiecewiseBicubicSplineInterpolatingFunction( xval1, yval, zval ); - Assert.fail( "Failed to detect insufficient x data" ); - } catch ( InsufficientDataException iae ) { + bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval1, yval, zval); + Assert.fail("Failed to detect insufficient x data"); + } catch (InsufficientDataException iae) { // Expected. } try { double yval1[] = { 0.0, 1.0, 2.0, 3.0 }; - bcf = new PiecewiseBicubicSplineInterpolatingFunction( xval, yval1, zval ); - Assert.fail( "Failed to detect insufficient y data" ); - } catch ( InsufficientDataException iae ) { + bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval, yval1, zval); + Assert.fail("Failed to detect insufficient y data"); + } catch (InsufficientDataException iae) { // Expected. } try { double zval1[][] = new double[4][4]; - bcf = new PiecewiseBicubicSplineInterpolatingFunction( xval, yval, zval1 ); - Assert.fail( "Failed to detect insufficient z data" ); - } catch ( InsufficientDataException iae ) { + bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval, yval, zval1); + Assert.fail("Failed to detect insufficient z data"); + } catch (InsufficientDataException iae) { // Expected. } try { double xval1[] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 }; - bcf = new PiecewiseBicubicSplineInterpolatingFunction( xval1, yval, zval ); - Assert.fail( "Failed to detect data set array with different sizes." ); - } catch ( DimensionMismatchException iae ) { + bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval1, yval, zval); + Assert.fail("Failed to detect data set array with different sizes."); + } catch (DimensionMismatchException iae) { // Expected. } try { double yval1[] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 }; - bcf = new PiecewiseBicubicSplineInterpolatingFunction( xval, yval1, zval ); - Assert.fail( "Failed to detect data set array with different sizes." ); - } catch ( DimensionMismatchException iae ) { + bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval, yval1, zval); + Assert.fail("Failed to detect data set array with different sizes."); + } catch (DimensionMismatchException iae) { // Expected. } // X values not sorted. try { double xval1[] = { 0.0, 1.0, 0.5, 7.0, 3.5 }; - bcf = new PiecewiseBicubicSplineInterpolatingFunction( xval1, yval, zval ); - Assert.fail( "Failed to detect unsorted x arguments." ); - } catch ( NonMonotonicSequenceException iae ) { + bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval1, yval, zval); + Assert.fail("Failed to detect unsorted x arguments."); + } catch (NonMonotonicSequenceException iae) { // Expected. } // Y values not sorted. try { double yval1[] = { 0.0, 1.0, 1.5, 0.0, 3.0 }; - bcf = new PiecewiseBicubicSplineInterpolatingFunction( xval, yval1, zval ); - Assert.fail( "Failed to detect unsorted y arguments." ); - } catch ( NonMonotonicSequenceException iae ) { + bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval, yval1, zval); + Assert.fail("Failed to detect unsorted y arguments."); + } catch (NonMonotonicSequenceException iae) { // Expected. } } @@ -134,25 +131,33 @@ public final class PiecewiseBicubicSplineInterpolatingFunctionTest { * z = 2 x - 3 y + 5 */ @Test - public void testInterpolatePlane() { + public void testPlane() { final int numberOfElements = 10; final double minimumX = -10; final double maximumX = 10; final double minimumY = -10; final double maximumY = 10; final int numberOfSamples = 100; + final double interpolationTolerance = 7e-15; final double maxTolerance = 6e-14; // Function values BivariateFunction f = new BivariateFunction() { - public double value( double x, double y ) { + public double value(double x, double y) { return 2 * x - 3 * y + 5; } }; - testInterpolation( minimumX, maximumX, minimumY, maximumY, numberOfElements, numberOfSamples, f, - interpolationTolerance, maxTolerance ); + testInterpolation(minimumX, + maximumX, + minimumY, + maximumY, + numberOfElements, + numberOfSamples, + f, + interpolationTolerance, + maxTolerance); } /** @@ -161,74 +166,103 @@ public final class PiecewiseBicubicSplineInterpolatingFunctionTest { * z = 2 x<sup>2</sup> - 3 y<sup>2</sup> + 4 x y - 5 */ @Test - public void testInterpolationParabaloid() { + public void testParabaloid() { final int numberOfElements = 10; final double minimumX = -10; final double maximumX = 10; final double minimumY = -10; final double maximumY = 10; final int numberOfSamples = 100; + final double interpolationTolerance = 2e-14; final double maxTolerance = 6e-14; // Function values BivariateFunction f = new BivariateFunction() { - public double value( double x, double y ) { + public double value(double x, double y) { return 2 * x * x - 3 * y * y + 4 * x * y - 5; } }; - testInterpolation( minimumX, maximumX, minimumY, maximumY, numberOfElements, numberOfSamples, f, - interpolationTolerance, maxTolerance ); + testInterpolation(minimumX, + maximumX, + minimumY, + maximumY, + numberOfElements, + numberOfSamples, + f, + interpolationTolerance, + maxTolerance); } - private void testInterpolation( double minimumX, double maximumX, double minimumY, double maximumY, - int numberOfElements, int numberOfSamples, BivariateFunction f, double tolerance, - double maxTolerance ) { + /** + * @param minimumX Lower bound of interpolation range along the x-coordinate. + * @param maximumX Higher bound of interpolation range along the x-coordinate. + * @param minimumY Lower bound of interpolation range along the y-coordinate. + * @param maximumY Higher bound of interpolation range along the y-coordinate. + * @param numberOfElements Number of data points (along each dimension). + * @param numberOfSamples Number of test points. + * @param f Function to test. + * @param meanTolerance Allowed average error (mean error on all interpolated values). + * @param maxTolerance Allowed error on each interpolated value. + */ + private void testInterpolation(double minimumX, + double maximumX, + double minimumY, + double maximumY, + int numberOfElements, + int numberOfSamples, + BivariateFunction f, + double meanTolerance, + double maxTolerance) { double expected; double actual; double currentX; double currentY; - final double deltaX = ( maximumX - minimumX ) / ( (double) numberOfElements ); - final double deltaY = ( maximumY - minimumY ) / ( (double) numberOfElements ); - double xValues[] = new double[numberOfElements]; - double yValues[] = new double[numberOfElements]; - double zValues[][] = new double[numberOfElements][numberOfElements]; + final double deltaX = (maximumX - minimumX) / ((double) numberOfElements); + final double deltaY = (maximumY - minimumY) / ((double) numberOfElements); + final double[] xValues = new double[numberOfElements]; + final double[] yValues = new double[numberOfElements]; + final double[][] zValues = new double[numberOfElements][numberOfElements]; - for ( int i = 0; i < numberOfElements; i++ ) { + for (int i = 0; i < numberOfElements; i++) { xValues[i] = minimumX + deltaX * (double) i; - for ( int j = 0; j < numberOfElements; j++ ) { + for (int j = 0; j < numberOfElements; j++) { yValues[j] = minimumY + deltaY * (double) j; - zValues[i][j] = f.value( xValues[i], yValues[j] ); + zValues[i][j] = f.value(xValues[i], yValues[j]); } } - BivariateFunction interpolation = new PiecewiseBicubicSplineInterpolatingFunction( xValues, yValues, zValues ); + final BivariateFunction interpolation + = new PiecewiseBicubicSplineInterpolatingFunction(xValues, + yValues, + zValues); - for ( int i = 0; i < numberOfElements; i++ ) { + for (int i = 0; i < numberOfElements; i++) { currentX = xValues[i]; - for ( int j = 0; j < numberOfElements; j++ ) { + for (int j = 0; j < numberOfElements; j++) { currentY = yValues[j]; - expected = f.value( currentX, currentY ); - actual = interpolation.value( currentX, currentY ); - assertTrue( Precision.equals( expected, actual ) ); + expected = f.value(currentX, currentY); + actual = interpolation.value(currentX, currentY); + Assert.assertTrue(Precision.equals(expected, actual)); } } - final RandomGenerator rng = new Well19937c( 1234567L ); // "tol" depends on the seed. - final UniformRealDistribution distX = new UniformRealDistribution( rng, xValues[0], xValues[xValues.length - 1] ); - final UniformRealDistribution distY = new UniformRealDistribution( rng, yValues[0], yValues[yValues.length - 1] ); + final RandomGenerator rng = new Well19937c(1234567L); + final UniformRealDistribution distX = new UniformRealDistribution(rng, xValues[0], xValues[xValues.length - 1]); + final UniformRealDistribution distY = new UniformRealDistribution(rng, yValues[0], yValues[yValues.length - 1]); double sumError = 0; - for ( int i = 0; i < numberOfSamples; i++ ) { + for (int i = 0; i < numberOfSamples; i++) { currentX = distX.sample(); currentY = distY.sample(); - expected = f.value( currentX, currentY ); - actual = interpolation.value( currentX, currentY ); - sumError += FastMath.abs( actual - expected ); - assertEquals( expected, actual, maxTolerance ); + expected = f.value(currentX, currentY); + actual = interpolation.value(currentX, currentY); + sumError += FastMath.abs(actual - expected); + Assert.assertEquals(expected, actual, maxTolerance); } - assertEquals( 0.0, ( sumError / (double) numberOfSamples ), tolerance ); + final double meanError = sumError / numberOfSamples; + Assert.assertEquals(0, meanError, meanTolerance); } }
