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

Formatting.

This commit is contained in:
Gilles 2014-11-20 12:50:41 +01:00
parent 301ad59214
commit f8a8ea748a
1 changed files with 102 additions and 68 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

170
src/test/java/org/apache/commons/math3/analysis/interpolation/PiecewiseBicubicSplineInterpolatingFunctionTest.java
View File

@ -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);
}
}