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Java example source code file (PiecewiseBicubicSplineInterpolatingFunctionTest.java)

This example Java source code file (PiecewiseBicubicSplineInterpolatingFunctionTest.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

bivariatefunction, dimensionmismatchexception, failed, insufficientdataexception, nonmonotonicsequenceexception, nullargumentexception, piecewisebicubicsplineinterpolatingfunction, piecewisebicubicsplineinterpolatingfunctiontest, randomgenerator, suppresswarnings, test, uniformrealdistribution, well19937c

The PiecewiseBicubicSplineInterpolatingFunctionTest.java Java example source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.commons.math3.analysis.interpolation;

import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.InsufficientDataException;
import org.apache.commons.math3.exception.NonMonotonicSequenceException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.analysis.BivariateFunction;
import org.apache.commons.math3.distribution.UniformRealDistribution;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.Well19937c;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
import org.junit.Assert;
import org.junit.Test;

/**
 * Test case for the piecewise bicubic function.
 */
public final class PiecewiseBicubicSplineInterpolatingFunctionTest {
    /**
     * Test preconditions.
     */
    @Test
    public void testPreconditions() {
        double[] xval = new double[] { 3, 4, 5, 6.5, 7.5 };
        double[] yval = new double[] { -4, -3, -1, 2.5, 3.5 };
        double[][] zval = new double[xval.length][yval.length];

        @SuppressWarnings("unused")
        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) {
            // Expected.
        }

        try {
            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) {
            // 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) {
            // 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) {
            // 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) {
            // 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) {
            // 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) {
            // 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) {
            // 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) {
            // Expected.
        }
    }

    /**
     * Interpolating a plane.
     * <p>
     * z = 2 x - 3 y + 5
     */
    @Test
    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) {
                    return 2 * x - 3 * y + 5;
                }
            };

        testInterpolation(minimumX,
                          maximumX,
                          minimumY,
                          maximumY,
                          numberOfElements,
                          numberOfSamples,
                          f,
                          interpolationTolerance,
                          maxTolerance);
    }

    /**
     * Interpolating a paraboloid.
     * <p>
     * z = 2 x<sup>2 - 3 y2 + 4 x y - 5
     */
    @Test
    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) {
                    return 2 * x * x - 3 * y * y + 4 * x * y - 5;
                }
            };

        testInterpolation(minimumX,
                          maximumX,
                          minimumY,
                          maximumY,
                          numberOfElements,
                          numberOfSamples,
                          f,
                          interpolationTolerance,
                          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);
        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++) {
            xValues[i] = minimumX + deltaX * (double) i;
            for (int j = 0; j < numberOfElements; j++) {
                yValues[j] = minimumY + deltaY * (double) j;
                zValues[i][j] = f.value(xValues[i], yValues[j]);
            }
        }

        final BivariateFunction interpolation
            = new PiecewiseBicubicSplineInterpolatingFunction(xValues,
                                                              yValues,
                                                              zValues);

        for (int i = 0; i < numberOfElements; i++) {
            currentX = xValues[i];
            for (int j = 0; j < numberOfElements; j++) {
                currentY = yValues[j];
                expected = f.value(currentX, currentY);
                actual = interpolation.value(currentX, currentY);
                Assert.assertTrue(Precision.equals(expected, actual));
            }
        }

        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++) {
            currentX = distX.sample();
            currentY = distY.sample();
            expected = f.value(currentX, currentY);
            actual = interpolation.value(currentX, currentY);
            sumError += FastMath.abs(actual - expected);
            Assert.assertEquals(expected, actual, maxTolerance);
        }

        final double meanError = sumError / numberOfSamples;
        Assert.assertEquals(0, meanError, meanTolerance);
    }
}

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