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Java example source code file (AkimaSplineInterpolatorTest.java)

This example Java source code file (AkimaSplineInterpolatorTest.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

akimasplineinterpolatortest, dimensionmismatchexception, failed, nonmonotonicsequenceexception, nullargumentexception, numberistoosmallexception, randomgenerator, test, uniformrealdistribution, univariatefunction, univariateinterpolator, well19937c

The AkimaSplineInterpolatorTest.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.analysis.UnivariateFunction;
import org.apache.commons.math3.distribution.UniformRealDistribution;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NonMonotonicSequenceException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
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;

import static org.junit.Assert.*;

public class AkimaSplineInterpolatorTest
{

    @Test
    public void testIllegalArguments()
    {
        // Data set arrays of different size.
        UnivariateInterpolator i = new AkimaSplineInterpolator();

        try
        {
            double yval[] = { 0.0, 1.0, 2.0, 3.0, 4.0 };
            i.interpolate( null, yval );
            Assert.fail( "Failed to detect x null pointer" );
        }
        catch ( NullArgumentException iae )
        {
            // Expected.
        }

        try
        {
            double xval[] = { 0.0, 1.0, 2.0, 3.0, 4.0 };
            i.interpolate( xval, null );
            Assert.fail( "Failed to detect y null pointer" );
        }
        catch ( NullArgumentException iae )
        {
            // Expected.
        }

        try
        {
            double xval[] = { 0.0, 1.0, 2.0, 3.0 };
            double yval[] = { 0.0, 1.0, 2.0, 3.0 };
            i.interpolate( xval, yval );
            Assert.fail( "Failed to detect insufficient data" );
        }
        catch ( NumberIsTooSmallException iae )
        {
            // Expected.
        }

        try
        {
            double xval[] = { 0.0, 1.0, 2.0, 3.0, 4.0 };
            double yval[] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 };
            i.interpolate( xval, yval );
            Assert.fail( "Failed to detect data set array with different sizes." );
        }
        catch ( DimensionMismatchException iae )
        {
            // Expected.
        }

        // X values not sorted.
        try
        {
            double xval[] = { 0.0, 1.0, 0.5, 7.0, 3.5, 2.2, 8.0 };
            double yval[] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
            i.interpolate( xval, yval );
            Assert.fail( "Failed to detect unsorted arguments." );
        }
        catch ( NonMonotonicSequenceException iae )
        {
            // Expected.
        }
    }

    /*
     * Interpolate a straight line. <p> y = 2 x - 5 

Tolerances determined by performing same calculation using * Math.NET over ten runs of 100 random number draws for the same function over the same span with the same number * of elements */ @Test public void testInterpolateLine() { final int numberOfElements = 10; final double minimumX = -10; final double maximumX = 10; final int numberOfSamples = 100; final double interpolationTolerance = 1e-15; final double maxTolerance = 1e-15; UnivariateFunction f = new UnivariateFunction() { public double value( double x ) { return 2 * x - 5; } }; testInterpolation( minimumX, maximumX, numberOfElements, numberOfSamples, f, interpolationTolerance, maxTolerance ); } /* * Interpolate a straight line. <p> y = 3 x2 - 5 x + 7

Tolerances determined by performing same * calculation using Math.NET over ten runs of 100 random number draws for the same function over the same span with * the same number of elements */ @Test public void testInterpolateParabola() { final int numberOfElements = 10; final double minimumX = -10; final double maximumX = 10; final int numberOfSamples = 100; final double interpolationTolerance = 7e-15; final double maxTolerance = 6e-14; UnivariateFunction f = new UnivariateFunction() { public double value( double x ) { return ( 3 * x * x ) - ( 5 * x ) + 7; } }; testInterpolation( minimumX, maximumX, numberOfElements, numberOfSamples, f, interpolationTolerance, maxTolerance ); } /* * Interpolate a straight line. <p> y = 3 x3 - 0.5 x2 + x - 1

Tolerances determined by * performing same calculation using Math.NET over ten runs of 100 random number draws for the same function over * the same span with the same number of elements */ @Test public void testInterpolateCubic() { final int numberOfElements = 10; final double minimumX = -3; final double maximumX = 3; final int numberOfSamples = 100; final double interpolationTolerance = 0.37; final double maxTolerance = 3.8; UnivariateFunction f = new UnivariateFunction() { public double value( double x ) { return ( 3 * x * x * x ) - ( 0.5 * x * x ) + ( 1 * x ) - 1; } }; testInterpolation( minimumX, maximumX, numberOfElements, numberOfSamples, f, interpolationTolerance, maxTolerance ); } private void testInterpolation( double minimumX, double maximumX, int numberOfElements, int numberOfSamples, UnivariateFunction f, double tolerance, double maxTolerance ) { double expected; double actual; double currentX; final double delta = ( maximumX - minimumX ) / ( (double) numberOfElements ); double xValues[] = new double[numberOfElements]; double yValues[] = new double[numberOfElements]; for ( int i = 0; i < numberOfElements; i++ ) { xValues[i] = minimumX + delta * (double) i; yValues[i] = f.value( xValues[i] ); } UnivariateFunction interpolation = new AkimaSplineInterpolator().interpolate( xValues, yValues ); for ( int i = 0; i < numberOfElements; i++ ) { currentX = xValues[i]; expected = f.value( currentX ); actual = interpolation.value( currentX ); 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] ); double sumError = 0; for ( int i = 0; i < numberOfSamples; i++ ) { currentX = distX.sample(); expected = f.value( currentX ); actual = interpolation.value( currentX ); sumError += FastMath.abs( actual - expected ); assertEquals( expected, actual, maxTolerance ); } assertEquals( 0.0, ( sumError / (double) numberOfSamples ), tolerance ); } }

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