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Java example source code file (RealTransformerAbstractTest.java)

This example Java source code file (RealTransformerAbstractTest.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

mathillegalargumentexception, notstrictlypositiveexception, numberistoolargeexception, random, realtransformer, realtransformerabstracttest, seed, string, test, transformtype, univariatefunction, util

The RealTransformerAbstractTest.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.transform;

import java.util.Random;

import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Test;

/**
 * Abstract test for classes implementing the {@link RealTransformer} interface.
 * This abstract test handles the automatic generation of random data of various
 * sizes. For each generated data array, actual values (returned by the
 * transformer to be tested) are compared to expected values, returned by the
 * {@link #transform(double[], TransformType)} (to be implemented by the user:
 * a naive method may be used). Methods are also provided to test that invalid
 * parameters throw the expected exceptions.
 *
 * @since 3.0
 */
public abstract class RealTransformerAbstractTest {

    /** The common seed of all random number generators used in this test. */
    private final static long SEED = 20110119L;

    /**
     * Returns a new instance of the {@link RealTransformer} to be tested.
     *
     * @return a the transformer to be tested
     */
    abstract RealTransformer createRealTransformer();

    /**
     * Returns an invalid data size. Transforms with this data size should
     * trigger a {@link MathIllegalArgumentException}.
     *
     * @param i the index of the invalid data size ({@code 0 <= i <}
     * {@link #getNumberOfInvalidDataSizes()}
     * @return an invalid data size
     */
    abstract int getInvalidDataSize(int i);

    /**
     * Returns the total number of invalid data sizes to be tested. If data
     * array of any
     * size can be handled by the {@link RealTransformer} to be tested, this
     * method should return {@code 0}.
     *
     * @return the total number of invalid data sizes
     */
    abstract int getNumberOfInvalidDataSizes();

    /**
     * Returns the total number of valid data sizes to be tested.
     *
     * @return the total number of valid data sizes
     */
    abstract int getNumberOfValidDataSizes();

    /**
     * Returns the expected relative accuracy for data arrays of size
     * {@code getValidDataSize(i)}.
     *
     * @param i the index of the valid data size
     * @return the expected relative accuracy
     */
    abstract double getRelativeTolerance(int i);

    /**
     * Returns a valid data size. This method allows for data arrays of various
     * sizes to be automatically tested (by allowing multiple values of the
     * specified index).
     *
     * @param i the index of the valid data size ({@code 0 <= i <}
     * {@link #getNumberOfValidDataSizes()}
     * @return a valid data size
     */
    abstract int getValidDataSize(int i);

    /**
     * Returns a function for the accuracy check of
     * {@link RealTransformer#transform(UnivariateFunction, double, double, int, TransformType)}.
     * This function should be valid. In other words, none of the above methods
     * should throw an exception when passed this function.
     *
     * @return a valid function
     */
    abstract UnivariateFunction getValidFunction();

    /**
     * Returns a sampling lower bound for the accuracy check of
     * {@link RealTransformer#transform(UnivariateFunction, double, double, int, TransformType)}.
     * This lower bound should be valid. In other words, none of the above
     * methods should throw an exception when passed this bound.
     *
     * @return a valid lower bound
     */
    abstract double getValidLowerBound();

    /**
     * Returns a sampling upper bound for the accuracy check of
     * {@link RealTransformer#transform(UnivariateFunction, double, double, int, TransformType)}.
     * This upper bound should be valid. In other words, none of the above
     * methods should throw an exception when passed this bound.
     *
     * @return a valid bound
     */
    abstract double getValidUpperBound();

    /**
     * Returns the expected transform of the specified real data array.
     *
     * @param x the real data array to be transformed
     * @param type the type of transform (forward, inverse) to be performed
     * @return the expected transform
     */
    abstract double[] transform(double[] x, TransformType type);

    /*
     * Check of preconditions.
     */

    /**
     * {@link RealTransformer#transform(double[], TransformType)} should throw a
     * {@link MathIllegalArgumentException} if data size is invalid.
     */
    @Test
    public void testTransformRealInvalidDataSize() {
        final TransformType[] type = TransformType.values();
        final RealTransformer transformer = createRealTransformer();
        for (int i = 0; i < getNumberOfInvalidDataSizes(); i++) {
            final int n = getInvalidDataSize(i);
            for (int j = 0; j < type.length; j++) {
                try {
                    transformer.transform(createRealData(n), type[j]);
                    Assert.fail(type[j] + ", " + n);
                } catch (MathIllegalArgumentException e) {
                    // Expected: do nothing
                }
            }
        }
    }

    /**
     * {@link RealTransformer#transform(UnivariateFunction, double, double, int, TransformType)}
     * should throw a {@link MathIllegalArgumentException} if number of samples
     * is invalid.
     */
    @Test
    public void testTransformFunctionInvalidDataSize() {
        final TransformType[] type = TransformType.values();
        final RealTransformer transformer = createRealTransformer();
        final UnivariateFunction f = getValidFunction();
        final double a = getValidLowerBound();
        final double b = getValidUpperBound();
        for (int i = 0; i < getNumberOfInvalidDataSizes(); i++) {
            final int n = getInvalidDataSize(i);
            for (int j = 0; j < type.length; j++) {
                try {
                    transformer.transform(f, a, b, n, type[j]);
                    Assert.fail(type[j] + ", " + n);
                } catch (MathIllegalArgumentException e) {
                    // Expected: do nothing
                }
            }
        }
    }

    /**
     * {@link RealTransformer#transform(UnivariateFunction, double, double, int, TransformType)}
     * should throw a {@link NotStrictlyPositiveException} if number of samples
     * is not strictly positive.
     */
    @Test
    public void testTransformFunctionNotStrictlyPositiveNumberOfSamples() {
        final TransformType[] type = TransformType.values();
        final RealTransformer transformer = createRealTransformer();
        final UnivariateFunction f = getValidFunction();
        final double a = getValidLowerBound();
        final double b = getValidUpperBound();
        for (int i = 0; i < getNumberOfValidDataSizes(); i++) {
            final int n = getValidDataSize(i);
            for (int j = 0; j < type.length; j++) {
                try {
                    transformer.transform(f, a, b, -n, type[j]);
                    Assert.fail(type[j] + ", " + (-n));
                } catch (NotStrictlyPositiveException e) {
                    // Expected: do nothing
                }
            }
        }
    }

    /**
     * {@link RealTransformer#transform(UnivariateFunction, double, double, int, TransformType)}
     * should throw a {@link NumberIsTooLargeException} if sampling bounds are
     * not correctly ordered.
     */
    @Test
    public void testTransformFunctionInvalidBounds() {
        final TransformType[] type = TransformType.values();
        final RealTransformer transformer = createRealTransformer();
        final UnivariateFunction f = getValidFunction();
        final double a = getValidLowerBound();
        final double b = getValidUpperBound();
        for (int i = 0; i < getNumberOfValidDataSizes(); i++) {
            final int n = getValidDataSize(i);
            for (int j = 0; j < type.length; j++) {
                try {
                    transformer.transform(f, b, a, n, type[j]);
                    Assert.fail(type[j] + ", " + b + ", " + a);
                } catch (NumberIsTooLargeException e) {
                    // Expected: do nothing
                }
            }
        }
    }

    /*
     * Accuracy tests of transform of valid data.
     */

    /**
     * Accuracy check of {@link RealTransformer#transform(double[], TransformType)}.
     * For each valid data size returned by
     * {@link #getValidDataSize(int) getValidDataSize(i)},
     * a random data array is generated with
     * {@link #createRealData(int) createRealData(i)}. The actual
     * transform is computed and compared to the expected transform, return by
     * {@link #transform(double[], TransformType)}. Actual and expected values
     * should be equal to within the relative error returned by
     * {@link #getRelativeTolerance(int) getRelativeTolerance(i)}.
     */
    @Test
    public void testTransformReal() {
        final TransformType[] type = TransformType.values();
        for (int i = 0; i < getNumberOfValidDataSizes(); i++) {
            final int n = getValidDataSize(i);
            final double tol = getRelativeTolerance(i);
            for (int j = 0; j < type.length; j++) {
                doTestTransformReal(n, tol, type[j]);
            }
        }
    }

    /**
     * Accuracy check of
     * {@link RealTransformer#transform(UnivariateFunction, double, double, int, TransformType)}.
     * For each valid data size returned by
     * {@link #getValidDataSize(int) getValidDataSize(i)},
     * the {@link UnivariateFunction} returned by {@link #getValidFunction()} is
     * sampled. The actual transform is computed and compared to the expected
     * transform, return by {@link #transform(double[], TransformType)}. Actual
     * and expected values should be equal to within the relative error returned
     * by {@link #getRelativeTolerance(int) getRelativeTolerance(i)}.
     */
    @Test
    public void testTransformFunction() {
        final TransformType[] type = TransformType.values();
        for (int i = 0; i < getNumberOfValidDataSizes(); i++) {
            final int n = getValidDataSize(i);
            final double tol = getRelativeTolerance(i);
            for (int j = 0; j < type.length; j++) {
                doTestTransformFunction(n, tol, type[j]);
            }
        }
    }

    /*
     * Utility methods.
     */

    /**
     * Returns a random array of doubles. Random generator always uses the same
     * seed.
     *
     * @param n the size of the array to be returned
     * @return a random array of specified size
     */
    double[] createRealData(final int n) {
        final Random random = new Random(SEED);
        final double[] data = new double[n];
        for (int i = 0; i < n; i++) {
            data[i] = 2.0 * random.nextDouble() - 1.0;
        }
        return data;
    }

    /*
     * The tests per se.
     */

    private void doTestTransformReal(final int n, final double tol,
        final TransformType type) {
        final RealTransformer transformer = createRealTransformer();
        final double[] x = createRealData(n);
        final double[] expected = transform(x, type);
        final double[] actual = transformer.transform(x, type);
        for (int i = 0; i < n; i++) {
            final String msg = String.format("%d, %d", n, i);
            final double delta = tol * FastMath.abs(expected[i]);
            Assert.assertEquals(msg, expected[i], actual[i], delta);
        }
    }

    private void doTestTransformFunction(final int n, final double tol,
        final TransformType type) {
        final RealTransformer transformer = createRealTransformer();
        final UnivariateFunction f = getValidFunction();
        final double a = getValidLowerBound();
        final double b = getValidUpperBound();
        final double[] x = createRealData(n);
        for (int i = 0; i < n; i++) {
            final double t = a + i * (b - a) / n;
            x[i] = f.value(t);
        }
        final double[] expected = transform(x, type);
        final double[] actual = transformer.transform(f, a, b, n, type);
        for (int i = 0; i < n; i++) {
            final String msg = String.format("%d, %d", n, i);
            final double delta = tol * FastMath.abs(expected[i]);
            Assert.assertEquals(msg, expected[i], actual[i], delta);
        }
    }
}

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