com.github.rinde.rinsim.util.StochasticSuppliersTest.java Source code

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/*
 * Copyright (C) 2011-2016 Rinde van Lon, iMinds-DistriNet, KU Leuven
 *
 * Licensed 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 com.github.rinde.rinsim.util;

import static com.github.rinde.rinsim.util.StochasticSuppliers.checked;
import static com.github.rinde.rinsim.util.StochasticSuppliers.constant;
import static com.github.rinde.rinsim.util.StochasticSuppliers.uniformInt;
import static com.google.common.collect.Lists.newArrayList;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;

import java.util.List;

import org.apache.commons.math3.distribution.IntegerDistribution;
import org.apache.commons.math3.distribution.UniformIntegerDistribution;
import org.apache.commons.math3.random.MersenneTwister;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.stat.descriptive.SummaryStatistics;
import org.apache.commons.math3.stat.inference.TestUtils;
import org.junit.Test;

import com.github.rinde.rinsim.util.StochasticSupplier;
import com.github.rinde.rinsim.util.StochasticSuppliers;
import com.google.common.base.Predicate;
import com.google.common.collect.Multiset;
import com.google.common.collect.Range;
import com.google.common.collect.TreeMultiset;
import com.google.common.primitives.Doubles;
import com.google.common.primitives.Longs;

public class StochasticSuppliersTest {

    @Test
    public void testUniform() {
        final RandomGenerator rng = new MersenneTwister(123L);
        final StochasticSupplier<Integer> sup = uniformInt(2, 10);
        final IntegerDistribution id = new UniformIntegerDistribution(2, 10);

        final Multiset<Integer> ms = TreeMultiset.create();
        for (int i = 0; i < 1000; i++) {
            ms.add(sup.get(rng.nextLong()));
        }
        final List<Integer> observations = newArrayList();
        final List<Double> expectations = newArrayList();
        for (final Multiset.Entry<Integer> entry : ms.entrySet()) {
            observations.add(entry.getCount());
            expectations.add(id.probability(entry.getElement()));
        }
        assertTrue(chiSquare(expectations, observations, .01));
    }

    boolean chiSquare(List<? extends Number> expectations, List<? extends Number> observations, double confidence) {
        final double chi = TestUtils.chiSquareTest(Doubles.toArray(expectations), Longs.toArray(observations));
        return !(chi < confidence);
    }

    /**
     * Tests for
     * {@link StochasticSuppliers#checked(StochasticSupplier, Predicate)}.
     */
    @Test
    public void testCheckedSupplier() {
        final Predicate<Double> positive = Range.closedOpen(0d, Double.POSITIVE_INFINITY);

        checked(constant(0d), positive).get(0);
        checked(constant(453453453.34), positive).get(0);
        checked(constant(Double.MAX_VALUE), positive).get(0);

        boolean fail = false;
        try {
            checked(constant(Double.POSITIVE_INFINITY), positive).get(0);
        } catch (final IllegalArgumentException e) {
            fail = true;
        }
        assertTrue(fail);
        fail = false;
        try {
            checked(constant(-0.0000000001), positive).get(0);
        } catch (final IllegalArgumentException e) {
            fail = true;
        }
        assertTrue(fail);
    }

    /**
     * Tests whether the rescaling of the mean of a truncated normal distribution
     * is implemented correctly.
     */
    @Test
    public void testNormalScaleMean() {
        final double[] means = new double[] { 1d, 2d, 3d, 10d, 100d };
        final double[] sds = new double[] { 1d, 1d, 3d, 5d, 100d };

        for (int i = 0; i < means.length; i++) {
            final StochasticSupplier<Double> ss = StochasticSuppliers.normal().mean(means[i]).std(sds[i])
                    .lowerBound(0).scaleMean().redrawWhenOutOfBounds().buildDouble();

            final RandomGenerator rng = new MersenneTwister(123);
            final SummaryStatistics stats = new SummaryStatistics();
            for (int j = 0; j < 10000; j++) {
                stats.addValue(ss.get(rng.nextLong()));
            }
            // 1 % deviation from mean is acceptable
            final double allowedDeviation = 0.01 * means[i];
            assertEquals(means[i], stats.getMean(), allowedDeviation);
        }

    }
}