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[yul-phaser] Population+Chromosome: Better tests for makeRandom()

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This commit is contained in:
Kamil Śliwak 2020-02-14 06:07:28 +01:00
parent 806891f494
commit 823e715902
2 changed files with 36 additions and 27 deletions
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36
test/yulPhaser/Chromosome.cpp
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@ -67,25 +67,31 @@ BOOST_AUTO_TEST_CASE(constructor_should_convert_from_string_to_optimisation_step
BOOST_TEST(Chromosome("ChrOmOsoMe").optimisationSteps() == expectedSteps); BOOST_TEST(Chromosome("ChrOmOsoMe").optimisationSteps() == expectedSteps);
} }
BOOST_AUTO_TEST_CASE(makeRandom_should_create_chromosome_with_random_optimisation_steps) BOOST_AUTO_TEST_CASE(makeRandom_should_return_different_chromosome_each_time)
{ {
constexpr uint32_t numSteps = 1000; SimulationRNG::reset(1);
for (size_t i = 0; i < 10; ++i)
BOOST_TEST(Chromosome::makeRandom(100) != Chromosome::makeRandom(100));
}
auto chromosome1 = Chromosome::makeRandom(numSteps); BOOST_AUTO_TEST_CASE(makeRandom_should_use_every_possible_step_with_the_same_probability)
auto chromosome2 = Chromosome::makeRandom(numSteps); {
BOOST_CHECK_EQUAL(chromosome1.length(), numSteps); SimulationRNG::reset(1);
BOOST_CHECK_EQUAL(chromosome2.length(), numSteps); constexpr int samplesPerStep = 100;
constexpr double relativeTolerance = 0.01;
multiset<string> steps1; map<string, size_t> stepIndices = enumerateOptmisationSteps();
multiset<string> steps2; auto chromosome = Chromosome::makeRandom(stepIndices.size() * samplesPerStep);
for (auto const& step: chromosome1.optimisationSteps())
steps1.insert(step);
for (auto const& step: chromosome2.optimisationSteps())
steps2.insert(step);
// Check if steps are different and also if they're not just a permutation of the same set. vector<size_t> samples;
// Technically they could be the same and still random but the probability is infinitesimally low. for (auto& step: chromosome.optimisationSteps())
BOOST_TEST(steps1 != steps2); samples.push_back(stepIndices.at(step));
const double expectedValue = (stepIndices.size() - 1) / 2.0;
const double variance = (stepIndices.size() * stepIndices.size() - 1) / 12.0;
BOOST_TEST(abs(mean(samples) - expectedValue) < expectedValue * relativeTolerance);
BOOST_TEST(abs(meanSquaredError(samples, expectedValue) - variance) < variance * relativeTolerance);
} }
BOOST_AUTO_TEST_CASE(constructor_should_store_optimisation_steps) BOOST_AUTO_TEST_CASE(constructor_should_store_optimisation_steps)

27
test/yulPhaser/Population.cpp
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@ -159,21 +159,24 @@ BOOST_FIXTURE_TEST_CASE(makeRandom_should_use_random_chromosome_length, Populati
BOOST_FIXTURE_TEST_CASE(makeRandom_should_return_population_with_random_chromosomes, PopulationFixture) BOOST_FIXTURE_TEST_CASE(makeRandom_should_return_population_with_random_chromosomes, PopulationFixture)
{ {
auto population1 = Population::makeRandom(m_program, 100, 30, 30); SimulationRNG::reset(1);
auto population2 = Population::makeRandom(m_program, 100, 30, 30); constexpr int populationSize = 100;
constexpr int chromosomeLength = 30;
constexpr double relativeTolerance = 0.01;
BOOST_TEST(population1.individuals().size() == 100); map<string, size_t> stepIndices = enumerateOptmisationSteps();
BOOST_TEST(population2.individuals().size() == 100); auto population = Population::makeRandom(m_program, populationSize, chromosomeLength, chromosomeLength);
int numMatchingPositions = 0; vector<size_t> samples;
for (size_t i = 0; i < 100; ++i) for (auto& individual: population.individuals())
if (population1.individuals()[i].chromosome == population2.individuals()[i].chromosome) for (auto& step: individual.chromosome.optimisationSteps())
++numMatchingPositions; samples.push_back(stepIndices.at(step));
// Assume that the results are random if there are no more than 10 identical chromosomes on the const double expectedValue = (stepIndices.size() - 1) / 2.0;
// same positions. One duplicate is very unlikely but still possible after billions of runs const double variance = (stepIndices.size() * stepIndices.size() - 1) / 12.0;
// (especially for short chromosomes). For ten the probability is so small that we can ignore it.
BOOST_TEST(numMatchingPositions < 10); BOOST_TEST(abs(mean(samples) - expectedValue) < expectedValue * relativeTolerance);
BOOST_TEST(abs(meanSquaredError(samples, expectedValue) - variance) < variance * relativeTolerance);
} }
BOOST_FIXTURE_TEST_CASE(makeRandom_should_not_compute_fitness, PopulationFixture) BOOST_FIXTURE_TEST_CASE(makeRandom_should_not_compute_fitness, PopulationFixture)