cerc-io/solidity
11
0
Fork 0
mirror of https://github.com/ethereum/solidity synced 2023-10-03 13:03:40 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

[yul-phaser] Selections+PairSelections: Add RandomSubset and PairsFromRandomSubset

Browse Source
This commit is contained in:
Kamil Śliwak
2020-04-06 19:06:08 +02:00
parent 7381068dcc
commit b6f8ecf755
6 changed files with 220 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
test/yulPhaser/PairSelections.cpp
+72
View File
@@ -119,6 +119,78 @@ BOOST_AUTO_TEST_CASE(materialise_should_return_no_pairs_if_collection_has_one_el
BOOST_TEST(RandomPairSelection(2.0).materialise(1).empty());
}
BOOST_AUTO_TEST_SUITE_END()
BOOST_AUTO_TEST_SUITE(PairsFromRandomSubsetTest)
BOOST_AUTO_TEST_CASE(materialise_should_return_random_values_with_equal_probabilities)
{
constexpr int collectionSize = 1000;
constexpr double selectionChance = 0.7;
constexpr double relativeTolerance = 0.001;
constexpr double expectedValue = selectionChance;
constexpr double variance = selectionChance * (1 - selectionChance);
SimulationRNG::reset(1);
vector<tuple<size_t, size_t>> pairs = PairsFromRandomSubset(selectionChance).materialise(collectionSize);
vector<double> bernoulliTrials(collectionSize, 0);
for (auto& pair: pairs)
{
BOOST_REQUIRE(get<1>(pair) < collectionSize);
BOOST_REQUIRE(get<1>(pair) < collectionSize);
bernoulliTrials[get<0>(pair)] = 1.0;
bernoulliTrials[get<1>(pair)] = 1.0;
}
BOOST_TEST(abs(mean(bernoulliTrials) - expectedValue) < expectedValue * relativeTolerance);
BOOST_TEST(abs(meanSquaredError(bernoulliTrials, expectedValue) - variance) < variance * relativeTolerance);
}
BOOST_AUTO_TEST_CASE(materialise_should_return_only_values_that_can_be_used_as_collection_indices)
{
const size_t collectionSize = 200;
constexpr double selectionChance = 0.5;
vector<tuple<size_t, size_t>> pairs = PairsFromRandomSubset(selectionChance).materialise(collectionSize);
BOOST_TEST(all_of(pairs.begin(), pairs.end(), [&](auto const& pair){ return get<0>(pair) <= collectionSize; }));
BOOST_TEST(all_of(pairs.begin(), pairs.end(), [&](auto const& pair){ return get<1>(pair) <= collectionSize; }));
}
BOOST_AUTO_TEST_CASE(materialise_should_use_unique_indices)
{
constexpr size_t collectionSize = 200;
constexpr double selectionChance = 0.5;
vector<tuple<size_t, size_t>> pairs = PairsFromRandomSubset(selectionChance).materialise(collectionSize);
set<size_t> indices;
for (auto& pair: pairs)
{
indices.insert(get<0>(pair));
indices.insert(get<1>(pair));
}
BOOST_TEST(indices.size() == 2 * pairs.size());
}
BOOST_AUTO_TEST_CASE(materialise_should_return_no_indices_if_collection_is_empty)
{
BOOST_TEST(PairsFromRandomSubset(0.0).materialise(0).empty());
BOOST_TEST(PairsFromRandomSubset(0.5).materialise(0).empty());
BOOST_TEST(PairsFromRandomSubset(1.0).materialise(0).empty());
}
BOOST_AUTO_TEST_CASE(materialise_should_return_no_pairs_if_selection_chance_is_zero)
{
BOOST_TEST(PairsFromRandomSubset(0.0).materialise(0).empty());
BOOST_TEST(PairsFromRandomSubset(0.0).materialise(100).empty());
}
BOOST_AUTO_TEST_CASE(materialise_should_return_all_pairs_if_selection_chance_is_one)
{
BOOST_TEST(PairsFromRandomSubset(1.0).materialise(0).empty());
BOOST_TEST(PairsFromRandomSubset(1.0).materialise(100).size() == 50);
}
BOOST_AUTO_TEST_SUITE_END()
BOOST_AUTO_TEST_SUITE(PairMosaicSelectionTest)