mirror of
https://github.com/ethereum/solidity
synced 2023-10-03 13:03:40 +00:00
196 lines
6.6 KiB
C++
196 lines
6.6 KiB
C++
/*
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This file is part of solidity.
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solidity is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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solidity is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with solidity. If not, see <http://www.gnu.org/licenses/>.
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*/
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// SPDX-License-Identifier: GPL-3.0
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#include <test/yulPhaser/TestHelpers.h>
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#include <tools/yulPhaser/SimulationRNG.h>
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#include <boost/test/unit_test.hpp>
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#include <cassert>
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using namespace std;
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namespace solidity::phaser::test
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{
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BOOST_AUTO_TEST_SUITE(Phaser, *boost::unit_test::label("nooptions"))
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BOOST_AUTO_TEST_SUITE(RandomTest)
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BOOST_AUTO_TEST_CASE(bernoulliTrial_should_produce_samples_with_right_expected_value_and_variance)
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{
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SimulationRNG::reset(1);
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constexpr size_t numSamples = 10000;
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constexpr double successProbability = 0.4;
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constexpr double relativeTolerance = 0.05;
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// For bernoulli distribution with success probability p: EX = p, VarX = p(1 - p)
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constexpr double expectedValue = successProbability;
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constexpr double variance = successProbability * (1 - successProbability);
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vector<uint32_t> samples;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples.push_back(static_cast<uint32_t>(SimulationRNG::bernoulliTrial(successProbability)));
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BOOST_TEST(abs(mean(samples) - expectedValue) < expectedValue * relativeTolerance);
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BOOST_TEST(abs(meanSquaredError(samples, expectedValue) - variance) < variance * relativeTolerance);
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}
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BOOST_AUTO_TEST_CASE(bernoulliTrial_can_be_reset)
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{
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constexpr size_t numSamples = 10;
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constexpr double successProbability = 0.4;
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SimulationRNG::reset(1);
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vector<uint32_t> samples1;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples1.push_back(static_cast<uint32_t>(SimulationRNG::bernoulliTrial(successProbability)));
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vector<uint32_t> samples2;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples2.push_back(static_cast<uint32_t>(SimulationRNG::bernoulliTrial(successProbability)));
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SimulationRNG::reset(1);
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vector<uint32_t> samples3;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples3.push_back(static_cast<uint32_t>(SimulationRNG::bernoulliTrial(successProbability)));
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SimulationRNG::reset(2);
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vector<uint32_t> samples4;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples4.push_back(static_cast<uint32_t>(SimulationRNG::bernoulliTrial(successProbability)));
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BOOST_TEST(samples1 != samples2);
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BOOST_TEST(samples1 == samples3);
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BOOST_TEST(samples1 != samples4);
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BOOST_TEST(samples2 != samples3);
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BOOST_TEST(samples2 != samples4);
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BOOST_TEST(samples3 != samples4);
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}
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BOOST_AUTO_TEST_CASE(uniformInt_returns_different_values_when_called_multiple_times)
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{
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SimulationRNG::reset(1);
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constexpr size_t numSamples = 1000;
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constexpr uint32_t minValue = 50;
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constexpr uint32_t maxValue = 80;
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constexpr double relativeTolerance = 0.05;
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// For uniform distribution from range a..b: EX = (a + b) / 2, VarX = ((b - a + 1)^2 - 1) / 12
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constexpr double expectedValue = (minValue + maxValue) / 2.0;
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constexpr double variance = ((maxValue - minValue + 1) * (maxValue - minValue + 1) - 1) / 12.0;
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vector<size_t> samples;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples.push_back(SimulationRNG::uniformInt(minValue, maxValue));
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BOOST_TEST(abs(mean(samples) - expectedValue) < expectedValue * relativeTolerance);
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BOOST_TEST(abs(meanSquaredError(samples, expectedValue) - variance) < variance * relativeTolerance);
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}
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BOOST_AUTO_TEST_CASE(uniformInt_can_be_reset)
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{
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constexpr size_t numSamples = 10;
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constexpr uint32_t minValue = 50;
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constexpr uint32_t maxValue = 80;
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SimulationRNG::reset(1);
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vector<size_t> samples1;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples1.push_back(SimulationRNG::uniformInt(minValue, maxValue));
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vector<size_t> samples2;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples2.push_back(SimulationRNG::uniformInt(minValue, maxValue));
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SimulationRNG::reset(1);
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vector<size_t> samples3;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples3.push_back(SimulationRNG::uniformInt(minValue, maxValue));
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SimulationRNG::reset(2);
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vector<size_t> samples4;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples4.push_back(SimulationRNG::uniformInt(minValue, maxValue));
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BOOST_TEST(samples1 != samples2);
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BOOST_TEST(samples1 == samples3);
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BOOST_TEST(samples1 != samples4);
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BOOST_TEST(samples2 != samples3);
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BOOST_TEST(samples2 != samples4);
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BOOST_TEST(samples3 != samples4);
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}
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BOOST_AUTO_TEST_CASE(binomialInt_should_produce_samples_with_right_expected_value_and_variance)
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{
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SimulationRNG::reset(1);
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constexpr size_t numSamples = 1000;
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constexpr uint32_t numTrials = 100;
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constexpr double successProbability = 0.2;
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constexpr double relativeTolerance = 0.05;
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// For binomial distribution with n trials and success probability p: EX = np, VarX = np(1 - p)
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constexpr double expectedValue = numTrials * successProbability;
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constexpr double variance = numTrials * successProbability * (1 - successProbability);
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vector<size_t> samples;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples.push_back(SimulationRNG::binomialInt(numTrials, successProbability));
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BOOST_TEST(abs(mean(samples) - expectedValue) < expectedValue * relativeTolerance);
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BOOST_TEST(abs(meanSquaredError(samples, expectedValue) - variance) < variance * relativeTolerance);
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}
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BOOST_AUTO_TEST_CASE(binomialInt_can_be_reset)
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{
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constexpr size_t numSamples = 10;
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constexpr uint32_t numTrials = 10;
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constexpr double successProbability = 0.6;
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SimulationRNG::reset(1);
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vector<size_t> samples1;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples1.push_back(SimulationRNG::binomialInt(numTrials, successProbability));
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vector<size_t> samples2;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples2.push_back(SimulationRNG::binomialInt(numTrials, successProbability));
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SimulationRNG::reset(1);
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vector<size_t> samples3;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples3.push_back(SimulationRNG::binomialInt(numTrials, successProbability));
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SimulationRNG::reset(2);
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vector<size_t> samples4;
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for (uint32_t i = 0; i < numSamples; ++i)
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samples4.push_back(SimulationRNG::binomialInt(numTrials, successProbability));
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BOOST_TEST(samples1 != samples2);
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BOOST_TEST(samples1 == samples3);
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BOOST_TEST(samples1 != samples4);
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BOOST_TEST(samples2 != samples3);
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BOOST_TEST(samples2 != samples4);
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BOOST_TEST(samples3 != samples4);
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}
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE_END()
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}
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