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https://github.com/ethereum/solidity
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[yul-phaser] Gather functions from Random into SimulationRNG class
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@ -29,7 +29,7 @@ namespace solidity::phaser::test
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BOOST_AUTO_TEST_SUITE(Phaser)
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BOOST_AUTO_TEST_SUITE(RandomTest)
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BOOST_AUTO_TEST_CASE(uniformRandomInt_returns_different_values_when_called_multiple_times)
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BOOST_AUTO_TEST_CASE(uniformInt_returns_different_values_when_called_multiple_times)
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{
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constexpr uint32_t numSamples = 1000;
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constexpr uint32_t numOutcomes = 100;
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@ -38,8 +38,8 @@ BOOST_AUTO_TEST_CASE(uniformRandomInt_returns_different_values_when_called_multi
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vector<uint32_t> samples2;
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for (uint32_t i = 0; i < numSamples; ++i)
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{
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samples1.push_back(uniformRandomInt(0, numOutcomes - 1));
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samples2.push_back(uniformRandomInt(0, numOutcomes - 1));
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samples1.push_back(SimulationRNG::uniformInt(0, numOutcomes - 1));
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samples2.push_back(SimulationRNG::uniformInt(0, numOutcomes - 1));
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}
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vector<uint32_t> counts1(numOutcomes, 0);
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@ -63,7 +63,7 @@ BOOST_AUTO_TEST_CASE(uniformRandomInt_returns_different_values_when_called_multi
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BOOST_TEST(counts1 != counts2);
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}
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BOOST_AUTO_TEST_CASE(binomialRandomInt_returns_different_values_when_called_multiple_times)
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BOOST_AUTO_TEST_CASE(binomialInt_returns_different_values_when_called_multiple_times)
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{
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constexpr uint32_t numSamples = 1000;
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constexpr uint32_t numTrials = 100;
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@ -73,8 +73,8 @@ BOOST_AUTO_TEST_CASE(binomialRandomInt_returns_different_values_when_called_mult
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vector<uint32_t> samples2;
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for (uint32_t i = 0; i < numSamples; ++i)
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{
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samples1.push_back(binomialRandomInt(numTrials, successProbability));
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samples2.push_back(binomialRandomInt(numTrials, successProbability));
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samples1.push_back(SimulationRNG::binomialInt(numTrials, successProbability));
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samples2.push_back(SimulationRNG::binomialInt(numTrials, successProbability));
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}
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vector<uint32_t> counts1(numTrials, 0);
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@ -85,7 +85,7 @@ BOOST_AUTO_TEST_CASE(binomialRandomInt_returns_different_values_when_called_mult
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++counts2[samples2[i]];
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}
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// See remark for uniformRandomInt() above. Same applies here.
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// See remark for uniformInt() above. Same applies here.
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BOOST_TEST(counts1 != counts2);
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}
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@ -66,5 +66,5 @@ string const& Chromosome::randomOptimisationStep()
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{
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static vector<string> stepNames = allStepNames();
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return stepNames[uniformRandomInt(0, stepNames.size() - 1)];
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return stepNames[SimulationRNG::uniformInt(0, stepNames.size() - 1)];
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}
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@ -61,7 +61,7 @@ public:
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std::vector<Individual> const& individuals() const { return m_individuals; }
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static size_t randomChromosomeLength() { return binomialRandomInt(MaxChromosomeLength, 0.5); }
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static size_t randomChromosomeLength() { return SimulationRNG::binomialInt(MaxChromosomeLength, 0.5); }
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static size_t measureFitness(Chromosome const& _chromosome, Program const& _program);
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friend std::ostream& operator<<(std::ostream& _stream, Population const& _population);
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@ -17,15 +17,15 @@
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#include <tools/yulPhaser/Random.h>
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#include <boost/random/mersenne_twister.hpp>
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#include <boost/random/uniform_int_distribution.hpp>
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#include <boost/random/binomial_distribution.hpp>
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#include <boost/random/uniform_int_distribution.hpp>
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#include <ctime>
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using namespace solidity;
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using namespace solidity::phaser;
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uint32_t phaser::uniformRandomInt(uint32_t _min, uint32_t _max)
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uint32_t SimulationRNG::uniformInt(uint32_t _min, uint32_t _max)
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{
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// TODO: Seed must be configurable
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static boost::random::mt19937 generator(time(0));
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@ -34,7 +34,7 @@ uint32_t phaser::uniformRandomInt(uint32_t _min, uint32_t _max)
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return distribution(generator);
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}
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uint32_t phaser::binomialRandomInt(uint32_t _numTrials, double _successProbability)
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uint32_t SimulationRNG::binomialInt(uint32_t _numTrials, double _successProbability)
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{
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// TODO: Seed must be configurable
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static boost::random::mt19937 generator(time(0));
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@ -17,12 +17,24 @@
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#pragma once
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#include <boost/random/mersenne_twister.hpp>
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#include <cstdint>
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namespace solidity::phaser
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{
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uint32_t uniformRandomInt(uint32_t _min, uint32_t _max);
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uint32_t binomialRandomInt(uint32_t _numTrials, double _successProbability);
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/**
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* A class that provides functions for generating random numbers good enough for simulation purposes.
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*
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* The numbers are not cryptographically secure so do not use this for anything that requires
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* them to be truly unpredictable.
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*/
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class SimulationRNG
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{
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public:
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static uint32_t uniformInt(uint32_t _min, uint32_t _max);
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static uint32_t binomialInt(uint32_t _numTrials, double _successProbability);
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};
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}
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