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yul-phaser: Switch from uint32_t to size_t in SimulationRNG

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- Also pass the appriopriate type internally to the distribution instead of relying on the default (which is uint32_t)
This commit is contained in:
Kamil Śliwak 2020-06-03 13:52:37 +02:00
parent 9b3d1c11ff
commit 8f55ead48d
3 changed files with 21 additions and 12 deletions
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2
tools/yulPhaser/GeneticAlgorithms.cpp
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@ -155,7 +155,7 @@ Population ClassicGeneticAlgorithm::select(Population _population, size_t _selec
vector<Individual> selectedIndividuals;
for (size_t i = 0; i < _selectionSize; ++i)
{
uint32_t ball = SimulationRNG::uniformInt(0, rouletteRange - 1);
size_t ball = SimulationRNG::uniformInt(0, rouletteRange - 1);
size_t cumulativeFitness = 0;
for (auto const& individual: _population.individuals())

27
tools/yulPhaser/SimulationRNG.cpp
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@ -17,12 +17,17 @@
#include <tools/yulPhaser/SimulationRNG.h>
// NOTE: The code would work with std::random but the results for a given seed would not be reproducible
// across different STL implementations. Boost does not guarantee this either but at least it has only one
// implementation. Reproducibility is not a hard requirement for yul-phaser but it's nice to have.
#include <boost/random/bernoulli_distribution.hpp>
#include <boost/random/binomial_distribution.hpp>
#include <boost/random/uniform_int_distribution.hpp>
#include <ctime>
#include <limits>
using namespace std;
using namespace solidity;
using namespace solidity::phaser;
@ -30,23 +35,27 @@ thread_local boost::random::mt19937 SimulationRNG::s_generator(SimulationRNG::ge
bool SimulationRNG::bernoulliTrial(double _successProbability)
{
boost::random::bernoulli_distribution<> distribution(_successProbability);
boost::random::bernoulli_distribution<double> distribution(_successProbability);
return static_cast<bool>(distribution(s_generator));
}
uint32_t SimulationRNG::uniformInt(uint32_t _min, uint32_t _max)
{
boost::random::uniform_int_distribution<> distribution(_min, _max);
return distribution(s_generator);
}
uint32_t SimulationRNG::binomialInt(uint32_t _numTrials, double _successProbability)
size_t SimulationRNG::uniformInt(size_t _min, size_t _max)
{
boost::random::binomial_distribution<> distribution(_numTrials, _successProbability);
boost::random::uniform_int_distribution<size_t> distribution(_min, _max);
return distribution(s_generator);
}
size_t SimulationRNG::binomialInt(size_t _numTrials, double _successProbability)
{
// NOTE: binomial_distribution<size_t> would not work because it internally tries to use abs()
// and fails to compile due to ambiguous conversion.
assert(_numTrials <= static_cast<size_t>(numeric_limits<long>::max()));
boost::random::binomial_distribution<long> distribution(static_cast<long>(_numTrials), _successProbability);
return static_cast<size_t>(distribution(s_generator));
}
uint32_t SimulationRNG::generateSeed()
{
// This is not a secure way to seed the generator but it's good enough for simulation purposes.

4
tools/yulPhaser/SimulationRNG.h
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@ -38,8 +38,8 @@ class SimulationRNG
{
public:
static bool bernoulliTrial(double _successProbability);
static uint32_t uniformInt(uint32_t _min, uint32_t _max);
static uint32_t binomialInt(uint32_t _numTrials, double _successProbability);
static size_t uniformInt(size_t _min, size_t _max);
static size_t binomialInt(size_t _numTrials, double _successProbability);
/// Resets generator to a known state given by the @a seed. Given the same seed, a fixed
/// sequence of calls to the members generating random values is guaranteed to produce the