int main()
{
// random number generator
std::random_device rd;
std::mt19937 urng(rd());
// Create a threadpool with 5 threads
::Uplinkzero::Common::ThreadPool<Uplinkzero::Common::ThreadData> threadPool(5);
// Pointer to the function we want the threads in the pool to execute
auto pFunctionForThreadToRun = std::function<void(Uplinkzero::Common::FunctionParams &)>(functionForThreadToRun);
// enque tasks into the threadpool
for (int i = 0; i < 1000; i++)
{
Uplinkzero::Common::FunctionParams params;
params.i = urng();
Uplinkzero::Common::ThreadData td;
td.setThreadFunction(pFunctionForThreadToRun);
td.setThreadFunctionParams(params);
threadPool.enqueue(td);
}
// sleep for 10 seconds
std::this_thread::sleep_for(std::chrono::milliseconds(10000));
std::cout << "Main thread finished!" << std::endl;
return 0;
}
int operator[](size_t index) const {
// Could be more specific exception std::out_of_range
if( index >= _size ) {
std::ostringstream msg;
msg << "Index is out of bounds:\n";
msg << "index = " << index << " not in expected ";
msg << "[0, " << _size << ") range\n";
SKYLARK_THROW_EXCEPTION (
base::random123_exception()
<< base::error_msg(msg.str()) );
}
ctr_t ctr;
ctr.v[0] = static_cast<ctr_t::value_type>(_base + index);
ctr.v[1] = static_cast<ctr_t::value_type>(0);
URNG_t urng(ctr, _key);
return urng();
}
/**
* Shuffles a pile of card objects
* @warning Implented with newer c++14 randomization. Requires <random> header
*/
void CardPile::shuffle() {
//c++14 'proper randomization
std::random_device rng;
std::mt19937 urng(rng());
std::shuffle(_cardPile.begin(), _cardPile.end(), urng);
//std::random_shuffle(_cardPile.begin(), _cardPile.end()); //deprecated randomization, required srand
}
void Friday::initRobinsonDeck()
{
//std::random_shuffle (m_startingCards.begin(), m_startingCards.end());
std::vector<RobinsonCard> aux(m_startingCards);
std::random_device rng;
std::mt19937 urng(rng());
std::shuffle(m_startingCards.begin(), m_startingCards.end(), urng);
for (auto row: m_startingCards)
{
m_robinsonDeck.push(&row);
}
}
inline std::vector<TestCase> sortTests( IConfig const& config, std::vector<TestCase> const& unsortedTestCases ) {
std::vector<TestCase> sorted = unsortedTestCases;
switch( config.runOrder() ) {
case RunTests::InLexicographicalOrder:
std::sort( sorted.begin(), sorted.end(), LexSort() );
break;
case RunTests::InRandomOrder:
{
seedRng( config );
std::random_device rng;
std::mt19937 urng(rng());
std::shuffle( sorted.begin(), sorted.end(), rng );
}
break;
case RunTests::InDeclarationOrder:
// already in declaration order
break;
}
return sorted;
}
int utils::pick_a_number(int from, int thru)
{
static std::uniform_int_distribution<> d{};
using parm_t = std::uniform_int_distribution<>::param_type;
return d(urng(), parm_t{ from, thru });
}
int utils::coin_toss(double p)
{
static std::bernoulli_distribution d{};
using parm_t = decltype(d)::param_type;
return d(urng(), parm_t{ p });
}
int main()
{
typedef double real_type;
typedef unsigned long uint_type;
typedef ::std::size_t size_type;
typedef ::dcs::math::la::dense_matrix<real_type> matrix_type;
typedef ::dcs::math::la::dense_vector<real_type> vector_type;
uint_type seed(5489);
uint_type num_obs(50);
real_type ff(0.98); // forgetting factor
uint_type n_a(2);
uint_type n_b(2);
uint_type delay(0);
::dcs::math::random::mt19937 urng(seed);
::dcs::math::stats::normal_distribution<real_type> dist;
// Create a SISO model with ARX structure
vector_type a(n_a);
a(0) = -1.5; a(1) = 1.0;
vector_type b(n_b+1);
b(0) = 0; b(1) = 1.0; b(2) = 0.5;
real_type c = 1.0; // noise variance
::dcs::sysid::darx_siso_model<vector_type,real_type,uint_type> siso_model(a, b, c);
::std::cout << siso_model << ::std::endl;
// Generate random input data
::dcs::math::la::dense_vector<real_type> u(num_obs);
for (size_type i = 0; i < num_obs; ++i)
{
u(i) = ::dcs::math::sign(::dcs::math::stats::rand(dist, urng));
}
// Generate random noise
::dcs::math::la::dense_vector<real_type> e(num_obs);
for (size_type i = 0; i < num_obs; ++i)
{
e(i) = 0.2*::dcs::math::stats::rand(dist, urng);
}
::std::cout << "RLS with forgetting factor for SISO models:" << ::std::endl;
vector_type theta_hat;
matrix_type P;
vector_type phi;
::std::cout << "N_A: " << n_a << ::std::endl;
::std::cout << "N_B: " << n_b << ::std::endl;
::std::cout << "D: " << delay << ::std::endl;
::dcs::sysid::rls_arx_siso_init(n_a, n_b, delay, theta_hat, P, phi);
::std::cout << "\tInput Data: " << u << ::std::endl;
::std::cout << "\tNoise Data: " << e << ::std::endl;
::std::cout << "\tInitial Estimated Parameters: " << theta_hat << ::std::endl;
::std::cout << "\tInitial Covariance Matrix: " << P << ::std::endl;
::std::cout << "\tInitial Regressor: " << phi << ::std::endl;
vector_type y;
y = ::dcs::sysid::simulate(siso_model, u, e);
for (size_type i = 0; i < num_obs; ++i)
{
::std::cout << "\n\tObservation #" << i << ::std::endl;
::dcs::sysid::rls_ff_arx_siso(y(i), u(i), ff, n_a, n_b, delay, theta_hat, P, phi);
//real_type y_hat = ::dcs::math::la::inner_prod(theta_hat, phi);
::std::cout << "\t\tInput Data: " << u(i) << ::std::endl;
::std::cout << "\t\tOutput Data: " << y(i) << ::std::endl;
::std::cout << "\t\tEstimated Parameters: " << theta_hat << ::std::endl;
::std::cout << "\t\tCovariance Matrix: " << P << ::std::endl;
::std::cout << "\t\tRegressor: " << phi << ::std::endl;
//::std::cout << "\t\tEstimated Output Data: " << y_hat << ::std::endl;
//::std::cout << "\t\tRelative Error: " << ::std::abs((y(i)-y_hat)/y(i)) << ::std::endl;
}
// ::std::cout << "Simulation without noise: " << ::std::endl;
// ::std::cout << "\tInput Data: " << u << ::std::endl;
// ::std::cout << "\tOutput Data: " << y << ::std::endl;
//
// y = ::dcs::sysid::simulate(siso_model, u, e);
// ::std::cout << "Simulation with noise: " << ::std::endl;
// ::std::cout << "\tInput Data: " << u << ::std::endl;
// ::std::cout << "\tNoise Data: " << e << ::std::endl;
// ::std::cout << "\tOutput Data: " << y << ::std::endl;
}