void InferenceEngine::allocateShuffledIndicesToThreads(vector<vector<uint> > * thread_index_allocation, const uint num_indices) {
mt19937 prng = mt19937(prng_seed);
assert(thread_index_allocation->empty());
uint num_indices_per_thread = num_indices/num_threads + 1;
uint num_last_indices = num_indices%num_threads;
for (uint i = 0; i < num_threads; i++) {
thread_index_allocation->push_back(vector<uint>(num_indices_per_thread));
for (uint j = 0; j < thread_index_allocation->back().size(); j++) {
thread_index_allocation->back().at(j) = i + j * num_threads;
}
if (i >= num_last_indices) {
thread_index_allocation->back().pop_back();
}
shuffle(thread_index_allocation->back().begin(), thread_index_allocation->back().end(), prng);
}
}
vector<vector<LPEdge>> swapRound(const vector<vector<LPEdge>> &trees, const vector<double> &betas)
{
//srand((unsigned)time(NULL));
system_clock::rep seed = system_clock::now().time_since_epoch().count();
mt19937 mersenne_generator = mt19937((unsigned int)seed);
vector<vector<LPEdge>> ts;
//sort all the tries such than e.end0 < e.end1 for all edges;
for (unsigned i = 0; i < trees.size(); i++)
{
vector<LPEdge> t;
for (unsigned j = 0; j < trees[i].size(); j++)
{
if (trees[i][j].end0 == trees[i][j].end1)
throw invalid_argument("");
if (trees[i][j].end0 < trees[i][j].end1)
t.push_back(LPEdge(trees[i][j].end0, trees[i][j].end1, trees[i][j].weight));
else
t.push_back(LPEdge(trees[i][j].end1, trees[i][j].end0, trees[i][j].weight));
}
ts.push_back(t);
}
//
vector<vector<LPEdge>> C;
C.push_back(ts[0]);
for (int i = 0; i <= (int)ts.size() - 2; i++)
{
double nextBeta = sum(betas, 0, i + 1);
//cout << "Next beta: " << nextBeta << endl;
vector<LPEdge> CNext = mergeBasesFast(nextBeta, C.back(), betas[i + 1], ts[i + 1], mersenne_generator);
C.push_back(CNext);
}
return C;
}
World::World(int id, simulation_options_t *options)
{
this -> solarLuminosity = 0;
this -> options = options;
size_t y;
this -> cell = new Cell*[WORLD_HEIGHT];
for(y = 0; y < WORLD_HEIGHT; y++) {
this -> cell[y] = new Cell[WORLD_WIDTH];
}
if(options -> seed == -1) {
random_device rd;
gen = mt19937(rd());
} else {
gen = mt19937(options -> seed);
}
dis = uniform_real_distribution<>(0, 1);
d = new Display(this, id);
}
void make_world(World* world, int width, int height, int num_cities, int seed)
{
/*
Makes a new world struct
world : Pointer to the world to create
width : The width of the world
height : The height of the world
num_cities : The number of cities in the world
seed : The random seed to use to select the cities
*/
// Random number generation
mt19937::result_type rseed = seed;
auto rgen = bind(uniform_real_distribution<>(0, 1), mt19937(rseed));
// Initialize the world
init_world(world, width, height, num_cities);
// Create a set to deal with uniqueness
set<tuple<int, int>> coordinates;
set<tuple<int, int>>::iterator it;
pair<set<tuple<int, int>>::iterator,bool> ret;
// Create some unique random cities
for (int i=0; i<num_cities; i++)
{
while (true)
{
// Try to add a new set of coordinates
tuple<int,int> coors((int)(rgen() * width), \
(int)(rgen() * height));
ret = coordinates.insert(coors);
// Break if the city was added successfully
if (ret.second)
break;
}
}
// Add those cities to the world
{
int i = 0;
for (it=coordinates.begin(); it!=coordinates.end(); it++)
{
world->cities[i].x = get<0>(*it);
world->cities[i].y = get<1>(*it);
i++;
}
}
}
void run() {
variate_generator<mt19937, normal_distribution<> > rand(mt19937(), normal_distribution<>(0, 10));
table table("table");
table.add<double>("x").add<double>("y");
for (int i = 0; i < 200; ++i)
table << double(i) << double(i) + rand();
table.write_file("test.csv");
linear_fit fit("fn", table);
cout << fit << endl;
plot("plot <table>, <fn>");
}
void Simulation_running::on_entry()
{
/*****************************/
//auto seed = chrono::high_resolution_clock::now().time_since_epoch().count();
//mt19937 mt_rand(seed);
auto dice_rand = std::bind(std::uniform_int_distribution<int>(1,9999),
mt19937(seed));
mt19937 nrg;
poisson_distribution<int> poisson(4.9);
/*****************************/
std::cout << "Simulation_running on_entry()\n";
std::cout<<"State: "<<state_to_text(
m_state_machine_controller.m_current_state->get_state())<<std::endl;
bool flag = false;
auto sim_run_thread = sim_running_thread(m_state_machine_controller);
while (!flag) // Infinite loop producing random numbers every "poisson" time
{
flag = check_me2(m_state_machine_controller);
int job = dice_rand();
int sth = random_disp();
std::cout<<"DA FAQ "<<flag<<" - - - Random Disp Number: "<<sth<<" - - - Random Job: "<<job<<std::endl;
std::cout<<"Job sending"<<std::endl;
vDisp.at(sth)->add_job_q(job);
vDisp.at(sth)->schedule_event(Events::DISP_JOB);
std::this_thread::sleep_for(std::chrono::seconds(poisson(nrg)));
//std::this_thread::sleep_for(std::chrono::milliseconds(2000));
}
//sim_run_thread.get();
//std::cout<<"State: "<<state_to_text(m_state_machine_controller.m_previous_state)<<std::endl;
}
UUIDGenerator::UUIDGenerator() : m_generator(mt19937(random_device()())) {}
SLuint SLRay::subsampledRays = 0;
SLuint SLRay::subsampledPixels = 0;
SLuint SLRay::tirRays = 0;
SLuint SLRay::tests = 0;
SLuint SLRay::intersections = 0;
SLint SLRay::depthReached = 1;
SLint SLRay::maxDepthReached = 0;
SLfloat SLRay::avgDepth = 0;
//-----------------------------------------------------------------------------
/*! Global uniform random number generator for numbers between 0 and 1 that are
used in SLRay, SLLightRect and SLPathtracer. So far they work perfectly with
CPP11 multithreading.
*/
auto random01 = bind(uniform_real_distribution<SLfloat>(0.0, 1.0),
mt19937((SLuint)time(nullptr)));
SLfloat rnd01();
SLfloat rnd01(){return random01();}
//-----------------------------------------------------------------------------
/*!
SLRay::SLRay default constructor
*/
SLRay::SLRay()
{
origin = SLVec3f::ZERO;
setDir(SLVec3f::ZERO);
type = PRIMARY;
length = FLT_MAX;
depth = 1;
hitTriangle = -1;
hitPoint = SLVec3f::ZERO;
SLuint SLRay::subsampledPixels = 0;
SLuint SLRay::tirRays = 0;
SLuint SLRay::tests = 0;
SLuint SLRay::intersections = 0;
SLint SLRay::depthReached = 1;
SLint SLRay::maxDepthReached = 0;
SLfloat SLRay::avgDepth = 0;
//-----------------------------------------------------------------------------
/*! Global uniform random number generator for numbers between 0 and 1 that are
used in SLRay, SLLightRect and SLPathtracer. So far they work perfectly with
CPP11 multithreading.
*/
#ifdef SL_CPP11
auto random01 = bind(uniform_real_distribution<SLfloat>(0.0, 1.0),
mt19937((SLuint)time(NULL)));
SLfloat rnd01();
SLfloat rnd01(){return random01();}
#else
SLfloat rnd01(){return (SLfloat)rand() / (SLfloat)RAND_MAX;}
#endif
//-----------------------------------------------------------------------------
/*!
SLRay::SLRay default constructor
*/
SLRay::SLRay()
{
origin = SLVec3f::ZERO;
setDir(SLVec3f::ZERO);
type = PRIMARY;
length = FLT_MAX;
void InferenceEngine::genotypeVariantClusterGroupsCallback(ProducerConsumerQueue<VariantClusterGroupBatch> * variant_cluster_group_batch_queue, KmerHash * kmer_hash, const CountDistribution & count_distribution, const vector<Sample> & samples, GenotypeWriter * genotype_writer, VariantClusterGroupCounts * variant_cluster_group_counts, mutex * thread_lock) {
assert(samples.size() == num_samples);
mt19937 prng = mt19937(prng_seed);
VariantClusterGroupCounts variant_cluster_group_counts_local;
VariantClusterGroupBatch variant_cluster_group_batch;
while (variant_cluster_group_batch_queue->pop(&variant_cluster_group_batch)) {
vector<Genotypes*> * variant_genotypes = new vector<Genotypes*>();
variant_genotypes->reserve(variant_cluster_group_batch.number_of_variants);
auto variant_cluster_group_it = variant_cluster_group_batch.start_it;
while (variant_cluster_group_it != variant_cluster_group_batch.end_it) {
if ((*variant_cluster_group_it)->isInChromosomeRegions(chromosome_regions)) {
for (ushort chain_idx = 0; chain_idx < num_gibbs_sampling_chains; chain_idx++) {
(*variant_cluster_group_it)->initialise(kmer_hash, samples, prng_seed, num_noise_sources, num_haplotype_candidates_per_sample, max_multicluster_kmers);
(*variant_cluster_group_it)->shuffleBranches(&prng);
for (ushort i = 0; i < gibbs_burn; i++) {
(*variant_cluster_group_it)->estimateGenotypes(count_distribution, chromosome_ploidy, false);
}
for (ushort i = 0; i < gibbs_samples; i++) {
(*variant_cluster_group_it)->estimateGenotypes(count_distribution, chromosome_ploidy, true);
}
}
if ((*variant_cluster_group_it)->hasMulticlusterKmer()) {
assert((*variant_cluster_group_it)->numberOfVariantClusters() > 1);
variant_cluster_group_counts_local.num_multicluster++;
} else if ((*variant_cluster_group_it)->hasInterclusterKmer()) {
variant_cluster_group_counts_local.num_intercluster++;
} else {
variant_cluster_group_counts_local.num_unique++;
}
(*variant_cluster_group_it)->collectGenotypes(variant_genotypes, chromosome_ploidy);
} else {
variant_cluster_group_counts_local.num_skipped++;
}
delete *variant_cluster_group_it;
variant_cluster_group_it++;
}
genotype_writer->addGenotypes(variant_genotypes);
}
lock_guard<mutex> tread_locker(*thread_lock);
*variant_cluster_group_counts += variant_cluster_group_counts_local;
}
